diff --git a/models/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/analytics/coremldata.bin b/AudioEncoder.mlmodelc/analytics/coremldata.bin similarity index 100% rename from models/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/analytics/coremldata.bin rename to AudioEncoder.mlmodelc/analytics/coremldata.bin diff --git a/models/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/coremldata.bin b/AudioEncoder.mlmodelc/coremldata.bin similarity index 100% rename from models/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/coremldata.bin rename to AudioEncoder.mlmodelc/coremldata.bin diff --git a/models/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/metadata.json b/AudioEncoder.mlmodelc/metadata.json similarity index 100% rename from models/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/metadata.json rename to AudioEncoder.mlmodelc/metadata.json diff --git a/models/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/model.mil b/AudioEncoder.mlmodelc/model.mil similarity index 100% rename from models/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/model.mil rename to AudioEncoder.mlmodelc/model.mil diff --git a/models/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/weights/weight.bin b/AudioEncoder.mlmodelc/weights/weight.bin similarity index 100% rename from models/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/weights/weight.bin rename to AudioEncoder.mlmodelc/weights/weight.bin diff --git a/models/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/analytics/coremldata.bin b/MelSpectrogram.mlmodelc/analytics/coremldata.bin similarity index 100% rename from models/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/analytics/coremldata.bin rename to MelSpectrogram.mlmodelc/analytics/coremldata.bin diff --git a/models/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/coremldata.bin b/MelSpectrogram.mlmodelc/coremldata.bin similarity index 100% rename from models/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/coremldata.bin rename to MelSpectrogram.mlmodelc/coremldata.bin diff --git a/models/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/metadata.json b/MelSpectrogram.mlmodelc/metadata.json similarity index 100% rename from models/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/metadata.json rename to MelSpectrogram.mlmodelc/metadata.json diff --git a/models/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/model.mil b/MelSpectrogram.mlmodelc/model.mil similarity index 100% rename from models/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/model.mil rename to MelSpectrogram.mlmodelc/model.mil diff --git a/models/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/weights/weight.bin b/MelSpectrogram.mlmodelc/weights/weight.bin similarity index 100% rename from models/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/weights/weight.bin rename to MelSpectrogram.mlmodelc/weights/weight.bin diff --git a/cli/CtcEarningsBenchmark.swift b/cli/CtcEarningsBenchmark.swift deleted file mode 100644 index 1366881f21e31fd699568575d0e3d519179391fc..0000000000000000000000000000000000000000 --- a/cli/CtcEarningsBenchmark.swift +++ /dev/null @@ -1,1048 +0,0 @@ -#if os(macOS) -import AVFoundation -import CoreML -import FluidAudio -import Foundation - -/// Earnings22 benchmark using TDT for transcription + CTC for keyword spotting. -/// TDT provides low WER transcription, CTC provides high recall dictionary detection. -public enum CtcEarningsBenchmark { - - private enum KeywordMode: String { - case chunk - case file - } - - /// Default CTC model directory - private static func defaultCtcModelPath() -> String? { - let appSupport = FileManager.default.urls( - for: .applicationSupportDirectory, in: .userDomainMask - ).first! - let modelPath = appSupport.appendingPathComponent("FluidAudio/Models/parakeet-ctc-110m-coreml") - if FileManager.default.fileExists(atPath: modelPath.path) { - return modelPath.path - } - return nil - } - - /// Default data directory (from download command) - private static func defaultDataDir() -> String? { - let dataDir = DatasetDownloader.getEarnings22Directory().appendingPathComponent("test-dataset") - if FileManager.default.fileExists(atPath: dataDir.path) { - return dataDir.path - } - return nil - } - - public static func runCLI(arguments: [String]) async { - // Check for help - if arguments.contains("--help") || arguments.contains("-h") { - printUsage() - return - } - - // Parse arguments - var dataDir: String? = nil - var outputFile = "ctc_earnings_benchmark.json" - var maxFiles: Int? = nil - var ctcModelPath: String? = nil - // Note: Using v2 by default because v3 has issues with certain audio files - // (returns empty transcription for ~7 files in Earnings22 dataset) - var tdtVersion: AsrModelVersion = .v2 - var autoDownload = false - var keywordMode: KeywordMode = .chunk - - var i = 0 - while i < arguments.count { - switch arguments[i] { - case "--data-dir": - if i + 1 < arguments.count { - dataDir = arguments[i + 1] - i += 1 - } - case "--output", "-o": - if i + 1 < arguments.count { - outputFile = arguments[i + 1] - i += 1 - } - case "--max-files": - if i + 1 < arguments.count { - maxFiles = Int(arguments[i + 1]) - i += 1 - } - case "--ctc-model": - if i + 1 < arguments.count { - ctcModelPath = arguments[i + 1] - i += 1 - } - case "--tdt-version": - if i + 1 < arguments.count { - if arguments[i + 1] == "v2" || arguments[i + 1] == "2" { - tdtVersion = .v2 - } - i += 1 - } - case "--auto-download": - autoDownload = true - case "--keyword-mode": - if i + 1 < arguments.count, let mode = parseKeywordMode(arguments[i + 1]) { - keywordMode = mode - i += 1 - } - default: - break - } - i += 1 - } - - // Use defaults if not specified - if dataDir == nil { - dataDir = defaultDataDir() - } - if ctcModelPath == nil { - ctcModelPath = defaultCtcModelPath() - } - - // Handle auto-download for dataset - if autoDownload && dataDir == nil { - print("đŸ“„ Downloading earnings22-kws dataset...") - await DatasetDownloader.downloadEarnings22KWS(force: false) - dataDir = defaultDataDir() - } - - // Handle auto-download for CTC models - if autoDownload && ctcModelPath == nil { - print("đŸ“„ Downloading CTC models...") - do { - _ = try await CtcModels.download() - ctcModelPath = defaultCtcModelPath() - } catch { - print("ERROR: Failed to download CTC models: \(error)") - } - } - - print("Earnings Benchmark (TDT transcription + CTC keyword spotting)") - print(" Data directory: \(dataDir ?? "not found")") - print(" Output file: \(outputFile)") - print(" TDT version: \(tdtVersion == .v2 ? "v2" : "v3")") - print(" CTC model: \(ctcModelPath ?? "not found")") - print(" Keyword mode: \(keywordMode.rawValue)") - - guard let finalDataDir = dataDir else { - print("ERROR: Data directory not found") - print("💡 Download with: fluidaudio download --dataset earnings22-kws") - print(" Or specify: --data-dir ") - printUsage() - return - } - - guard let modelPath = ctcModelPath else { - print("ERROR: CTC model not found") - print("💡 Download parakeet-ctc-110m-coreml model to:") - print(" ~/Library/Application Support/FluidAudio/Models/parakeet-ctc-110m-coreml/") - print(" Or specify: --ctc-model ") - printUsage() - return - } - - let dataDirResolved = finalDataDir - - do { - // Load TDT models for transcription - print("Loading TDT models (\(tdtVersion == .v2 ? "v2" : "v3")) for transcription...") - let tdtModels = try await AsrModels.downloadAndLoad(version: tdtVersion) - let asrManager = AsrManager(config: .default) - try await asrManager.initialize(models: tdtModels) - print("TDT models loaded successfully") - - // Load CTC models for keyword spotting - print("Loading CTC models from: \(modelPath)") - let modelDir = URL(fileURLWithPath: modelPath) - let ctcModels = try await CtcModels.loadDirect(from: modelDir) - print("Loaded CTC vocabulary with \(ctcModels.vocabulary.count) tokens") - - // Create keyword spotter - let vocabSize = ctcModels.vocabulary.count - let blankId = vocabSize // Blank is at index = vocab_size - let spotter = CtcKeywordSpotter(models: ctcModels, blankId: blankId) - print("Created CTC spotter with blankId=\(blankId)") - - // Collect test files - let dataDirURL = URL(fileURLWithPath: dataDirResolved) - let fileIds = try collectFileIds(from: dataDirURL, maxFiles: maxFiles) - let keywordIndex = try buildKeywordIndex(dataDir: dataDirURL, keywordMode: keywordMode) - - if fileIds.isEmpty { - print("ERROR: No test files found in \(dataDirResolved)") - return - } - - print("Processing \(fileIds.count) test files...") - - var results: [[String: Any]] = [] - var totalWer = 0.0 - var totalKeywordReference = 0 - var totalKeywordPredicted = 0 - var totalKeywordTruePositives = 0 - var totalKeywordFalsePositives = 0 - var totalKeywordFalseNegatives = 0 - var totalAudioDuration = 0.0 - var totalProcessingTime = 0.0 - - for (index, fileId) in fileIds.enumerated() { - print("[\(index + 1)/\(fileIds.count)] \(fileId)") - - if let result = try await processFile( - fileId: fileId, - dataDir: dataDirURL, - asrManager: asrManager, - ctcModels: ctcModels, - spotter: spotter, - keywordMode: keywordMode, - keywordIndex: keywordIndex - ) { - results.append(result) - totalWer += result["wer"] as? Double ?? 0 - totalKeywordReference += result["keywordReference"] as? Int ?? 0 - totalKeywordPredicted += result["keywordPredicted"] as? Int ?? 0 - totalKeywordTruePositives += result["keywordTruePositives"] as? Int ?? 0 - totalKeywordFalsePositives += result["keywordFalsePositives"] as? Int ?? 0 - totalKeywordFalseNegatives += result["keywordFalseNegatives"] as? Int ?? 0 - totalAudioDuration += result["audioLength"] as? Double ?? 0 - totalProcessingTime += result["processingTime"] as? Double ?? 0 - - let wer = result["wer"] as? Double ?? 0 - let precision = result["keywordPrecision"] as? Double ?? 0 - let recall = result["keywordRecall"] as? Double ?? 0 - let fscore = result["keywordFscore"] as? Double ?? 0 - print( - " WER: \(String(format: "%.1f", wer))%, " + - "KW P/R/F: \(String(format: "%.2f", precision))/" + - "\(String(format: "%.2f", recall))/" + - "\(String(format: "%.2f", fscore))" - ) - } - } - - // Calculate summary - let avgWer = results.isEmpty ? 0.0 : totalWer / Double(results.count) - let keywordPrecision = - totalKeywordPredicted > 0 - ? Double(totalKeywordTruePositives) / Double(totalKeywordPredicted) - : 0 - let keywordRecall = - totalKeywordReference > 0 - ? Double(totalKeywordTruePositives) / Double(totalKeywordReference) - : 0 - let keywordFscore = - (keywordPrecision + keywordRecall) > 0 - ? 2 * keywordPrecision * keywordRecall / (keywordPrecision + keywordRecall) - : 0 - - // Print summary - print("\n" + String(repeating: "=", count: 60)) - print("EARNINGS22 BENCHMARK (TDT + CTC)") - print(String(repeating: "=", count: 60)) - print("Model: \(modelPath)") - print("Total tests: \(results.count)") - print("Average WER: \(String(format: "%.2f", avgWer))%") - print( - "Keyword Precision/Recall/F1: " + - "\(String(format: "%.2f", keywordPrecision))/" + - "\(String(format: "%.2f", keywordRecall))/" + - "\(String(format: "%.2f", keywordFscore))" - ) - print("Total audio: \(String(format: "%.1f", totalAudioDuration))s") - print("Total processing: \(String(format: "%.1f", totalProcessingTime))s") - if totalProcessingTime > 0 { - print("RTFx: \(String(format: "%.2f", totalAudioDuration / totalProcessingTime))x") - } - print(String(repeating: "=", count: 60)) - - // Sort results by WER descending (worst first) - let sortedResults = results.sorted { r1, r2 in - let wer1 = r1["wer"] as? Double ?? 0 - let wer2 = r2["wer"] as? Double ?? 0 - return wer1 > wer2 - } - - // Save to JSON - let summaryDict: [String: Any] = [ - "totalTests": results.count, - "avgWer": round(avgWer * 100) / 100, - "keywordTruePositives": totalKeywordTruePositives, - "keywordFalsePositives": totalKeywordFalsePositives, - "keywordFalseNegatives": totalKeywordFalseNegatives, - "keywordPredicted": totalKeywordPredicted, - "keywordReference": totalKeywordReference, - "keywordPrecision": round(keywordPrecision * 1000) / 1000, - "keywordRecall": round(keywordRecall * 1000) / 1000, - "keywordFscore": round(keywordFscore * 1000) / 1000, - "totalAudioDuration": round(totalAudioDuration * 100) / 100, - "totalProcessingTime": round(totalProcessingTime * 100) / 100, - ] - - let output: [String: Any] = [ - "model": modelPath, - "keywordMode": keywordMode.rawValue, - "summary": summaryDict, - "results": sortedResults, - ] - - let jsonData = try JSONSerialization.data(withJSONObject: output, options: [.prettyPrinted, .sortedKeys]) - try jsonData.write(to: URL(fileURLWithPath: outputFile)) - print("\nResults written to: \(outputFile)") - - } catch { - print("ERROR: Benchmark failed: \(error)") - } - } - - private static func collectFileIds(from dataDir: URL, maxFiles: Int?) throws -> [String] { - var fileIds: [String] = [] - let suffix = ".dictionary.txt" - - let fileManager = FileManager.default - let contents = try fileManager.contentsOfDirectory(at: dataDir, includingPropertiesForKeys: nil) - - for url in contents.sorted(by: { $0.path < $1.path }) { - let name = url.lastPathComponent - if name.hasSuffix(suffix) { - let data = try? Data(contentsOf: url) - if let data = data, !data.isEmpty { - let fileId = String(name.dropLast(suffix.count)) - fileIds.append(fileId) - } - } - } - - if let maxFiles = maxFiles { - return Array(fileIds.prefix(maxFiles)) - } - return fileIds - } - - private static func processFile( - fileId: String, - dataDir: URL, - asrManager: AsrManager, - ctcModels: CtcModels, - spotter: CtcKeywordSpotter, - keywordMode: KeywordMode, - keywordIndex: [String: [String]] - ) async throws -> [String: Any]? { - let wavFile = dataDir.appendingPathComponent("\(fileId).wav") - let dictionaryFile = dataDir.appendingPathComponent("\(fileId).dictionary.txt") - let textFile = dataDir.appendingPathComponent("\(fileId).text.txt") - - let fm = FileManager.default - guard fm.fileExists(atPath: wavFile.path), - fm.fileExists(atPath: dictionaryFile.path) - else { - return nil - } - - // Load dictionary words (chunk or file keywords) - let dictionaryWords = try loadDictionaryWords( - fileId: fileId, - dictionaryFile: dictionaryFile, - keywordMode: keywordMode, - keywordIndex: keywordIndex - ) - - // Load reference text - let referenceRaw = - (try? String(contentsOf: textFile, encoding: .utf8))?.trimmingCharacters(in: .whitespacesAndNewlines) ?? "" - - // Get audio samples - let audioFile = try AVAudioFile(forReading: wavFile) - let audioLength = Double(audioFile.length) / audioFile.processingFormat.sampleRate - let format = audioFile.processingFormat - let frameCount = AVAudioFrameCount(audioFile.length) - - guard let buffer = AVAudioPCMBuffer(pcmFormat: format, frameCapacity: frameCount) else { - throw NSError( - domain: "CtcEarningsBenchmark", code: 1, - userInfo: [NSLocalizedDescriptionKey: "Failed to create audio buffer"]) - } - try audioFile.read(into: buffer) - - // Resample to 16kHz - let converter = AudioConverter() - let samples = try converter.resampleBuffer(buffer) - - let startTime = Date() - - // 1. TDT transcription for low WER - let tdtResult = try await asrManager.transcribe(wavFile) - - // Skip files where TDT returns empty (some audio files cause model issues) - if tdtResult.text.isEmpty { - print(" SKIPPED: TDT returned empty transcription") - return nil - } - - // 2. Build custom vocabulary for CTC keyword spotting - var vocabTerms: [CustomVocabularyTerm] = [] - for word in dictionaryWords { - let tokenIds = tokenize(word, vocabulary: ctcModels.vocabulary) - if !tokenIds.isEmpty { - let term = CustomVocabularyTerm( - text: word, - weight: nil, - aliases: nil, - tokenIds: nil, - ctcTokenIds: tokenIds - ) - vocabTerms.append(term) - } - } - let customVocab = CustomVocabularyContext(terms: vocabTerms) - - // 3. CTC keyword spotting for high recall dictionary detection - let spotResult = try await spotter.spotKeywordsWithLogProbs( - audioSamples: samples, - customVocabulary: customVocab, - minScore: nil - ) - - // 4. Post-process: Use VocabularyRescorer with Argmax-style parameters - // Argmax uses cbw=3.0 (context-biasing weight) for boosting vocab terms - let useRescorer = ProcessInfo.processInfo.environment["NO_CTC_RESCORING"] != "1" - let hypothesis: String - if useRescorer { - let rescorerConfig = VocabularyRescorer.Config( - minScoreAdvantage: 1.0, // Lower threshold - rely more on CTC scoring - minVocabScore: -15.0, // Permissive to include more detections - maxOriginalScoreForReplacement: -2.0, // Don't replace very confident words - vocabBoostWeight: 3.0 // Argmax cbw=3.0 - ) - let rescorer = VocabularyRescorer( - spotter: spotter, - vocabulary: customVocab, - config: rescorerConfig - ) - let rescoreResult = rescorer.rescore(transcript: tdtResult.text, spotResult: spotResult) - hypothesis = rescoreResult.text - } else { - hypothesis = tdtResult.text // Baseline: no CTC corrections - } - - let processingTime = Date().timeIntervalSince(startTime) - - // Normalize texts - let referenceNormalized = TextNormalizer.normalize(referenceRaw) - let hypothesisNormalized = TextNormalizer.normalize(hypothesis) - - // Keyword sets for precision/recall - let referenceKeywords = keywordsInText(referenceNormalized, dictionaryWords: dictionaryWords) - let predictedKeywords = keywordsInText(hypothesisNormalized, dictionaryWords: dictionaryWords) - let truePositives = referenceKeywords.intersection(predictedKeywords) - let falsePositives = predictedKeywords.subtracting(referenceKeywords) - let falseNegatives = referenceKeywords.subtracting(predictedKeywords) - let keywordPrecision = predictedKeywords.isEmpty ? 0 : Double(truePositives.count) / Double(predictedKeywords.count) - let keywordRecall = referenceKeywords.isEmpty ? 0 : Double(truePositives.count) / Double(referenceKeywords.count) - let keywordFscore = - (keywordPrecision + keywordRecall) > 0 - ? 2 * keywordPrecision * keywordRecall / (keywordPrecision + keywordRecall) - : 0 - - let referenceWords = referenceNormalized.components(separatedBy: CharacterSet.whitespacesAndNewlines).filter { - !$0.isEmpty - } - let hypothesisWords = hypothesisNormalized.components(separatedBy: CharacterSet.whitespacesAndNewlines).filter { - !$0.isEmpty - } - - // Calculate WER - let wer: Double - if referenceWords.isEmpty { - wer = hypothesisWords.isEmpty ? 0.0 : 1.0 - } else { - wer = calculateWER(reference: referenceWords, hypothesis: hypothesisWords) - } - - // Count dictionary detections (debug only) - let minCtcScore: Float = -15.0 // Permissive threshold for detection - var detectionDetails: [[String: Any]] = [] - var ctcFoundWords: Set = [] - - // 1. CTC detections - for detection in spotResult.detections { - let inRef = referenceKeywords.contains(detection.term.text.lowercased()) - let detail: [String: Any] = [ - "word": detection.term.text, - "score": round(Double(detection.score) * 100) / 100, - "startTime": round(detection.startTime * 100) / 100, - "endTime": round(detection.endTime * 100) / 100, - "source": "ctc", - "inReference": inRef, - ] - detectionDetails.append(detail) - - if detection.score >= minCtcScore { // Use >= to include edge cases - ctcFoundWords.insert(detection.term.text.lowercased()) - } - } - - // 2. Fallback: check hypothesis for dictionary words not found by CTC - let hypothesisLower = hypothesis.lowercased() - for word in dictionaryWords { - let wordLower = word.lowercased() - if !ctcFoundWords.contains(wordLower) { - // Check if word appears as whole word in hypothesis (avoid substring false positives) - let pattern = "\\b\(NSRegularExpression.escapedPattern(for: wordLower))\\b" - if let regex = try? NSRegularExpression(pattern: pattern, options: []), - regex.firstMatch( - in: hypothesisLower, options: [], - range: NSRange(hypothesisLower.startIndex..., in: hypothesisLower)) != nil - { - ctcFoundWords.insert(wordLower) - let inRef = referenceKeywords.contains(wordLower) - let detail: [String: Any] = [ - "word": word, - "score": 0.0, - "startTime": 0.0, - "endTime": 0.0, - "source": "hypothesis", - "inReference": inRef, - ] - detectionDetails.append(detail) - } - } - } - - let result: [String: Any] = [ - "fileId": fileId, - "reference": referenceNormalized, - "hypothesis": hypothesisNormalized, - "wer": round(wer * 10000) / 100, - "dictFound": predictedKeywords.count, - "dictTotal": referenceKeywords.count, - "keywordPredicted": predictedKeywords.count, - "keywordReference": referenceKeywords.count, - "keywordTruePositives": truePositives.count, - "keywordFalsePositives": falsePositives.count, - "keywordFalseNegatives": falseNegatives.count, - "keywordPrecision": round(keywordPrecision * 1000) / 1000, - "keywordRecall": round(keywordRecall * 1000) / 1000, - "keywordFscore": round(keywordFscore * 1000) / 1000, - "audioLength": round(audioLength * 100) / 100, - "processingTime": round(processingTime * 1000) / 1000, - "ctcDetections": detectionDetails, - ] - return result - } - - /// Simple tokenization using vocabulary lookup - private static func tokenize(_ text: String, vocabulary: [Int: String]) -> [Int] { - // Build reverse vocabulary (token -> id) - var tokenToId: [String: Int] = [:] - for (id, token) in vocabulary { - tokenToId[token] = id - } - - let normalizedText = text.lowercased() - var result: [Int] = [] - var position = normalizedText.startIndex - var isWordStart = true - - while position < normalizedText.endIndex { - var matched = false - let remaining = normalizedText.distance(from: position, to: normalizedText.endIndex) - var matchLength = min(20, remaining) - - while matchLength > 0 { - let endPos = normalizedText.index(position, offsetBy: matchLength) - let substring = String(normalizedText[position.. String { - // Filter detections by score - let validDetections = detections.filter { $0.score >= minScore } - guard !validDetections.isEmpty else { - return tdtResult.text - } - - var text = tdtResult.text - var usedDetections: Set = [] - - // PASS 1: Fuzzy matching for phonetically similar words - for detection in validDetections { - let keyword = detection.term.text - let keywordLower = keyword.lowercased() - let keywordParts = keywordLower.components(separatedBy: " ").filter { !$0.isEmpty } - - let words = text.components(separatedBy: .whitespacesAndNewlines).filter { !$0.isEmpty } - - // Handle multi-word keywords - if keywordParts.count > 1 { - for i in 0..<(words.count - keywordParts.count + 1) { - var allMatch = true - var matchedWords: [String] = [] - - for j in 0..= 3 - && !stopWords.contains(keywordLower) - - guard isProperNoun else { continue } - - // Look for "this is X" pattern specifically for names - let thisIsPattern = try? NSRegularExpression(pattern: "this is ([A-Z][a-z]+)", options: []) - if let regex = thisIsPattern { - let textRange = NSRange(text.startIndex..., in: text) - if let match = regex.firstMatch(in: text, options: [], range: textRange), - match.numberOfRanges > 1, - let captureRange = Range(match.range(at: 1), in: text) - { - let capturedWord = String(text[captureRange]) - let capturedLower = capturedWord.lowercased() - - // Skip if captured word is already a detected keyword - let isOtherKeyword = validDetections.contains { det in - det.term.text.lowercased() == capturedLower - } - - if !isOtherKeyword && !stopWords.contains(capturedLower) { - // Similar length check - if abs(capturedWord.count - keyword.count) <= 3 { - text = text.replacingOccurrences(of: capturedWord, with: keyword) - usedDetections.insert(keyword) - } - } - } - } - } - - return text - } - - /// Build word timings by merging subword tokens (tokens starting with "▁" begin new words) - private static func buildWordTimings( - from tokenTimings: [TokenTiming] - ) -> [(word: String, startTime: Double, endTime: Double)] { - var wordTimings: [(word: String, startTime: Double, endTime: Double)] = [] - var currentWord = "" - var wordStart: Double = 0 - var wordEnd: Double = 0 - - for timing in tokenTimings { - let token = timing.token - - // Skip special tokens - if token.isEmpty || token == "" || token == "" { - continue - } - - // Check if this starts a new word (has ▁ prefix or is first token) - let startsNewWord = token.hasPrefix("▁") || currentWord.isEmpty - - if startsNewWord && !currentWord.isEmpty { - // Save previous word - wordTimings.append((word: currentWord, startTime: wordStart, endTime: wordEnd)) - currentWord = "" - } - - if startsNewWord { - currentWord = token.hasPrefix("▁") ? String(token.dropFirst()) : token - wordStart = timing.startTime - } else { - currentWord += token - } - wordEnd = timing.endTime - } - - // Save final word - if !currentWord.isEmpty { - wordTimings.append((word: currentWord, startTime: wordStart, endTime: wordEnd)) - } - - return wordTimings - } - - /// Common English words that should never be replaced by keyword matching - private static let stopWords: Set = [ - // Pronouns - "i", "you", "he", "she", "it", "we", "they", "me", "him", "her", "us", "them", - "my", "your", "his", "its", "our", "their", "mine", "yours", "hers", "ours", "theirs", - "this", "that", "these", "those", "who", "whom", "what", "which", "whose", - // Common verbs - "is", "are", "was", "were", "be", "been", "being", "am", - "have", "has", "had", "having", "do", "does", "did", "doing", "done", - "will", "would", "shall", "should", "may", "might", "must", "can", "could", - "get", "got", "getting", "go", "goes", "went", "going", "gone", - "come", "came", "coming", "see", "saw", "seen", "know", "knew", "known", - "think", "thought", "make", "made", "take", "took", "taken", "give", "gave", "given", - "say", "said", "tell", "told", "ask", "asked", "use", "used", "want", "wanted", - "need", "needed", "try", "tried", "let", "put", "keep", "kept", "look", "looked", - // Articles and determiners - "a", "an", "the", "some", "any", "no", "every", "each", "all", "both", "few", "many", - "much", "more", "most", "other", "another", "such", - // Prepositions - "in", "on", "at", "to", "for", "of", "with", "by", "from", "up", "down", "out", - "about", "into", "over", "after", "before", "between", "under", "through", "during", - // Conjunctions - "and", "or", "but", "so", "yet", "nor", "if", "then", "than", "because", "while", - "although", "unless", "since", "when", "where", "as", - // Adverbs - "not", "very", "just", "also", "only", "even", "still", "already", "always", "never", - "often", "sometimes", "usually", "really", "well", "now", "here", "there", "how", "why", - // Common words - "yes", "no", "okay", "ok", "thank", "thanks", "please", "sorry", "hello", "hi", "bye", - "good", "great", "bad", "new", "old", "first", "last", "long", "short", "big", "small", - "high", "low", "right", "left", "next", "back", "same", "different", "own", "able", - "way", "thing", "things", "time", "times", "year", "years", "day", "days", "week", "weeks", - "part", "place", "case", "point", "fact", "end", "kind", "lot", "set", - // Numbers - "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", - "hundred", "thousand", "million", "billion", - ] - - /// Check if two words are similar (edit distance / length ratio) - private static func isSimilar(_ a: String, _ b: String) -> Bool { - // Never match stop words - they're too common to be proper nouns - if stopWords.contains(a) || stopWords.contains(b) { - return false - } - - let maxLen = max(a.count, b.count) - let minLen = min(a.count, b.count) - guard maxLen > 0, minLen >= 3 else { return false } - - // Allow more length difference for longer words - let lenDiff = abs(a.count - b.count) - if lenDiff > max(3, maxLen / 2) { return false } - - // Calculate edit distance - let distance = editDistance(a, b) - - // More aggressive threshold: allow up to 40% of max length as edits - let threshold = max(2, Int(Double(maxLen) * 0.4)) - - // Also check if one is substring of other (handles "Erik" in "Ririek") - if a.contains(b) || b.contains(a) { - return true - } - - // Check common prefix/suffix (handles "Heri" vs "Harry") - let commonPrefix = commonPrefixLength(a, b) - let commonSuffix = commonSuffixLength(a, b) - if commonPrefix >= 2 || commonSuffix >= 2 { - return distance <= threshold + 1 - } - - return distance <= threshold - } - - /// Get length of common prefix - private static func commonPrefixLength(_ a: String, _ b: String) -> Int { - let aChars = Array(a) - let bChars = Array(b) - var count = 0 - for i in 0.. Int { - let aChars = Array(a.reversed()) - let bChars = Array(b.reversed()) - var count = 0 - for i in 0.. Int { - let a = Array(a) - let b = Array(b) - let m = a.count - let n = b.count - - if m == 0 { return n } - if n == 0 { return m } - - var dp = Array(repeating: Array(repeating: 0, count: n + 1), count: m + 1) - - for i in 0...m { dp[i][0] = i } - for j in 0...n { dp[0][j] = j } - - for i in 1...m { - for j in 1...n { - if a[i - 1] == b[j - 1] { - dp[i][j] = dp[i - 1][j - 1] - } else { - dp[i][j] = 1 + min(dp[i - 1][j - 1], min(dp[i - 1][j], dp[i][j - 1])) - } - } - } - - return dp[m][n] - } - - /// Match the case pattern of the original word - private static func matchCase(_ keyword: String, to original: String) -> String { - let origClean = original.trimmingCharacters(in: .punctuationCharacters) - - // Check case pattern - if origClean.first?.isUppercase == true { - // Capitalize first letter - return keyword.prefix(1).uppercased() + keyword.dropFirst() - } - return keyword - } - - private static func calculateWER(reference: [String], hypothesis: [String]) -> Double { - if reference.isEmpty { - return hypothesis.isEmpty ? 0.0 : 1.0 - } - - let m = reference.count - let n = hypothesis.count - var dp = Array(repeating: Array(repeating: 0, count: n + 1), count: m + 1) - - for i in 0...m { dp[i][0] = i } - for j in 0...n { dp[0][j] = j } - - for i in 1...m { - for j in 1...n { - if reference[i - 1] == hypothesis[j - 1] { - dp[i][j] = dp[i - 1][j - 1] - } else { - dp[i][j] = min(dp[i - 1][j - 1], min(dp[i - 1][j], dp[i][j - 1])) + 1 - } - } - } - - return Double(dp[m][n]) / Double(m) - } - - private static func printUsage() { - print( - """ - CTC Earnings Benchmark (TDT + CTC keyword spotting) - - Usage: fluidaudio ctc-earnings-benchmark [options] - - Options: - --data-dir Path to earnings test dataset (auto-detected if downloaded) - --ctc-model Path to CTC model directory (auto-detected if in standard location) - --max-files Maximum number of files to process - --output, -o Output JSON file (default: ctc_earnings_benchmark.json) - --auto-download Download earnings22-kws dataset if not found - --keyword-mode Keyword mode: chunk or file (default: chunk) - - Default locations: - Dataset: ~/Library/Application Support/FluidAudio/earnings22-kws/test-dataset/ - CTC Model: ~/Library/Application Support/FluidAudio/Models/parakeet-ctc-110m-coreml/ - - Setup: - 1. Download dataset: fluidaudio download --dataset earnings22-kws - 2. Place CTC model in standard location - 3. Run: fluidaudio ctc-earnings-benchmark - - Examples: - # Run with auto-detected paths - fluidaudio ctc-earnings-benchmark - - # Run with auto-download - fluidaudio ctc-earnings-benchmark --auto-download - - # Run with explicit paths - fluidaudio ctc-earnings-benchmark \\ - --data-dir /path/to/test-dataset \\ - --ctc-model /path/to/parakeet-ctc-110m-coreml \\ - --max-files 100 - """) - } - - private static func parseKeywordMode(_ value: String) -> KeywordMode? { - switch value.lowercased() { - case "chunk", "chunk-keywords": - return .chunk - case "file", "file-keywords": - return .file - default: - return nil - } - } - - private static func parentId(from fileId: String) -> String { - guard let range = fileId.range(of: "_chunk") else { - return fileId - } - return String(fileId[.. [String: [String]] { - guard keywordMode == .file else { - return [:] - } - - var index: [String: Set] = [:] - let suffix = ".dictionary.txt" - let fileManager = FileManager.default - let contents = try fileManager.contentsOfDirectory(at: dataDir, includingPropertiesForKeys: nil) - - for url in contents { - let name = url.lastPathComponent - guard name.hasSuffix(suffix) else { continue } - let fileId = String(name.dropLast(suffix.count)) - let parent = parentId(from: fileId) - let words = try loadDictionaryWords(from: url) - var set = index[parent] ?? Set() - set.formUnion(words) - index[parent] = set - } - - return index.mapValues { Array($0).sorted() } - } - - private static func loadDictionaryWords( - fileId: String, - dictionaryFile: URL, - keywordMode: KeywordMode, - keywordIndex: [String: [String]] - ) throws -> [String] { - switch keywordMode { - case .chunk: - return try loadDictionaryWords(from: dictionaryFile) - case .file: - let parent = parentId(from: fileId) - if let words = keywordIndex[parent] { - return words - } - return try loadDictionaryWords(from: dictionaryFile) - } - } - - private static func loadDictionaryWords(from url: URL) throws -> [String] { - let dictionaryContent = try String(contentsOf: url, encoding: .utf8) - return dictionaryContent - .components(separatedBy: .newlines) - .map { $0.trimmingCharacters(in: .whitespacesAndNewlines) } - .filter { !$0.isEmpty } - } - - private static func keywordsInText(_ text: String, dictionaryWords: [String]) -> Set { - let textLower = text.lowercased() - var result: Set = [] - - for word in dictionaryWords { - let wordLower = word.lowercased() - let pattern = "\\b\(NSRegularExpression.escapedPattern(for: wordLower))\\b" - guard let regex = try? NSRegularExpression(pattern: pattern, options: []) else { continue } - let range = NSRange(textLower.startIndex..., in: textLower) - if regex.firstMatch(in: textLower, options: [], range: range) != nil { - result.insert(wordLower) - } - } - return result - } -} -#endif diff --git a/cli/HybridEarningsBenchmark.swift b/cli/HybridEarningsBenchmark.swift deleted file mode 100644 index dcd8023757245b94deed24d634195358afc82c7d..0000000000000000000000000000000000000000 --- a/cli/HybridEarningsBenchmark.swift +++ /dev/null @@ -1,554 +0,0 @@ -#if os(macOS) -import AVFoundation -import FluidAudio -import Foundation - -/// Earnings22 benchmark using ONLY the Hybrid 110M model (single encoder). -/// CTC head provides both transcription AND keyword spotting from the same encoder. -public enum HybridEarningsBenchmark { - - private enum KeywordMode: String { - case chunk - case file - } - - public static func runCLI(arguments: [String]) async { - if arguments.contains("--help") || arguments.contains("-h") { - printUsage() - return - } - - // Parse arguments - var outputFile = "hybrid_earnings_benchmark.json" - var maxFiles: Int? = nil - var decodingMode: HybridDecodingMode = .tdt - var useRescoring = false - var keywordMode: KeywordMode = .chunk - - var i = 0 - while i < arguments.count { - switch arguments[i] { - case "--output", "-o": - if i + 1 < arguments.count { - outputFile = arguments[i + 1] - i += 1 - } - case "--max-files": - if i + 1 < arguments.count { - maxFiles = Int(arguments[i + 1]) - i += 1 - } - case "--ctc": - decodingMode = .ctc - case "--tdt": - decodingMode = .tdt - case "--rescore": - useRescoring = true - case "--keyword-mode": - if i + 1 < arguments.count, let mode = parseKeywordMode(arguments[i + 1]) { - keywordMode = mode - i += 1 - } - default: - break - } - i += 1 - } - - let dataDir = DatasetDownloader.getEarnings22Directory().appendingPathComponent("test-dataset") - guard FileManager.default.fileExists(atPath: dataDir.path) else { - print("ERROR: Earnings dataset not found at \(dataDir.path)") - print("Download with: fluidaudio download --dataset earnings22-kws") - return - } - - let modeStr = decodingMode == .ctc ? "CTC" : "TDT" - let rescoringStr = useRescoring ? " + Rescoring" : "" - print("Hybrid 110M Earnings Benchmark (Decoding: \(modeStr)\(rescoringStr))") - print(" Output file: \(outputFile)") - print(" Decoding mode: \(modeStr)") - print(" Rescoring: \(useRescoring ? "enabled" : "disabled")") - print(" Keyword mode: \(keywordMode.rawValue)") - - do { - // Load Hybrid 110M model (single encoder with CTC head) - print("Loading Hybrid 110M model...") - let hybridModels = try await HybridAsrModels.downloadAndLoad() - let hybridManager = HybridAsrManager(models: hybridModels, decodingMode: decodingMode) - let spotter = HybridKeywordSpotter(vocabulary: hybridModels.vocabulary, blankId: hybridModels.blankId) - print(" Vocab size: \(hybridModels.vocabSize)") - - // Collect test files - let fileIds = try collectFileIds(from: dataDir, maxFiles: maxFiles) - let keywordIndex = try buildKeywordIndex(dataDir: dataDir, keywordMode: keywordMode) - - if fileIds.isEmpty { - print("ERROR: No test files found") - return - } - - print("Processing \(fileIds.count) test files...") - - var results: [[String: Any]] = [] - var totalWer = 0.0 - var totalKeywordReference = 0 - var totalKeywordPredicted = 0 - var totalKeywordTruePositives = 0 - var totalKeywordFalsePositives = 0 - var totalKeywordFalseNegatives = 0 - var totalAudioDuration = 0.0 - var totalProcessingTime = 0.0 - - for (index, fileId) in fileIds.enumerated() { - print("[\(index + 1)/\(fileIds.count)] \(fileId)") - - if let result = try await processFile( - fileId: fileId, - dataDir: dataDir, - hybridManager: hybridManager, - spotter: spotter, - useRescoring: useRescoring, - keywordMode: keywordMode, - keywordIndex: keywordIndex - ) { - results.append(result) - totalWer += result["wer"] as? Double ?? 0 - totalKeywordReference += result["keywordReference"] as? Int ?? 0 - totalKeywordPredicted += result["keywordPredicted"] as? Int ?? 0 - totalKeywordTruePositives += result["keywordTruePositives"] as? Int ?? 0 - totalKeywordFalsePositives += result["keywordFalsePositives"] as? Int ?? 0 - totalKeywordFalseNegatives += result["keywordFalseNegatives"] as? Int ?? 0 - totalAudioDuration += result["audioLength"] as? Double ?? 0 - totalProcessingTime += result["processingTime"] as? Double ?? 0 - - let wer = result["wer"] as? Double ?? 0 - let precision = result["keywordPrecision"] as? Double ?? 0 - let recall = result["keywordRecall"] as? Double ?? 0 - let fscore = result["keywordFscore"] as? Double ?? 0 - print( - " WER: \(String(format: "%.1f", wer))%, " + - "KW P/R/F: \(String(format: "%.2f", precision))/" + - "\(String(format: "%.2f", recall))/" + - "\(String(format: "%.2f", fscore))" - ) - } - } - - // Calculate summary - let avgWer = results.isEmpty ? 0.0 : totalWer / Double(results.count) - let keywordPrecision = - totalKeywordPredicted > 0 - ? Double(totalKeywordTruePositives) / Double(totalKeywordPredicted) - : 0 - let keywordRecall = - totalKeywordReference > 0 - ? Double(totalKeywordTruePositives) / Double(totalKeywordReference) - : 0 - let keywordFscore = - (keywordPrecision + keywordRecall) > 0 - ? 2 * keywordPrecision * keywordRecall / (keywordPrecision + keywordRecall) - : 0 - - // Print summary - print("\n" + String(repeating: "=", count: 60)) - print("HYBRID 110M BENCHMARK (\(modeStr)\(rescoringStr))") - print(String(repeating: "=", count: 60)) - print("Model: parakeet-tdt-ctc-110m-hybrid") - print("Decoding: \(modeStr), Rescoring: \(useRescoring ? "yes" : "no")") - print("Total tests: \(results.count)") - print("Average WER: \(String(format: "%.2f", avgWer))%") - print( - "Keyword Precision/Recall/F1: " + - "\(String(format: "%.2f", keywordPrecision))/" + - "\(String(format: "%.2f", keywordRecall))/" + - "\(String(format: "%.2f", keywordFscore))" - ) - print("Total audio: \(String(format: "%.1f", totalAudioDuration))s") - print("Total processing: \(String(format: "%.1f", totalProcessingTime))s") - if totalProcessingTime > 0 { - print("RTFx: \(String(format: "%.2f", totalAudioDuration / totalProcessingTime))x") - } - print(String(repeating: "=", count: 60)) - - // Sort results by WER descending (worst first) - let sortedResults = results.sorted { r1, r2 in - let wer1 = r1["wer"] as? Double ?? 0 - let wer2 = r2["wer"] as? Double ?? 0 - return wer1 > wer2 - } - - // Save to JSON - let summaryDict: [String: Any] = [ - "totalTests": results.count, - "avgWer": round(avgWer * 100) / 100, - "keywordTruePositives": totalKeywordTruePositives, - "keywordFalsePositives": totalKeywordFalsePositives, - "keywordFalseNegatives": totalKeywordFalseNegatives, - "keywordPredicted": totalKeywordPredicted, - "keywordReference": totalKeywordReference, - "keywordPrecision": round(keywordPrecision * 1000) / 1000, - "keywordRecall": round(keywordRecall * 1000) / 1000, - "keywordFscore": round(keywordFscore * 1000) / 1000, - "totalAudioDuration": round(totalAudioDuration * 100) / 100, - "totalProcessingTime": round(totalProcessingTime * 100) / 100, - ] - - let output: [String: Any] = [ - "model": "parakeet-tdt-ctc-110m-hybrid", - "approach": "single-encoder", - "decodingMode": modeStr, - "rescoring": useRescoring, - "keywordMode": keywordMode.rawValue, - "summary": summaryDict, - "results": sortedResults, - ] - - let jsonData = try JSONSerialization.data(withJSONObject: output, options: [.prettyPrinted, .sortedKeys]) - try jsonData.write(to: URL(fileURLWithPath: outputFile)) - print("\nResults written to: \(outputFile)") - - } catch { - print("ERROR: \(error)") - } - } - - private static func collectFileIds(from dataDir: URL, maxFiles: Int?) throws -> [String] { - var fileIds: [String] = [] - let suffix = ".dictionary.txt" - - let fileManager = FileManager.default - let contents = try fileManager.contentsOfDirectory(at: dataDir, includingPropertiesForKeys: nil) - - for url in contents.sorted(by: { $0.path < $1.path }) { - let name = url.lastPathComponent - if name.hasSuffix(suffix) { - let data = try? Data(contentsOf: url) - if let data = data, !data.isEmpty { - let fileId = String(name.dropLast(suffix.count)) - fileIds.append(fileId) - } - } - } - - if let maxFiles = maxFiles { - return Array(fileIds.prefix(maxFiles)) - } - return fileIds - } - - private static func processFile( - fileId: String, - dataDir: URL, - hybridManager: HybridAsrManager, - spotter: HybridKeywordSpotter, - useRescoring: Bool, - keywordMode: KeywordMode, - keywordIndex: [String: [String]] - ) async throws -> [String: Any]? { - let wavFile = dataDir.appendingPathComponent("\(fileId).wav") - let dictionaryFile = dataDir.appendingPathComponent("\(fileId).dictionary.txt") - let textFile = dataDir.appendingPathComponent("\(fileId).text.txt") - - let fm = FileManager.default - guard fm.fileExists(atPath: wavFile.path), - fm.fileExists(atPath: dictionaryFile.path) - else { - return nil - } - - // Load dictionary words (chunk or file keywords) - let dictionaryWords = try loadDictionaryWords( - fileId: fileId, - dictionaryFile: dictionaryFile, - keywordMode: keywordMode, - keywordIndex: keywordIndex - ) - - // Load reference text - let referenceRaw = - (try? String(contentsOf: textFile, encoding: .utf8))?.trimmingCharacters(in: .whitespacesAndNewlines) ?? "" - - // Get audio samples - let audioFile = try AVAudioFile(forReading: wavFile) - let audioLength = Double(audioFile.length) / audioFile.processingFormat.sampleRate - let format = audioFile.processingFormat - let frameCount = AVAudioFrameCount(audioFile.length) - - guard let buffer = AVAudioPCMBuffer(pcmFormat: format, frameCapacity: frameCount) else { - return nil - } - try audioFile.read(into: buffer) - - // Resample to 16kHz - let converter = AudioConverter() - let samples = try converter.resampleBuffer(buffer) - - // Build custom vocabulary for keyword spotting - var vocabTerms: [CustomVocabularyTerm] = [] - for word in dictionaryWords { - let term = CustomVocabularyTerm( - text: word, - weight: nil, - aliases: nil, - tokenIds: nil, - ctcTokenIds: nil - ) - vocabTerms.append(term) - } - let customVocab = CustomVocabularyContext(terms: vocabTerms) - - // Run Hybrid 110M using new API (TDT transcription + CTC keyword detection) - let rescorerConfig: HybridTextRescorer.Config? = useRescoring ? .default : nil - let hybridResult = try await hybridManager.transcribeHybrid( - audioSamples: samples, - customVocabulary: customVocab, - rescorerConfig: rescorerConfig - ) - - // Skip if empty transcription - if hybridResult.text.isEmpty { - print(" SKIPPED: Empty transcription") - return nil - } - - let detections = hybridResult.keywordDetections - let processingTime = hybridResult.processingTime - - // Use hybrid transcription as hypothesis (may be rescored if enabled) - let hypothesis = hybridResult.text - - // Normalize texts - let referenceNormalized = TextNormalizer.normalize(referenceRaw) - let hypothesisNormalized = TextNormalizer.normalize(hypothesis) - - // Keyword sets for precision/recall - let referenceKeywords = keywordsInText(referenceNormalized, dictionaryWords: dictionaryWords) - let predictedKeywords = keywordsInText(hypothesisNormalized, dictionaryWords: dictionaryWords) - let truePositives = referenceKeywords.intersection(predictedKeywords) - let falsePositives = predictedKeywords.subtracting(referenceKeywords) - let falseNegatives = referenceKeywords.subtracting(predictedKeywords) - let keywordPrecision = predictedKeywords.isEmpty ? 0 : Double(truePositives.count) / Double(predictedKeywords.count) - let keywordRecall = referenceKeywords.isEmpty ? 0 : Double(truePositives.count) / Double(referenceKeywords.count) - let keywordFscore = - (keywordPrecision + keywordRecall) > 0 - ? 2 * keywordPrecision * keywordRecall / (keywordPrecision + keywordRecall) - : 0 - - let referenceWords = referenceNormalized.components(separatedBy: CharacterSet.whitespacesAndNewlines).filter { - !$0.isEmpty - } - let hypothesisWords = hypothesisNormalized.components(separatedBy: CharacterSet.whitespacesAndNewlines).filter { - !$0.isEmpty - } - - // Calculate WER - let wer: Double - if referenceWords.isEmpty { - wer = hypothesisWords.isEmpty ? 0.0 : 1.0 - } else { - wer = calculateWER(reference: referenceWords, hypothesis: hypothesisWords) - } - - // Count dictionary detections for debugging - let minCtcScore: Float = -15.0 - var detectionDetails: [[String: Any]] = [] - var foundWords: Set = [] - - // CTC detections - for detection in detections { - let inRef = referenceKeywords.contains(detection.term.text.lowercased()) - let detail: [String: Any] = [ - "word": detection.term.text, - "score": round(Double(detection.score) * 100) / 100, - "startTime": round(detection.startTime * 100) / 100, - "endTime": round(detection.endTime * 100) / 100, - "source": "ctc", - "inReference": inRef, - ] - detectionDetails.append(detail) - - if detection.score >= minCtcScore { - foundWords.insert(detection.term.text.lowercased()) - } - } - - // Fallback: check hypothesis for dictionary words not found by CTC - let hypothesisLower = hypothesis.lowercased() - for word in dictionaryWords { - let wordLower = word.lowercased() - if !foundWords.contains(wordLower) { - let pattern = "\\b\(NSRegularExpression.escapedPattern(for: wordLower))\\b" - if let regex = try? NSRegularExpression(pattern: pattern, options: []), - regex.firstMatch( - in: hypothesisLower, options: [], - range: NSRange(hypothesisLower.startIndex..., in: hypothesisLower)) != nil - { - foundWords.insert(wordLower) - let inRef = referenceKeywords.contains(wordLower) - let detail: [String: Any] = [ - "word": word, - "score": 0.0, - "startTime": 0.0, - "endTime": 0.0, - "source": "hypothesis", - "inReference": inRef, - ] - detectionDetails.append(detail) - } - } - } - - let result: [String: Any] = [ - "fileId": fileId, - "reference": referenceNormalized, - "hypothesis": hypothesisNormalized, - "wer": round(wer * 10000) / 100, - "dictFound": predictedKeywords.count, - "dictTotal": referenceKeywords.count, - "keywordPredicted": predictedKeywords.count, - "keywordReference": referenceKeywords.count, - "keywordTruePositives": truePositives.count, - "keywordFalsePositives": falsePositives.count, - "keywordFalseNegatives": falseNegatives.count, - "keywordPrecision": round(keywordPrecision * 1000) / 1000, - "keywordRecall": round(keywordRecall * 1000) / 1000, - "keywordFscore": round(keywordFscore * 1000) / 1000, - "audioLength": round(audioLength * 100) / 100, - "processingTime": round(processingTime * 1000) / 1000, - "ctcDetections": detectionDetails, - ] - return result - } - - private static func calculateWER(reference: [String], hypothesis: [String]) -> Double { - if reference.isEmpty { - return hypothesis.isEmpty ? 0.0 : 1.0 - } - - let m = reference.count - let n = hypothesis.count - var dp = Array(repeating: Array(repeating: 0, count: n + 1), count: m + 1) - - for i in 0...m { dp[i][0] = i } - for j in 0...n { dp[0][j] = j } - - for i in 1...m { - for j in 1...n { - if reference[i - 1] == hypothesis[j - 1] { - dp[i][j] = dp[i - 1][j - 1] - } else { - dp[i][j] = min(dp[i - 1][j - 1], min(dp[i - 1][j], dp[i][j - 1])) + 1 - } - } - } - - return Double(dp[m][n]) / Double(m) - } - - private static func printUsage() { - print( - """ - Hybrid 110M Earnings Benchmark (Single Encoder) - - Usage: fluidaudio hybrid-earnings-benchmark [options] - - This benchmark uses ONLY the Hybrid 110M model: - - Single encoder provides CTC log-probs - - CTC greedy decode for transcription - - CTC keyword spotting from same encoder output - - Options: - --max-files Maximum number of files to process - --output, -o Output JSON file (default: hybrid_earnings_benchmark.json) - --keyword-mode Keyword mode: chunk or file (default: chunk) - - Compare with: - fluidaudio ctc-earnings-benchmark (Canary-CTC + TDT 0.6B, two encoders) - """) - } - - private static func parseKeywordMode(_ value: String) -> KeywordMode? { - switch value.lowercased() { - case "chunk", "chunk-keywords": - return .chunk - case "file", "file-keywords": - return .file - default: - return nil - } - } - - private static func parentId(from fileId: String) -> String { - guard let range = fileId.range(of: "_chunk") else { - return fileId - } - return String(fileId[.. [String: [String]] { - guard keywordMode == .file else { - return [:] - } - - var index: [String: Set] = [:] - let suffix = ".dictionary.txt" - let fileManager = FileManager.default - let contents = try fileManager.contentsOfDirectory(at: dataDir, includingPropertiesForKeys: nil) - - for url in contents { - let name = url.lastPathComponent - guard name.hasSuffix(suffix) else { continue } - let fileId = String(name.dropLast(suffix.count)) - let parent = parentId(from: fileId) - let words = try loadDictionaryWords(from: url) - var set = index[parent] ?? Set() - set.formUnion(words) - index[parent] = set - } - - return index.mapValues { Array($0).sorted() } - } - - private static func loadDictionaryWords( - fileId: String, - dictionaryFile: URL, - keywordMode: KeywordMode, - keywordIndex: [String: [String]] - ) throws -> [String] { - switch keywordMode { - case .chunk: - return try loadDictionaryWords(from: dictionaryFile) - case .file: - let parent = parentId(from: fileId) - if let words = keywordIndex[parent] { - return words - } - return try loadDictionaryWords(from: dictionaryFile) - } - } - - private static func loadDictionaryWords(from url: URL) throws -> [String] { - let dictionaryContent = try String(contentsOf: url, encoding: .utf8) - return dictionaryContent - .components(separatedBy: .newlines) - .map { $0.trimmingCharacters(in: .whitespacesAndNewlines) } - .filter { !$0.isEmpty } - } - - private static func keywordsInText(_ text: String, dictionaryWords: [String]) -> Set { - let textLower = text.lowercased() - var result: Set = [] - - for word in dictionaryWords { - let wordLower = word.lowercased() - let pattern = "\\b\(NSRegularExpression.escapedPattern(for: wordLower))\\b" - guard let regex = try? NSRegularExpression(pattern: pattern, options: []) else { continue } - let range = NSRange(textLower.startIndex..., in: textLower) - if regex.firstMatch(in: textLower, options: [], range: range) != nil { - result.insert(wordLower) - } - } - return result - } -} -#endif diff --git a/config.json b/config.json deleted file mode 100644 index 9e26dfeeb6e641a33dae4961196235bdb965b21b..0000000000000000000000000000000000000000 --- a/config.json +++ /dev/null @@ -1 +0,0 @@ -{} \ No newline at end of file diff --git a/convert/.DS_Store b/convert/.DS_Store deleted file mode 100644 index 75ff82f86c29bd5d5eabcec4b41e4db80ef621da..0000000000000000000000000000000000000000 Binary files a/convert/.DS_Store and /dev/null differ diff --git a/convert/parakeet-tdt-ctc-110m/convert_tdt_decoder.py b/convert/parakeet-tdt-ctc-110m/convert_tdt_decoder.py deleted file mode 100644 index 4ed15a38318bb788204bfdbd2bc466cff9ff6e5c..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/convert_tdt_decoder.py +++ /dev/null @@ -1,323 +0,0 @@ -#!/usr/bin/env python3 -""" -Convert Parakeet TDT-CTC 110M decoder components to CoreML. - -This script exports the TDT decoder (prediction network) and joint network -with the SAME format as the working 0.6B model: -- JointDecision outputs token_id, token_prob, duration (argmax done inside) -- Uses shape [1, dim, 1] for encoder/decoder steps -- Matches the interface expected by TdtDecoderV3 -""" - -import argparse -import os -import torch -import torch.nn.functional as F -import coremltools as ct -import numpy as np -from pathlib import Path - -# NeMo imports -import nemo.collections.asr as nemo_asr - - -def get_model_config(model): - """Extract model configuration.""" - encoder_dim = None - pred_hidden = 640 # Default for parakeet models - num_layers = 1 - vocab_size = 1024 - num_durations = 5 - - # Get encoder dimension - if hasattr(model, 'encoder'): - encoder = model.encoder - if hasattr(encoder, 'd_model'): - encoder_dim = encoder.d_model - elif hasattr(encoder, '_feat_out'): - encoder_dim = encoder._feat_out - - # Get decoder config - if hasattr(model, 'decoder'): - decoder = model.decoder - if hasattr(decoder, 'pred_hidden'): - pred_hidden = decoder.pred_hidden - if hasattr(decoder, 'pred_rnn_layers'): - num_layers = decoder.pred_rnn_layers - - # Get joint config - if hasattr(model, 'joint'): - joint = model.joint - if hasattr(joint, 'num_extra_outputs'): - num_durations = joint.num_extra_outputs - if hasattr(joint, 'num_classes'): - vocab_size = joint.num_classes - num_durations - - return { - 'encoder_dim': encoder_dim, - 'pred_hidden': pred_hidden, - 'num_layers': num_layers, - 'vocab_size': vocab_size, - 'num_durations': num_durations, - } - - -class DecoderWrapper(torch.nn.Module): - """ - Wrapper for the RNNT/TDT decoder (prediction network). - - Matches 0.6B format: - - Input: targets[1,1], target_lengths[1], h_in[num_layers,1,pred_hidden], c_in[...] - - Output: decoder_output[1,pred_hidden,2], h_out[...], c_out[...] - """ - - def __init__(self, decoder, pred_hidden): - super().__init__() - self.decoder = decoder - self.pred_hidden = pred_hidden - - def forward(self, targets, target_lengths, h_in, c_in): - """ - Args: - targets: [1, 1] - previous token ID - target_lengths: [1] - always 1 - h_in: [num_layers, 1, pred_hidden] - c_in: [num_layers, 1, pred_hidden] - Returns: - decoder_output: [1, pred_hidden, 2] - prediction network output (transposed) - h_out: [num_layers, 1, pred_hidden] - c_out: [num_layers, 1, pred_hidden] - """ - state = (h_in, c_in) - # pred_output shape: [batch, time, pred_hidden] = [1, 1, pred_hidden] - pred_output, new_state = self.decoder.predict(targets, state=state, add_sos=False) - h_out, c_out = new_state - - # Transpose to [batch, pred_hidden, time] and concat two time steps - # (0.6B outputs [1, 640, 2] - we match this by duplicating) - pred_transposed = pred_output.transpose(1, 2) # [1, pred_hidden, 1] - decoder_output = torch.cat([pred_transposed, pred_transposed], dim=2) # [1, pred_hidden, 2] - - return decoder_output, h_out, c_out - - -class JointWrapper(torch.nn.Module): - """ - Wrapper for the TDT joint network with internal argmax. - - Matches 0.6B format: - - Input: encoder_step[1,encoder_dim,1], decoder_step[1,pred_hidden,1] - - Output: token_id[1,1,1], token_prob[1,1,1], duration[1,1,1] - """ - - def __init__(self, joint, vocab_size, num_durations=5): - super().__init__() - self.joint = joint - self.vocab_size = vocab_size - self.num_durations = num_durations - - def forward(self, encoder_step, decoder_step): - """ - Args: - encoder_step: [1, encoder_dim, 1] - decoder_step: [1, pred_hidden, 1] - Returns: - token_id: [1, 1, 1] - argmax token ID - token_prob: [1, 1, 1] - probability of selected token - duration: [1, 1, 1] - argmax duration bin - """ - # Transpose to [batch, 1, dim] for joint network - enc = encoder_step.transpose(1, 2) # [1, 1, encoder_dim] - dec = decoder_step.transpose(1, 2) # [1, 1, pred_hidden] - - # Run joint network - # Joint output: [1, 1, 1, vocab_size + 1 (blank) + num_durations] - joint_out = self.joint.joint(enc, dec) - - # Debug: print shape on first call - if not hasattr(self, '_debug_printed'): - self._debug_printed = True - print(f" Joint output shape: {joint_out.shape}") - print(f" Expected: vocab={self.vocab_size} + blank=1 + durations={self.num_durations} = {self.vocab_size + 1 + self.num_durations}") - - # Split: token logits include vocab + blank, durations are separate - # vocab_size = 1024 tokens (0-1023), blank = index 1024, durations = indices 1025+ - num_tokens = self.vocab_size + 1 # Include blank at vocab_size - logits = joint_out[..., :num_tokens] # [1, 1, 1, vocab_size + 1] - duration_logits = joint_out[..., num_tokens:] # [1, 1, 1, num_durations] - - # Apply softmax and get probabilities - probs = F.softmax(logits, dim=-1) - - # Argmax for token - token_id = torch.argmax(logits, dim=-1, keepdim=True) # [1, 1, 1, 1] - token_id = token_id.squeeze(-1) # [1, 1, 1] - - # Get probability of selected token - token_prob = torch.gather(probs, -1, token_id.unsqueeze(-1)) # [1, 1, 1, 1] - token_prob = token_prob.squeeze(-1) # [1, 1, 1] - - # Argmax for duration - duration = torch.argmax(duration_logits, dim=-1, keepdim=False) # [1, 1, 1] - - return token_id.int(), token_prob, duration.int() - - -def convert_decoder(model, config, output_dir: Path): - """Convert decoder to CoreML.""" - print(f"Converting Decoder...") - print(f" pred_hidden={config['pred_hidden']}, num_layers={config['num_layers']}") - - wrapper = DecoderWrapper(model.decoder, config['pred_hidden']) - wrapper.eval() - - # Create example inputs - targets = torch.zeros(1, 1, dtype=torch.long) - target_lengths = torch.ones(1, dtype=torch.long) - h_in = torch.zeros(config['num_layers'], 1, config['pred_hidden']) - c_in = torch.zeros(config['num_layers'], 1, config['pred_hidden']) - - # Trace the model - with torch.no_grad(): - traced = torch.jit.trace(wrapper, (targets, target_lengths, h_in, c_in)) - - # Convert to CoreML - mlmodel = ct.convert( - traced, - inputs=[ - ct.TensorType(name="targets", shape=(1, 1), dtype=np.int32), - ct.TensorType(name="target_lengths", shape=(1,), dtype=np.int32), - ct.TensorType(name="h_in", shape=(config['num_layers'], 1, config['pred_hidden']), dtype=np.float32), - ct.TensorType(name="c_in", shape=(config['num_layers'], 1, config['pred_hidden']), dtype=np.float32), - ], - outputs=[ - ct.TensorType(name="decoder_output"), - ct.TensorType(name="h_out"), - ct.TensorType(name="c_out"), - ], - minimum_deployment_target=ct.target.iOS17, - compute_precision=ct.precision.FLOAT16, - ) - - # Add metadata - mlmodel.author = "Fluid Inference" - mlmodel.short_description = "Hybrid TDT Decoder (110M)" - - # Save - output_path = output_dir / "Decoder.mlpackage" - mlmodel.save(str(output_path)) - print(f" Saved to {output_path}") - - return mlmodel - - -def convert_joint(model, config, output_dir: Path): - """Convert joint network to CoreML.""" - print(f"Converting JointDecision...") - print(f" encoder_dim={config['encoder_dim']}, pred_hidden={config['pred_hidden']}") - print(f" vocab_size={config['vocab_size']}, num_durations={config['num_durations']}") - - wrapper = JointWrapper( - model.joint, - vocab_size=config['vocab_size'], - num_durations=config['num_durations'] - ) - wrapper.eval() - - # Create example inputs - shape [1, dim, 1] - encoder_step = torch.randn(1, config['encoder_dim'], 1) - decoder_step = torch.randn(1, config['pred_hidden'], 1) - - # Trace the model - with torch.no_grad(): - traced = torch.jit.trace(wrapper, (encoder_step, decoder_step)) - - # Convert to CoreML - mlmodel = ct.convert( - traced, - inputs=[ - ct.TensorType(name="encoder_step", shape=(1, config['encoder_dim'], 1), dtype=np.float32), - ct.TensorType(name="decoder_step", shape=(1, config['pred_hidden'], 1), dtype=np.float32), - ], - outputs=[ - ct.TensorType(name="token_id"), - ct.TensorType(name="token_prob"), - ct.TensorType(name="duration"), - ], - minimum_deployment_target=ct.target.iOS17, - compute_precision=ct.precision.FLOAT16, - ) - - # Add metadata - mlmodel.author = "Fluid Inference" - mlmodel.short_description = "Hybrid Joint Decision (110M)" - - # Save - output_path = output_dir / "JointDecision.mlpackage" - mlmodel.save(str(output_path)) - print(f" Saved to {output_path}") - - return mlmodel - - -def main(): - parser = argparse.ArgumentParser(description="Convert TDT decoder to CoreML (0.6B format)") - parser.add_argument( - "--model-name", - default="nvidia/parakeet-tdt_ctc-110m", - help="NeMo model name or path" - ) - parser.add_argument( - "--output-dir", - type=Path, - default=Path("./output"), - help="Output directory for CoreML models" - ) - args = parser.parse_args() - - # Create output directory - args.output_dir.mkdir(parents=True, exist_ok=True) - - # Load model - print(f"Loading model: {args.model_name}") - model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(args.model_name) - model.eval() - - # Get model configuration - config = get_model_config(model) - - # Auto-detect encoder dim if not found - if config['encoder_dim'] is None: - print("Auto-detecting encoder dimension...") - dummy_audio = torch.randn(1, 16000) - dummy_length = torch.tensor([16000]) - with torch.no_grad(): - enc_out, enc_len = model.encoder( - audio_signal=dummy_audio, - length=dummy_length - ) - config['encoder_dim'] = enc_out.shape[-1] - - print(f"\nModel config:") - for k, v in config.items(): - print(f" {k}: {v}") - - # Convert components - print() - convert_decoder(model, config, args.output_dir) - convert_joint(model, config, args.output_dir) - - print("\nConversion complete!") - print(f"Models saved to: {args.output_dir}") - print("\nNext steps:") - print("1. Compile to .mlmodelc:") - print(f" cd {args.output_dir}") - print(" xcrun coremlcompiler compile Decoder.mlpackage .") - print(" xcrun coremlcompiler compile JointDecision.mlpackage .") - print("2. Copy to model cache:") - print(" cp -r Decoder.mlmodelc JointDecision.mlmodelc ~/Library/Application\\ Support/FluidAudio/Models/parakeet-ctc-110m-coreml/") - print("3. Test with: swift run fluidaudio hybrid-earnings-benchmark --max-files 1") - - -if __name__ == "__main__": - main() diff --git a/convert/parakeet-tdt-ctc-110m/coreml/audio/yc_first_minute_16k_15s.wav b/convert/parakeet-tdt-ctc-110m/coreml/audio/yc_first_minute_16k_15s.wav deleted file mode 100644 index 0b8040f5f124d20a1d7e812c576c831a7573eddf..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/audio/yc_first_minute_16k_15s.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c79c8bc763b4efccb3e12f199ec0a59aa2edc5e9e4d21ca70fde8f36762d4147 -size 480078 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/analytics/coremldata.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 4093e2df453ec508a533d293a574198b2372845b..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fc681823d92eca3dbece3a30c975afa7251eedae0e718b07ffbf1a8b4313b87e -size 243 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/coremldata.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/coremldata.bin deleted file mode 100644 index db4ae8de920867977480465fffece41bc606c0a2..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2ebec8fc38c063de4b2159e21b1f981309fa5947c24d7e4883aca20f7c15fbb9 -size 377 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/metadata.json b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/metadata.json deleted file mode 100644 index 4abe30bf0b35b72d2c9c013b61cc78b4f26a85e1..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/metadata.json +++ /dev/null @@ -1,66 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M CTC decoder head", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 188 × 1025)", - "shortDescription" : "", - "shape" : "[1, 188, 1025]", - "name" : "ctc_logits", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.cast" : 2, - "Ios17.conv" : 1, - "Ios17.transpose" : 1, - "Ios16.softmax" : 1, - "Ios17.log" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - 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index 67b3b5f87ce33caed71f9233828f90775bb8ac9d..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/model.mil +++ /dev/null @@ -1,24 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor encoder_output) { - tensor var_4 = const()[name = tensor("op_4"), val = tensor(-1)]; - tensor var_18_pad_type_0 = const()[name = tensor("op_18_pad_type_0"), val = tensor("valid")]; - tensor var_18_strides_0 = const()[name = tensor("op_18_strides_0"), val = tensor([1])]; - tensor var_18_pad_0 = const()[name = tensor("op_18_pad_0"), val = tensor([0, 0])]; - tensor var_18_dilations_0 = const()[name = tensor("op_18_dilations_0"), val = tensor([1])]; - tensor var_18_groups_0 = const()[name = tensor("op_18_groups_0"), val = tensor(1)]; - tensor encoder_output_to_fp16_dtype_0 = const()[name = tensor("encoder_output_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor module_decoder_layers_0_weight_to_fp16 = const()[name = tensor("module_decoder_layers_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor module_decoder_layers_0_bias_to_fp16 = const()[name = tensor("module_decoder_layers_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049728)))]; - tensor encoder_output_to_fp16 = cast(dtype = encoder_output_to_fp16_dtype_0, x = encoder_output)[name = tensor("cast_1")]; - tensor var_18_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = var_18_dilations_0, groups = var_18_groups_0, pad = var_18_pad_0, pad_type = var_18_pad_type_0, strides = var_18_strides_0, weight = module_decoder_layers_0_weight_to_fp16, x = encoder_output_to_fp16)[name = tensor("op_18_cast_fp16")]; - tensor input_perm_0 = const()[name = tensor("input_perm_0"), val = tensor([0, 2, 1])]; - tensor input_cast_fp16 = transpose(perm = input_perm_0, x = var_18_cast_fp16)[name = tensor("transpose_0")]; - tensor out_objects_softmax_cast_fp16 = softmax(axis = var_4, x = input_cast_fp16)[name = tensor("out_objects_softmax_cast_fp16")]; - tensor out_objects_epsilon_0 = const()[name = tensor("out_objects_epsilon_0"), val = tensor(0x1p-149)]; - tensor out_objects_cast_fp16 = log(epsilon = out_objects_epsilon_0, x = out_objects_softmax_cast_fp16)[name = tensor("out_objects_cast_fp16")]; - tensor out_objects_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("out_objects_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor ctc_logits = cast(dtype = out_objects_cast_fp16_to_fp32_dtype_0, x = out_objects_cast_fp16)[name = tensor("cast_0")]; - } -> (ctc_logits); -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/weights/weight.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/weights/weight.bin deleted file mode 100644 index 23cedd570125191064c6111997f08c48898245f7..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fb9bead064427ffcb7529c0e3f378e421b4dde8e6d81447b6d1ca3352ca850e1 -size 1051842 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/analytics/coremldata.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index dd015548a03cc453e6477a24374e8fee955e75ef..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 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"storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.squeeze" : 2, - "Ios17.gather" : 1, - "Ios17.cast" : 6, - "Ios17.lstm" : 1, - "Ios17.transpose" : 2, - "Identity" : 1, - "Ios17.expandDims" : 2 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1]", - "name" : "targets", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : 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a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/model.mil b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/model.mil deleted file mode 100644 index f69c7247525bc9d95f9341acc4f11eca709c7d00..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/model.mil +++ /dev/null @@ -1,45 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor c_in, tensor h_in, tensor target_length, tensor targets) { - tensor y_axis_0 = const()[name = tensor("y_axis_0"), val = tensor(0)]; - tensor y_batch_dims_0 = const()[name = tensor("y_batch_dims_0"), val = tensor(0)]; - tensor y_validate_indices_0 = const()[name = tensor("y_validate_indices_0"), val = tensor(false)]; - tensor module_prediction_embed_weight_to_fp16 = const()[name = tensor("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor targets_to_int16_dtype_0 = const()[name = tensor("targets_to_int16_dtype_0"), val = tensor("int16")]; - tensor targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor("cast_8")]; - tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = targets_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = tensor("y_cast_fp16_cast_uint16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([1, 0, 2])]; - tensor input_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; - tensor h_in_to_fp16_dtype_0 = const()[name = tensor("h_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor("cast_7")]; - tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = h_in_to_fp16)[name = tensor("input_lstm_h0_squeeze_cast_fp16")]; - tensor input_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; - tensor c_in_to_fp16_dtype_0 = const()[name = tensor("c_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor("cast_6")]; - tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = c_in_to_fp16)[name = tensor("input_lstm_c0_squeeze_cast_fp16")]; - tensor input_direction_0 = const()[name = tensor("input_direction_0"), val = tensor("forward")]; - tensor input_output_sequence_0 = const()[name = tensor("input_output_sequence_0"), val = tensor(true)]; - tensor input_recurrent_activation_0 = const()[name = tensor("input_recurrent_activation_0"), val = tensor("sigmoid")]; - tensor input_cell_activation_0 = const()[name = tensor("input_cell_activation_0"), val = tensor("tanh")]; - tensor input_activation_0 = const()[name = tensor("input_activation_0"), val = tensor("tanh")]; - tensor concat_1_to_fp16 = const()[name = tensor("concat_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312128)))]; - tensor concat_2_to_fp16 = const()[name = tensor("concat_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4588992)))]; - tensor concat_0_to_fp16 = const()[name = tensor("concat_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7865856)))]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = tensor("transpose_2")]; - tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_0_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor obj_3_axes_0 = const()[name = tensor("obj_3_axes_0"), val = tensor([0])]; - tensor obj_3_cast_fp16 = expand_dims(axes = obj_3_axes_0, x = input_cast_fp16_1)[name = tensor("obj_3_cast_fp16")]; - tensor obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_3_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_axes_0 = const()[name = tensor("obj_axes_0"), val = tensor([0])]; - tensor obj_cast_fp16 = expand_dims(axes = obj_axes_0, x = input_cast_fp16_2)[name = tensor("obj_cast_fp16")]; - tensor obj_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([1, 2, 0])]; - tensor transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("transpose_0_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor("transpose_1")]; - tensor decoder = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = tensor("cast_3")]; - tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = tensor("cast_4")]; - tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = tensor("cast_5")]; - tensor target_length_tmp = identity(x = target_length)[name = tensor("target_length_tmp")]; - } -> (decoder, h_out, c_out); -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/weights/weight.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/weights/weight.bin deleted file mode 100644 index 4543ad4ea89b21ebed0de0d5ec3975d07eace7fb..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:dd90b58597ee2c172c672dffe13b1110898ba07394c1a15efc96cc8c6b18411b -size 7871040 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/analytics/coremldata.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index d2787d4e5376d7f894d2b0a5a09f497a9b486c73..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 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"Ios17.transpose" : 123, - "Split" : 17, - "Ios17.expandDims" : 17, - "Select" : 51, - "Ios17.add" : 128, - "Tile" : 8, - "Ios17.sliceByIndex" : 34, - "Ios16.sigmoid" : 17, - "Pad" : 34, - "Ios17.logicalNot" : 2, - "Ios17.layerNorm" : 85, - "Ios16.silu" : 51, - "Ios17.less" : 5, - "Ios17.sub" : 3, - "Ios17.conv" : 56, - "Ios16.relu" : 3, - "Ios17.linear" : 137, - "Ios17.cast" : 11, - "Ios17.floorDiv" : 3, - "Ios17.mul" : 77 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 80 × 1501)", - "shortDescription" : "", - "shape" : "[1, 80, 1501]", - "name" : "mel_features", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "generatedClassName" : "parakeet_encoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/model.mil b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/model.mil deleted file mode 100644 index 344bd1abf313c03c9ed9287c00893739f5d361bd..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/model.mil +++ /dev/null @@ -1,2690 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor mel_features, tensor mel_length) { - tensor var_23 = const()[name = tensor("op_23"), val = tensor(-1)]; - tensor x_1_perm_0 = const()[name = tensor("x_1_perm_0"), val = tensor([0, 2, 1])]; - tensor mel_features_to_fp16_dtype_0 = const()[name = tensor("mel_features_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor tensor_1_axes_0 = const()[name = tensor("tensor_1_axes_0"), val = tensor([1])]; - tensor mel_features_to_fp16 = cast(dtype = mel_features_to_fp16_dtype_0, x = mel_features)[name = tensor("cast_182")]; - tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_features_to_fp16)[name = tensor("transpose_207")]; - tensor tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_1_cast_fp16)[name = tensor("tensor_1_cast_fp16")]; - tensor expand_dims_0 = const()[name = tensor("expand_dims_0"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 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608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 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1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500]])]; - tensor var_87_axes_0 = const()[name = tensor("op_87_axes_0"), val = tensor([1])]; - tensor var_87 = expand_dims(axes = var_87_axes_0, x = mel_length)[name = tensor("op_87")]; - tensor time_mask_1 = less(x = expand_dims_0, y = var_87)[name = tensor("time_mask_1")]; - tensor var_89_axes_0 = const()[name = tensor("op_89_axes_0"), val = tensor([-1])]; - tensor var_89 = expand_dims(axes = var_89_axes_0, x = time_mask_1)[name = tensor("op_89")]; - tensor var_91_reps_0 = const()[name = tensor("op_91_reps_0"), val = tensor([1, 1, 80])]; - tensor var_91 = tile(reps = var_91_reps_0, x = var_89)[name = tensor("op_91")]; - tensor var_97_axes_0 = const()[name = tensor("op_97_axes_0"), val = tensor([1])]; - tensor cast_3_to_fp16_dtype_0 = const()[name = tensor("cast_3_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_91_to_fp16 = cast(dtype = cast_3_to_fp16_dtype_0, x = var_91)[name = tensor("cast_181")]; - tensor var_97_cast_fp16 = expand_dims(axes = var_97_axes_0, x = var_91_to_fp16)[name = tensor("op_97_cast_fp16")]; - tensor input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_97_cast_fp16)[name = tensor("input_1_cast_fp16")]; - tensor tensor_3_pad_type_0 = const()[name = tensor("tensor_3_pad_type_0"), val = tensor("custom")]; - tensor tensor_3_pad_0 = const()[name = tensor("tensor_3_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_3_strides_0 = const()[name = tensor("tensor_3_strides_0"), val = tensor([2, 2])]; - tensor tensor_3_dilations_0 = const()[name = tensor("tensor_3_dilations_0"), val = tensor([1, 1])]; - tensor tensor_3_groups_0 = const()[name = tensor("tensor_3_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_0_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor module_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4736)))]; - tensor tensor_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("tensor_3_cast_fp16")]; - tensor cast_1_to_fp16_dtype_0 = const()[name = tensor("cast_1_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_108_promoted_to_fp16 = const()[name = tensor("op_108_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor mel_length_to_fp16 = cast(dtype = cast_1_to_fp16_dtype_0, x = mel_length)[name = tensor("cast_180")]; - tensor var_109_cast_fp16 = add(x = mel_length_to_fp16, y = var_108_promoted_to_fp16)[name = tensor("op_109_cast_fp16")]; - tensor var_110_promoted_to_fp16 = const()[name = tensor("op_110_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_111_cast_fp16 = add(x = var_109_cast_fp16, y = var_110_promoted_to_fp16)[name = tensor("op_111_cast_fp16")]; - tensor var_112_promoted_to_fp16 = const()[name = tensor("op_112_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_113_cast_fp16 = sub(x = var_111_cast_fp16, y = var_112_promoted_to_fp16)[name = tensor("op_113_cast_fp16")]; - tensor var_21_promoted_to_fp16 = const()[name = tensor("op_21_promoted_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_0_cast_fp16 = floor_div(x = var_113_cast_fp16, y = var_21_promoted_to_fp16)[name = tensor("floor_div_0_cast_fp16")]; - tensor var_115_promoted_to_fp16 = const()[name = tensor("op_115_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_115_promoted_to_fp16)[name = tensor("current_lengths_3_cast_fp16")]; - tensor cast_4_dtype_0 = const()[name = tensor("cast_4_dtype_0"), val = tensor("int32")]; - tensor expand_dims_1 = const()[name = tensor("expand_dims_1"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750]])]; - tensor var_124_axes_0 = const()[name = tensor("op_124_axes_0"), val = tensor([1])]; - tensor current_lengths_3_cast_fp16_to_int32 = cast(dtype = cast_4_dtype_0, x = current_lengths_3_cast_fp16)[name = tensor("cast_179")]; - tensor var_124 = expand_dims(axes = var_124_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = tensor("op_124")]; - tensor time_mask_3 = less(x = expand_dims_1, y = var_124)[name = tensor("time_mask_3")]; - tensor var_126_axes_0 = const()[name = tensor("op_126_axes_0"), val = tensor([-1])]; - tensor var_126 = expand_dims(axes = var_126_axes_0, x = time_mask_3)[name = tensor("op_126")]; - tensor var_128_reps_0 = const()[name = tensor("op_128_reps_0"), val = tensor([1, 1, 40])]; - tensor var_128 = tile(reps = var_128_reps_0, x = var_126)[name = tensor("op_128")]; - tensor var_134_axes_0 = const()[name = tensor("op_134_axes_0"), val = tensor([1])]; - tensor cast_5_to_fp16_dtype_0 = const()[name = tensor("cast_5_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_128_to_fp16 = cast(dtype = cast_5_to_fp16_dtype_0, x = var_128)[name = tensor("cast_178")]; - tensor var_134_cast_fp16 = expand_dims(axes = var_134_axes_0, x = var_128_to_fp16)[name = tensor("op_134_cast_fp16")]; - tensor expanded_mask_3_reps_0 = const()[name = tensor("expanded_mask_3_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_134_cast_fp16)[name = tensor("expanded_mask_3_cast_fp16")]; - tensor input_3_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor tensor_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor("tensor_5_cast_fp16")]; - tensor input_5_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor tensor_7_pad_type_0 = const()[name = tensor("tensor_7_pad_type_0"), val = tensor("custom")]; - tensor tensor_7_pad_0 = const()[name = tensor("tensor_7_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_7_strides_0 = const()[name = tensor("tensor_7_strides_0"), val = tensor([2, 2])]; - tensor tensor_7_groups_0 = const()[name = tensor("tensor_7_groups_0"), val = tensor(256)]; - tensor tensor_7_dilations_0 = const()[name = tensor("tensor_7_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_2_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5312)))]; - tensor module_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9984)))]; - tensor tensor_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16, x = input_5_cast_fp16)[name = tensor("tensor_7_cast_fp16")]; - tensor var_154_promoted_to_fp16 = const()[name = tensor("op_154_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_155_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_154_promoted_to_fp16)[name = tensor("op_155_cast_fp16")]; - tensor var_156_promoted_to_fp16 = const()[name = tensor("op_156_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_157_cast_fp16 = add(x = var_155_cast_fp16, y = var_156_promoted_to_fp16)[name = tensor("op_157_cast_fp16")]; - tensor var_158_promoted_to_fp16 = const()[name = tensor("op_158_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_159_cast_fp16 = sub(x = var_157_cast_fp16, y = var_158_promoted_to_fp16)[name = tensor("op_159_cast_fp16")]; - tensor var_21_promoted_1_to_fp16 = const()[name = tensor("op_21_promoted_1_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_1_cast_fp16 = floor_div(x = var_159_cast_fp16, y = var_21_promoted_1_to_fp16)[name = tensor("floor_div_1_cast_fp16")]; - tensor var_161_promoted_to_fp16 = const()[name = tensor("op_161_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_161_promoted_to_fp16)[name = tensor("current_lengths_5_cast_fp16")]; - tensor cast_6_dtype_0 = const()[name = tensor("cast_6_dtype_0"), val = tensor("int32")]; - tensor expand_dims_2 = const()[name = tensor("expand_dims_2"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375]])]; - tensor var_170_axes_0 = const()[name = tensor("op_170_axes_0"), val = tensor([1])]; - tensor current_lengths_5_cast_fp16_to_int32 = cast(dtype = cast_6_dtype_0, x = current_lengths_5_cast_fp16)[name = tensor("cast_177")]; - tensor var_170 = expand_dims(axes = var_170_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = tensor("op_170")]; - tensor time_mask_5 = less(x = expand_dims_2, y = var_170)[name = tensor("time_mask_5")]; - tensor var_172_axes_0 = const()[name = tensor("op_172_axes_0"), val = tensor([-1])]; - tensor var_172 = expand_dims(axes = var_172_axes_0, x = time_mask_5)[name = tensor("op_172")]; - tensor var_174_reps_0 = const()[name = tensor("op_174_reps_0"), val = tensor([1, 1, 20])]; - tensor var_174 = tile(reps = var_174_reps_0, x = var_172)[name = tensor("op_174")]; - tensor var_180_axes_0 = const()[name = tensor("op_180_axes_0"), val = tensor([1])]; - tensor cast_7_to_fp16_dtype_0 = const()[name = tensor("cast_7_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_174_to_fp16 = cast(dtype = cast_7_to_fp16_dtype_0, x = var_174)[name = tensor("cast_176")]; - tensor var_180_cast_fp16 = expand_dims(axes = var_180_axes_0, x = var_174_to_fp16)[name = tensor("op_180_cast_fp16")]; - tensor expanded_mask_7_reps_0 = const()[name = tensor("expanded_mask_7_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_180_cast_fp16)[name = tensor("expanded_mask_7_cast_fp16")]; - tensor input_7_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor tensor_9_pad_type_0 = const()[name = tensor("tensor_9_pad_type_0"), val = tensor("valid")]; - tensor tensor_9_strides_0 = const()[name = tensor("tensor_9_strides_0"), val = tensor([1, 1])]; - tensor tensor_9_pad_0 = const()[name = tensor("tensor_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor tensor_9_dilations_0 = const()[name = tensor("tensor_9_dilations_0"), val = tensor([1, 1])]; - tensor tensor_9_groups_0 = const()[name = tensor("tensor_9_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_3_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_3_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10560)))]; - tensor module_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141696)))]; - tensor tensor_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("tensor_9_cast_fp16")]; - tensor input_9_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; - tensor tensor_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor("tensor_11_cast_fp16")]; - tensor input_11_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_11_cast_fp16")]; - tensor tensor_13_pad_type_0 = const()[name = tensor("tensor_13_pad_type_0"), val = tensor("custom")]; - tensor tensor_13_pad_0 = const()[name = tensor("tensor_13_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_13_strides_0 = const()[name = tensor("tensor_13_strides_0"), val = tensor([2, 2])]; - tensor tensor_13_groups_0 = const()[name = tensor("tensor_13_groups_0"), val = tensor(256)]; - tensor tensor_13_dilations_0 = const()[name = tensor("tensor_13_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_5_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_5_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142272)))]; - tensor module_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146944)))]; - tensor tensor_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("tensor_13_cast_fp16")]; - tensor var_215_promoted_to_fp16 = const()[name = tensor("op_215_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_216_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_215_promoted_to_fp16)[name = tensor("op_216_cast_fp16")]; - tensor var_217_promoted_to_fp16 = const()[name = tensor("op_217_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_218_cast_fp16 = add(x = var_216_cast_fp16, y = var_217_promoted_to_fp16)[name = tensor("op_218_cast_fp16")]; - tensor var_219_promoted_to_fp16 = const()[name = tensor("op_219_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_220_cast_fp16 = sub(x = var_218_cast_fp16, y = var_219_promoted_to_fp16)[name = tensor("op_220_cast_fp16")]; - tensor var_21_promoted_2_to_fp16 = const()[name = tensor("op_21_promoted_2_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_2_cast_fp16 = floor_div(x = var_220_cast_fp16, y = var_21_promoted_2_to_fp16)[name = tensor("floor_div_2_cast_fp16")]; - tensor var_222_promoted_to_fp16 = const()[name = tensor("op_222_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_222_promoted_to_fp16)[name = tensor("current_lengths_cast_fp16")]; - tensor cast_8_dtype_0 = const()[name = tensor("cast_8_dtype_0"), val = tensor("int32")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187]])]; - tensor var_231_axes_0 = const()[name = tensor("op_231_axes_0"), val = tensor([1])]; - tensor current_lengths_cast_fp16_to_int32 = cast(dtype = cast_8_dtype_0, x = current_lengths_cast_fp16)[name = tensor("cast_175")]; - tensor var_231 = expand_dims(axes = var_231_axes_0, x = current_lengths_cast_fp16_to_int32)[name = tensor("op_231")]; - tensor time_mask = less(x = expand_dims_3, y = var_231)[name = tensor("time_mask")]; - tensor var_233_axes_0 = const()[name = tensor("op_233_axes_0"), val = tensor([-1])]; - tensor var_233 = expand_dims(axes = var_233_axes_0, x = time_mask)[name = tensor("op_233")]; - tensor var_235_reps_0 = const()[name = tensor("op_235_reps_0"), val = tensor([1, 1, 10])]; - tensor var_235 = tile(reps = var_235_reps_0, x = var_233)[name = tensor("op_235")]; - tensor var_241_axes_0 = const()[name = tensor("op_241_axes_0"), val = tensor([1])]; - tensor cast_9_to_fp16_dtype_0 = const()[name = tensor("cast_9_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_235_to_fp16 = cast(dtype = cast_9_to_fp16_dtype_0, x = var_235)[name = tensor("cast_174")]; - tensor var_241_cast_fp16 = expand_dims(axes = var_241_axes_0, x = var_235_to_fp16)[name = tensor("op_241_cast_fp16")]; - tensor expanded_mask_13_reps_0 = const()[name = tensor("expanded_mask_13_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_241_cast_fp16)[name = tensor("expanded_mask_13_cast_fp16")]; - tensor input_13_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("input_13_cast_fp16")]; - tensor tensor_15_pad_type_0 = const()[name = tensor("tensor_15_pad_type_0"), val = tensor("valid")]; - tensor tensor_15_strides_0 = const()[name = tensor("tensor_15_strides_0"), val = tensor([1, 1])]; - tensor tensor_15_pad_0 = const()[name = tensor("tensor_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor tensor_15_dilations_0 = const()[name = tensor("tensor_15_dilations_0"), val = tensor([1, 1])]; - tensor tensor_15_groups_0 = const()[name = tensor("tensor_15_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_6_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_6_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147520)))]; - tensor module_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278656)))]; - tensor tensor_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16, x = input_13_cast_fp16)[name = tensor("tensor_15_cast_fp16")]; - tensor input_15_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; - tensor tensor_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor("tensor_cast_fp16")]; - tensor x_3_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("x_3_cast_fp16")]; - tensor var_275_perm_0 = const()[name = tensor("op_275_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_276 = const()[name = tensor("op_276"), val = tensor([1, 188, -1])]; - tensor var_275_cast_fp16 = transpose(perm = var_275_perm_0, x = x_3_cast_fp16)[name = tensor("transpose_206")]; - tensor input_17_cast_fp16 = reshape(shape = var_276, x = var_275_cast_fp16)[name = tensor("input_17_cast_fp16")]; - tensor module_pre_encode_out_weight_to_fp16 = const()[name = tensor("module_pre_encode_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279232)))]; - tensor module_pre_encode_out_bias_to_fp16 = const()[name = tensor("module_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2900736)))]; - tensor linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16, x = input_17_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor cast_12_dtype_0 = const()[name = tensor("cast_12_dtype_0"), val = tensor("int32")]; - tensor var_314_axes_0 = const()[name = tensor("op_314_axes_0"), val = tensor([-1])]; - tensor encoder_length = cast(dtype = cast_12_dtype_0, x = current_lengths_cast_fp16)[name = tensor("cast_173")]; - tensor var_314 = expand_dims(axes = var_314_axes_0, x = encoder_length)[name = tensor("op_314")]; - tensor pad_mask_1 = less(x = expand_dims_3, y = var_314)[name = tensor("pad_mask_1")]; - tensor var_316_axes_0 = const()[name = tensor("op_316_axes_0"), val = tensor([1])]; - tensor var_316 = expand_dims(axes = var_316_axes_0, x = pad_mask_1)[name = tensor("op_316")]; - tensor var_317 = const()[name = tensor("op_317"), val = tensor([1, 188, 1])]; - tensor pad_mask_for_att_mask_1 = tile(reps = var_317, x = var_316)[name = tensor("pad_mask_for_att_mask_1")]; - tensor var_319_perm_0 = const()[name = tensor("op_319_perm_0"), val = tensor([0, 2, 1])]; - tensor var_319 = transpose(perm = var_319_perm_0, x = pad_mask_for_att_mask_1)[name = tensor("transpose_205")]; - tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_319)[name = tensor("pad_mask_for_att_mask")]; - tensor const_63 = const()[name = tensor("const_63"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; - tensor att_mask = logical_and(x = pad_mask_for_att_mask, y = const_63)[name = tensor("att_mask")]; - tensor mask_9 = logical_not(x = att_mask)[name = tensor("mask_9")]; - tensor pad_mask = logical_not(x = pad_mask_1)[name = tensor("pad_mask")]; - tensor input_21_axes_0 = const()[name = tensor("input_21_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2901824)))]; - tensor module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2902912)))]; - tensor var_9_to_fp16 = const()[name = tensor("op_9_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = tensor("input_21_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2904000)))]; - tensor module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001216)))]; - tensor linear_1_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16, x = input_21_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor("input_25_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5005376)))]; - tensor module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7102592)))]; - tensor linear_2_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16, x = input_25_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor var_352_to_fp16 = const()[name = tensor("op_352_to_fp16"), val = tensor(0x1p-1)]; - tensor var_353_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_352_to_fp16)[name = tensor("op_353_cast_fp16")]; - tensor input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_353_cast_fp16)[name = tensor("input_31_cast_fp16")]; - tensor query_1_axes_0 = const()[name = tensor("query_1_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7103680)))]; - tensor module_layers_0_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7104768)))]; - tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("query_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7105856)))]; - tensor module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7630208)))]; - tensor linear_3_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_q_bias_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_3_cast_fp16")]; - tensor var_370 = const()[name = tensor("op_370"), val = tensor([1, -1, 8, 64])]; - tensor q_1_cast_fp16 = reshape(shape = var_370, x = linear_3_cast_fp16)[name = tensor("q_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7631296)))]; - tensor module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8155648)))]; - tensor linear_4_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_k_bias_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_4_cast_fp16")]; - tensor var_375 = const()[name = tensor("op_375"), val = tensor([1, -1, 8, 64])]; - tensor k_1_cast_fp16 = reshape(shape = var_375, x = linear_4_cast_fp16)[name = tensor("k_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8156736)))]; - tensor module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8681088)))]; - tensor linear_5_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_v_bias_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_5_cast_fp16")]; - tensor var_380 = const()[name = tensor("op_380"), val = tensor([1, -1, 8, 64])]; - tensor v_1_cast_fp16 = reshape(shape = var_380, x = linear_5_cast_fp16)[name = tensor("v_1_cast_fp16")]; - tensor value_3_perm_0 = const()[name = tensor("value_3_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8682176)))]; - tensor var_392_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = tensor("op_392_cast_fp16")]; - tensor module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8683264)))]; - tensor var_394_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = tensor("op_394_cast_fp16")]; - tensor q_with_bias_v_1_perm_0 = const()[name = tensor("q_with_bias_v_1_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_7_transpose_x_0 = const()[name = tensor("x_7_transpose_x_0"), val = tensor(false)]; - tensor x_7_transpose_y_0 = const()[name = tensor("x_7_transpose_y_0"), val = tensor(false)]; - tensor var_396_to_fp16 = const()[name = tensor("op_396_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8684352)))]; - tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_394_cast_fp16)[name = tensor("transpose_203")]; - tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = var_396_to_fp16)[name = tensor("x_7_cast_fp16")]; - tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_9_mode_0 = const()[name = tensor("x_9_mode_0"), val = tensor("constant")]; - tensor const_70_to_fp16 = const()[name = tensor("const_70_to_fp16"), val = tensor(0x0p+0)]; - tensor x_9_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = tensor("x_9_cast_fp16")]; - tensor var_404 = const()[name = tensor("op_404"), val = tensor([1, 8, -1, 188])]; - tensor x_11_cast_fp16 = reshape(shape = var_404, x = x_9_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor var_408_begin_0 = const()[name = tensor("op_408_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_408_end_0 = const()[name = tensor("op_408_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_408_end_mask_0 = const()[name = tensor("op_408_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_408_cast_fp16 = slice_by_index(begin = var_408_begin_0, end = var_408_end_0, end_mask = var_408_end_mask_0, x = x_11_cast_fp16)[name = tensor("op_408_cast_fp16")]; - tensor var_409 = const()[name = tensor("op_409"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_1_cast_fp16 = reshape(shape = var_409, x = var_408_cast_fp16)[name = tensor("matrix_bd_1_cast_fp16")]; - tensor matrix_ac_1_transpose_x_0 = const()[name = tensor("matrix_ac_1_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_1_transpose_y_0 = const()[name = tensor("matrix_ac_1_transpose_y_0"), val = tensor(false)]; - tensor transpose_51_perm_0 = const()[name = tensor("transpose_51_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_52_perm_0 = const()[name = tensor("transpose_52_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = k_1_cast_fp16)[name = tensor("transpose_201")]; - tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_392_cast_fp16)[name = tensor("transpose_202")]; - tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_51, y = transpose_52)[name = tensor("matrix_ac_1_cast_fp16")]; - tensor matrix_bd_3_begin_0 = const()[name = tensor("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_3_end_0 = const()[name = tensor("matrix_bd_3_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_3_end_mask_0 = const()[name = tensor("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = tensor("matrix_bd_3_cast_fp16")]; - tensor var_418_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = tensor("op_418_cast_fp16")]; - tensor _inversed_scores_1_y_0_to_fp16 = const()[name = tensor("_inversed_scores_1_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_1_cast_fp16 = mul(x = var_418_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = tensor("_inversed_scores_1_cast_fp16")]; - tensor mask_11_axes_0 = const()[name = tensor("mask_11_axes_0"), val = tensor([1])]; - tensor mask_11 = expand_dims(axes = mask_11_axes_0, x = mask_9)[name = tensor("mask_11")]; - tensor var_12_to_fp16 = const()[name = tensor("op_12_to_fp16"), val = tensor(-0x1.388p+13)]; - tensor scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_11)[name = tensor("scores_3_cast_fp16")]; - tensor var_424_cast_fp16 = softmax(axis = var_23, x = scores_3_cast_fp16)[name = tensor("op_424_cast_fp16")]; - tensor var_11_to_fp16 = const()[name = tensor("op_11_to_fp16"), val = tensor(0x0p+0)]; - tensor input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_424_cast_fp16, cond = mask_11)[name = tensor("input_33_cast_fp16")]; - tensor x_13_transpose_x_0 = const()[name = tensor("x_13_transpose_x_0"), val = tensor(false)]; - tensor x_13_transpose_y_0 = const()[name = tensor("x_13_transpose_y_0"), val = tensor(false)]; - tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = tensor("transpose_204")]; - tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_3_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_428_perm_0 = const()[name = tensor("op_428_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_429 = const()[name = tensor("op_429"), val = tensor([1, -1, 512])]; - tensor var_428_cast_fp16 = transpose(perm = var_428_perm_0, x = x_13_cast_fp16)[name = tensor("transpose_200")]; - tensor input_35_cast_fp16 = reshape(shape = var_429, x = var_428_cast_fp16)[name = tensor("input_35_cast_fp16")]; - tensor module_layers_0_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9068416)))]; - tensor module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9592768)))]; - tensor linear_7_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_out_bias_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16, x = input_35_cast_fp16)[name = tensor("linear_7_cast_fp16")]; - tensor input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = tensor("input_39_cast_fp16")]; - tensor x_17_axes_0 = const()[name = tensor("x_17_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9593856)))]; - tensor module_layers_0_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9594944)))]; - tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("x_17_cast_fp16")]; - tensor input_41_perm_0 = const()[name = tensor("input_41_perm_0"), val = tensor([0, 2, 1])]; - tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; - tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([1])]; - tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0])]; - tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1])]; - tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9596032)))]; - tensor module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10644672)))]; - tensor input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = tensor("transpose_199")]; - tensor input_43_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv1_bias_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16, x = input_41_cast_fp16)[name = tensor("input_43_cast_fp16")]; - tensor x_19_split_num_splits_0 = const()[name = tensor("x_19_split_num_splits_0"), val = tensor(2)]; - tensor x_19_split_axis_0 = const()[name = tensor("x_19_split_axis_0"), val = tensor(1)]; - tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = tensor("x_19_split_cast_fp16")]; - tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = tensor("x_19_split_1_sigmoid_cast_fp16")]; - tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = tensor("x_19_cast_fp16")]; - tensor var_453_axes_0 = const()[name = tensor("op_453_axes_0"), val = tensor([1])]; - tensor var_453 = expand_dims(axes = var_453_axes_0, x = pad_mask)[name = tensor("op_453")]; - tensor input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_453)[name = tensor("input_45_cast_fp16")]; - tensor input_47_pad_0 = const()[name = tensor("input_47_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_47_mode_0 = const()[name = tensor("input_47_mode_0"), val = tensor("constant")]; - tensor const_73_to_fp16 = const()[name = tensor("const_73_to_fp16"), val = tensor(0x0p+0)]; - tensor input_47_cast_fp16 = pad(constant_val = const_73_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = tensor("input_47_cast_fp16")]; - tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("valid")]; - tensor input_49_groups_0 = const()[name = tensor("input_49_groups_0"), val = tensor(512)]; - tensor input_49_strides_0 = const()[name = tensor("input_49_strides_0"), val = tensor([1])]; - tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([0, 0])]; - tensor input_49_dilations_0 = const()[name = tensor("input_49_dilations_0"), val = tensor([1])]; - tensor const_234_to_fp16 = const()[name = tensor("const_234_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10646784)))]; - tensor const_235_to_fp16 = const()[name = tensor("const_235_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10656064)))]; - tensor input_51_cast_fp16 = conv(bias = const_235_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_234_to_fp16, x = input_47_cast_fp16)[name = tensor("input_51_cast_fp16")]; - tensor input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = tensor("input_53_cast_fp16")]; - tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("valid")]; - tensor x_21_strides_0 = const()[name = tensor("x_21_strides_0"), val = tensor([1])]; - tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0])]; - tensor x_21_dilations_0 = const()[name = tensor("x_21_dilations_0"), val = tensor([1])]; - tensor x_21_groups_0 = const()[name = tensor("x_21_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10657152)))]; - tensor module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11181504)))]; - tensor x_21_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv2_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_layers_0_conv_pointwise_conv2_weight_to_fp16, x = input_53_cast_fp16)[name = tensor("x_21_cast_fp16")]; - tensor input_55_perm_0 = const()[name = tensor("input_55_perm_0"), val = tensor([0, 2, 1])]; - tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = tensor("transpose_198")]; - tensor input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; - tensor input_59_axes_0 = const()[name = tensor("input_59_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11182592)))]; - tensor module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11183680)))]; - tensor input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("input_59_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11184768)))]; - tensor module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13281984)))]; - tensor linear_8_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("linear_8_cast_fp16")]; - tensor input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = tensor("input_63_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13286144)))]; - tensor module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15383360)))]; - tensor linear_9_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16, x = input_63_cast_fp16)[name = tensor("linear_9_cast_fp16")]; - tensor var_495_to_fp16 = const()[name = tensor("op_495_to_fp16"), val = tensor(0x1p-1)]; - tensor var_496_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_495_to_fp16)[name = tensor("op_496_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_496_cast_fp16)[name = tensor("input_69_cast_fp16")]; - tensor input_71_axes_0 = const()[name = tensor("input_71_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15384448)))]; - tensor module_layers_0_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15385536)))]; - tensor input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = tensor("input_71_cast_fp16")]; - tensor input_73_axes_0 = const()[name = tensor("input_73_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15386624)))]; - tensor module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15387712)))]; - tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = tensor("input_73_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15388800)))]; - tensor module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17486016)))]; - tensor linear_10_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16, x = input_73_cast_fp16)[name = tensor("linear_10_cast_fp16")]; - tensor input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor("input_77_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17490176)))]; - tensor module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19587392)))]; - tensor linear_11_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16, x = input_77_cast_fp16)[name = tensor("linear_11_cast_fp16")]; - tensor var_526_to_fp16 = const()[name = tensor("op_526_to_fp16"), val = tensor(0x1p-1)]; - tensor var_527_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_526_to_fp16)[name = tensor("op_527_cast_fp16")]; - tensor input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_527_cast_fp16)[name = tensor("input_83_cast_fp16")]; - tensor query_3_axes_0 = const()[name = tensor("query_3_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19588480)))]; - tensor module_layers_1_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19589568)))]; - tensor query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = tensor("query_3_cast_fp16")]; - tensor module_layers_1_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19590656)))]; - tensor module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20115008)))]; - tensor linear_12_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_q_bias_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_12_cast_fp16")]; - tensor var_544 = const()[name = tensor("op_544"), val = tensor([1, -1, 8, 64])]; - tensor q_7_cast_fp16 = reshape(shape = var_544, x = linear_12_cast_fp16)[name = tensor("q_7_cast_fp16")]; - tensor module_layers_1_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20116096)))]; - tensor module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20640448)))]; - tensor linear_13_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_k_bias_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_13_cast_fp16")]; - tensor var_549 = const()[name = tensor("op_549"), val = tensor([1, -1, 8, 64])]; - tensor k_5_cast_fp16 = reshape(shape = var_549, x = linear_13_cast_fp16)[name = tensor("k_5_cast_fp16")]; - tensor module_layers_1_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20641536)))]; - tensor module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21165888)))]; - tensor linear_14_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_v_bias_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_14_cast_fp16")]; - tensor var_554 = const()[name = tensor("op_554"), val = tensor([1, -1, 8, 64])]; - tensor v_3_cast_fp16 = reshape(shape = var_554, x = linear_14_cast_fp16)[name = tensor("v_3_cast_fp16")]; - tensor value_5_perm_0 = const()[name = tensor("value_5_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21166976)))]; - tensor var_566_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = tensor("op_566_cast_fp16")]; - tensor module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21168064)))]; - tensor var_568_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = tensor("op_568_cast_fp16")]; - tensor q_with_bias_v_3_perm_0 = const()[name = tensor("q_with_bias_v_3_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_29_transpose_x_0 = const()[name = tensor("x_29_transpose_x_0"), val = tensor(false)]; - tensor x_29_transpose_y_0 = const()[name = tensor("x_29_transpose_y_0"), val = tensor(false)]; - tensor var_570_to_fp16 = const()[name = tensor("op_570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21169152)))]; - tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_568_cast_fp16)[name = tensor("transpose_196")]; - tensor x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = var_570_to_fp16)[name = tensor("x_29_cast_fp16")]; - tensor x_31_pad_0 = const()[name = tensor("x_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_31_mode_0 = const()[name = tensor("x_31_mode_0"), val = tensor("constant")]; - tensor const_80_to_fp16 = const()[name = tensor("const_80_to_fp16"), val = tensor(0x0p+0)]; - tensor x_31_cast_fp16 = pad(constant_val = const_80_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = tensor("x_31_cast_fp16")]; - tensor var_578 = const()[name = tensor("op_578"), val = tensor([1, 8, -1, 188])]; - tensor x_33_cast_fp16 = reshape(shape = var_578, x = x_31_cast_fp16)[name = tensor("x_33_cast_fp16")]; - tensor var_582_begin_0 = const()[name = tensor("op_582_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_582_end_0 = const()[name = tensor("op_582_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_582_end_mask_0 = const()[name = tensor("op_582_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_582_cast_fp16 = slice_by_index(begin = var_582_begin_0, end = var_582_end_0, end_mask = var_582_end_mask_0, x = x_33_cast_fp16)[name = tensor("op_582_cast_fp16")]; - tensor var_583 = const()[name = tensor("op_583"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_5_cast_fp16 = reshape(shape = var_583, x = var_582_cast_fp16)[name = tensor("matrix_bd_5_cast_fp16")]; - tensor matrix_ac_3_transpose_x_0 = const()[name = tensor("matrix_ac_3_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_3_transpose_y_0 = const()[name = tensor("matrix_ac_3_transpose_y_0"), val = tensor(false)]; - tensor transpose_53_perm_0 = const()[name = tensor("transpose_53_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_54_perm_0 = const()[name = tensor("transpose_54_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = k_5_cast_fp16)[name = tensor("transpose_194")]; - tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = var_566_cast_fp16)[name = tensor("transpose_195")]; - tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_53, y = transpose_54)[name = tensor("matrix_ac_3_cast_fp16")]; - tensor matrix_bd_7_begin_0 = const()[name = tensor("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_7_end_0 = const()[name = tensor("matrix_bd_7_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_7_end_mask_0 = const()[name = tensor("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = tensor("matrix_bd_7_cast_fp16")]; - tensor var_592_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = tensor("op_592_cast_fp16")]; - tensor _inversed_scores_5_y_0_to_fp16 = const()[name = tensor("_inversed_scores_5_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_5_cast_fp16 = mul(x = var_592_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = tensor("_inversed_scores_5_cast_fp16")]; - tensor scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_11)[name = tensor("scores_7_cast_fp16")]; - tensor var_598_cast_fp16 = softmax(axis = var_23, x = scores_7_cast_fp16)[name = tensor("op_598_cast_fp16")]; - tensor input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_598_cast_fp16, cond = mask_11)[name = tensor("input_85_cast_fp16")]; - tensor x_35_transpose_x_0 = const()[name = tensor("x_35_transpose_x_0"), val = tensor(false)]; - tensor x_35_transpose_y_0 = const()[name = tensor("x_35_transpose_y_0"), val = tensor(false)]; - tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = tensor("transpose_197")]; - tensor x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_5_cast_fp16)[name = tensor("x_35_cast_fp16")]; - tensor var_602_perm_0 = const()[name = tensor("op_602_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_603 = const()[name = tensor("op_603"), val = tensor([1, -1, 512])]; - tensor var_602_cast_fp16 = transpose(perm = var_602_perm_0, x = x_35_cast_fp16)[name = tensor("transpose_193")]; - tensor input_87_cast_fp16 = reshape(shape = var_603, x = var_602_cast_fp16)[name = tensor("input_87_cast_fp16")]; - tensor module_layers_1_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21553216)))]; - tensor module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22077568)))]; - tensor linear_16_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_out_bias_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16, x = input_87_cast_fp16)[name = tensor("linear_16_cast_fp16")]; - tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = tensor("input_91_cast_fp16")]; - tensor x_39_axes_0 = const()[name = tensor("x_39_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22078656)))]; - tensor module_layers_1_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22079744)))]; - tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = tensor("x_39_cast_fp16")]; - tensor input_93_perm_0 = const()[name = tensor("input_93_perm_0"), val = tensor([0, 2, 1])]; - tensor input_95_pad_type_0 = const()[name = tensor("input_95_pad_type_0"), val = tensor("valid")]; - tensor input_95_strides_0 = const()[name = tensor("input_95_strides_0"), val = tensor([1])]; - tensor input_95_pad_0 = const()[name = tensor("input_95_pad_0"), val = tensor([0, 0])]; - tensor input_95_dilations_0 = const()[name = tensor("input_95_dilations_0"), val = tensor([1])]; - tensor input_95_groups_0 = const()[name = tensor("input_95_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22080832)))]; - tensor module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23129472)))]; - tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = tensor("transpose_192")]; - tensor input_95_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv1_bias_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16, x = input_93_cast_fp16)[name = tensor("input_95_cast_fp16")]; - tensor x_41_split_num_splits_0 = const()[name = tensor("x_41_split_num_splits_0"), val = tensor(2)]; - tensor x_41_split_axis_0 = const()[name = tensor("x_41_split_axis_0"), val = tensor(1)]; - tensor x_41_split_cast_fp16_0, tensor x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = tensor("x_41_split_cast_fp16")]; - tensor x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = tensor("x_41_split_1_sigmoid_cast_fp16")]; - tensor x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = tensor("x_41_cast_fp16")]; - tensor input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_453)[name = tensor("input_97_cast_fp16")]; - tensor input_99_pad_0 = const()[name = tensor("input_99_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_99_mode_0 = const()[name = tensor("input_99_mode_0"), val = tensor("constant")]; - tensor const_83_to_fp16 = const()[name = tensor("const_83_to_fp16"), val = tensor(0x0p+0)]; - tensor input_99_cast_fp16 = pad(constant_val = const_83_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = tensor("input_99_cast_fp16")]; - tensor input_101_pad_type_0 = const()[name = tensor("input_101_pad_type_0"), val = tensor("valid")]; - tensor input_101_groups_0 = const()[name = tensor("input_101_groups_0"), val = tensor(512)]; - tensor input_101_strides_0 = const()[name = tensor("input_101_strides_0"), val = tensor([1])]; - tensor input_101_pad_0 = const()[name = tensor("input_101_pad_0"), val = tensor([0, 0])]; - tensor input_101_dilations_0 = const()[name = tensor("input_101_dilations_0"), val = tensor([1])]; - tensor const_236_to_fp16 = const()[name = tensor("const_236_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23131584)))]; - tensor const_237_to_fp16 = const()[name = tensor("const_237_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23140864)))]; - tensor input_103_cast_fp16 = conv(bias = const_237_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_236_to_fp16, x = input_99_cast_fp16)[name = tensor("input_103_cast_fp16")]; - tensor input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = tensor("input_105_cast_fp16")]; - tensor x_43_pad_type_0 = const()[name = tensor("x_43_pad_type_0"), val = tensor("valid")]; - tensor x_43_strides_0 = const()[name = tensor("x_43_strides_0"), val = tensor([1])]; - tensor x_43_pad_0 = const()[name = tensor("x_43_pad_0"), val = tensor([0, 0])]; - tensor x_43_dilations_0 = const()[name = tensor("x_43_dilations_0"), val = tensor([1])]; - tensor x_43_groups_0 = const()[name = tensor("x_43_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23141952)))]; - tensor module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23666304)))]; - tensor x_43_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv2_bias_to_fp16, dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = module_layers_1_conv_pointwise_conv2_weight_to_fp16, x = input_105_cast_fp16)[name = tensor("x_43_cast_fp16")]; - tensor input_107_perm_0 = const()[name = tensor("input_107_perm_0"), val = tensor([0, 2, 1])]; - tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = tensor("transpose_191")]; - tensor input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = tensor("input_109_cast_fp16")]; - tensor input_111_axes_0 = const()[name = tensor("input_111_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23667392)))]; - tensor module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23668480)))]; - tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("input_111_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23669568)))]; - tensor module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25766784)))]; - tensor linear_17_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16, x = input_111_cast_fp16)[name = tensor("linear_17_cast_fp16")]; - tensor input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = tensor("input_115_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25770944)))]; - tensor module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27868160)))]; - tensor linear_18_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16, x = input_115_cast_fp16)[name = tensor("linear_18_cast_fp16")]; - tensor var_669_to_fp16 = const()[name = tensor("op_669_to_fp16"), val = tensor(0x1p-1)]; - tensor var_670_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_669_to_fp16)[name = tensor("op_670_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_670_cast_fp16)[name = tensor("input_121_cast_fp16")]; - tensor input_123_axes_0 = const()[name = tensor("input_123_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27869248)))]; - tensor module_layers_1_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27870336)))]; - tensor input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("input_123_cast_fp16")]; - tensor input_125_axes_0 = const()[name = tensor("input_125_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27871424)))]; - tensor module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27872512)))]; - tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor("input_125_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27873600)))]; - tensor module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29970816)))]; - tensor linear_19_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16, x = input_125_cast_fp16)[name = tensor("linear_19_cast_fp16")]; - tensor input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor("input_129_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29974976)))]; - tensor module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32072192)))]; - tensor linear_20_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("linear_20_cast_fp16")]; - tensor var_700_to_fp16 = const()[name = tensor("op_700_to_fp16"), val = tensor(0x1p-1)]; - tensor var_701_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_700_to_fp16)[name = tensor("op_701_cast_fp16")]; - tensor input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_701_cast_fp16)[name = tensor("input_135_cast_fp16")]; - tensor query_5_axes_0 = const()[name = tensor("query_5_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32073280)))]; - tensor module_layers_2_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32074368)))]; - tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = tensor("query_5_cast_fp16")]; - tensor module_layers_2_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32075456)))]; - tensor module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32599808)))]; - tensor linear_21_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_q_bias_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_21_cast_fp16")]; - tensor var_718 = const()[name = tensor("op_718"), val = tensor([1, -1, 8, 64])]; - tensor q_13_cast_fp16 = reshape(shape = var_718, x = linear_21_cast_fp16)[name = tensor("q_13_cast_fp16")]; - tensor module_layers_2_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32600896)))]; - tensor module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33125248)))]; - tensor linear_22_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_k_bias_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_22_cast_fp16")]; - tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, -1, 8, 64])]; - tensor k_9_cast_fp16 = reshape(shape = var_723, x = linear_22_cast_fp16)[name = tensor("k_9_cast_fp16")]; - tensor module_layers_2_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33126336)))]; - tensor module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33650688)))]; - tensor linear_23_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_v_bias_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_23_cast_fp16")]; - tensor var_728 = const()[name = tensor("op_728"), val = tensor([1, -1, 8, 64])]; - tensor v_5_cast_fp16 = reshape(shape = var_728, x = linear_23_cast_fp16)[name = tensor("v_5_cast_fp16")]; - tensor value_7_perm_0 = const()[name = tensor("value_7_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33651776)))]; - tensor var_740_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = tensor("op_740_cast_fp16")]; - tensor module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33652864)))]; - tensor var_742_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = tensor("op_742_cast_fp16")]; - tensor q_with_bias_v_5_perm_0 = const()[name = tensor("q_with_bias_v_5_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_51_transpose_x_0 = const()[name = tensor("x_51_transpose_x_0"), val = tensor(false)]; - tensor x_51_transpose_y_0 = const()[name = tensor("x_51_transpose_y_0"), val = tensor(false)]; - tensor var_744_to_fp16 = const()[name = tensor("op_744_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33653952)))]; - tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_742_cast_fp16)[name = tensor("transpose_189")]; - tensor x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = var_744_to_fp16)[name = tensor("x_51_cast_fp16")]; - tensor x_53_pad_0 = const()[name = tensor("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_53_mode_0 = const()[name = tensor("x_53_mode_0"), val = tensor("constant")]; - tensor const_90_to_fp16 = const()[name = tensor("const_90_to_fp16"), val = tensor(0x0p+0)]; - tensor x_53_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = tensor("x_53_cast_fp16")]; - tensor var_752 = const()[name = tensor("op_752"), val = tensor([1, 8, -1, 188])]; - tensor x_55_cast_fp16 = reshape(shape = var_752, x = x_53_cast_fp16)[name = tensor("x_55_cast_fp16")]; - tensor var_756_begin_0 = const()[name = tensor("op_756_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_756_end_0 = const()[name = tensor("op_756_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_756_end_mask_0 = const()[name = tensor("op_756_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_756_cast_fp16 = slice_by_index(begin = var_756_begin_0, end = var_756_end_0, end_mask = var_756_end_mask_0, x = x_55_cast_fp16)[name = tensor("op_756_cast_fp16")]; - tensor var_757 = const()[name = tensor("op_757"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_9_cast_fp16 = reshape(shape = var_757, x = var_756_cast_fp16)[name = tensor("matrix_bd_9_cast_fp16")]; - tensor matrix_ac_5_transpose_x_0 = const()[name = tensor("matrix_ac_5_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_5_transpose_y_0 = const()[name = tensor("matrix_ac_5_transpose_y_0"), val = tensor(false)]; - tensor transpose_55_perm_0 = const()[name = tensor("transpose_55_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_56_perm_0 = const()[name = tensor("transpose_56_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = k_9_cast_fp16)[name = tensor("transpose_187")]; - tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_740_cast_fp16)[name = tensor("transpose_188")]; - tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_55, y = transpose_56)[name = tensor("matrix_ac_5_cast_fp16")]; - tensor matrix_bd_11_begin_0 = const()[name = tensor("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_11_end_0 = const()[name = tensor("matrix_bd_11_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_11_end_mask_0 = const()[name = tensor("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = tensor("matrix_bd_11_cast_fp16")]; - tensor var_766_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = tensor("op_766_cast_fp16")]; - tensor _inversed_scores_9_y_0_to_fp16 = const()[name = tensor("_inversed_scores_9_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_9_cast_fp16 = mul(x = var_766_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = tensor("_inversed_scores_9_cast_fp16")]; - tensor scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_11)[name = tensor("scores_11_cast_fp16")]; - tensor var_772_cast_fp16 = softmax(axis = var_23, x = scores_11_cast_fp16)[name = tensor("op_772_cast_fp16")]; - tensor input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_772_cast_fp16, cond = mask_11)[name = tensor("input_137_cast_fp16")]; - tensor x_57_transpose_x_0 = const()[name = tensor("x_57_transpose_x_0"), val = tensor(false)]; - tensor x_57_transpose_y_0 = const()[name = tensor("x_57_transpose_y_0"), val = tensor(false)]; - tensor value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = tensor("transpose_190")]; - tensor x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_7_cast_fp16)[name = tensor("x_57_cast_fp16")]; - tensor var_776_perm_0 = const()[name = tensor("op_776_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_777 = const()[name = tensor("op_777"), val = tensor([1, -1, 512])]; - tensor var_776_cast_fp16 = transpose(perm = var_776_perm_0, x = x_57_cast_fp16)[name = tensor("transpose_186")]; - tensor input_139_cast_fp16 = reshape(shape = var_777, x = var_776_cast_fp16)[name = tensor("input_139_cast_fp16")]; - tensor module_layers_2_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34038016)))]; - tensor module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34562368)))]; - tensor linear_25_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_out_bias_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("linear_25_cast_fp16")]; - tensor input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = tensor("input_143_cast_fp16")]; - tensor x_61_axes_0 = const()[name = tensor("x_61_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34563456)))]; - tensor module_layers_2_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34564544)))]; - tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = tensor("x_61_cast_fp16")]; - tensor input_145_perm_0 = const()[name = tensor("input_145_perm_0"), val = tensor([0, 2, 1])]; - tensor input_147_pad_type_0 = const()[name = tensor("input_147_pad_type_0"), val = tensor("valid")]; - tensor input_147_strides_0 = const()[name = tensor("input_147_strides_0"), val = tensor([1])]; - tensor input_147_pad_0 = const()[name = tensor("input_147_pad_0"), val = tensor([0, 0])]; - tensor input_147_dilations_0 = const()[name = tensor("input_147_dilations_0"), val = tensor([1])]; - tensor input_147_groups_0 = const()[name = tensor("input_147_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34565632)))]; - tensor module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35614272)))]; - tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = tensor("transpose_185")]; - tensor input_147_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16, x = input_145_cast_fp16)[name = tensor("input_147_cast_fp16")]; - tensor x_63_split_num_splits_0 = const()[name = tensor("x_63_split_num_splits_0"), val = tensor(2)]; - tensor x_63_split_axis_0 = const()[name = tensor("x_63_split_axis_0"), val = tensor(1)]; - tensor x_63_split_cast_fp16_0, tensor x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = tensor("x_63_split_cast_fp16")]; - tensor x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = tensor("x_63_split_1_sigmoid_cast_fp16")]; - tensor x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = tensor("x_63_cast_fp16")]; - tensor input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_453)[name = tensor("input_149_cast_fp16")]; - tensor input_151_pad_0 = const()[name = tensor("input_151_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_151_mode_0 = const()[name = tensor("input_151_mode_0"), val = tensor("constant")]; - tensor const_93_to_fp16 = const()[name = tensor("const_93_to_fp16"), val = tensor(0x0p+0)]; - tensor input_151_cast_fp16 = pad(constant_val = const_93_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = tensor("input_151_cast_fp16")]; - tensor input_153_pad_type_0 = const()[name = tensor("input_153_pad_type_0"), val = tensor("valid")]; - tensor input_153_groups_0 = const()[name = tensor("input_153_groups_0"), val = tensor(512)]; - tensor input_153_strides_0 = const()[name = tensor("input_153_strides_0"), val = tensor([1])]; - tensor input_153_pad_0 = const()[name = tensor("input_153_pad_0"), val = tensor([0, 0])]; - tensor input_153_dilations_0 = const()[name = tensor("input_153_dilations_0"), val = tensor([1])]; - tensor const_238_to_fp16 = const()[name = tensor("const_238_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35616384)))]; - tensor const_239_to_fp16 = const()[name = tensor("const_239_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35625664)))]; - tensor input_155_cast_fp16 = conv(bias = const_239_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_238_to_fp16, x = input_151_cast_fp16)[name = tensor("input_155_cast_fp16")]; - tensor input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = tensor("input_157_cast_fp16")]; - tensor x_65_pad_type_0 = const()[name = tensor("x_65_pad_type_0"), val = tensor("valid")]; - tensor x_65_strides_0 = const()[name = tensor("x_65_strides_0"), val = tensor([1])]; - tensor x_65_pad_0 = const()[name = tensor("x_65_pad_0"), val = tensor([0, 0])]; - tensor x_65_dilations_0 = const()[name = tensor("x_65_dilations_0"), val = tensor([1])]; - tensor x_65_groups_0 = const()[name = tensor("x_65_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35626752)))]; - tensor module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36151104)))]; - tensor x_65_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv2_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_layers_2_conv_pointwise_conv2_weight_to_fp16, x = input_157_cast_fp16)[name = tensor("x_65_cast_fp16")]; - tensor input_159_perm_0 = const()[name = tensor("input_159_perm_0"), val = tensor([0, 2, 1])]; - tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = tensor("transpose_184")]; - tensor input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = tensor("input_161_cast_fp16")]; - tensor input_163_axes_0 = const()[name = tensor("input_163_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36152192)))]; - tensor module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36153280)))]; - tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("input_163_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36154368)))]; - tensor module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38251584)))]; - tensor linear_26_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16, x = input_163_cast_fp16)[name = tensor("linear_26_cast_fp16")]; - tensor input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = tensor("input_167_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38255744)))]; - tensor module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40352960)))]; - tensor linear_27_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16, x = input_167_cast_fp16)[name = tensor("linear_27_cast_fp16")]; - tensor var_843_to_fp16 = const()[name = tensor("op_843_to_fp16"), val = tensor(0x1p-1)]; - tensor var_844_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_843_to_fp16)[name = tensor("op_844_cast_fp16")]; - tensor input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_844_cast_fp16)[name = tensor("input_173_cast_fp16")]; - tensor input_175_axes_0 = const()[name = tensor("input_175_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40354048)))]; - tensor module_layers_2_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40355136)))]; - tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = tensor("input_175_cast_fp16")]; - tensor input_177_axes_0 = const()[name = tensor("input_177_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40356224)))]; - tensor module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40357312)))]; - tensor input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor("input_177_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40358400)))]; - tensor module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42455616)))]; - tensor linear_28_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16, x = input_177_cast_fp16)[name = tensor("linear_28_cast_fp16")]; - tensor input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = tensor("input_181_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42459776)))]; - tensor module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44556992)))]; - tensor linear_29_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16, x = input_181_cast_fp16)[name = tensor("linear_29_cast_fp16")]; - tensor var_874_to_fp16 = const()[name = tensor("op_874_to_fp16"), val = tensor(0x1p-1)]; - tensor var_875_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_874_to_fp16)[name = tensor("op_875_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_875_cast_fp16)[name = tensor("input_187_cast_fp16")]; - tensor query_7_axes_0 = const()[name = tensor("query_7_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44558080)))]; - tensor module_layers_3_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44559168)))]; - tensor query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = tensor("query_7_cast_fp16")]; - tensor module_layers_3_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44560256)))]; - tensor module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45084608)))]; - tensor linear_30_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_q_bias_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_30_cast_fp16")]; - tensor var_892 = const()[name = tensor("op_892"), val = tensor([1, -1, 8, 64])]; - tensor q_19_cast_fp16 = reshape(shape = var_892, x = linear_30_cast_fp16)[name = tensor("q_19_cast_fp16")]; - tensor module_layers_3_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45085696)))]; - tensor module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45610048)))]; - tensor linear_31_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_k_bias_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_31_cast_fp16")]; - tensor var_897 = const()[name = tensor("op_897"), val = tensor([1, -1, 8, 64])]; - tensor k_13_cast_fp16 = reshape(shape = var_897, x = linear_31_cast_fp16)[name = tensor("k_13_cast_fp16")]; - tensor module_layers_3_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45611136)))]; - tensor module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46135488)))]; - tensor linear_32_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_v_bias_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_32_cast_fp16")]; - tensor var_902 = const()[name = tensor("op_902"), val = tensor([1, -1, 8, 64])]; - tensor v_7_cast_fp16 = reshape(shape = var_902, x = linear_32_cast_fp16)[name = tensor("v_7_cast_fp16")]; - tensor value_9_perm_0 = const()[name = tensor("value_9_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46136576)))]; - tensor var_914_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = tensor("op_914_cast_fp16")]; - tensor module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46137664)))]; - tensor var_916_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = tensor("op_916_cast_fp16")]; - tensor q_with_bias_v_7_perm_0 = const()[name = tensor("q_with_bias_v_7_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_73_transpose_x_0 = const()[name = tensor("x_73_transpose_x_0"), val = tensor(false)]; - tensor x_73_transpose_y_0 = const()[name = tensor("x_73_transpose_y_0"), val = tensor(false)]; - tensor var_918_to_fp16 = const()[name = tensor("op_918_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46138752)))]; - tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_916_cast_fp16)[name = tensor("transpose_182")]; - tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = var_918_to_fp16)[name = tensor("x_73_cast_fp16")]; - tensor x_75_pad_0 = const()[name = tensor("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_75_mode_0 = const()[name = tensor("x_75_mode_0"), val = tensor("constant")]; - tensor const_100_to_fp16 = const()[name = tensor("const_100_to_fp16"), val = tensor(0x0p+0)]; - tensor x_75_cast_fp16 = pad(constant_val = const_100_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = tensor("x_75_cast_fp16")]; - tensor var_926 = const()[name = tensor("op_926"), val = tensor([1, 8, -1, 188])]; - tensor x_77_cast_fp16 = reshape(shape = var_926, x = x_75_cast_fp16)[name = tensor("x_77_cast_fp16")]; - tensor var_930_begin_0 = const()[name = tensor("op_930_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_930_end_0 = const()[name = tensor("op_930_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_930_end_mask_0 = const()[name = tensor("op_930_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_930_cast_fp16 = slice_by_index(begin = var_930_begin_0, end = var_930_end_0, end_mask = var_930_end_mask_0, x = x_77_cast_fp16)[name = tensor("op_930_cast_fp16")]; - tensor var_931 = const()[name = tensor("op_931"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_13_cast_fp16 = reshape(shape = var_931, x = var_930_cast_fp16)[name = tensor("matrix_bd_13_cast_fp16")]; - tensor matrix_ac_7_transpose_x_0 = const()[name = tensor("matrix_ac_7_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_7_transpose_y_0 = const()[name = tensor("matrix_ac_7_transpose_y_0"), val = tensor(false)]; - tensor transpose_57_perm_0 = const()[name = tensor("transpose_57_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_58_perm_0 = const()[name = tensor("transpose_58_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = k_13_cast_fp16)[name = tensor("transpose_180")]; - tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_914_cast_fp16)[name = tensor("transpose_181")]; - tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_57, y = transpose_58)[name = tensor("matrix_ac_7_cast_fp16")]; - tensor matrix_bd_15_begin_0 = const()[name = tensor("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_15_end_0 = const()[name = tensor("matrix_bd_15_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_15_end_mask_0 = const()[name = tensor("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = tensor("matrix_bd_15_cast_fp16")]; - tensor var_940_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = tensor("op_940_cast_fp16")]; - tensor _inversed_scores_13_y_0_to_fp16 = const()[name = tensor("_inversed_scores_13_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_13_cast_fp16 = mul(x = var_940_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = tensor("_inversed_scores_13_cast_fp16")]; - tensor scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_11)[name = tensor("scores_15_cast_fp16")]; - tensor var_946_cast_fp16 = softmax(axis = var_23, x = scores_15_cast_fp16)[name = tensor("op_946_cast_fp16")]; - tensor input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_946_cast_fp16, cond = mask_11)[name = tensor("input_189_cast_fp16")]; - tensor x_79_transpose_x_0 = const()[name = tensor("x_79_transpose_x_0"), val = tensor(false)]; - tensor x_79_transpose_y_0 = const()[name = tensor("x_79_transpose_y_0"), val = tensor(false)]; - tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = tensor("transpose_183")]; - tensor x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_9_cast_fp16)[name = tensor("x_79_cast_fp16")]; - tensor var_950_perm_0 = const()[name = tensor("op_950_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_951 = const()[name = tensor("op_951"), val = tensor([1, -1, 512])]; - tensor var_950_cast_fp16 = transpose(perm = var_950_perm_0, x = x_79_cast_fp16)[name = tensor("transpose_179")]; - tensor input_191_cast_fp16 = reshape(shape = var_951, x = var_950_cast_fp16)[name = tensor("input_191_cast_fp16")]; - tensor module_layers_3_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46522816)))]; - tensor module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47047168)))]; - tensor linear_34_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_out_bias_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16, x = input_191_cast_fp16)[name = tensor("linear_34_cast_fp16")]; - tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = tensor("input_195_cast_fp16")]; - tensor x_83_axes_0 = const()[name = tensor("x_83_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47048256)))]; - tensor module_layers_3_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47049344)))]; - tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = tensor("x_83_cast_fp16")]; - tensor input_197_perm_0 = const()[name = tensor("input_197_perm_0"), val = tensor([0, 2, 1])]; - tensor input_199_pad_type_0 = const()[name = tensor("input_199_pad_type_0"), val = tensor("valid")]; - tensor input_199_strides_0 = const()[name = tensor("input_199_strides_0"), val = tensor([1])]; - tensor input_199_pad_0 = const()[name = tensor("input_199_pad_0"), val = tensor([0, 0])]; - tensor input_199_dilations_0 = const()[name = tensor("input_199_dilations_0"), val = tensor([1])]; - tensor input_199_groups_0 = const()[name = tensor("input_199_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47050432)))]; - tensor module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48099072)))]; - tensor input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = tensor("transpose_178")]; - tensor input_199_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv1_bias_to_fp16, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16, x = input_197_cast_fp16)[name = tensor("input_199_cast_fp16")]; - tensor x_85_split_num_splits_0 = const()[name = tensor("x_85_split_num_splits_0"), val = tensor(2)]; - tensor x_85_split_axis_0 = const()[name = tensor("x_85_split_axis_0"), val = tensor(1)]; - tensor x_85_split_cast_fp16_0, tensor x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = tensor("x_85_split_cast_fp16")]; - tensor x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = tensor("x_85_split_1_sigmoid_cast_fp16")]; - tensor x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = tensor("x_85_cast_fp16")]; - tensor input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_453)[name = tensor("input_201_cast_fp16")]; - tensor input_203_pad_0 = const()[name = tensor("input_203_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_203_mode_0 = const()[name = tensor("input_203_mode_0"), val = tensor("constant")]; - tensor const_103_to_fp16 = const()[name = tensor("const_103_to_fp16"), val = tensor(0x0p+0)]; - tensor input_203_cast_fp16 = pad(constant_val = const_103_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = tensor("input_203_cast_fp16")]; - tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("valid")]; - tensor input_205_groups_0 = const()[name = tensor("input_205_groups_0"), val = tensor(512)]; - tensor input_205_strides_0 = const()[name = tensor("input_205_strides_0"), val = tensor([1])]; - tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([0, 0])]; - tensor input_205_dilations_0 = const()[name = tensor("input_205_dilations_0"), val = tensor([1])]; - tensor const_240_to_fp16 = const()[name = tensor("const_240_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48101184)))]; - tensor const_241_to_fp16 = const()[name = tensor("const_241_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48110464)))]; - tensor input_207_cast_fp16 = conv(bias = const_241_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_240_to_fp16, x = input_203_cast_fp16)[name = tensor("input_207_cast_fp16")]; - tensor input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = tensor("input_209_cast_fp16")]; - tensor x_87_pad_type_0 = const()[name = tensor("x_87_pad_type_0"), val = tensor("valid")]; - tensor x_87_strides_0 = const()[name = tensor("x_87_strides_0"), val = tensor([1])]; - tensor x_87_pad_0 = const()[name = tensor("x_87_pad_0"), val = tensor([0, 0])]; - tensor x_87_dilations_0 = const()[name = tensor("x_87_dilations_0"), val = tensor([1])]; - tensor x_87_groups_0 = const()[name = tensor("x_87_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48111552)))]; - tensor module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48635904)))]; - tensor x_87_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv2_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16, x = input_209_cast_fp16)[name = tensor("x_87_cast_fp16")]; - tensor input_211_perm_0 = const()[name = tensor("input_211_perm_0"), val = tensor([0, 2, 1])]; - tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = tensor("transpose_177")]; - tensor input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = tensor("input_213_cast_fp16")]; - tensor input_215_axes_0 = const()[name = tensor("input_215_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48636992)))]; - tensor module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48638080)))]; - tensor input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = tensor("input_215_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48639168)))]; - tensor module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50736384)))]; - tensor linear_35_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16, x = input_215_cast_fp16)[name = tensor("linear_35_cast_fp16")]; - tensor input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = tensor("input_219_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50740544)))]; - tensor module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52837760)))]; - tensor linear_36_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16, x = input_219_cast_fp16)[name = tensor("linear_36_cast_fp16")]; - tensor var_1017_to_fp16 = const()[name = tensor("op_1017_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1018_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1017_to_fp16)[name = tensor("op_1018_cast_fp16")]; - tensor input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_1018_cast_fp16)[name = tensor("input_225_cast_fp16")]; - tensor input_227_axes_0 = const()[name = tensor("input_227_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52838848)))]; - tensor module_layers_3_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52839936)))]; - tensor input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = tensor("input_227_cast_fp16")]; - tensor input_229_axes_0 = const()[name = tensor("input_229_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52841024)))]; - tensor module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52842112)))]; - tensor input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = tensor("input_229_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52843200)))]; - tensor module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54940416)))]; - tensor linear_37_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16, x = input_229_cast_fp16)[name = tensor("linear_37_cast_fp16")]; - tensor input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = tensor("input_233_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54944576)))]; - tensor module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57041792)))]; - tensor linear_38_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16, x = input_233_cast_fp16)[name = tensor("linear_38_cast_fp16")]; - tensor var_1048_to_fp16 = const()[name = tensor("op_1048_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1049_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1048_to_fp16)[name = tensor("op_1049_cast_fp16")]; - tensor input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_1049_cast_fp16)[name = tensor("input_239_cast_fp16")]; - tensor query_9_axes_0 = const()[name = tensor("query_9_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57042880)))]; - tensor module_layers_4_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57043968)))]; - tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = tensor("query_9_cast_fp16")]; - tensor module_layers_4_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57045056)))]; - tensor module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57569408)))]; - tensor linear_39_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_q_bias_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_39_cast_fp16")]; - tensor var_1066 = const()[name = tensor("op_1066"), val = tensor([1, -1, 8, 64])]; - tensor q_25_cast_fp16 = reshape(shape = var_1066, x = linear_39_cast_fp16)[name = tensor("q_25_cast_fp16")]; - tensor module_layers_4_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57570496)))]; - tensor module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58094848)))]; - tensor linear_40_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_k_bias_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_40_cast_fp16")]; - tensor var_1071 = const()[name = tensor("op_1071"), val = tensor([1, -1, 8, 64])]; - tensor k_17_cast_fp16 = reshape(shape = var_1071, x = linear_40_cast_fp16)[name = tensor("k_17_cast_fp16")]; - tensor module_layers_4_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58095936)))]; - tensor module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58620288)))]; - tensor linear_41_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_v_bias_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_41_cast_fp16")]; - tensor var_1076 = const()[name = tensor("op_1076"), val = tensor([1, -1, 8, 64])]; - tensor v_9_cast_fp16 = reshape(shape = var_1076, x = linear_41_cast_fp16)[name = tensor("v_9_cast_fp16")]; - tensor value_11_perm_0 = const()[name = tensor("value_11_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58621376)))]; - tensor var_1088_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1088_cast_fp16")]; - tensor module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58622464)))]; - tensor var_1090_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1090_cast_fp16")]; - tensor q_with_bias_v_9_perm_0 = const()[name = tensor("q_with_bias_v_9_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_95_transpose_x_0 = const()[name = tensor("x_95_transpose_x_0"), val = tensor(false)]; - tensor x_95_transpose_y_0 = const()[name = tensor("x_95_transpose_y_0"), val = tensor(false)]; - tensor var_1092_to_fp16 = const()[name = tensor("op_1092_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58623552)))]; - tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1090_cast_fp16)[name = tensor("transpose_175")]; - tensor x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = var_1092_to_fp16)[name = tensor("x_95_cast_fp16")]; - tensor x_97_pad_0 = const()[name = tensor("x_97_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_97_mode_0 = const()[name = tensor("x_97_mode_0"), val = tensor("constant")]; - tensor const_110_to_fp16 = const()[name = tensor("const_110_to_fp16"), val = tensor(0x0p+0)]; - tensor x_97_cast_fp16 = pad(constant_val = const_110_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = tensor("x_97_cast_fp16")]; - tensor var_1100 = const()[name = tensor("op_1100"), val = tensor([1, 8, -1, 188])]; - tensor x_99_cast_fp16 = reshape(shape = var_1100, x = x_97_cast_fp16)[name = tensor("x_99_cast_fp16")]; - tensor var_1104_begin_0 = const()[name = tensor("op_1104_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1104_end_0 = const()[name = tensor("op_1104_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1104_end_mask_0 = const()[name = tensor("op_1104_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1104_cast_fp16 = slice_by_index(begin = var_1104_begin_0, end = var_1104_end_0, end_mask = var_1104_end_mask_0, x = x_99_cast_fp16)[name = tensor("op_1104_cast_fp16")]; - tensor var_1105 = const()[name = tensor("op_1105"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_17_cast_fp16 = reshape(shape = var_1105, x = var_1104_cast_fp16)[name = tensor("matrix_bd_17_cast_fp16")]; - tensor matrix_ac_9_transpose_x_0 = const()[name = tensor("matrix_ac_9_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_9_transpose_y_0 = const()[name = tensor("matrix_ac_9_transpose_y_0"), val = tensor(false)]; - tensor transpose_59_perm_0 = const()[name = tensor("transpose_59_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_60_perm_0 = const()[name = tensor("transpose_60_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = k_17_cast_fp16)[name = tensor("transpose_173")]; - tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = var_1088_cast_fp16)[name = tensor("transpose_174")]; - tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_59, y = transpose_60)[name = tensor("matrix_ac_9_cast_fp16")]; - tensor matrix_bd_19_begin_0 = const()[name = tensor("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_19_end_0 = const()[name = tensor("matrix_bd_19_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_19_end_mask_0 = const()[name = tensor("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = tensor("matrix_bd_19_cast_fp16")]; - tensor var_1114_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = tensor("op_1114_cast_fp16")]; - tensor _inversed_scores_17_y_0_to_fp16 = const()[name = tensor("_inversed_scores_17_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_17_cast_fp16 = mul(x = var_1114_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = tensor("_inversed_scores_17_cast_fp16")]; - tensor scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_11)[name = tensor("scores_19_cast_fp16")]; - tensor var_1120_cast_fp16 = softmax(axis = var_23, x = scores_19_cast_fp16)[name = tensor("op_1120_cast_fp16")]; - tensor input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_1120_cast_fp16, cond = mask_11)[name = tensor("input_241_cast_fp16")]; - tensor x_101_transpose_x_0 = const()[name = tensor("x_101_transpose_x_0"), val = tensor(false)]; - tensor x_101_transpose_y_0 = const()[name = tensor("x_101_transpose_y_0"), val = tensor(false)]; - tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = tensor("transpose_176")]; - tensor x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_11_cast_fp16)[name = tensor("x_101_cast_fp16")]; - tensor var_1124_perm_0 = const()[name = tensor("op_1124_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1125 = const()[name = tensor("op_1125"), val = tensor([1, -1, 512])]; - tensor var_1124_cast_fp16 = transpose(perm = var_1124_perm_0, x = x_101_cast_fp16)[name = tensor("transpose_172")]; - tensor input_243_cast_fp16 = reshape(shape = var_1125, x = var_1124_cast_fp16)[name = tensor("input_243_cast_fp16")]; - tensor module_layers_4_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59007616)))]; - tensor module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59531968)))]; - tensor linear_43_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_out_bias_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16, x = input_243_cast_fp16)[name = tensor("linear_43_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = tensor("input_247_cast_fp16")]; - tensor x_105_axes_0 = const()[name = tensor("x_105_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59533056)))]; - tensor module_layers_4_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59534144)))]; - tensor x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = tensor("x_105_cast_fp16")]; - tensor input_249_perm_0 = const()[name = tensor("input_249_perm_0"), val = tensor([0, 2, 1])]; - tensor input_251_pad_type_0 = const()[name = tensor("input_251_pad_type_0"), val = tensor("valid")]; - tensor input_251_strides_0 = const()[name = tensor("input_251_strides_0"), val = tensor([1])]; - tensor input_251_pad_0 = const()[name = tensor("input_251_pad_0"), val = tensor([0, 0])]; - tensor input_251_dilations_0 = const()[name = tensor("input_251_dilations_0"), val = tensor([1])]; - tensor input_251_groups_0 = const()[name = tensor("input_251_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59535232)))]; - tensor module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60583872)))]; - tensor input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = tensor("transpose_171")]; - tensor input_251_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv1_bias_to_fp16, dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16, x = input_249_cast_fp16)[name = tensor("input_251_cast_fp16")]; - tensor x_107_split_num_splits_0 = const()[name = tensor("x_107_split_num_splits_0"), val = tensor(2)]; - tensor x_107_split_axis_0 = const()[name = tensor("x_107_split_axis_0"), val = tensor(1)]; - tensor x_107_split_cast_fp16_0, tensor x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = tensor("x_107_split_cast_fp16")]; - tensor x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = tensor("x_107_split_1_sigmoid_cast_fp16")]; - tensor x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = tensor("x_107_cast_fp16")]; - tensor input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_453)[name = tensor("input_253_cast_fp16")]; - tensor input_255_pad_0 = const()[name = tensor("input_255_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_255_mode_0 = const()[name = tensor("input_255_mode_0"), val = tensor("constant")]; - tensor const_113_to_fp16 = const()[name = tensor("const_113_to_fp16"), val = tensor(0x0p+0)]; - tensor input_255_cast_fp16 = pad(constant_val = const_113_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = tensor("input_255_cast_fp16")]; - tensor input_257_pad_type_0 = const()[name = tensor("input_257_pad_type_0"), val = tensor("valid")]; - tensor input_257_groups_0 = const()[name = tensor("input_257_groups_0"), val = tensor(512)]; - tensor input_257_strides_0 = const()[name = tensor("input_257_strides_0"), val = tensor([1])]; - tensor input_257_pad_0 = const()[name = tensor("input_257_pad_0"), val = tensor([0, 0])]; - tensor input_257_dilations_0 = const()[name = tensor("input_257_dilations_0"), val = tensor([1])]; - tensor const_242_to_fp16 = const()[name = tensor("const_242_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60585984)))]; - tensor const_243_to_fp16 = const()[name = tensor("const_243_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60595264)))]; - tensor input_259_cast_fp16 = conv(bias = const_243_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_242_to_fp16, x = input_255_cast_fp16)[name = tensor("input_259_cast_fp16")]; - tensor input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = tensor("input_261_cast_fp16")]; - tensor x_109_pad_type_0 = const()[name = tensor("x_109_pad_type_0"), val = tensor("valid")]; - tensor x_109_strides_0 = const()[name = tensor("x_109_strides_0"), val = tensor([1])]; - tensor x_109_pad_0 = const()[name = tensor("x_109_pad_0"), val = tensor([0, 0])]; - tensor x_109_dilations_0 = const()[name = tensor("x_109_dilations_0"), val = tensor([1])]; - tensor x_109_groups_0 = const()[name = tensor("x_109_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60596352)))]; - tensor module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61120704)))]; - tensor x_109_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv2_bias_to_fp16, dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16, x = input_261_cast_fp16)[name = tensor("x_109_cast_fp16")]; - tensor input_263_perm_0 = const()[name = tensor("input_263_perm_0"), val = tensor([0, 2, 1])]; - tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = tensor("transpose_170")]; - tensor input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = tensor("input_265_cast_fp16")]; - tensor input_267_axes_0 = const()[name = tensor("input_267_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61121792)))]; - tensor module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61122880)))]; - tensor input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor("input_267_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61123968)))]; - tensor module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63221184)))]; - tensor linear_44_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16, x = input_267_cast_fp16)[name = tensor("linear_44_cast_fp16")]; - tensor input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = tensor("input_271_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63225344)))]; - tensor module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65322560)))]; - tensor linear_45_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16, x = input_271_cast_fp16)[name = tensor("linear_45_cast_fp16")]; - tensor var_1191_to_fp16 = const()[name = tensor("op_1191_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1192_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1191_to_fp16)[name = tensor("op_1192_cast_fp16")]; - tensor input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1192_cast_fp16)[name = tensor("input_277_cast_fp16")]; - tensor input_279_axes_0 = const()[name = tensor("input_279_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65323648)))]; - tensor module_layers_4_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65324736)))]; - tensor input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = tensor("input_279_cast_fp16")]; - tensor input_281_axes_0 = const()[name = tensor("input_281_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65325824)))]; - tensor module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65326912)))]; - tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = tensor("input_281_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65328000)))]; - tensor module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67425216)))]; - tensor linear_46_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16, x = input_281_cast_fp16)[name = tensor("linear_46_cast_fp16")]; - tensor input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = tensor("input_285_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67429376)))]; - tensor module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69526592)))]; - tensor linear_47_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16, x = input_285_cast_fp16)[name = tensor("linear_47_cast_fp16")]; - tensor var_1222_to_fp16 = const()[name = tensor("op_1222_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1223_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1222_to_fp16)[name = tensor("op_1223_cast_fp16")]; - tensor input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1223_cast_fp16)[name = tensor("input_291_cast_fp16")]; - tensor query_11_axes_0 = const()[name = tensor("query_11_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69527680)))]; - tensor module_layers_5_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69528768)))]; - tensor query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = tensor("query_11_cast_fp16")]; - tensor module_layers_5_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69529856)))]; - tensor module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70054208)))]; - tensor linear_48_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_q_bias_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_48_cast_fp16")]; - tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1, -1, 8, 64])]; - tensor q_31_cast_fp16 = reshape(shape = var_1240, x = linear_48_cast_fp16)[name = tensor("q_31_cast_fp16")]; - tensor module_layers_5_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70055296)))]; - tensor module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70579648)))]; - tensor linear_49_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_k_bias_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_49_cast_fp16")]; - tensor var_1245 = const()[name = tensor("op_1245"), val = tensor([1, -1, 8, 64])]; - tensor k_21_cast_fp16 = reshape(shape = var_1245, x = linear_49_cast_fp16)[name = tensor("k_21_cast_fp16")]; - tensor module_layers_5_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70580736)))]; - tensor module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71105088)))]; - tensor linear_50_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_v_bias_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_50_cast_fp16")]; - tensor var_1250 = const()[name = tensor("op_1250"), val = tensor([1, -1, 8, 64])]; - tensor v_11_cast_fp16 = reshape(shape = var_1250, x = linear_50_cast_fp16)[name = tensor("v_11_cast_fp16")]; - tensor value_13_perm_0 = const()[name = tensor("value_13_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71106176)))]; - tensor var_1262_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1262_cast_fp16")]; - tensor module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71107264)))]; - tensor var_1264_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1264_cast_fp16")]; - tensor q_with_bias_v_11_perm_0 = const()[name = tensor("q_with_bias_v_11_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_117_transpose_x_0 = const()[name = tensor("x_117_transpose_x_0"), val = tensor(false)]; - tensor x_117_transpose_y_0 = const()[name = tensor("x_117_transpose_y_0"), val = tensor(false)]; - tensor var_1266_to_fp16 = const()[name = tensor("op_1266_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71108352)))]; - tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1264_cast_fp16)[name = tensor("transpose_168")]; - tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = var_1266_to_fp16)[name = tensor("x_117_cast_fp16")]; - tensor x_119_pad_0 = const()[name = tensor("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_119_mode_0 = const()[name = tensor("x_119_mode_0"), val = tensor("constant")]; - tensor const_120_to_fp16 = const()[name = tensor("const_120_to_fp16"), val = tensor(0x0p+0)]; - tensor x_119_cast_fp16 = pad(constant_val = const_120_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = tensor("x_119_cast_fp16")]; - tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([1, 8, -1, 188])]; - tensor x_121_cast_fp16 = reshape(shape = var_1274, x = x_119_cast_fp16)[name = tensor("x_121_cast_fp16")]; - tensor var_1278_begin_0 = const()[name = tensor("op_1278_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1278_end_0 = const()[name = tensor("op_1278_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1278_end_mask_0 = const()[name = tensor("op_1278_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1278_cast_fp16 = slice_by_index(begin = var_1278_begin_0, end = var_1278_end_0, end_mask = var_1278_end_mask_0, x = x_121_cast_fp16)[name = tensor("op_1278_cast_fp16")]; - tensor var_1279 = const()[name = tensor("op_1279"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1279, x = var_1278_cast_fp16)[name = tensor("matrix_bd_21_cast_fp16")]; - tensor matrix_ac_11_transpose_x_0 = const()[name = tensor("matrix_ac_11_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_11_transpose_y_0 = const()[name = tensor("matrix_ac_11_transpose_y_0"), val = tensor(false)]; - tensor transpose_61_perm_0 = const()[name = tensor("transpose_61_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_62_perm_0 = const()[name = tensor("transpose_62_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = k_21_cast_fp16)[name = tensor("transpose_166")]; - tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_1262_cast_fp16)[name = tensor("transpose_167")]; - tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_61, y = transpose_62)[name = tensor("matrix_ac_11_cast_fp16")]; - tensor matrix_bd_23_begin_0 = const()[name = tensor("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_23_end_0 = const()[name = tensor("matrix_bd_23_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_23_end_mask_0 = const()[name = tensor("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = tensor("matrix_bd_23_cast_fp16")]; - tensor var_1288_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = tensor("op_1288_cast_fp16")]; - tensor _inversed_scores_21_y_0_to_fp16 = const()[name = tensor("_inversed_scores_21_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_21_cast_fp16 = mul(x = var_1288_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = tensor("_inversed_scores_21_cast_fp16")]; - tensor scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_11)[name = tensor("scores_23_cast_fp16")]; - tensor var_1294_cast_fp16 = softmax(axis = var_23, x = scores_23_cast_fp16)[name = tensor("op_1294_cast_fp16")]; - tensor input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1294_cast_fp16, cond = mask_11)[name = tensor("input_293_cast_fp16")]; - tensor x_123_transpose_x_0 = const()[name = tensor("x_123_transpose_x_0"), val = tensor(false)]; - tensor x_123_transpose_y_0 = const()[name = tensor("x_123_transpose_y_0"), val = tensor(false)]; - tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = tensor("transpose_169")]; - tensor x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_13_cast_fp16)[name = tensor("x_123_cast_fp16")]; - tensor var_1298_perm_0 = const()[name = tensor("op_1298_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1299 = const()[name = tensor("op_1299"), val = tensor([1, -1, 512])]; - tensor var_1298_cast_fp16 = transpose(perm = var_1298_perm_0, x = x_123_cast_fp16)[name = tensor("transpose_165")]; - tensor input_295_cast_fp16 = reshape(shape = var_1299, x = var_1298_cast_fp16)[name = tensor("input_295_cast_fp16")]; - tensor module_layers_5_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71492416)))]; - tensor module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72016768)))]; - tensor linear_52_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_out_bias_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16, x = input_295_cast_fp16)[name = tensor("linear_52_cast_fp16")]; - tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = tensor("input_299_cast_fp16")]; - tensor x_127_axes_0 = const()[name = tensor("x_127_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72017856)))]; - tensor module_layers_5_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72018944)))]; - tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = tensor("x_127_cast_fp16")]; - tensor input_301_perm_0 = const()[name = tensor("input_301_perm_0"), val = tensor([0, 2, 1])]; - tensor input_303_pad_type_0 = const()[name = tensor("input_303_pad_type_0"), val = tensor("valid")]; - tensor input_303_strides_0 = const()[name = tensor("input_303_strides_0"), val = tensor([1])]; - tensor input_303_pad_0 = const()[name = tensor("input_303_pad_0"), val = tensor([0, 0])]; - tensor input_303_dilations_0 = const()[name = tensor("input_303_dilations_0"), val = tensor([1])]; - tensor input_303_groups_0 = const()[name = tensor("input_303_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72020032)))]; - tensor module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73068672)))]; - tensor input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = tensor("transpose_164")]; - tensor input_303_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv1_bias_to_fp16, dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16, x = input_301_cast_fp16)[name = tensor("input_303_cast_fp16")]; - tensor x_129_split_num_splits_0 = const()[name = tensor("x_129_split_num_splits_0"), val = tensor(2)]; - tensor x_129_split_axis_0 = const()[name = tensor("x_129_split_axis_0"), val = tensor(1)]; - tensor x_129_split_cast_fp16_0, tensor x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = tensor("x_129_split_cast_fp16")]; - tensor x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = tensor("x_129_split_1_sigmoid_cast_fp16")]; - tensor x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = tensor("x_129_cast_fp16")]; - tensor input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_453)[name = tensor("input_305_cast_fp16")]; - tensor input_307_pad_0 = const()[name = tensor("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_307_mode_0 = const()[name = tensor("input_307_mode_0"), val = tensor("constant")]; - tensor const_123_to_fp16 = const()[name = tensor("const_123_to_fp16"), val = tensor(0x0p+0)]; - tensor input_307_cast_fp16 = pad(constant_val = const_123_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = tensor("input_307_cast_fp16")]; - tensor input_309_pad_type_0 = const()[name = tensor("input_309_pad_type_0"), val = tensor("valid")]; - tensor input_309_groups_0 = const()[name = tensor("input_309_groups_0"), val = tensor(512)]; - tensor input_309_strides_0 = const()[name = tensor("input_309_strides_0"), val = tensor([1])]; - tensor input_309_pad_0 = const()[name = tensor("input_309_pad_0"), val = tensor([0, 0])]; - tensor input_309_dilations_0 = const()[name = tensor("input_309_dilations_0"), val = tensor([1])]; - tensor const_244_to_fp16 = const()[name = tensor("const_244_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73070784)))]; - tensor const_245_to_fp16 = const()[name = tensor("const_245_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73080064)))]; - tensor input_311_cast_fp16 = conv(bias = const_245_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_244_to_fp16, x = input_307_cast_fp16)[name = tensor("input_311_cast_fp16")]; - tensor input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = tensor("input_313_cast_fp16")]; - tensor x_131_pad_type_0 = const()[name = tensor("x_131_pad_type_0"), val = tensor("valid")]; - tensor x_131_strides_0 = const()[name = tensor("x_131_strides_0"), val = tensor([1])]; - tensor x_131_pad_0 = const()[name = tensor("x_131_pad_0"), val = tensor([0, 0])]; - tensor x_131_dilations_0 = const()[name = tensor("x_131_dilations_0"), val = tensor([1])]; - tensor x_131_groups_0 = const()[name = tensor("x_131_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73081152)))]; - tensor module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73605504)))]; - tensor x_131_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv2_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_layers_5_conv_pointwise_conv2_weight_to_fp16, x = input_313_cast_fp16)[name = tensor("x_131_cast_fp16")]; - tensor input_315_perm_0 = const()[name = tensor("input_315_perm_0"), val = tensor([0, 2, 1])]; - tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = tensor("transpose_163")]; - tensor input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = tensor("input_317_cast_fp16")]; - tensor input_319_axes_0 = const()[name = tensor("input_319_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73606592)))]; - tensor module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73607680)))]; - tensor input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor("input_319_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73608768)))]; - tensor module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75705984)))]; - tensor linear_53_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16, x = input_319_cast_fp16)[name = tensor("linear_53_cast_fp16")]; - tensor input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = tensor("input_323_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75710144)))]; - tensor module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77807360)))]; - tensor linear_54_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16, x = input_323_cast_fp16)[name = tensor("linear_54_cast_fp16")]; - tensor var_1365_to_fp16 = const()[name = tensor("op_1365_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1366_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1365_to_fp16)[name = tensor("op_1366_cast_fp16")]; - tensor input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1366_cast_fp16)[name = tensor("input_329_cast_fp16")]; - tensor input_331_axes_0 = const()[name = tensor("input_331_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77808448)))]; - tensor module_layers_5_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77809536)))]; - tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("input_331_cast_fp16")]; - tensor input_333_axes_0 = const()[name = tensor("input_333_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77810624)))]; - tensor module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77811712)))]; - tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("input_333_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77812800)))]; - tensor module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79910016)))]; - tensor linear_55_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16, x = input_333_cast_fp16)[name = tensor("linear_55_cast_fp16")]; - tensor input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = tensor("input_337_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79914176)))]; - tensor module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82011392)))]; - tensor linear_56_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16, x = input_337_cast_fp16)[name = tensor("linear_56_cast_fp16")]; - tensor var_1396_to_fp16 = const()[name = tensor("op_1396_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1397_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1396_to_fp16)[name = tensor("op_1397_cast_fp16")]; - tensor input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1397_cast_fp16)[name = tensor("input_343_cast_fp16")]; - tensor query_13_axes_0 = const()[name = tensor("query_13_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82012480)))]; - tensor module_layers_6_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82013568)))]; - tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = tensor("query_13_cast_fp16")]; - tensor module_layers_6_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82014656)))]; - tensor module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82539008)))]; - tensor linear_57_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_q_bias_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_57_cast_fp16")]; - tensor var_1414 = const()[name = tensor("op_1414"), val = tensor([1, -1, 8, 64])]; - tensor q_37_cast_fp16 = reshape(shape = var_1414, x = linear_57_cast_fp16)[name = tensor("q_37_cast_fp16")]; - tensor module_layers_6_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82540096)))]; - tensor module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83064448)))]; - tensor linear_58_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_k_bias_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_58_cast_fp16")]; - tensor var_1419 = const()[name = tensor("op_1419"), val = tensor([1, -1, 8, 64])]; - tensor k_25_cast_fp16 = reshape(shape = var_1419, x = linear_58_cast_fp16)[name = tensor("k_25_cast_fp16")]; - tensor module_layers_6_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83065536)))]; - tensor module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83589888)))]; - tensor linear_59_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_v_bias_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_59_cast_fp16")]; - tensor var_1424 = const()[name = tensor("op_1424"), val = tensor([1, -1, 8, 64])]; - tensor v_13_cast_fp16 = reshape(shape = var_1424, x = linear_59_cast_fp16)[name = tensor("v_13_cast_fp16")]; - tensor value_15_perm_0 = const()[name = tensor("value_15_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83590976)))]; - tensor var_1436_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1436_cast_fp16")]; - tensor module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83592064)))]; - tensor var_1438_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1438_cast_fp16")]; - tensor q_with_bias_v_13_perm_0 = const()[name = tensor("q_with_bias_v_13_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_139_transpose_x_0 = const()[name = tensor("x_139_transpose_x_0"), val = tensor(false)]; - tensor x_139_transpose_y_0 = const()[name = tensor("x_139_transpose_y_0"), val = tensor(false)]; - tensor var_1440_to_fp16 = const()[name = tensor("op_1440_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83593152)))]; - tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1438_cast_fp16)[name = tensor("transpose_161")]; - tensor x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = var_1440_to_fp16)[name = tensor("x_139_cast_fp16")]; - tensor x_141_pad_0 = const()[name = tensor("x_141_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_141_mode_0 = const()[name = tensor("x_141_mode_0"), val = tensor("constant")]; - tensor const_130_to_fp16 = const()[name = tensor("const_130_to_fp16"), val = tensor(0x0p+0)]; - tensor x_141_cast_fp16 = pad(constant_val = const_130_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = tensor("x_141_cast_fp16")]; - tensor var_1448 = const()[name = tensor("op_1448"), val = tensor([1, 8, -1, 188])]; - tensor x_143_cast_fp16 = reshape(shape = var_1448, x = x_141_cast_fp16)[name = tensor("x_143_cast_fp16")]; - tensor var_1452_begin_0 = const()[name = tensor("op_1452_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1452_end_0 = const()[name = tensor("op_1452_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1452_end_mask_0 = const()[name = tensor("op_1452_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1452_cast_fp16 = slice_by_index(begin = var_1452_begin_0, end = var_1452_end_0, end_mask = var_1452_end_mask_0, x = x_143_cast_fp16)[name = tensor("op_1452_cast_fp16")]; - tensor var_1453 = const()[name = tensor("op_1453"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1453, x = var_1452_cast_fp16)[name = tensor("matrix_bd_25_cast_fp16")]; - tensor matrix_ac_13_transpose_x_0 = const()[name = tensor("matrix_ac_13_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_13_transpose_y_0 = const()[name = tensor("matrix_ac_13_transpose_y_0"), val = tensor(false)]; - tensor transpose_63_perm_0 = const()[name = tensor("transpose_63_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_64_perm_0 = const()[name = tensor("transpose_64_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = k_25_cast_fp16)[name = tensor("transpose_159")]; - tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_1436_cast_fp16)[name = tensor("transpose_160")]; - tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_63, y = transpose_64)[name = tensor("matrix_ac_13_cast_fp16")]; - tensor matrix_bd_27_begin_0 = const()[name = tensor("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_27_end_0 = const()[name = tensor("matrix_bd_27_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_27_end_mask_0 = const()[name = tensor("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = tensor("matrix_bd_27_cast_fp16")]; - tensor var_1462_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = tensor("op_1462_cast_fp16")]; - tensor _inversed_scores_25_y_0_to_fp16 = const()[name = tensor("_inversed_scores_25_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_25_cast_fp16 = mul(x = var_1462_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = tensor("_inversed_scores_25_cast_fp16")]; - tensor scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_11)[name = tensor("scores_27_cast_fp16")]; - tensor var_1468_cast_fp16 = softmax(axis = var_23, x = scores_27_cast_fp16)[name = tensor("op_1468_cast_fp16")]; - tensor input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1468_cast_fp16, cond = mask_11)[name = tensor("input_345_cast_fp16")]; - tensor x_145_transpose_x_0 = const()[name = tensor("x_145_transpose_x_0"), val = tensor(false)]; - tensor x_145_transpose_y_0 = const()[name = tensor("x_145_transpose_y_0"), val = tensor(false)]; - tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = tensor("transpose_162")]; - tensor x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_15_cast_fp16)[name = tensor("x_145_cast_fp16")]; - tensor var_1472_perm_0 = const()[name = tensor("op_1472_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1473 = const()[name = tensor("op_1473"), val = tensor([1, -1, 512])]; - tensor var_1472_cast_fp16 = transpose(perm = var_1472_perm_0, x = x_145_cast_fp16)[name = tensor("transpose_158")]; - tensor input_347_cast_fp16 = reshape(shape = var_1473, x = var_1472_cast_fp16)[name = tensor("input_347_cast_fp16")]; - tensor module_layers_6_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83977216)))]; - tensor module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84501568)))]; - tensor linear_61_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_out_bias_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16, x = input_347_cast_fp16)[name = tensor("linear_61_cast_fp16")]; - tensor input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = tensor("input_351_cast_fp16")]; - tensor x_149_axes_0 = const()[name = tensor("x_149_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84502656)))]; - tensor module_layers_6_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84503744)))]; - tensor x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = tensor("x_149_cast_fp16")]; - tensor input_353_perm_0 = const()[name = tensor("input_353_perm_0"), val = tensor([0, 2, 1])]; - tensor input_355_pad_type_0 = const()[name = tensor("input_355_pad_type_0"), val = tensor("valid")]; - tensor input_355_strides_0 = const()[name = tensor("input_355_strides_0"), val = tensor([1])]; - tensor input_355_pad_0 = const()[name = tensor("input_355_pad_0"), val = tensor([0, 0])]; - tensor input_355_dilations_0 = const()[name = tensor("input_355_dilations_0"), val = tensor([1])]; - tensor input_355_groups_0 = const()[name = tensor("input_355_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84504832)))]; - tensor module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85553472)))]; - tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = tensor("transpose_157")]; - tensor input_355_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv1_bias_to_fp16, dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16, x = input_353_cast_fp16)[name = tensor("input_355_cast_fp16")]; - tensor x_151_split_num_splits_0 = const()[name = tensor("x_151_split_num_splits_0"), val = tensor(2)]; - tensor x_151_split_axis_0 = const()[name = tensor("x_151_split_axis_0"), val = tensor(1)]; - tensor x_151_split_cast_fp16_0, tensor x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = tensor("x_151_split_cast_fp16")]; - tensor x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = tensor("x_151_split_1_sigmoid_cast_fp16")]; - tensor x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = tensor("x_151_cast_fp16")]; - tensor input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_453)[name = tensor("input_357_cast_fp16")]; - tensor input_359_pad_0 = const()[name = tensor("input_359_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_359_mode_0 = const()[name = tensor("input_359_mode_0"), val = tensor("constant")]; - tensor const_133_to_fp16 = const()[name = tensor("const_133_to_fp16"), val = tensor(0x0p+0)]; - tensor input_359_cast_fp16 = pad(constant_val = const_133_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = tensor("input_359_cast_fp16")]; - tensor input_361_pad_type_0 = const()[name = tensor("input_361_pad_type_0"), val = tensor("valid")]; - tensor input_361_groups_0 = const()[name = tensor("input_361_groups_0"), val = tensor(512)]; - tensor input_361_strides_0 = const()[name = tensor("input_361_strides_0"), val = tensor([1])]; - tensor input_361_pad_0 = const()[name = tensor("input_361_pad_0"), val = tensor([0, 0])]; - tensor input_361_dilations_0 = const()[name = tensor("input_361_dilations_0"), val = tensor([1])]; - tensor const_246_to_fp16 = const()[name = tensor("const_246_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85555584)))]; - tensor const_247_to_fp16 = const()[name = tensor("const_247_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85564864)))]; - tensor input_363_cast_fp16 = conv(bias = const_247_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_246_to_fp16, x = input_359_cast_fp16)[name = tensor("input_363_cast_fp16")]; - tensor input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; - tensor x_153_pad_type_0 = const()[name = tensor("x_153_pad_type_0"), val = tensor("valid")]; - tensor x_153_strides_0 = const()[name = tensor("x_153_strides_0"), val = tensor([1])]; - tensor x_153_pad_0 = const()[name = tensor("x_153_pad_0"), val = tensor([0, 0])]; - tensor x_153_dilations_0 = const()[name = tensor("x_153_dilations_0"), val = tensor([1])]; - tensor x_153_groups_0 = const()[name = tensor("x_153_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85565952)))]; - tensor module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86090304)))]; - tensor x_153_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv2_bias_to_fp16, dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = module_layers_6_conv_pointwise_conv2_weight_to_fp16, x = input_365_cast_fp16)[name = tensor("x_153_cast_fp16")]; - tensor input_367_perm_0 = const()[name = tensor("input_367_perm_0"), val = tensor([0, 2, 1])]; - tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = tensor("transpose_156")]; - tensor input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = tensor("input_369_cast_fp16")]; - tensor input_371_axes_0 = const()[name = tensor("input_371_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86091392)))]; - tensor module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86092480)))]; - tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor("input_371_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86093568)))]; - tensor module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88190784)))]; - tensor linear_62_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16, x = input_371_cast_fp16)[name = tensor("linear_62_cast_fp16")]; - tensor input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = tensor("input_375_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88194944)))]; - tensor module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90292160)))]; - tensor linear_63_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16, x = input_375_cast_fp16)[name = tensor("linear_63_cast_fp16")]; - tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1540_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1539_to_fp16)[name = tensor("op_1540_cast_fp16")]; - tensor input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1540_cast_fp16)[name = tensor("input_381_cast_fp16")]; - tensor input_383_axes_0 = const()[name = tensor("input_383_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90293248)))]; - tensor module_layers_6_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90294336)))]; - tensor input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = tensor("input_383_cast_fp16")]; - tensor input_385_axes_0 = const()[name = tensor("input_385_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90295424)))]; - tensor module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90296512)))]; - tensor input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = tensor("input_385_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90297600)))]; - tensor module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92394816)))]; - tensor linear_64_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16, x = input_385_cast_fp16)[name = tensor("linear_64_cast_fp16")]; - tensor input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = tensor("input_389_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92398976)))]; - tensor module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94496192)))]; - tensor linear_65_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16, x = input_389_cast_fp16)[name = tensor("linear_65_cast_fp16")]; - tensor var_1570_to_fp16 = const()[name = tensor("op_1570_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1571_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1570_to_fp16)[name = tensor("op_1571_cast_fp16")]; - tensor input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1571_cast_fp16)[name = tensor("input_395_cast_fp16")]; - tensor query_15_axes_0 = const()[name = tensor("query_15_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94497280)))]; - tensor module_layers_7_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94498368)))]; - tensor query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = tensor("query_15_cast_fp16")]; - tensor module_layers_7_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94499456)))]; - tensor module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95023808)))]; - tensor linear_66_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_q_bias_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_66_cast_fp16")]; - tensor var_1588 = const()[name = tensor("op_1588"), val = tensor([1, -1, 8, 64])]; - tensor q_43_cast_fp16 = reshape(shape = var_1588, x = linear_66_cast_fp16)[name = tensor("q_43_cast_fp16")]; - tensor module_layers_7_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95024896)))]; - tensor module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95549248)))]; - tensor linear_67_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_k_bias_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_67_cast_fp16")]; - tensor var_1593 = const()[name = tensor("op_1593"), val = tensor([1, -1, 8, 64])]; - tensor k_29_cast_fp16 = reshape(shape = var_1593, x = linear_67_cast_fp16)[name = tensor("k_29_cast_fp16")]; - tensor module_layers_7_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95550336)))]; - tensor module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96074688)))]; - tensor linear_68_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_v_bias_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_68_cast_fp16")]; - tensor var_1598 = const()[name = tensor("op_1598"), val = tensor([1, -1, 8, 64])]; - tensor v_15_cast_fp16 = reshape(shape = var_1598, x = linear_68_cast_fp16)[name = tensor("v_15_cast_fp16")]; - tensor value_17_perm_0 = const()[name = tensor("value_17_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96075776)))]; - tensor var_1610_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1610_cast_fp16")]; - tensor module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96076864)))]; - tensor var_1612_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1612_cast_fp16")]; - tensor q_with_bias_v_15_perm_0 = const()[name = tensor("q_with_bias_v_15_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_161_transpose_x_0 = const()[name = tensor("x_161_transpose_x_0"), val = tensor(false)]; - tensor x_161_transpose_y_0 = const()[name = tensor("x_161_transpose_y_0"), val = tensor(false)]; - tensor var_1614_to_fp16 = const()[name = tensor("op_1614_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96077952)))]; - tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1612_cast_fp16)[name = tensor("transpose_154")]; - tensor x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = var_1614_to_fp16)[name = tensor("x_161_cast_fp16")]; - tensor x_163_pad_0 = const()[name = tensor("x_163_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_163_mode_0 = const()[name = tensor("x_163_mode_0"), val = tensor("constant")]; - tensor const_140_to_fp16 = const()[name = tensor("const_140_to_fp16"), val = tensor(0x0p+0)]; - tensor x_163_cast_fp16 = pad(constant_val = const_140_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = tensor("x_163_cast_fp16")]; - tensor var_1622 = const()[name = tensor("op_1622"), val = tensor([1, 8, -1, 188])]; - tensor x_165_cast_fp16 = reshape(shape = var_1622, x = x_163_cast_fp16)[name = tensor("x_165_cast_fp16")]; - tensor var_1626_begin_0 = const()[name = tensor("op_1626_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1626_end_0 = const()[name = tensor("op_1626_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1626_end_mask_0 = const()[name = tensor("op_1626_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1626_cast_fp16 = slice_by_index(begin = var_1626_begin_0, end = var_1626_end_0, end_mask = var_1626_end_mask_0, x = x_165_cast_fp16)[name = tensor("op_1626_cast_fp16")]; - tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1627, x = var_1626_cast_fp16)[name = tensor("matrix_bd_29_cast_fp16")]; - tensor matrix_ac_15_transpose_x_0 = const()[name = tensor("matrix_ac_15_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_15_transpose_y_0 = const()[name = tensor("matrix_ac_15_transpose_y_0"), val = tensor(false)]; - tensor transpose_65_perm_0 = const()[name = tensor("transpose_65_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_66_perm_0 = const()[name = tensor("transpose_66_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = k_29_cast_fp16)[name = tensor("transpose_152")]; - tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = var_1610_cast_fp16)[name = tensor("transpose_153")]; - tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_65, y = transpose_66)[name = tensor("matrix_ac_15_cast_fp16")]; - tensor matrix_bd_31_begin_0 = const()[name = tensor("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_31_end_0 = const()[name = tensor("matrix_bd_31_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_31_end_mask_0 = const()[name = tensor("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = tensor("matrix_bd_31_cast_fp16")]; - tensor var_1636_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = tensor("op_1636_cast_fp16")]; - tensor _inversed_scores_29_y_0_to_fp16 = const()[name = tensor("_inversed_scores_29_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_29_cast_fp16 = mul(x = var_1636_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = tensor("_inversed_scores_29_cast_fp16")]; - tensor scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_11)[name = tensor("scores_31_cast_fp16")]; - tensor var_1642_cast_fp16 = softmax(axis = var_23, x = scores_31_cast_fp16)[name = tensor("op_1642_cast_fp16")]; - tensor input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1642_cast_fp16, cond = mask_11)[name = tensor("input_397_cast_fp16")]; - tensor x_167_transpose_x_0 = const()[name = tensor("x_167_transpose_x_0"), val = tensor(false)]; - tensor x_167_transpose_y_0 = const()[name = tensor("x_167_transpose_y_0"), val = tensor(false)]; - tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = tensor("transpose_155")]; - tensor x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_17_cast_fp16)[name = tensor("x_167_cast_fp16")]; - tensor var_1646_perm_0 = const()[name = tensor("op_1646_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1647 = const()[name = tensor("op_1647"), val = tensor([1, -1, 512])]; - tensor var_1646_cast_fp16 = transpose(perm = var_1646_perm_0, x = x_167_cast_fp16)[name = tensor("transpose_151")]; - tensor input_399_cast_fp16 = reshape(shape = var_1647, x = var_1646_cast_fp16)[name = tensor("input_399_cast_fp16")]; - tensor module_layers_7_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96462016)))]; - tensor module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96986368)))]; - tensor linear_70_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_out_bias_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16, x = input_399_cast_fp16)[name = tensor("linear_70_cast_fp16")]; - tensor input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = tensor("input_403_cast_fp16")]; - tensor x_171_axes_0 = const()[name = tensor("x_171_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96987456)))]; - tensor module_layers_7_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96988544)))]; - tensor x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = tensor("x_171_cast_fp16")]; - tensor input_405_perm_0 = const()[name = tensor("input_405_perm_0"), val = tensor([0, 2, 1])]; - tensor input_407_pad_type_0 = const()[name = tensor("input_407_pad_type_0"), val = tensor("valid")]; - tensor input_407_strides_0 = const()[name = tensor("input_407_strides_0"), val = tensor([1])]; - tensor input_407_pad_0 = const()[name = tensor("input_407_pad_0"), val = tensor([0, 0])]; - tensor input_407_dilations_0 = const()[name = tensor("input_407_dilations_0"), val = tensor([1])]; - tensor input_407_groups_0 = const()[name = tensor("input_407_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96989632)))]; - tensor module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98038272)))]; - tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = tensor("transpose_150")]; - tensor input_407_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv1_bias_to_fp16, dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16, x = input_405_cast_fp16)[name = tensor("input_407_cast_fp16")]; - tensor x_173_split_num_splits_0 = const()[name = tensor("x_173_split_num_splits_0"), val = tensor(2)]; - tensor x_173_split_axis_0 = const()[name = tensor("x_173_split_axis_0"), val = tensor(1)]; - tensor x_173_split_cast_fp16_0, tensor x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = tensor("x_173_split_cast_fp16")]; - tensor x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = tensor("x_173_split_1_sigmoid_cast_fp16")]; - tensor x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = tensor("x_173_cast_fp16")]; - tensor input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_453)[name = tensor("input_409_cast_fp16")]; - tensor input_411_pad_0 = const()[name = tensor("input_411_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_411_mode_0 = const()[name = tensor("input_411_mode_0"), val = tensor("constant")]; - tensor const_143_to_fp16 = const()[name = tensor("const_143_to_fp16"), val = tensor(0x0p+0)]; - tensor input_411_cast_fp16 = pad(constant_val = const_143_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = tensor("input_411_cast_fp16")]; - tensor input_413_pad_type_0 = const()[name = tensor("input_413_pad_type_0"), val = tensor("valid")]; - tensor input_413_groups_0 = const()[name = tensor("input_413_groups_0"), val = tensor(512)]; - tensor input_413_strides_0 = const()[name = tensor("input_413_strides_0"), val = tensor([1])]; - tensor input_413_pad_0 = const()[name = tensor("input_413_pad_0"), val = tensor([0, 0])]; - tensor input_413_dilations_0 = const()[name = tensor("input_413_dilations_0"), val = tensor([1])]; - tensor const_248_to_fp16 = const()[name = tensor("const_248_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98040384)))]; - tensor const_249_to_fp16 = const()[name = tensor("const_249_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98049664)))]; - tensor input_415_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_248_to_fp16, x = input_411_cast_fp16)[name = tensor("input_415_cast_fp16")]; - tensor input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = tensor("input_417_cast_fp16")]; - tensor x_175_pad_type_0 = const()[name = tensor("x_175_pad_type_0"), val = tensor("valid")]; - tensor x_175_strides_0 = const()[name = tensor("x_175_strides_0"), val = tensor([1])]; - tensor x_175_pad_0 = const()[name = tensor("x_175_pad_0"), val = tensor([0, 0])]; - tensor x_175_dilations_0 = const()[name = tensor("x_175_dilations_0"), val = tensor([1])]; - tensor x_175_groups_0 = const()[name = tensor("x_175_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98050752)))]; - tensor module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98575104)))]; - tensor x_175_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv2_bias_to_fp16, dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = module_layers_7_conv_pointwise_conv2_weight_to_fp16, x = input_417_cast_fp16)[name = tensor("x_175_cast_fp16")]; - tensor input_419_perm_0 = const()[name = tensor("input_419_perm_0"), val = tensor([0, 2, 1])]; - tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = tensor("transpose_149")]; - tensor input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = tensor("input_421_cast_fp16")]; - tensor input_423_axes_0 = const()[name = tensor("input_423_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98576192)))]; - tensor module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98577280)))]; - tensor input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = tensor("input_423_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98578368)))]; - tensor module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100675584)))]; - tensor linear_71_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor("linear_71_cast_fp16")]; - tensor input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = tensor("input_427_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100679744)))]; - tensor module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102776960)))]; - tensor linear_72_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16, x = input_427_cast_fp16)[name = tensor("linear_72_cast_fp16")]; - tensor var_1713_to_fp16 = const()[name = tensor("op_1713_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1714_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1713_to_fp16)[name = tensor("op_1714_cast_fp16")]; - tensor input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1714_cast_fp16)[name = tensor("input_433_cast_fp16")]; - tensor input_435_axes_0 = const()[name = tensor("input_435_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102778048)))]; - tensor module_layers_7_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102779136)))]; - tensor input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = tensor("input_435_cast_fp16")]; - tensor input_437_axes_0 = const()[name = tensor("input_437_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102780224)))]; - tensor module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102781312)))]; - tensor input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = tensor("input_437_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102782400)))]; - tensor module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104879616)))]; - tensor linear_73_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16, x = input_437_cast_fp16)[name = tensor("linear_73_cast_fp16")]; - tensor input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = tensor("input_441_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104883776)))]; - tensor module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106980992)))]; - tensor linear_74_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16, x = input_441_cast_fp16)[name = tensor("linear_74_cast_fp16")]; - tensor var_1744_to_fp16 = const()[name = tensor("op_1744_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1745_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1744_to_fp16)[name = tensor("op_1745_cast_fp16")]; - tensor input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1745_cast_fp16)[name = tensor("input_447_cast_fp16")]; - tensor query_17_axes_0 = const()[name = tensor("query_17_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106982080)))]; - tensor module_layers_8_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106983168)))]; - tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = tensor("query_17_cast_fp16")]; - tensor module_layers_8_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106984256)))]; - tensor module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107508608)))]; - tensor linear_75_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_q_bias_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_75_cast_fp16")]; - tensor var_1762 = const()[name = tensor("op_1762"), val = tensor([1, -1, 8, 64])]; - tensor q_49_cast_fp16 = reshape(shape = var_1762, x = linear_75_cast_fp16)[name = tensor("q_49_cast_fp16")]; - tensor module_layers_8_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107509696)))]; - tensor module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108034048)))]; - tensor linear_76_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_k_bias_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_76_cast_fp16")]; - tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, -1, 8, 64])]; - tensor k_33_cast_fp16 = reshape(shape = var_1767, x = linear_76_cast_fp16)[name = tensor("k_33_cast_fp16")]; - tensor module_layers_8_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108035136)))]; - tensor module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108559488)))]; - tensor linear_77_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_v_bias_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_77_cast_fp16")]; - tensor var_1772 = const()[name = tensor("op_1772"), val = tensor([1, -1, 8, 64])]; - tensor v_17_cast_fp16 = reshape(shape = var_1772, x = linear_77_cast_fp16)[name = tensor("v_17_cast_fp16")]; - tensor value_19_perm_0 = const()[name = tensor("value_19_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108560576)))]; - tensor var_1784_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1784_cast_fp16")]; - tensor module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108561664)))]; - tensor var_1786_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1786_cast_fp16")]; - tensor q_with_bias_v_17_perm_0 = const()[name = tensor("q_with_bias_v_17_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_183_transpose_x_0 = const()[name = tensor("x_183_transpose_x_0"), val = tensor(false)]; - tensor x_183_transpose_y_0 = const()[name = tensor("x_183_transpose_y_0"), val = tensor(false)]; - tensor var_1788_to_fp16 = const()[name = tensor("op_1788_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108562752)))]; - tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1786_cast_fp16)[name = tensor("transpose_147")]; - tensor x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = var_1788_to_fp16)[name = tensor("x_183_cast_fp16")]; - tensor x_185_pad_0 = const()[name = tensor("x_185_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_185_mode_0 = const()[name = tensor("x_185_mode_0"), val = tensor("constant")]; - tensor const_150_to_fp16 = const()[name = tensor("const_150_to_fp16"), val = tensor(0x0p+0)]; - tensor x_185_cast_fp16 = pad(constant_val = const_150_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = tensor("x_185_cast_fp16")]; - tensor var_1796 = const()[name = tensor("op_1796"), val = tensor([1, 8, -1, 188])]; - tensor x_187_cast_fp16 = reshape(shape = var_1796, x = x_185_cast_fp16)[name = tensor("x_187_cast_fp16")]; - tensor var_1800_begin_0 = const()[name = tensor("op_1800_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1800_end_0 = const()[name = tensor("op_1800_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1800_end_mask_0 = const()[name = tensor("op_1800_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1800_cast_fp16 = slice_by_index(begin = var_1800_begin_0, end = var_1800_end_0, end_mask = var_1800_end_mask_0, x = x_187_cast_fp16)[name = tensor("op_1800_cast_fp16")]; - tensor var_1801 = const()[name = tensor("op_1801"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_33_cast_fp16 = reshape(shape = var_1801, x = var_1800_cast_fp16)[name = tensor("matrix_bd_33_cast_fp16")]; - tensor matrix_ac_17_transpose_x_0 = const()[name = tensor("matrix_ac_17_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_17_transpose_y_0 = const()[name = tensor("matrix_ac_17_transpose_y_0"), val = tensor(false)]; - tensor transpose_67_perm_0 = const()[name = tensor("transpose_67_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_68_perm_0 = const()[name = tensor("transpose_68_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = k_33_cast_fp16)[name = tensor("transpose_145")]; - tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_1784_cast_fp16)[name = tensor("transpose_146")]; - tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_67, y = transpose_68)[name = tensor("matrix_ac_17_cast_fp16")]; - tensor matrix_bd_35_begin_0 = const()[name = tensor("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_35_end_0 = const()[name = tensor("matrix_bd_35_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_35_end_mask_0 = const()[name = tensor("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = tensor("matrix_bd_35_cast_fp16")]; - tensor var_1810_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = tensor("op_1810_cast_fp16")]; - tensor _inversed_scores_33_y_0_to_fp16 = const()[name = tensor("_inversed_scores_33_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_33_cast_fp16 = mul(x = var_1810_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = tensor("_inversed_scores_33_cast_fp16")]; - tensor scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_11)[name = tensor("scores_35_cast_fp16")]; - tensor var_1816_cast_fp16 = softmax(axis = var_23, x = scores_35_cast_fp16)[name = tensor("op_1816_cast_fp16")]; - tensor input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1816_cast_fp16, cond = mask_11)[name = tensor("input_449_cast_fp16")]; - tensor x_189_transpose_x_0 = const()[name = tensor("x_189_transpose_x_0"), val = tensor(false)]; - tensor x_189_transpose_y_0 = const()[name = tensor("x_189_transpose_y_0"), val = tensor(false)]; - tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = tensor("transpose_148")]; - tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_19_cast_fp16)[name = tensor("x_189_cast_fp16")]; - tensor var_1820_perm_0 = const()[name = tensor("op_1820_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1821 = const()[name = tensor("op_1821"), val = tensor([1, -1, 512])]; - tensor var_1820_cast_fp16 = transpose(perm = var_1820_perm_0, x = x_189_cast_fp16)[name = tensor("transpose_144")]; - tensor input_451_cast_fp16 = reshape(shape = var_1821, x = var_1820_cast_fp16)[name = tensor("input_451_cast_fp16")]; - tensor module_layers_8_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108946816)))]; - tensor module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109471168)))]; - tensor linear_79_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_out_bias_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16, x = input_451_cast_fp16)[name = tensor("linear_79_cast_fp16")]; - tensor input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = tensor("input_455_cast_fp16")]; - tensor x_193_axes_0 = const()[name = tensor("x_193_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109472256)))]; - tensor module_layers_8_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109473344)))]; - tensor x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = tensor("x_193_cast_fp16")]; - tensor input_457_perm_0 = const()[name = tensor("input_457_perm_0"), val = tensor([0, 2, 1])]; - tensor input_459_pad_type_0 = const()[name = tensor("input_459_pad_type_0"), val = tensor("valid")]; - tensor input_459_strides_0 = const()[name = tensor("input_459_strides_0"), val = tensor([1])]; - tensor input_459_pad_0 = const()[name = tensor("input_459_pad_0"), val = tensor([0, 0])]; - tensor input_459_dilations_0 = const()[name = tensor("input_459_dilations_0"), val = tensor([1])]; - tensor input_459_groups_0 = const()[name = tensor("input_459_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109474432)))]; - tensor module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110523072)))]; - tensor input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = tensor("transpose_143")]; - tensor input_459_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv1_bias_to_fp16, dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16, x = input_457_cast_fp16)[name = tensor("input_459_cast_fp16")]; - tensor x_195_split_num_splits_0 = const()[name = tensor("x_195_split_num_splits_0"), val = tensor(2)]; - tensor x_195_split_axis_0 = const()[name = tensor("x_195_split_axis_0"), val = tensor(1)]; - tensor x_195_split_cast_fp16_0, tensor x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = tensor("x_195_split_cast_fp16")]; - tensor x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = tensor("x_195_split_1_sigmoid_cast_fp16")]; - tensor x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = tensor("x_195_cast_fp16")]; - tensor input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_453)[name = tensor("input_461_cast_fp16")]; - tensor input_463_pad_0 = const()[name = tensor("input_463_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_463_mode_0 = const()[name = tensor("input_463_mode_0"), val = tensor("constant")]; - tensor const_153_to_fp16 = const()[name = tensor("const_153_to_fp16"), val = tensor(0x0p+0)]; - tensor input_463_cast_fp16 = pad(constant_val = const_153_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = tensor("input_463_cast_fp16")]; - tensor input_465_pad_type_0 = const()[name = tensor("input_465_pad_type_0"), val = tensor("valid")]; - tensor input_465_groups_0 = const()[name = tensor("input_465_groups_0"), val = tensor(512)]; - tensor input_465_strides_0 = const()[name = tensor("input_465_strides_0"), val = tensor([1])]; - tensor input_465_pad_0 = const()[name = tensor("input_465_pad_0"), val = tensor([0, 0])]; - tensor input_465_dilations_0 = const()[name = tensor("input_465_dilations_0"), val = tensor([1])]; - tensor const_250_to_fp16 = const()[name = tensor("const_250_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110525184)))]; - tensor const_251_to_fp16 = const()[name = tensor("const_251_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110534464)))]; - tensor input_467_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_250_to_fp16, x = input_463_cast_fp16)[name = tensor("input_467_cast_fp16")]; - tensor input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = tensor("input_469_cast_fp16")]; - tensor x_197_pad_type_0 = const()[name = tensor("x_197_pad_type_0"), val = tensor("valid")]; - tensor x_197_strides_0 = const()[name = tensor("x_197_strides_0"), val = tensor([1])]; - tensor x_197_pad_0 = const()[name = tensor("x_197_pad_0"), val = tensor([0, 0])]; - tensor x_197_dilations_0 = const()[name = tensor("x_197_dilations_0"), val = tensor([1])]; - tensor x_197_groups_0 = const()[name = tensor("x_197_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110535552)))]; - tensor module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111059904)))]; - tensor x_197_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv2_bias_to_fp16, dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = module_layers_8_conv_pointwise_conv2_weight_to_fp16, x = input_469_cast_fp16)[name = tensor("x_197_cast_fp16")]; - tensor input_471_perm_0 = const()[name = tensor("input_471_perm_0"), val = tensor([0, 2, 1])]; - tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = tensor("transpose_142")]; - tensor input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = tensor("input_473_cast_fp16")]; - tensor input_475_axes_0 = const()[name = tensor("input_475_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111060992)))]; - tensor module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111062080)))]; - tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = tensor("input_475_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111063168)))]; - tensor module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113160384)))]; - tensor linear_80_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16, x = input_475_cast_fp16)[name = tensor("linear_80_cast_fp16")]; - tensor input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = tensor("input_479_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113164544)))]; - tensor module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115261760)))]; - tensor linear_81_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16, x = input_479_cast_fp16)[name = tensor("linear_81_cast_fp16")]; - tensor var_1887_to_fp16 = const()[name = tensor("op_1887_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1888_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1887_to_fp16)[name = tensor("op_1888_cast_fp16")]; - tensor input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1888_cast_fp16)[name = tensor("input_485_cast_fp16")]; - tensor input_487_axes_0 = const()[name = tensor("input_487_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115262848)))]; - tensor module_layers_8_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115263936)))]; - tensor input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = tensor("input_487_cast_fp16")]; - tensor input_489_axes_0 = const()[name = tensor("input_489_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115265024)))]; - tensor module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115266112)))]; - tensor input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = tensor("input_489_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115267200)))]; - tensor module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117364416)))]; - tensor linear_82_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16, x = input_489_cast_fp16)[name = tensor("linear_82_cast_fp16")]; - tensor input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = tensor("input_493_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117368576)))]; - tensor module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119465792)))]; - tensor linear_83_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16, x = input_493_cast_fp16)[name = tensor("linear_83_cast_fp16")]; - tensor var_1918_to_fp16 = const()[name = tensor("op_1918_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1919_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1918_to_fp16)[name = tensor("op_1919_cast_fp16")]; - tensor input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1919_cast_fp16)[name = tensor("input_499_cast_fp16")]; - tensor query_19_axes_0 = const()[name = tensor("query_19_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119466880)))]; - tensor module_layers_9_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119467968)))]; - tensor query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = tensor("query_19_cast_fp16")]; - tensor module_layers_9_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119469056)))]; - tensor module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119993408)))]; - tensor linear_84_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_q_bias_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_84_cast_fp16")]; - tensor var_1936 = const()[name = tensor("op_1936"), val = tensor([1, -1, 8, 64])]; - tensor q_55_cast_fp16 = reshape(shape = var_1936, x = linear_84_cast_fp16)[name = tensor("q_55_cast_fp16")]; - tensor module_layers_9_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119994496)))]; - tensor module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120518848)))]; - tensor linear_85_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_k_bias_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_85_cast_fp16")]; - tensor var_1941 = const()[name = tensor("op_1941"), val = tensor([1, -1, 8, 64])]; - tensor k_37_cast_fp16 = reshape(shape = var_1941, x = linear_85_cast_fp16)[name = tensor("k_37_cast_fp16")]; - tensor module_layers_9_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120519936)))]; - tensor module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121044288)))]; - tensor linear_86_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_v_bias_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_86_cast_fp16")]; - tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([1, -1, 8, 64])]; - tensor v_19_cast_fp16 = reshape(shape = var_1946, x = linear_86_cast_fp16)[name = tensor("v_19_cast_fp16")]; - tensor value_21_perm_0 = const()[name = tensor("value_21_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121045376)))]; - tensor var_1958_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1958_cast_fp16")]; - tensor module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121046464)))]; - tensor var_1960_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1960_cast_fp16")]; - tensor q_with_bias_v_19_perm_0 = const()[name = tensor("q_with_bias_v_19_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_205_transpose_x_0 = const()[name = tensor("x_205_transpose_x_0"), val = tensor(false)]; - tensor x_205_transpose_y_0 = const()[name = tensor("x_205_transpose_y_0"), val = tensor(false)]; - tensor var_1962_to_fp16 = const()[name = tensor("op_1962_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121047552)))]; - tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1960_cast_fp16)[name = tensor("transpose_140")]; - tensor x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = var_1962_to_fp16)[name = tensor("x_205_cast_fp16")]; - tensor x_207_pad_0 = const()[name = tensor("x_207_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_207_mode_0 = const()[name = tensor("x_207_mode_0"), val = tensor("constant")]; - tensor const_160_to_fp16 = const()[name = tensor("const_160_to_fp16"), val = tensor(0x0p+0)]; - tensor x_207_cast_fp16 = pad(constant_val = const_160_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = tensor("x_207_cast_fp16")]; - tensor var_1970 = const()[name = tensor("op_1970"), val = tensor([1, 8, -1, 188])]; - tensor x_209_cast_fp16 = reshape(shape = var_1970, x = x_207_cast_fp16)[name = tensor("x_209_cast_fp16")]; - tensor var_1974_begin_0 = const()[name = tensor("op_1974_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1974_end_0 = const()[name = tensor("op_1974_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1974_end_mask_0 = const()[name = tensor("op_1974_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1974_cast_fp16 = slice_by_index(begin = var_1974_begin_0, end = var_1974_end_0, end_mask = var_1974_end_mask_0, x = x_209_cast_fp16)[name = tensor("op_1974_cast_fp16")]; - tensor var_1975 = const()[name = tensor("op_1975"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1975, x = var_1974_cast_fp16)[name = tensor("matrix_bd_37_cast_fp16")]; - tensor matrix_ac_19_transpose_x_0 = const()[name = tensor("matrix_ac_19_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_19_transpose_y_0 = const()[name = tensor("matrix_ac_19_transpose_y_0"), val = tensor(false)]; - tensor transpose_69_perm_0 = const()[name = tensor("transpose_69_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_70_perm_0 = const()[name = tensor("transpose_70_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = k_37_cast_fp16)[name = tensor("transpose_138")]; - tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1958_cast_fp16)[name = tensor("transpose_139")]; - tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_69, y = transpose_70)[name = tensor("matrix_ac_19_cast_fp16")]; - tensor matrix_bd_39_begin_0 = const()[name = tensor("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_39_end_0 = const()[name = tensor("matrix_bd_39_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_39_end_mask_0 = const()[name = tensor("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = tensor("matrix_bd_39_cast_fp16")]; - tensor var_1984_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = tensor("op_1984_cast_fp16")]; - tensor _inversed_scores_37_y_0_to_fp16 = const()[name = tensor("_inversed_scores_37_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_37_cast_fp16 = mul(x = var_1984_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = tensor("_inversed_scores_37_cast_fp16")]; - tensor scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_11)[name = tensor("scores_39_cast_fp16")]; - tensor var_1990_cast_fp16 = softmax(axis = var_23, x = scores_39_cast_fp16)[name = tensor("op_1990_cast_fp16")]; - tensor input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1990_cast_fp16, cond = mask_11)[name = tensor("input_501_cast_fp16")]; - tensor x_211_transpose_x_0 = const()[name = tensor("x_211_transpose_x_0"), val = tensor(false)]; - tensor x_211_transpose_y_0 = const()[name = tensor("x_211_transpose_y_0"), val = tensor(false)]; - tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = tensor("transpose_141")]; - tensor x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_21_cast_fp16)[name = tensor("x_211_cast_fp16")]; - tensor var_1994_perm_0 = const()[name = tensor("op_1994_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1995 = const()[name = tensor("op_1995"), val = tensor([1, -1, 512])]; - tensor var_1994_cast_fp16 = transpose(perm = var_1994_perm_0, x = x_211_cast_fp16)[name = tensor("transpose_137")]; - tensor input_503_cast_fp16 = reshape(shape = var_1995, x = var_1994_cast_fp16)[name = tensor("input_503_cast_fp16")]; - tensor module_layers_9_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121431616)))]; - tensor module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121955968)))]; - tensor linear_88_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_out_bias_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16, x = input_503_cast_fp16)[name = tensor("linear_88_cast_fp16")]; - tensor input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = tensor("input_507_cast_fp16")]; - tensor x_215_axes_0 = const()[name = tensor("x_215_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121957056)))]; - tensor module_layers_9_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121958144)))]; - tensor x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = tensor("x_215_cast_fp16")]; - tensor input_509_perm_0 = const()[name = tensor("input_509_perm_0"), val = tensor([0, 2, 1])]; - tensor input_511_pad_type_0 = const()[name = tensor("input_511_pad_type_0"), val = tensor("valid")]; - tensor input_511_strides_0 = const()[name = tensor("input_511_strides_0"), val = tensor([1])]; - tensor input_511_pad_0 = const()[name = tensor("input_511_pad_0"), val = tensor([0, 0])]; - tensor input_511_dilations_0 = const()[name = tensor("input_511_dilations_0"), val = tensor([1])]; - tensor input_511_groups_0 = const()[name = tensor("input_511_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121959232)))]; - tensor module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123007872)))]; - tensor input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = tensor("transpose_136")]; - tensor input_511_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv1_bias_to_fp16, dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16, x = input_509_cast_fp16)[name = tensor("input_511_cast_fp16")]; - tensor x_217_split_num_splits_0 = const()[name = tensor("x_217_split_num_splits_0"), val = tensor(2)]; - tensor x_217_split_axis_0 = const()[name = tensor("x_217_split_axis_0"), val = tensor(1)]; - tensor x_217_split_cast_fp16_0, tensor x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = tensor("x_217_split_cast_fp16")]; - tensor x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = tensor("x_217_split_1_sigmoid_cast_fp16")]; - tensor x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = tensor("x_217_cast_fp16")]; - tensor input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_453)[name = tensor("input_513_cast_fp16")]; - tensor input_515_pad_0 = const()[name = tensor("input_515_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_515_mode_0 = const()[name = tensor("input_515_mode_0"), val = tensor("constant")]; - tensor const_163_to_fp16 = const()[name = tensor("const_163_to_fp16"), val = tensor(0x0p+0)]; - tensor input_515_cast_fp16 = pad(constant_val = const_163_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = tensor("input_515_cast_fp16")]; - tensor input_517_pad_type_0 = const()[name = tensor("input_517_pad_type_0"), val = tensor("valid")]; - tensor input_517_groups_0 = const()[name = tensor("input_517_groups_0"), val = tensor(512)]; - tensor input_517_strides_0 = const()[name = tensor("input_517_strides_0"), val = tensor([1])]; - tensor input_517_pad_0 = const()[name = tensor("input_517_pad_0"), val = tensor([0, 0])]; - tensor input_517_dilations_0 = const()[name = tensor("input_517_dilations_0"), val = tensor([1])]; - tensor const_252_to_fp16 = const()[name = tensor("const_252_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123009984)))]; - tensor const_253_to_fp16 = const()[name = tensor("const_253_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123019264)))]; - tensor input_519_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_252_to_fp16, x = input_515_cast_fp16)[name = tensor("input_519_cast_fp16")]; - tensor input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = tensor("input_521_cast_fp16")]; - tensor x_219_pad_type_0 = const()[name = tensor("x_219_pad_type_0"), val = tensor("valid")]; - tensor x_219_strides_0 = const()[name = tensor("x_219_strides_0"), val = tensor([1])]; - tensor x_219_pad_0 = const()[name = tensor("x_219_pad_0"), val = tensor([0, 0])]; - tensor x_219_dilations_0 = const()[name = tensor("x_219_dilations_0"), val = tensor([1])]; - tensor x_219_groups_0 = const()[name = tensor("x_219_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123020352)))]; - tensor module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123544704)))]; - tensor x_219_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv2_bias_to_fp16, dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = module_layers_9_conv_pointwise_conv2_weight_to_fp16, x = input_521_cast_fp16)[name = tensor("x_219_cast_fp16")]; - tensor input_523_perm_0 = const()[name = tensor("input_523_perm_0"), val = tensor([0, 2, 1])]; - tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = tensor("transpose_135")]; - tensor input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = tensor("input_525_cast_fp16")]; - tensor input_527_axes_0 = const()[name = tensor("input_527_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123545792)))]; - tensor module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123546880)))]; - tensor input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = tensor("input_527_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123547968)))]; - tensor module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125645184)))]; - tensor linear_89_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16, x = input_527_cast_fp16)[name = tensor("linear_89_cast_fp16")]; - tensor input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = tensor("input_531_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125649344)))]; - tensor module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127746560)))]; - tensor linear_90_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16, x = input_531_cast_fp16)[name = tensor("linear_90_cast_fp16")]; - tensor var_2061_to_fp16 = const()[name = tensor("op_2061_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2062_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2061_to_fp16)[name = tensor("op_2062_cast_fp16")]; - tensor input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_2062_cast_fp16)[name = tensor("input_537_cast_fp16")]; - tensor input_539_axes_0 = const()[name = tensor("input_539_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127747648)))]; - tensor module_layers_9_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127748736)))]; - tensor input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = tensor("input_539_cast_fp16")]; - tensor input_541_axes_0 = const()[name = tensor("input_541_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127749824)))]; - tensor module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127750912)))]; - tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = tensor("input_541_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127752000)))]; - tensor module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129849216)))]; - tensor linear_91_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16, x = input_541_cast_fp16)[name = tensor("linear_91_cast_fp16")]; - tensor input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = tensor("input_545_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129853376)))]; - tensor module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131950592)))]; - tensor linear_92_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16, x = input_545_cast_fp16)[name = tensor("linear_92_cast_fp16")]; - tensor var_2092_to_fp16 = const()[name = tensor("op_2092_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2093_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2092_to_fp16)[name = tensor("op_2093_cast_fp16")]; - tensor input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_2093_cast_fp16)[name = tensor("input_551_cast_fp16")]; - tensor query_21_axes_0 = const()[name = tensor("query_21_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131951680)))]; - tensor module_layers_10_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131952768)))]; - tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = tensor("query_21_cast_fp16")]; - tensor module_layers_10_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131953856)))]; - tensor module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132478208)))]; - tensor linear_93_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_q_bias_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_93_cast_fp16")]; - tensor var_2110 = const()[name = tensor("op_2110"), val = tensor([1, -1, 8, 64])]; - tensor q_61_cast_fp16 = reshape(shape = var_2110, x = linear_93_cast_fp16)[name = tensor("q_61_cast_fp16")]; - tensor module_layers_10_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132479296)))]; - tensor module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133003648)))]; - tensor linear_94_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_k_bias_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_94_cast_fp16")]; - tensor var_2115 = const()[name = tensor("op_2115"), val = tensor([1, -1, 8, 64])]; - tensor k_41_cast_fp16 = reshape(shape = var_2115, x = linear_94_cast_fp16)[name = tensor("k_41_cast_fp16")]; - tensor module_layers_10_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133004736)))]; - tensor module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133529088)))]; - tensor linear_95_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_v_bias_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_95_cast_fp16")]; - tensor var_2120 = const()[name = tensor("op_2120"), val = tensor([1, -1, 8, 64])]; - tensor v_21_cast_fp16 = reshape(shape = var_2120, x = linear_95_cast_fp16)[name = tensor("v_21_cast_fp16")]; - tensor value_23_perm_0 = const()[name = tensor("value_23_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133530176)))]; - tensor var_2132_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2132_cast_fp16")]; - tensor module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133531264)))]; - tensor var_2134_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2134_cast_fp16")]; - tensor q_with_bias_v_21_perm_0 = const()[name = tensor("q_with_bias_v_21_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_227_transpose_x_0 = const()[name = tensor("x_227_transpose_x_0"), val = tensor(false)]; - tensor x_227_transpose_y_0 = const()[name = tensor("x_227_transpose_y_0"), val = tensor(false)]; - tensor var_2136_to_fp16 = const()[name = tensor("op_2136_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133532352)))]; - tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2134_cast_fp16)[name = tensor("transpose_133")]; - tensor x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = var_2136_to_fp16)[name = tensor("x_227_cast_fp16")]; - tensor x_229_pad_0 = const()[name = tensor("x_229_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_229_mode_0 = const()[name = tensor("x_229_mode_0"), val = tensor("constant")]; - tensor const_170_to_fp16 = const()[name = tensor("const_170_to_fp16"), val = tensor(0x0p+0)]; - tensor x_229_cast_fp16 = pad(constant_val = const_170_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = tensor("x_229_cast_fp16")]; - tensor var_2144 = const()[name = tensor("op_2144"), val = tensor([1, 8, -1, 188])]; - tensor x_231_cast_fp16 = reshape(shape = var_2144, x = x_229_cast_fp16)[name = tensor("x_231_cast_fp16")]; - tensor var_2148_begin_0 = const()[name = tensor("op_2148_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2148_end_0 = const()[name = tensor("op_2148_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2148_end_mask_0 = const()[name = tensor("op_2148_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2148_cast_fp16 = slice_by_index(begin = var_2148_begin_0, end = var_2148_end_0, end_mask = var_2148_end_mask_0, x = x_231_cast_fp16)[name = tensor("op_2148_cast_fp16")]; - tensor var_2149 = const()[name = tensor("op_2149"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_41_cast_fp16 = reshape(shape = var_2149, x = var_2148_cast_fp16)[name = tensor("matrix_bd_41_cast_fp16")]; - tensor matrix_ac_21_transpose_x_0 = const()[name = tensor("matrix_ac_21_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_21_transpose_y_0 = const()[name = tensor("matrix_ac_21_transpose_y_0"), val = tensor(false)]; - tensor transpose_71_perm_0 = const()[name = tensor("transpose_71_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_72_perm_0 = const()[name = tensor("transpose_72_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = k_41_cast_fp16)[name = tensor("transpose_131")]; - tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = var_2132_cast_fp16)[name = tensor("transpose_132")]; - tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_71, y = transpose_72)[name = tensor("matrix_ac_21_cast_fp16")]; - tensor matrix_bd_43_begin_0 = const()[name = tensor("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_43_end_0 = const()[name = tensor("matrix_bd_43_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_43_end_mask_0 = const()[name = tensor("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = tensor("matrix_bd_43_cast_fp16")]; - tensor var_2158_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = tensor("op_2158_cast_fp16")]; - tensor _inversed_scores_41_y_0_to_fp16 = const()[name = tensor("_inversed_scores_41_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_41_cast_fp16 = mul(x = var_2158_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = tensor("_inversed_scores_41_cast_fp16")]; - tensor scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_11)[name = tensor("scores_43_cast_fp16")]; - tensor var_2164_cast_fp16 = softmax(axis = var_23, x = scores_43_cast_fp16)[name = tensor("op_2164_cast_fp16")]; - tensor input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_2164_cast_fp16, cond = mask_11)[name = tensor("input_553_cast_fp16")]; - tensor x_233_transpose_x_0 = const()[name = tensor("x_233_transpose_x_0"), val = tensor(false)]; - tensor x_233_transpose_y_0 = const()[name = tensor("x_233_transpose_y_0"), val = tensor(false)]; - tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = tensor("transpose_134")]; - tensor x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_23_cast_fp16)[name = tensor("x_233_cast_fp16")]; - tensor var_2168_perm_0 = const()[name = tensor("op_2168_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2169 = const()[name = tensor("op_2169"), val = tensor([1, -1, 512])]; - tensor var_2168_cast_fp16 = transpose(perm = var_2168_perm_0, x = x_233_cast_fp16)[name = tensor("transpose_130")]; - tensor input_555_cast_fp16 = reshape(shape = var_2169, x = var_2168_cast_fp16)[name = tensor("input_555_cast_fp16")]; - tensor module_layers_10_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133916416)))]; - tensor module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134440768)))]; - tensor linear_97_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_out_bias_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16, x = input_555_cast_fp16)[name = tensor("linear_97_cast_fp16")]; - tensor input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = tensor("input_559_cast_fp16")]; - tensor x_237_axes_0 = const()[name = tensor("x_237_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134441856)))]; - tensor module_layers_10_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134442944)))]; - tensor x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = tensor("x_237_cast_fp16")]; - tensor input_561_perm_0 = const()[name = tensor("input_561_perm_0"), val = tensor([0, 2, 1])]; - tensor input_563_pad_type_0 = const()[name = tensor("input_563_pad_type_0"), val = tensor("valid")]; - tensor input_563_strides_0 = const()[name = tensor("input_563_strides_0"), val = tensor([1])]; - tensor input_563_pad_0 = const()[name = tensor("input_563_pad_0"), val = tensor([0, 0])]; - tensor input_563_dilations_0 = const()[name = tensor("input_563_dilations_0"), val = tensor([1])]; - tensor input_563_groups_0 = const()[name = tensor("input_563_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134444032)))]; - tensor module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135492672)))]; - tensor input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = tensor("transpose_129")]; - tensor input_563_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv1_bias_to_fp16, dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16, x = input_561_cast_fp16)[name = tensor("input_563_cast_fp16")]; - tensor x_239_split_num_splits_0 = const()[name = tensor("x_239_split_num_splits_0"), val = tensor(2)]; - tensor x_239_split_axis_0 = const()[name = tensor("x_239_split_axis_0"), val = tensor(1)]; - tensor x_239_split_cast_fp16_0, tensor x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = tensor("x_239_split_cast_fp16")]; - tensor x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = tensor("x_239_split_1_sigmoid_cast_fp16")]; - tensor x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = tensor("x_239_cast_fp16")]; - tensor input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_453)[name = tensor("input_565_cast_fp16")]; - tensor input_567_pad_0 = const()[name = tensor("input_567_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_567_mode_0 = const()[name = tensor("input_567_mode_0"), val = tensor("constant")]; - tensor const_173_to_fp16 = const()[name = tensor("const_173_to_fp16"), val = tensor(0x0p+0)]; - tensor input_567_cast_fp16 = pad(constant_val = const_173_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = tensor("input_567_cast_fp16")]; - tensor input_569_pad_type_0 = const()[name = tensor("input_569_pad_type_0"), val = tensor("valid")]; - tensor input_569_groups_0 = const()[name = tensor("input_569_groups_0"), val = tensor(512)]; - tensor input_569_strides_0 = const()[name = tensor("input_569_strides_0"), val = tensor([1])]; - tensor input_569_pad_0 = const()[name = tensor("input_569_pad_0"), val = tensor([0, 0])]; - tensor input_569_dilations_0 = const()[name = tensor("input_569_dilations_0"), val = tensor([1])]; - tensor const_254_to_fp16 = const()[name = tensor("const_254_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135494784)))]; - tensor const_255_to_fp16 = const()[name = tensor("const_255_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135504064)))]; - tensor input_571_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_254_to_fp16, x = input_567_cast_fp16)[name = tensor("input_571_cast_fp16")]; - tensor input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = tensor("input_573_cast_fp16")]; - tensor x_241_pad_type_0 = const()[name = tensor("x_241_pad_type_0"), val = tensor("valid")]; - tensor x_241_strides_0 = const()[name = tensor("x_241_strides_0"), val = tensor([1])]; - tensor x_241_pad_0 = const()[name = tensor("x_241_pad_0"), val = tensor([0, 0])]; - tensor x_241_dilations_0 = const()[name = tensor("x_241_dilations_0"), val = tensor([1])]; - tensor x_241_groups_0 = const()[name = tensor("x_241_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135505152)))]; - tensor module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136029504)))]; - tensor x_241_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv2_bias_to_fp16, dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = module_layers_10_conv_pointwise_conv2_weight_to_fp16, x = input_573_cast_fp16)[name = tensor("x_241_cast_fp16")]; - tensor input_575_perm_0 = const()[name = tensor("input_575_perm_0"), val = tensor([0, 2, 1])]; - tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = tensor("transpose_128")]; - tensor input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = tensor("input_577_cast_fp16")]; - tensor input_579_axes_0 = const()[name = tensor("input_579_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136030592)))]; - tensor module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136031680)))]; - tensor input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = tensor("input_579_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136032768)))]; - tensor module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138129984)))]; - tensor linear_98_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16, x = input_579_cast_fp16)[name = tensor("linear_98_cast_fp16")]; - tensor input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = tensor("input_583_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138134144)))]; - tensor module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140231360)))]; - tensor linear_99_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16, x = input_583_cast_fp16)[name = tensor("linear_99_cast_fp16")]; - tensor var_2235_to_fp16 = const()[name = tensor("op_2235_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2236_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2235_to_fp16)[name = tensor("op_2236_cast_fp16")]; - tensor input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_2236_cast_fp16)[name = tensor("input_589_cast_fp16")]; - tensor input_591_axes_0 = const()[name = tensor("input_591_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140232448)))]; - tensor module_layers_10_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140233536)))]; - tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = tensor("input_591_cast_fp16")]; - tensor input_593_axes_0 = const()[name = tensor("input_593_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140234624)))]; - tensor module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140235712)))]; - tensor input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = tensor("input_593_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140236800)))]; - tensor module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142334016)))]; - tensor linear_100_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16, x = input_593_cast_fp16)[name = tensor("linear_100_cast_fp16")]; - tensor input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = tensor("input_597_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142338176)))]; - tensor module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144435392)))]; - tensor linear_101_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16, x = input_597_cast_fp16)[name = tensor("linear_101_cast_fp16")]; - tensor var_2266_to_fp16 = const()[name = tensor("op_2266_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2267_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2266_to_fp16)[name = tensor("op_2267_cast_fp16")]; - tensor input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2267_cast_fp16)[name = tensor("input_603_cast_fp16")]; - tensor query_23_axes_0 = const()[name = tensor("query_23_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144436480)))]; - tensor module_layers_11_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144437568)))]; - tensor query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = tensor("query_23_cast_fp16")]; - tensor module_layers_11_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144438656)))]; - tensor module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144963008)))]; - tensor linear_102_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_q_bias_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_102_cast_fp16")]; - tensor var_2284 = const()[name = tensor("op_2284"), val = tensor([1, -1, 8, 64])]; - tensor q_67_cast_fp16 = reshape(shape = var_2284, x = linear_102_cast_fp16)[name = tensor("q_67_cast_fp16")]; - tensor module_layers_11_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144964096)))]; - tensor module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145488448)))]; - tensor linear_103_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_k_bias_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_103_cast_fp16")]; - tensor var_2289 = const()[name = tensor("op_2289"), val = tensor([1, -1, 8, 64])]; - tensor k_45_cast_fp16 = reshape(shape = var_2289, x = linear_103_cast_fp16)[name = tensor("k_45_cast_fp16")]; - tensor module_layers_11_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145489536)))]; - tensor module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146013888)))]; - tensor linear_104_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_v_bias_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_104_cast_fp16")]; - tensor var_2294 = const()[name = tensor("op_2294"), val = tensor([1, -1, 8, 64])]; - tensor v_23_cast_fp16 = reshape(shape = var_2294, x = linear_104_cast_fp16)[name = tensor("v_23_cast_fp16")]; - tensor value_25_perm_0 = const()[name = tensor("value_25_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146014976)))]; - tensor var_2306_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2306_cast_fp16")]; - tensor module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146016064)))]; - tensor var_2308_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2308_cast_fp16")]; - tensor q_with_bias_v_23_perm_0 = const()[name = tensor("q_with_bias_v_23_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_249_transpose_x_0 = const()[name = tensor("x_249_transpose_x_0"), val = tensor(false)]; - tensor x_249_transpose_y_0 = const()[name = tensor("x_249_transpose_y_0"), val = tensor(false)]; - tensor var_2310_to_fp16 = const()[name = tensor("op_2310_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146017152)))]; - tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2308_cast_fp16)[name = tensor("transpose_126")]; - tensor x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = var_2310_to_fp16)[name = tensor("x_249_cast_fp16")]; - tensor x_251_pad_0 = const()[name = tensor("x_251_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_251_mode_0 = const()[name = tensor("x_251_mode_0"), val = tensor("constant")]; - tensor const_180_to_fp16 = const()[name = tensor("const_180_to_fp16"), val = tensor(0x0p+0)]; - tensor x_251_cast_fp16 = pad(constant_val = const_180_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = tensor("x_251_cast_fp16")]; - tensor var_2318 = const()[name = tensor("op_2318"), val = tensor([1, 8, -1, 188])]; - tensor x_253_cast_fp16 = reshape(shape = var_2318, x = x_251_cast_fp16)[name = tensor("x_253_cast_fp16")]; - tensor var_2322_begin_0 = const()[name = tensor("op_2322_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2322_end_0 = const()[name = tensor("op_2322_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2322_end_mask_0 = const()[name = tensor("op_2322_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2322_cast_fp16 = slice_by_index(begin = var_2322_begin_0, end = var_2322_end_0, end_mask = var_2322_end_mask_0, x = x_253_cast_fp16)[name = tensor("op_2322_cast_fp16")]; - tensor var_2323 = const()[name = tensor("op_2323"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2323, x = var_2322_cast_fp16)[name = tensor("matrix_bd_45_cast_fp16")]; - tensor matrix_ac_23_transpose_x_0 = const()[name = tensor("matrix_ac_23_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_23_transpose_y_0 = const()[name = tensor("matrix_ac_23_transpose_y_0"), val = tensor(false)]; - tensor transpose_73_perm_0 = const()[name = tensor("transpose_73_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_74_perm_0 = const()[name = tensor("transpose_74_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = k_45_cast_fp16)[name = tensor("transpose_124")]; - tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_2306_cast_fp16)[name = tensor("transpose_125")]; - tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_73, y = transpose_74)[name = tensor("matrix_ac_23_cast_fp16")]; - tensor matrix_bd_47_begin_0 = const()[name = tensor("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_47_end_0 = const()[name = tensor("matrix_bd_47_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_47_end_mask_0 = const()[name = tensor("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = tensor("matrix_bd_47_cast_fp16")]; - tensor var_2332_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = tensor("op_2332_cast_fp16")]; - tensor _inversed_scores_45_y_0_to_fp16 = const()[name = tensor("_inversed_scores_45_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_45_cast_fp16 = mul(x = var_2332_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = tensor("_inversed_scores_45_cast_fp16")]; - tensor scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_11)[name = tensor("scores_47_cast_fp16")]; - tensor var_2338_cast_fp16 = softmax(axis = var_23, x = scores_47_cast_fp16)[name = tensor("op_2338_cast_fp16")]; - tensor input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2338_cast_fp16, cond = mask_11)[name = tensor("input_605_cast_fp16")]; - tensor x_255_transpose_x_0 = const()[name = tensor("x_255_transpose_x_0"), val = tensor(false)]; - tensor x_255_transpose_y_0 = const()[name = tensor("x_255_transpose_y_0"), val = tensor(false)]; - tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = tensor("transpose_127")]; - tensor x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_25_cast_fp16)[name = tensor("x_255_cast_fp16")]; - tensor var_2342_perm_0 = const()[name = tensor("op_2342_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2343 = const()[name = tensor("op_2343"), val = tensor([1, -1, 512])]; - tensor var_2342_cast_fp16 = transpose(perm = var_2342_perm_0, x = x_255_cast_fp16)[name = tensor("transpose_123")]; - tensor input_607_cast_fp16 = reshape(shape = var_2343, x = var_2342_cast_fp16)[name = tensor("input_607_cast_fp16")]; - tensor module_layers_11_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146401216)))]; - tensor module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146925568)))]; - tensor linear_106_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_out_bias_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16, x = input_607_cast_fp16)[name = tensor("linear_106_cast_fp16")]; - tensor input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = tensor("input_611_cast_fp16")]; - tensor x_259_axes_0 = const()[name = tensor("x_259_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146926656)))]; - tensor module_layers_11_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146927744)))]; - tensor x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = tensor("x_259_cast_fp16")]; - tensor input_613_perm_0 = const()[name = tensor("input_613_perm_0"), val = tensor([0, 2, 1])]; - tensor input_615_pad_type_0 = const()[name = tensor("input_615_pad_type_0"), val = tensor("valid")]; - tensor input_615_strides_0 = const()[name = tensor("input_615_strides_0"), val = tensor([1])]; - tensor input_615_pad_0 = const()[name = tensor("input_615_pad_0"), val = tensor([0, 0])]; - tensor input_615_dilations_0 = const()[name = tensor("input_615_dilations_0"), val = tensor([1])]; - tensor input_615_groups_0 = const()[name = tensor("input_615_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146928832)))]; - tensor module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147977472)))]; - tensor input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = tensor("transpose_122")]; - tensor input_615_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv1_bias_to_fp16, dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16, x = input_613_cast_fp16)[name = tensor("input_615_cast_fp16")]; - tensor x_261_split_num_splits_0 = const()[name = tensor("x_261_split_num_splits_0"), val = tensor(2)]; - tensor x_261_split_axis_0 = const()[name = tensor("x_261_split_axis_0"), val = tensor(1)]; - tensor x_261_split_cast_fp16_0, tensor x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = tensor("x_261_split_cast_fp16")]; - tensor x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = tensor("x_261_split_1_sigmoid_cast_fp16")]; - tensor x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = tensor("x_261_cast_fp16")]; - tensor input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_453)[name = tensor("input_617_cast_fp16")]; - tensor input_619_pad_0 = const()[name = tensor("input_619_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_619_mode_0 = const()[name = tensor("input_619_mode_0"), val = tensor("constant")]; - tensor const_183_to_fp16 = const()[name = tensor("const_183_to_fp16"), val = tensor(0x0p+0)]; - tensor input_619_cast_fp16 = pad(constant_val = const_183_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = tensor("input_619_cast_fp16")]; - tensor input_621_pad_type_0 = const()[name = tensor("input_621_pad_type_0"), val = tensor("valid")]; - tensor input_621_groups_0 = const()[name = tensor("input_621_groups_0"), val = tensor(512)]; - tensor input_621_strides_0 = const()[name = tensor("input_621_strides_0"), val = tensor([1])]; - tensor input_621_pad_0 = const()[name = tensor("input_621_pad_0"), val = tensor([0, 0])]; - tensor input_621_dilations_0 = const()[name = tensor("input_621_dilations_0"), val = tensor([1])]; - tensor const_256_to_fp16 = const()[name = tensor("const_256_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147979584)))]; - tensor const_257_to_fp16 = const()[name = tensor("const_257_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147988864)))]; - tensor input_623_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_256_to_fp16, x = input_619_cast_fp16)[name = tensor("input_623_cast_fp16")]; - tensor input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = tensor("input_625_cast_fp16")]; - tensor x_263_pad_type_0 = const()[name = tensor("x_263_pad_type_0"), val = tensor("valid")]; - tensor x_263_strides_0 = const()[name = tensor("x_263_strides_0"), val = tensor([1])]; - tensor x_263_pad_0 = const()[name = tensor("x_263_pad_0"), val = tensor([0, 0])]; - tensor x_263_dilations_0 = const()[name = tensor("x_263_dilations_0"), val = tensor([1])]; - tensor x_263_groups_0 = const()[name = tensor("x_263_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147989952)))]; - tensor module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148514304)))]; - tensor x_263_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv2_bias_to_fp16, dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = module_layers_11_conv_pointwise_conv2_weight_to_fp16, x = input_625_cast_fp16)[name = tensor("x_263_cast_fp16")]; - tensor input_627_perm_0 = const()[name = tensor("input_627_perm_0"), val = tensor([0, 2, 1])]; - tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = tensor("transpose_121")]; - tensor input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = tensor("input_629_cast_fp16")]; - tensor input_631_axes_0 = const()[name = tensor("input_631_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148515392)))]; - tensor module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148516480)))]; - tensor input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = tensor("input_631_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148517568)))]; - tensor module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150614784)))]; - tensor linear_107_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16, x = input_631_cast_fp16)[name = tensor("linear_107_cast_fp16")]; - tensor input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = tensor("input_635_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150618944)))]; - tensor module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152716160)))]; - tensor linear_108_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16, x = input_635_cast_fp16)[name = tensor("linear_108_cast_fp16")]; - tensor var_2409_to_fp16 = const()[name = tensor("op_2409_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2410_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2409_to_fp16)[name = tensor("op_2410_cast_fp16")]; - tensor input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2410_cast_fp16)[name = tensor("input_641_cast_fp16")]; - tensor input_643_axes_0 = const()[name = tensor("input_643_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152717248)))]; - tensor module_layers_11_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152718336)))]; - tensor input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = tensor("input_643_cast_fp16")]; - tensor input_645_axes_0 = const()[name = tensor("input_645_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152719424)))]; - tensor module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152720512)))]; - tensor input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = tensor("input_645_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152721600)))]; - tensor module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154818816)))]; - tensor linear_109_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16, x = input_645_cast_fp16)[name = tensor("linear_109_cast_fp16")]; - tensor input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = tensor("input_649_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154822976)))]; - tensor module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156920192)))]; - tensor linear_110_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16, x = input_649_cast_fp16)[name = tensor("linear_110_cast_fp16")]; - tensor var_2440_to_fp16 = const()[name = tensor("op_2440_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2441_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2440_to_fp16)[name = tensor("op_2441_cast_fp16")]; - tensor input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2441_cast_fp16)[name = tensor("input_655_cast_fp16")]; - tensor query_25_axes_0 = const()[name = tensor("query_25_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156921280)))]; - tensor module_layers_12_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156922368)))]; - tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = tensor("query_25_cast_fp16")]; - tensor module_layers_12_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156923456)))]; - tensor module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157447808)))]; - tensor linear_111_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_q_bias_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_111_cast_fp16")]; - tensor var_2458 = const()[name = tensor("op_2458"), val = tensor([1, -1, 8, 64])]; - tensor q_73_cast_fp16 = reshape(shape = var_2458, x = linear_111_cast_fp16)[name = tensor("q_73_cast_fp16")]; - tensor module_layers_12_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157448896)))]; - tensor module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157973248)))]; - tensor linear_112_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_k_bias_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_112_cast_fp16")]; - tensor var_2463 = const()[name = tensor("op_2463"), val = tensor([1, -1, 8, 64])]; - tensor k_49_cast_fp16 = reshape(shape = var_2463, x = linear_112_cast_fp16)[name = tensor("k_49_cast_fp16")]; - tensor module_layers_12_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157974336)))]; - tensor module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158498688)))]; - tensor linear_113_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_v_bias_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_113_cast_fp16")]; - tensor var_2468 = const()[name = tensor("op_2468"), val = tensor([1, -1, 8, 64])]; - tensor v_25_cast_fp16 = reshape(shape = var_2468, x = linear_113_cast_fp16)[name = tensor("v_25_cast_fp16")]; - tensor value_27_perm_0 = const()[name = tensor("value_27_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158499776)))]; - tensor var_2480_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2480_cast_fp16")]; - tensor module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158500864)))]; - tensor var_2482_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2482_cast_fp16")]; - tensor q_with_bias_v_25_perm_0 = const()[name = tensor("q_with_bias_v_25_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_271_transpose_x_0 = const()[name = tensor("x_271_transpose_x_0"), val = tensor(false)]; - tensor x_271_transpose_y_0 = const()[name = tensor("x_271_transpose_y_0"), val = tensor(false)]; - tensor var_2484_to_fp16 = const()[name = tensor("op_2484_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158501952)))]; - tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2482_cast_fp16)[name = tensor("transpose_119")]; - tensor x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = var_2484_to_fp16)[name = tensor("x_271_cast_fp16")]; - tensor x_273_pad_0 = const()[name = tensor("x_273_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_273_mode_0 = const()[name = tensor("x_273_mode_0"), val = tensor("constant")]; - tensor const_190_to_fp16 = const()[name = tensor("const_190_to_fp16"), val = tensor(0x0p+0)]; - tensor x_273_cast_fp16 = pad(constant_val = const_190_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = tensor("x_273_cast_fp16")]; - tensor var_2492 = const()[name = tensor("op_2492"), val = tensor([1, 8, -1, 188])]; - tensor x_275_cast_fp16 = reshape(shape = var_2492, x = x_273_cast_fp16)[name = tensor("x_275_cast_fp16")]; - tensor var_2496_begin_0 = const()[name = tensor("op_2496_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2496_end_0 = const()[name = tensor("op_2496_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2496_end_mask_0 = const()[name = tensor("op_2496_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2496_cast_fp16 = slice_by_index(begin = var_2496_begin_0, end = var_2496_end_0, end_mask = var_2496_end_mask_0, x = x_275_cast_fp16)[name = tensor("op_2496_cast_fp16")]; - tensor var_2497 = const()[name = tensor("op_2497"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_49_cast_fp16 = reshape(shape = var_2497, x = var_2496_cast_fp16)[name = tensor("matrix_bd_49_cast_fp16")]; - tensor matrix_ac_25_transpose_x_0 = const()[name = tensor("matrix_ac_25_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_25_transpose_y_0 = const()[name = tensor("matrix_ac_25_transpose_y_0"), val = tensor(false)]; - tensor transpose_75_perm_0 = const()[name = tensor("transpose_75_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_76_perm_0 = const()[name = tensor("transpose_76_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = k_49_cast_fp16)[name = tensor("transpose_117")]; - tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_2480_cast_fp16)[name = tensor("transpose_118")]; - tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_75, y = transpose_76)[name = tensor("matrix_ac_25_cast_fp16")]; - tensor matrix_bd_51_begin_0 = const()[name = tensor("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_51_end_0 = const()[name = tensor("matrix_bd_51_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_51_end_mask_0 = const()[name = tensor("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = tensor("matrix_bd_51_cast_fp16")]; - tensor var_2506_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = tensor("op_2506_cast_fp16")]; - tensor _inversed_scores_49_y_0_to_fp16 = const()[name = tensor("_inversed_scores_49_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_49_cast_fp16 = mul(x = var_2506_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = tensor("_inversed_scores_49_cast_fp16")]; - tensor scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_11)[name = tensor("scores_51_cast_fp16")]; - tensor var_2512_cast_fp16 = softmax(axis = var_23, x = scores_51_cast_fp16)[name = tensor("op_2512_cast_fp16")]; - tensor input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2512_cast_fp16, cond = mask_11)[name = tensor("input_657_cast_fp16")]; - tensor x_277_transpose_x_0 = const()[name = tensor("x_277_transpose_x_0"), val = tensor(false)]; - tensor x_277_transpose_y_0 = const()[name = tensor("x_277_transpose_y_0"), val = tensor(false)]; - tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = tensor("transpose_120")]; - tensor x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_27_cast_fp16)[name = tensor("x_277_cast_fp16")]; - tensor var_2516_perm_0 = const()[name = tensor("op_2516_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2517 = const()[name = tensor("op_2517"), val = tensor([1, -1, 512])]; - tensor var_2516_cast_fp16 = transpose(perm = var_2516_perm_0, x = x_277_cast_fp16)[name = tensor("transpose_116")]; - tensor input_659_cast_fp16 = reshape(shape = var_2517, x = var_2516_cast_fp16)[name = tensor("input_659_cast_fp16")]; - tensor module_layers_12_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158886016)))]; - tensor module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159410368)))]; - tensor linear_115_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_out_bias_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16, x = input_659_cast_fp16)[name = tensor("linear_115_cast_fp16")]; - tensor input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = tensor("input_663_cast_fp16")]; - tensor x_281_axes_0 = const()[name = tensor("x_281_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159411456)))]; - tensor module_layers_12_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159412544)))]; - tensor x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = tensor("x_281_cast_fp16")]; - tensor input_665_perm_0 = const()[name = tensor("input_665_perm_0"), val = tensor([0, 2, 1])]; - tensor input_667_pad_type_0 = const()[name = tensor("input_667_pad_type_0"), val = tensor("valid")]; - tensor input_667_strides_0 = const()[name = tensor("input_667_strides_0"), val = tensor([1])]; - tensor input_667_pad_0 = const()[name = tensor("input_667_pad_0"), val = tensor([0, 0])]; - tensor input_667_dilations_0 = const()[name = tensor("input_667_dilations_0"), val = tensor([1])]; - tensor input_667_groups_0 = const()[name = tensor("input_667_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159413632)))]; - tensor module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160462272)))]; - tensor input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = tensor("transpose_115")]; - tensor input_667_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv1_bias_to_fp16, dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16, x = input_665_cast_fp16)[name = tensor("input_667_cast_fp16")]; - tensor x_283_split_num_splits_0 = const()[name = tensor("x_283_split_num_splits_0"), val = tensor(2)]; - tensor x_283_split_axis_0 = const()[name = tensor("x_283_split_axis_0"), val = tensor(1)]; - tensor x_283_split_cast_fp16_0, tensor x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = tensor("x_283_split_cast_fp16")]; - tensor x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = tensor("x_283_split_1_sigmoid_cast_fp16")]; - tensor x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = tensor("x_283_cast_fp16")]; - tensor input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_453)[name = tensor("input_669_cast_fp16")]; - tensor input_671_pad_0 = const()[name = tensor("input_671_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_671_mode_0 = const()[name = tensor("input_671_mode_0"), val = tensor("constant")]; - tensor const_193_to_fp16 = const()[name = tensor("const_193_to_fp16"), val = tensor(0x0p+0)]; - tensor input_671_cast_fp16 = pad(constant_val = const_193_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = tensor("input_671_cast_fp16")]; - tensor input_673_pad_type_0 = const()[name = tensor("input_673_pad_type_0"), val = tensor("valid")]; - tensor input_673_groups_0 = const()[name = tensor("input_673_groups_0"), val = tensor(512)]; - tensor input_673_strides_0 = const()[name = tensor("input_673_strides_0"), val = tensor([1])]; - tensor input_673_pad_0 = const()[name = tensor("input_673_pad_0"), val = tensor([0, 0])]; - tensor input_673_dilations_0 = const()[name = tensor("input_673_dilations_0"), val = tensor([1])]; - tensor const_258_to_fp16 = const()[name = tensor("const_258_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160464384)))]; - tensor const_259_to_fp16 = const()[name = tensor("const_259_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160473664)))]; - tensor input_675_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_258_to_fp16, x = input_671_cast_fp16)[name = tensor("input_675_cast_fp16")]; - tensor input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = tensor("input_677_cast_fp16")]; - tensor x_285_pad_type_0 = const()[name = tensor("x_285_pad_type_0"), val = tensor("valid")]; - tensor x_285_strides_0 = const()[name = tensor("x_285_strides_0"), val = tensor([1])]; - tensor x_285_pad_0 = const()[name = tensor("x_285_pad_0"), val = tensor([0, 0])]; - tensor x_285_dilations_0 = const()[name = tensor("x_285_dilations_0"), val = tensor([1])]; - tensor x_285_groups_0 = const()[name = tensor("x_285_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160474752)))]; - tensor module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160999104)))]; - tensor x_285_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv2_bias_to_fp16, dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = module_layers_12_conv_pointwise_conv2_weight_to_fp16, x = input_677_cast_fp16)[name = tensor("x_285_cast_fp16")]; - tensor input_679_perm_0 = const()[name = tensor("input_679_perm_0"), val = tensor([0, 2, 1])]; - tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = tensor("transpose_114")]; - tensor input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = tensor("input_681_cast_fp16")]; - tensor input_683_axes_0 = const()[name = tensor("input_683_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161000192)))]; - tensor module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161001280)))]; - tensor input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = tensor("input_683_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161002368)))]; - tensor module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163099584)))]; - tensor linear_116_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16, x = input_683_cast_fp16)[name = tensor("linear_116_cast_fp16")]; - tensor input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = tensor("input_687_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163103744)))]; - tensor module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165200960)))]; - tensor linear_117_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16, x = input_687_cast_fp16)[name = tensor("linear_117_cast_fp16")]; - tensor var_2583_to_fp16 = const()[name = tensor("op_2583_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2584_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2583_to_fp16)[name = tensor("op_2584_cast_fp16")]; - tensor input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2584_cast_fp16)[name = tensor("input_693_cast_fp16")]; - tensor input_695_axes_0 = const()[name = tensor("input_695_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165202048)))]; - tensor module_layers_12_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165203136)))]; - tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = tensor("input_695_cast_fp16")]; - tensor input_697_axes_0 = const()[name = tensor("input_697_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165204224)))]; - tensor module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165205312)))]; - tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = tensor("input_697_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165206400)))]; - tensor module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167303616)))]; - tensor linear_118_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16, x = input_697_cast_fp16)[name = tensor("linear_118_cast_fp16")]; - tensor input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = tensor("input_701_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167307776)))]; - tensor module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169404992)))]; - tensor linear_119_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16, x = input_701_cast_fp16)[name = tensor("linear_119_cast_fp16")]; - tensor var_2614_to_fp16 = const()[name = tensor("op_2614_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2615_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2614_to_fp16)[name = tensor("op_2615_cast_fp16")]; - tensor input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2615_cast_fp16)[name = tensor("input_707_cast_fp16")]; - tensor query_27_axes_0 = const()[name = tensor("query_27_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169406080)))]; - tensor module_layers_13_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169407168)))]; - tensor query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = tensor("query_27_cast_fp16")]; - tensor module_layers_13_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169408256)))]; - tensor module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169932608)))]; - tensor linear_120_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_q_bias_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_120_cast_fp16")]; - tensor var_2632 = const()[name = tensor("op_2632"), val = tensor([1, -1, 8, 64])]; - tensor q_79_cast_fp16 = reshape(shape = var_2632, x = linear_120_cast_fp16)[name = tensor("q_79_cast_fp16")]; - tensor module_layers_13_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169933696)))]; - tensor module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170458048)))]; - tensor linear_121_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_k_bias_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_121_cast_fp16")]; - tensor var_2637 = const()[name = tensor("op_2637"), val = tensor([1, -1, 8, 64])]; - tensor k_53_cast_fp16 = reshape(shape = var_2637, x = linear_121_cast_fp16)[name = tensor("k_53_cast_fp16")]; - tensor module_layers_13_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170459136)))]; - tensor module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170983488)))]; - tensor linear_122_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_v_bias_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_122_cast_fp16")]; - tensor var_2642 = const()[name = tensor("op_2642"), val = tensor([1, -1, 8, 64])]; - tensor v_27_cast_fp16 = reshape(shape = var_2642, x = linear_122_cast_fp16)[name = tensor("v_27_cast_fp16")]; - tensor value_29_perm_0 = const()[name = tensor("value_29_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170984576)))]; - tensor var_2654_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2654_cast_fp16")]; - tensor module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170985664)))]; - tensor var_2656_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2656_cast_fp16")]; - tensor q_with_bias_v_27_perm_0 = const()[name = tensor("q_with_bias_v_27_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_293_transpose_x_0 = const()[name = tensor("x_293_transpose_x_0"), val = tensor(false)]; - tensor x_293_transpose_y_0 = const()[name = tensor("x_293_transpose_y_0"), val = tensor(false)]; - tensor var_2658_to_fp16 = const()[name = tensor("op_2658_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170986752)))]; - tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2656_cast_fp16)[name = tensor("transpose_112")]; - tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = var_2658_to_fp16)[name = tensor("x_293_cast_fp16")]; - tensor x_295_pad_0 = const()[name = tensor("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_295_mode_0 = const()[name = tensor("x_295_mode_0"), val = tensor("constant")]; - tensor const_200_to_fp16 = const()[name = tensor("const_200_to_fp16"), val = tensor(0x0p+0)]; - tensor x_295_cast_fp16 = pad(constant_val = const_200_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = tensor("x_295_cast_fp16")]; - tensor var_2666 = const()[name = tensor("op_2666"), val = tensor([1, 8, -1, 188])]; - tensor x_297_cast_fp16 = reshape(shape = var_2666, x = x_295_cast_fp16)[name = tensor("x_297_cast_fp16")]; - tensor var_2670_begin_0 = const()[name = tensor("op_2670_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2670_end_0 = const()[name = tensor("op_2670_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2670_end_mask_0 = const()[name = tensor("op_2670_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2670_cast_fp16 = slice_by_index(begin = var_2670_begin_0, end = var_2670_end_0, end_mask = var_2670_end_mask_0, x = x_297_cast_fp16)[name = tensor("op_2670_cast_fp16")]; - tensor var_2671 = const()[name = tensor("op_2671"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2671, x = var_2670_cast_fp16)[name = tensor("matrix_bd_53_cast_fp16")]; - tensor matrix_ac_27_transpose_x_0 = const()[name = tensor("matrix_ac_27_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_27_transpose_y_0 = const()[name = tensor("matrix_ac_27_transpose_y_0"), val = tensor(false)]; - tensor transpose_77_perm_0 = const()[name = tensor("transpose_77_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_78_perm_0 = const()[name = tensor("transpose_78_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = k_53_cast_fp16)[name = tensor("transpose_110")]; - tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = var_2654_cast_fp16)[name = tensor("transpose_111")]; - tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_77, y = transpose_78)[name = tensor("matrix_ac_27_cast_fp16")]; - tensor matrix_bd_55_begin_0 = const()[name = tensor("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_55_end_0 = const()[name = tensor("matrix_bd_55_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_55_end_mask_0 = const()[name = tensor("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = tensor("matrix_bd_55_cast_fp16")]; - tensor var_2680_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = tensor("op_2680_cast_fp16")]; - tensor _inversed_scores_53_y_0_to_fp16 = const()[name = tensor("_inversed_scores_53_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_53_cast_fp16 = mul(x = var_2680_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = tensor("_inversed_scores_53_cast_fp16")]; - tensor scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_11)[name = tensor("scores_55_cast_fp16")]; - tensor var_2686_cast_fp16 = softmax(axis = var_23, x = scores_55_cast_fp16)[name = tensor("op_2686_cast_fp16")]; - tensor input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2686_cast_fp16, cond = mask_11)[name = tensor("input_709_cast_fp16")]; - tensor x_299_transpose_x_0 = const()[name = tensor("x_299_transpose_x_0"), val = tensor(false)]; - tensor x_299_transpose_y_0 = const()[name = tensor("x_299_transpose_y_0"), val = tensor(false)]; - tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = tensor("transpose_113")]; - tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_29_cast_fp16)[name = tensor("x_299_cast_fp16")]; - tensor var_2690_perm_0 = const()[name = tensor("op_2690_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2691 = const()[name = tensor("op_2691"), val = tensor([1, -1, 512])]; - tensor var_2690_cast_fp16 = transpose(perm = var_2690_perm_0, x = x_299_cast_fp16)[name = tensor("transpose_109")]; - tensor input_711_cast_fp16 = reshape(shape = var_2691, x = var_2690_cast_fp16)[name = tensor("input_711_cast_fp16")]; - tensor module_layers_13_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171370816)))]; - tensor module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171895168)))]; - tensor linear_124_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_out_bias_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16, x = input_711_cast_fp16)[name = tensor("linear_124_cast_fp16")]; - tensor input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = tensor("input_715_cast_fp16")]; - tensor x_303_axes_0 = const()[name = tensor("x_303_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171896256)))]; - tensor module_layers_13_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171897344)))]; - tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = tensor("x_303_cast_fp16")]; - tensor input_717_perm_0 = const()[name = tensor("input_717_perm_0"), val = tensor([0, 2, 1])]; - tensor input_719_pad_type_0 = const()[name = tensor("input_719_pad_type_0"), val = tensor("valid")]; - tensor input_719_strides_0 = const()[name = tensor("input_719_strides_0"), val = tensor([1])]; - tensor input_719_pad_0 = const()[name = tensor("input_719_pad_0"), val = tensor([0, 0])]; - tensor input_719_dilations_0 = const()[name = tensor("input_719_dilations_0"), val = tensor([1])]; - tensor input_719_groups_0 = const()[name = tensor("input_719_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171898432)))]; - tensor module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172947072)))]; - tensor input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = tensor("transpose_108")]; - tensor input_719_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv1_bias_to_fp16, dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16, x = input_717_cast_fp16)[name = tensor("input_719_cast_fp16")]; - tensor x_305_split_num_splits_0 = const()[name = tensor("x_305_split_num_splits_0"), val = tensor(2)]; - tensor x_305_split_axis_0 = const()[name = tensor("x_305_split_axis_0"), val = tensor(1)]; - tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = tensor("x_305_split_cast_fp16")]; - tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = tensor("x_305_split_1_sigmoid_cast_fp16")]; - tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = tensor("x_305_cast_fp16")]; - tensor input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_453)[name = tensor("input_721_cast_fp16")]; - tensor input_723_pad_0 = const()[name = tensor("input_723_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_723_mode_0 = const()[name = tensor("input_723_mode_0"), val = tensor("constant")]; - tensor const_203_to_fp16 = const()[name = tensor("const_203_to_fp16"), val = tensor(0x0p+0)]; - tensor input_723_cast_fp16 = pad(constant_val = const_203_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = tensor("input_723_cast_fp16")]; - tensor input_725_pad_type_0 = const()[name = tensor("input_725_pad_type_0"), val = tensor("valid")]; - tensor input_725_groups_0 = const()[name = tensor("input_725_groups_0"), val = tensor(512)]; - tensor input_725_strides_0 = const()[name = tensor("input_725_strides_0"), val = tensor([1])]; - tensor input_725_pad_0 = const()[name = tensor("input_725_pad_0"), val = tensor([0, 0])]; - tensor input_725_dilations_0 = const()[name = tensor("input_725_dilations_0"), val = tensor([1])]; - tensor const_260_to_fp16 = const()[name = tensor("const_260_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172949184)))]; - tensor const_261_to_fp16 = const()[name = tensor("const_261_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172958464)))]; - tensor input_727_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_260_to_fp16, x = input_723_cast_fp16)[name = tensor("input_727_cast_fp16")]; - tensor input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = tensor("input_729_cast_fp16")]; - tensor x_307_pad_type_0 = const()[name = tensor("x_307_pad_type_0"), val = tensor("valid")]; - tensor x_307_strides_0 = const()[name = tensor("x_307_strides_0"), val = tensor([1])]; - tensor x_307_pad_0 = const()[name = tensor("x_307_pad_0"), val = tensor([0, 0])]; - tensor x_307_dilations_0 = const()[name = tensor("x_307_dilations_0"), val = tensor([1])]; - tensor x_307_groups_0 = const()[name = tensor("x_307_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172959552)))]; - tensor module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173483904)))]; - tensor x_307_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv2_bias_to_fp16, dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = module_layers_13_conv_pointwise_conv2_weight_to_fp16, x = input_729_cast_fp16)[name = tensor("x_307_cast_fp16")]; - tensor input_731_perm_0 = const()[name = tensor("input_731_perm_0"), val = tensor([0, 2, 1])]; - tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = tensor("transpose_107")]; - tensor input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = tensor("input_733_cast_fp16")]; - tensor input_735_axes_0 = const()[name = tensor("input_735_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173484992)))]; - tensor module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173486080)))]; - tensor input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = tensor("input_735_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173487168)))]; - tensor module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175584384)))]; - tensor linear_125_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16, x = input_735_cast_fp16)[name = tensor("linear_125_cast_fp16")]; - tensor input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = tensor("input_739_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175588544)))]; - tensor module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177685760)))]; - tensor linear_126_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16, x = input_739_cast_fp16)[name = tensor("linear_126_cast_fp16")]; - tensor var_2757_to_fp16 = const()[name = tensor("op_2757_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2758_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2757_to_fp16)[name = tensor("op_2758_cast_fp16")]; - tensor input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2758_cast_fp16)[name = tensor("input_745_cast_fp16")]; - tensor input_747_axes_0 = const()[name = tensor("input_747_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177686848)))]; - tensor module_layers_13_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177687936)))]; - tensor input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = tensor("input_747_cast_fp16")]; - tensor input_749_axes_0 = const()[name = tensor("input_749_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177689024)))]; - tensor module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177690112)))]; - tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = tensor("input_749_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177691200)))]; - tensor module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179788416)))]; - tensor linear_127_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16, x = input_749_cast_fp16)[name = tensor("linear_127_cast_fp16")]; - tensor input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = tensor("input_753_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179792576)))]; - tensor module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181889792)))]; - tensor linear_128_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16, x = input_753_cast_fp16)[name = tensor("linear_128_cast_fp16")]; - tensor var_2788_to_fp16 = const()[name = tensor("op_2788_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2789_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2788_to_fp16)[name = tensor("op_2789_cast_fp16")]; - tensor input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2789_cast_fp16)[name = tensor("input_759_cast_fp16")]; - tensor query_29_axes_0 = const()[name = tensor("query_29_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181890880)))]; - tensor module_layers_14_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181891968)))]; - tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = tensor("query_29_cast_fp16")]; - tensor module_layers_14_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181893056)))]; - tensor module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182417408)))]; - tensor linear_129_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_q_bias_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_129_cast_fp16")]; - tensor var_2806 = const()[name = tensor("op_2806"), val = tensor([1, -1, 8, 64])]; - tensor q_85_cast_fp16 = reshape(shape = var_2806, x = linear_129_cast_fp16)[name = tensor("q_85_cast_fp16")]; - tensor module_layers_14_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182418496)))]; - tensor module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182942848)))]; - tensor linear_130_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_k_bias_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_130_cast_fp16")]; - tensor var_2811 = const()[name = tensor("op_2811"), val = tensor([1, -1, 8, 64])]; - tensor k_57_cast_fp16 = reshape(shape = var_2811, x = linear_130_cast_fp16)[name = tensor("k_57_cast_fp16")]; - tensor module_layers_14_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182943936)))]; - tensor module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183468288)))]; - tensor linear_131_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_v_bias_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_131_cast_fp16")]; - tensor var_2816 = const()[name = tensor("op_2816"), val = tensor([1, -1, 8, 64])]; - tensor v_29_cast_fp16 = reshape(shape = var_2816, x = linear_131_cast_fp16)[name = tensor("v_29_cast_fp16")]; - tensor value_31_perm_0 = const()[name = tensor("value_31_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183469376)))]; - tensor var_2828_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2828_cast_fp16")]; - tensor module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183470464)))]; - tensor var_2830_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2830_cast_fp16")]; - tensor q_with_bias_v_29_perm_0 = const()[name = tensor("q_with_bias_v_29_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_315_transpose_x_0 = const()[name = tensor("x_315_transpose_x_0"), val = tensor(false)]; - tensor x_315_transpose_y_0 = const()[name = tensor("x_315_transpose_y_0"), val = tensor(false)]; - tensor var_2832_to_fp16 = const()[name = tensor("op_2832_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183471552)))]; - tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2830_cast_fp16)[name = tensor("transpose_105")]; - tensor x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = var_2832_to_fp16)[name = tensor("x_315_cast_fp16")]; - tensor x_317_pad_0 = const()[name = tensor("x_317_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_317_mode_0 = const()[name = tensor("x_317_mode_0"), val = tensor("constant")]; - tensor const_210_to_fp16 = const()[name = tensor("const_210_to_fp16"), val = tensor(0x0p+0)]; - tensor x_317_cast_fp16 = pad(constant_val = const_210_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = tensor("x_317_cast_fp16")]; - tensor var_2840 = const()[name = tensor("op_2840"), val = tensor([1, 8, -1, 188])]; - tensor x_319_cast_fp16 = reshape(shape = var_2840, x = x_317_cast_fp16)[name = tensor("x_319_cast_fp16")]; - tensor var_2844_begin_0 = const()[name = tensor("op_2844_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2844_end_0 = const()[name = tensor("op_2844_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2844_end_mask_0 = const()[name = tensor("op_2844_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2844_cast_fp16 = slice_by_index(begin = var_2844_begin_0, end = var_2844_end_0, end_mask = var_2844_end_mask_0, x = x_319_cast_fp16)[name = tensor("op_2844_cast_fp16")]; - tensor var_2845 = const()[name = tensor("op_2845"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_57_cast_fp16 = reshape(shape = var_2845, x = var_2844_cast_fp16)[name = tensor("matrix_bd_57_cast_fp16")]; - tensor matrix_ac_29_transpose_x_0 = const()[name = tensor("matrix_ac_29_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_29_transpose_y_0 = const()[name = tensor("matrix_ac_29_transpose_y_0"), val = tensor(false)]; - tensor transpose_79_perm_0 = const()[name = tensor("transpose_79_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_80_perm_0 = const()[name = tensor("transpose_80_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = k_57_cast_fp16)[name = tensor("transpose_103")]; - tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_2828_cast_fp16)[name = tensor("transpose_104")]; - tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_79, y = transpose_80)[name = tensor("matrix_ac_29_cast_fp16")]; - tensor matrix_bd_59_begin_0 = const()[name = tensor("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_59_end_0 = const()[name = tensor("matrix_bd_59_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_59_end_mask_0 = const()[name = tensor("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = tensor("matrix_bd_59_cast_fp16")]; - tensor var_2854_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = tensor("op_2854_cast_fp16")]; - tensor _inversed_scores_57_y_0_to_fp16 = const()[name = tensor("_inversed_scores_57_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_57_cast_fp16 = mul(x = var_2854_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = tensor("_inversed_scores_57_cast_fp16")]; - tensor scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_11)[name = tensor("scores_59_cast_fp16")]; - tensor var_2860_cast_fp16 = softmax(axis = var_23, x = scores_59_cast_fp16)[name = tensor("op_2860_cast_fp16")]; - tensor input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2860_cast_fp16, cond = mask_11)[name = tensor("input_761_cast_fp16")]; - tensor x_321_transpose_x_0 = const()[name = tensor("x_321_transpose_x_0"), val = tensor(false)]; - tensor x_321_transpose_y_0 = const()[name = tensor("x_321_transpose_y_0"), val = tensor(false)]; - tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = tensor("transpose_106")]; - tensor x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_31_cast_fp16)[name = tensor("x_321_cast_fp16")]; - tensor var_2864_perm_0 = const()[name = tensor("op_2864_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2865 = const()[name = tensor("op_2865"), val = tensor([1, -1, 512])]; - tensor var_2864_cast_fp16 = transpose(perm = var_2864_perm_0, x = x_321_cast_fp16)[name = tensor("transpose_102")]; - tensor input_763_cast_fp16 = reshape(shape = var_2865, x = var_2864_cast_fp16)[name = tensor("input_763_cast_fp16")]; - tensor module_layers_14_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183855616)))]; - tensor module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184379968)))]; - tensor linear_133_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_out_bias_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16, x = input_763_cast_fp16)[name = tensor("linear_133_cast_fp16")]; - tensor input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = tensor("input_767_cast_fp16")]; - tensor x_325_axes_0 = const()[name = tensor("x_325_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184381056)))]; - tensor module_layers_14_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184382144)))]; - tensor x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = tensor("x_325_cast_fp16")]; - tensor input_769_perm_0 = const()[name = tensor("input_769_perm_0"), val = tensor([0, 2, 1])]; - tensor input_771_pad_type_0 = const()[name = tensor("input_771_pad_type_0"), val = tensor("valid")]; - tensor input_771_strides_0 = const()[name = tensor("input_771_strides_0"), val = tensor([1])]; - tensor input_771_pad_0 = const()[name = tensor("input_771_pad_0"), val = tensor([0, 0])]; - tensor input_771_dilations_0 = const()[name = tensor("input_771_dilations_0"), val = tensor([1])]; - tensor input_771_groups_0 = const()[name = tensor("input_771_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184383232)))]; - tensor module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185431872)))]; - tensor input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = tensor("transpose_101")]; - tensor input_771_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv1_bias_to_fp16, dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16, x = input_769_cast_fp16)[name = tensor("input_771_cast_fp16")]; - tensor x_327_split_num_splits_0 = const()[name = tensor("x_327_split_num_splits_0"), val = tensor(2)]; - tensor x_327_split_axis_0 = const()[name = tensor("x_327_split_axis_0"), val = tensor(1)]; - tensor x_327_split_cast_fp16_0, tensor x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = tensor("x_327_split_cast_fp16")]; - tensor x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = tensor("x_327_split_1_sigmoid_cast_fp16")]; - tensor x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = tensor("x_327_cast_fp16")]; - tensor input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_453)[name = tensor("input_773_cast_fp16")]; - tensor input_775_pad_0 = const()[name = tensor("input_775_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_775_mode_0 = const()[name = tensor("input_775_mode_0"), val = tensor("constant")]; - tensor const_213_to_fp16 = const()[name = tensor("const_213_to_fp16"), val = tensor(0x0p+0)]; - tensor input_775_cast_fp16 = pad(constant_val = const_213_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = tensor("input_775_cast_fp16")]; - tensor input_777_pad_type_0 = const()[name = tensor("input_777_pad_type_0"), val = tensor("valid")]; - tensor input_777_groups_0 = const()[name = tensor("input_777_groups_0"), val = tensor(512)]; - tensor input_777_strides_0 = const()[name = tensor("input_777_strides_0"), val = tensor([1])]; - tensor input_777_pad_0 = const()[name = tensor("input_777_pad_0"), val = tensor([0, 0])]; - tensor input_777_dilations_0 = const()[name = tensor("input_777_dilations_0"), val = tensor([1])]; - tensor const_262_to_fp16 = const()[name = tensor("const_262_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185433984)))]; - tensor const_263_to_fp16 = const()[name = tensor("const_263_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185443264)))]; - tensor input_779_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_262_to_fp16, x = input_775_cast_fp16)[name = tensor("input_779_cast_fp16")]; - tensor input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = tensor("input_781_cast_fp16")]; - tensor x_329_pad_type_0 = const()[name = tensor("x_329_pad_type_0"), val = tensor("valid")]; - tensor x_329_strides_0 = const()[name = tensor("x_329_strides_0"), val = tensor([1])]; - tensor x_329_pad_0 = const()[name = tensor("x_329_pad_0"), val = tensor([0, 0])]; - tensor x_329_dilations_0 = const()[name = tensor("x_329_dilations_0"), val = tensor([1])]; - tensor x_329_groups_0 = const()[name = tensor("x_329_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185444352)))]; - tensor module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185968704)))]; - tensor x_329_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv2_bias_to_fp16, dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = module_layers_14_conv_pointwise_conv2_weight_to_fp16, x = input_781_cast_fp16)[name = tensor("x_329_cast_fp16")]; - tensor input_783_perm_0 = const()[name = tensor("input_783_perm_0"), val = tensor([0, 2, 1])]; - tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = tensor("transpose_100")]; - tensor input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = tensor("input_785_cast_fp16")]; - tensor input_787_axes_0 = const()[name = tensor("input_787_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185969792)))]; - tensor module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185970880)))]; - tensor input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = tensor("input_787_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185971968)))]; - tensor module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188069184)))]; - tensor linear_134_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16, x = input_787_cast_fp16)[name = tensor("linear_134_cast_fp16")]; - tensor input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = tensor("input_791_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188073344)))]; - tensor module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190170560)))]; - tensor linear_135_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16, x = input_791_cast_fp16)[name = tensor("linear_135_cast_fp16")]; - tensor var_2931_to_fp16 = const()[name = tensor("op_2931_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2932_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2931_to_fp16)[name = tensor("op_2932_cast_fp16")]; - tensor input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2932_cast_fp16)[name = tensor("input_797_cast_fp16")]; - tensor input_799_axes_0 = const()[name = tensor("input_799_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190171648)))]; - tensor module_layers_14_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190172736)))]; - tensor input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = tensor("input_799_cast_fp16")]; - tensor input_801_axes_0 = const()[name = tensor("input_801_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190173824)))]; - tensor module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190174912)))]; - tensor input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = tensor("input_801_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190176000)))]; - tensor module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192273216)))]; - tensor linear_136_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16, x = input_801_cast_fp16)[name = tensor("linear_136_cast_fp16")]; - tensor input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = tensor("input_805_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192277376)))]; - tensor module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194374592)))]; - tensor linear_137_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16, x = input_805_cast_fp16)[name = tensor("linear_137_cast_fp16")]; - tensor var_2962_to_fp16 = const()[name = tensor("op_2962_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2963_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2962_to_fp16)[name = tensor("op_2963_cast_fp16")]; - tensor input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2963_cast_fp16)[name = tensor("input_811_cast_fp16")]; - tensor query_31_axes_0 = const()[name = tensor("query_31_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194375680)))]; - tensor module_layers_15_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194376768)))]; - tensor query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = tensor("query_31_cast_fp16")]; - tensor module_layers_15_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194377856)))]; - tensor module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194902208)))]; - tensor linear_138_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_q_bias_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_138_cast_fp16")]; - tensor var_2980 = const()[name = tensor("op_2980"), val = tensor([1, -1, 8, 64])]; - tensor q_91_cast_fp16 = reshape(shape = var_2980, x = linear_138_cast_fp16)[name = tensor("q_91_cast_fp16")]; - tensor module_layers_15_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194903296)))]; - tensor module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195427648)))]; - tensor linear_139_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_k_bias_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_139_cast_fp16")]; - tensor var_2985 = const()[name = tensor("op_2985"), val = tensor([1, -1, 8, 64])]; - tensor k_61_cast_fp16 = reshape(shape = var_2985, x = linear_139_cast_fp16)[name = tensor("k_61_cast_fp16")]; - tensor module_layers_15_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195428736)))]; - tensor module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195953088)))]; - tensor linear_140_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_v_bias_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_140_cast_fp16")]; - tensor var_2990 = const()[name = tensor("op_2990"), val = tensor([1, -1, 8, 64])]; - tensor v_31_cast_fp16 = reshape(shape = var_2990, x = linear_140_cast_fp16)[name = tensor("v_31_cast_fp16")]; - tensor value_33_perm_0 = const()[name = tensor("value_33_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195954176)))]; - tensor var_3002_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3002_cast_fp16")]; - tensor module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195955264)))]; - tensor var_3004_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3004_cast_fp16")]; - tensor q_with_bias_v_31_perm_0 = const()[name = tensor("q_with_bias_v_31_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_337_transpose_x_0 = const()[name = tensor("x_337_transpose_x_0"), val = tensor(false)]; - tensor x_337_transpose_y_0 = const()[name = tensor("x_337_transpose_y_0"), val = tensor(false)]; - tensor var_3006_to_fp16 = const()[name = tensor("op_3006_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195956352)))]; - tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3004_cast_fp16)[name = tensor("transpose_98")]; - tensor x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = var_3006_to_fp16)[name = tensor("x_337_cast_fp16")]; - tensor x_339_pad_0 = const()[name = tensor("x_339_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_339_mode_0 = const()[name = tensor("x_339_mode_0"), val = tensor("constant")]; - tensor const_220_to_fp16 = const()[name = tensor("const_220_to_fp16"), val = tensor(0x0p+0)]; - tensor x_339_cast_fp16 = pad(constant_val = const_220_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = tensor("x_339_cast_fp16")]; - tensor var_3014 = const()[name = tensor("op_3014"), val = tensor([1, 8, -1, 188])]; - tensor x_341_cast_fp16 = reshape(shape = var_3014, x = x_339_cast_fp16)[name = tensor("x_341_cast_fp16")]; - tensor var_3018_begin_0 = const()[name = tensor("op_3018_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3018_end_0 = const()[name = tensor("op_3018_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3018_end_mask_0 = const()[name = tensor("op_3018_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3018_cast_fp16 = slice_by_index(begin = var_3018_begin_0, end = var_3018_end_0, end_mask = var_3018_end_mask_0, x = x_341_cast_fp16)[name = tensor("op_3018_cast_fp16")]; - tensor var_3019 = const()[name = tensor("op_3019"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_61_cast_fp16 = reshape(shape = var_3019, x = var_3018_cast_fp16)[name = tensor("matrix_bd_61_cast_fp16")]; - tensor matrix_ac_31_transpose_x_0 = const()[name = tensor("matrix_ac_31_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_31_transpose_y_0 = const()[name = tensor("matrix_ac_31_transpose_y_0"), val = tensor(false)]; - tensor transpose_81_perm_0 = const()[name = tensor("transpose_81_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_82_perm_0 = const()[name = tensor("transpose_82_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = k_61_cast_fp16)[name = tensor("transpose_96")]; - tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_3002_cast_fp16)[name = tensor("transpose_97")]; - tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_81, y = transpose_82)[name = tensor("matrix_ac_31_cast_fp16")]; - tensor matrix_bd_63_begin_0 = const()[name = tensor("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_63_end_0 = const()[name = tensor("matrix_bd_63_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_63_end_mask_0 = const()[name = tensor("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = tensor("matrix_bd_63_cast_fp16")]; - tensor var_3028_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = tensor("op_3028_cast_fp16")]; - tensor _inversed_scores_61_y_0_to_fp16 = const()[name = tensor("_inversed_scores_61_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_61_cast_fp16 = mul(x = var_3028_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = tensor("_inversed_scores_61_cast_fp16")]; - tensor scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_11)[name = tensor("scores_63_cast_fp16")]; - tensor var_3034_cast_fp16 = softmax(axis = var_23, x = scores_63_cast_fp16)[name = tensor("op_3034_cast_fp16")]; - tensor input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_3034_cast_fp16, cond = mask_11)[name = tensor("input_813_cast_fp16")]; - tensor x_343_transpose_x_0 = const()[name = tensor("x_343_transpose_x_0"), val = tensor(false)]; - tensor x_343_transpose_y_0 = const()[name = tensor("x_343_transpose_y_0"), val = tensor(false)]; - tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = tensor("transpose_99")]; - tensor x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_33_cast_fp16)[name = tensor("x_343_cast_fp16")]; - tensor var_3038_perm_0 = const()[name = tensor("op_3038_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3039 = const()[name = tensor("op_3039"), val = tensor([1, -1, 512])]; - tensor var_3038_cast_fp16 = transpose(perm = var_3038_perm_0, x = x_343_cast_fp16)[name = tensor("transpose_95")]; - tensor input_815_cast_fp16 = reshape(shape = var_3039, x = var_3038_cast_fp16)[name = tensor("input_815_cast_fp16")]; - tensor module_layers_15_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196340416)))]; - tensor module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196864768)))]; - tensor linear_142_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_out_bias_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16, x = input_815_cast_fp16)[name = tensor("linear_142_cast_fp16")]; - tensor input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = tensor("input_819_cast_fp16")]; - tensor x_347_axes_0 = const()[name = tensor("x_347_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196865856)))]; - tensor module_layers_15_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196866944)))]; - tensor x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = tensor("x_347_cast_fp16")]; - tensor input_821_perm_0 = const()[name = tensor("input_821_perm_0"), val = tensor([0, 2, 1])]; - tensor input_823_pad_type_0 = const()[name = tensor("input_823_pad_type_0"), val = tensor("valid")]; - tensor input_823_strides_0 = const()[name = tensor("input_823_strides_0"), val = tensor([1])]; - tensor input_823_pad_0 = const()[name = tensor("input_823_pad_0"), val = tensor([0, 0])]; - tensor input_823_dilations_0 = const()[name = tensor("input_823_dilations_0"), val = tensor([1])]; - tensor input_823_groups_0 = const()[name = tensor("input_823_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196868032)))]; - tensor module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197916672)))]; - tensor input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = tensor("transpose_94")]; - tensor input_823_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv1_bias_to_fp16, dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16, x = input_821_cast_fp16)[name = tensor("input_823_cast_fp16")]; - tensor x_349_split_num_splits_0 = const()[name = tensor("x_349_split_num_splits_0"), val = tensor(2)]; - tensor x_349_split_axis_0 = const()[name = tensor("x_349_split_axis_0"), val = tensor(1)]; - tensor x_349_split_cast_fp16_0, tensor x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = tensor("x_349_split_cast_fp16")]; - tensor x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = tensor("x_349_split_1_sigmoid_cast_fp16")]; - tensor x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = tensor("x_349_cast_fp16")]; - tensor input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_453)[name = tensor("input_825_cast_fp16")]; - tensor input_827_pad_0 = const()[name = tensor("input_827_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_827_mode_0 = const()[name = tensor("input_827_mode_0"), val = tensor("constant")]; - tensor const_223_to_fp16 = const()[name = tensor("const_223_to_fp16"), val = tensor(0x0p+0)]; - tensor input_827_cast_fp16 = pad(constant_val = const_223_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = tensor("input_827_cast_fp16")]; - tensor input_829_pad_type_0 = const()[name = tensor("input_829_pad_type_0"), val = tensor("valid")]; - tensor input_829_groups_0 = const()[name = tensor("input_829_groups_0"), val = tensor(512)]; - tensor input_829_strides_0 = const()[name = tensor("input_829_strides_0"), val = tensor([1])]; - tensor input_829_pad_0 = const()[name = tensor("input_829_pad_0"), val = tensor([0, 0])]; - tensor input_829_dilations_0 = const()[name = tensor("input_829_dilations_0"), val = tensor([1])]; - tensor const_264_to_fp16 = const()[name = tensor("const_264_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197918784)))]; - tensor const_265_to_fp16 = const()[name = tensor("const_265_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197928064)))]; - tensor input_831_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_264_to_fp16, x = input_827_cast_fp16)[name = tensor("input_831_cast_fp16")]; - tensor input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = tensor("input_833_cast_fp16")]; - tensor x_351_pad_type_0 = const()[name = tensor("x_351_pad_type_0"), val = tensor("valid")]; - tensor x_351_strides_0 = const()[name = tensor("x_351_strides_0"), val = tensor([1])]; - tensor x_351_pad_0 = const()[name = tensor("x_351_pad_0"), val = tensor([0, 0])]; - tensor x_351_dilations_0 = const()[name = tensor("x_351_dilations_0"), val = tensor([1])]; - tensor x_351_groups_0 = const()[name = tensor("x_351_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197929152)))]; - tensor module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198453504)))]; - tensor x_351_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv2_bias_to_fp16, dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = module_layers_15_conv_pointwise_conv2_weight_to_fp16, x = input_833_cast_fp16)[name = tensor("x_351_cast_fp16")]; - tensor input_835_perm_0 = const()[name = tensor("input_835_perm_0"), val = tensor([0, 2, 1])]; - tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = tensor("transpose_93")]; - tensor input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = tensor("input_837_cast_fp16")]; - tensor input_839_axes_0 = const()[name = tensor("input_839_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198454592)))]; - tensor module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198455680)))]; - tensor input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = tensor("input_839_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198456768)))]; - tensor module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200553984)))]; - tensor linear_143_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16, x = input_839_cast_fp16)[name = tensor("linear_143_cast_fp16")]; - tensor input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = tensor("input_843_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200558144)))]; - tensor module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202655360)))]; - tensor linear_144_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16, x = input_843_cast_fp16)[name = tensor("linear_144_cast_fp16")]; - tensor var_3105_to_fp16 = const()[name = tensor("op_3105_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3106_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3105_to_fp16)[name = tensor("op_3106_cast_fp16")]; - tensor input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_3106_cast_fp16)[name = tensor("input_849_cast_fp16")]; - tensor input_851_axes_0 = const()[name = tensor("input_851_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202656448)))]; - tensor module_layers_15_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202657536)))]; - tensor input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = tensor("input_851_cast_fp16")]; - tensor input_853_axes_0 = const()[name = tensor("input_853_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202658624)))]; - tensor module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202659712)))]; - tensor input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = tensor("input_853_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202660800)))]; - tensor module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204758016)))]; - tensor linear_145_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16, x = input_853_cast_fp16)[name = tensor("linear_145_cast_fp16")]; - tensor input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = tensor("input_857_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204762176)))]; - tensor module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206859392)))]; - tensor linear_146_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16, x = input_857_cast_fp16)[name = tensor("linear_146_cast_fp16")]; - tensor var_3136_to_fp16 = const()[name = tensor("op_3136_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3137_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3136_to_fp16)[name = tensor("op_3137_cast_fp16")]; - tensor input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_3137_cast_fp16)[name = tensor("input_863_cast_fp16")]; - tensor query_axes_0 = const()[name = tensor("query_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206860480)))]; - tensor module_layers_16_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206861568)))]; - tensor query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = tensor("query_cast_fp16")]; - tensor module_layers_16_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206862656)))]; - tensor module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207387008)))]; - tensor linear_147_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_q_bias_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_147_cast_fp16")]; - tensor var_3154 = const()[name = tensor("op_3154"), val = tensor([1, -1, 8, 64])]; - tensor q_97_cast_fp16 = reshape(shape = var_3154, x = linear_147_cast_fp16)[name = tensor("q_97_cast_fp16")]; - tensor module_layers_16_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207388096)))]; - tensor module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207912448)))]; - tensor linear_148_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_k_bias_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_148_cast_fp16")]; - tensor var_3159 = const()[name = tensor("op_3159"), val = tensor([1, -1, 8, 64])]; - tensor k_65_cast_fp16 = reshape(shape = var_3159, x = linear_148_cast_fp16)[name = tensor("k_65_cast_fp16")]; - tensor module_layers_16_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207913536)))]; - tensor module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208437888)))]; - tensor linear_149_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_v_bias_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_149_cast_fp16")]; - tensor var_3164 = const()[name = tensor("op_3164"), val = tensor([1, -1, 8, 64])]; - tensor v_cast_fp16 = reshape(shape = var_3164, x = linear_149_cast_fp16)[name = tensor("v_cast_fp16")]; - tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208438976)))]; - tensor var_3176_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3176_cast_fp16")]; - tensor module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208440064)))]; - tensor var_3178_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3178_cast_fp16")]; - tensor q_with_bias_v_perm_0 = const()[name = tensor("q_with_bias_v_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_359_transpose_x_0 = const()[name = tensor("x_359_transpose_x_0"), val = tensor(false)]; - tensor x_359_transpose_y_0 = const()[name = tensor("x_359_transpose_y_0"), val = tensor(false)]; - tensor var_3180_to_fp16 = const()[name = tensor("op_3180_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208441152)))]; - tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_3178_cast_fp16)[name = tensor("transpose_91")]; - tensor x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_cast_fp16, y = var_3180_to_fp16)[name = tensor("x_359_cast_fp16")]; - tensor x_361_pad_0 = const()[name = tensor("x_361_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_361_mode_0 = const()[name = tensor("x_361_mode_0"), val = tensor("constant")]; - tensor const_230_to_fp16 = const()[name = tensor("const_230_to_fp16"), val = tensor(0x0p+0)]; - tensor x_361_cast_fp16 = pad(constant_val = const_230_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = tensor("x_361_cast_fp16")]; - tensor var_3188 = const()[name = tensor("op_3188"), val = tensor([1, 8, -1, 188])]; - tensor x_363_cast_fp16 = reshape(shape = var_3188, x = x_361_cast_fp16)[name = tensor("x_363_cast_fp16")]; - tensor var_3192_begin_0 = const()[name = tensor("op_3192_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3192_end_0 = const()[name = tensor("op_3192_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3192_end_mask_0 = const()[name = tensor("op_3192_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3192_cast_fp16 = slice_by_index(begin = var_3192_begin_0, end = var_3192_end_0, end_mask = var_3192_end_mask_0, x = x_363_cast_fp16)[name = tensor("op_3192_cast_fp16")]; - tensor var_3193 = const()[name = tensor("op_3193"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_65_cast_fp16 = reshape(shape = var_3193, x = var_3192_cast_fp16)[name = tensor("matrix_bd_65_cast_fp16")]; - tensor matrix_ac_transpose_x_0 = const()[name = tensor("matrix_ac_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_transpose_y_0 = const()[name = tensor("matrix_ac_transpose_y_0"), val = tensor(false)]; - tensor transpose_83_perm_0 = const()[name = tensor("transpose_83_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_84_perm_0 = const()[name = tensor("transpose_84_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = k_65_cast_fp16)[name = tensor("transpose_89")]; - tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = var_3176_cast_fp16)[name = tensor("transpose_90")]; - tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_83, y = transpose_84)[name = tensor("matrix_ac_cast_fp16")]; - tensor matrix_bd_begin_0 = const()[name = tensor("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_end_0 = const()[name = tensor("matrix_bd_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_end_mask_0 = const()[name = tensor("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_65_cast_fp16)[name = tensor("matrix_bd_cast_fp16")]; - tensor var_3202_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = tensor("op_3202_cast_fp16")]; - tensor _inversed_scores_65_y_0_to_fp16 = const()[name = tensor("_inversed_scores_65_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_65_cast_fp16 = mul(x = var_3202_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = tensor("_inversed_scores_65_cast_fp16")]; - tensor scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_11)[name = tensor("scores_cast_fp16")]; - tensor var_3208_cast_fp16 = softmax(axis = var_23, x = scores_cast_fp16)[name = tensor("op_3208_cast_fp16")]; - tensor input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_3208_cast_fp16, cond = mask_11)[name = tensor("input_865_cast_fp16")]; - tensor x_365_transpose_x_0 = const()[name = tensor("x_365_transpose_x_0"), val = tensor(false)]; - tensor x_365_transpose_y_0 = const()[name = tensor("x_365_transpose_y_0"), val = tensor(false)]; - tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = tensor("transpose_92")]; - tensor x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_cast_fp16)[name = tensor("x_365_cast_fp16")]; - tensor var_3212_perm_0 = const()[name = tensor("op_3212_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3213 = const()[name = tensor("op_3213"), val = tensor([1, -1, 512])]; - tensor var_3212_cast_fp16 = transpose(perm = var_3212_perm_0, x = x_365_cast_fp16)[name = tensor("transpose_88")]; - tensor input_867_cast_fp16 = reshape(shape = var_3213, x = var_3212_cast_fp16)[name = tensor("input_867_cast_fp16")]; - tensor module_layers_16_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208825216)))]; - tensor module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209349568)))]; - tensor linear_151_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_out_bias_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16, x = input_867_cast_fp16)[name = tensor("linear_151_cast_fp16")]; - tensor input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = tensor("input_871_cast_fp16")]; - tensor x_369_axes_0 = const()[name = tensor("x_369_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209350656)))]; - tensor module_layers_16_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209351744)))]; - tensor x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = tensor("x_369_cast_fp16")]; - tensor input_873_perm_0 = const()[name = tensor("input_873_perm_0"), val = tensor([0, 2, 1])]; - tensor input_875_pad_type_0 = const()[name = tensor("input_875_pad_type_0"), val = tensor("valid")]; - tensor input_875_strides_0 = const()[name = tensor("input_875_strides_0"), val = tensor([1])]; - tensor input_875_pad_0 = const()[name = tensor("input_875_pad_0"), val = tensor([0, 0])]; - tensor input_875_dilations_0 = const()[name = tensor("input_875_dilations_0"), val = tensor([1])]; - tensor input_875_groups_0 = const()[name = tensor("input_875_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209352832)))]; - tensor module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210401472)))]; - tensor input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = tensor("transpose_87")]; - tensor input_875_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv1_bias_to_fp16, dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16, x = input_873_cast_fp16)[name = tensor("input_875_cast_fp16")]; - tensor x_371_split_num_splits_0 = const()[name = tensor("x_371_split_num_splits_0"), val = tensor(2)]; - tensor x_371_split_axis_0 = const()[name = tensor("x_371_split_axis_0"), val = tensor(1)]; - tensor x_371_split_cast_fp16_0, tensor x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = tensor("x_371_split_cast_fp16")]; - tensor x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = tensor("x_371_split_1_sigmoid_cast_fp16")]; - tensor x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = tensor("x_371_cast_fp16")]; - tensor input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_453)[name = tensor("input_877_cast_fp16")]; - tensor input_879_pad_0 = const()[name = tensor("input_879_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_879_mode_0 = const()[name = tensor("input_879_mode_0"), val = tensor("constant")]; - tensor const_233_to_fp16 = const()[name = tensor("const_233_to_fp16"), val = tensor(0x0p+0)]; - tensor input_879_cast_fp16 = pad(constant_val = const_233_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = tensor("input_879_cast_fp16")]; - tensor input_881_pad_type_0 = const()[name = tensor("input_881_pad_type_0"), val = tensor("valid")]; - tensor input_881_groups_0 = const()[name = tensor("input_881_groups_0"), val = tensor(512)]; - tensor input_881_strides_0 = const()[name = tensor("input_881_strides_0"), val = tensor([1])]; - tensor input_881_pad_0 = const()[name = tensor("input_881_pad_0"), val = tensor([0, 0])]; - tensor input_881_dilations_0 = const()[name = tensor("input_881_dilations_0"), val = tensor([1])]; - tensor const_266_to_fp16 = const()[name = tensor("const_266_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210403584)))]; - tensor const_267_to_fp16 = const()[name = tensor("const_267_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210412864)))]; - tensor input_883_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_266_to_fp16, x = input_879_cast_fp16)[name = tensor("input_883_cast_fp16")]; - tensor input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = tensor("input_885_cast_fp16")]; - tensor x_373_pad_type_0 = const()[name = tensor("x_373_pad_type_0"), val = tensor("valid")]; - tensor x_373_strides_0 = const()[name = tensor("x_373_strides_0"), val = tensor([1])]; - tensor x_373_pad_0 = const()[name = tensor("x_373_pad_0"), val = tensor([0, 0])]; - tensor x_373_dilations_0 = const()[name = tensor("x_373_dilations_0"), val = tensor([1])]; - tensor x_373_groups_0 = const()[name = tensor("x_373_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210413952)))]; - tensor module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210938304)))]; - tensor x_373_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv2_bias_to_fp16, dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = module_layers_16_conv_pointwise_conv2_weight_to_fp16, x = input_885_cast_fp16)[name = tensor("x_373_cast_fp16")]; - tensor input_887_perm_0 = const()[name = tensor("input_887_perm_0"), val = tensor([0, 2, 1])]; - tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = tensor("transpose_86")]; - tensor input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = tensor("input_889_cast_fp16")]; - tensor input_891_axes_0 = const()[name = tensor("input_891_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210939392)))]; - tensor module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210940480)))]; - tensor input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = tensor("input_891_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210941568)))]; - tensor module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213038784)))]; - tensor linear_152_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16, x = input_891_cast_fp16)[name = tensor("linear_152_cast_fp16")]; - tensor input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = tensor("input_895_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213042944)))]; - tensor module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215140160)))]; - tensor linear_153_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16, x = input_895_cast_fp16)[name = tensor("linear_153_cast_fp16")]; - tensor var_3279_to_fp16 = const()[name = tensor("op_3279_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3280_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3279_to_fp16)[name = tensor("op_3280_cast_fp16")]; - tensor input_cast_fp16 = add(x = input_889_cast_fp16, y = var_3280_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor audio_signal_axes_0 = const()[name = tensor("audio_signal_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215141248)))]; - tensor module_layers_16_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215142336)))]; - tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("audio_signal_cast_fp16")]; - tensor obj_1_perm_0 = const()[name = tensor("obj_1_perm_0"), val = tensor([0, 2, 1])]; - tensor obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_1_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = tensor("transpose_85")]; - tensor encoder_output = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = tensor("cast_172")]; - } -> (encoder_output, encoder_length); -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/weights/weight.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/weights/weight.bin deleted file mode 100644 index c5bffa9667270bb21c093e55898f4fb2e299e426..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:cecf7994b2758397d992802a4f6e5d656e3a1aeb7bbedc2aa430b1316d62474c -size 215143424 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/analytics/coremldata.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 4b4b92e5e8e2b45c67e95237edd74d11f323a65a..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid 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a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/metadata.json +++ /dev/null @@ -1,102 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M joint + decision head (split, softmax, argmax)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 188 × 1)", - "shortDescription" : "", - "shape" : "[1, 188, 1]", - "name" : "token_id", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 188 × 1)", - "shortDescription" : "", - "shape" : "[1, 188, 1]", - "name" : "token_prob", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 188 × 1)", - "shortDescription" : "", - "shape" : "[1, 188, 1]", - "name" : "duration", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.reduceArgmax" : 2, - "Ios17.squeeze" : 1, - "Ios17.cast" : 4, - "Ios17.linear" : 3, - "Ios17.transpose" : 2, - "Ios17.sliceByIndex" : 2, - "Ios17.add" : 1, - "Ios16.relu" : 1, - "Ios16.softmax" : 1, - "Ios17.gatherAlongAxis" : 1, - "Ios17.expandDims" : 3 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 512 × 188)", - "shortDescription" : "", - "shape" : "[1, 512, 188]", - "name" : "encoder", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.version" : "8.3.0", - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0" - }, - "generatedClassName" : "parakeet_joint_decision", - "method" : "predict" - } -] \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/model.mil b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/model.mil deleted file mode 100644 index edfc5050f47ffbe1f5344799cef5dc0b21c392e0..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/model.mil +++ /dev/null @@ -1,58 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor decoder, tensor encoder) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_to_fp16_dtype_0 = const()[name = tensor("encoder_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_to_fp16_dtype_0 = const()[name = tensor("decoder_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488)))]; - tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = tensor("cast_6")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656832)))]; - tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476096)))]; - tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = tensor("cast_5")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; - tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; - tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; - tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1477440)))]; - tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2795904)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 188, 1, 1025])]; - tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; - tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 188, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; - tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; - tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; - tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; - tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; - tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; - tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; - tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; - tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_4")]; - tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; - tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; - tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; - tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; - tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; - tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; - tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_3")]; - } -> (token_id, token_prob, duration); -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/weights/weight.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b3f771cb65b190f1873e39629676ed79b65a8361522f451b37bdba8b1106e6ff -size 2798028 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/analytics/coremldata.bin 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a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/model.mil +++ /dev/null @@ -1,69 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor decoder_step, tensor encoder_step) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_step_to_fp16_dtype_0 = const()[name = tensor("encoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_step_to_fp16_dtype_0 = const()[name = tensor("decoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488)))]; - tensor encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor("cast_9")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656832)))]; - tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476096)))]; - tensor decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor("cast_8")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; - tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; - tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; - tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1477440)))]; - tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2795904)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 1, 1, 1025])]; - tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; - tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 1, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; - tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; - tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; - tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; - tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; - tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; - tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; - tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; - tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_7")]; - tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; - tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; - tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; - tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; - tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; - tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; - tensor var_72 = const()[name = tensor("op_72"), val = tensor(64)]; - tensor var_76_axis_0 = const()[name = tensor("op_76_axis_0"), val = tensor(-1)]; - tensor var_76_ascending_0 = const()[name = tensor("op_76_ascending_0"), val = tensor(false)]; - tensor var_76_sort_0 = const()[name = tensor("op_76_sort_0"), val = tensor(true)]; - tensor var_76_return_indices_0 = const()[name = tensor("op_76_return_indices_0"), val = tensor(true)]; - tensor var_76_cast_fp16_cast_int16_output_indices_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_output_indices_dtype_0"), val = tensor("uint16")]; - tensor var_76_cast_fp16_cast_int16_0, tensor var_76_cast_fp16_cast_int16_1 = topk(ascending = var_76_ascending_0, axis = var_76_axis_0, k = var_72, output_indices_dtype = var_76_cast_fp16_cast_int16_output_indices_dtype_0, return_indices = var_76_return_indices_0, sort = var_76_sort_0, x = token_logits_cast_fp16)[name = tensor("op_76_cast_fp16_cast_int16")]; - tensor var_76_cast_fp16_cast_int16_1_to_int32_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_1_to_int32_dtype_0"), val = tensor("int32")]; - tensor var_76_cast_fp16_0_to_fp32_dtype_0 = const()[name = tensor("op_76_cast_fp16_0_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor top_k_logits = cast(dtype = var_76_cast_fp16_0_to_fp32_dtype_0, x = var_76_cast_fp16_cast_int16_0)[name = tensor("cast_4")]; - tensor top_k_ids = cast(dtype = var_76_cast_fp16_cast_int16_1_to_int32_dtype_0, x = var_76_cast_fp16_cast_int16_1)[name = tensor("cast_5")]; - tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_6")]; - } -> (token_id, token_prob, duration, top_k_ids, top_k_logits); -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/weights/weight.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b3f771cb65b190f1873e39629676ed79b65a8361522f451b37bdba8b1106e6ff -size 2798028 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/analytics/coremldata.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index d34457003aea6648633a14e7cd6134edc9c30f17..0000000000000000000000000000000000000000 --- 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b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/metadata.json deleted file mode 100644 index 6087d5e76f7dd060cf4f135f1f3ae0bbbeb2f30a..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/metadata.json +++ /dev/null @@ -1,112 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M preprocessor (15 s window)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32)", - "shortDescription" : "", - "shape" : "[]", - "name" : "mel_features", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Range1d" : 3, - "Ios17.equal" : 1, - "Ios17.notEqual" : 1, - "Ios17.reshape" : 2, - "Identity" : 1, - "Ios17.matmul" : 1, - "Select" : 6, - "Ios17.expandDims" : 12, - "Ios17.add" : 3, - "Tile" : 2, - "Ios17.sliceByIndex" : 3, - "Ios16.reduceSum" : 4, - "Shape" : 4, - "Ios17.gather" : 4, - "Ios17.logicalNot" : 1, - "Pad" : 1, - "Ios17.log" : 1, - "Ios17.less" : 2, - "Ios17.sub" : 4, - "Ios17.conv" : 2, - "Ios17.pow" : 2, - "Ios17.cast" : 10, - "Ios17.concat" : 3, - "Stack" : 1, - "Ios17.floorDiv" : 1, - "Ios17.realDiv" : 4, - "Ios17.sqrt" : 1, - "Ios17.greaterEqual" : 1, - "Ios17.mul" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32, UInt16)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "dataType" : "Float32", - "hasShapeFlexibility" : "1", - "isOptional" : "0", - "shapeFlexibility" : "1 × 1...240000", - "shapeRange" : "[[1, 1], [1, 240000]]", - "formattedType" : "MultiArray (Float32 1 × 1)", - "type" : "MultiArray", - "shape" : "[1, 1]", - "name" : "audio", - "shortDescription" : "" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "audio_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "generatedClassName" : "parakeet_preprocessor", - "method" : "predict" - } -] \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/model.mil b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/model.mil deleted file mode 100644 index ae325b6071a8ee5dad74a2e516e60889d89771d8..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/model.mil +++ /dev/null @@ -1,191 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor audio, tensor audio_length) [FlexibleShapeInformation = tuple, dict, tensor>>, tuple, dict, list, ?>>>>((("DefaultShapes", {{"audio", [1, 1]}}), ("RangeDims", {{"audio", [[1, 1], [1, 240000]]}})))] { - tensor var_9 = const()[name = tensor("op_9"), val = tensor(1)]; - tensor var_10 = const()[name = tensor("op_10"), val = tensor(160)]; - tensor var_12 = const()[name = tensor("op_12"), val = tensor(0)]; - tensor var_34 = const()[name = tensor("op_34"), val = tensor(512)]; - tensor var_35 = add(x = audio_length, y = var_34)[name = tensor("op_35")]; - tensor var_36 = const()[name = tensor("op_36"), val = tensor(512)]; - tensor var_37 = sub(x = var_35, y = var_36)[name = tensor("op_37")]; - tensor floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor("floor_div_0")]; - tensor var_40 = equal(x = audio_length, y = var_12)[name = tensor("op_40")]; - tensor var_41 = const()[name = tensor("op_41"), val = tensor([0])]; - tensor mel_length = select(a = var_41, b = floor_div_0, cond = var_40)[name = tensor("seq_len")]; - tensor audio_to_fp16_dtype_0 = const()[name = tensor("audio_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = tensor("cast_27")]; - tensor var_43_shape_cast_fp16 = shape(x = audio_to_fp16)[name = tensor("op_43_shape_cast_fp16")]; - tensor gather_0_axis_0 = const()[name = tensor("gather_0_axis_0"), val = tensor(0)]; - tensor gather_0_batch_dims_0 = const()[name = tensor("gather_0_batch_dims_0"), val = tensor(0)]; - tensor gather_0_validate_indices_0 = const()[name = tensor("gather_0_validate_indices_0"), val = tensor(false)]; - tensor var_43_shape_cast_fp16_to_int16_dtype_0 = const()[name = tensor("op_43_shape_cast_fp16_to_int16_dtype_0"), val = tensor("int16")]; - tensor select_0_to_uint16 = const()[name = tensor("select_0_to_uint16"), val = tensor(1)]; - tensor var_43_shape_cast_fp16_to_int16 = cast(dtype = var_43_shape_cast_fp16_to_int16_dtype_0, x = var_43_shape_cast_fp16)[name = tensor("cast_26")]; - tensor gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_43_shape_cast_fp16_to_int16)[name = tensor("gather_0_cast_uint16")]; - tensor gather_0_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_0_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_0 = const()[name = tensor("const_0"), val = tensor(0)]; - tensor const_1 = const()[name = tensor("const_1"), val = tensor(1)]; - tensor gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = tensor("cast_25")]; - tensor var_44 = range_1d(end = gather_0_cast_uint16_to_int32, start = const_0, step = const_1)[name = tensor("op_44")]; - tensor var_45_axes_0 = const()[name = tensor("op_45_axes_0"), val = tensor([0])]; - tensor var_45 = expand_dims(axes = var_45_axes_0, x = var_44)[name = tensor("op_45")]; - tensor var_46_axes_0 = const()[name = tensor("op_46_axes_0"), val = tensor([1])]; - tensor var_46 = expand_dims(axes = var_46_axes_0, x = audio_length)[name = tensor("op_46")]; - tensor timemask = less(x = var_45, y = var_46)[name = tensor("timemask")]; - tensor var_49_begin_0 = const()[name = tensor("op_49_begin_0"), val = tensor([0, 0])]; - tensor var_49_end_0 = const()[name = tensor("op_49_end_0"), val = tensor([1, 1])]; - tensor var_49_end_mask_0 = const()[name = tensor("op_49_end_mask_0"), val = tensor([true, false])]; - tensor var_49_squeeze_mask_0 = const()[name = tensor("op_49_squeeze_mask_0"), val = tensor([false, true])]; - tensor var_49_cast_fp16 = slice_by_index(begin = var_49_begin_0, end = var_49_end_0, end_mask = var_49_end_mask_0, squeeze_mask = var_49_squeeze_mask_0, x = audio_to_fp16)[name = tensor("op_49_cast_fp16")]; - tensor var_50_axes_0 = const()[name = tensor("op_50_axes_0"), val = tensor([1])]; - tensor var_50_cast_fp16 = expand_dims(axes = var_50_axes_0, x = var_49_cast_fp16)[name = tensor("op_50_cast_fp16")]; - tensor var_52_begin_0 = const()[name = tensor("op_52_begin_0"), val = tensor([0, 1])]; - tensor var_52_end_0 = const()[name = tensor("op_52_end_0"), val = tensor([1, 0])]; - tensor var_52_end_mask_0 = const()[name = tensor("op_52_end_mask_0"), val = tensor([true, true])]; - tensor var_52_cast_fp16 = slice_by_index(begin = var_52_begin_0, end = var_52_end_0, end_mask = var_52_end_mask_0, x = audio_to_fp16)[name = tensor("op_52_cast_fp16")]; - tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 0])]; - tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([1, -1])]; - tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, false])]; - tensor var_54_cast_fp16 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = audio_to_fp16)[name = tensor("op_54_cast_fp16")]; - tensor var_55_to_fp16 = const()[name = tensor("op_55_to_fp16"), val = tensor(0x1.f0cp-1)]; - tensor var_56_cast_fp16 = mul(x = var_54_cast_fp16, y = var_55_to_fp16)[name = tensor("op_56_cast_fp16")]; - tensor var_57_cast_fp16 = sub(x = var_52_cast_fp16, y = var_56_cast_fp16)[name = tensor("op_57_cast_fp16")]; - tensor x_3_interleave_0 = const()[name = tensor("x_3_interleave_0"), val = tensor(false)]; - tensor x_3_cast_fp16 = concat(axis = var_9, interleave = x_3_interleave_0, values = (var_50_cast_fp16, var_57_cast_fp16))[name = tensor("x_3_cast_fp16")]; - tensor var_60 = logical_not(x = timemask)[name = tensor("op_60")]; - tensor var_16_to_fp16 = const()[name = tensor("op_16_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_60)[name = tensor("input_1_cast_fp16")]; - tensor concat_1x = const()[name = tensor("concat_1x"), val = tensor([1, 1, -1])]; - tensor input_3_cast_fp16 = reshape(shape = concat_1x, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; - tensor input_5_mode_0 = const()[name = tensor("input_5_mode_0"), val = tensor("constant")]; - tensor const_3_to_fp16 = const()[name = tensor("const_3_to_fp16"), val = tensor(0x0p+0)]; - tensor input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor concat_2x = const()[name = tensor("concat_2x"), val = tensor([1, -1])]; - tensor input_cast_fp16 = reshape(shape = concat_2x, x = input_5_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([160])]; - tensor expand_dims_4_axes_0 = const()[name = tensor("expand_dims_4_axes_0"), val = tensor([1])]; - tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = tensor("expand_dims_4_cast_fp16")]; - tensor conv_0_pad_type_0 = const()[name = tensor("conv_0_pad_type_0"), val = tensor("valid")]; - tensor conv_0_pad_0 = const()[name = tensor("conv_0_pad_0"), val = tensor([0, 0])]; - tensor conv_0_dilations_0 = const()[name = tensor("conv_0_dilations_0"), val = tensor([1])]; - tensor conv_0_groups_0 = const()[name = tensor("conv_0_groups_0"), val = tensor(1)]; - tensor expand_dims_1_to_fp16 = const()[name = tensor("expand_dims_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_0_cast_fp16")]; - tensor conv_1_pad_type_0 = const()[name = tensor("conv_1_pad_type_0"), val = tensor("valid")]; - tensor conv_1_pad_0 = const()[name = tensor("conv_1_pad_0"), val = tensor([0, 0])]; - tensor conv_1_dilations_0 = const()[name = tensor("conv_1_dilations_0"), val = tensor([1])]; - tensor conv_1_groups_0 = const()[name = tensor("conv_1_groups_0"), val = tensor(1)]; - tensor expand_dims_2_to_fp16 = const()[name = tensor("expand_dims_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263296)))]; - tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_1_cast_fp16")]; - tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(-1)]; - tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor("stack_0_cast_fp16")]; - tensor var_19_promoted_to_fp16 = const()[name = tensor("op_19_promoted_to_fp16"), val = tensor(0x1p+1)]; - tensor var_75_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_19_promoted_to_fp16)[name = tensor("op_75_cast_fp16")]; - tensor var_77_axes_0 = const()[name = tensor("op_77_axes_0"), val = tensor([-1])]; - tensor var_77_keep_dims_0 = const()[name = tensor("op_77_keep_dims_0"), val = tensor(false)]; - tensor var_77_cast_fp16 = reduce_sum(axes = var_77_axes_0, keep_dims = var_77_keep_dims_0, x = var_75_cast_fp16)[name = tensor("op_77_cast_fp16")]; - tensor x_11_cast_fp16 = identity(x = var_77_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor x_13_transpose_x_0 = const()[name = tensor("x_13_transpose_x_0"), val = tensor(false)]; - tensor x_13_transpose_y_0 = const()[name = tensor("x_13_transpose_y_0"), val = tensor(false)]; - tensor const_4_to_fp16 = const()[name = tensor("const_4_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526528)))]; - tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_4_to_fp16, y = x_11_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_84_to_fp16 = const()[name = tensor("op_84_to_fp16"), val = tensor(0x1p-24)]; - tensor var_85_cast_fp16 = add(x = x_13_cast_fp16, y = var_84_to_fp16)[name = tensor("op_85_cast_fp16")]; - tensor x_15_epsilon_0 = const()[name = tensor("x_15_epsilon_0"), val = tensor(0x1p-149)]; - tensor x_15_cast_fp16 = log(epsilon = x_15_epsilon_0, x = var_85_cast_fp16)[name = tensor("x_15_cast_fp16")]; - tensor var_87_shape_cast_fp16 = shape(x = x_15_cast_fp16)[name = tensor("op_87_shape_cast_fp16")]; - tensor gather_5 = const()[name = tensor("gather_5"), val = tensor(1)]; - tensor gather_6_axis_0 = const()[name = tensor("gather_6_axis_0"), val = tensor(0)]; - tensor gather_6_batch_dims_0 = const()[name = tensor("gather_6_batch_dims_0"), val = tensor(0)]; - tensor gather_6_validate_indices_0 = const()[name = tensor("gather_6_validate_indices_0"), val = tensor(false)]; - tensor var_87_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_87_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor select_6_to_uint16 = const()[name = tensor("select_6_to_uint16"), val = tensor(2)]; - tensor var_87_shape_cast_fp16_to_uint16 = cast(dtype = var_87_shape_cast_fp16_to_uint16_dtype_0, x = var_87_shape_cast_fp16)[name = tensor("cast_24")]; - tensor gather_6_cast_uint16 = gather(axis = gather_6_axis_0, batch_dims = gather_6_batch_dims_0, indices = select_6_to_uint16, validate_indices = gather_6_validate_indices_0, x = var_87_shape_cast_fp16_to_uint16)[name = tensor("gather_6_cast_uint16")]; - tensor gather_6_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_6_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_5 = const()[name = tensor("const_5"), val = tensor(0)]; - tensor const_6 = const()[name = tensor("const_6"), val = tensor(1)]; - tensor gather_6_cast_uint16_to_int32 = cast(dtype = gather_6_cast_uint16_to_int32_dtype_0, x = gather_6_cast_uint16)[name = tensor("cast_23")]; - tensor var_89 = range_1d(end = gather_6_cast_uint16_to_int32, start = const_5, step = const_6)[name = tensor("op_89")]; - tensor var_90_axes_0 = const()[name = tensor("op_90_axes_0"), val = tensor([0])]; - tensor var_90 = expand_dims(axes = var_90_axes_0, x = var_89)[name = tensor("op_90")]; - tensor concat_3_axis_0 = const()[name = tensor("concat_3_axis_0"), val = tensor(0)]; - tensor concat_3_interleave_0 = const()[name = tensor("concat_3_interleave_0"), val = tensor(false)]; - tensor concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (gather_5, gather_6_cast_uint16_to_int32))[name = tensor("concat_3")]; - tensor shape_8 = shape(x = var_90)[name = tensor("shape_8")]; - tensor real_div_0 = real_div(x = concat_3, y = shape_8)[name = tensor("real_div_0")]; - tensor time_steps = tile(reps = real_div_0, x = var_90)[name = tensor("time_steps")]; - tensor var_93_axes_0 = const()[name = tensor("op_93_axes_0"), val = tensor([1])]; - tensor var_93 = expand_dims(axes = var_93_axes_0, x = mel_length)[name = tensor("op_93")]; - tensor valid_mask = less(x = time_steps, y = var_93)[name = tensor("valid_mask")]; - tensor var_95_axes_0 = const()[name = tensor("op_95_axes_0"), val = tensor([1])]; - tensor var_95 = expand_dims(axes = var_95_axes_0, x = valid_mask)[name = tensor("op_95")]; - tensor var_96_cast_fp16 = select(a = x_15_cast_fp16, b = var_16_to_fp16, cond = var_95)[name = tensor("op_96_cast_fp16")]; - tensor x_mean_numerator_axes_0 = const()[name = tensor("x_mean_numerator_axes_0"), val = tensor([2])]; - tensor x_mean_numerator_keep_dims_0 = const()[name = tensor("x_mean_numerator_keep_dims_0"), val = tensor(false)]; - tensor x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_96_cast_fp16)[name = tensor("x_mean_numerator_cast_fp16")]; - tensor x_mean_denominator_axes_0 = const()[name = tensor("x_mean_denominator_axes_0"), val = tensor([1])]; - tensor x_mean_denominator_keep_dims_0 = const()[name = tensor("x_mean_denominator_keep_dims_0"), val = tensor(false)]; - tensor cast_6_to_fp16_dtype_0 = const()[name = tensor("cast_6_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor valid_mask_to_fp16 = cast(dtype = cast_6_to_fp16_dtype_0, x = valid_mask)[name = tensor("cast_22")]; - tensor x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor("x_mean_denominator_cast_fp16")]; - tensor var_101_axes_0 = const()[name = tensor("op_101_axes_0"), val = tensor([1])]; - tensor var_101_cast_fp16 = expand_dims(axes = var_101_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor("op_101_cast_fp16")]; - tensor x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_101_cast_fp16)[name = tensor("x_mean_cast_fp16")]; - tensor var_104_axes_0 = const()[name = tensor("op_104_axes_0"), val = tensor([2])]; - tensor var_104_cast_fp16 = expand_dims(axes = var_104_axes_0, x = x_mean_cast_fp16)[name = tensor("op_104_cast_fp16")]; - tensor var_105_cast_fp16 = sub(x = x_15_cast_fp16, y = var_104_cast_fp16)[name = tensor("op_105_cast_fp16")]; - tensor var_106_cast_fp16 = select(a = var_105_cast_fp16, b = var_16_to_fp16, cond = var_95)[name = tensor("op_106_cast_fp16")]; - tensor var_19_promoted_1_to_fp16 = const()[name = tensor("op_19_promoted_1_to_fp16"), val = tensor(0x1p+1)]; - tensor var_107_cast_fp16 = pow(x = var_106_cast_fp16, y = var_19_promoted_1_to_fp16)[name = tensor("op_107_cast_fp16")]; - tensor var_109_axes_0 = const()[name = tensor("op_109_axes_0"), val = tensor([2])]; - tensor var_109_keep_dims_0 = const()[name = tensor("op_109_keep_dims_0"), val = tensor(false)]; - tensor var_109_cast_fp16 = reduce_sum(axes = var_109_axes_0, keep_dims = var_109_keep_dims_0, x = var_107_cast_fp16)[name = tensor("op_109_cast_fp16")]; - tensor var_111_to_fp16 = const()[name = tensor("op_111_to_fp16"), val = tensor(0x1p+0)]; - tensor var_112_cast_fp16 = sub(x = var_101_cast_fp16, y = var_111_to_fp16)[name = tensor("op_112_cast_fp16")]; - tensor var_113_cast_fp16 = real_div(x = var_109_cast_fp16, y = var_112_cast_fp16)[name = tensor("op_113_cast_fp16")]; - tensor x_std_1_cast_fp16 = sqrt(x = var_113_cast_fp16)[name = tensor("x_std_1_cast_fp16")]; - tensor var_115_cast_fp16 = not_equal(x = x_std_1_cast_fp16, y = x_std_1_cast_fp16)[name = tensor("op_115_cast_fp16")]; - tensor x_std_3_cast_fp16 = select(a = var_16_to_fp16, b = x_std_1_cast_fp16, cond = var_115_cast_fp16)[name = tensor("x_std_3_cast_fp16")]; - tensor var_25_to_fp16 = const()[name = tensor("op_25_to_fp16"), val = tensor(0x1.5p-17)]; - tensor x_std_cast_fp16 = add(x = x_std_3_cast_fp16, y = var_25_to_fp16)[name = tensor("x_std_cast_fp16")]; - tensor var_120_axes_0 = const()[name = tensor("op_120_axes_0"), val = tensor([2])]; - tensor var_120_cast_fp16 = expand_dims(axes = var_120_axes_0, x = x_std_cast_fp16)[name = tensor("op_120_cast_fp16")]; - tensor x_cast_fp16 = real_div(x = var_105_cast_fp16, y = var_120_cast_fp16)[name = tensor("x_cast_fp16")]; - tensor var_122_shape_cast_fp16 = shape(x = x_cast_fp16)[name = tensor("op_122_shape_cast_fp16")]; - tensor gather_7_axis_0 = const()[name = tensor("gather_7_axis_0"), val = tensor(0)]; - tensor gather_7_batch_dims_0 = const()[name = tensor("gather_7_batch_dims_0"), val = tensor(0)]; - tensor gather_7_validate_indices_0 = const()[name = tensor("gather_7_validate_indices_0"), val = tensor(false)]; - tensor var_122_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_122_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor select_7_to_uint16 = const()[name = tensor("select_7_to_uint16"), val = tensor(2)]; - tensor var_122_shape_cast_fp16_to_uint16 = cast(dtype = var_122_shape_cast_fp16_to_uint16_dtype_0, x = var_122_shape_cast_fp16)[name = tensor("cast_21")]; - tensor gather_7_cast_uint16 = gather(axis = gather_7_axis_0, batch_dims = gather_7_batch_dims_0, indices = select_7_to_uint16, validate_indices = gather_7_validate_indices_0, x = var_122_shape_cast_fp16_to_uint16)[name = tensor("gather_7_cast_uint16")]; - tensor gather_7_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_7_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_7 = const()[name = tensor("const_7"), val = tensor(0)]; - tensor const_8 = const()[name = tensor("const_8"), val = tensor(1)]; - tensor gather_7_cast_uint16_to_int32 = cast(dtype = gather_7_cast_uint16_to_int32_dtype_0, x = gather_7_cast_uint16)[name = tensor("cast_20")]; - tensor mask_1 = range_1d(end = gather_7_cast_uint16_to_int32, start = const_7, step = const_8)[name = tensor("mask_1")]; - tensor gather_8_axis_0 = const()[name = tensor("gather_8_axis_0"), val = tensor(0)]; - tensor gather_8_batch_dims_0 = const()[name = tensor("gather_8_batch_dims_0"), val = tensor(0)]; - tensor gather_8_validate_indices_0 = const()[name = tensor("gather_8_validate_indices_0"), val = tensor(false)]; - tensor select_8_to_uint16 = const()[name = tensor("select_8_to_uint16"), val = tensor(0)]; - tensor gather_8_cast_uint16 = gather(axis = gather_8_axis_0, batch_dims = gather_8_batch_dims_0, indices = select_8_to_uint16, validate_indices = gather_8_validate_indices_0, x = var_122_shape_cast_fp16_to_uint16)[name = tensor("gather_8_cast_uint16")]; - tensor gather_8_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_8_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor concat_4_axis_0 = const()[name = tensor("concat_4_axis_0"), val = tensor(0)]; - tensor concat_4_interleave_0 = const()[name = tensor("concat_4_interleave_0"), val = tensor(false)]; - tensor gather_8_cast_uint16_to_int32 = cast(dtype = gather_8_cast_uint16_to_int32_dtype_0, x = gather_8_cast_uint16)[name = tensor("cast_19")]; - tensor concat_4 = concat(axis = concat_4_axis_0, interleave = concat_4_interleave_0, values = (gather_8_cast_uint16_to_int32, var_9))[name = tensor("concat_4")]; - tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0])]; - tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = tensor("expand_dims_0")]; - tensor var_126 = tile(reps = concat_4, x = expand_dims_0)[name = tensor("op_126")]; - tensor mask = greater_equal(x = var_126, y = var_93)[name = tensor("mask")]; - tensor var_129_axes_0 = const()[name = tensor("op_129_axes_0"), val = tensor([1])]; - tensor var_129 = expand_dims(axes = var_129_axes_0, x = mask)[name = tensor("op_129")]; - tensor cast_15_to_fp16 = const()[name = tensor("cast_15_to_fp16"), val = tensor(0x0p+0)]; - tensor processed_signal_cast_fp16 = select(a = cast_15_to_fp16, b = x_cast_fp16, cond = var_129)[name = tensor("processed_signal_cast_fp16")]; - tensor processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("processed_signal_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor mel_features = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = tensor("cast_18")]; - } -> (mel_features, mel_length); -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/weights/weight.bin b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/weights/weight.bin deleted file mode 100644 index d0c6ac87e5f78315662149af9ab4ea3658497dbe..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c062338de852a26607ce4101f74e6895de3a4134a57b07232bd72bfc6f1d7f1a -size 567712 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/metadata.json b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/metadata.json deleted file mode 100644 index 6db872ae8a7d463c95dc180210ace0fce019f4a6..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/metadata.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "model_id": "nvidia/parakeet-tdt_ctc-110m", - "model_type": "hybrid_rnnt_ctc", - "sample_rate": 16000, - "max_audio_seconds": 15.0, - "max_audio_samples": 240000, - "max_symbol_steps": 1, - "vocab_size": 1024, - "joint_extra_outputs": 5, - "encoder_dim": 512, - "decoder_dim": 640, - "decoder_hidden": 640, - "decoder_layers": 1, - "blank_id": 1024, - "checkpoint": { - "type": "pretrained", - "model_id": "nvidia/parakeet-tdt_ctc-110m" - }, - "coreml": { - "compute_units": "CPU_ONLY", - "compute_precision": "FLOAT32" - }, - "components": { - "preprocessor": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "path": "parakeet_preprocessor.mlpackage" - }, - "encoder": { - "inputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_encoder.mlpackage" - }, - "ctc_head": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ] - }, - "outputs": { - "log_probs": [ - 1, - 188, - 1025 - ] - }, - "path": "parakeet_ctc_head.mlpackage" - }, - "mel_encoder": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_mel_encoder.mlpackage" - }, - "decoder": { - "inputs": { - "targets": [ - 1, - 1 - ], - "target_length": [ - 1 - ], - "h_in": [ - 1, - 1, - 640 - ], - "c_in": [ - 1, - 1, - 640 - ] - }, - "outputs": { - "decoder": [ - 1, - 640, - 1 - ], - "h_out": [ - 1, - 1, - 640 - ], - "c_out": [ - 1, - 1, - 640 - ] - }, - "path": "parakeet_decoder.mlpackage" - }, - "joint": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "logits": [ - 1, - 188, - 1, - 1030 - ] - }, - "path": "parakeet_joint.mlpackage" - }, - "joint_decision": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 188, - 1 - ], - "token_prob": [ - 1, - 188, - 1 - ], - "duration": [ - 1, - 188, - 1 - ] - }, - "path": "parakeet_joint_decision.mlpackage" - }, - "joint_decision_single_step": { - "inputs": { - "encoder_step": [ - 1, - 512, - 1 - ], - "decoder_step": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 1, - 1 - ], - "token_prob": [ - 1, - 1, - 1 - ], - "duration": [ - 1, - 1, - 1 - ], - "top_k_ids": [ - 1, - 1, - 1, - 64 - ], - "top_k_logits": [ - 1, - 1, - 1, - 64 - ] - }, - "path": "parakeet_joint_decision_single_step.mlpackage" - } - } -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/vocab.json b/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/vocab.json deleted file mode 100644 index 33b95831722563db5362641673b37885e25fb47a..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/compiled_models/vocab.json +++ /dev/null @@ -1 +0,0 @@ -{"0": "", "1": "▁t", "2": "▁th", "3": "▁a", "4": "in", "5": "re", "6": "▁the", "7": "▁w", "8": "▁s", "9": "▁o", "10": "er", "11": "ou", "12": "at", "13": "nd", "14": "it", "15": "▁h", "16": "▁c", "17": "▁b", "18": "is", "19": "en", "20": "on", "21": "ing", "22": "▁f", "23": "▁to", "24": "▁m", "25": "es", "26": "▁p", "27": "or", "28": "an", "29": "▁d", "30": "ll", "31": "▁I", "32": "ed", "33": "▁and", "34": "▁l", "35": "▁of", "36": "▁in", "37": "▁y", "38": "ar", "39": "▁g", "40": "▁you", "41": "as", "42": "om", "43": "▁n", "44": "ve", "45": "▁that", "46": "le", "47": "ic", "48": "us", "49": "ow", "50": "et", "51": "al", "52": "▁e", "53": "ut", "54": "▁it", "55": "ot", "56": "▁be", "57": "▁T", "58": "ion", "59": "▁is", "60": "▁wh", "61": "▁re", "62": "▁on", "63": "▁we", "64": "ent", "65": "▁A", "66": "ay", "67": "▁ha", "68": "▁Th", "69": "id", "70": "▁S", "71": "ac", "72": "gh", "73": "ver", "74": "ke", "75": "▁for", "76": "im", "77": "ly", "78": "ur", "79": "ld", "80": "▁he", "81": "▁st", "82": "all", "83": "ro", "84": "st", "85": "se", "86": "ct", "87": "ith", "88": "ir", "89": "am", "90": "▁this", "91": "if", "92": "▁W", "93": "oo", "94": "ri", "95": "▁was", "96": "ght", "97": "▁u", "98": "▁with", "99": "ad", "100": "ch", "101": "▁se", "102": "▁k", "103": "▁an", "104": "▁The", "105": "▁li", "106": "▁do", "107": "▁B", "108": "▁have", "109": "▁as", "110": "th", "111": "▁are", "112": "▁sh", "113": "ust", "114": "ce", "115": "ally", "116": "ill", "117": "▁H", "118": "▁j", "119": "ter", "120": "▁go", "121": "▁And", "122": "ation", "123": "▁C", "124": "▁so", "125": "ome", "126": "▁not", "127": "op", "128": "il", "129": "ore", "130": "▁ne", "131": "▁can", "132": "▁me", "133": "▁at", "134": "ould", "135": "ant", "136": "▁M", "137": "▁like", "138": "ere", "139": "▁they", "140": "ra", "141": "ers", "142": "▁ab", "143": "▁de", "144": "▁kn", "145": "ge", "146": "▁Y", "147": "▁ch", "148": "ul", "149": "pp", "150": "▁or", "151": "▁al", "152": "▁con", "153": "▁com", "154": "ess", "155": "▁su", "156": "out", "157": "▁your", "158": "▁So", "159": "ate", "160": "▁one", "161": "▁all", "162": "▁ex", "163": "est", "164": "▁fr", "165": "▁just", "166": "▁pro", "167": "▁know", "168": "▁O", "169": "ain", "170": "▁but", "171": "ol", "172": "ive", "173": "▁v", "174": "use", "175": "very", "176": "art", "177": "qu", "178": "▁my", "179": "el", "180": "▁N", "181": "nt", "182": "▁It", "183": "▁what", "184": "ab", "185": "▁P", "186": "▁wor", "187": "▁out", "188": "▁there", "189": "▁up", "190": "um", "191": "▁from", "192": "pe", "193": "▁tw", "194": "▁r", "195": "and", "196": "ight", "197": "ort", "198": "un", "199": "▁L", "200": "ist", "201": "▁about", "202": "ide", "203": "ig", "204": "ake", "205": "▁D", "206": "em", "207": "os", "208": "king", "209": "rou", "210": "ind", "211": "our", "212": "res", "213": "▁We", "214": "▁get", "215": "▁E", "216": "▁G", "217": "ack", "218": "▁le", "219": "ity", "220": "od", "221": "▁F", "222": "ard", "223": "▁pl", "224": "▁our", "225": "▁int", "226": "ment", "227": "▁will", "228": "ies", "229": "▁by", "230": "ink", "231": "ca", "232": "▁if", "233": "red", "234": "her", "235": "ie", "236": "▁us", "237": "▁some", "238": "▁don", "239": "ven", "240": "ood", "241": "ast", "242": "▁R", "243": "▁his", "244": "▁tim", "245": "▁tr", "246": "▁more", "247": "ich", "248": "ous", "249": "ame", "250": "▁going", "251": "▁had", "252": "▁them", "253": "ook", "254": "▁pe", "255": "▁Wh", "256": "▁You", "257": "▁But", "258": "ine", "259": "▁here", "260": "▁would", "261": "cause", "262": "right", "263": "so", "264": "ost", "265": "ure", "266": "▁has", "267": "ect", "268": "▁think", "269": "▁fe", "270": "ong", "271": "▁see", "272": "▁when", "273": "▁who", "274": "▁were", "275": "▁really", "276": "▁their", "277": "▁want", "278": "one", "279": "ople", "280": "▁then", "281": "▁time", "282": "▁sa", "283": "ap", "284": "▁te", "285": "▁He", "286": "▁ye", "287": "ck", "288": "▁her", "289": "▁thing", "290": "▁right", "291": "▁which", "292": "itt", "293": "ice", "294": "act", "295": "▁people", "296": "ty", "297": "▁two", "298": "▁J", "299": "▁im", "300": "ther", "301": "ci", "302": "ose", "303": "▁cl", "304": "▁qu", "305": "▁man", "306": "▁also", "307": "ree", "308": "▁en", "309": "ud", "310": "▁how", "311": "reat", "312": "ak", "313": "hing", "314": "ag", "315": "▁any", "316": "ff", "317": "ace", "318": "per", "319": "▁because", "320": "▁very", "321": "own", "322": "▁ad", "323": "▁act", "324": "▁been", "325": "▁now", "326": "▁ag", "327": "▁into", "328": "▁comp", "329": "ars", "330": "ions", "331": "are", "332": "ite", "333": "iv", "334": "▁these", "335": "ays", "336": "ep", "337": "▁This", "338": "▁she", "339": "ans", "340": "ah", "341": "een", "342": "▁over", "343": "ry", "344": "▁lo", "345": "age", "346": "▁pr", "347": "▁sp", "348": "ue", "349": "▁co", "350": "ick", "351": "ber", "352": "▁did", "353": "ip", "354": "ach", "355": "▁back", "356": "▁no", "357": "▁cont", "358": "▁other", "359": "▁every", "360": "pt", "361": "▁need", "362": "▁him", "363": "▁U", "364": "▁In", "365": "▁work", "366": "irst", "367": "▁part", "368": "▁look", "369": "ittle", "370": "ble", "371": "iz", "372": "▁un", "373": "▁make", "374": "omet", "375": "nder", "376": "ish", "377": "na", "378": "▁little", "379": "▁off", "380": "▁than", "381": "▁got", "382": "ually", "383": "▁per", "384": "▁good", "385": "▁way", "386": "▁could", "387": "▁ac", "388": "▁imp", "389": "able", "390": "▁where", "391": "iff", "392": "▁That", "393": "▁res", "394": "ount", "395": "pl", "396": "ance", "397": "▁first", "398": "▁ro", "399": "▁pre", "400": "ass", "401": "▁say", "402": "int", "403": "ated", "404": "ire", "405": "uch", "406": "ase", "407": "▁somet", "408": "ound", "409": "▁down", "410": "▁diff", "411": "sel", "412": "▁gu", "413": "▁am", "414": "ress", "415": "▁lot", "416": "ence", "417": "▁dis", "418": "orm", "419": "ix", "420": "▁po", "421": "ving", "422": "enty", "423": "▁K", "424": "▁spe", "425": "und", "426": "he", "427": "▁much", "428": "▁ar", "429": "round", "430": "▁app", "431": "co", "432": "ark", "433": "▁new", "434": "ater", "435": "ult", "436": "end", "437": "▁even", "438": "▁start", "439": "ations", "440": "rough", "441": "ile", "442": "fter", "443": "▁well", "444": "be", "445": "▁They", "446": "▁three", "447": "ign", "448": "ild", "449": "▁said", "450": "ough", "451": "ang", "452": "▁too", "453": "ade", "454": "▁bl", "455": "ens", "456": "▁inc", "457": "ia", "458": "▁those", "459": "▁mo", "460": "▁take", "461": "▁through", "462": "▁fl", "463": "▁kind", "464": "▁things", "465": "▁bet", "466": "▁only", "467": "▁St", "468": "▁let", "469": "cess", "470": "▁Ch", "471": "ary", "472": "vel", "473": "▁If", "474": "xt", "475": "other", "476": "av", "477": "ical", "478": "ord", "479": "▁again", "480": "▁something", "481": "onna", "482": "fore", "483": "▁may", "484": "ting", "485": "▁bu", "486": "▁differe", "487": "urn", "488": "▁gonna", "489": "▁does", "490": "uct", "491": "og", "492": "▁twenty", "493": "▁gr", "494": "▁Ye", "495": "wn", "496": "▁should", "497": "▁comm", "498": "ition", "499": "▁under", "500": "▁hel", "501": "ory", "502": "▁fo", "503": "▁use", "504": "igh", "505": "ife", "506": "▁actually", "507": "▁tal", "508": "▁call", "509": "ents", "510": "ious", "511": "ull", "512": "▁There", "513": "▁Yeah", "514": "▁most", "515": "▁ke", "516": "ors", "517": "ved", "518": "ys", "519": "▁sc", "520": "▁happ", "521": "ope", "522": "▁help", "523": "atch", "524": "▁What", "525": "▁rem", "526": "ple", "527": "▁Now", "528": "▁br", "529": "ool", "530": "oth", "531": "▁four", "532": "self", "533": "▁str", "534": "ne", "535": "thing", "536": "▁put", "537": "ial", "538": "▁great", "539": "ail", "540": "ub", "541": "ning", "542": "▁sm", "543": "▁feel", "544": "▁five", "545": "ody", "546": "undred", "547": "iss", "548": "ank", "549": "get", "550": "aking", "551": "▁many", "552": "▁hundred", "553": "▁years", "554": "▁being", "555": "▁come", "556": "▁mean", "557": "ily", "558": "▁different", "559": "▁after", "560": "▁ser", "561": "▁show", "562": "form", "563": "ful", "564": "oy", "565": "▁six", "566": "▁vide", "567": "▁V", "568": "▁its", "569": "▁point", "570": "▁day", "571": "▁des", "572": "ons", "573": "▁bit", "574": "▁bel", "575": "▁before", "576": "▁aw", "577": "▁end", "578": "▁Oh", "579": "▁still", "580": "ath", "581": "▁long", "582": "▁'", "583": "ise", "584": "ob", "585": "day", "586": "▁add", "587": "ft", "588": "ves", "589": "ces", "590": "ady", "591": "▁cr", "592": "▁around", "593": "▁try", "594": "les", "595": "vers", "596": "kay", "597": "ian", "598": "ates", "599": "▁find", "600": "ward", "601": "▁As", "602": "▁eight", "603": "lic", "604": "▁same", "605": "▁pos", "606": "▁em", "607": "▁made", "608": "▁supp", "609": "▁life", "610": "▁Be", "611": "pect", "612": "▁dec", "613": "▁play", "614": "ange", "615": "▁att", "616": "▁pers", "617": "ways", "618": "▁high", "619": "▁hand", "620": "▁next", "621": "▁cons", "622": "▁own", "623": "▁inv", "624": "ower", "625": "▁ind", "626": "ert", "627": "ng", "628": "ave", "629": "▁year", "630": "▁big", "631": "ating", "632": "▁world", "633": "▁rel", "634": "▁sure", "635": "▁tra", "636": "ew", "637": "ered", "638": "▁fin", "639": "▁Well", "640": "▁sl", "641": "▁doing", "642": "bs", "643": "▁set", "644": "▁rec", "645": "ual", "646": "cial", "647": "▁ph", "648": "erm", "649": "▁love", "650": "ph", "651": "▁real", "652": "▁last", "653": "ict", "654": "▁bo", "655": "▁ra", "656": "ible", "657": "▁wr", "658": "mer", "659": "▁count", "660": "ities", "661": "▁always", "662": "inet", "663": "ments", "664": "uc", "665": "▁might", "666": "▁inter", "667": "▁video", "668": "gin", "669": "▁tell", "670": "▁never", "671": "vent", "672": "▁import", "673": "ied", "674": "▁sy", "675": "▁How", "676": "ically", "677": "ought", "678": "▁thir", "679": "▁rep", "680": "ks", "681": "ib", "682": "▁fam", "683": "ject", "684": "▁bas", "685": "▁She", "686": "▁give", "687": "akes", "688": "▁ninet", "689": "▁reg", "690": "▁min", "691": "▁op", "692": "▁def", "693": "▁didn", "694": "te", "695": "▁cour", "696": "▁why", "697": "▁ent", "698": "▁place", "699": "▁ins", "700": "▁car", "701": "ather", "702": "▁person", "703": "ular", "704": "▁inst", "705": "▁prod", "706": "lect", "707": "▁Al", "708": "▁today", "709": "▁bec", "710": "▁sur", "711": "▁All", "712": "▁another", "713": "▁bus", "714": "▁keep", "715": "ell", "716": "ese", "717": "riend", "718": "▁quest", "719": "▁talk", "720": "als", "721": "ings", "722": "▁mon", "723": "cond", "724": "old", "725": "▁acc", "726": "▁la", "727": "▁num", "728": "ident", "729": "▁che", "730": "iness", "731": "▁turn", "732": "▁ear", "733": "▁No", "734": "ousand", "735": "▁better", "736": "ific", "737": "▁loo", "738": "▁gl", "739": "oc", "740": "▁important", "741": "ited", "742": "▁An", "743": "▁thousand", "744": "ility", "745": "llow", "746": "▁used", "747": "▁gen", "748": "▁sim", "749": "li", "750": "▁happen", "751": "▁Un", "752": "▁Let", "753": "air", "754": "ock", "755": "ably", "756": "gg", "757": "▁watch", "758": "▁For", "759": "▁sw", "760": "ren", "761": "ute", "762": "ever", "763": "▁pol", "764": "▁sch", "765": "▁When", "766": "▁such", "767": "▁fif", "768": "▁home", "769": "▁cle", "770": "▁contin", "771": "ouse", "772": "▁friend", "773": "uring", "774": "▁Okay", "775": "gr", "776": "▁able", "777": "▁stud", "778": "▁eff", "779": "hip", "780": "body", "781": "▁top", "782": "ness", "783": "▁exper", "784": "▁pret", "785": "▁both", "786": "▁done", "787": "cri", "788": "▁mark", "789": "▁while", "790": "▁old", "791": "ros", "792": "ont", "793": "▁second", "794": "ative", "795": "▁thought", "796": "▁best", "797": "▁found", "798": "iew", "799": "▁belie", "800": "▁each", "801": "erest", "802": "▁tri", "803": "▁eas", "804": "▁ca", "805": "▁fact", "806": "▁care", "807": "▁fun", "808": "atter", "809": "ures", "810": "▁head", "811": "▁lear", "812": "▁water", "813": "▁hard", "814": "▁few", "815": "▁side", "816": "ween", "817": "▁exp", "818": "▁away", "819": "its", "820": "▁ext", "821": "lud", "822": "▁run", "823": "▁trans", "824": "ince", "825": "▁sk", "826": "▁open", "827": "cus", "828": "▁between", "829": "▁called", "830": "▁wee", "831": "▁pretty", "832": "ason", "833": "▁far", "834": "ember", "835": "omm", "836": "▁interest", "837": "any", "838": "ner", "839": "uff", "840": "▁pres", "841": "▁cur", "842": "▁child", "843": "ee", "844": "▁toget", "845": "▁together", "846": "olog", "847": "▁God", "848": "ond", "849": "▁char", "850": "▁looking", "851": "stem", "852": "az", "853": "cent", "854": "▁ob", "855": "▁ass", "856": "land", "857": "▁doesn", "858": "▁business", "859": "▁course", "860": "▁ten", "861": "ps", "862": "arch", "863": "ced", "864": "ms", "865": "ize", "866": "nce", "867": "▁ref", "868": "▁name", "869": "ross", "870": "▁grow", "871": "oney", "872": "▁went", "873": "ics", "874": "teen", "875": "▁cou", "876": "▁prob", "877": "▁ret", "878": "▁guys", "879": "▁came", "880": "ash", "881": "led", "882": "▁Eur", "883": "ues", "884": "▁ide", "885": "gan", "886": "▁everything", "887": "▁getting", "888": "▁ask", "889": "▁cor", "890": "▁build", "891": "▁sign", "892": "▁small", "893": "uck", "894": "▁el", "895": "▁col", "896": "▁Is", "897": "ational", "898": "stand", "899": "cy", "900": "▁conf", "901": "der", "902": "▁bre", "903": "▁cap", "904": "▁mod", "905": "ets", "906": "ike", "907": "▁number", "908": "▁comple", "909": "ertain", "910": "▁ever", "911": "▁coll", "912": "▁hum", "913": "▁Europe", "914": "▁cre", "915": "▁met", "916": "▁exam", "917": "▁move", "918": "▁pass", "919": "▁left", "920": "▁system", "921": "▁includ", "922": "▁Thank", "923": "cept", "924": "▁wom", "925": "▁product", "926": "ten", "927": "▁rest", "928": "▁probably", "929": "▁dri", "930": "▁Do", "931": "▁gener", "932": "▁anything", "933": "▁lar", "934": "▁My", "935": "▁school", "936": "▁lead", "937": "▁sub", "938": "▁ty", "939": "▁plan", "940": "▁seem", "941": "▁whole", "942": "irect", "943": "▁light", "944": "▁must", "945": "▁mom", "946": "▁opp", "947": "▁support", "948": "▁family", "949": "ices", "950": "amp", "951": "▁proble", "952": "▁dr", "953": "ready", "954": "▁using", "955": "ense", "956": "▁prov", "957": "ush", "958": "ax", "959": "▁power", "960": "▁Re", "961": "alth", "962": "▁ev", "963": "▁stand", "964": "▁war", "965": "ts", "966": "▁", "967": "e", "968": "t", "969": "o", "970": "a", "971": "n", "972": "i", "973": "s", "974": "r", "975": "h", "976": "l", "977": "d", "978": "u", "979": "c", "980": "m", "981": "y", "982": "g", "983": "w", "984": "f", "985": "p", "986": ".", "987": "b", "988": ",", "989": "v", "990": "k", "991": "'", "992": "I", "993": "T", "994": "A", "995": "S", "996": "x", "997": "W", "998": "j", "999": "B", "1000": "C", "1001": "H", "1002": "?", "1003": "M", "1004": "O", "1005": "Y", "1006": "N", "1007": "P", "1008": "E", "1009": "q", "1010": "L", "1011": "D", "1012": "z", "1013": "G", "1014": "F", "1015": "R", "1016": "!", "1017": "J", "1018": "U", "1019": "K", "1020": "V", "1021": "Q", "1022": "Z", "1023": "X"} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/convert-parakeet.py b/convert/parakeet-tdt-ctc-110m/coreml/convert-parakeet.py deleted file mode 100644 index 6cd1c871c81f26b9449bf6313077afe8e27527d9..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/convert-parakeet.py +++ /dev/null @@ -1,697 +0,0 @@ -#!/usr/bin/env python3 -"""CLI for exporting Parakeet TDT-CTC 110M Hybrid components to CoreML.""" -from __future__ import annotations - -import json -from dataclasses import asdict -from pathlib import Path -from typing import Dict, Optional, Tuple - -import coremltools as ct -import numpy as np -import soundfile as sf -import torch -import typer - -import nemo.collections.asr as nemo_asr - -from individual_components import ( - CTCHeadWrapper, - DecoderWrapper, - EncoderWrapper, - ExportSettings, - JointWrapper, - JointDecisionWrapper, - JointDecisionSingleStep, - PreprocessorWrapper, - MelEncoderWrapper, - _coreml_convert, -) - -DEFAULT_MODEL_ID = "nvidia/parakeet-tdt_ctc-110m" -AUTHOR = "Fluid Inference" - - -def _compute_length(seconds: float, sample_rate: int) -> int: - return int(round(seconds * sample_rate)) - - -def _prepare_audio( - validation_audio: Optional[Path], - sample_rate: int, - max_samples: int, - seed: Optional[int], -) -> torch.Tensor: - if validation_audio is None: - if seed is not None: - torch.manual_seed(seed) - audio = torch.randn(1, max_samples, dtype=torch.float32) - return audio - - data, sr = sf.read(str(validation_audio), dtype="float32") - if sr != sample_rate: - raise typer.BadParameter( - f"Validation audio sample rate {sr} does not match model rate {sample_rate}" - ) - - if data.ndim > 1: - data = data[:, 0] - - if data.size == 0: - raise typer.BadParameter("Validation audio is empty") - - if data.size < max_samples: - pad_width = max_samples - data.size - data = np.pad(data, (0, pad_width)) - elif data.size > max_samples: - data = data[:max_samples] - - audio = torch.from_numpy(data).unsqueeze(0).to(dtype=torch.float32) - return audio - - -def _save_mlpackage(model: ct.models.MLModel, path: Path, description: str) -> None: - # Ensure iOS 17+ target for MLProgram ops and ANE readiness - try: - model.minimum_deployment_target = ct.target.iOS17 - except Exception: - pass - model.short_description = description - model.author = AUTHOR - path.parent.mkdir(parents=True, exist_ok=True) - model.save(str(path)) - - -def _tensor_shape(tensor: torch.Tensor) -> Tuple[int, ...]: - return tuple(int(dim) for dim in tensor.shape) - - -def _parse_compute_units(name: str) -> ct.ComputeUnit: - """Parse a human-friendly compute units string into ct.ComputeUnit. - - Accepted (case-insensitive): ALL, CPU_ONLY, CPU_AND_GPU, CPU_AND_NE. - """ - normalized = str(name).strip().upper() - mapping = { - "ALL": ct.ComputeUnit.ALL, - "CPU_ONLY": ct.ComputeUnit.CPU_ONLY, - "CPU_AND_GPU": ct.ComputeUnit.CPU_AND_GPU, - "CPU_AND_NE": ct.ComputeUnit.CPU_AND_NE, - "CPU_AND_NEURALENGINE": ct.ComputeUnit.CPU_AND_NE, - } - if normalized not in mapping: - raise typer.BadParameter( - f"Unknown compute units '{name}'. Choose from: " + ", ".join(mapping.keys()) - ) - return mapping[normalized] - - -def _parse_compute_precision(name: Optional[str]) -> Optional[ct.precision]: - """Parse compute precision string into ct.precision or None. - - Accepted (case-insensitive): FLOAT32, FLOAT16. If None/empty, returns None (tool default). - """ - if name is None: - return None - normalized = str(name).strip().upper() - if normalized == "": - return None - mapping = { - "FLOAT32": ct.precision.FLOAT32, - "FLOAT16": ct.precision.FLOAT16, - } - if normalized not in mapping: - raise typer.BadParameter( - f"Unknown compute precision '{name}'. Choose from: " + ", ".join(mapping.keys()) - ) - return mapping[normalized] - - -# Fixed export choices: CPU_ONLY + FP32, min target iOS17 - - -app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) - - -@app.command() -def convert( - nemo_path: Optional[Path] = typer.Option( - None, - "--nemo-path", - exists=True, - resolve_path=True, - help="Path to parakeet-tdt_ctc-110m .nemo checkpoint (skip to auto-download)", - ), - model_id: str = typer.Option( - DEFAULT_MODEL_ID, - "--model-id", - help="Model identifier to download when --nemo-path is omitted", - ), - output_dir: Path = typer.Option(Path("parakeet_110m_coreml"), help="Directory where mlpackages and metadata will be written"), - preprocessor_cu: str = typer.Option( - "CPU_ONLY", - "--preprocessor-cu", - help="Compute units for preprocessor (default CPU_ONLY)", - ), - mel_encoder_cu: str = typer.Option( - "CPU_ONLY", - "--mel-encoder-cu", - help="Compute units for fused mel+encoder (default CPU_ONLY)", - ), - compute_precision: Optional[str] = typer.Option( - None, - "--compute-precision", - help="Export precision: FLOAT32 (default) or FLOAT16 to shrink non-quantized weights.", - ), -) -> None: - """Export all Parakeet TDT-CTC 110M Hybrid sub-modules to CoreML with a fixed 15-second window. - - This exports both CTC and TDT components from the hybrid model. - """ - # Runtime CoreML contract keeps U=1 so the prediction net matches the streaming decoder. - export_settings = ExportSettings( - output_dir=output_dir, - compute_units=ct.ComputeUnit.CPU_ONLY, # Default: CPU-only for all components - deployment_target=ct.target.iOS17, # iOS 17+ features and kernels - compute_precision=_parse_compute_precision(compute_precision), - max_audio_seconds=15.0, - max_symbol_steps=1, - ) - - typer.echo("Export configuration:") - typer.echo(asdict(export_settings)) - - output_dir.mkdir(parents=True, exist_ok=True) - pre_cu = _parse_compute_units(preprocessor_cu) - melenc_cu = _parse_compute_units(mel_encoder_cu) - - if nemo_path is not None: - typer.echo(f"Loading NeMo model from {nemo_path}
") - # 110M is a hybrid model: EncDecHybridRNNTCTCBPEModel - asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.restore_from( - str(nemo_path), map_location="cpu" - ) - checkpoint_meta = { - "type": "file", - "path": str(nemo_path), - } - else: - typer.echo(f"Downloading NeMo model via {model_id}
") - # 110M is a hybrid model: EncDecHybridRNNTCTCBPEModel - asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained( - model_id, map_location="cpu" - ) - checkpoint_meta = { - "type": "pretrained", - "model_id": model_id, - } - asr_model.eval() - - sample_rate = int(asr_model.cfg.preprocessor.sample_rate) - max_samples = _compute_length(export_settings.max_audio_seconds, sample_rate) - - # Look for a bundled 15s 16kHz audio file - default_audio = (Path(__file__).parent / "audio" / "yc_first_minute_16k_15s.wav").resolve() - if default_audio.exists(): - typer.echo(f"Using trace audio: {default_audio}") - audio_tensor = _prepare_audio(default_audio, sample_rate, max_samples, seed=None) - else: - typer.echo("No trace audio found, using random noise for tracing") - audio_tensor = _prepare_audio(None, sample_rate, max_samples, seed=42) - audio_length = torch.tensor([max_samples], dtype=torch.int32) - - preprocessor = PreprocessorWrapper(asr_model.preprocessor.eval()) - encoder = EncoderWrapper(asr_model.encoder.eval()) - decoder = DecoderWrapper(asr_model.decoder.eval()) - joint = JointWrapper(asr_model.joint.eval()) - # CTC head for hybrid model - ctc_head = CTCHeadWrapper(asr_model.ctc_decoder.eval()) - - decoder_export_flag = getattr(asr_model.decoder, "_rnnt_export", False) - asr_model.decoder._rnnt_export = True - - try: - with torch.inference_mode(): - mel_ref, mel_length_ref = preprocessor(audio_tensor, audio_length) - mel_length_ref = mel_length_ref.to(dtype=torch.int32) - encoder_ref, encoder_length_ref = encoder(mel_ref, mel_length_ref) - encoder_length_ref = encoder_length_ref.to(dtype=torch.int32) - # CTC log probs - ctc_log_probs_ref = ctc_head(encoder_ref) - - # Clone Tensors to drop the inference tensor flag before tracing - mel_ref = mel_ref.clone() - mel_length_ref = mel_length_ref.clone() - encoder_ref = encoder_ref.clone() - encoder_length_ref = encoder_length_ref.clone() - ctc_log_probs_ref = ctc_log_probs_ref.clone() - - vocab_size = int(asr_model.tokenizer.vocab_size) - num_extra = int(asr_model.joint.num_extra_outputs) - decoder_hidden = int(asr_model.decoder.pred_hidden) - decoder_layers = int(asr_model.decoder.pred_rnn_layers) - - typer.echo(f"Model info:") - typer.echo(f" Vocab size: {vocab_size}") - typer.echo(f" Num extra (duration bins): {num_extra}") - typer.echo(f" Decoder hidden: {decoder_hidden}") - typer.echo(f" Decoder layers: {decoder_layers}") - typer.echo(f" Encoder output shape: {_tensor_shape(encoder_ref)}") - - targets = torch.full( - (1, export_settings.max_symbol_steps), - fill_value=asr_model.decoder.blank_idx, - dtype=torch.int32, - ) - target_lengths = torch.tensor( - [export_settings.max_symbol_steps], dtype=torch.int32 - ) - zero_state = torch.zeros( - decoder_layers, - 1, - decoder_hidden, - dtype=torch.float32, - ) - - with torch.inference_mode(): - decoder_ref, h_ref, c_ref = decoder(targets, target_lengths, zero_state, zero_state) - joint_ref = joint(encoder_ref, decoder_ref) - - decoder_ref = decoder_ref.clone() - h_ref = h_ref.clone() - c_ref = c_ref.clone() - joint_ref = joint_ref.clone() - - typer.echo(f" Decoder output shape: {_tensor_shape(decoder_ref)}") - typer.echo(f" Joint output shape: {_tensor_shape(joint_ref)}") - typer.echo(f" CTC log probs shape: {_tensor_shape(ctc_log_probs_ref)}") - - typer.echo("Tracing and converting preprocessor
") - # Ensure tracing happens on CPU explicitly - preprocessor = preprocessor.cpu() - audio_tensor = audio_tensor.cpu() - audio_length = audio_length.cpu() - traced_preprocessor = torch.jit.trace( - preprocessor, (audio_tensor, audio_length), strict=False - ) - traced_preprocessor.eval() - preprocessor_inputs = [ - # Allow variable-length audio up to the fixed 15s window using RangeDim - ct.TensorType( - name="audio", - shape=(1, ct.RangeDim(1, max_samples)), - dtype=np.float32, - ), - ct.TensorType(name="audio_length", shape=(1,), dtype=np.int32), - ] - preprocessor_outputs = [ - ct.TensorType(name="mel_features", dtype=np.float32), - ct.TensorType(name="mel_length", dtype=np.int32), - ] - # Preprocessor compute units (parametrized; default CPU_ONLY) - preprocessor_model = _coreml_convert( - traced_preprocessor, - preprocessor_inputs, - preprocessor_outputs, - export_settings, - compute_units_override=pre_cu, - ) - preprocessor_path = output_dir / "parakeet_preprocessor.mlpackage" - _save_mlpackage( - preprocessor_model, - preprocessor_path, - "Parakeet 110M preprocessor (15 s window)", - ) - - typer.echo("Tracing and converting encoder
") - traced_encoder = torch.jit.trace( - encoder, (mel_ref, mel_length_ref), strict=False - ) - traced_encoder.eval() - encoder_inputs = [ - ct.TensorType(name="mel_features", shape=_tensor_shape(mel_ref), dtype=np.float32), - ct.TensorType(name="mel_length", shape=(1,), dtype=np.int32), - ] - encoder_outputs = [ - ct.TensorType(name="encoder_output", dtype=np.float32), - ct.TensorType(name="encoder_length", dtype=np.int32), - ] - # Encoder: CPU only - encoder_model = _coreml_convert( - traced_encoder, - encoder_inputs, - encoder_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - encoder_path = output_dir / "parakeet_encoder.mlpackage" - _save_mlpackage( - encoder_model, - encoder_path, - "Parakeet 110M encoder (15 s window)", - ) - - # CTC Head for hybrid model - typer.echo("Tracing and converting CTC head
") - traced_ctc_head = torch.jit.trace( - ctc_head, (encoder_ref,), strict=False - ) - traced_ctc_head.eval() - ctc_head_inputs = [ - ct.TensorType(name="encoder_output", shape=_tensor_shape(encoder_ref), dtype=np.float32), - ] - ctc_head_outputs = [ - ct.TensorType(name="ctc_logits", dtype=np.float32), - ] - ctc_head_model = _coreml_convert( - traced_ctc_head, - ctc_head_inputs, - ctc_head_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - ctc_head_path = output_dir / "parakeet_ctc_head.mlpackage" - _save_mlpackage( - ctc_head_model, - ctc_head_path, - "Parakeet 110M CTC decoder head", - ) - - # Optional fused export: Preprocessor + Encoder - typer.echo("Tracing and converting fused mel+encoder
") - mel_encoder = MelEncoderWrapper(preprocessor, encoder) - traced_mel_encoder = torch.jit.trace( - mel_encoder, (audio_tensor, audio_length), strict=False - ) - traced_mel_encoder.eval() - mel_encoder_inputs = [ - # Keep fixed 15s window for fused Mel+Encoder - ct.TensorType(name="audio", shape=(1, max_samples), dtype=np.float32), - ct.TensorType(name="audio_length", shape=(1,), dtype=np.int32), - ] - mel_encoder_outputs = [ - ct.TensorType(name="encoder_output", dtype=np.float32), - ct.TensorType(name="encoder_length", dtype=np.int32), - ] - # Fused mel+encoder compute units (parametrized; default CPU_ONLY) - mel_encoder_model = _coreml_convert( - traced_mel_encoder, - mel_encoder_inputs, - mel_encoder_outputs, - export_settings, - compute_units_override=melenc_cu, - ) - mel_encoder_path = output_dir / "parakeet_mel_encoder.mlpackage" - _save_mlpackage( - mel_encoder_model, - mel_encoder_path, - "Parakeet 110M fused Mel+Encoder (15 s window)", - ) - - typer.echo("Tracing and converting decoder
") - traced_decoder = torch.jit.trace( - decoder, - (targets, target_lengths, zero_state, zero_state), - strict=False, - ) - traced_decoder.eval() - decoder_inputs = [ - ct.TensorType(name="targets", shape=_tensor_shape(targets), dtype=np.int32), - ct.TensorType(name="target_length", shape=(1,), dtype=np.int32), - ct.TensorType(name="h_in", shape=_tensor_shape(zero_state), dtype=np.float32), - ct.TensorType(name="c_in", shape=_tensor_shape(zero_state), dtype=np.float32), - ] - decoder_outputs = [ - ct.TensorType(name="decoder", dtype=np.float32), - ct.TensorType(name="h_out", dtype=np.float32), - ct.TensorType(name="c_out", dtype=np.float32), - ] - # Decoder: CPU only - decoder_model = _coreml_convert( - traced_decoder, - decoder_inputs, - decoder_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - decoder_path = output_dir / "parakeet_decoder.mlpackage" - _save_mlpackage( - decoder_model, - decoder_path, - "Parakeet 110M decoder (RNNT prediction network)", - ) - - typer.echo("Tracing and converting joint
") - traced_joint = torch.jit.trace( - joint, - (encoder_ref, decoder_ref), - strict=False, - ) - traced_joint.eval() - joint_inputs = [ - ct.TensorType(name="encoder", shape=_tensor_shape(encoder_ref), dtype=np.float32), - ct.TensorType(name="decoder", shape=_tensor_shape(decoder_ref), dtype=np.float32), - ] - joint_outputs = [ - ct.TensorType(name="logits", dtype=np.float32), - ] - # Joint: CPU only - joint_model = _coreml_convert( - traced_joint, - joint_inputs, - joint_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - joint_path = output_dir / "parakeet_joint.mlpackage" - _save_mlpackage( - joint_model, - joint_path, - "Parakeet 110M joint network (RNNT)", - ) - - # Joint + decision head (split logits, softmax, argmax) - typer.echo("Tracing and converting joint decision head
") - vocab_size = int(asr_model.tokenizer.vocab_size) - num_extra = int(asr_model.joint.num_extra_outputs) - joint_decision = JointDecisionWrapper(joint, vocab_size=vocab_size, num_extra=num_extra) - traced_joint_decision = torch.jit.trace( - joint_decision, - (encoder_ref, decoder_ref), - strict=False, - ) - traced_joint_decision.eval() - joint_decision_inputs = [ - ct.TensorType(name="encoder", shape=_tensor_shape(encoder_ref), dtype=np.float32), - ct.TensorType(name="decoder", shape=_tensor_shape(decoder_ref), dtype=np.float32), - ] - joint_decision_outputs = [ - ct.TensorType(name="token_id", dtype=np.int32), - ct.TensorType(name="token_prob", dtype=np.float32), - ct.TensorType(name="duration", dtype=np.int32), - ] - # JointDecision: CPU only - joint_decision_model = _coreml_convert( - traced_joint_decision, - joint_decision_inputs, - joint_decision_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - joint_decision_path = output_dir / "parakeet_joint_decision.mlpackage" - _save_mlpackage( - joint_decision_model, - joint_decision_path, - "Parakeet 110M joint + decision head (split, softmax, argmax)", - ) - - # Single-step JointDecision for [1,512,1] x [1,640,1] -> [1,1,1] - # Note: 110M encoder dim is 512 (not 1024 like 0.6B) - typer.echo("Tracing and converting single-step joint decision
") - jd_single = JointDecisionSingleStep(joint, vocab_size=vocab_size, num_extra=num_extra) - # Create single-step slices from refs - enc_step = encoder_ref[:, :, :1].contiguous() - dec_step = decoder_ref[:, :, :1].contiguous() - traced_jd_single = torch.jit.trace( - jd_single, - (enc_step, dec_step), - strict=False, - ) - traced_jd_single.eval() - jd_single_inputs = [ - ct.TensorType(name="encoder_step", shape=(1, enc_step.shape[1], 1), dtype=np.float32), - ct.TensorType(name="decoder_step", shape=(1, dec_step.shape[1], 1), dtype=np.float32), - ] - jd_single_outputs = [ - ct.TensorType(name="token_id", dtype=np.int32), - ct.TensorType(name="token_prob", dtype=np.float32), - ct.TensorType(name="duration", dtype=np.int32), - ct.TensorType(name="top_k_ids", dtype=np.int32), - ct.TensorType(name="top_k_logits", dtype=np.float32), - ] - # Single-step JointDecision: CPU only - jd_single_model = _coreml_convert( - traced_jd_single, - jd_single_inputs, - jd_single_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - jd_single_path = output_dir / "parakeet_joint_decision_single_step.mlpackage" - _save_mlpackage( - jd_single_model, - jd_single_path, - "Parakeet 110M single-step joint decision (current frame)", - ) - - # Export vocabulary - typer.echo("Exporting vocabulary
") - vocab_path = output_dir / "vocab.json" - vocab_dict = { - "vocab_size": vocab_size, - "blank_id": int(asr_model.decoder.blank_idx), - "tokens": asr_model.tokenizer.vocab, - } - vocab_path.write_text(json.dumps(vocab_dict, indent=2, ensure_ascii=False)) - - metadata: Dict[str, object] = { - "model_id": model_id, - "model_type": "hybrid_rnnt_ctc", - "sample_rate": sample_rate, - "max_audio_seconds": export_settings.max_audio_seconds, - "max_audio_samples": max_samples, - "max_symbol_steps": export_settings.max_symbol_steps, - "vocab_size": vocab_size, - "joint_extra_outputs": num_extra, - "encoder_dim": int(encoder_ref.shape[1]), # 512 for 110M - "decoder_dim": int(decoder_ref.shape[1]), # 640 - "decoder_hidden": decoder_hidden, - "decoder_layers": decoder_layers, - "blank_id": int(asr_model.decoder.blank_idx), - "checkpoint": checkpoint_meta, - "coreml": { - "compute_units": export_settings.compute_units.name, - "compute_precision": ( - export_settings.compute_precision.name - if export_settings.compute_precision is not None - else "FLOAT32" - ), - }, - "components": { - "preprocessor": { - "inputs": { - "audio_signal": list(_tensor_shape(audio_tensor)), - "audio_length": [1], - }, - "outputs": { - "mel": list(_tensor_shape(mel_ref)), - "mel_length": [1], - }, - "path": preprocessor_path.name, - }, - "encoder": { - "inputs": { - "mel": list(_tensor_shape(mel_ref)), - "mel_length": [1], - }, - "outputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "encoder_length": [1], - }, - "path": encoder_path.name, - }, - "ctc_head": { - "inputs": { - "encoder": list(_tensor_shape(encoder_ref)), - }, - "outputs": { - "log_probs": list(_tensor_shape(ctc_log_probs_ref)), - }, - "path": ctc_head_path.name, - }, - "mel_encoder": { - "inputs": { - "audio_signal": [1, max_samples], - "audio_length": [1], - }, - "outputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "encoder_length": [1], - }, - "path": mel_encoder_path.name, - }, - "decoder": { - "inputs": { - "targets": list(_tensor_shape(targets)), - "target_length": [1], - "h_in": list(_tensor_shape(zero_state)), - "c_in": list(_tensor_shape(zero_state)), - }, - "outputs": { - "decoder": list(_tensor_shape(decoder_ref)), - "h_out": list(_tensor_shape(h_ref)), - "c_out": list(_tensor_shape(c_ref)), - }, - "path": decoder_path.name, - }, - "joint": { - "inputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "decoder": list(_tensor_shape(decoder_ref)), - }, - "outputs": { - "logits": list(_tensor_shape(joint_ref)), - }, - "path": joint_path.name, - }, - "joint_decision": { - "inputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "decoder": list(_tensor_shape(decoder_ref)), - }, - "outputs": { - "token_id": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[2], - _tensor_shape(decoder_ref)[2], - ], - "token_prob": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[2], - _tensor_shape(decoder_ref)[2], - ], - "duration": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[2], - _tensor_shape(decoder_ref)[2], - ], - }, - "path": joint_decision_path.name, - }, - "joint_decision_single_step": { - "inputs": { - "encoder_step": [1, int(encoder_ref.shape[1]), 1], - "decoder_step": [1, int(decoder_ref.shape[1]), 1], - }, - "outputs": { - "token_id": [1, 1, 1], - "token_prob": [1, 1, 1], - "duration": [1, 1, 1], - "top_k_ids": [1, 1, 1, 64], - "top_k_logits": [1, 1, 1, 64], - }, - "path": jd_single_path.name, - }, - }, - } - - metadata_path = output_dir / "metadata.json" - metadata_path.write_text(json.dumps(metadata, indent=2)) - typer.echo(f"Export complete. Metadata written to {metadata_path}") - - finally: - asr_model.decoder._rnnt_export = decoder_export_flag - - -if __name__ == "__main__": - app() diff --git a/convert/parakeet-tdt-ctc-110m/coreml/hybrid_earnings_benchmark.json b/convert/parakeet-tdt-ctc-110m/coreml/hybrid_earnings_benchmark.json deleted file mode 100644 index 3dd6bc7551d07ef8a966ba06590d2454e4324983..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/hybrid_earnings_benchmark.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "approach" : "single-encoder", - "model" : "parakeet-tdt-ctc-110m-hybrid", - "results" : [ - { - "audioLength" : 15, - "ctcDetections" : [ - { - "endTime" : 6.0800000000000001, - "inReference" : true, - "score" : -8.3699999999999992, - "source" : "ctc", - "startTime" : 4.96, - "word" : "LATAM Airlines" - } - ], - "dictFound" : 1, - "dictTotal" : 1, - "fileId" : "4329526_chunk0", - "hypothesis" : "goodday everyone and welcome to latam airlines group earnings release confonference call just as a reminder this conference is being recorded lat tam airlines group eararnings releaseed for the", - "processingTime" : 0.070000000000000007, - "reference" : "good day everyone and welcome to latam airlines group earnings release conference call just as a reminder this conference is being recorded latam airlines group earnings released for the", - "wer" : 24.140000000000001 - } - ], - "summary" : { - "avgWer" : 24.140000000000001, - "dictPass" : 1, - "dictRate" : 100, - "dictTotal" : 1, - "totalAudioDuration" : 15, - "totalProcessingTime" : 0.070000000000000007, - "totalTests" : 1 - } -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/individual_components.py b/convert/parakeet-tdt-ctc-110m/coreml/individual_components.py deleted file mode 100644 index 0f6ae75f46ab359edc5c6dd6799dccf0b04a7ed7..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/individual_components.py +++ /dev/null @@ -1,265 +0,0 @@ -#!/usr/bin/env python3 -"""Export Parakeet TDT-CTC 110M Hybrid RNNT components into CoreML and validate outputs.""" -from __future__ import annotations - -from dataclasses import dataclass -from pathlib import Path -from typing import Optional, Tuple - -import coremltools as ct -import torch - - -@dataclass -class ExportSettings: - output_dir: Path - compute_units: ct.ComputeUnit - deployment_target: Optional[ct.target.iOS17] - compute_precision: Optional[ct.precision] - max_audio_seconds: float - max_symbol_steps: int - - -@dataclass -class ValidationSettings: - audio_path: Optional[Path] - seconds: float - seed: Optional[int] - rtol: float - atol: float - skip: bool - - -@dataclass -class ValidationDiff: - name: str - max_abs_diff: float - max_rel_diff: float - - -@dataclass -class ValidationResult: - source: str - audio_num_samples: int - audio_seconds: float - token_length: int - atol: float - rtol: float - diffs: Tuple[ValidationDiff, ...] - - -class PreprocessorWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, audio_signal: torch.Tensor, length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - mel, mel_length = self.module(input_signal=audio_signal, length=length.to(dtype=torch.long)) - return mel, mel_length - - -class EncoderWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, features: torch.Tensor, length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - encoded, encoded_lengths = self.module(audio_signal=features, length=length.to(dtype=torch.long)) - return encoded, encoded_lengths - - -class DecoderWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward( - self, - targets: torch.Tensor, - target_lengths: torch.Tensor, - h_in: torch.Tensor, - c_in: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - state = [h_in, c_in] - decoder_output, _, new_state = self.module( - targets=targets.to(dtype=torch.long), - target_length=target_lengths.to(dtype=torch.long), - states=state, - ) - return decoder_output, new_state[0], new_state[1] - - -class JointWrapper(torch.nn.Module): - """Joint network for 110M hybrid model. - - Note: The 110M model has encoder_dim=512 and decoder_dim=640. - The joint network projects both to 640, then combines them. - """ - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor) -> torch.Tensor: - # Input: encoder_outputs [B, D_enc, T], decoder_outputs [B, D_dec, U] - # For 110M: D_enc=512, D_dec=640 - # Transpose to match what projection layers expect - encoder_outputs = encoder_outputs.transpose(1, 2) # [B, T, D_enc] - decoder_outputs = decoder_outputs.transpose(1, 2) # [B, U, D_dec] - - # Apply projections - enc_proj = self.module.enc(encoder_outputs) # [B, T, 640] - dec_proj = self.module.pred(decoder_outputs) # [B, U, 640] - - # Explicit broadcasting along T and U to avoid converter ambiguity - x = enc_proj.unsqueeze(2) + dec_proj.unsqueeze(1) # [B, T, U, 640] - x = self.module.joint_net[0](x) # ReLU - x = self.module.joint_net[1](x) # Dropout (no-op in eval) - out = self.module.joint_net[2](x) # Linear -> logits [B, T, U, vocab+1+durations] - return out - - -class CTCHeadWrapper(torch.nn.Module): - """CTC decoder head for 110M hybrid model. - - Takes encoder output and produces log probabilities over vocabulary. - The NeMo CTC decoder (ConvASRDecoder) uses Conv1d so it expects [B, D, T] format. - """ - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, encoder_outputs: torch.Tensor) -> torch.Tensor: - # Input: encoder_outputs [B, D_enc, T] - already in the format CTC decoder expects - # The NeMo CTC decoder uses Conv1d internally, so it expects [B, D, T] - # Output: log probabilities [B, T, vocab+1] - log_probs = self.module(encoder_output=encoder_outputs) - return log_probs - - -class MelEncoderWrapper(torch.nn.Module): - """Fused wrapper: waveform -> mel -> encoder. - - Inputs: - - audio_signal: [B, S] - - audio_length: [B] - - Outputs: - - encoder: [B, D, T_enc] - - encoder_length: [B] - """ - def __init__(self, preprocessor: PreprocessorWrapper, encoder: EncoderWrapper) -> None: - super().__init__() - self.preprocessor = preprocessor - self.encoder = encoder - - def forward(self, audio_signal: torch.Tensor, audio_length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - mel, mel_length = self.preprocessor(audio_signal, audio_length) - encoded, enc_len = self.encoder(mel, mel_length.to(dtype=torch.int32)) - return encoded, enc_len - - -class JointDecisionWrapper(torch.nn.Module): - """Joint + decision head: outputs label id, label prob, duration frames. - - Splits joint logits into token logits and duration logits, applies softmax - over tokens, argmax for both heads, and gathers probability of the chosen token. - - Inputs: - - encoder_outputs: [B, D, T] - - decoder_outputs: [B, D, U] - - Returns: - - token_id: [B, T, U] int32 - - token_prob: [B, T, U] float32 - - duration: [B, T, U] int32 (frames; for v3 bins=[0,1,2,3,4]) - """ - def __init__(self, joint: JointWrapper, vocab_size: int, num_extra: int) -> None: - super().__init__() - self.joint = joint - self.vocab_with_blank = int(vocab_size) + 1 - self.num_extra = int(num_extra) - - def forward(self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor): - logits = self.joint(encoder_outputs, decoder_outputs) - token_logits = logits[..., : self.vocab_with_blank] - duration_logits = logits[..., -self.num_extra :] - - # Token selection - token_ids = torch.argmax(token_logits, dim=-1).to(dtype=torch.int32) - token_probs_all = torch.softmax(token_logits, dim=-1) - # gather expects int64 (long) indices; cast only for gather - token_prob = torch.gather( - token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) - ).squeeze(-1) - - # Duration prediction (bins are identity mapping to frames for v3) - duration = torch.argmax(duration_logits, dim=-1).to(dtype=torch.int32) - return token_ids, token_prob, duration - - -class JointDecisionSingleStep(torch.nn.Module): - """Single-step variant for streaming: encoder_step [1, 512, 1] -> [1,1,1]. - - Note: For 110M model, encoder_dim is 512 (not 1024 like 0.6B). - - Inputs: - - encoder_step: [B=1, D=512, T=1] - - decoder_step: [B=1, D=640, U=1] - - Returns: - - token_id: [1, 1, 1] int32 - - token_prob: [1, 1, 1] float32 - - duration: [1, 1, 1] int32 - - top_k_ids: [1, 1, 1, K] int32 - - top_k_logits: [1, 1, 1, K] float32 - """ - def __init__(self, joint: JointWrapper, vocab_size: int, num_extra: int, top_k: int = 64) -> None: - super().__init__() - self.joint = joint - self.vocab_with_blank = int(vocab_size) + 1 - self.num_extra = int(num_extra) - # Emit top-K candidates to enable host-side re-ranking with contextual biasing - self.top_k = int(top_k) - - def forward(self, encoder_step: torch.Tensor, decoder_step: torch.Tensor): - # Reuse JointWrapper which expects [B, D, T] and [B, D, U] - logits = self.joint(encoder_step, decoder_step) # [1, 1, 1, V+extra] - token_logits = logits[..., : self.vocab_with_blank] - duration_logits = logits[..., -self.num_extra :] - - token_ids = torch.argmax(token_logits, dim=-1, keepdim=False).to(dtype=torch.int32) - token_probs_all = torch.softmax(token_logits, dim=-1) - token_prob = torch.gather( - token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) - ).squeeze(-1) - duration = torch.argmax(duration_logits, dim=-1, keepdim=False).to(dtype=torch.int32) - - # Also expose top-K candidates for host-side re-ranking. - # Shapes preserved as [1, 1, 1, K] to match CoreML broadcasting expectations. - # Note: topk expects last dimension; original shape is [1, 1, 1, V]. - topk_logits, topk_ids_long = torch.topk(token_logits, k=min(self.top_k, token_logits.shape[-1]), dim=-1) - topk_ids = topk_ids_long.to(dtype=torch.int32) - return token_ids, token_prob, duration, topk_ids, topk_logits - - -def _coreml_convert( - traced: torch.jit.ScriptModule, - inputs, - outputs, - settings: ExportSettings, - compute_units_override: Optional[ct.ComputeUnit] = None, -) -> ct.models.MLModel: - cu = compute_units_override if compute_units_override is not None else settings.compute_units - kwargs = { - "convert_to": "mlprogram", - "inputs": inputs, - "outputs": outputs, - "compute_units": cu, - } - print("Converting:", traced.__class__.__name__) - print("Conversion kwargs:", kwargs) - if settings.deployment_target is not None: - kwargs["minimum_deployment_target"] = settings.deployment_target - if settings.compute_precision is not None: - kwargs["compute_precision"] = settings.compute_precision - return ct.convert(traced, **kwargs) diff --git a/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/metadata.json b/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/metadata.json deleted file mode 100644 index 6db872ae8a7d463c95dc180210ace0fce019f4a6..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/metadata.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "model_id": "nvidia/parakeet-tdt_ctc-110m", - "model_type": "hybrid_rnnt_ctc", - "sample_rate": 16000, - "max_audio_seconds": 15.0, - "max_audio_samples": 240000, - "max_symbol_steps": 1, - "vocab_size": 1024, - "joint_extra_outputs": 5, - "encoder_dim": 512, - "decoder_dim": 640, - "decoder_hidden": 640, - "decoder_layers": 1, - "blank_id": 1024, - "checkpoint": { - "type": "pretrained", - "model_id": "nvidia/parakeet-tdt_ctc-110m" - }, - "coreml": { - "compute_units": "CPU_ONLY", - "compute_precision": "FLOAT32" - }, - "components": { - "preprocessor": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "path": "parakeet_preprocessor.mlpackage" - }, - "encoder": { - "inputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_encoder.mlpackage" - }, - "ctc_head": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ] - }, - "outputs": { - "log_probs": [ - 1, - 188, - 1025 - ] - }, - "path": "parakeet_ctc_head.mlpackage" - }, - "mel_encoder": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_mel_encoder.mlpackage" - }, - "decoder": { - "inputs": { - "targets": [ - 1, - 1 - ], - "target_length": [ - 1 - ], - "h_in": [ - 1, - 1, - 640 - ], - "c_in": [ - 1, - 1, - 640 - ] - }, - "outputs": { - "decoder": [ - 1, - 640, - 1 - ], - "h_out": [ - 1, - 1, - 640 - ], - "c_out": [ - 1, - 1, - 640 - ] - }, - "path": "parakeet_decoder.mlpackage" - }, - "joint": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "logits": [ - 1, - 188, - 1, - 1030 - ] - }, - "path": "parakeet_joint.mlpackage" - }, - "joint_decision": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 188, - 1 - ], - "token_prob": [ - 1, - 188, - 1 - ], - "duration": [ - 1, - 188, - 1 - ] - }, - "path": "parakeet_joint_decision.mlpackage" - }, - "joint_decision_single_step": { - "inputs": { - "encoder_step": [ - 1, - 512, - 1 - ], - "decoder_step": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 1, - 1 - ], - "token_prob": [ - 1, - 1, - 1 - ], - "duration": [ - 1, - 1, - 1 - ], - "top_k_ids": [ - 1, - 1, - 1, - 64 - ], - "top_k_logits": [ - 1, - 1, - 1, - 64 - ] - }, - "path": "parakeet_joint_decision_single_step.mlpackage" - } - } -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/model.mlmodel b/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/model.mlmodel deleted file mode 100644 index b807d0d0234624c649b82a7bd3e36bd9095fa961..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/model.mlmodel +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:6459b9564e0630f2eec300eb732fceccbc1d2d16f12cb0694ce310d84bfbecf2 -size 3366 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/weights/weight.bin deleted file mode 100644 index 23cedd570125191064c6111997f08c48898245f7..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fb9bead064427ffcb7529c0e3f378e421b4dde8e6d81447b6d1ca3352ca850e1 -size 1051842 diff --git a/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Manifest.json b/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Manifest.json deleted file mode 100644 index 6fa9ee67551a75a4b78fabbd02c58c0b39b247ed..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Manifest.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - 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"▁important", - "ited", - "▁An", - "▁thousand", - "ility", - "llow", - "▁used", - "▁gen", - "▁sim", - "li", - "▁happen", - "▁Un", - "▁Let", - "air", - "ock", - "ably", - "gg", - "▁watch", - "▁For", - "▁sw", - "ren", - "ute", - "ever", - "▁pol", - "▁sch", - "▁When", - "▁such", - "▁fif", - "▁home", - "▁cle", - "▁contin", - "ouse", - "▁friend", - "uring", - "▁Okay", - "gr", - "▁able", - "▁stud", - "▁eff", - "hip", - "body", - "▁top", - "ness", - "▁exper", - "▁pret", - "▁both", - "▁done", - "cri", - "▁mark", - "▁while", - "▁old", - "ros", - "ont", - "▁second", - "ative", - "▁thought", - "▁best", - "▁found", - "iew", - "▁belie", - "▁each", - "erest", - "▁tri", - "▁eas", - "▁ca", - "▁fact", - "▁care", - "▁fun", - "atter", - "ures", - "▁head", - "▁lear", - "▁water", - "▁hard", - "▁few", - "▁side", - "ween", - "▁exp", - "▁away", - "its", - "▁ext", - "lud", - "▁run", - "▁trans", - "ince", - "▁sk", - "▁open", - "cus", - "▁between", - "▁called", - "▁wee", - "▁pretty", - "ason", - "▁far", - "ember", - "omm", - "▁interest", - "any", - "ner", - "uff", - "▁pres", - "▁cur", - "▁child", - "ee", - "▁toget", - "▁together", - "olog", - "▁God", - "ond", - "▁char", - "▁looking", - "stem", - "az", - "cent", - "▁ob", - "▁ass", - "land", - "▁doesn", - "▁business", - "▁course", - "▁ten", - "ps", - "arch", - "ced", - "ms", - "ize", - "nce", - "▁ref", - "▁name", - "ross", - "▁grow", - "oney", - "▁went", - "ics", - "teen", - "▁cou", - "▁prob", - "▁ret", - "▁guys", - "▁came", - "ash", - "led", - "▁Eur", - "ues", - "▁ide", - "gan", - "▁everything", - "▁getting", - "▁ask", - "▁cor", - "▁build", - "▁sign", - "▁small", - "uck", - "▁el", - "▁col", - "▁Is", - "ational", - "stand", - "cy", - "▁conf", - "der", - "▁bre", - "▁cap", - "▁mod", - "ets", - "ike", - "▁number", - "▁comple", - "ertain", - "▁ever", - "▁coll", - "▁hum", - "▁Europe", - "▁cre", - "▁met", - "▁exam", - "▁move", - "▁pass", - "▁left", - "▁system", - "▁includ", - "▁Thank", - "cept", - "▁wom", - "▁product", - "ten", - "▁rest", - "▁probably", - "▁dri", - "▁Do", - "▁gener", - "▁anything", - "▁lar", - "▁My", - "▁school", - "▁lead", - "▁sub", - "▁ty", - "▁plan", - "▁seem", - "▁whole", - "irect", - "▁light", - "▁must", - "▁mom", - "▁opp", - "▁support", - "▁family", - "ices", - "amp", - "▁proble", - "▁dr", - "ready", - "▁using", - "ense", - "▁prov", - "ush", - "ax", - "▁power", - "▁Re", - "alth", - "▁ev", - "▁stand", - "▁war", - "ts", - "▁", - "e", - "t", - "o", - "a", - "n", - "i", - "s", - "r", - "h", - "l", - "d", - "u", - "c", - "m", - "y", - "g", - "w", - "f", - "p", - ".", - "b", - ",", - "v", - "k", - "'", - "I", - "T", - "A", - "S", - "x", - "W", - "j", - "B", - "C", - "H", - "?", - "M", - "O", - "Y", - "N", - "P", - "E", - "q", - "L", - "D", - "z", - "G", - "F", - "R", - "!", - "J", - "U", - "K", - "V", - "Q", - "Z", - "X" - ] -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/coreml/parakeet_ctc_coreml/.DS_Store b/convert/parakeet-tdt-ctc-110m/coreml/parakeet_ctc_coreml/.DS_Store deleted file mode 100644 index 7b37950bb8ce3a439220b30b68fcd5a01d75cb8d..0000000000000000000000000000000000000000 Binary files a/convert/parakeet-tdt-ctc-110m/coreml/parakeet_ctc_coreml/.DS_Store and /dev/null differ diff --git a/convert/parakeet-tdt-ctc-110m/get-pip.py b/convert/parakeet-tdt-ctc-110m/get-pip.py deleted file mode 100644 index 22f29f881487fe04461440ad6bc7b6a2e5e781de..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/get-pip.py +++ /dev/null @@ -1,27368 +0,0 @@ -#!/usr/bin/env python -# -# Hi There! -# -# You may be wondering what this giant blob of binary data here is, you might -# even be worried that we're up to something nefarious (good for you for being -# paranoid!). This is a base85 encoding of a zip file, this zip file contains -# an entire copy of pip (version 25.3). -# -# Pip is a thing that installs packages, pip itself is a package that someone -# might want to install, especially if they're looking to run this get-pip.py -# script. Pip has a lot of code to deal with the security of installing -# packages, various edge cases on various platforms, and other such sort of -# "tribal knowledge" that has been encoded in its code base. Because of this -# we basically include an entire copy of pip inside this blob. We do this -# because the alternatives are attempt to implement a "minipip" that probably -# doesn't do things correctly and has weird edge cases, or compress pip itself -# down into a single file. -# -# If you're wondering how this is created, it is generated using -# `scripts/generate.py` in https://github.com/pypa/get-pip. - -import sys - -this_python = sys.version_info[:2] -min_version = (3, 9) -if this_python < min_version: - message_parts = [ - "This script does not work on Python {}.{}.".format(*this_python), - "The minimum supported Python version is {}.{}.".format(*min_version), - "Please use https://bootstrap.pypa.io/pip/{}.{}/get-pip.py instead.".format(*this_python), - ] - print("ERROR: " + " ".join(message_parts)) - sys.exit(1) - - -import os.path -import pkgutil -import shutil -import tempfile -import argparse -import importlib -from base64 import b85decode - - -def include_setuptools(args): - """ - Install setuptools only if absent, not excluded and when using Python <3.12. - """ - cli = not args.no_setuptools - env = not os.environ.get("PIP_NO_SETUPTOOLS") - absent = not importlib.util.find_spec("setuptools") - python_lt_3_12 = this_python < (3, 12) - return cli and env and absent and python_lt_3_12 - - -def include_wheel(args): - """ - Install wheel only if absent, not excluded and when using Python <3.12. - """ - cli = not args.no_wheel - env = not os.environ.get("PIP_NO_WHEEL") - absent = not importlib.util.find_spec("wheel") - python_lt_3_12 = this_python < (3, 12) - return cli and env and absent and python_lt_3_12 - - -def determine_pip_install_arguments(): - pre_parser = argparse.ArgumentParser() - pre_parser.add_argument("--no-setuptools", action="store_true") - pre_parser.add_argument("--no-wheel", action="store_true") - pre, args = pre_parser.parse_known_args() - - args.append("pip") - - if include_setuptools(pre): - args.append("setuptools") - - if include_wheel(pre): - args.append("wheel") - - return ["install", "--upgrade", "--force-reinstall"] + args - - -def monkeypatch_for_cert(tmpdir): - """Patches `pip install` to provide default certificate with the lowest priority. - - This ensures that the bundled certificates are used unless the user specifies a - custom cert via any of pip's option passing mechanisms (config, env-var, CLI). - - A monkeypatch is the easiest way to achieve this, without messing too much with - the rest of pip's internals. - """ - from pip._internal.commands.install import InstallCommand - - # We want to be using the internal certificates. - cert_path = os.path.join(tmpdir, "cacert.pem") - with open(cert_path, "wb") as cert: - cert.write(pkgutil.get_data("pip._vendor.certifi", "cacert.pem")) - - install_parse_args = InstallCommand.parse_args - - def cert_parse_args(self, args): - if not self.parser.get_default_values().cert: - # There are no user provided cert -- force use of bundled cert - self.parser.defaults["cert"] = cert_path # calculated above - return install_parse_args(self, args) - - InstallCommand.parse_args = cert_parse_args - - -def bootstrap(tmpdir): - monkeypatch_for_cert(tmpdir) - - # Execute the included pip and use it to install the latest pip and - # any user-requested packages from PyPI. - from pip._internal.cli.main import main as pip_entry_point - args = determine_pip_install_arguments() - sys.exit(pip_entry_point(args)) - - -def main(): - tmpdir = None - try: - # Create a temporary working directory - tmpdir = tempfile.mkdtemp() - - # Unpack the zipfile into the temporary directory - pip_zip = os.path.join(tmpdir, "pip.zip") - with open(pip_zip, "wb") as fp: - fp.write(b85decode(DATA.replace(b"\n", b""))) - - # Add the zipfile to sys.path so that we can import it - sys.path.insert(0, pip_zip) - - # Run the bootstrap - bootstrap(tmpdir=tmpdir) - finally: - # Clean up our temporary working directory - if tmpdir: - shutil.rmtree(tmpdir, ignore_errors=True) - - -DATA = b""" 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sha256:fefed2be21166b9afcc5913192d8b86b4a621ee236b459a2cc754584020d4878 -size 243 diff --git a/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/coremldata.bin b/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/coremldata.bin deleted file mode 100644 index f6cebbf78c8aa9626ae27c1a9d8b681c3bf0f441..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:66953a7662cfdc8247e5f3da0ec38847d1e198a5b5dd94b51fa1c711f7b777ac -size 543 diff --git a/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/metadata.json b/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/metadata.json deleted file mode 100644 index 84ebb9b5a894782e6def44d0b3707784e36a1757..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/metadata.json +++ /dev/null @@ -1,123 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Hybrid TDT Decoder (110M)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 640 × 2)", - "shortDescription" : "", - "shape" : "[1, 640, 2]", - "name" : "decoder_output", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "h_out", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "c_out", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Select" : 1, - "Ios17.squeeze" : 2, - "Ios17.gather" : 1, - "Ios17.cast" : 5, - "Ios17.lstm" : 1, - "Ios17.transpose" : 2, - "Ios17.add" : 1, - "Ios17.concat" : 1, - "Ios17.greaterEqual" : 1, - "Identity" : 1, - "Ios17.expandDims" : 2 - }, - "computePrecision" : "Mixed (Float16, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1]", - "name" : "targets", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "target_lengths", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "h_in", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "c_in", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2026-01-01", - "com.github.apple.coremltools.source" : "torch==2.8.0", - "com.github.apple.coremltools.version" : "9.0", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "Decoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/model.mil b/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/model.mil deleted file mode 100644 index 3d8abb8f2d6b323d3d0fd6e3d34bc1f8145e5ca4..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/model.mil +++ /dev/null @@ -1,51 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] -{ - func main(tensor c_in, tensor h_in, tensor target_lengths, tensor targets) { - tensor y_batch_dims_0 = const()[name = tensor("y_batch_dims_0"), val = tensor(0)]; - tensor y_validate_indices_0 = const()[name = tensor("y_validate_indices_0"), val = tensor(false)]; - tensor decoder_prediction_embed_weight_to_fp16 = const()[name = tensor("decoder_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor targets_to_int16_dtype_0 = const()[name = tensor("targets_to_int16_dtype_0"), val = tensor("int16")]; - tensor cast_4_dtype_0 = const()[name = tensor("cast_4_dtype_0"), val = tensor("int32")]; - tensor greater_equal_0_y_0 = const()[name = tensor("greater_equal_0_y_0"), val = tensor(0)]; - tensor targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor("cast_9")]; - tensor cast_4 = cast(dtype = cast_4_dtype_0, x = targets_to_int16)[name = tensor("cast_8")]; - tensor greater_equal_0 = greater_equal(x = cast_4, y = greater_equal_0_y_0)[name = tensor("greater_equal_0")]; - tensor slice_by_index_0 = const()[name = tensor("slice_by_index_0"), val = tensor(1025)]; - tensor add_1 = add(x = cast_4, y = slice_by_index_0)[name = tensor("add_1")]; - tensor select_0 = select(a = cast_4, b = add_1, cond = greater_equal_0)[name = tensor("select_0")]; - tensor y_cast_fp16_cast_uint16_axis_0 = const()[name = tensor("y_cast_fp16_cast_uint16_axis_0"), val = tensor(0)]; - tensor select_0_to_int16_dtype_0 = const()[name = tensor("select_0_to_int16_dtype_0"), val = tensor("int16")]; - tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor("cast_7")]; - tensor y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = decoder_prediction_embed_weight_to_fp16)[name = tensor("y_cast_fp16_cast_uint16_cast_uint16")]; - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([1, 0, 2])]; - tensor input_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; - tensor h_in_to_fp16_dtype_0 = const()[name = tensor("h_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor("cast_6")]; - tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = h_in_to_fp16)[name = tensor("input_lstm_h0_squeeze_cast_fp16")]; - tensor input_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; - tensor c_in_to_fp16_dtype_0 = const()[name = tensor("c_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor("cast_5")]; - tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = c_in_to_fp16)[name = tensor("input_lstm_c0_squeeze_cast_fp16")]; - tensor input_direction_0 = const()[name = tensor("input_direction_0"), val = tensor("forward")]; - tensor input_output_sequence_0 = const()[name = tensor("input_output_sequence_0"), val = tensor(true)]; - tensor input_recurrent_activation_0 = const()[name = tensor("input_recurrent_activation_0"), val = tensor("sigmoid")]; - tensor input_cell_activation_0 = const()[name = tensor("input_cell_activation_0"), val = tensor("tanh")]; - tensor input_activation_0 = const()[name = tensor("input_activation_0"), val = tensor("tanh")]; - tensor concat_1_to_fp16 = const()[name = tensor("concat_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312128)))]; - tensor concat_2_to_fp16 = const()[name = tensor("concat_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4588992)))]; - tensor concat_0_to_fp16 = const()[name = tensor("concat_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7865856)))]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = tensor("transpose_2")]; - tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_0_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_1_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor var_30_axes_0 = const()[name = tensor("op_30_axes_0"), val = tensor([0])]; - tensor h_out = expand_dims(axes = var_30_axes_0, x = input_cast_fp16_1)[name = tensor("op_30_cast_fp16")]; - tensor var_31_axes_0 = const()[name = tensor("op_31_axes_0"), val = tensor([0])]; - tensor c_out = expand_dims(axes = var_31_axes_0, x = input_cast_fp16_2)[name = tensor("op_31_cast_fp16")]; - tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([1, 2, 0])]; - tensor var_48 = const()[name = tensor("op_48"), val = tensor(2)]; - tensor var_49_interleave_0 = const()[name = tensor("op_49_interleave_0"), val = tensor(false)]; - tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor("transpose_1")]; - tensor decoder_output = concat(axis = var_48, interleave = var_49_interleave_0, values = (transpose_0_cast_fp16, transpose_0_cast_fp16))[name = tensor("op_49_cast_fp16")]; - tensor target_lengths_tmp = identity(x = target_lengths)[name = tensor("target_lengths_tmp")]; - } -> (decoder_output, h_out, c_out); -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/weights/weight.bin b/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/weights/weight.bin deleted file mode 100644 index 4543ad4ea89b21ebed0de0d5ec3975d07eace7fb..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:dd90b58597ee2c172c672dffe13b1110898ba07394c1a15efc96cc8c6b18411b -size 7871040 diff --git 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"6BDFA15B-7DF3-44B6-A308-B95282CBC63A" -} diff --git a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/analytics/coremldata.bin b/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index f7d619494d2f2061ae52418edbb5c5fc2ec1f11e..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e51c4ee19e8d7e8392896e5d3b5464892f9e16ba271335ff7eb3d2eb4a015f27 -size 243 diff --git a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/coremldata.bin b/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/coremldata.bin deleted file mode 100644 index cc76621d9ad55dc03a554246d198a4851607172f..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:5c45adaff9e0e6d8c49949666f07a6fcde0affc7a631a8f93d12a0e51c1393d3 -size 509 diff --git a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/metadata.json b/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/metadata.json deleted file mode 100644 index 4d8cd2c6a2393dfafad11338ff96f388b2d59295..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/metadata.json +++ /dev/null @@ -1,104 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Hybrid Joint Decision (110M)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_id", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_prob", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "duration", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.reduceArgmax" : 2, - "Ios17.linear" : 3, - "Ios17.transpose" : 2, - "Ios17.log" : 1, - "Ios17.add" : 1, - "Ios17.sliceByIndex" : 2, - "Ios16.relu" : 1, - "Ios16.softmax" : 2, - "Ios17.expandDims" : 3, - "Ios17.squeeze" : 2, - "Ios17.cast" : 5, - "Ios17.gatherAlongAxis" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 512 × 1)", - "shortDescription" : "", - "shape" : "[1, 512, 1]", - "name" : "encoder_step", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder_step", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2026-01-01", - "com.github.apple.coremltools.source" : "torch==2.8.0", - "com.github.apple.coremltools.version" : "9.0", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "JointDecision", - "method" : "predict" - } -] \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/model.mil b/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/model.mil deleted file mode 100644 index 7057845c459a15cef5cb1a4baf0695b949c7f703..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/model.mil +++ /dev/null @@ -1,66 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] -{ - func main(tensor decoder_step, tensor encoder_step) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_step_to_fp16_dtype_0 = const()[name = tensor("encoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_step_to_fp16_dtype_0 = const()[name = tensor("decoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_enc_weight_to_fp16 = const()[name = tensor("joint_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_enc_bias_to_fp16 = const()[name = tensor("joint_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488)))]; - tensor encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor("cast_5")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_enc_bias_to_fp16, weight = joint_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor f_axes_0 = const()[name = tensor("f_axes_0"), val = tensor([2])]; - tensor f_cast_fp16 = expand_dims(axes = f_axes_0, x = linear_0_cast_fp16)[name = tensor("f_cast_fp16")]; - tensor joint_pred_weight_to_fp16 = const()[name = tensor("joint_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656832)))]; - tensor joint_pred_bias_to_fp16 = const()[name = tensor("joint_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476096)))]; - tensor decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor("cast_4")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_pred_bias_to_fp16, weight = joint_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor g_axes_0 = const()[name = tensor("g_axes_0"), val = tensor([1])]; - tensor g_cast_fp16 = expand_dims(axes = g_axes_0, x = linear_1_cast_fp16)[name = tensor("g_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = f_cast_fp16, y = g_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1477440)))]; - tensor joint_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2795904)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_joint_net_2_bias_to_fp16, weight = joint_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor var_35 = const()[name = tensor("op_35"), val = tensor(-1)]; - tensor joint_out_softmax_cast_fp16 = softmax(axis = var_35, x = linear_2_cast_fp16)[name = tensor("joint_out_softmax_cast_fp16")]; - tensor joint_out_epsilon_0 = const()[name = tensor("joint_out_epsilon_0"), val = tensor(0x1p-149)]; - tensor joint_out_cast_fp16 = log(epsilon = joint_out_epsilon_0, x = joint_out_softmax_cast_fp16)[name = tensor("joint_out_cast_fp16")]; - tensor input_begin_0 = const()[name = tensor("input_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor input_end_0 = const()[name = tensor("input_end_0"), val = tensor([1, 1, 1, 1025])]; - tensor input_end_mask_0 = const()[name = tensor("input_end_mask_0"), val = tensor([true, true, true, false])]; - tensor input_cast_fp16 = slice_by_index(begin = input_begin_0, end = input_end_0, end_mask = input_end_mask_0, x = joint_out_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 1, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = joint_out_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_48 = const()[name = tensor("op_48"), val = tensor(-1)]; - tensor probs_cast_fp16 = softmax(axis = var_48, x = input_cast_fp16)[name = tensor("probs_cast_fp16")]; - tensor token_id_1_axis_0 = const()[name = tensor("token_id_1_axis_0"), val = tensor(-1)]; - tensor token_id_1_keep_dims_0 = const()[name = tensor("token_id_1_keep_dims_0"), val = tensor(true)]; - tensor token_id_1_output_dtype_0 = const()[name = tensor("token_id_1_output_dtype_0"), val = tensor("int32")]; - tensor token_id_1_cast_fp16 = reduce_argmax(axis = token_id_1_axis_0, keep_dims = token_id_1_keep_dims_0, output_dtype = token_id_1_output_dtype_0, x = input_cast_fp16)[name = tensor("token_id_1_cast_fp16")]; - tensor token_id_axes_0 = const()[name = tensor("token_id_axes_0"), val = tensor([-1])]; - tensor token_id_1_cast_fp16_to_int16_dtype_0 = const()[name = tensor("token_id_1_cast_fp16_to_int16_dtype_0"), val = tensor("int16")]; - tensor token_id_1_cast_fp16_to_int16 = cast(dtype = token_id_1_cast_fp16_to_int16_dtype_0, x = token_id_1_cast_fp16)[name = tensor("cast_3")]; - tensor token_id_cast_int16 = squeeze(axes = token_id_axes_0, x = token_id_1_cast_fp16_to_int16)[name = tensor("token_id_cast_int16")]; - tensor token_id_cast_int16_to_int32_dtype_0 = const()[name = tensor("token_id_cast_int16_to_int32_dtype_0"), val = tensor("int32")]; - tensor var_57_axes_0 = const()[name = tensor("op_57_axes_0"), val = tensor([-1])]; - tensor token_id = cast(dtype = token_id_cast_int16_to_int32_dtype_0, x = token_id_cast_int16)[name = tensor("cast_2")]; - tensor var_57 = expand_dims(axes = var_57_axes_0, x = token_id)[name = tensor("op_57")]; - tensor var_58 = const()[name = tensor("op_58"), val = tensor(-1)]; - tensor token_prob_validate_indices_0 = const()[name = tensor("token_prob_validate_indices_0"), val = tensor(false)]; - tensor var_57_to_int16_dtype_0 = const()[name = tensor("op_57_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_57_to_int16 = cast(dtype = var_57_to_int16_dtype_0, x = var_57)[name = tensor("cast_1")]; - tensor token_prob_cast_fp16_cast_int16 = gather_along_axis(axis = var_58, indices = var_57_to_int16, validate_indices = token_prob_validate_indices_0, x = probs_cast_fp16)[name = tensor("token_prob_cast_fp16_cast_int16")]; - tensor var_62_axes_0 = const()[name = tensor("op_62_axes_0"), val = tensor([-1])]; - tensor token_prob = squeeze(axes = var_62_axes_0, x = token_prob_cast_fp16_cast_int16)[name = tensor("op_62_cast_fp16")]; - tensor duration_axis_0 = const()[name = tensor("duration_axis_0"), val = tensor(-1)]; - tensor duration_keep_dims_0 = const()[name = tensor("duration_keep_dims_0"), val = tensor(false)]; - tensor duration_output_dtype_0 = const()[name = tensor("duration_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = duration_axis_0, keep_dims = duration_keep_dims_0, output_dtype = duration_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("duration_cast_fp16")]; - } -> (token_id, token_prob, duration); -} \ No newline at end of file diff --git a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/weights/weight.bin b/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b3f771cb65b190f1873e39629676ed79b65a8361522f451b37bdba8b1106e6ff -size 2798028 diff --git a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/model.mlmodel b/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/model.mlmodel deleted file mode 100644 index 00748a425eda1d051f13fc524fcfc160d1cb7ca1..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/model.mlmodel +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:73d76247715622313f066e93874f5eff0706410e8572e1b3c51f6f7f2b14a78b -size 9790 diff --git a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b3f771cb65b190f1873e39629676ed79b65a8361522f451b37bdba8b1106e6ff -size 2798028 diff --git a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Manifest.json b/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Manifest.json deleted file mode 100644 index 51edbc5622d98d269493af788ab212f926544b0c..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Manifest.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "fileFormatVersion": "1.0.0", - "itemInfoEntries": { - "601AC192-A217-4675-8F63-01F426D7BC52": { - "author": "com.apple.CoreML", - "description": "CoreML Model Weights", - "name": "weights", - "path": "com.apple.CoreML/weights" - }, - "76B2BF33-9547-4031-8668-01C0E473C306": { - "author": "com.apple.CoreML", - "description": "CoreML Model Specification", - "name": "model.mlmodel", - "path": "com.apple.CoreML/model.mlmodel" - } - }, - "rootModelIdentifier": "76B2BF33-9547-4031-8668-01C0E473C306" -} diff --git a/convert/parakeet-tdt-v2-0.6b/.DS_Store b/convert/parakeet-tdt-v2-0.6b/.DS_Store deleted file mode 100644 index 4a654c3e785ab1ee5f435f3eda3c58ea35b780d4..0000000000000000000000000000000000000000 Binary files a/convert/parakeet-tdt-v2-0.6b/.DS_Store and /dev/null differ diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/.gitignore b/convert/parakeet-tdt-v2-0.6b/coreml/.gitignore deleted file mode 100644 index e2e12b1c4fd2deeef4b38477dccac90f560a627c..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -mlpackages/ -parakeet_coreml/ -parakeet_coreml_quantized/ -compiled/ diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/README.md b/convert/parakeet-tdt-v2-0.6b/coreml/README.md deleted file mode 100644 index 8ef855d29d09b61830ca71b880c99e953f44f667..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/README.md +++ /dev/null @@ -1,136 +0,0 @@ -# Parakeet‑TDT v2 (0.6B) — CoreML Export, Parity, and Quantization - -Tools to export NVIDIA Parakeet‑TDT v2 (0.6B) RNNT ASR to CoreML, validate numerical parity with the NeMo reference, measure latency, and explore quantization trade‑offs. All CoreML components use a fixed 15‑second audio window for export and validation. - -## Environment - -1. Create or reuse the local environment with `uv venv`. -2. Activate the repo `.venv` and install deps via `uv pip sync`. -3. Run everything through `uv run` to keep resolutions reproducible. - -## Test Environment - -All tests and measurements referenced here were run on an Apple M4 Pro with 48 GB of RAM. - -## Export CoreML packages - -Exports preprocessor, encoder, decoder, joint, and two fused variants (mel+encoder, joint+decision). Shapes and I/O match the fixed 15‑second window contract. - -``` -uv run python convert-parakeet.py convert \ - --nemo-path /path/to/parakeet-tdt-0.6b-v2.nemo \ - --output-dir parakeet_coreml -``` - -Notes -- Minimum deployment target: iOS 17. Export uses CPU_ONLY by default; runtime compute units can be set when loading the model (Python or Swift). -- Audio is 16 kHz, single‑channel. The 15 s window is enforced during export and validation. - -## Validate parity and speed (Torch vs CoreML) - -Runs Torch and CoreML side‑by‑side on the same 15 s input, records diffs and latency, and saves plots under `plots/compare-components/`. The tool updates `parakeet_coreml/metadata.json` with all measurements. - -``` -uv run python compare-components.py compare \ - --output-dir parakeet_coreml \ - --model-id nvidia/parakeet-tdt-0.6b-v2 \ - --runs 10 --warmup 3 -``` - -Output comparison: - -![./plots/compare-components/mel_encoder_time_l2.png](./plots/compare-components/mel_encoder_time_l2.png) -![./plots/compare-components/joint_decision_prob_u0.png](./plots/compare-components/joint_decision_prob_u0.png) -![./plots/compare-components/decoder_steps_l2.png](./plots/compare-components/decoder_steps_l2.png) - -Latency: - -![./plots/quantize/all/all_components_compile.png](./plots/quantize/all/all_components_compile.png) -![./plots/quantize/all/all_components_compression.png](./plots/quantize/all/all_components_compression.png) -![./plots/quantize/all/all_components_quality.png](./plots/quantize/all/all_components_quality.png) -![./plots/quantize/all/all_components_latency.png](./plots/quantize/all/all_components_latency.png) - - -Quants: - - - -### Key results (quality first) - -Numerical parity is strong across components on the fixed window: -- Preprocessor mel: match=true; max_abs≈0.484, max_rel≈2.00 (near‑zero bins inflate relative error). -- Encoder: match=true; max_abs≈0.0054, strong agreement over time (see plot). -- Decoder h/c state: match=true; value deltas within tolerance. -- Joint logits: match=true; max_abs≈0.099, distributions align (see top‑k plot). -- Joint+Decision: Fused CoreML head exactly matches decisions computed on CoreML logits (token_id/prob/duration). PyTorch logits produce slightly different argmax paths (expected from small logit differences). - -### Speed (latency and RTF) - -Component latency on a 15 s clip, Torch CPU vs CoreML (CPU+NE) from `parakeet_coreml/metadata.json`: -- Encoder: Torch 1030.48 ms → CoreML 25.44 ms (≈40.5× faster, RTF 0.00170) -- Preprocessor: 1.99 ms → 1.19 ms (≈1.68×) -- Joint: 28.34 ms → 22.66 ms (≈1.25×) -- Decoder (U=1): 7.51 ms → 4.32 ms (≈1.73×) - -Fused paths: -- Mel+Encoder (Torch separate vs CoreML fused): 1032.48 ms → 27.10 ms (≈38.1× faster) -- Joint+Decision (CoreML joint + CPU post vs fused CoreML head): 50.05 ms → 64.09 ms (fused is slower here; prefer CoreML joint + lightweight CPU decision on host). - -Plots -- Latency bars and speedups: `plots/compare-components/latency_summary.png`, `plots/compare-components/latency_speedup.png` -- Fused vs separate: `plots/compare-components/latency_fused_vs_separate.png`, `plots/compare-components/latency_fused_speedup.png` -- Quality visuals: mel composite (`mel_composite.png`), encoder L2 over time (`encoder_time_l2.png`), decoder step L2 (`decoder_steps_l2.png`), joint top‑k/time L2 (`joint_top50.png`, `joint_time_l2.png`), joint‑decision agreement (`joint_decision_token_agree.png`, `joint_decision_prob_u0.png`). - -## Quantization (size ‱ quality ‱ speed) - -`uv run python quantize_coreml.py` evaluates several variants and writes a roll‑up to `parakeet_coreml_quantized/quantization_summary.json`. Plots are mirrored to `plots/quantize//` (we include `plots/quantize/all/`). Quality here is reported as 1 − normalized L2 error (1.0 = identical). For JointDecision we report token‑id match rate, duration match, and token‑prob MAE. - -Quick highlights (ComputeUnits=ALL): -- int8 linear (per‑channel): ~2.0× smaller across components with minimal quality loss - - MelEncoder quality≈0.963; latency≈31.13 ms (baseline≈29.34 ms) - - JointDecision acc≈0.995; latency≈1.96 ms (baseline≈2.15 ms) -- int8 linear (per‑tensor symmetric): large encoder quality drop (≈0.50) — not recommended - -Quantization plots (ALL) -- Fused: `plots/quantize/all/fused_quality.png`, `fused_latency.png`, `fused_compression.png`, `fused_size.png` -- Component breakdown: `plots/quantize/all/all_components_quality.png`, `all_components_latency.png`, `all_components_compression.png`, `all_components_size.png`, `all_components_compile.png` - -## Reproduce the figures - -1) Export baseline CoreML packages -``` -uv run python convert-parakeet.py convert --model-id nvidia/parakeet-tdt-0.6b-v2 --output-dir parakeet_coreml -``` - -2) Compare Torch vs CoreML and generate parity/latency plots -``` -uv run python compare-components.py compare --output-dir parakeet_coreml --runs 10 --warmup 3 -``` - -3) Run quantization sweeps (mirrors plots into `plots/quantize//`) -``` -uv run python quantize_coreml.py \ - --input-dir parakeet_coreml \ - --output-root parakeet_coreml_quantized \ - --compute-units ALL --runs 10 -``` - -Examples -- Encoder 6‑bit palette only: - `uv run python quantize_coreml.py -c encoder-palettize` -- MelEncoder 6‑bit palette only: - `uv run python quantize_coreml.py -c mel-palettize` - (By default, the script derives the component whitelist from the selected - variants. Use `-m/--component` to explicitly restrict or `-m all` to force all.) - -## Notes & limits - -- Fixed 15‑second window shapes are required for all CoreML exports and validations. -- Latency measurements are host‑side CoreML predictions (CPU+NE or ALL); on‑device results can differ by chip/OS. -- For streaming decode, the exported decoder uses U=1 inputs with explicit LSTM state I/O. -- Minimum deployment target is iOS 17; models are saved as MLProgram and eligible for ANE when loaded with `ComputeUnits=ALL`. - -## Acknowledgements - -- Parakeet‑TDT v2 model from NVIDIA NeMo (`nvidia/parakeet-tdt-0.6b-v2`). -- This directory provides export/validation utilities and plots to help the community reproduce quality and performance on Apple devices. diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/agents.md b/convert/parakeet-tdt-v2-0.6b/coreml/agents.md deleted file mode 100644 index d6765848d59f76846549712d8c31a4fce733a98a..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/agents.md +++ /dev/null @@ -1,7 +0,0 @@ -# Agent Notes - -- Preferred Python workflow uses `uv` (https://github.com/astral-sh/uv). - - Create and manage environments with `uv venv`. - - Install dependencies with `uv pip install` or `uv pip sync` as needed. -- When working in this repo, activate the local `.venv` and run tooling through `uv run` to keep resolutions reproducible. -- Keep CoreML conversions constrained to the fixed 15-second audio window when exporting or validating Parakeet components. diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute.wav b/convert/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute.wav deleted file mode 100644 index 8682a32b66200c635a60706e722c2b03b5701db0..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:004afd9a8b6265d321b1faeb3b22ca5efcc78b570566fca633327d73f545f3fa -size 10584142 diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k.wav b/convert/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k.wav deleted file mode 100644 index de71da9ab403797061b7051b820337a754336481..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:555404115420c8b4a77fcfacc450ecfc767211ca7b449a9dcb292b1734256103 -size 1920090 diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k_15s.wav b/convert/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k_15s.wav deleted file mode 100644 index 0b8040f5f124d20a1d7e812c576c831a7573eddf..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k_15s.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c79c8bc763b4efccb3e12f199ec0a59aa2edc5e9e4d21ca70fde8f36762d4147 -size 480078 diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/compare-components.py b/convert/parakeet-tdt-v2-0.6b/coreml/compare-components.py deleted file mode 100644 index 114b74c388a2562cc2576f1ac331b8cd61dc518e..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/compare-components.py +++ /dev/null @@ -1,958 +0,0 @@ -#!/usr/bin/env python3 -"""Compare Parakeet TDT v2 Torch vs CoreML components on a fixed 15s window. - -Writes numeric diffs to the specified output directory (metadata.json) and -saves plots under a repo-tracked directory: plots//. -""" -from __future__ import annotations - -import json -import time -from dataclasses import dataclass -from pathlib import Path -from typing import Dict, Optional, Tuple - -import coremltools as ct -import numpy as np -import soundfile as sf -import torch -import typer - -import nemo.collections.asr as nemo_asr - -# Optional plotting -try: - import matplotlib - matplotlib.use("Agg") - import matplotlib.pyplot as plt - HAS_MPL = True -except Exception: - HAS_MPL = False - - -@dataclass -class ValidationSettings: - audio_path: Optional[Path] - seconds: float - seed: Optional[int] - rtol: float - atol: float - - -def _compute_length(seconds: float, sample_rate: int) -> int: - return int(round(seconds * sample_rate)) - - -def _prepare_audio( - validation_audio: Optional[Path], - sample_rate: int, - max_samples: int, - seed: Optional[int], -) -> torch.Tensor: - if validation_audio is None: - if seed is not None: - torch.manual_seed(seed) - return torch.randn(1, max_samples, dtype=torch.float32) - - data, sr = sf.read(str(validation_audio), dtype="float32") - if sr != sample_rate: - raise typer.BadParameter( - f"Validation audio sample rate {sr} does not match model rate {sample_rate}" - ) - if data.ndim > 1: - data = data[:, 0] - if data.size == 0: - raise typer.BadParameter("Validation audio is empty") - if data.size < max_samples: - data = np.pad(data, (0, max_samples - data.size)) - elif data.size > max_samples: - data = data[:max_samples] - return torch.from_numpy(data).unsqueeze(0).to(dtype=torch.float32) - - -def _np(x: torch.Tensor, dtype=None) -> np.ndarray: - arr = x.detach().cpu().numpy() - if dtype is not None: - return arr.astype(dtype, copy=False) - return arr - - -def _to_t(x) -> torch.Tensor: - if isinstance(x, torch.Tensor): - return x.detach().cpu() - elif isinstance(x, np.ndarray): - # Ensure a separate tensor (avoid shared memory weirdness) - return torch.from_numpy(np.array(x, copy=True)) - else: - return torch.tensor(x) - - -def _max_diffs(a, b, rtol: float, atol: float) -> Tuple[float, float, bool]: - # Use NumPy for comparisons to avoid invoking the PyTorch C-API in contexts - # where the GIL may not be held (which can trigger PyEval_SaveThread errors). - na = np.array(a, dtype=np.float32, copy=True) - nb = np.array(b, dtype=np.float32, copy=True) - if na.size == 0: - return 0.0, 0.0, True - diff = np.abs(na - nb) - max_abs = float(diff.max()) - denom = np.maximum(np.abs(na), np.abs(nb)) - with np.errstate(divide="ignore", invalid="ignore"): - rel = np.where(denom == 0.0, 0.0, diff / denom) - max_rel = float(rel.max()) - ok = bool(np.allclose(na, nb, rtol=rtol, atol=atol)) - return max_abs, max_rel, ok - - -def _plot_line(x_ref: np.ndarray, x_ml: np.ndarray, title: str, path: Path, also_delta: bool = False): - if not HAS_MPL: - return None - if also_delta: - fig, axes = plt.subplots(2, 1, figsize=(8, 5), sharex=True) - axes[0].plot(x_ref, label="torch", linewidth=1) - axes[0].plot(x_ml, label="coreml", linewidth=1, alpha=0.8) - axes[0].set_title(title) - axes[0].legend() - delta = np.asarray(x_ref) - np.asarray(x_ml) - axes[1].plot(delta, color="C3", linewidth=1) - axes[1].set_title("Delta (torch - coreml)") - axes[1].set_xlabel("time/step") - plt.tight_layout() - plt.savefig(path) - plt.close(fig) - else: - plt.figure(figsize=(8, 3)) - plt.plot(x_ref, label="torch", linewidth=1) - plt.plot(x_ml, label="coreml", linewidth=1, alpha=0.8) - plt.title(title) - plt.legend() - plt.tight_layout() - plt.savefig(path) - plt.close() - return str(path.name) - - -def _plot_image(img: np.ndarray, title: str, path: Path, vmin=None, vmax=None): - if not HAS_MPL: - return None - plt.figure(figsize=(6, 4)) - plt.imshow(img, aspect='auto', origin='lower', interpolation='nearest', vmin=vmin, vmax=vmax) - plt.title(title) - plt.colorbar(shrink=0.8) - plt.tight_layout() - plt.savefig(path) - plt.close() - return str(path.name) - - -def _plot_mel_composite( - mel_torch: np.ndarray, - mel_coreml: np.ndarray, - path: Path, - vmin=None, - vmax=None, -): - """Create a single PNG with mel torch, mel coreml, abs diff heatmap, and mean-over-time curves with delta.""" - if not HAS_MPL: - return None - mel_torch = np.asarray(mel_torch) - mel_coreml = np.asarray(mel_coreml) - absdiff = np.abs(mel_torch - mel_coreml) - mean_t = mel_torch.mean(axis=0) - mean_c = mel_coreml.mean(axis=0) - delta = mean_t - mean_c - - fig = plt.figure(figsize=(12, 8)) - gs = fig.add_gridspec(2, 2, height_ratios=[1, 1]) - ax1 = fig.add_subplot(gs[0, 0]) - im1 = ax1.imshow(mel_torch, aspect='auto', origin='lower', interpolation='nearest', vmin=vmin, vmax=vmax) - ax1.set_title("Mel (Torch)") - fig.colorbar(im1, ax=ax1, shrink=0.8) - - ax2 = fig.add_subplot(gs[0, 1]) - im2 = ax2.imshow(mel_coreml, aspect='auto', origin='lower', interpolation='nearest', vmin=vmin, vmax=vmax) - ax2.set_title("Mel (CoreML)") - fig.colorbar(im2, ax=ax2, shrink=0.8) - - ax3 = fig.add_subplot(gs[1, 0]) - im3 = ax3.imshow(absdiff, aspect='auto', origin='lower', interpolation='nearest') - ax3.set_title("Mel |diff|") - fig.colorbar(im3, ax=ax3, shrink=0.8) - - ax4 = fig.add_subplot(gs[1, 1]) - ax4.plot(mean_t, label="torch", linewidth=1) - ax4.plot(mean_c, label="coreml", linewidth=1, alpha=0.8) - ax4.plot(delta, label="delta", linewidth=1, color="C3") - ax4.set_title("Mel mean over time + delta") - ax4.legend() - - plt.tight_layout() - plt.savefig(path) - plt.close(fig) - return str(path.name) - - -def _plot_latency_bars( - labels, - torch_means, - torch_stds, - coreml_means, - coreml_stds, - path: Path, -): - if not HAS_MPL: - return None - x = np.arange(len(labels)) - width = 0.35 - fig, ax = plt.subplots(figsize=(8, 4)) - b1 = ax.bar(x - width/2, torch_means, width, yerr=torch_stds, label="torch", color="C0", alpha=0.9) - b2 = ax.bar(x + width/2, coreml_means, width, yerr=coreml_stds, label="coreml", color="C1", alpha=0.9) - ax.set_xticks(x, labels, rotation=15) - ax.set_ylabel("latency (ms)") - ax.set_title("Component latency (15s window inputs)") - ax.legend() - # Add value labels on bars - def _annotate(bars): - for bar in bars: - h = bar.get_height() - if np.isnan(h): - continue - ax.annotate(f"{h:.0f}", - xy=(bar.get_x() + bar.get_width()/2, h), - xytext=(0, 3), textcoords="offset points", - ha='center', va='bottom', fontsize=8) - _annotate(b1) - _annotate(b2) - plt.tight_layout() - plt.savefig(path) - plt.close(fig) - return str(path.name) - - -def _plot_speedup_bars(labels, torch_means, coreml_means, path: Path): - if not HAS_MPL: - return None - speedup = [] - for t, c in zip(torch_means, coreml_means): - if c and c > 0: - speedup.append(float(t) / float(c)) - else: - speedup.append(np.nan) - x = np.arange(len(labels)) - fig, ax = plt.subplots(figsize=(8, 4)) - bars = ax.bar(x, speedup, color="C2") - ax.set_xticks(x, labels, rotation=15) - ax.set_ylabel("torch/coreml speedup") - ax.set_title("CoreML speedup vs Torch (higher is better)") - ax.axhline(1.0, color="gray", linestyle="--", linewidth=1) - # Add value labels - for bar in bars: - h = bar.get_height() - if np.isnan(h): - continue - ax.annotate(f"{h:.2f}", - xy=(bar.get_x() + bar.get_width()/2, h), - xytext=(0, 3), textcoords="offset points", - ha='center', va='bottom', fontsize=8) - plt.tight_layout() - plt.savefig(path) - plt.close(fig) - return str(path.name) - - - -app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) - - -@app.command() -def compare( - output_dir: Path = typer.Option(Path("parakeet_coreml"), help="Directory containing mlpackages + metadata.json"), - nemo_path: Optional[Path] = typer.Option(None, "--nemo-path", exists=True, resolve_path=True, help="Path to .nemo checkpoint"), - model_id: str = typer.Option("nvidia/parakeet-tdt-0.6b-v2", "--model-id", help="HF model id if --nemo-path omitted"), - validation_audio: Optional[Path] = typer.Option(None, exists=True, resolve_path=True, help="15s, 16kHz wav for validation (defaults to audio/yc_first_minute_16k_15s.wav if present)"), - seed: Optional[int] = typer.Option(None, help="Random seed for synthetic input when audio is not provided"), - rtol: float = typer.Option(1e-3, help="Relative tolerance for comparisons"), - atol: float = typer.Option(1e-4, help="Absolute tolerance for comparisons"), - runs: int = typer.Option(10, help="Timed runs per model for latency measurement"), - warmup: int = typer.Option(3, help="Warmup runs before timing (compilation, caches)"), - symbol_steps: int = typer.Option( - 32, - help="Number of sequential decoder steps to validate with streaming U=1 inputs", - ), -) -> None: - """Run Torch vs CoreML comparisons and update metadata.json with plots and diffs.""" - output_dir.mkdir(parents=True, exist_ok=True) - if symbol_steps < 1: - raise typer.BadParameter("symbol_steps must be >= 1") - - meta_path = output_dir / "metadata.json" - exported_meta: Dict[str, object] = {} - if meta_path.exists(): - try: - exported_meta = json.loads(meta_path.read_text()) - except Exception: - exported_meta = {} - exported_max_u = int(exported_meta.get("max_symbol_steps", 1)) - if exported_max_u != 1: - typer.echo( - f"Note: CoreML export reports max_symbol_steps={exported_max_u}; " - "comparison still drives decoder step-wise with U=1 inputs." - ) - if nemo_path is not None: - typer.echo(f"Loading NeMo model from {nemo_path}
") - asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(str(nemo_path), map_location="cpu") - else: - typer.echo(f"Downloading NeMo model via {model_id}
") - asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_id, map_location="cpu") - asr_model.eval() - - sample_rate = int(asr_model.cfg.preprocessor.sample_rate) - max_samples = _compute_length(15.0, sample_rate) - default_audio = (Path(__file__).parent / "audio" / "yc_first_minute_16k_15s.wav").resolve() - chosen_audio = validation_audio if validation_audio is not None else (default_audio if default_audio.exists() else None) - if chosen_audio is not None and validation_audio is None: - typer.echo(f"Using default validation audio: {chosen_audio}") - - audio_tensor = _prepare_audio(chosen_audio, sample_rate, max_samples, seed) - audio_length = torch.tensor([max_samples], dtype=torch.int32) - - asr_model.decoder._rnnt_export = True - # Disable fused loss/WER computation for simpler joint inference - asr_model.joint.set_fuse_loss_wer(False) - # Important: ensure the joint returns raw logits (not log-softmax) - # RNNTJoint applies log_softmax on CPU by default when `log_softmax is None`. - # Our exported CoreML joint emits pre-softmax logits, so make the Torch - # reference do the same to avoid systematic offsets in comparisons/plots. - try: - # Some versions expose this as a plain attribute - asr_model.joint.log_softmax = False - except Exception: - pass - - # Generate reference outputs directly from NeMo model components - with torch.inference_mode(): - # Preprocessor - direct NeMo call - mel_ref, mel_length_ref = asr_model.preprocessor( - input_signal=audio_tensor, - length=audio_length.to(dtype=torch.long) - ) - mel_length_ref = mel_length_ref.to(dtype=torch.int32) - - # Encoder - direct NeMo call - encoder_ref, encoder_length_ref = asr_model.encoder( - audio_signal=mel_ref, - length=mel_length_ref.to(dtype=torch.long) - ) - encoder_length_ref = encoder_length_ref.to(dtype=torch.int32) - - vocab_size = int(asr_model.tokenizer.vocab_size) - num_extra = int(asr_model.joint.num_extra_outputs) - decoder_hidden = int(asr_model.decoder.pred_hidden) - decoder_layers = int(asr_model.decoder.pred_rnn_layers) - blank_id = int(asr_model.decoder.blank_idx) - - blank_targets = torch.tensor([[blank_id]], dtype=torch.int32) - blank_target_lengths = torch.tensor([1], dtype=torch.int32) - blank_targets_long = blank_targets.to(dtype=torch.long) - blank_target_lengths_long = blank_target_lengths.to(dtype=torch.long) - - def _decoder_rollout_torch(num_steps: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - outputs = [] - h_state = torch.zeros(decoder_layers, 1, decoder_hidden, dtype=torch.float32) - c_state = torch.zeros(decoder_layers, 1, decoder_hidden, dtype=torch.float32) - state = [h_state, c_state] - with torch.inference_mode(): - for _ in range(num_steps): - y, _, new_state = asr_model.decoder( - targets=blank_targets_long, - target_length=blank_target_lengths_long, - states=state, - ) - outputs.append(y.detach()) - state = [new_state[0].detach(), new_state[1].detach()] - if outputs: - decoder_seq = torch.cat(outputs, dim=-1) - else: - decoder_seq = torch.zeros(1, decoder_hidden, 0, dtype=torch.float32) - return decoder_seq, state[0], state[1] - - decoder_ref, h_ref, c_ref = _decoder_rollout_torch(symbol_steps) - - with torch.inference_mode(): - logits_ref = asr_model.joint( - encoder_outputs=encoder_ref, - decoder_outputs=decoder_ref, - ) - - # Convert tensors to numpy for CoreML - def _np32(x): - return np.array(x.detach().cpu().numpy(), dtype=np.float32, copy=True) - - # Prepare plot dir (write to repo-tracked plots//) - plots_root = Path(__file__).parent / "plots" - plots_dir = plots_root / Path(__file__).stem - plots_dir.mkdir(parents=True, exist_ok=True) - - encoder_np = _np32(encoder_ref) - decoder_ref_np = _np32(decoder_ref) - - summary: Dict[str, object] = { - "requested": True, - "status": "ok", - "atol": atol, - "rtol": rtol, - "symbol_steps": int(symbol_steps), - "audio_path": None if validation_audio is None else str(validation_audio), - "components": {}, - } - - # Preprocessor - pre = ct.models.MLModel(str(output_dir / "parakeet_preprocessor.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - t0 = time.perf_counter() - pre_out = pre.predict({"audio_signal": _np32(audio_tensor), "audio_length": _np32(audio_length).astype(np.int32)}) - t1 = time.perf_counter() - pre_first_ms = (t1 - t0) * 1000.0 - mel_ml = np.array(pre_out["mel"], dtype=np.float32, copy=True) - mel_len_ml = np.array(pre_out["mel_length"], dtype=np.int32, copy=True) - pre_atol, pre_rtol = max(atol, 1.0), max(rtol, 1e-2) - a_mel, r_mel, ok_mel = _max_diffs(_np32(mel_ref), mel_ml, pre_rtol, pre_atol) - ok_len = int(_np32(mel_length_ref).astype(np.int32)[0]) == int(np.array(mel_len_ml).astype(np.int32)[0]) - mel_t = _np32(mel_ref)[0] - mel_c = mel_ml[0] - vmin = float(min(mel_t.min(), mel_c.min())) - vmax = float(max(mel_t.max(), mel_c.max())) - pre_plots = { - "mel_composite.png": _plot_mel_composite(mel_t, mel_c, plots_dir / "mel_composite.png", vmin=vmin, vmax=vmax), - } - # Latency measurements: Torch and CoreML - def _time_coreml(model: ct.models.MLModel, inputs: Dict[str, np.ndarray]) -> Tuple[float, float]: - # Warmup - for _ in range(max(0, warmup)): - _ = model.predict(inputs) - times = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _ = model.predict(inputs) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - def _time_torch(fn, *args, **kwargs) -> Tuple[float, float]: - with torch.inference_mode(): - for _ in range(max(0, warmup)): - _ = fn(*args, **kwargs) - times = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _ = fn(*args, **kwargs) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - pre_torch_ms_mean, pre_torch_ms_std = _time_torch( - asr_model.preprocessor, input_signal=audio_tensor, length=audio_length.to(dtype=torch.long) - ) - pre_coreml_ms_mean, pre_coreml_ms_std = _time_coreml( - pre, - {"audio_signal": _np32(audio_tensor), "audio_length": _np32(audio_length).astype(np.int32)}, - ) - seconds = 15.0 - pre_coreml_rtf = float(pre_coreml_ms_mean / (seconds * 1000.0)) if pre_coreml_ms_mean > 0 else None - pre_torch_rtf = float(pre_torch_ms_mean / (seconds * 1000.0)) if pre_torch_ms_mean > 0 else None - - summary["components"]["preprocessor"] = { - "mel": {"max_abs": a_mel, "max_rel": r_mel, "match": bool(ok_mel)}, - "length_match": bool(ok_len), - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "coreml_first_ms": pre_first_ms, - "torch_ms": {"mean": pre_torch_ms_mean, "std": pre_torch_ms_std}, - "coreml_ms": {"mean": pre_coreml_ms_mean, "std": pre_coreml_ms_std}, - "rtf": {"torch": pre_torch_rtf, "coreml": pre_coreml_rtf}, - }, - "plots": {k: v for k, v in pre_plots.items() if v}, - } - - # Encoder - enc = ct.models.MLModel(str(output_dir / "parakeet_encoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - t0 = time.perf_counter() - enc_out = enc.predict({"mel": _np32(mel_ref), "mel_length": _np32(mel_length_ref).astype(np.int32)}) - t1 = time.perf_counter() - enc_first_ms = (t1 - t0) * 1000.0 - enc_ml = np.array(enc_out["encoder"], dtype=np.float32, copy=True) - enc_len_ml = np.array(enc_out["encoder_length"], dtype=np.int32, copy=True) - a_enc, r_enc, ok_enc = _max_diffs(_np32(encoder_ref), enc_ml, max(rtol, 5e-3), max(atol, 5e-2)) - ok_enc_len = int(_np32(encoder_length_ref).astype(np.int32)[0]) == int(np.array(enc_len_ml).astype(np.int32)[0]) - enc_t = _np32(encoder_ref)[0] - enc_c = enc_ml[0] - enc_plots = { - "encoder_time_l2.png": _plot_line( - np.linalg.norm(enc_t, axis=0), # L2 norm over features (D) for each time step - np.linalg.norm(enc_c, axis=0), # enc_t shape is (D, T), so axis=0 is features - "Encoder L2 over time", - plots_dir / "encoder_time_l2.png", - also_delta=True, - ), - } - enc_torch_ms_mean, enc_torch_ms_std = _time_torch( - asr_model.encoder, audio_signal=mel_ref, length=mel_length_ref.to(dtype=torch.long) - ) - enc_coreml_ms_mean, enc_coreml_ms_std = _time_coreml( - enc, {"mel": _np32(mel_ref), "mel_length": _np32(mel_length_ref).astype(np.int32)} - ) - enc_coreml_rtf = float(enc_coreml_ms_mean / (seconds * 1000.0)) if enc_coreml_ms_mean > 0 else None - enc_torch_rtf = float(enc_torch_ms_mean / (seconds * 1000.0)) if enc_torch_ms_mean > 0 else None - - summary["components"]["encoder"] = { - "encoder": {"max_abs": a_enc, "max_rel": r_enc, "match": bool(ok_enc)}, - "length_match": bool(ok_enc_len), - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "coreml_first_ms": enc_first_ms, - "torch_ms": {"mean": enc_torch_ms_mean, "std": enc_torch_ms_std}, - "coreml_ms": {"mean": enc_coreml_ms_mean, "std": enc_coreml_ms_std}, - "rtf": {"torch": enc_torch_rtf, "coreml": enc_coreml_rtf}, - }, - "plots": {k: v for k, v in enc_plots.items() if v}, - } - - # Decoder (sequential U=1 rollout) - dec = ct.models.MLModel(str(output_dir / "parakeet_decoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - - zero_state_np = np.zeros((decoder_layers, 1, decoder_hidden), dtype=np.float32) - blank_targets_np = np.array(blank_targets.detach().cpu().numpy(), dtype=np.int32, copy=True) - blank_target_lengths_np = np.array(blank_target_lengths.detach().cpu().numpy(), dtype=np.int32, copy=True) - - def _decoder_rollout_coreml(num_steps: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray, float]: - outputs = [] - h_np = zero_state_np.copy() - c_np = zero_state_np.copy() - first_ms: Optional[float] = None - for i in range(num_steps): - t0_i = time.perf_counter() if i == 0 else None - res = dec.predict( - { - "targets": blank_targets_np, - "target_length": blank_target_lengths_np, - "h_in": h_np, - "c_in": c_np, - } - ) - if t0_i is not None: - t1_i = time.perf_counter() - first_ms = (t1_i - t0_i) * 1000.0 - outputs.append(np.array(res["decoder"], dtype=np.float32, copy=True)) - h_np = np.array(res["h_out"], dtype=np.float32, copy=True) - c_np = np.array(res["c_out"], dtype=np.float32, copy=True) - if outputs: - decoder_seq = np.concatenate(outputs, axis=-1) - else: - decoder_seq = np.zeros((1, decoder_hidden, 0), dtype=np.float32) - return decoder_seq, h_np, c_np, (0.0 if first_ms is None else float(first_ms)) - - dec_ml, h_ml, c_ml, dec_first_ms = _decoder_rollout_coreml(symbol_steps) - h_ref_np = _np32(h_ref) - c_ref_np = _np32(c_ref) - - a_dec, r_dec, ok_dec = _max_diffs(decoder_ref_np, dec_ml, max(rtol, 1e-2), max(atol, 1e-1)) - a_h, r_h, ok_h = _max_diffs(h_ref_np, h_ml, max(rtol, 1e-2), max(atol, 2.5e-1)) - a_c, r_c, ok_c = _max_diffs(c_ref_np, c_ml, max(rtol, 5e-2), max(atol, 1.5e0)) - - dec_t = decoder_ref_np[0] - dec_c = dec_ml[0] - dec_plots = { - "decoder_steps_l2.png": _plot_line( - np.linalg.norm(dec_t, axis=0), - np.linalg.norm(dec_c, axis=0), - "Decoder L2 over steps", - plots_dir / "decoder_steps_l2.png", - also_delta=True, - ), - } - - def _time_decoder_coreml() -> Tuple[float, float]: - for _ in range(max(0, warmup)): - _decoder_rollout_coreml(symbol_steps) - times = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _decoder_rollout_coreml(symbol_steps) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - dec_torch_ms_mean, dec_torch_ms_std = _time_torch(lambda: _decoder_rollout_torch(symbol_steps)) - dec_coreml_ms_mean, dec_coreml_ms_std = _time_decoder_coreml() - dec_coreml_rtf = float(dec_coreml_ms_mean / (seconds * 1000.0)) if dec_coreml_ms_mean > 0 else None - dec_torch_rtf = float(dec_torch_ms_mean / (seconds * 1000.0)) if dec_torch_ms_mean > 0 else None - - summary["components"]["decoder"] = { - "decoder": {"max_abs": a_dec, "max_rel": r_dec, "match": bool(ok_dec)}, - "h_out": {"max_abs": a_h, "max_rel": r_h, "match": bool(ok_h)}, - "c_out": {"max_abs": a_c, "max_rel": r_c, "match": bool(ok_c)}, - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "coreml_first_ms": dec_first_ms, - "torch_ms": {"mean": dec_torch_ms_mean, "std": dec_torch_ms_std}, - "coreml_ms": {"mean": dec_coreml_ms_mean, "std": dec_coreml_ms_std}, - "rtf": {"torch": dec_torch_rtf, "coreml": dec_coreml_rtf}, - }, - "plots": {k: v for k, v in dec_plots.items() if v}, - } - - # Joint (sequential U=1 rollouts) - j = ct.models.MLModel(str(output_dir / "parakeet_joint.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - - def _joint_rollout_coreml(decoder_seq_np: np.ndarray) -> Tuple[np.ndarray, float]: - logits_steps = [] - first_ms: Optional[float] = None - for u in range(decoder_seq_np.shape[2]): - dec_slice = decoder_seq_np[:, :, u : u + 1] - t0_u = time.perf_counter() if u == 0 else None - res = j.predict({"encoder": encoder_np, "decoder": dec_slice}) - if t0_u is not None: - t1_u = time.perf_counter() - first_ms = (t1_u - t0_u) * 1000.0 - logits_steps.append(np.array(res["logits"], dtype=np.float32, copy=True)) - if not logits_steps: - raise RuntimeError("No decoder steps provided for joint rollout") - return np.concatenate(logits_steps, axis=2), (0.0 if first_ms is None else float(first_ms)) - - logits_ml, joint_first_ms = _joint_rollout_coreml(decoder_ref_np) - logits_ref_np = _np32(logits_ref) - a_j, r_j, ok_j = _max_diffs(logits_ref_np, logits_ml, max(rtol, 1e-2), max(atol, 1e-1)) - joint_plots = {} - if HAS_MPL: - lt = logits_ref_np[0, 0, 0, :] - lc = logits_ml[0, 0, 0, :] - top_idx = np.argsort(-np.abs(lt))[:50] - path = plots_dir / "joint_top50.png" - plt.figure(figsize=(8, 3)) - plt.plot(lt[top_idx], label="torch") - plt.plot(lc[top_idx], label="coreml", alpha=0.8) - plt.title("Joint logits (t=0,u=0) top-50 |torch|") - plt.legend(); plt.tight_layout(); plt.savefig(path); plt.close() - joint_plots["joint_top50.png"] = str(path.name) - - # Delta-over-time visualization (fix u=0; summarize over vocab) - jt = logits_ref_np[0, :, 0, :] - jc = logits_ml[0, :, 0, :] - l2_t = np.linalg.norm(jt, axis=1) - l2_c = np.linalg.norm(jc, axis=1) - path2 = plots_dir / "joint_time_l2.png" - _plot_line(l2_t, l2_c, "Joint L2 over time (u=0)", path2, also_delta=True) - joint_plots["joint_time_l2.png"] = str(path2.name) - - def _time_joint_coreml() -> Tuple[float, float]: - for _ in range(max(0, warmup)): - _joint_rollout_coreml(decoder_ref_np) - times = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _joint_rollout_coreml(decoder_ref_np) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - joint_torch_ms_mean, joint_torch_ms_std = _time_torch( - asr_model.joint, encoder_outputs=encoder_ref, decoder_outputs=decoder_ref - ) - joint_coreml_ms_mean, joint_coreml_ms_std = _time_joint_coreml() - joint_coreml_rtf = float(joint_coreml_ms_mean / (seconds * 1000.0)) if joint_coreml_ms_mean > 0 else None - joint_torch_rtf = float(joint_torch_ms_mean / (seconds * 1000.0)) if joint_torch_ms_mean > 0 else None - - summary["components"]["joint"] = { - "logits": {"max_abs": a_j, "max_rel": r_j, "match": bool(ok_j)}, - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "coreml_first_ms": joint_first_ms, - "torch_ms": {"mean": joint_torch_ms_mean, "std": joint_torch_ms_std}, - "coreml_ms": {"mean": joint_coreml_ms_mean, "std": joint_coreml_ms_std}, - "rtf": {"torch": joint_torch_rtf, "coreml": joint_coreml_rtf}, - }, - "plots": joint_plots, - } - - # Fused components - # 1) Mel+Encoder fused vs separate - mel_enc_plots = {} - try: - mel_enc = ct.models.MLModel(str(output_dir / "parakeet_mel_encoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - t0 = time.perf_counter() - mel_enc_out = mel_enc.predict({ - "audio_signal": _np32(audio_tensor), - "audio_length": _np32(audio_length).astype(np.int32), - }) - t1 = time.perf_counter() - mel_enc_first_ms = (t1 - t0) * 1000.0 - mel_enc_ml = np.array(mel_enc_out["encoder"], dtype=np.float32, copy=True) - mel_enc_len_ml = np.array(mel_enc_out["encoder_length"], dtype=np.int32, copy=True) - # Compare fused output vs Torch reference encoder - a_melenc, r_melenc, ok_melenc = _max_diffs(_np32(encoder_ref), mel_enc_ml, max(rtol, 5e-3), max(atol, 5e-2)) - ok_melenc_len = int(_np32(encoder_length_ref).astype(np.int32)[0]) == int(mel_enc_len_ml.astype(np.int32)[0]) - # Also compare fused vs separate CoreML pipeline (pre -> enc) - a_melenc_vs_sep, r_melenc_vs_sep, ok_melenc_vs_sep = _max_diffs(enc_ml, mel_enc_ml, max(rtol, 5e-3), max(atol, 5e-2)) - - # Plots: L2 over time (fused vs torch) - enc_t_ref = _np32(encoder_ref)[0] - enc_c_fused = mel_enc_ml[0] - mel_enc_plots["mel_encoder_time_l2.png"] = _plot_line( - np.linalg.norm(enc_t_ref, axis=0), - np.linalg.norm(enc_c_fused, axis=0), - "Mel+Encoder (fused) L2 over time", - plots_dir / "mel_encoder_time_l2.png", - also_delta=True, - ) - - # Latency: fused CoreML vs separate (CoreML pre + CoreML enc) - mel_enc_coreml_ms_mean, mel_enc_coreml_ms_std = _time_coreml( - mel_enc, - {"audio_signal": _np32(audio_tensor), "audio_length": _np32(audio_length).astype(np.int32)}, - ) - sep_coreml_ms_mean = float(pre_coreml_ms_mean + enc_coreml_ms_mean) - sep_coreml_ms_std = float((pre_coreml_ms_std ** 2 + enc_coreml_ms_std ** 2) ** 0.5) - # Torch baseline (separate torch pre + enc) - sep_torch_ms_mean = float(pre_torch_ms_mean + enc_torch_ms_mean) - sep_torch_ms_std = float((pre_torch_ms_std ** 2 + enc_torch_ms_std ** 2) ** 0.5) - - mel_enc_coreml_rtf = float(mel_enc_coreml_ms_mean / (seconds * 1000.0)) if mel_enc_coreml_ms_mean > 0 else None - sep_coreml_rtf = float(sep_coreml_ms_mean / (seconds * 1000.0)) if sep_coreml_ms_mean > 0 else None - sep_torch_rtf = float(sep_torch_ms_mean / (seconds * 1000.0)) if sep_torch_ms_mean > 0 else None - - summary["components"]["mel_encoder"] = { - "encoder": {"max_abs": a_melenc, "max_rel": r_melenc, "match": bool(ok_melenc)}, - "length_match": bool(ok_melenc_len), - "vs_separate_coreml": {"max_abs": a_melenc_vs_sep, "max_rel": r_melenc_vs_sep, "match": bool(ok_melenc_vs_sep)}, - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "fused_coreml_first_ms": mel_enc_first_ms, - "fused_coreml_ms": {"mean": mel_enc_coreml_ms_mean, "std": mel_enc_coreml_ms_std}, - "separate_coreml_ms": {"mean": sep_coreml_ms_mean, "std": sep_coreml_ms_std}, - "separate_torch_ms": {"mean": sep_torch_ms_mean, "std": sep_torch_ms_std}, - "rtf": {"fused_coreml": mel_enc_coreml_rtf, "separate_coreml": sep_coreml_rtf, "separate_torch": sep_torch_rtf}, - }, - "plots": {k: v for k, v in mel_enc_plots.items() if v}, - } - except Exception as e: - summary["components"]["mel_encoder_error"] = str(e) - - # 2) JointDecision fused vs CPU PyTorch post-processing - jd_plots = {} - try: - # Fused CoreML joint decision - jd = ct.models.MLModel(str(output_dir / "parakeet_joint_decision.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - def _joint_decision_rollout_coreml(decoder_seq_np: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, float]: - token_ids = [] - token_probs = [] - durations = [] - first_ms: Optional[float] = None - for u in range(decoder_seq_np.shape[2]): - dec_slice = decoder_seq_np[:, :, u : u + 1] - t0_u = time.perf_counter() if u == 0 else None - res = jd.predict({"encoder": encoder_np, "decoder": dec_slice}) - if t0_u is not None: - t1_u = time.perf_counter() - first_ms = (t1_u - t0_u) * 1000.0 - token_ids.append(np.array(res["token_id"], dtype=np.int32, copy=True)) - token_probs.append(np.array(res["token_prob"], dtype=np.float32, copy=True)) - durations.append(np.array(res["duration"], dtype=np.int32, copy=True)) - if not token_ids: - raise RuntimeError("No decoder steps provided for joint decision rollout") - return ( - np.concatenate(token_ids, axis=2), - np.concatenate(token_probs, axis=2), - np.concatenate(durations, axis=2), - (0.0 if first_ms is None else float(first_ms)), - ) - - token_id_ml, token_prob_ml, duration_ml, jd_first_ms = _joint_decision_rollout_coreml(decoder_ref_np) - - # CPU PyTorch decision using Torch logits - vocab_with_blank = int(vocab_size) + 1 - with torch.inference_mode(): - logits_t = logits_ref - token_logits_t = logits_t[..., :vocab_with_blank] - duration_logits_t = logits_t[..., -num_extra:] if num_extra > 0 else None - token_ids_t = torch.argmax(token_logits_t, dim=-1).to(dtype=torch.int32) - token_probs_all_t = torch.softmax(token_logits_t, dim=-1) - token_prob_t = torch.gather( - token_probs_all_t, dim=-1, index=token_ids_t.long().unsqueeze(-1) - ).squeeze(-1) - if duration_logits_t is not None and duration_logits_t.numel() > 0: - duration_t = torch.argmax(duration_logits_t, dim=-1).to(dtype=torch.int32) - else: - duration_t = torch.zeros_like(token_ids_t, dtype=torch.int32) - - # Also derive CPU decision from CoreML joint logits for "separate" path - token_logits_c = _to_t(logits_ml)[..., :vocab_with_blank] - duration_logits_c = _to_t(logits_ml)[..., -num_extra:] if num_extra > 0 else None - token_ids_c = torch.argmax(token_logits_c, dim=-1).to(dtype=torch.int32) - token_probs_all_c = torch.softmax(token_logits_c, dim=-1) - token_prob_c = torch.gather( - token_probs_all_c, dim=-1, index=token_ids_c.long().unsqueeze(-1) - ).squeeze(-1) - if duration_logits_c is not None and duration_logits_c.numel() > 0: - duration_c = torch.argmax(duration_logits_c, dim=-1).to(dtype=torch.int32) - else: - duration_c = torch.zeros_like(token_ids_c, dtype=torch.int32) - - # Compare fused outputs to CPU PyTorch decisions - a_tid_t, r_tid_t, ok_tid_t = _max_diffs(_np(token_ids_t), token_id_ml, 0.0, 0.0) - a_tprob_t, r_tprob_t, ok_tprob_t = _max_diffs(_np(token_prob_t), token_prob_ml, max(rtol, 1e-2), max(atol, 1e-1)) - a_dur_t, r_dur_t, ok_dur_t = _max_diffs(_np(duration_t), duration_ml, 0.0, 0.0) - - a_tid_c, r_tid_c, ok_tid_c = _max_diffs(_np(token_ids_c), token_id_ml, 0.0, 0.0) - a_tprob_c, r_tprob_c, ok_tprob_c = _max_diffs(_np(token_prob_c), token_prob_ml, max(rtol, 1e-2), max(atol, 1e-1)) - a_dur_c, r_dur_c, ok_dur_c = _max_diffs(_np(duration_c), duration_ml, 0.0, 0.0) - - # Plots: token_prob over time for u=0 (fused vs torch CPU) - if HAS_MPL: - prob_t = _np(token_prob_t)[0, :, 0] - prob_ml = token_prob_ml[0, :, 0] - jd_plots["joint_decision_prob_u0.png"] = _plot_line( - prob_t, - prob_ml, - "JointDecision token_prob (u=0)", - plots_dir / "joint_decision_prob_u0.png", - also_delta=True, - ) - - # Agreement heatmap for token_id - agree = (_np(token_ids_t)[0] == token_id_ml[0]).astype(np.float32) - jd_plots["joint_decision_token_agree.png"] = _plot_image( - agree, - "token_id agreement (torch CPU vs fused)", - plots_dir / "joint_decision_token_agree.png", - vmin=0.0, - vmax=1.0, - ) - - # Latency: fused CoreML vs separate (CoreML joint + CPU PyTorch decision) - def _time_joint_decision_coreml() -> Tuple[float, float]: - for _ in range(max(0, warmup)): - _joint_decision_rollout_coreml(decoder_ref_np) - times = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _joint_decision_rollout_coreml(decoder_ref_np) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - jd_coreml_ms_mean, jd_coreml_ms_std = _time_joint_decision_coreml() - - # Time CPU post-processing only (Torch) on top of CoreML or Torch logits. Use Torch logits. - def _decision_torch_call(): - with torch.inference_mode(): - tl = logits_ref - tl_token = tl[..., :vocab_with_blank] - tl_ids = torch.argmax(tl_token, dim=-1) - tl_probs = torch.softmax(tl_token, dim=-1) - _ = torch.gather(tl_probs, -1, tl_ids.long().unsqueeze(-1)).squeeze(-1) - if num_extra > 0: - _ = torch.argmax(tl[..., -num_extra:], dim=-1) - return None - - jd_decision_torch_ms_mean, jd_decision_torch_ms_std = _time_torch(lambda: _decision_torch_call()) - sep_joint_plus_cpu_ms_mean = float(joint_coreml_ms_mean + jd_decision_torch_ms_mean) - sep_joint_plus_cpu_ms_std = float((joint_coreml_ms_std ** 2 + jd_decision_torch_ms_std ** 2) ** 0.5) - jd_coreml_rtf = float(jd_coreml_ms_mean / (seconds * 1000.0)) if jd_coreml_ms_mean > 0 else None - sep_joint_cpu_rtf = float(sep_joint_plus_cpu_ms_mean / (seconds * 1000.0)) if sep_joint_plus_cpu_ms_mean > 0 else None - - summary["components"]["joint_decision"] = { - "vs_torch_cpu": { - "token_id": {"max_abs": a_tid_t, "max_rel": r_tid_t, "match": bool(ok_tid_t)}, - "token_prob": {"max_abs": a_tprob_t, "max_rel": r_tprob_t, "match": bool(ok_tprob_t)}, - "duration": {"max_abs": a_dur_t, "max_rel": r_dur_t, "match": bool(ok_dur_t)}, - }, - "vs_coreml_joint_cpu": { - "token_id": {"max_abs": a_tid_c, "max_rel": r_tid_c, "match": bool(ok_tid_c)}, - "token_prob": {"max_abs": a_tprob_c, "max_rel": r_tprob_c, "match": bool(ok_tprob_c)}, - "duration": {"max_abs": a_dur_c, "max_rel": r_dur_c, "match": bool(ok_dur_c)}, - }, - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "fused_coreml_first_ms": jd_first_ms, - "fused_coreml_ms": {"mean": jd_coreml_ms_mean, "std": jd_coreml_ms_std}, - "separate_joint_coreml_plus_cpu_ms": {"mean": sep_joint_plus_cpu_ms_mean, "std": sep_joint_plus_cpu_ms_std}, - "rtf": {"fused_coreml": jd_coreml_rtf, "separate_joint_coreml_plus_cpu": sep_joint_cpu_rtf}, - }, - "plots": {k: v for k, v in jd_plots.items() if v}, - } - except Exception as e: - summary["components"]["joint_decision_error"] = str(e) - - # Latency overview plots (saved alongside component plots) - latency_plots = {} - labels = ["preprocessor", "encoder", "decoder", "joint"] - torch_means = [pre_torch_ms_mean, enc_torch_ms_mean, dec_torch_ms_mean, joint_torch_ms_mean] - torch_stds = [pre_torch_ms_std, enc_torch_ms_std, dec_torch_ms_std, joint_torch_ms_std] - coreml_means = [pre_coreml_ms_mean, enc_coreml_ms_mean, dec_coreml_ms_mean, joint_coreml_ms_mean] - coreml_stds = [pre_coreml_ms_std, enc_coreml_ms_std, dec_coreml_ms_std, joint_coreml_ms_std] - lat_path = plots_dir / "latency_summary.png" - spd_path = plots_dir / "latency_speedup.png" - latency_plots["latency_summary.png"] = _plot_latency_bars( - labels, torch_means, torch_stds, coreml_means, coreml_stds, lat_path - ) - latency_plots["latency_speedup.png"] = _plot_speedup_bars( - labels, torch_means, coreml_means, spd_path - ) - - # Fused vs separate latency summary - fused_labels = ["mel+encoder", "joint_decision"] - fused_baseline_means = [ - float(pre_torch_ms_mean + enc_torch_ms_mean), - float(joint_coreml_ms_mean + jd_decision_torch_ms_mean if 'jd_coreml_ms_mean' in locals() else joint_coreml_ms_mean), - ] - fused_coreml_means = [ - float(mel_enc_coreml_ms_mean if 'mel_enc_coreml_ms_mean' in locals() else np.nan), - float(jd_coreml_ms_mean if 'jd_coreml_ms_mean' in locals() else np.nan), - ] - fused_latency_path = plots_dir / "latency_fused_vs_separate.png" - fused_speedup_path = plots_dir / "latency_fused_speedup.png" - latency_plots["latency_fused_vs_separate.png"] = _plot_latency_bars( - fused_labels, fused_baseline_means, [0, 0], fused_coreml_means, [0, 0], fused_latency_path - ) - latency_plots["latency_fused_speedup.png"] = _plot_speedup_bars( - fused_labels, fused_baseline_means, fused_coreml_means, fused_speedup_path - ) - - all_ok = ( - summary["components"]["preprocessor"]["mel"]["match"] - and summary["components"]["preprocessor"]["length_match"] - and summary["components"]["encoder"]["encoder"]["match"] - and summary["components"]["encoder"]["length_match"] - and summary["components"]["decoder"]["decoder"]["match"] - and summary["components"]["decoder"]["h_out"]["match"] - and summary["components"]["decoder"]["c_out"]["match"] - and summary["components"]["joint"]["logits"]["match"] - ) - summary["status"] = "ok" if all_ok else "mismatch" - - # Update metadata.json - meta_path = output_dir / "metadata.json" - try: - meta = json.loads(meta_path.read_text()) - except Exception: - meta = {} - meta["validation"] = summary - meta_path.write_text(json.dumps(meta, indent=2)) - - typer.echo(f"Validation {'passed' if all_ok else 'mismatched'}. Updated {meta_path}") - if HAS_MPL: - typer.echo(f"Saved plots to {plots_dir}") - - -if __name__ == "__main__": - app() diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/compile_modelc.py b/convert/parakeet-tdt-v2-0.6b/coreml/compile_modelc.py deleted file mode 100644 index 9695e03b41fce9280f2ca2ee0e9585c26b2393cc..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/compile_modelc.py +++ /dev/null @@ -1,91 +0,0 @@ -#!/usr/bin/env python3 -"""Compile Core ML packages into ``.mlmodelc`` bundles via ``xcrun``. - -This script walks through the default Parakeet CoreML directories, finds -all ``*.mlpackage`` bundles, and compiles each of them with -``xcrun coremlcompiler`` into ``./compiled`` while preserving the -relative directory structure. -""" -from __future__ import annotations - -import shutil -import subprocess -import sys -from pathlib import Path - -BASE_DIR = Path(__file__).resolve().parent -OUTPUT_ROOT = BASE_DIR / "compiled" -SOURCE_DIRS = [BASE_DIR / "parakeet_coreml", BASE_DIR / "parakeet_coreml_quantized"] - - -def ensure_coremlcompiler() -> None: - """Ensure ``xcrun coremlcompiler`` is available for the active Xcode.""" - xcrun_path = shutil.which("xcrun") - if xcrun_path is None: - print("Error: 'xcrun' not found on PATH. Install Xcode command line tools.", file=sys.stderr) - sys.exit(1) - - try: - subprocess.run([ - xcrun_path, - "--find", - "coremlcompiler", - ], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - except subprocess.CalledProcessError: - print("Error: 'coremlcompiler' not found via xcrun. Check your Xcode installation.", file=sys.stderr) - sys.exit(1) - - -def gather_packages() -> list[Path]: - """Return a list of all ``*.mlpackage`` bundles under the source dirs.""" - packages: list[Path] = [] - for source in SOURCE_DIRS: - if not source.exists(): - print(f"Warning: {source.relative_to(BASE_DIR)} does not exist; skipping", file=sys.stderr) - continue - packages.extend(source.rglob("*.mlpackage")) - return packages - - -def compile_package(package: Path) -> None: - """Compile a single ``.mlpackage`` bundle using ``xcrun coremlcompiler``.""" - relative_pkg = package.relative_to(BASE_DIR) - output_dir = OUTPUT_ROOT / relative_pkg.parent - output_dir.mkdir(parents=True, exist_ok=True) - output_path = output_dir / f"{package.stem}.mlmodelc" - - if output_path.exists(): - shutil.rmtree(output_path) - - cmd = [ - "xcrun", - "coremlcompiler", - "compile", - str(package), - str(output_dir), - ] - - print(f"Compiling {relative_pkg} -> {output_path.relative_to(BASE_DIR)}") - subprocess.run(cmd, check=True) - - -def main() -> None: - ensure_coremlcompiler() - packages = gather_packages() - - if not packages: - print("No .mlpackage bundles found to compile.") - return - - for package in packages: - try: - compile_package(package) - except subprocess.CalledProcessError as exc: - print(f"Failed to compile {package}: {exc}", file=sys.stderr) - sys.exit(exc.returncode) - - print(f"Finished compiling {len(packages)} package(s) into {OUTPUT_ROOT.relative_to(BASE_DIR)}.") - - -if __name__ == "__main__": - main() diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/context/Efficient Sequence Transduction by Jointly Predicting Tokens and Durations.pdf b/convert/parakeet-tdt-v2-0.6b/coreml/context/Efficient Sequence Transduction by Jointly Predicting Tokens and Durations.pdf deleted file mode 100644 index b3fdd0192810231ef4b0ac61b608a7e06d9d4f6c..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/context/Efficient Sequence Transduction by Jointly Predicting Tokens and Durations.pdf +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a29e637f2f977a44bece33863aeb01b45b0fc73a9bb723aea2960986e41c5bba -size 3071390 diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/context/FAST CONFORMER WITH LINEARLY SCALABLE ATTENTION.pdf b/convert/parakeet-tdt-v2-0.6b/coreml/context/FAST CONFORMER WITH LINEARLY SCALABLE ATTENTION.pdf deleted file mode 100644 index d0aef3ea5d69a76e18bbd94161a522246ccfd1fc..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/context/FAST CONFORMER WITH LINEARLY SCALABLE ATTENTION.pdf +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:eb8c7706a567ec5795333139f8cd41ff9c127ba99b6dc85d378eb3fa3213bf8d -size 586484 diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/context/coreml_component_io.md b/convert/parakeet-tdt-v2-0.6b/coreml/context/coreml_component_io.md deleted file mode 100644 index 85a34b5927f4e7e98a4c672ebc7cc249992c3100..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/context/coreml_component_io.md +++ /dev/null @@ -1,96 +0,0 @@ -# CoreML Component I/O (Parakeet‑TDT‑0.6B‑v2) - -This file documents the CoreML I/O contracts for each exported sub‑module. All shapes below target the fixed 15‑second window at 16 kHz (240,000 samples). Batch is fixed to 1 for on-device use, and we do not plan to support other window sizes in CoreML (re-export if the requirement changes). - -## Conventions - -- Dtypes: `float32` for real‑valued tensors, `int32` for lengths and token IDs. -- Batch: `B=1` everywhere. -- Vocab: `V=8192` BPE tokens; RNNT adds blank (+1). TDT adds 5 duration logits appended to the class axis. -- Subsampling: overall encoder time reduction ×8. - -## PreprocessorWrapper - -- Inputs - - `audio_signal`: `[1, 240000]` float32, PCM range ~[-1, 1] - - `length`: `[1]` int32, number of valid samples (<= 240000) -- Outputs - - `mel`: `[1, 128, 1501]` float32 (center=True => frames = floor(240000/160)+1 = 1501) - - `mel_length`: `[1]` int32 (=1501 for full 15 s) - -## EncoderWrapper - -- Inputs - - `features`: `[1, 128, 1501]` float32 (mel) - - `length`: `[1]` int32 (=1501) -- Outputs - - `encoded`: `[1, 188, 1024]` float32 (wrapper has time‑major last: it transposes `[B,D,T] -> [B,T,D]`) - - `encoded_length`: `[1]` int32 (`ceil(1501/8)=188`) - -## DecoderWrapper (stateful LSTM, 2 layers) - -- Inputs - - `targets`: `[1, U]` int32 (token IDs in [0, V)) - - `target_lengths`: `[1]` int32 (=U) - - `h_in`: `[2, 1, 640]` float32 (LSTM hidden, L=2 layers) - - `c_in`: `[2, 1, 640]` float32 (LSTM cell, L=2 layers) -- Outputs - - `decoder_output`: `[1, U, 640]` float32 - - `h_out`: `[2, 1, 640]` float32 - - `c_out`: `[2, 1, 640]` float32 - -Notes - -- For step‑wise greedy decoding, set `U=1` and feed back `h_out`, `c_out`. -- For batched token scoring, `U` can be >1 to evaluate multiple symbols per call. - -## JointWrapper - -- Inputs - - `encoder_outputs`: `[1, 188, 1024]` float32 (time‑major) - - `decoder_outputs`: `[1, U, 640]` float32 -- Output - - `logits`: `[1, 188, U, 8192 + 1 + 5]` float32 - -Notes - -- Split the last dimension into `[token_logits: V+1]` and `[duration_logits: 5]` when post‑processing. -- `log_softmax` is disabled for export; apply on CPU if needed. - -## MelEncoderWrapper (fused) - -- Inputs - - `audio_signal`: `[1, 240000]` float32 - - `audio_length`: `[1]` int32 -- Outputs - - `encoder`: `[1, 188, 1024]` float32 - - `encoder_length`: `[1]` int32 - -Notes - -- Fuses preprocessor + encoder to avoid a mel round‑trip. Shapes match chaining `PreprocessorWrapper` then `EncoderWrapper` on the fixed 15 s window. - -## JointDecisionWrapper (fused post‑processing) - -- Inputs - - `encoder`: `[1, 188, 1024]` float32 - - `decoder`: `[1, U, 640]` float32 -- Outputs - - `token_id`: `[1, 188, U]` int32 (argmax over token logits, includes blank) - - `token_prob`: `[1, 188, U]` float32 (softmax probability of chosen token) - - `duration`: `[1, 188, U]` int32 (argmax over 5 duration logits) - -Notes - -- Embeds the common host‑side steps: split joint logits, softmax over token logits, argmax over both heads, and gather chosen token probability. Replaces separate `runJoint` → `splitLogits` → `softmax/argmax` in Swift. - -## Suggested CoreML tensor names - -- Preprocessor: `audio_signal`, `audio_length` → `mel`, `mel_length` -- Encoder: `mel`, `mel_length` → `encoder`, `encoder_length` -- Decoder: `targets`, `target_length`, `h_in`, `c_in` → `decoder`, `h_out`, `c_out` -- Joint: `encoder`, `decoder` → `logits` -- MelEncoder (fused): `audio_signal`, `audio_length` → `encoder`, `encoder_length` -- JointDecision (fused): `encoder`, `decoder` → `token_id`, `token_prob`, `duration` - -These names align with wrappers in `individual_components.py` and simplify downstream wiring. diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/context/coreml_conversion_plan.md b/convert/parakeet-tdt-v2-0.6b/coreml/context/coreml_conversion_plan.md deleted file mode 100644 index cf3cd4c09dbd02b882cca3f37f0802f7bfa569b1..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/context/coreml_conversion_plan.md +++ /dev/null @@ -1,65 +0,0 @@ -# CoreML Conversion Plan (Parakeet‑TDT‑0.6B‑v2) - -This plan describes how we export Parakeet’s sub‑modules to CoreML, validate numerics, and prepare for on‑device decoding. The pipeline keeps a fixed 15‑second audio window. - -## Goals - -- Export preprocessor, encoder, decoder, and joint as separate mlpackages. -- Also export fused variants: `mel_encoder` (preprocessor+encoder) and `joint_decision` (joint + split/softmax/argmax). -- Preserve streaming decoder state I/O for on‑device greedy decoding. -- Validate component outputs against the NeMo reference on a 15‑second clip. - -## Environment - -- Use `uv venv` and run via `uv run` to ensure reproducible resolutions. -- Python 3.10.12, `torch 2.5.0`, `coremltools 8.3.0`, `nemo-toolkit 2.3.1`. - -## Export settings - -- `convert_to = "mlprogram"` -- `minimum_deployment_target` = `ct.target.iOS17` (we only target iOS 17+ for the CoreML deployment) -- `compute_units` = `.CPU_AND_GPU` (or `.ALL` on iOS) -- `compute_precision` = `ct.precision.FLOAT16` or `None` (start with fp32 for validation, then try fp16) -- Fixed window: 15 seconds at 16 kHz → shapes as in `context/coreml_component_io.md`. No variable-length support is required; CoreML graphs can assume the 15 s window. - -## Steps - -- Load the NeMo checkpoint with `ASRModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v2")`. -- Extract modules: preprocessor, encoder, decoder, joint. -- Wrap modules with `PreprocessorWrapper`, `EncoderWrapper`, `DecoderWrapper`, `JointWrapper`; derive `MelEncoderWrapper` and `JointDecisionWrapper` for fused exports. -- Trace each wrapper with static 15‑second inputs (batch=1). Ensure outputs match I/O contracts in `coreml_component_io.md`. -- Define CoreML inputs with explicit names and fixed shapes (15 s audio → mel 1501 → encoder 188). Keeping the window fixed simplifies CoreML deployment; re‑export if window size changes. -- Convert via `ct.convert(...)` with the settings above. -- Save each mlpackage under the chosen `output_dir`, along with a `metadata.json` capturing shapes, sample rate, vocab size, tokenizer path, and export metadata (author, conversion commit, etc.). -- Emit fused graphs (`parakeet_mel_encoder.mlpackage` and `parakeet_joint_decision.mlpackage`) alongside standalone components to reduce host I/O and post‑processing. -- Provide a helper like `compile_modelc.py` to batch `xcrun coremlcompiler` invocations when packaging for release. - -## CLI layout - -- `convert_parakeet_components.py` → conversion-only entry point (no validation), parameterized by compute units, precision, and optional fused exports. Model inspection (encoder strides, etc.) should auto-populate config, inspired by Silero VAD’s converter. -- `compare_parakeet_models.py` → validation/plotting script that loads reference NeMo modules and CoreML packages, runs diffs per component, and reports max/mean errors plus RTF metrics. Use lightweight wrappers similar to Silero’s `PyTorchJITWrapper` / `CoreMLVADWrapper`. -- Future helpers: `compile_modelc.py` (reuse pattern from VAD project) and optional quantization experiments once parity is established. - -## Validation - -- Run inference on a known 16 kHz clip (trim/pad to 15 s): - - Compare preprocessor mel and lengths (max abs/rel diff thresholds: atol=1e-4, rtol=1e-3). - - Compare encoder outputs and lengths. - - Compare decoder outputs for a fixed target sequence and initial zero states. - - Compare joint logits on the same `(T_enc, U)` grid; split `[V+1]` token logits from the last 5 duration logits when computing metrics. -- Record per‑component diffs in `metadata.json` for auditability. - -## Decoding (device) - -- Implement greedy RNNT in app code calling CoreML: - - Either: use modular `joint` and perform split/softmax/argmax on host. - - Or: call fused `joint_decision` to receive `token_id`, `token_prob`, and `duration` directly. - - Preprocess → Encode once per window (or call fused `mel_encoder`). - - Maintain `(h, c)` across symbol steps. - - Blank handling: `token_id == V` indicates blank. - -## Known caveats - -- RNNT joint logits are large: `[188 × U × ~8200]` per window; consider fp16 and/or tiling over `U` to reduce memory. -- Length maths: mel frames use `center=True`, yielding 1501 frames for exactly 240,000 samples; encoder length computed via ceil divide by 8. -- For accurate timestamping, use the original NeMo decoder on server to validate any device‑side greedy implementation. diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/context/hf_model_card_notes.md b/convert/parakeet-tdt-v2-0.6b/coreml/context/hf_model_card_notes.md deleted file mode 100644 index 082919ab08fdfb6067b8537d0af73b3c5001ff5a..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/context/hf_model_card_notes.md +++ /dev/null @@ -1,24 +0,0 @@ -# Hugging Face Model Card Notes: nvidia/parakeet-tdt-0.6b-v2 - -Key details extracted from the public model card for quick reference. - -## Summary -- Task: Multilingual automatic speech recognition (ASR) -- Architecture: FastConformer encoder + RNNT + TDT -- Params: ~0.6B -- License: CC‑BY‑4.0 - -## Languages (25 EU + Russian/Ukrainian) -bg, hr, cs, da, nl, et, fi, fr, de, el, hu, it, lv, lt, mt, pl, pt, ro, sk, sl, sv, es, en, ru, uk - -## Input / Output -- Input: 16 kHz mono audio (`.wav`, `.flac`) -- Output: text with punctuation and capitalization - -## Notes relevant to conversion -- The model card reports accurate word/segment timestamps; Parakeet TDT uses duration buckets which we expose from the joint head (last 5 logits along the class axis). -- Long‑audio inference is reported for GPU; our CoreML export intentionally uses a fixed 15‑second window to fit on‑device constraints. - -References -- HF repo: https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2 -- NeMo FastConformer: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/context/mel_encoder_ane_behavior.md b/convert/parakeet-tdt-v2-0.6b/coreml/context/mel_encoder_ane_behavior.md deleted file mode 100644 index e55cf015d7b17e89d1ce1dedeb54512a056cf5ae..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/context/mel_encoder_ane_behavior.md +++ /dev/null @@ -1,67 +0,0 @@ -# MelEncoder ANE behavior and iPhone 13 issue - -Summary of the failure seen on iPhone 13 (A15 / E5): - -> ANE model load has failed for on-device compiled macho. Must re-compile the E5 bundle. Invalid layer: Tensor dimensions N1D1C1H1W240000 are not within supported range, N[1-65536] D[1-16384] C[1-65536] H[1-16384] W[1-16384]. - -## Root cause - -- The fused MelEncoder model takes raw waveform input shaped `[1, 240000]` for the fixed 15 s window (16 kHz × 15 s). -- In Core ML’s internal layout this maps to `N1 D1 C1 H1 W240000`. -- Apple Neural Engine (ANE) enforces a per-dimension cap of ≀ 16384 for H and W. `W=240000` violates this, so any ANE partition that “sees” the waveform will fail validation on A15 (and other iPhone ANE generations). -- The failure is caused by the preprocessor stage inside the fused MelEncoder (waveform → STFT/mel). The standalone encoder (mel → encoder) uses inputs around `[1, 128, 1501]` and is ANE-safe. - -## Why earlier models “worked” - -- Prior exports either: - - Avoided fusing the waveform preprocessor with the encoder; or - - Exported a “mel→encoder” model only; or - - Relied on Core ML partitioning that kept the preprocessor on CPU/GPU while the encoder ran on ANE. -- Even with `computeUnits = .cpuAndNeuralEngine`, Core ML will fall back to CPU for non-ANE-eligible subgraphs. With split models, the preprocessor silently ran on CPU and the encoder on ANE. -- With the new fused MelEncoder, the compiler tries to start an ANE partition earlier (due to the encoder being ANE-friendly). That exposes the 240000-wide waveform to the ANE plan and triggers the dimension error. - -## Device differences (M‑series Macs vs iPhones) - -- Inputs are identical across devices; what differs is how Core ML partitions the graph. -- On M‑series Macs, the waveform preprocessor commonly stays on CPU/GPU; the ANE (if present) or GPU is used for the encoder. No ANE error is logged. -- On iPhones (A14/A15/A16/A17), the ANE compiler (E-series) may attempt to include earlier elementwise ops, making the NE subgraph input be the waveform itself, which fails validation with `W>16384`. -- Treat this limitation as universal across iPhones with ANE; the precise logging/behavior can vary, but no iPhone ANE can accept a subgraph that sees `W=240000`. - -## Current repo behavior and fix - -- The exporter now sets CPU+GPU compute units for waveform‑in components to avoid ANE attempts while preserving the 15 s window: - - `parakeet_preprocessor.mlpackage` - - `parakeet_mel_encoder.mlpackage` (fused waveform→mel→encoder) -- See the changes in `convert-parakeet.py`: - - `convert-parakeet.py:225` and `convert-parakeet.py:237` — preprocessor exported with `ct.ComputeUnit.CPU_AND_GPU`. - - `convert-parakeet.py:289` and `convert-parakeet.py:301` — fused mel+encoder exported with `ct.ComputeUnit.CPU_AND_GPU`. - -## Recommended runtime settings (iOS) - -- Preprocessor (waveform→mel): set `MLModelConfiguration.computeUnits = .cpuAndGPU` to skip ANE attempts and use CPU/GPU efficiently. -- Encoder / Decoder / Joint: set `computeUnits = .cpuAndNeuralEngine` (or `.all`) to leverage ANE. -- Using `.cpuAndNeuralEngine` for all models also works with the split setup (preprocessor falls back to CPU), but setting `.cpuAndGPU` on the preprocessor avoids ANE compile warnings and extra load-time overhead. - -## Using the split models (recommended) - -- Pipeline: - 1. `parakeet_preprocessor.mlmodelc` (CPU+GPU): input `audio_signal [1, 240000]`, `audio_length [1]` → output `mel [1, 128, 1501]`, `mel_length [1]`. - 2. `parakeet_encoder.mlmodelc` (ANE): input `mel [1, 128, 1501]`, `mel_length [1]` → output `encoder [1, 1024, 188]`, `encoder_length [1]`. - 3. Decoder / Joint / Decision: keep on ANE. -- Shapes are captured in `parakeet_coreml/metadata.json`. - -## If fused‑on‑ANE is required - -- The current fused API (`[1, 240000]` waveform input) cannot compile to ANE on iPhones due to the hard ≀16384 per‑dimension cap. -- Viable approach: introduce a chunked fused variant with input like `[1, 15, 16000]` and implement overlap‑aware preemphasis + STFT + mel inside the model to preserve exact parity with global STFT (center padding, hop=160, win=400). Ensure no op sees a dimension >16384. This changes the model API and requires careful seam handling. -- Alternative: stream ≀1.024 s windows with state and accumulate outputs, but this changes runtime control flow. - -## FAQ - -- Q: Which model causes the iPhone 13 error? - - A: The preprocessor stage inside the fused MelEncoder (not the standalone encoder). -- Q: Is this only iPhone 13? - - A: No. Treat it as affecting all iPhone ANE generations; some may just silently partition or fall back differently. -- Q: Why does M4 work? - - A: Likely because the preprocessor subgraph runs on CPU/GPU; the ANE never “sees” `W=240000` on macOS. - diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/context/nemo-joint-fuse-mode.md b/convert/parakeet-tdt-v2-0.6b/coreml/context/nemo-joint-fuse-mode.md deleted file mode 100644 index 1c5eeae7a0f89608d7a6abddf40a1412c13c642a..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/context/nemo-joint-fuse-mode.md +++ /dev/null @@ -1,87 +0,0 @@ -# NeMo Joint Network Fuse Mode - -## Overview - -The NeMo RNNT Joint network has two operational modes controlled by the `fuse_loss_wer` flag. Understanding this is crucial for proper validation and inference. - -## Fuse Mode Details - -### Normal Mode (`fuse_loss_wer = False`) - -**Purpose**: Standard inference and model export -**Interface**: `joint(encoder_outputs, decoder_outputs)` -**Returns**: Raw logits `[B, T, U, V + 1]` -**Use case**: -- Model inference -- Model export (CoreML, ONNX, etc.) -- Validation and comparison - -```python -# Simple joint computation -logits = model.joint(encoder_outputs=encoder_out, decoder_outputs=decoder_out) -``` - -### Fused Mode (`fuse_loss_wer = True`) - -**Purpose**: Memory-optimized training with integrated loss/WER computation -**Interface**: Requires additional parameters -**Returns**: Loss and WER metrics -**Use case**: Training with memory constraints - -**Required parameters when fused**: -- `encoder_outputs` (mandatory) -- `decoder_outputs` (optional, required for loss) -- `encoder_lengths` (required) -- `transcripts` (optional, required for WER) -- `transcript_lengths` (required) - -**Memory optimization**: Processes batch in sub-batches to reduce memory usage - -```python -# Fused mode - more complex interface -result = model.joint( - encoder_outputs=encoder_out, - decoder_outputs=decoder_out, - encoder_lengths=enc_lengths, - transcripts=transcripts, # Ground truth tokens - transcript_lengths=trans_lengths -) -``` - -## Why We Disable Fused Mode for Validation - -### 1. Interface Simplicity -- Fused mode requires ground truth transcripts that we don't have during validation -- Normal mode only needs encoder/decoder outputs, matching CoreML model interface - -### 2. Export Compatibility -```python -def _prepare_for_export(self, **kwargs): - self._fuse_loss_wer = False # Automatically disabled for export - self.log_softmax = False -``` -NeMo automatically disables fused mode during model export, indicating this is the correct mode for inference. - -### 3. Validation Purpose -- We want raw logits for comparison, not loss/WER calculations -- Fused mode is training-oriented, not inference-oriented - -### 4. CoreML Conversion -CoreML models are converted from the non-fused mode, so validation should use the same mode to ensure fair comparison. - -## Implementation in compare-components.py - -```python -# Disable fused mode for proper inference comparison -asr_model.joint.set_fuse_loss_wer(False) - -# Now we can use the simple interface that matches CoreML -logits_ref = asr_model.joint( - encoder_outputs=encoder_ref, - decoder_outputs=decoder_ref -) -``` - -## Key Takeaway - -For validation and inference, always use `fuse_loss_wer = False` to get the standard joint network behavior that matches exported models and enables fair comparison with CoreML implementations. \ No newline at end of file diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/context/parakeet_tdt_v2_architecture.md b/convert/parakeet-tdt-v2-0.6b/coreml/context/parakeet_tdt_v2_architecture.md deleted file mode 100644 index ca086b16c6a2b05b0bcf49a3d31cd9c98d7844d9..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/context/parakeet_tdt_v2_architecture.md +++ /dev/null @@ -1,61 +0,0 @@ -# Parakeet‑TDT‑0.6B‑v2 Architecture Overview - -## Model topology - -- Streaming‑capable RNN‑Transducer (RNNT) ASR model trained at 16 kHz. -- Time‑Depth Transducer (TDT) extends RNNT to jointly predict token and duration buckets (commonly [0, 1, 2, 3, 4]). -- Four major learnable blocks packaged in the `.nemo` checkpoint: - 1. `AudioToMelSpectrogramPreprocessor` – waveform → 128×T mel‑spectrogram. - 2. `ConformerEncoder` (FastConformer‑style) – 24 conformer blocks, `d_model=1024`, `n_heads=8`, depthwise CNN subsampling with total reduction ×8. - 3. `RNNTDecoder` – 2‑layer LSTM prediction network, hidden size 640, `blank_as_pad=True` for efficient batching and blank handling. - 4. `RNNTJoint` – linear projections from encoder (1024) and decoder (640) into joint space 640 with ReLU, then to class logits. - -## Audio front‑end - -- `sample_rate = 16000`, Hann window, 25 ms window, 10 ms hop (`n_fft=512`, `win_length=400`, `hop_length=160`, `center=True`). -- 128 mel filters (Slaney‑style), log‑magnitude power 2.0 and per‑feature normalization. -- Outputs `(mel, mel_length)` feed the encoder. For CoreML, both tensors must be surfaced to preserve valid‑lengths through subsampling. - -## Encoder (FastConformer) - -- Implemented as `nemo.collections.asr.modules.ConformerEncoder` configured per FastConformer. -- 24 blocks, `d_model=1024`, FFN expansion ×4, 8‑head relative‑position attention, conv kernel 9, batch‑norm. -- Dropouts: 0.1 general, 0.1 pre‑encoder, 0.0 embedding, 0.1 attention; stochastic depth disabled. -- Depthwise CNN subsampling yields overall time reduction ×8. Returns features `[B, D=1024, T_enc]` and lengths. - -## Decoder (`RNNTDecoder`) - -- Stateful LSTM prediction network (2 layers, hidden=640). See `nemo/collections/asr/modules/rnnt.py`. -- `blank_as_pad=True` adds a learnable pad/blank to the embedding; embedding returns zeros for blank, simplifying blank handling. -- Export toggles `_rnnt_export` and uses `.predict()` without prepending SOS so decoder outputs shape `[B, U, 640]`. -- Streaming state is a tuple `(h, c)`, each `[L=2, B, H=640]`. Utilities exist to batch and select states during beam search. - -## Joint network & TDT - -- `nemo.collections.asr.modules.RNNTJoint` with `joint_hidden=640`, activation=ReLU, dropout=0.2. -- Input shapes: encoder `[B, T_enc, 1024]`, decoder `[B, U, 640]` → output logits `[B, T_enc, U, V+1+E]`. -- Vocabulary `V=8192` (SentencePiece BPE). RNNT blank adds `+1`. TDT uses `num_extra_outputs=E=5`, appended in the last dimension of the joint logits. The TDT loss splits the tail 5 entries as duration logits for buckets `[0,1,2,3,4]`. -- Training often enables `fuse_loss_wer=true` with `fused_batch_size=4`; export/inference use just `joint.joint(...)`. - -## Auxiliary heads - -- No separate CTC decoder is attached in the public v2 checkpoint. -- Loss is TDT: `fastemit_lambda=0.0`, `durations=[0,1,2,3,4]`, `sigma=0.02`, `omega=0.1`. - -## Tokenizer - -- SentencePiece BPE (`tokenizer.model`) with 8192 subword units plus control symbols (e.g., punctuation/timestamp markers). RNNT adds blank internally. - -## Notes for CoreML export - -- Exportable subgraphs map 1:1 to NeMo modules: preprocessor, encoder, decoder, joint, and (optionally) a greedy decoder. In addition, we export two fused variants for on‑device efficiency: (1) `mel_encoder` which combines preprocessor+encoder, and (2) `joint_decision` which applies split/softmax/argmax to joint logits to output `token_id`, `token_prob`, and `duration`. -- Decoder state I/O: expose `h` and `c` separately as `[L=2, B, 640]` multi‑arrays to enable on‑device streaming. -- TDT duration logits are appended to the class dimension; downstream code must split `[V+1]` token logits from the last `5` duration logits. - -## Fixed 15‑second window (CoreML constraint) - -- Audio: `B=1`, `T_audio = 15s × 16kHz = 240,000` samples. -- Mel frames (center=True): `T_mel ≈ floor(T_audio / 160) + 1 = 1,500 + 1 = 1,501` → mel `[1, 128, 1501]`. -- Encoder length (×8 subsampling): `T_enc = ceil(1501 / 8) = 188` → encoder `[1, 1024, 188]` (wrapper transposes to `[1, 188, 1024]`). -- Decoder step budget: choose `U_max` to cap per‑window decoding (e.g., 256–384). Joint outputs then shape `[1, T_enc, U_max, 8192+1+5]`. - diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/context/silero_coreml_reference.md b/convert/parakeet-tdt-v2-0.6b/coreml/context/silero_coreml_reference.md deleted file mode 100644 index 2efa8c5dad24884352d705092051bc1fedce9ca7..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/context/silero_coreml_reference.md +++ /dev/null @@ -1,53 +0,0 @@ -# Silero VAD CoreML Reference (Techniques to Reuse) - -This note distills patterns from `/models/vad/silero-vad/coreml` that we can reuse for Parakeet-TDT and highlights CoreML limits to plan around. - -## Tooling Patterns Worth Copying - -- **Dedicated CLI entry points**: `convert-coreml.py` performs all exports, while `compare-models.py` focuses solely on validation/plotting. Keeping convert vs. compare independent shortens iteration when only one side changes. -- **Module wrappers**: Lightweight `nn.Module` wrappers (`STFTModel`, `EncoderModel`, `DecoderModel`) map state-dict weights into export-friendly graphs. They surface state tensors explicitly (hidden/cell) and clamp activations where CoreML would otherwise overflow. -- **Shape contracts via CoreML types**: Inputs/outputs use `ct.TensorType` to document tensor layouts. Silero leans on `ct.RangeDim` for flexible windows; for Parakeet we can simplify by locking to the fixed 15 s window (240 k samples → 1501 mels → 188 encoder frames). -- **Metadata & publish scripts**: Conversion annotates author/version on each mlpackage and reports disk size. `compile_modelc.py` shows how to mass-compile mlpackages to `.mlmodelc` bundles via `xcrun`—handy when preparing release artifacts. -- **Multiple deployment variants**: The project exports both modular (STFT→Encoder→Decoder) and unified pipelines. The 256 ms unified variant demonstrates how to encode fixed iteration counts inside the traced graph (8 chunks + noisy-OR aggregation) for batch workflows. - -## Techniques Directly Applicable to Parakeet-TDT - -- **Separate convert & compare CLIs** - - `convert_parakeet_components.py` should mirror the Silero convert script: accept output directory, compute-unit/precision knobs, optional fused graph exports (e.g., preprocessor+encoder). - - A new `compare_parakeet_models.py` can follow the VAD wrapper pattern: run NeMo vs. CoreML components, collect per-layer diffs, and optionally chart mel/encoder/token probabilities over time. -- **Wrapper modules per component** - - We already have `PreprocessorWrapper`, `EncoderWrapper`, `DecoderWrapper`, `JointWrapper`; extend them with deterministic input padding/casts just like Silero’s wrappers to avoid CoreML runtime surprises. -- **Fixed-window CoreML shapes** - - Keep CoreML inputs fixed to the 15 s window. If we ever change the window length, re-export the models instead of relying on dynamic shape bounds. -- **Explicit state I/O** - - Follow Silero’s practice of exposing LSTM states as inputs/outputs every call. For Parakeet, that means separate `h`/`c` arrays of shape `[num_layers, batch, 640]`, enabling streaming decode loops outside CoreML. -- **Release automation** - - Borrow `compile_modelc.py` to generate `.mlmodelc`; consider a similar helper for quantization experiments once baseline parity is achieved. - -## Parakeet Components to Export - -1. **Audio front-end (Mel)** – same strategy as Silero STFT: wrap the NeMo preprocessor, trace with fixed 15 s audio (240 k samples), and export with constant shapes. -2. **Encoder (FastConformer)** – export with constant shapes for the 188 encoder frames corresponding to the 15 s window (transpose to time-major for CoreML compatibility). -3. **Decoder (RNNT Prediction Network)** – stateful LSTM; replicate Silero’s explicit state tensors but with `[2, 1, 640]` dims and integer targets. Provide zero-state helper for CoreML consumers. -4. **Joint network + TDT head** – trace head-only module; outputs `[B, T_enc, U, V+1+5]`. Document token vs. duration slices (see `tdt_decoding_notes.md`). -5. **Fused graphs** – export both: - - `mel_encoder` (preprocessor+encoder) to reduce I/O. - - `joint_decision` (joint + split/softmax/argmax) to avoid host-side post-processing on logits and return `token_id`, `token_prob`, and `duration` directly. - -## Likely CoreML Pain Points - -- **Joint tensor size**: At 15 s the joint produces ~188×U×(8192+6) floats. Even at `U=256`, that is >400 MB of fp32 activations. Plan for fp16 exports and/or tiled decoding (call joint per-step rather than full grid). -- **Dynamic loops**: RNNT greedy/beam search loops can’t be represented in a single CoreML graph (no `while` support). Expect to orchestrate decoding in Swift/Python by issuing repeated `decoder` and `joint` calls. -- **Large vocabulary softmax**: Full RNNT softmax over 8193 classes is CoreML-friendly but expensive. Avoid embedding beam-search logic in CoreML; keep it host-side. -- **Duration logits**: Splitting the final 5 logits inside CoreML is trivial, but combining them with token control flow should stay in host code (mirrors Silero’s noisy-OR aggregation done in Python before tracing). -- **Tokenizer & text post-processing**: SentencePiece decoding, punctuation fixes, and timestamp formatting remain in Python/Swift; CoreML returns logits only. -- **Feature normalization stats**: Stick to constant statistics baked into the wrapper as Silero does for STFT weights; do not rely on CoreML `LayerNormalization` with dynamic reductions over time. - -## Python / Host Responsibilities - -- Audio chunking, state caching, and greedy/beam decoding loops (leveraging exported decoder + joint). -- Token → text conversion (`sentencepiece`), duration bucket interpretation, punctuation/TDT alignment heuristics. -- Validation tooling (diff calculations, plotting) analogous to Silero’s `compare-models.py`. -- Optional post-processing (e.g., noise suppression, segmentation) that require dynamic control flow. - -Reuse these patterns to keep Parakeet’s conversion pipeline maintainable while respecting CoreML’s graph restrictions. diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/context/tdt_decoding_notes.md b/convert/parakeet-tdt-v2-0.6b/coreml/context/tdt_decoding_notes.md deleted file mode 100644 index a1c5fb6ada701bc349921fbf288504b95fe34840..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/context/tdt_decoding_notes.md +++ /dev/null @@ -1,36 +0,0 @@ -# TDT Decoding Notes (RNNT + Duration Buckets) - -Parakeet‑TDT extends RNNT by appending 5 duration logits to the joint’s class axis. These describe coarse duration buckets associated with an emitted symbol. This note summarizes how to consume them alongside standard RNNT greedy decoding. - -## Joint output - -- Shape: `logits[B, T_enc, U, V+1+5]` where: - - `V=8192` BPE vocab, `+1` RNNT blank, `+5` TDT duration buckets. -- Split last dimension: - - `token_logits = logits[..., :V+1]` - - `duration_logits = logits[..., V+1:] # shape [..., 5]` - -## Greedy decoding sketch - -- Initialize decoder state `(h, c)` and `u=0`; iterate over encoder time `t=0..T_enc-1`. -- At each `(t,u)`: - - Either use raw joint logits: retrieve `token_logits[t, u]` and pick `k = argmax(token_logits)`. - - Or call the fused `JointDecisionWrapper` CoreML model to get `token_id[t,u]`, `token_prob[t,u]`, and `duration[t,u]` directly. - - If `k` is blank: advance `t += 1` (RNNT time advance), keep `u`. - - Else (emit token): append token `k` to hypothesis, update decoder state via `DecoderWrapper` with that token, increment `u += 1`. - - Duration (optional): `d = argmax(duration_logits[t, u_prev])`. Map `d ∈ {0..4}` to bucket durations per training config. Use to assign sub‑frame timestamp estimates. - -## Using duration buckets - -- Bucket IDs 0..4 correspond to the configured `tdt_durations = [0,1,2,3,4]` used during training. -- A simple heuristic for timestamps per emitted token: - - Keep track of current mel/encoder frame index. - - When emitting a token at `(t,u)`, associate bucket `d = argmax(duration_logits[t,u])`. - - Convert bucket to time by multiplying with the encoder frame stride (`8× hop_length / sr = 8×0.01 s = 80 ms` per encoder step) or use mel frame stride if preferred. -- For better quality, prefer server‑side timestamp decoding identical to NeMo’s implementation and use duration buckets as a hint when post‑processing on device. - -## Notes - -- NeMo trains the joint with `num_extra_outputs=5`. These logits are not part of the token softmax and should not affect argmax over `V+1`. -- The model card claims accurate word/segment timestamps; TDT buckets contribute to that, but exact use may vary per downstream aligner. -- If durations are not needed, you can ignore the last 5 logits entirely. diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/convert-parakeet.py b/convert/parakeet-tdt-v2-0.6b/coreml/convert-parakeet.py deleted file mode 100644 index eaaa19ce95913996b3772611443ad6a7c0be7044..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/convert-parakeet.py +++ /dev/null @@ -1,619 +0,0 @@ -#!/usr/bin/env python3 -"""CLI for exporting Parakeet TDT v2 components to CoreML.""" -from __future__ import annotations - -import json -from dataclasses import asdict -from pathlib import Path -from typing import Dict, Optional, Tuple - -import coremltools as ct -import numpy as np -import soundfile as sf -import torch -import typer - -import nemo.collections.asr as nemo_asr - -from individual_components import ( - DecoderWrapper, - EncoderWrapper, - ExportSettings, - JointWrapper, - JointDecisionWrapper, - JointDecisionSingleStep, - PreprocessorWrapper, - MelEncoderWrapper, - _coreml_convert, -) - -DEFAULT_MODEL_ID = "nvidia/parakeet-tdt-0.6b-v2" -AUTHOR = "Fluid Inference" - - -def _compute_length(seconds: float, sample_rate: int) -> int: - return int(round(seconds * sample_rate)) - - -def _prepare_audio( - validation_audio: Optional[Path], - sample_rate: int, - max_samples: int, - seed: Optional[int], -) -> torch.Tensor: - if validation_audio is None: - if seed is not None: - torch.manual_seed(seed) - audio = torch.randn(1, max_samples, dtype=torch.float32) - return audio - - data, sr = sf.read(str(validation_audio), dtype="float32") - if sr != sample_rate: - raise typer.BadParameter( - f"Validation audio sample rate {sr} does not match model rate {sample_rate}" - ) - - if data.ndim > 1: - data = data[:, 0] - - if data.size == 0: - raise typer.BadParameter("Validation audio is empty") - - if data.size < max_samples: - pad_width = max_samples - data.size - data = np.pad(data, (0, pad_width)) - elif data.size > max_samples: - data = data[:max_samples] - - audio = torch.from_numpy(data).unsqueeze(0).to(dtype=torch.float32) - return audio - - -def _save_mlpackage(model: ct.models.MLModel, path: Path, description: str) -> None: - # Ensure iOS 17+ target for MLProgram ops and ANE readiness - try: - model.minimum_deployment_target = ct.target.iOS17 - except Exception: - pass - model.short_description = description - model.author = AUTHOR - path.parent.mkdir(parents=True, exist_ok=True) - model.save(str(path)) - - -def _tensor_shape(tensor: torch.Tensor) -> Tuple[int, ...]: - return tuple(int(dim) for dim in tensor.shape) - - -def _parse_compute_units(name: str) -> ct.ComputeUnit: - """Parse a human-friendly compute units string into ct.ComputeUnit. - - Accepted (case-insensitive): ALL, CPU_ONLY, CPU_AND_GPU, CPU_AND_NE. - """ - normalized = str(name).strip().upper() - mapping = { - "ALL": ct.ComputeUnit.ALL, - "CPU_ONLY": ct.ComputeUnit.CPU_ONLY, - "CPU_AND_GPU": ct.ComputeUnit.CPU_AND_GPU, - "CPU_AND_NE": ct.ComputeUnit.CPU_AND_NE, - "CPU_AND_NEURALENGINE": ct.ComputeUnit.CPU_AND_NE, - } - if normalized not in mapping: - raise typer.BadParameter( - f"Unknown compute units '{name}'. Choose from: " + ", ".join(mapping.keys()) - ) - return mapping[normalized] - - -def _parse_compute_precision(name: Optional[str]) -> Optional[ct.precision]: - """Parse compute precision string into ct.precision or None. - - Accepted (case-insensitive): FLOAT32, FLOAT16. If None/empty, returns None (tool default). - """ - if name is None: - return None - normalized = str(name).strip().upper() - if normalized == "": - return None - mapping = { - "FLOAT32": ct.precision.FLOAT32, - "FLOAT16": ct.precision.FLOAT16, - } - if normalized not in mapping: - raise typer.BadParameter( - f"Unknown compute precision '{name}'. Choose from: " + ", ".join(mapping.keys()) - ) - return mapping[normalized] - - -# Validation logic removed; use compare-compnents.py for comparisons. - - -# Fixed export choices: CPU_ONLY + FP32, min target iOS17 - - -app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) - - -@app.command() -def convert( - nemo_path: Optional[Path] = typer.Option( - None, - "--nemo-path", - exists=True, - resolve_path=True, - help="Path to parakeet-tdt-0.6b-v2 .nemo checkpoint (skip to auto-download)", - ), - model_id: str = typer.Option( - DEFAULT_MODEL_ID, - "--model-id", - help="Model identifier to download when --nemo-path is omitted", - ), - output_dir: Path = typer.Option(Path("parakeet_coreml"), help="Directory where mlpackages and metadata will be written"), - preprocessor_cu: str = typer.Option( - "CPU_ONLY", - "--preprocessor-cu", - help="Compute units for preprocessor (default CPU_ONLY)", - ), - mel_encoder_cu: str = typer.Option( - "CPU_ONLY", - "--mel-encoder-cu", - help="Compute units for fused mel+encoder (default CPU_ONLY)", - ), - compute_precision: Optional[str] = typer.Option( - None, - "--compute-precision", - help="Export precision: FLOAT32 (default) or FLOAT16 to shrink non-quantized weights.", - ), -) -> None: - """Export all Parakeet sub-modules to CoreML with a fixed 15-second window.""" - # Runtime CoreML contract keeps U=1 so the prediction net matches the streaming decoder. - export_settings = ExportSettings( - output_dir=output_dir, - compute_units=ct.ComputeUnit.CPU_ONLY, # Default: CPU-only for all components - deployment_target=ct.target.iOS17, # iOS 17+ features and kernels - compute_precision=_parse_compute_precision(compute_precision), - max_audio_seconds=15.0, - max_symbol_steps=1, - ) - - typer.echo("Export configuration:") - typer.echo(asdict(export_settings)) - - output_dir.mkdir(parents=True, exist_ok=True) - pre_cu = _parse_compute_units(preprocessor_cu) - melenc_cu = _parse_compute_units(mel_encoder_cu) - - if nemo_path is not None: - typer.echo(f"Loading NeMo model from {nemo_path}
") - asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from( - str(nemo_path), map_location="cpu" - ) - checkpoint_meta = { - "type": "file", - "path": str(nemo_path), - } - else: - typer.echo(f"Downloading NeMo model via {model_id}
") - asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained( - model_id, map_location="cpu" - ) - checkpoint_meta = { - "type": "pretrained", - "model_id": model_id, - } - asr_model.eval() - - sample_rate = int(asr_model.cfg.preprocessor.sample_rate) - max_samples = _compute_length(export_settings.max_audio_seconds, sample_rate) - # Prefer a bundled 15s 16kHz audio if available - default_audio = (Path(__file__).parent / "audio" / "yc_first_minute_16k_15s.wav").resolve() - if not default_audio.exists(): - raise typer.BadParameter(f"Expected 15s trace audio at {default_audio}; add the file to proceed.") - typer.echo(f"Using trace audio: {default_audio}") - audio_tensor = _prepare_audio(default_audio, sample_rate, max_samples, seed=None) - audio_length = torch.tensor([max_samples], dtype=torch.int32) - - preprocessor = PreprocessorWrapper(asr_model.preprocessor.eval()) - encoder = EncoderWrapper(asr_model.encoder.eval()) - decoder = DecoderWrapper(asr_model.decoder.eval()) - joint = JointWrapper(asr_model.joint.eval()) - - decoder_export_flag = getattr(asr_model.decoder, "_rnnt_export", False) - asr_model.decoder._rnnt_export = True - - try: - with torch.inference_mode(): - mel_ref, mel_length_ref = preprocessor(audio_tensor, audio_length) - mel_length_ref = mel_length_ref.to(dtype=torch.int32) - encoder_ref, encoder_length_ref = encoder(mel_ref, mel_length_ref) - encoder_length_ref = encoder_length_ref.to(dtype=torch.int32) - - # Clone Tensors to drop the inference tensor flag before tracing - mel_ref = mel_ref.clone() - mel_length_ref = mel_length_ref.clone() - encoder_ref = encoder_ref.clone() - encoder_length_ref = encoder_length_ref.clone() - - vocab_size = int(asr_model.tokenizer.vocab_size) - num_extra = int(asr_model.joint.num_extra_outputs) - decoder_hidden = int(asr_model.decoder.pred_hidden) - decoder_layers = int(asr_model.decoder.pred_rnn_layers) - - targets = torch.full( - (1, export_settings.max_symbol_steps), - fill_value=asr_model.decoder.blank_idx, - dtype=torch.int32, - ) - target_lengths = torch.tensor( - [export_settings.max_symbol_steps], dtype=torch.int32 - ) - zero_state = torch.zeros( - decoder_layers, - 1, - decoder_hidden, - dtype=torch.float32, - ) - - with torch.inference_mode(): - decoder_ref, h_ref, c_ref = decoder(targets, target_lengths, zero_state, zero_state) - joint_ref = joint(encoder_ref, decoder_ref) - - decoder_ref = decoder_ref.clone() - h_ref = h_ref.clone() - c_ref = c_ref.clone() - joint_ref = joint_ref.clone() - - typer.echo("Tracing and converting preprocessor
") - # Ensure tracing happens on CPU explicitly - preprocessor = preprocessor.cpu() - audio_tensor = audio_tensor.cpu() - audio_length = audio_length.cpu() - traced_preprocessor = torch.jit.trace( - preprocessor, (audio_tensor, audio_length), strict=False - ) - traced_preprocessor.eval() - preprocessor_inputs = [ - # Allow variable-length audio up to the fixed 15s window using RangeDim - ct.TensorType( - name="audio_signal", - shape=(1, ct.RangeDim(1, max_samples)), - dtype=np.float32, - ), - ct.TensorType(name="audio_length", shape=(1,), dtype=np.int32), - ] - preprocessor_outputs = [ - ct.TensorType(name="mel", dtype=np.float32), - ct.TensorType(name="mel_length", dtype=np.int32), - ] - # Preprocessor compute units (parametrized; default CPU_ONLY) - preprocessor_model = _coreml_convert( - traced_preprocessor, - preprocessor_inputs, - preprocessor_outputs, - export_settings, - compute_units_override=pre_cu, - ) - preprocessor_path = output_dir / "parakeet_preprocessor.mlpackage" - _save_mlpackage( - preprocessor_model, - preprocessor_path, - "Parakeet preprocessor (15 s window)", - ) - - typer.echo("Tracing and converting encoder
") - traced_encoder = torch.jit.trace( - encoder, (mel_ref, mel_length_ref), strict=False - ) - traced_encoder.eval() - encoder_inputs = [ - ct.TensorType(name="mel", shape=_tensor_shape(mel_ref), dtype=np.float32), - ct.TensorType(name="mel_length", shape=(1,), dtype=np.int32), - ] - encoder_outputs = [ - ct.TensorType(name="encoder", dtype=np.float32), - ct.TensorType(name="encoder_length", dtype=np.int32), - ] - # Encoder: CPU only - encoder_model = _coreml_convert( - traced_encoder, - encoder_inputs, - encoder_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - encoder_path = output_dir / "parakeet_encoder.mlpackage" - _save_mlpackage( - encoder_model, - encoder_path, - "Parakeet encoder (15 s window)", - ) - - # Optional fused export: Preprocessor + Encoder - typer.echo("Tracing and converting fused mel+encoder
") - mel_encoder = MelEncoderWrapper(preprocessor, encoder) - traced_mel_encoder = torch.jit.trace( - mel_encoder, (audio_tensor, audio_length), strict=False - ) - traced_mel_encoder.eval() - mel_encoder_inputs = [ - # Keep fixed 15s window for fused Mel+Encoder - ct.TensorType(name="audio_signal", shape=(1, max_samples), dtype=np.float32), - ct.TensorType(name="audio_length", shape=(1,), dtype=np.int32), - ] - mel_encoder_outputs = [ - ct.TensorType(name="encoder", dtype=np.float32), - ct.TensorType(name="encoder_length", dtype=np.int32), - ] - # Fused mel+encoder compute units (parametrized; default CPU_ONLY) - mel_encoder_model = _coreml_convert( - traced_mel_encoder, - mel_encoder_inputs, - mel_encoder_outputs, - export_settings, - compute_units_override=melenc_cu, - ) - mel_encoder_path = output_dir / "parakeet_mel_encoder.mlpackage" - _save_mlpackage( - mel_encoder_model, - mel_encoder_path, - "Parakeet fused Mel+Encoder (15 s window)", - ) - - typer.echo("Tracing and converting decoder
") - traced_decoder = torch.jit.trace( - decoder, - (targets, target_lengths, zero_state, zero_state), - strict=False, - ) - traced_decoder.eval() - decoder_inputs = [ - ct.TensorType(name="targets", shape=_tensor_shape(targets), dtype=np.int32), - ct.TensorType(name="target_length", shape=(1,), dtype=np.int32), - ct.TensorType(name="h_in", shape=_tensor_shape(zero_state), dtype=np.float32), - ct.TensorType(name="c_in", shape=_tensor_shape(zero_state), dtype=np.float32), - ] - decoder_outputs = [ - ct.TensorType(name="decoder", dtype=np.float32), - ct.TensorType(name="h_out", dtype=np.float32), - ct.TensorType(name="c_out", dtype=np.float32), - ] - # Decoder: CPU only - decoder_model = _coreml_convert( - traced_decoder, - decoder_inputs, - decoder_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - decoder_path = output_dir / "parakeet_decoder.mlpackage" - _save_mlpackage( - decoder_model, - decoder_path, - "Parakeet decoder (RNNT prediction network)", - ) - - typer.echo("Tracing and converting joint
") - traced_joint = torch.jit.trace( - joint, - (encoder_ref, decoder_ref), - strict=False, - ) - traced_joint.eval() - joint_inputs = [ - ct.TensorType(name="encoder", shape=_tensor_shape(encoder_ref), dtype=np.float32), - ct.TensorType(name="decoder", shape=_tensor_shape(decoder_ref), dtype=np.float32), - ] - joint_outputs = [ - ct.TensorType(name="logits", dtype=np.float32), - ] - # Joint: CPU only - joint_model = _coreml_convert( - traced_joint, - joint_inputs, - joint_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - joint_path = output_dir / "parakeet_joint.mlpackage" - _save_mlpackage( - joint_model, - joint_path, - "Parakeet joint network (RNNT)", - ) - - # Joint + decision head (split logits, softmax, argmax) - typer.echo("Tracing and converting joint decision head
") - vocab_size = int(asr_model.tokenizer.vocab_size) - num_extra = int(asr_model.joint.num_extra_outputs) - joint_decision = JointDecisionWrapper(joint, vocab_size=vocab_size, num_extra=num_extra) - traced_joint_decision = torch.jit.trace( - joint_decision, - (encoder_ref, decoder_ref), - strict=False, - ) - traced_joint_decision.eval() - joint_decision_inputs = [ - ct.TensorType(name="encoder", shape=_tensor_shape(encoder_ref), dtype=np.float32), - ct.TensorType(name="decoder", shape=_tensor_shape(decoder_ref), dtype=np.float32), - ] - joint_decision_outputs = [ - ct.TensorType(name="token_id", dtype=np.int32), - ct.TensorType(name="token_prob", dtype=np.float32), - ct.TensorType(name="duration", dtype=np.int32), - ] - # JointDecision: CPU only - joint_decision_model = _coreml_convert( - traced_joint_decision, - joint_decision_inputs, - joint_decision_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - joint_decision_path = output_dir / "parakeet_joint_decision.mlpackage" - _save_mlpackage( - joint_decision_model, - joint_decision_path, - "Parakeet joint + decision head (split, softmax, argmax)", - ) - - # Single-step JointDecision for [1,1024,1] x [1,640,1] -> [1,1,1] - typer.echo("Tracing and converting single-step joint decision
") - jd_single = JointDecisionSingleStep(joint, vocab_size=vocab_size, num_extra=num_extra) - # Create single-step slices from refs - enc_step = encoder_ref[:, :, :1].contiguous() - dec_step = decoder_ref[:, :, :1].contiguous() - traced_jd_single = torch.jit.trace( - jd_single, - (enc_step, dec_step), - strict=False, - ) - traced_jd_single.eval() - jd_single_inputs = [ - ct.TensorType(name="encoder_step", shape=(1, enc_step.shape[1], 1), dtype=np.float32), - ct.TensorType(name="decoder_step", shape=(1, dec_step.shape[1], 1), dtype=np.float32), - ] - jd_single_outputs = [ - ct.TensorType(name="token_id", dtype=np.int32), - ct.TensorType(name="token_prob", dtype=np.float32), - ct.TensorType(name="duration", dtype=np.int32), - ] - # Single-step JointDecision: CPU only - jd_single_model = _coreml_convert( - traced_jd_single, - jd_single_inputs, - jd_single_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - jd_single_path = output_dir / "parakeet_joint_decision_single_step.mlpackage" - _save_mlpackage( - jd_single_model, - jd_single_path, - "Parakeet single-step joint decision (current frame)", - ) - - metadata: Dict[str, object] = { - "model_id": model_id, - "sample_rate": sample_rate, - "max_audio_seconds": export_settings.max_audio_seconds, - "max_audio_samples": max_samples, - "max_symbol_steps": export_settings.max_symbol_steps, - "vocab_size": vocab_size, - "joint_extra_outputs": num_extra, - "checkpoint": checkpoint_meta, - "coreml": { - "compute_units": export_settings.compute_units.name, - "compute_precision": ( - export_settings.compute_precision.name - if export_settings.compute_precision is not None - else "FLOAT32" - ), - }, - "components": { - "preprocessor": { - "inputs": { - "audio_signal": list(_tensor_shape(audio_tensor)), - "audio_length": [1], - }, - "outputs": { - "mel": list(_tensor_shape(mel_ref)), - "mel_length": [1], - }, - "path": preprocessor_path.name, - }, - "encoder": { - "inputs": { - "mel": list(_tensor_shape(mel_ref)), - "mel_length": [1], - }, - "outputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "encoder_length": [1], - }, - "path": encoder_path.name, - }, - "mel_encoder": { - "inputs": { - "audio_signal": [1, max_samples], - "audio_length": [1], - }, - "outputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "encoder_length": [1], - }, - "path": mel_encoder_path.name, - }, - "decoder": { - "inputs": { - "targets": list(_tensor_shape(targets)), - "target_length": [1], - "h_in": list(_tensor_shape(zero_state)), - "c_in": list(_tensor_shape(zero_state)), - }, - "outputs": { - "decoder": list(_tensor_shape(decoder_ref)), - "h_out": list(_tensor_shape(h_ref)), - "c_out": list(_tensor_shape(c_ref)), - }, - "path": decoder_path.name, - }, - "joint": { - "inputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "decoder": list(_tensor_shape(decoder_ref)), - }, - "outputs": { - "logits": list(_tensor_shape(joint_ref)), - }, - "path": joint_path.name, - }, - "joint_decision": { - "inputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "decoder": list(_tensor_shape(decoder_ref)), - }, - "outputs": { - "token_id": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[1], - _tensor_shape(decoder_ref)[1], - ], - "token_prob": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[1], - _tensor_shape(decoder_ref)[1], - ], - "duration": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[1], - _tensor_shape(decoder_ref)[1], - ], - }, - "path": joint_decision_path.name, - }, - "joint_decision_single_step": { - "inputs": { - "encoder_step": [1, _tensor_shape(encoder_ref)[2-1], 1], - "decoder_step": [1, _tensor_shape(decoder_ref)[2-1], 1], - }, - "outputs": { - "token_id": [1, 1, 1], - "token_prob": [1, 1, 1], - "duration": [1, 1, 1], - }, - "path": jd_single_path.name, - }, - }, - } - - metadata_path = output_dir / "metadata.json" - metadata_path.write_text(json.dumps(metadata, indent=2)) - typer.echo(f"Export complete. Metadata written to {metadata_path}") - - finally: - asr_model.decoder._rnnt_export = decoder_export_flag - - -if __name__ == "__main__": - app() diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/individual_components.py b/convert/parakeet-tdt-v2-0.6b/coreml/individual_components.py deleted file mode 100644 index 5640330bbaa9e73ef18644458bcf023971ca70cf..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/individual_components.py +++ /dev/null @@ -1,229 +0,0 @@ -#!/usr/bin/env python3 -"""Export Parakeet TDT v2 RNNT components into CoreML and validate outputs.""" -from __future__ import annotations - -from dataclasses import dataclass -from pathlib import Path -from typing import Optional, Tuple - -import coremltools as ct -import torch - - -@dataclass -class ExportSettings: - output_dir: Path - compute_units: ct.ComputeUnit - deployment_target: Optional[ct.target.iOS17] - compute_precision: Optional[ct.precision] - max_audio_seconds: float - max_symbol_steps: int - - -@dataclass -class ValidationSettings: - audio_path: Optional[Path] - seconds: float - seed: Optional[int] - rtol: float - atol: float - skip: bool - - -@dataclass -class ValidationDiff: - name: str - max_abs_diff: float - max_rel_diff: float - - -@dataclass -class ValidationResult: - source: str - audio_num_samples: int - audio_seconds: float - token_length: int - atol: float - rtol: float - diffs: Tuple[ValidationDiff, ...] - - -class PreprocessorWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, audio_signal: torch.Tensor, length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - mel, mel_length = self.module(input_signal=audio_signal, length=length.to(dtype=torch.long)) - return mel, mel_length - - -class EncoderWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, features: torch.Tensor, length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - encoded, encoded_lengths = self.module(audio_signal=features, length=length.to(dtype=torch.long)) - return encoded, encoded_lengths - - -class DecoderWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward( - self, - targets: torch.Tensor, - target_lengths: torch.Tensor, - h_in: torch.Tensor, - c_in: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - state = [h_in, c_in] - decoder_output, _, new_state = self.module( - targets=targets.to(dtype=torch.long), - target_length=target_lengths.to(dtype=torch.long), - states=state, - ) - return decoder_output, new_state[0], new_state[1] - - -class JointWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor) -> torch.Tensor: - # Input: encoder_outputs [B, D, T], decoder_outputs [B, D, U] - # Transpose to match what projection layers expect - encoder_outputs = encoder_outputs.transpose(1, 2) # [B, T, D] - decoder_outputs = decoder_outputs.transpose(1, 2) # [B, U, D] - - # Apply projections - enc_proj = self.module.enc(encoder_outputs) # [B, T, 640] - dec_proj = self.module.pred(decoder_outputs) # [B, U, 640] - - # Explicit broadcasting along T and U to avoid converter ambiguity - x = enc_proj.unsqueeze(2) + dec_proj.unsqueeze(1) # [B, T, U, 640] - x = self.module.joint_net[0](x) # ReLU - x = self.module.joint_net[1](x) # Dropout (no-op in eval) - out = self.module.joint_net[2](x) # Linear -> logits [B, T, U, 8198] - return out - - -class MelEncoderWrapper(torch.nn.Module): - """Fused wrapper: waveform -> mel -> encoder. - - Inputs: - - audio_signal: [B, S] - - audio_length: [B] - - Outputs: - - encoder: [B, D, T_enc] - - encoder_length: [B] - """ - def __init__(self, preprocessor: PreprocessorWrapper, encoder: EncoderWrapper) -> None: - super().__init__() - self.preprocessor = preprocessor - self.encoder = encoder - - def forward(self, audio_signal: torch.Tensor, audio_length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - mel, mel_length = self.preprocessor(audio_signal, audio_length) - encoded, enc_len = self.encoder(mel, mel_length.to(dtype=torch.int32)) - return encoded, enc_len - - -class JointDecisionWrapper(torch.nn.Module): - """Joint + decision head: outputs label id, label prob, duration frames. - - Splits joint logits into token logits and duration logits, applies softmax - over tokens, argmax for both heads, and gathers probability of the chosen token. - - Inputs: - - encoder_outputs: [B, D, T] - - decoder_outputs: [B, D, U] - - Returns: - - token_id: [B, T, U] int32 - - token_prob: [B, T, U] float32 - - duration: [B, T, U] int32 (frames; duration buckets as defined by the model) - """ - def __init__(self, joint: JointWrapper, vocab_size: int, num_extra: int) -> None: - super().__init__() - self.joint = joint - self.vocab_with_blank = int(vocab_size) + 1 - self.num_extra = int(num_extra) - - def forward(self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor): - logits = self.joint(encoder_outputs, decoder_outputs) - token_logits = logits[..., : self.vocab_with_blank] - duration_logits = logits[..., -self.num_extra :] - - # Token selection - token_ids = torch.argmax(token_logits, dim=-1).to(dtype=torch.int32) - token_probs_all = torch.softmax(token_logits, dim=-1) - # gather expects int64 (long) indices; cast only for gather - token_prob = torch.gather( - token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) - ).squeeze(-1) - - # Duration prediction (duration bucket argmax → frame increments) - duration = torch.argmax(duration_logits, dim=-1).to(dtype=torch.int32) - return token_ids, token_prob, duration - - -class JointDecisionSingleStep(torch.nn.Module): - """Single-step variant for streaming: encoder_step [1, 1024, 1] -> [1,1,1]. - - Inputs: - - encoder_step: [B=1, D=1024, T=1] - - decoder_step: [B=1, D=640, U=1] - - Returns: - - token_id: [1, 1, 1] int32 - - token_prob: [1, 1, 1] float32 - - duration: [1, 1, 1] int32 - """ - def __init__(self, joint: JointWrapper, vocab_size: int, num_extra: int) -> None: - super().__init__() - self.joint = joint - self.vocab_with_blank = int(vocab_size) + 1 - self.num_extra = int(num_extra) - - def forward(self, encoder_step: torch.Tensor, decoder_step: torch.Tensor): - # Reuse JointWrapper which expects [B, D, T] and [B, D, U] - logits = self.joint(encoder_step, decoder_step) # [1, 1, 1, V+extra] - token_logits = logits[..., : self.vocab_with_blank] - duration_logits = logits[..., -self.num_extra :] - - token_ids = torch.argmax(token_logits, dim=-1, keepdim=False).to(dtype=torch.int32) - token_probs_all = torch.softmax(token_logits, dim=-1) - token_prob = torch.gather( - token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) - ).squeeze(-1) - duration = torch.argmax(duration_logits, dim=-1, keepdim=False).to(dtype=torch.int32) - return token_ids, token_prob, duration - - -def _coreml_convert( - traced: torch.jit.ScriptModule, - inputs, - outputs, - settings: ExportSettings, - compute_units_override: Optional[ct.ComputeUnit] = None, -) -> ct.models.MLModel: - cu = compute_units_override if compute_units_override is not None else settings.compute_units - kwargs = { - "convert_to": "mlprogram", - "inputs": inputs, - "outputs": outputs, - "compute_units": cu, - } - print("Converting:", traced.__class__.__name__) - print("Conversion kwargs:", kwargs) - if settings.deployment_target is not None: - kwargs["minimum_deployment_target"] = settings.deployment_target - if settings.compute_precision is not None: - kwargs["compute_precision"] = settings.compute_precision - return ct.convert(traced, **kwargs) diff --git 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-description = "Add your description here" -readme = "README.md" -requires-python = "==3.10.12" -dependencies = [ - "absl-py==2.3.0", - "accelerate==1.8.1", - "aiohappyeyeballs==2.6.1", - "aiohttp==3.12.13", - "aiosignal==1.3.2", - "alembic==1.16.2", - "annotated-types==0.7.0", - "antlr4-python3-runtime==4.9.3", - "anyio==4.9.0", - "appnope==0.1.4", - "argon2-cffi-bindings==21.2.0", - "argon2-cffi==25.1.0", - "arrow==1.3.0", - "asttokens==3.0.0", - "async-lru==2.0.5", - "async-timeout==5.0.1", - "attrs==25.3.0", - "audioread==3.0.1", - "babel==2.17.0", - "backports-datetime-fromisoformat==2.0.3", - "beautifulsoup4==4.13.4", - "bleach==6.2.0", - "braceexpand==0.1.7", - "cattrs==25.1.1", - "certifi==2025.6.15", - "cffi==1.17.1", - "charset-normalizer==3.4.2", - "click==8.2.1", - "cloudpickle==3.1.1", - "colorlog==6.9.0", - "comm==0.2.2", - "contourpy==1.3.2", - "coremltools==9.0b1", - "cycler==0.12.1", - "cytoolz==1.0.1", - "datasets==3.6.0", - "debugpy==1.8.14", - "decorator==5.2.1", - "defusedxml==0.7.1", - "dill==0.3.8", - "distance==0.1.3", - "docopt==0.6.2", - "editdistance==0.8.1", - "einops==0.8.1", - "exceptiongroup==1.3.0", - "executing==2.2.0", - "fastjsonschema==2.21.1", - "fiddle==0.3.0", - "filelock==3.18.0", - "fonttools==4.58.4", - "fqdn==1.5.1", - "frozenlist==1.7.0", - "fsspec==2024.12.0", - "future==1.0.0", - "g2p-en==2.1.0", - "gitdb==4.0.12", - "gitpython==3.1.44", - "graphviz==0.21", - "grpcio==1.73.1", - "h11==0.16.0", - "hf-xet==1.1.5", - "httpcore==1.0.9", - "httpx==0.28.1", - "huggingface-hub==0.33.1", - "hydra-core==1.3.2", - "idna==3.10", - "inflect==7.5.0", - "intervaltree==3.1.0", - "ipykernel==6.29.5", - "ipython==8.37.0", - "ipywidgets==8.1.7", - "isoduration==20.11.0", - "jedi==0.19.2", - "jinja2==3.1.6", - "jiwer==4.0.0", - "joblib==1.5.1", - "json5==0.12.0", - "jsonpointer==3.0.0", - "jsonschema==4.24.0", - "jsonschema-specifications==2025.4.1", - "jupyter==1.1.1", - "jupyter-console==6.6.3", - "jupyter-events==0.12.0", - "jupyter-lsp==2.2.5", - "jupyter-client==8.6.3", - "jupyter-core==5.8.1", - "jupyter-server==2.16.0", - "jupyter-server-terminals==0.5.3", - "jupyterlab==4.4.4", - "jupyterlab-pygments==0.3.0", - "jupyterlab-server==2.27.3", - "jupyterlab-widgets==3.0.15", - "kaldi-python-io==1.2.2", - "kaldiio==2.18.1", - "kiwisolver==1.4.8", - "lazy-loader==0.4", - "levenshtein==0.27.1", - "lhotse==1.30.3", - "libcst==1.8.2", - "librosa==0.11.0", - "lightning==2.4.0", - "lightning-utilities==0.14.3", - "lilcom==1.8.1", - "llvmlite==0.44.0", - "loguru==0.7.3", - "mako==1.3.10", - "markdown==3.8.2", - "markdown-it-py==3.0.0", - "markupsafe==3.0.2", - "marshmallow==4.0.0", - "matplotlib==3.10.3", - "matplotlib-inline==0.1.7", - "mdurl==0.1.2", - "mediapy==1.1.6", - "mistune==3.1.3", - "more-itertools==10.7.0", - "mpmath==1.3.0", - "msgpack==1.1.1", - "multidict==6.6.2", - "multiprocess==0.70.16", - "nbclient==0.10.2", - "nbconvert==7.16.6", - "nbformat==5.10.4", - "nemo-toolkit==2.3.1", - "nest-asyncio==1.6.0", - "networkx==3.4.2", - "nltk==3.9.1", - "notebook==7.4.3", - "notebook-shim==0.2.4", - "num2words==0.5.14", - "numba==0.61.0", - "numpy==1.26.4", - "omegaconf==2.3.0", - "onnx==1.17.0", - "optuna==4.4.0", - "overrides==7.7.0", - "packaging==24.2", - "pandas==2.3.0", - "pandocfilters==1.5.1", - "parso==0.8.4", - "peft==0.15.2", - "pexpect==4.9.0", - "pillow==11.2.1", - "plac==1.4.5", - "platformdirs==4.3.8", - "pooch==1.8.2", - "prometheus-client==0.22.1", - "prompt-toolkit==3.0.51", - "propcache==0.3.2", - "psutil==7.0.0", - "ptyprocess==0.7.0", - "pure-eval==0.2.3", - "pyaml==25.5.0", - "pyannote-core==5.0.0", - "pyannote-database==5.1.3", - "pyannote-metrics==3.2.1", - "pyarrow==20.0.0", - "pybind11==2.13.6", - "pycparser==2.22", - "pydantic==2.11.7", - "pydantic-core==2.33.2", - "pydub==0.25.1", - "pygments==2.19.2", - "pyloudnorm==0.1.1", - "pyparsing==3.2.3", - "python-dateutil==2.9.0.post0", - "python-json-logger==3.3.0", - "pytorch-lightning==2.5.2", - "pytz==2025.2", - "pyyaml==6.0.2", - "pyzmq==27.0.0", - "rapidfuzz==3.13.0", - "referencing==0.36.2", - "regex==2024.11.6", - "requests==2.32.4", - "resampy==0.4.3", - "rfc3339-validator==0.1.4", - "rfc3986-validator==0.1.1", - "rich==14.0.0", - "rpds-py==0.25.1", - "ruamel-yaml==0.18.14", - "ruamel-yaml-clib==0.2.12", - "sacremoses==0.1.1", - "safetensors==0.5.3", - "scikit-learn==1.5.1", - "scipy==1.15.3", - "send2trash==1.8.3", - "sentencepiece==0.2.0", - "sentry-sdk==2.32.0", - "setproctitle==1.3.6", - "shellingham==1.5.4", - "six==1.17.0", - "smmap==5.0.2", - "sniffio==1.3.1", - "sortedcontainers==2.4.0", - "soundfile==0.13.1", - "soupsieve==2.7", - "sox==1.5.0", - "soxr==0.5.0.post1", - "sqlalchemy==2.0.41", - "stack-data==0.6.3", - "tabulate==0.9.0", - "tensorboard==2.19.0", - "tensorboard-data-server==0.7.2", - "termcolor==3.1.0", - "terminado==0.18.1", - "text-unidecode==1.3", - "texterrors==0.5.1", - "threadpoolctl==3.6.0", - "tinycss2==1.4.0", - "tokenizers==0.21.2", - "tomli==2.2.1", - "toolz==1.0.0", - "torch==2.7.0", - "torchmetrics==1.7.3", - "tornado==6.5.1", - "tqdm==4.67.1", - "traitlets==5.14.3", - "transformers==4.51.3", - "typeguard==4.4.4", - "typer==0.16.0", - "types-python-dateutil==2.9.0.20250516", - "typing-inspection==0.4.1", - "typing-extensions==4.14.0", - "tzdata==2025.2", - "uri-template==1.3.0", - "urllib3==2.5.0", - "wandb==0.20.1", - "wcwidth==0.2.13", - "webcolors==24.11.1", - "webdataset==1.0.2", - "webencodings==0.5.1", - "websocket-client==1.8.0", - "werkzeug==3.1.3", - "wget==3.2", - "widgetsnbextension==4.0.14", - "wrapt==1.17.2", - "xxhash==3.5.0", - "yarl==1.20.1", - "pip>=25.1.1", - "seaborn>=0.13.2", - "pyannote-audio>=3.3.2", - "funasr>=1.2.6", -] diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/quantize_coreml.py b/convert/parakeet-tdt-v2-0.6b/coreml/quantize_coreml.py deleted file mode 100644 index 2067bd179da56ba63fe260b8334c4adc56321970..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/quantize_coreml.py +++ /dev/null @@ -1,1207 +0,0 @@ -#!/usr/bin/env python3 -"""Quantize CoreML mlpackages and compare quality, compression, latency, and compile time. - -This script focuses on the fused models: - - parakeet_mel_encoder.mlpackage (waveform -> encoder) - - parakeet_joint_decision.mlpackage (joint + softmax/argmax) - -It also quantizes the rest of the components in the directory for completeness. - -Variants tried by default (examples): - - int8-linear (per-channel) - - palettize 6-bit (Mel-only) - - jd-only variants (int8 and palette) - - prune + int8 - -Outputs: - - A new directory per variant under // with quantized mlpackages - - quantization_summary.json with aggregate metrics (baseline vs each variant) - - Plots saved under /plots/ and mirrored to /plots/quantize// - - fused_quality.png / fused_latency.png / fused_compression.png / fused_size.png (latency chart also shows compile time) - - all_components_quality.png / all_components_latency.png / all_components_compression.png / all_components_size.png (latency chart also shows compile time) - -Notes: - - Uses CoreMLTools optimize.coreml linear/palettize/prune for MLProgram models. - - Keeps the fixed 15-second window shapes as per context/coreml_component_io.md. - - Run via `uv run` to ensure reproducible environment. - - Sets minimum deployment target to iOS 17 for all outputs and loads models with `compute_units=ALL` by default to enable ANE. - - Tracks offline compile time (mlpackage->mlmodelc) and includes that metric in plots. -""" -from __future__ import annotations - -import json -import platform -import shutil -import subprocess -import time -from dataclasses import dataclass -from pathlib import Path -from typing import Dict, List, Optional, Set, Tuple - -import numpy as np -import soundfile as sf -import typer - -import coremltools as ct -from coremltools.optimize.coreml import ( - OptimizationConfig, - OpLinearQuantizerConfig, - OpPalettizerConfig, - OpThresholdPrunerConfig, - linear_quantize_weights, - palettize_weights, - prune_weights, -) - - -# Optional plotting -try: - import matplotlib - matplotlib.use("Agg") - import matplotlib.pyplot as plt - HAS_MPL = True -except Exception: - HAS_MPL = False - - -BASE_DIR = Path(__file__).resolve().parent -BYTES_IN_MB = 1024 * 1024 - - -DISPLAY_LABEL_OVERRIDES = { - "mel6bit-palettize": "mel6bit", - "enc6bit-palettize": "enc6bit", - "int8-linear": "int8 linear", -} - - -# When no explicit components are provided, we derive the set of -# components to quantize from the selected variants' whitelists. -# If any selected variant has no whitelist (i.e., applies globally), -# we quantize all components. -DEFAULT_TARGET_COMPONENTS: Tuple[str, str] = () - - -# Formatting helpers ------------------------------------------------------- - - -def _format_labels(labels: List[str]) -> List[str]: - formatted: List[str] = [] - for label in labels: - pretty = DISPLAY_LABEL_OVERRIDES.get(label, label) - pretty = pretty.replace("_", " ") - pretty = pretty.replace("-", "\n") - formatted.append(pretty) - return formatted - - -def _friendly_component_name(name: str) -> str: - return name.replace("_", " ").title() - - -def _get_metric_series( - metrics: Dict[str, Dict[str, List[float]]], - component: str, - key: str, - length: int, -) -> List[float]: - values = metrics.get(component, {}).get(key) - if values is None: - return [float("nan")] * length - if len(values) >= length: - return list(values[:length]) - padded = list(values) - padded.extend([float("nan")] * (length - len(values))) - return padded - - -def _plot_bar_rows( - out_path: Path, - display_labels: List[str], - rows: List[Dict[str, object]], - title_suffix: str, -) -> None: - if not HAS_MPL: - return - if not rows or not display_labels: - return - - n_labels = len(display_labels) - fig_width = max(8.0, 1.3 * n_labels) - fig_height = max(2.6 * len(rows), 3.0) - - fig, axes = plt.subplots(len(rows), 1, figsize=(fig_width, fig_height), squeeze=False) - axes = axes.flatten() - x = np.arange(n_labels) - - for ax, row in zip(axes, rows): - values = np.asarray(row.get("values", [float("nan")] * n_labels), dtype=np.float64) - color = row.get("color") - bars = ax.bar(x, values, width=0.6, color=color) - ax.set_title(str(row.get("title", ""))) - ax.set_xticks(x) - ax.set_xticklabels(display_labels, rotation=25, ha="right") - ylim = row.get("ylim") - if isinstance(ylim, tuple) and len(ylim) == 2: - ax.set_ylim(ylim) - ax.grid(axis="y", linestyle="--", alpha=0.3) - # Add value labels for bars - for bar in bars: - h = bar.get_height() - if np.isnan(h): - continue - ax.annotate( - f"{h:.2f}", - xy=(bar.get_x() + bar.get_width() / 2, h), - xytext=(0, 3), - textcoords="offset points", - ha="center", - va="bottom", - fontsize=8, - ) - - if title_suffix: - fig.suptitle(title_suffix, fontsize=12) - fig.tight_layout(rect=(0, 0, 1, 0.95)) - else: - fig.tight_layout() - - out_path.parent.mkdir(parents=True, exist_ok=True) - plt.savefig(out_path) - plt.close(fig) - - -def _plot_fused_category_charts( - plot_dir: Path, - labels: List[str], - mel_quality: List[float], - mel_latency_ms: List[float], - mel_compression: List[float], - mel_size_mb: List[float], - jd_acc: List[float], - jd_latency_ms: List[float], - jd_compression: List[float], - jd_size_mb: List[float], - mel_compile_ms: Optional[List[float]] = None, - jd_compile_ms: Optional[List[float]] = None, - title_suffix: str = "", -) -> List[Path]: - if not HAS_MPL: - return [] - outputs: List[Path] = [] - display = _format_labels(labels) - prefix = f"Fused Components — {title_suffix}" if title_suffix else "Fused Components" - - quality_path = plot_dir / "fused_quality.png" - _plot_bar_rows( - quality_path, - display, - [ - { - "title": "MelEncoder quality (1 - norm err)", - "values": mel_quality, - "color": "C0", - "ylim": (0.0, 1.05), - }, - { - "title": "JointDecision token-id match rate", - "values": jd_acc, - "color": "C0", - "ylim": (0.0, 1.05), - }, - ], - f"{prefix} — Quality", - ) - outputs.append(quality_path) - - compression_path = plot_dir / "fused_compression.png" - _plot_bar_rows( - compression_path, - display, - [ - { - "title": "MelEncoder compression ratio", - "values": mel_compression, - "color": "C2", - }, - { - "title": "JointDecision compression ratio", - "values": jd_compression, - "color": "C2", - }, - ], - f"{prefix} — Compression", - ) - outputs.append(compression_path) - - size_path = plot_dir / "fused_size.png" - _plot_bar_rows( - size_path, - display, - [ - { - "title": "MelEncoder size (MB)", - "values": mel_size_mb, - "color": "C4", - }, - { - "title": "JointDecision size (MB)", - "values": jd_size_mb, - "color": "C4", - }, - ], - f"{prefix} — Size", - ) - outputs.append(size_path) - - latency_rows = [ - { - "title": "MelEncoder latency (ms)", - "values": mel_latency_ms, - "color": "C1", - }, - { - "title": "JointDecision latency (ms)", - "values": jd_latency_ms, - "color": "C1", - }, - ] - if mel_compile_ms is not None and jd_compile_ms is not None: - latency_rows.extend( - [ - { - "title": "MelEncoder compile (ms)", - "values": mel_compile_ms, - "color": "C3", - }, - { - "title": "JointDecision compile (ms)", - "values": jd_compile_ms, - "color": "C3", - }, - ] - ) - - latency_path = plot_dir / "fused_latency.png" - _plot_bar_rows( - latency_path, - display, - latency_rows, - f"{prefix} — Latency", - ) - outputs.append(latency_path) - - return outputs - - -def _plot_all_component_category_charts( - plot_dir: Path, - labels: List[str], - metrics: Dict[str, Dict[str, List[float]]], - title_suffix: str = "", -) -> List[Path]: - if not HAS_MPL: - return [] - outputs: List[Path] = [] - display = _format_labels(labels) - prefix = f"Component Breakdown — {title_suffix}" if title_suffix else "Component Breakdown" - comp_order = [ - "preprocessor", - "encoder", - "mel_encoder", - "decoder", - "joint", - "joint_decision", - ] - n = len(labels) - - quality_rows: List[Dict[str, object]] = [] - for comp in comp_order: - friendly = _friendly_component_name(comp) - if comp == "joint_decision": - values = _get_metric_series(metrics, comp, "acc", n) - title = f"{friendly} token-id match rate" - else: - values = _get_metric_series(metrics, comp, "quality", n) - title = f"{friendly} quality (1 - norm err)" - quality_rows.append({"title": title, "values": values, "color": "C0", "ylim": (0.0, 1.05)}) - - compression_rows: List[Dict[str, object]] = [] - for comp in comp_order: - friendly = _friendly_component_name(comp) - compression_rows.append( - { - "title": f"{friendly} compression ratio", - "values": _get_metric_series(metrics, comp, "compression", n), - "color": "C2", - } - ) - - size_rows: List[Dict[str, object]] = [] - for comp in comp_order: - friendly = _friendly_component_name(comp) - size_rows.append( - { - "title": f"{friendly} size (MB)", - "values": _get_metric_series(metrics, comp, "size_mb", n), - "color": "C4", - } - ) - - latency_rows: List[Dict[str, object]] = [] - for comp in comp_order: - friendly = _friendly_component_name(comp) - latency_rows.append( - { - "title": f"{friendly} latency (ms)", - "values": _get_metric_series(metrics, comp, "latency_ms", n), - "color": "C1", - } - ) - - compile_rows: List[Dict[str, object]] = [] - for comp in comp_order: - friendly = _friendly_component_name(comp) - compile_rows.append( - { - "title": f"{friendly} compile (ms)", - "values": _get_metric_series(metrics, comp, "compile_ms", n), - "color": "C3", - } - ) - - quality_path = plot_dir / "all_components_quality.png" - _plot_bar_rows(quality_path, display, quality_rows, f"{prefix} — Quality") - outputs.append(quality_path) - - compression_path = plot_dir / "all_components_compression.png" - _plot_bar_rows(compression_path, display, compression_rows, f"{prefix} — Compression") - outputs.append(compression_path) - - size_path = plot_dir / "all_components_size.png" - _plot_bar_rows(size_path, display, size_rows, f"{prefix} — Size") - outputs.append(size_path) - - latency_path = plot_dir / "all_components_latency.png" - _plot_bar_rows(latency_path, display, latency_rows, f"{prefix} — Latency") - outputs.append(latency_path) - - compile_path = plot_dir / "all_components_compile.png" - _plot_bar_rows(compile_path, display, compile_rows, f"{prefix} — Compile") - outputs.append(compile_path) - - return outputs - - -app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) - - -@dataclass -class VariantConfig: - name: str - # List of (op_kind, opt_config) steps applied in order; op_kind in {'linear','palettize','prune'} - steps: List[Tuple[str, OptimizationConfig]] - category: str - whitelist: Optional[List[str]] = None # component names to apply; others copied - - -def _dir_size_bytes(path: Path) -> int: - total = 0 - for p in path.rglob("*"): - if p.is_file(): - try: - total += p.stat().st_size - except OSError: - pass - return total - - -def _load_metadata(input_dir: Path) -> Dict[str, object]: - meta_path = input_dir / "metadata.json" - if not meta_path.exists(): - raise typer.BadParameter(f"Expected metadata.json in {input_dir}") - return json.loads(meta_path.read_text()) - - -def _prepare_audio( - seconds: float, - sample_rate: int, - audio_path: Optional[Path], -) -> Tuple[np.ndarray, np.ndarray]: - max_samples = int(round(seconds * sample_rate)) - if audio_path is None: - # random but deterministic sample - rng = np.random.default_rng(1234) - audio = rng.standard_normal(size=(1, max_samples), dtype=np.float32).astype(np.float32) - else: - data, sr = sf.read(str(audio_path), dtype="float32") - if sr != sample_rate: - raise typer.BadParameter( - f"Validation audio sample rate {sr} != expected {sample_rate}" - ) - if data.ndim > 1: - data = data[:, 0] - if data.size < max_samples: - data = np.pad(data, (0, max_samples - data.size)) - elif data.size > max_samples: - data = data[:max_samples] - audio = data.reshape(1, -1).astype(np.float32, copy=False) - length = np.array([max_samples], dtype=np.int32) - return audio, length - - -def _predict_latency(model: ct.models.MLModel, inputs: Dict[str, np.ndarray], runs: int = 10, warmup: int = 3) -> Tuple[float, float]: - # Warmup - for _ in range(max(0, warmup)): - _ = model.predict(inputs) - # Timed runs - times: List[float] = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _ = model.predict(inputs) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) # ms - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - -def _max_abs_rel(a: np.ndarray, b: np.ndarray) -> Tuple[float, float]: - a = np.asarray(a, dtype=np.float32) - b = np.asarray(b, dtype=np.float32) - if a.size == 0: - return 0.0, 0.0 - diff = np.abs(a - b) - max_abs = float(diff.max()) - denom = np.maximum(np.abs(a), np.abs(b)) - with np.errstate(divide="ignore", invalid="ignore"): - rel = np.where(denom == 0.0, 0.0, diff / denom) - max_rel = float(rel.max()) - return max_abs, max_rel - - -def _save_mlpackage(model: ct.models.MLModel, path: Path, description: str) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - # Ensure iOS 17 target for proper MLProgram ops (e.g., blockwise shift/scale) - try: - model.minimum_deployment_target = ct.target.iOS17 - except Exception: - pass - model.short_description = description - model.author = "Fluid Inference" - model.save(str(path)) - - -def _offline_compile_time_ms(model_path: Path) -> float: - """Compile mlpackage -> mlmodelc and return wall time in ms (host offline compile). - - Returns NaN on failure. - """ - compiled_dir: Optional[Path] = None - expected_dir = model_path.with_suffix(".mlmodelc") - try: - # Delete any prior compile artifact in-place so we can measure a fresh build - if expected_dir.exists(): - shutil.rmtree(expected_dir, ignore_errors=True) - - t0 = time.perf_counter() - compiled_path = ct.utils.compile_model(str(model_path)) - t1 = time.perf_counter() - compiled_dir = Path(compiled_path) - return (t1 - t0) * 1000.0 - except Exception: - return float("nan") - finally: - if ( - compiled_dir is not None - and compiled_dir.exists() - and compiled_dir == expected_dir - ): - shutil.rmtree(compiled_dir, ignore_errors=True) - -def _chip_spec_string(compute_units: str) -> str: - try: - chip = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"]).decode().strip() - except Exception: - chip = platform.processor() or platform.machine() - mac_ver = platform.mac_ver()[0] or platform.platform() - return f"Host: {chip} ‱ macOS {mac_ver} ‱ CoreMLTools {ct.__version__} ‱ ComputeUnits={compute_units} ‱ Min Target: iOS17" - - -def _quantize_dir( - input_dir: Path, - output_dir: Path, - variant: VariantConfig, - global_whitelist: Optional[Set[str]] = None, -) -> Dict[str, str]: - """Quantize all mlpackages in input_dir into output_dir using given variant config. - - Returns a map of component name -> saved relative path. - """ - meta = _load_metadata(input_dir) - comps = meta.get("components", {}) - saved: Dict[str, str] = {} - for name, cfg in comps.items(): - src_name = cfg.get("path") - if not src_name: - continue - src_path = input_dir / src_name - if not src_path.exists(): - continue - dst_path = output_dir / src_name - # Use CPU+GPU for preprocessor to avoid NE preprocessor input size issues; others use CPU+NE - cu = ct.ComputeUnit.CPU_AND_GPU if name == "preprocessor" else ct.ComputeUnit.CPU_AND_NE - base_model = ct.models.MLModel(str(src_path), compute_units=cu) - # Target iOS17 when running optimizations so the right ops are chosen - try: - base_model.minimum_deployment_target = ct.target.iOS17 - except Exception: - pass - - if name == "decoder": - typer.echo(f"[{variant.name}] Skipping decoder quantization; copying baseline: {src_name}") - _save_mlpackage(base_model, dst_path, "Baseline copy (decoder quantization disabled) - decoder") - saved[name] = dst_path.name - continue - - skip_reasons: List[str] = [] - if variant.whitelist is not None and name not in variant.whitelist: - skip_reasons.append("not targeted by variant") - if global_whitelist is not None and name not in global_whitelist: - skip_reasons.append("not in requested components") - - if skip_reasons: - reason = "; ".join(skip_reasons) - typer.echo(f"[{variant.name}] Skipping {name} ({reason}); copying baseline: {src_name}") - _save_mlpackage(base_model, dst_path, f"Baseline copy ({reason}) - {name}") - saved[name] = dst_path.name - continue - - typer.echo(f"[{variant.name}] Quantizing {name}: {src_name}") - - try: - q_model = base_model - for step_kind, step_cfg in variant.steps: - if step_kind == 'linear': - q_model = linear_quantize_weights(q_model, step_cfg) - elif step_kind == 'palettize': - q_model = palettize_weights(q_model, step_cfg) - elif step_kind == 'prune': - q_model = prune_weights(q_model, step_cfg) - else: - raise ValueError(f"Unknown variant step: {step_kind}") - except Exception as e: - # If quantization fails (e.g., unsupported op), fall back to copying baseline. - typer.echo(f" ! Failed to quantize {name} with {variant.name}: {e}. Copying baseline.") - _save_mlpackage(base_model, dst_path, f"Baseline copy (failed to quantize) - {name}") - else: - _save_mlpackage(q_model, dst_path, f"{variant.name} quantized - {name}") - saved[name] = dst_path.name - # Persist a variant metadata shim - out_meta = { - "variant": variant.name, - "base_dir": str(input_dir.resolve()), - "components": saved, - } - (output_dir / "quantization_metadata.json").write_text(json.dumps(out_meta, indent=2)) - return saved - - -@app.command() -def quantize( - input_dir: Path = typer.Option(Path("parakeet_coreml"), help="Directory containing baseline mlpackages + metadata.json"), - output_root: Path = typer.Option(Path("parakeet_coreml_quantized"), help="Root output dir for quantized variants"), - validation_audio: Optional[Path] = typer.Option(None, exists=True, resolve_path=True, help="Optional 15s, 16kHz wav for evaluation (defaults to bundled audio if present)"), - compute_units: str = typer.Option("CPU_AND_NE", help="Compute units for evaluation of non-preprocessor models. Preprocessor is forced to CPU_AND_GPU."), - runs: int = typer.Option(10, help="Timed runs per model for latency measurement"), - categories: Optional[List[str]] = typer.Option( - None, - "--category", - "-c", - help="Only run quantization variants in these categories (e.g., linear, mel-palettize). Can be repeated.", - ), - components: Optional[List[str]] = typer.Option( - None, - "--component", - "-m", - help="Component names to quantize. Defaults to mel_encoder and joint_decision. Use 'all' to keep every component enabled.", - ), -) -> None: - """Quantize models, then compare quality, compression, latency, and compile time. - - Variants include int8-linear (per-channel/per-tensor/block), palettization (6-bit), - jd-only probes, and prune+int8. Baseline is the pre-converted models. - """ - meta = _load_metadata(input_dir) - sr = int(meta.get("sample_rate", 16000)) - seconds = float(meta.get("max_audio_seconds", 15.0)) - - components_meta = meta.get("components", {}) - component_lookup = {name.lower(): name for name in components_meta.keys()} - # Defer computing component_filter until after variant/category selection so - # that, by default, we quantize exactly the components targeted by the - # selected variants (instead of a hard-coded subset). - component_filter: Optional[Set[str]] = None - - # Default audio if present - default_audio = (BASE_DIR / "audio" / "yc_first_minute_16k_15s.wav").resolve() - audio_path = validation_audio if validation_audio is not None else (default_audio if default_audio.exists() else None) - if audio_path is not None and validation_audio is None: - typer.echo(f"Using default validation audio: {audio_path}") - audio, audio_len = _prepare_audio(seconds, sr, audio_path) - - # Load baseline models and helpers for inputs - # Baseline models for input preparation - # Force preprocessor to CPU+GPU and all other components to CPU+NE for evaluation - pre_base = ct.models.MLModel(str(input_dir / "parakeet_preprocessor.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_GPU) - enc_base = ct.models.MLModel(str(input_dir / "parakeet_encoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - mel_encoder_base = ct.models.MLModel(str(input_dir / "parakeet_mel_encoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - decoder_base = ct.models.MLModel(str(input_dir / "parakeet_decoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - joint_decision_base = ct.models.MLModel(str(input_dir / "parakeet_joint_decision.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - joint_base = ct.models.MLModel(str(input_dir / "parakeet_joint.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - - # Prepare typical inputs once using baseline models - pre_out = pre_base.predict({"audio_signal": audio, "audio_length": audio_len}) - mel_ref = np.array(pre_out["mel"], dtype=np.float32, copy=True) - mel_len = np.array(pre_out["mel_length"], dtype=np.int32, copy=True) - - enc_out = enc_base.predict({"mel": mel_ref, "mel_length": mel_len}) - encoder_ref = np.array(enc_out["encoder"], dtype=np.float32, copy=True) - encoder_len = np.array(enc_out["encoder_length"], dtype=np.int32, copy=True) - - # Decoder inputs from metadata - dec_in = meta["components"]["decoder"]["inputs"] - targets_shape = tuple(int(x) for x in dec_in["targets"]) # e.g., (1, 256) - h_shape = tuple(int(x) for x in dec_in["h_in"]) # e.g., (2, 1, 640) - # Use zeros as targets for reproducibility without needing blank idx - targets = np.zeros(targets_shape, dtype=np.int32) - target_length = np.array([targets_shape[1]], dtype=np.int32) - h0 = np.zeros(h_shape, dtype=np.float32) - c0 = np.zeros(h_shape, dtype=np.float32) - dec_out = decoder_base.predict({ - "targets": targets, - "target_length": target_length, - "h_in": h0, - "c_in": c0, - }) - decoder_ref = np.array(dec_out["decoder"], dtype=np.float32, copy=True) - - # Baseline sizes per component - pre_base_size = _dir_size_bytes(input_dir / "parakeet_preprocessor.mlpackage") - enc_base_size = _dir_size_bytes(input_dir / "parakeet_encoder.mlpackage") - mel_base_size = _dir_size_bytes(input_dir / "parakeet_mel_encoder.mlpackage") - dec_base_size = _dir_size_bytes(input_dir / "parakeet_decoder.mlpackage") - joint_base_size = _dir_size_bytes(input_dir / "parakeet_joint.mlpackage") - jd_base_size = _dir_size_bytes(input_dir / "parakeet_joint_decision.mlpackage") - - # Baseline latencies - pre_base_inputs = {"audio_signal": audio, "audio_length": audio_len} - enc_base_inputs = {"mel": mel_ref, "mel_length": mel_len} - mel_base_inputs = {"audio_signal": audio, "audio_length": audio_len} - dec_base_inputs = {"targets": targets, "target_length": target_length, "h_in": h0, "c_in": c0} - joint_base_inputs = {"encoder": encoder_ref, "decoder": decoder_ref} - jd_base_inputs = {"encoder": encoder_ref, "decoder": decoder_ref} - pre_base_ms, _ = _predict_latency(pre_base, pre_base_inputs, runs=runs) - enc_base_ms, _ = _predict_latency(enc_base, enc_base_inputs, runs=runs) - mel_base_ms, _ = _predict_latency(mel_encoder_base, mel_base_inputs, runs=runs) - dec_base_ms, _ = _predict_latency(decoder_base, dec_base_inputs, runs=runs) - joint_base_ms, _ = _predict_latency(joint_base, joint_base_inputs, runs=runs) - jd_base_ms, _ = _predict_latency(joint_decision_base, jd_base_inputs, runs=runs) - # Cache baseline joint logits for comparisons - logits_base = np.array(joint_base.predict(joint_base_inputs)["logits"], dtype=np.float32, copy=True) - - # Variants - variants: List[VariantConfig] = [ - VariantConfig( - name="int8-linear", - steps=[( - "linear", - OptimizationConfig(global_config=OpLinearQuantizerConfig(mode="linear", granularity="per_channel")), - )], - category="linear", - ), - # 6-bit palettization for MelEncoder only - VariantConfig( - name="mel6bit-palettize", - steps=[( - "palettize", - OptimizationConfig( - global_config=OpPalettizerConfig(mode="kmeans", nbits=6) - ), - )], - category="mel-palettize", - whitelist=["mel_encoder"], - ), - # 6-bit palettization for Encoder only - VariantConfig( - name="enc6bit-palettize", - steps=[( - "palettize", - OptimizationConfig( - global_config=OpPalettizerConfig(mode="kmeans", nbits=6) - ), - )], - category="encoder-palettize", - whitelist=["encoder"], - ), - # (removed) Global palettization variants - ] - - available_categories = {variant.category for variant in variants} - category_lookup = {cat.lower(): cat for cat in available_categories} - selected_categories: Set[str] - if categories: - normalized_categories = [cat.strip().lower() for cat in categories if cat.strip()] - if normalized_categories: - invalid_categories = [cat for cat in normalized_categories if cat not in category_lookup] - if invalid_categories: - available_str = ", ".join(sorted(available_categories)) or "none" - bad = ", ".join(sorted(set(invalid_categories))) - raise typer.BadParameter( - f"Unknown category (--category): {bad}. Available categories: {available_str}." - ) - selected_categories = {category_lookup[cat] for cat in normalized_categories} - variants = [variant for variant in variants if variant.category in selected_categories] - else: - selected_categories = available_categories - else: - selected_categories = available_categories - - if not variants: - typer.echo("No quantization variants matched the requested categories; nothing to do.") - raise typer.Exit(code=0) - - typer.echo("Running variant categories: " + ", ".join(sorted(selected_categories))) - - # Resolve the component whitelist now that variants are known. - if components: - normalized_components = [comp.strip().lower() for comp in components if comp.strip()] - if any(comp == "all" for comp in normalized_components): - component_filter = None - else: - resolved: List[str] = [] - invalid: List[str] = [] - for comp in normalized_components: - match = component_lookup.get(comp) - if match is None: - invalid.append(comp) - else: - resolved.append(match) - if invalid: - available = ", ".join(sorted(component_lookup.values())) or "none" - bad = ", ".join(sorted(set(invalid))) - raise typer.BadParameter( - f"Unknown component(s) for --component: {bad}. Available components: {available}." - ) - component_filter = set(resolved) - else: - # Derive from selected variants' whitelists - derived: Set[str] = set() - has_global = False - for v in variants: - if v.whitelist is None: - has_global = True - break - derived.update(v.whitelist) - component_filter = None if has_global or not derived else derived - - if component_filter is None: - typer.echo("Quantizing components: all components (derived)") - else: - typer.echo("Quantizing components: " + ", ".join(sorted(component_filter)) + " (derived)") - - # Aggregate results (baseline + variants) - summary: Dict[str, Dict[str, object]] = {} - variants_names: List[str] = [] - # Build arrays including baseline as first label - fused_labels: List[str] = ["baseline"] - mel_quality_scores: List[float] = [1.0] - mel_latency_ms: List[float] = [mel_base_ms] - mel_compression: List[float] = [1.0] - mel_size_mb: List[float] = [float(mel_base_size) / BYTES_IN_MB] - # Offline compile time (host) for fused models - mel_compile_ms: List[float] = [_offline_compile_time_ms(input_dir / "parakeet_mel_encoder.mlpackage")] - jd_accuracy: List[float] = [1.0] - jd_latency_ms: List[float] = [jd_base_ms] - jd_compression: List[float] = [1.0] - jd_size_mb: List[float] = [float(jd_base_size) / BYTES_IN_MB] - jd_compile_ms: List[float] = [_offline_compile_time_ms(input_dir / "parakeet_joint_decision.mlpackage")] - - # For the all-components chart, collect per-component metrics similarly - all_metrics: Dict[str, Dict[str, List[float]]] = { - "preprocessor": { - "quality": [1.0], - "compression": [1.0], - "latency_ms": [pre_base_ms], - "compile_ms": [_offline_compile_time_ms(input_dir / "parakeet_preprocessor.mlpackage")], - "size_mb": [float(pre_base_size) / BYTES_IN_MB], - }, - "encoder": { - "quality": [1.0], - "compression": [1.0], - "latency_ms": [enc_base_ms], - "compile_ms": [_offline_compile_time_ms(input_dir / "parakeet_encoder.mlpackage")], - "size_mb": [float(enc_base_size) / BYTES_IN_MB], - }, - "mel_encoder": { - "quality": [1.0], - "compression": [1.0], - "latency_ms": [mel_base_ms], - "compile_ms": [mel_compile_ms[0]], - "size_mb": [float(mel_base_size) / BYTES_IN_MB], - }, - "decoder": { - "quality": [1.0], - "compression": [1.0], - "latency_ms": [dec_base_ms], - "compile_ms": [_offline_compile_time_ms(input_dir / "parakeet_decoder.mlpackage")], - "size_mb": [float(dec_base_size) / BYTES_IN_MB], - }, - "joint": { - "quality": [1.0], - "compression": [1.0], - "latency_ms": [joint_base_ms], - "compile_ms": [_offline_compile_time_ms(input_dir / "parakeet_joint.mlpackage")], - "size_mb": [float(joint_base_size) / BYTES_IN_MB], - }, - "joint_decision": { - "acc": [1.0], - "compression": [1.0], - "latency_ms": [jd_base_ms], - "compile_ms": [jd_compile_ms[0]], - "size_mb": [float(jd_base_size) / BYTES_IN_MB], - }, - } - - # Populate baseline entry - summary["baseline"] = { - "components": { - "preprocessor": { - "quality": 1.0, - "latency_ms": pre_base_ms, - "size_bytes": float(pre_base_size), - "size_mb": float(pre_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["preprocessor"]["compile_ms"][0], - }, - "encoder": { - "quality": 1.0, - "latency_ms": enc_base_ms, - "size_bytes": float(enc_base_size), - "size_mb": float(enc_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["encoder"]["compile_ms"][0], - }, - "mel_encoder": { - "quality": 1.0, - "latency_ms": mel_base_ms, - "size_bytes": float(mel_base_size), - "size_mb": float(mel_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["mel_encoder"]["compile_ms"][0], - }, - "decoder": { - "quality": 1.0, - "latency_ms": dec_base_ms, - "size_bytes": float(dec_base_size), - "size_mb": float(dec_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["decoder"]["compile_ms"][0], - }, - "joint": { - "quality": 1.0, - "latency_ms": joint_base_ms, - "size_bytes": float(joint_base_size), - "size_mb": float(joint_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["joint"]["compile_ms"][0], - }, - "joint_decision": { - "acc": 1.0, - "latency_ms": jd_base_ms, - "size_bytes": float(jd_base_size), - "size_mb": float(jd_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["joint_decision"]["compile_ms"][0], - }, - } - } - - for var in variants: - variants_names.append(var.name) - out_dir = output_root / var.name - out_dir_exists = out_dir.exists() - out_dir.mkdir(parents=True, exist_ok=True) - - expected_components = [] - for comp_cfg in meta.get("components", {}).values(): - rel = comp_cfg.get("path") - if rel: - expected_components.append(out_dir / rel) - - missing = [p for p in expected_components if not p.exists()] - if out_dir_exists and not missing: - typer.echo(f"[{var.name}] Output already present at {out_dir}; skipping quantization step.") - else: - if out_dir_exists and missing: - missing_names = ", ".join(sorted(p.name for p in missing)) or "unknown" - typer.echo(f"[{var.name}] Output directory exists but is incomplete (missing: {missing_names}). Re-quantizing.") - shutil.rmtree(out_dir, ignore_errors=True) - out_dir.mkdir(parents=True, exist_ok=True) - _quantize_dir(input_dir, out_dir, var, component_filter) - - # Load quantized models and pre-compute offline compile time - pre_q_path = out_dir / "parakeet_preprocessor.mlpackage" - enc_q_path = out_dir / "parakeet_encoder.mlpackage" - mel_q_path = out_dir / "parakeet_mel_encoder.mlpackage" - dec_q_path = out_dir / "parakeet_decoder.mlpackage" - joint_q_path = out_dir / "parakeet_joint.mlpackage" - jd_q_path = out_dir / "parakeet_joint_decision.mlpackage" - - pre_compile_ms = _offline_compile_time_ms(pre_q_path) - enc_compile_ms = _offline_compile_time_ms(enc_q_path) - mel_compile_q_ms = _offline_compile_time_ms(mel_q_path) - dec_compile_ms = _offline_compile_time_ms(dec_q_path) - joint_compile_ms = _offline_compile_time_ms(joint_q_path) - jd_compile_q_ms = _offline_compile_time_ms(jd_q_path) - - # Match compute units for quantized artifacts: preprocessor on CPU+GPU; others on CPU+NE - pre_q = ct.models.MLModel(str(pre_q_path), compute_units=ct.ComputeUnit.CPU_AND_GPU) - enc_q = ct.models.MLModel(str(enc_q_path), compute_units=ct.ComputeUnit.CPU_AND_NE) - mel_q = ct.models.MLModel(str(mel_q_path), compute_units=ct.ComputeUnit.CPU_AND_NE) - dec_q = ct.models.MLModel(str(dec_q_path), compute_units=ct.ComputeUnit.CPU_AND_NE) - joint_q = ct.models.MLModel(str(joint_q_path), compute_units=ct.ComputeUnit.CPU_AND_NE) - jd_q = ct.models.MLModel(str(jd_q_path), compute_units=ct.ComputeUnit.CPU_AND_NE) - - # Preprocessor quality vs baseline - pre_q_out = pre_q.predict(pre_base_inputs) - mel_q_in = np.array(pre_q_out["mel"], dtype=np.float32, copy=True) - a_pre, r_pre = _max_abs_rel(mel_ref, mel_q_in) - l2_pre_ref = float(np.linalg.norm(mel_ref)) - l2_pre_err = float(np.linalg.norm(mel_ref - mel_q_in)) - pre_norm_err = (l2_pre_err / (l2_pre_ref + 1e-8)) if l2_pre_ref > 0 else 0.0 - pre_quality = float(max(0.0, 1.0 - pre_norm_err)) - pre_q_ms, _ = _predict_latency(pre_q, pre_base_inputs, runs=runs) - pre_q_size = _dir_size_bytes(out_dir / "parakeet_preprocessor.mlpackage") - all_metrics["preprocessor"]["quality"].append(pre_quality) - all_metrics["preprocessor"]["latency_ms"].append(pre_q_ms) - all_metrics["preprocessor"]["compression"].append(float(pre_base_size) / float(pre_q_size if pre_q_size > 0 else 1)) - all_metrics["preprocessor"].setdefault("compile_ms", []).append(pre_compile_ms) - all_metrics["preprocessor"]["size_mb"].append(float(pre_q_size) / BYTES_IN_MB) - - # Encoder quality vs baseline (feed baseline mel to both) - enc_q_out = enc_q.predict({"mel": mel_ref, "mel_length": mel_len}) - encoder_q = np.array(enc_q_out["encoder"], dtype=np.float32, copy=True) - l2_enc_ref = float(np.linalg.norm(encoder_ref)) - l2_enc_err = float(np.linalg.norm(encoder_ref - encoder_q)) - enc_norm_err = (l2_enc_err / (l2_enc_ref + 1e-8)) if l2_enc_ref > 0 else 0.0 - enc_quality = float(max(0.0, 1.0 - enc_norm_err)) - enc_q_ms, _ = _predict_latency(enc_q, enc_base_inputs, runs=runs) - enc_q_size = _dir_size_bytes(out_dir / "parakeet_encoder.mlpackage") - all_metrics["encoder"]["quality"].append(enc_quality) - all_metrics["encoder"]["latency_ms"].append(enc_q_ms) - all_metrics["encoder"]["compression"].append(float(enc_base_size) / float(enc_q_size if enc_q_size > 0 else 1)) - all_metrics["encoder"].setdefault("compile_ms", []).append(enc_compile_ms) - all_metrics["encoder"]["size_mb"].append(float(enc_q_size) / BYTES_IN_MB) - - # MelEncoder quality - mel_q_out = mel_q.predict(mel_base_inputs) - enc_q_fused = np.array(mel_q_out["encoder"], dtype=np.float32, copy=True) - a_mel, r_mel = _max_abs_rel(encoder_ref, enc_q_fused) - # Normalize error into a [0,1] quality score: 1 / (1 + normalized L2) - # Use relative measure derived from L2 norms as more stable than max. - l2_ref = float(np.linalg.norm(encoder_ref)) - l2_err = float(np.linalg.norm(encoder_ref - enc_q_fused)) - norm_err = (l2_err / (l2_ref + 1e-8)) if l2_ref > 0 else 0.0 - mel_quality = float(max(0.0, 1.0 - norm_err)) - mel_q_ms, _ = _predict_latency(mel_q, mel_base_inputs, runs=runs) - mel_q_size = _dir_size_bytes(out_dir / "parakeet_mel_encoder.mlpackage") - mel_ratio = float(mel_base_size) / float(mel_q_size if mel_q_size > 0 else 1) - mel_size_mb.append(float(mel_q_size) / BYTES_IN_MB) - all_metrics["mel_encoder"]["quality"].append(mel_quality) - all_metrics["mel_encoder"]["latency_ms"].append(mel_q_ms) - all_metrics["mel_encoder"]["compression"].append(float(mel_base_size) / float(mel_q_size if mel_q_size > 0 else 1)) - all_metrics["mel_encoder"].setdefault("compile_ms", []).append(mel_compile_q_ms) - all_metrics["mel_encoder"]["size_mb"].append(float(mel_q_size) / BYTES_IN_MB) - - # JointDecision quality: token-id and duration match rates - jd_base_out = joint_decision_base.predict(jd_base_inputs) - token_id_base = np.array(jd_base_out["token_id"], dtype=np.int32, copy=True) - duration_base = np.array(jd_base_out["duration"], dtype=np.int32, copy=True) - token_prob_base = np.array(jd_base_out["token_prob"], dtype=np.float32, copy=True) - - jd_q_out = jd_q.predict(jd_base_inputs) - token_id_q = np.array(jd_q_out["token_id"], dtype=np.int32, copy=True) - duration_q = np.array(jd_q_out["duration"], dtype=np.int32, copy=True) - token_prob_q = np.array(jd_q_out["token_prob"], dtype=np.float32, copy=True) - - # Accuracy metrics - id_match = float((token_id_q == token_id_base).mean()) - dur_match = float((duration_q == duration_base).mean()) - # Aggregate a single "accuracy" number as token-id match rate (primary) - jd_acc = id_match - jd_q_ms, _ = _predict_latency(jd_q, jd_base_inputs, runs=runs) - jd_q_size = _dir_size_bytes(out_dir / "parakeet_joint_decision.mlpackage") - jd_ratio = float(jd_base_size) / float(jd_q_size if jd_q_size > 0 else 1) - jd_size_mb.append(float(jd_q_size) / BYTES_IN_MB) - all_metrics["joint_decision"].setdefault("acc", []).append(jd_acc) - all_metrics["joint_decision"]["latency_ms"].append(jd_q_ms) - all_metrics["joint_decision"]["compression"].append(float(jd_base_size) / float(jd_q_size if jd_q_size > 0 else 1)) - all_metrics["joint_decision"].setdefault("compile_ms", []).append(jd_compile_q_ms) - all_metrics["joint_decision"]["size_mb"].append(float(jd_q_size) / BYTES_IN_MB) - - # Decoder quality vs baseline - dec_q_out = dec_q.predict(dec_base_inputs) - decoder_q = np.array(dec_q_out["decoder"], dtype=np.float32, copy=True) - l2_dec_ref = float(np.linalg.norm(decoder_ref)) - l2_dec_err = float(np.linalg.norm(decoder_ref - decoder_q)) - dec_norm_err = (l2_dec_err / (l2_dec_ref + 1e-8)) if l2_dec_ref > 0 else 0.0 - dec_quality = float(max(0.0, 1.0 - dec_norm_err)) - dec_q_ms, _ = _predict_latency(dec_q, dec_base_inputs, runs=runs) - dec_q_size = _dir_size_bytes(out_dir / "parakeet_decoder.mlpackage") - all_metrics["decoder"]["quality"].append(dec_quality) - all_metrics["decoder"]["latency_ms"].append(dec_q_ms) - all_metrics["decoder"]["compression"].append(float(dec_base_size) / float(dec_q_size if dec_q_size > 0 else 1)) - all_metrics["decoder"].setdefault("compile_ms", []).append(dec_compile_ms) - all_metrics["decoder"]["size_mb"].append(float(dec_q_size) / BYTES_IN_MB) - - # Joint quality vs baseline (compare logits) - joint_q_out = joint_q.predict(joint_base_inputs) - logits_q = np.array(joint_q_out["logits"], dtype=np.float32, copy=True) - l2_joint_ref = float(np.linalg.norm(logits_base)) - l2_joint_err = float(np.linalg.norm(logits_base - logits_q)) - joint_norm_err = (l2_joint_err / (l2_joint_ref + 1e-8)) if l2_joint_ref > 0 else 0.0 - joint_quality = float(max(0.0, 1.0 - joint_norm_err)) - joint_q_ms, _ = _predict_latency(joint_q, joint_base_inputs, runs=runs) - joint_q_size = _dir_size_bytes(out_dir / "parakeet_joint.mlpackage") - all_metrics["joint"]["quality"].append(joint_quality) - all_metrics["joint"]["latency_ms"].append(joint_q_ms) - all_metrics["joint"]["compression"].append(float(joint_base_size) / float(joint_q_size if joint_q_size > 0 else 1)) - all_metrics["joint"].setdefault("compile_ms", []).append(joint_compile_ms) - all_metrics["joint"]["size_mb"].append(float(joint_q_size) / BYTES_IN_MB) - - # Decoder deltas for JSON - a_dec, r_dec = _max_abs_rel(decoder_ref, decoder_q) - # Joint deltas for JSON - a_joint, r_joint = _max_abs_rel(logits_base, logits_q) - - # Store metrics - summary[var.name] = { - "components": { - "preprocessor": { - "quality": pre_quality, - "latency_ms": pre_q_ms, - "size_bytes": float(pre_q_size), - "size_mb": float(pre_q_size) / BYTES_IN_MB, - "compression_ratio": float(pre_base_size) / float(pre_q_size if pre_q_size > 0 else 1), - "max_abs": a_pre, - "max_rel": r_pre, - "compile_ms": pre_compile_ms, - }, - "encoder": { - "quality": enc_quality, - "latency_ms": enc_q_ms, - "size_bytes": float(enc_q_size), - "size_mb": float(enc_q_size) / BYTES_IN_MB, - "compression_ratio": float(enc_base_size) / float(enc_q_size if enc_q_size > 0 else 1), - "compile_ms": enc_compile_ms, - }, - "mel_encoder": { - "quality": mel_quality, - "latency_ms": mel_q_ms, - "size_bytes": float(mel_q_size), - "size_mb": float(mel_q_size) / BYTES_IN_MB, - "compression_ratio": mel_ratio, - "max_abs": a_mel, - "max_rel": r_mel, - "compile_ms": mel_compile_q_ms, - }, - "decoder": { - "quality": dec_quality, - "latency_ms": dec_q_ms, - "size_bytes": float(dec_q_size), - "size_mb": float(dec_q_size) / BYTES_IN_MB, - "compression_ratio": float(dec_base_size) / float(dec_q_size if dec_q_size > 0 else 1), - "max_abs": a_dec, - "max_rel": r_dec, - "compile_ms": dec_compile_ms, - }, - "joint": { - "quality": joint_quality, - "latency_ms": joint_q_ms, - "size_bytes": float(joint_q_size), - "size_mb": float(joint_q_size) / BYTES_IN_MB, - "compression_ratio": float(joint_base_size) / float(joint_q_size if joint_q_size > 0 else 1), - "max_abs": a_joint, - "max_rel": r_joint, - "compile_ms": joint_compile_ms, - }, - "joint_decision": { - "acc": jd_acc, - "duration_match": dur_match, - "prob_mae": float(np.mean(np.abs(token_prob_q - token_prob_base))), - "latency_ms": jd_q_ms, - "size_bytes": float(jd_q_size), - "size_mb": float(jd_q_size) / BYTES_IN_MB, - "compression_ratio": jd_ratio, - "compile_ms": jd_compile_q_ms, - }, - } - } - - fused_labels.append(var.name) - mel_quality_scores.append(mel_quality) - mel_latency_ms.append(mel_q_ms) - mel_compression.append(mel_ratio) - jd_accuracy.append(jd_acc) - jd_latency_ms.append(jd_q_ms) - jd_compression.append(jd_ratio) - mel_compile_ms.append(mel_compile_q_ms) - jd_compile_ms.append(jd_compile_q_ms) - - # Write summary JSON - out_root = output_root - out_root.mkdir(parents=True, exist_ok=True) - (out_root / "quantization_summary.json").write_text(json.dumps(summary, indent=2)) - - # Plot - plot_dir = out_root / "plots" - title_suffix = _chip_spec_string(compute_units) - fused_paths = _plot_fused_category_charts( - plot_dir, - fused_labels, - mel_quality_scores, - mel_latency_ms, - mel_compression, - mel_size_mb, - jd_accuracy, - jd_latency_ms, - jd_compression, - jd_size_mb, - mel_compile_ms, - jd_compile_ms, - title_suffix, - ) - component_paths = _plot_all_component_category_charts( - plot_dir, - fused_labels, - all_metrics, - title_suffix, - ) - - typer.echo(f"Wrote summary JSON: {out_root / 'quantization_summary.json'}") - if HAS_MPL: - all_plot_paths = fused_paths + component_paths - repo_plot_dir = BASE_DIR / "plots" / "quantize" / compute_units.lower() - repo_plot_dir.mkdir(parents=True, exist_ok=True) - for path in all_plot_paths: - typer.echo(f"Wrote plot: {path}") - if path.exists(): - dest = repo_plot_dir / path.name - shutil.copy2(path, dest) - typer.echo(f"Mirrored plot: {dest}") - else: - typer.echo("matplotlib unavailable; skipped plotting.") - - -if __name__ == "__main__": - app() diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/speech_to_text_streaming_infer_rnnt.py b/convert/parakeet-tdt-v2-0.6b/coreml/speech_to_text_streaming_infer_rnnt.py deleted file mode 100644 index 07d8fcc04df44b41c758ad52f518d36a1ad797ae..0000000000000000000000000000000000000000 --- a/convert/parakeet-tdt-v2-0.6b/coreml/speech_to_text_streaming_infer_rnnt.py +++ /dev/null @@ -1,341 +0,0 @@ -"""Streaming inference helper that stitches Parakeet CoreML components using the RNNT greedy loop from Nemo.""" -from __future__ import annotations - -import argparse -import json -import logging -from pathlib import Path -from typing import Iterable, List, Optional, Sequence - -import coremltools as ct -import librosa -import numpy as np -import torch - -from parakeet_components import CoreMLModelBundle - -LOGGER = logging.getLogger("parakeet_streaming") - - -class BatchedHyps: - """Minimal port of Nemo's batched hypothesis buffer.""" - - def __init__( - self, - batch_size: int, - init_length: int, - device: torch.device, - float_dtype: torch.dtype, - ) -> None: - if init_length <= 0: - raise ValueError("init_length must be > 0") - self._max_length = init_length - self.current_lengths = torch.zeros(batch_size, device=device, dtype=torch.long) - self.transcript = torch.zeros((batch_size, self._max_length), device=device, dtype=torch.long) - self.timestamps = torch.zeros((batch_size, self._max_length), device=device, dtype=torch.long) - self.scores = torch.zeros(batch_size, device=device, dtype=float_dtype) - self.last_timestamp = torch.full((batch_size,), -1, device=device, dtype=torch.long) - self.last_timestamp_lasts = torch.zeros(batch_size, device=device, dtype=torch.long) - self._batch_indices = torch.arange(batch_size, device=device) - self._ones = torch.ones_like(self._batch_indices) - - def add_results( - self, - active_mask: torch.Tensor, - labels: torch.Tensor, - time_indices: torch.Tensor, - scores: torch.Tensor, - ) -> None: - self.scores = torch.where(active_mask, self.scores + scores, self.scores) - self.transcript[self._batch_indices, self.current_lengths] = labels - self.timestamps[self._batch_indices, self.current_lengths] = time_indices - torch.where( - torch.logical_and(active_mask, self.last_timestamp == time_indices), - self.last_timestamp_lasts + 1, - self.last_timestamp_lasts, - out=self.last_timestamp_lasts, - ) - torch.where( - torch.logical_and(active_mask, self.last_timestamp != time_indices), - self._ones, - self.last_timestamp_lasts, - out=self.last_timestamp_lasts, - ) - torch.where(active_mask, time_indices, self.last_timestamp, out=self.last_timestamp) - self.current_lengths += active_mask - - -class CoreMLStreamingDecoder: - """Use exported decoder and joint CoreML models with Nemo's greedy RNNT loop.""" - - def __init__( - self, - decoder_model: ct.models.MLModel, - joint_model: ct.models.MLModel, - *, - vocab_size: int, - blank_id: int, - num_layers: int, - hidden_size: int, - durations: Sequence[int] = (0, 1, 2, 3, 4), - max_symbols: int = 10, - device: torch.device = torch.device("cpu"), - ) -> None: - self.decoder_model = decoder_model - self.joint_model = joint_model - self.vocab_size = vocab_size - self.blank_id = blank_id - self.num_layers = num_layers - self.hidden_size = hidden_size - self.durations = torch.tensor(durations, dtype=torch.long, device=device) - self.max_symbols = max_symbols - self.device = device - - def _predict_decoder(self, labels: torch.Tensor, h_in: np.ndarray, c_in: np.ndarray) -> tuple[torch.Tensor, np.ndarray, np.ndarray]: - outputs = self.decoder_model.predict( - { - "targets": np.array([labels.cpu().numpy()], dtype=np.int32), - "target_lengths": np.array([labels.numel()], dtype=np.int32), - "h_in": h_in, - "c_in": c_in, - } - ) - decoder_output = torch.from_numpy(outputs["decoder_output"]).to(self.device) - return decoder_output, outputs["h_out"], outputs["c_out"] - - def _predict_joint(self, encoder_frame: torch.Tensor, decoder_output: torch.Tensor) -> torch.Tensor: - outputs = self.joint_model.predict( - { - "encoder_outputs": encoder_frame.unsqueeze(1).cpu().numpy().astype(np.float32), - "decoder_outputs": decoder_output.cpu().numpy().astype(np.float32), - } - ) - logits = torch.from_numpy(outputs["logits"]).to(self.device) - return logits.squeeze(1).squeeze(1) - - def decode(self, encoder_output: torch.Tensor, encoder_lengths: torch.Tensor) -> List[List[int]]: - batch_size, max_time, _ = encoder_output.shape - encoder_output = encoder_output.to(self.device) - encoder_lengths = encoder_lengths.to(self.device) - - float_dtype = encoder_output.dtype - batch_indices = torch.arange(batch_size, device=self.device) - labels = torch.full((batch_size,), fill_value=self.blank_id, device=self.device, dtype=torch.long) - time_indices = torch.zeros_like(labels) - safe_time_indices = torch.zeros_like(labels) - time_indices_current = torch.zeros_like(labels) - last_timesteps = encoder_lengths - 1 - active_mask = encoder_lengths > 0 - advance_mask = torch.empty_like(active_mask) - active_mask_prev = torch.empty_like(active_mask) - became_inactive = torch.empty_like(active_mask) - - hyps = BatchedHyps( - batch_size=batch_size, - init_length=max_time * self.max_symbols if self.max_symbols else max_time, - device=self.device, - float_dtype=float_dtype, - ) - - h_in = np.zeros((self.num_layers, batch_size, self.hidden_size), dtype=np.float32) - c_in = np.zeros((self.num_layers, batch_size, self.hidden_size), dtype=np.float32) - - while active_mask.any(): - active_mask_prev.copy_(active_mask) - decoder_output, h_in, c_in = self._predict_decoder(labels, h_in, c_in) - logits = self._predict_joint(encoder_output[batch_indices, safe_time_indices], decoder_output) - - scores, labels = logits[:, : self.vocab_size].max(dim=-1) - duration_indices = logits[:, self.vocab_size : self.vocab_size + len(self.durations)].argmax(dim=-1) - durations = self.durations[duration_indices] - - blank_mask = labels == self.blank_id - durations.masked_fill_(torch.logical_and(durations == 0, blank_mask), 1) - time_indices_current.copy_(time_indices) - time_indices += durations - torch.minimum(time_indices, last_timesteps, out=safe_time_indices) - torch.less(time_indices, encoder_lengths, out=active_mask) - torch.logical_and(active_mask, blank_mask, out=advance_mask) - - while advance_mask.any(): - torch.where(advance_mask, time_indices, time_indices_current, out=time_indices_current) - logits = self._predict_joint(encoder_output[batch_indices, safe_time_indices], decoder_output) - more_scores, more_labels = logits[:, : self.vocab_size].max(dim=-1) - labels = torch.where(advance_mask, more_labels, labels) - scores = torch.where(advance_mask, more_scores, scores) - duration_indices = logits[:, self.vocab_size : self.vocab_size + len(self.durations)].argmax(dim=-1) - durations = self.durations[duration_indices] - blank_mask = labels == self.blank_id - durations.masked_fill_(torch.logical_and(durations == 0, blank_mask), 1) - torch.where(advance_mask, time_indices + durations, time_indices, out=time_indices) - torch.minimum(time_indices, last_timesteps, out=safe_time_indices) - torch.less(time_indices, encoder_lengths, out=active_mask) - torch.logical_and(active_mask, blank_mask, out=advance_mask) - - torch.ne(active_mask, active_mask_prev, out=became_inactive) - hyps.add_results(active_mask, labels, time_indices_current, scores) - - if self.max_symbols is not None: - force_blank = torch.logical_and( - active_mask, - torch.logical_and( - torch.logical_and(labels != self.blank_id, hyps.last_timestamp_lasts >= self.max_symbols), - hyps.last_timestamp == time_indices, - ), - ) - time_indices += force_blank - torch.minimum(time_indices, last_timesteps, out=safe_time_indices) - torch.less(time_indices, encoder_lengths, out=active_mask) - - results: List[List[int]] = [] - for hyp in hyps.transcript: - tokens = [int(token) for token in hyp.tolist() if 0 < token < self.vocab_size] - results.append(tokens) - return results - - -class StreamingTranscriber: - def __init__( - self, - bundle: CoreMLModelBundle, - *, - blank_id: Optional[int] = None, - num_layers: int = 2, - hidden_size: int = 640, - durations: Sequence[int] = (0, 1, 2, 3, 4), - ) -> None: - self.preprocessor = ct.models.MLModel(str(bundle.preprocessor), compute_units=ct.ComputeUnit.CPU_ONLY) - self.encoder = ct.models.MLModel(str(bundle.encoder), compute_units=ct.ComputeUnit.CPU_ONLY) - self.decoder = ct.models.MLModel(str(bundle.decoder), compute_units=ct.ComputeUnit.CPU_ONLY) - self.joint = ct.models.MLModel(str(bundle.joint), compute_units=ct.ComputeUnit.CPU_ONLY) - self.tokenizer = self._load_tokenizer(bundle.tokenizer) - - vocab_size = max(self.tokenizer.keys()) + 1 - if blank_id is None: - blank_id = vocab_size - 1 - self.decoder_helper = CoreMLStreamingDecoder( - self.decoder, - self.joint, - vocab_size=vocab_size, - blank_id=blank_id, - num_layers=num_layers, - hidden_size=hidden_size, - durations=durations, - ) - self.blank_id = blank_id - - @staticmethod - def _load_tokenizer(tokenizer_path: Optional[Path]) -> dict[int, str]: - if tokenizer_path is None: - raise ValueError("Tokenizer JSON is required") - with Path(tokenizer_path).open() as f: - data = json.load(f) - return {int(k): v for k, v in data.items()} - - def _tokens_to_text(self, tokens: Iterable[int]) -> str: - pieces: List[str] = [] - for token in tokens: - piece = self.tokenizer.get(token) - if piece is None: - continue - if piece.startswith("▁"): - if pieces: - pieces.append(" ") - pieces.append(piece[1:]) - else: - pieces.append(piece) - return "".join(pieces).strip() - - def _preprocess(self, audio: np.ndarray) -> tuple[np.ndarray, int]: - audio_2d = audio.reshape(1, -1).astype(np.float32) - length = np.array([audio_2d.shape[-1]], dtype=np.int32) - outputs = self.preprocessor.predict({ - "audio_signal": audio_2d, - "audio_length": length, - }) - return outputs["melspectrogram"], int(outputs["melspectrogram_length"][0]) - - def _encode(self, mel: np.ndarray, mel_length: int) -> tuple[torch.Tensor, torch.Tensor]: - outputs = self.encoder.predict({ - "melspectrogram": mel.astype(np.float32), - "melspectrogram_length": np.array([mel_length], dtype=np.int32), - }) - encoder_output = outputs["encoder_output"] - if encoder_output.ndim == 3: - encoder_output = np.transpose(encoder_output, (0, 2, 1)) - length = torch.tensor(outputs["encoder_output_length"], dtype=torch.long) - return torch.from_numpy(encoder_output.astype(np.float32)), length - - def transcribe(self, audio_path: Path) -> str: - audio, _ = librosa.load(str(audio_path), sr=16000) - mel, mel_length = self._preprocess(audio) - encoder_output, encoder_length = self._encode(mel, mel_length) - token_ids = self.decoder_helper.decode(encoder_output, encoder_length)[0] - return self._tokens_to_text(token_ids) - - def transcribe_many(self, audio_paths: Sequence[Path]) -> List[str]: - results: List[str] = [] - for path in audio_paths: - LOGGER.info("Transcribing %s", path) - results.append(self.transcribe(path)) - return results - - -def _resolve_bundle(args: argparse.Namespace) -> CoreMLModelBundle: - base = Path(args.model_dir) if args.model_dir else None - if base is None and not all([args.preprocessor, args.encoder, args.decoder, args.joint, args.tokenizer]): - raise ValueError("Either --model-dir or explicit model paths are required") - return CoreMLModelBundle( - preprocessor=Path(args.preprocessor) if args.preprocessor else base / "Melspectrogram.mlpackage", - encoder=Path(args.encoder) if args.encoder else base / "ParakeetEncoder.mlpackage", - decoder=Path(args.decoder) if args.decoder else base / "ParakeetDecoder.mlpackage", - joint=Path(args.joint) if args.joint else base / "RNNTJoint.mlpackage", - tokenizer=Path(args.tokenizer) if args.tokenizer else base / "tokenizer.json", - ) - - -def _build_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser(description="Streaming RNNT inference with CoreML components") - parser.add_argument("--model-dir", type=Path, help="Directory containing exported CoreML models") - parser.add_argument("--preprocessor", type=Path, help="Path to the preprocessor .mlpackage") - parser.add_argument("--encoder", type=Path, help="Path to the encoder .mlpackage") - parser.add_argument("--decoder", type=Path, help="Path to the decoder .mlpackage") - parser.add_argument("--joint", type=Path, help="Path to the joint .mlpackage") - parser.add_argument("--tokenizer", type=Path, help="Path to tokenizer JSON") - parser.add_argument("audio", nargs="+", help="Audio files to transcribe") - parser.add_argument("--blank-id", type=int, help="Blank token id") - parser.add_argument("--num-layers", type=int, default=2, help="Prediction network layer count") - parser.add_argument("--hidden-size", type=int, default=640, help="Prediction network hidden size") - parser.add_argument("--durations", type=int, nargs="+", default=[0, 1, 2, 3, 4], help="RNNT duration bucket values") - parser.add_argument("--verbose", "-v", action="count", default=0, help="Increase log verbosity") - return parser - - -def _configure_logging(verbosity: int) -> None: - level = logging.WARNING - (10 * verbosity) - logging.basicConfig(level=max(logging.DEBUG, level), format="[%(levelname)s] %(message)s") - - -def main(argv: Optional[Sequence[str]] = None) -> None: - parser = _build_parser() - args = parser.parse_args(argv) - _configure_logging(args.verbose) - - try: - bundle = _resolve_bundle(args) - transcriber = StreamingTranscriber( - bundle, - blank_id=args.blank_id, - num_layers=args.num_layers, - hidden_size=args.hidden_size, - durations=tuple(args.durations), - ) - transcripts = transcriber.transcribe_many([Path(p) for p in args.audio]) - for path, text in zip(args.audio, transcripts): - print(f"{path}: {text}") - except ValueError as exc: - parser.error(str(exc)) - - -if __name__ == "__main__": # pragma: no cover - main() diff --git a/convert/parakeet-tdt-v2-0.6b/coreml/uv.lock b/convert/parakeet-tdt-v2-0.6b/coreml/uv.lock deleted 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a/models/parakeet-ctc-110m-coreml/CTCHead.mlmodelc/model.mil +++ /dev/null @@ -1,24 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor encoder_output) { - tensor var_4 = const()[name = tensor("op_4"), val = tensor(-1)]; - tensor var_18_pad_type_0 = const()[name = tensor("op_18_pad_type_0"), val = tensor("valid")]; - tensor var_18_strides_0 = const()[name = tensor("op_18_strides_0"), val = tensor([1])]; - tensor var_18_pad_0 = const()[name = tensor("op_18_pad_0"), val = tensor([0, 0])]; - tensor var_18_dilations_0 = const()[name = tensor("op_18_dilations_0"), val = tensor([1])]; - tensor var_18_groups_0 = const()[name = tensor("op_18_groups_0"), val = tensor(1)]; - tensor encoder_output_to_fp16_dtype_0 = const()[name = tensor("encoder_output_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor module_decoder_layers_0_weight_to_fp16 = const()[name = tensor("module_decoder_layers_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor module_decoder_layers_0_bias_to_fp16 = const()[name = tensor("module_decoder_layers_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049728)))]; - tensor encoder_output_to_fp16 = cast(dtype = encoder_output_to_fp16_dtype_0, x = encoder_output)[name = tensor("cast_1")]; - tensor var_18_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = var_18_dilations_0, groups = var_18_groups_0, pad = var_18_pad_0, pad_type = var_18_pad_type_0, strides = var_18_strides_0, weight = module_decoder_layers_0_weight_to_fp16, x = encoder_output_to_fp16)[name = tensor("op_18_cast_fp16")]; - tensor input_perm_0 = const()[name = tensor("input_perm_0"), val = tensor([0, 2, 1])]; - tensor input_cast_fp16 = transpose(perm = input_perm_0, x = var_18_cast_fp16)[name = tensor("transpose_0")]; - tensor out_objects_softmax_cast_fp16 = softmax(axis = var_4, x = input_cast_fp16)[name = tensor("out_objects_softmax_cast_fp16")]; - tensor out_objects_epsilon_0 = const()[name = tensor("out_objects_epsilon_0"), val = tensor(0x1p-149)]; - tensor out_objects_cast_fp16 = log(epsilon = out_objects_epsilon_0, x = out_objects_softmax_cast_fp16)[name = tensor("out_objects_cast_fp16")]; - tensor out_objects_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("out_objects_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor ctc_logits = cast(dtype = out_objects_cast_fp16_to_fp32_dtype_0, x = out_objects_cast_fp16)[name = tensor("cast_0")]; - } -> (ctc_logits); -} \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/CTCHead.mlmodelc/weights/weight.bin b/models/parakeet-ctc-110m-coreml/CTCHead.mlmodelc/weights/weight.bin deleted file mode 100644 index 23cedd570125191064c6111997f08c48898245f7..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/CTCHead.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fb9bead064427ffcb7529c0e3f378e421b4dde8e6d81447b6d1ca3352ca850e1 -size 1051842 diff --git a/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/analytics/coremldata.bin b/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index dd015548a03cc453e6477a24374e8fee955e75ef..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:990455f6431342750254f66edf27bfb41be62a7ba17a18e1dd6afd4f5f56e9eb -size 243 diff --git a/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/coremldata.bin b/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/coremldata.bin deleted file mode 100644 index cbea0affef0c3bb63be750e134dc0c9ed70aadb1..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:29009727821ad8551ab5fe9271e93c597d92a9714f64b94aa533a9ceb6e22b93 -size 498 diff --git a/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/metadata.json b/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/metadata.json deleted file mode 100644 index 786f4bb2823b1aa7ebb55fe335c9385ee0065b7c..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/metadata.json +++ /dev/null @@ -1,118 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M decoder (RNNT prediction network)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "h_out", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "c_out", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.squeeze" : 2, - "Ios17.gather" : 1, - "Ios17.cast" : 6, - "Ios17.lstm" : 1, - "Ios17.transpose" : 2, - "Identity" : 1, - "Ios17.expandDims" : 2 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1]", - "name" : "targets", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "target_length", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "h_in", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "c_in", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.version" : "8.3.0", - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0" - }, - "generatedClassName" : "parakeet_decoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/model.mil b/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/model.mil deleted file mode 100644 index f69c7247525bc9d95f9341acc4f11eca709c7d00..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/model.mil +++ /dev/null @@ -1,45 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor c_in, tensor h_in, tensor target_length, tensor targets) { - tensor y_axis_0 = const()[name = tensor("y_axis_0"), val = tensor(0)]; - tensor y_batch_dims_0 = const()[name = tensor("y_batch_dims_0"), val = tensor(0)]; - tensor y_validate_indices_0 = const()[name = tensor("y_validate_indices_0"), val = tensor(false)]; - tensor module_prediction_embed_weight_to_fp16 = const()[name = tensor("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor targets_to_int16_dtype_0 = const()[name = tensor("targets_to_int16_dtype_0"), val = tensor("int16")]; - tensor targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor("cast_8")]; - tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = targets_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = tensor("y_cast_fp16_cast_uint16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([1, 0, 2])]; - tensor input_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; - tensor h_in_to_fp16_dtype_0 = const()[name = tensor("h_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor("cast_7")]; - tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = h_in_to_fp16)[name = tensor("input_lstm_h0_squeeze_cast_fp16")]; - tensor input_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; - tensor c_in_to_fp16_dtype_0 = const()[name = tensor("c_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor("cast_6")]; - tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = c_in_to_fp16)[name = tensor("input_lstm_c0_squeeze_cast_fp16")]; - tensor input_direction_0 = const()[name = tensor("input_direction_0"), val = tensor("forward")]; - tensor input_output_sequence_0 = const()[name = tensor("input_output_sequence_0"), val = tensor(true)]; - tensor input_recurrent_activation_0 = const()[name = tensor("input_recurrent_activation_0"), val = tensor("sigmoid")]; - tensor input_cell_activation_0 = const()[name = tensor("input_cell_activation_0"), val = tensor("tanh")]; - tensor input_activation_0 = const()[name = tensor("input_activation_0"), val = tensor("tanh")]; - tensor concat_1_to_fp16 = const()[name = tensor("concat_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312128)))]; - tensor concat_2_to_fp16 = const()[name = tensor("concat_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4588992)))]; - tensor concat_0_to_fp16 = const()[name = tensor("concat_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7865856)))]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = tensor("transpose_2")]; - tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_0_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor obj_3_axes_0 = const()[name = tensor("obj_3_axes_0"), val = tensor([0])]; - tensor obj_3_cast_fp16 = expand_dims(axes = obj_3_axes_0, x = input_cast_fp16_1)[name = tensor("obj_3_cast_fp16")]; - tensor obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_3_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_axes_0 = const()[name = tensor("obj_axes_0"), val = tensor([0])]; - tensor obj_cast_fp16 = expand_dims(axes = obj_axes_0, x = input_cast_fp16_2)[name = tensor("obj_cast_fp16")]; - tensor obj_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([1, 2, 0])]; - tensor transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("transpose_0_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor("transpose_1")]; - tensor decoder = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = tensor("cast_3")]; - tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = tensor("cast_4")]; - tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = tensor("cast_5")]; - tensor target_length_tmp = identity(x = target_length)[name = tensor("target_length_tmp")]; - } -> (decoder, h_out, c_out); -} \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/weights/weight.bin b/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/weights/weight.bin deleted file mode 100644 index 4543ad4ea89b21ebed0de0d5ec3975d07eace7fb..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Decoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:dd90b58597ee2c172c672dffe13b1110898ba07394c1a15efc96cc8c6b18411b -size 7871040 diff --git a/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/analytics/coremldata.bin b/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index d2787d4e5376d7f894d2b0a5a09f497a9b486c73..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b7ae65e2af616df46066b7efca2d7c19941666ac0685f4ed005666890a052b0d -size 243 diff --git a/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/coremldata.bin b/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/coremldata.bin deleted file mode 100644 index 9d9cdbfd392e8f7391b47c1c96f0fcbaab440389..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0713c2d6ac5f8f6fb9582be250351ebd8efc925f71f4261191165f1406f2ee5d -size 437 diff --git a/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/metadata.json b/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/metadata.json deleted file mode 100644 index 8e3f6cc9ddca206205987b2c828d2e595e19bfc2..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/metadata.json +++ /dev/null @@ -1,105 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M encoder (15 s window)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 512 × 188)", - "shortDescription" : "", - "shape" : "[1, 512, 188]", - "name" : "encoder_output", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "encoder_length", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.logicalAnd" : 2, - "Ios17.reshape" : 103, - "Ios16.softmax" : 17, - "Ios17.matmul" : 51, - "Ios17.transpose" : 123, - "Split" : 17, - "Ios17.expandDims" : 17, - "Select" : 51, - "Ios17.add" : 128, - "Tile" : 8, - "Ios17.sliceByIndex" : 34, - "Ios16.sigmoid" : 17, - "Pad" : 34, - "Ios17.logicalNot" : 2, - "Ios17.layerNorm" : 85, - "Ios16.silu" : 51, - "Ios17.less" : 5, - "Ios17.sub" : 3, - "Ios17.conv" : 56, - "Ios16.relu" : 3, - "Ios17.linear" : 137, - "Ios17.cast" : 11, - "Ios17.floorDiv" : 3, - "Ios17.mul" : 77 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 80 × 1501)", - "shortDescription" : "", - "shape" : "[1, 80, 1501]", - "name" : "mel_features", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "generatedClassName" : "parakeet_encoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/model.mil b/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/model.mil deleted file mode 100644 index 344bd1abf313c03c9ed9287c00893739f5d361bd..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/model.mil +++ /dev/null @@ -1,2690 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor mel_features, tensor mel_length) { - tensor var_23 = const()[name = tensor("op_23"), val = tensor(-1)]; - tensor x_1_perm_0 = const()[name = tensor("x_1_perm_0"), val = tensor([0, 2, 1])]; - tensor mel_features_to_fp16_dtype_0 = const()[name = tensor("mel_features_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor tensor_1_axes_0 = const()[name = tensor("tensor_1_axes_0"), val = tensor([1])]; - tensor mel_features_to_fp16 = cast(dtype = mel_features_to_fp16_dtype_0, x = mel_features)[name = tensor("cast_182")]; - tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_features_to_fp16)[name = tensor("transpose_207")]; - tensor tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_1_cast_fp16)[name = tensor("tensor_1_cast_fp16")]; - tensor expand_dims_0 = const()[name = tensor("expand_dims_0"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 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583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500]])]; - tensor var_87_axes_0 = const()[name = tensor("op_87_axes_0"), val = tensor([1])]; - tensor var_87 = expand_dims(axes = var_87_axes_0, x = mel_length)[name = tensor("op_87")]; - tensor time_mask_1 = less(x = expand_dims_0, y = var_87)[name = tensor("time_mask_1")]; - tensor var_89_axes_0 = const()[name = tensor("op_89_axes_0"), val = tensor([-1])]; - tensor var_89 = expand_dims(axes = var_89_axes_0, x = time_mask_1)[name = tensor("op_89")]; - tensor var_91_reps_0 = const()[name = tensor("op_91_reps_0"), val = tensor([1, 1, 80])]; - tensor var_91 = tile(reps = var_91_reps_0, x = var_89)[name = tensor("op_91")]; - tensor var_97_axes_0 = const()[name = tensor("op_97_axes_0"), val = tensor([1])]; - tensor cast_3_to_fp16_dtype_0 = const()[name = tensor("cast_3_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_91_to_fp16 = cast(dtype = cast_3_to_fp16_dtype_0, x = var_91)[name = tensor("cast_181")]; - tensor var_97_cast_fp16 = expand_dims(axes = var_97_axes_0, x = var_91_to_fp16)[name = tensor("op_97_cast_fp16")]; - tensor input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_97_cast_fp16)[name = tensor("input_1_cast_fp16")]; - tensor tensor_3_pad_type_0 = const()[name = tensor("tensor_3_pad_type_0"), val = tensor("custom")]; - tensor tensor_3_pad_0 = const()[name = tensor("tensor_3_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_3_strides_0 = const()[name = tensor("tensor_3_strides_0"), val = tensor([2, 2])]; - tensor tensor_3_dilations_0 = const()[name = tensor("tensor_3_dilations_0"), val = tensor([1, 1])]; - tensor tensor_3_groups_0 = const()[name = tensor("tensor_3_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_0_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor module_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4736)))]; - tensor tensor_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("tensor_3_cast_fp16")]; - tensor cast_1_to_fp16_dtype_0 = const()[name = tensor("cast_1_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_108_promoted_to_fp16 = const()[name = tensor("op_108_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor mel_length_to_fp16 = cast(dtype = cast_1_to_fp16_dtype_0, x = mel_length)[name = tensor("cast_180")]; - tensor var_109_cast_fp16 = add(x = mel_length_to_fp16, y = var_108_promoted_to_fp16)[name = tensor("op_109_cast_fp16")]; - tensor var_110_promoted_to_fp16 = const()[name = tensor("op_110_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_111_cast_fp16 = add(x = var_109_cast_fp16, y = var_110_promoted_to_fp16)[name = tensor("op_111_cast_fp16")]; - tensor var_112_promoted_to_fp16 = const()[name = tensor("op_112_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_113_cast_fp16 = sub(x = var_111_cast_fp16, y = var_112_promoted_to_fp16)[name = tensor("op_113_cast_fp16")]; - tensor var_21_promoted_to_fp16 = const()[name = tensor("op_21_promoted_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_0_cast_fp16 = floor_div(x = var_113_cast_fp16, y = var_21_promoted_to_fp16)[name = tensor("floor_div_0_cast_fp16")]; - tensor var_115_promoted_to_fp16 = const()[name = tensor("op_115_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_115_promoted_to_fp16)[name = tensor("current_lengths_3_cast_fp16")]; - tensor cast_4_dtype_0 = const()[name = tensor("cast_4_dtype_0"), val = tensor("int32")]; - tensor expand_dims_1 = const()[name = tensor("expand_dims_1"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750]])]; - tensor var_124_axes_0 = const()[name = tensor("op_124_axes_0"), val = tensor([1])]; - tensor current_lengths_3_cast_fp16_to_int32 = cast(dtype = cast_4_dtype_0, x = current_lengths_3_cast_fp16)[name = tensor("cast_179")]; - tensor var_124 = expand_dims(axes = var_124_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = tensor("op_124")]; - tensor time_mask_3 = less(x = expand_dims_1, y = var_124)[name = tensor("time_mask_3")]; - tensor var_126_axes_0 = const()[name = tensor("op_126_axes_0"), val = tensor([-1])]; - tensor var_126 = expand_dims(axes = var_126_axes_0, x = time_mask_3)[name = tensor("op_126")]; - tensor var_128_reps_0 = const()[name = tensor("op_128_reps_0"), val = tensor([1, 1, 40])]; - tensor var_128 = tile(reps = var_128_reps_0, x = var_126)[name = tensor("op_128")]; - tensor var_134_axes_0 = const()[name = tensor("op_134_axes_0"), val = tensor([1])]; - tensor cast_5_to_fp16_dtype_0 = const()[name = tensor("cast_5_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_128_to_fp16 = cast(dtype = cast_5_to_fp16_dtype_0, x = var_128)[name = tensor("cast_178")]; - tensor var_134_cast_fp16 = expand_dims(axes = var_134_axes_0, x = var_128_to_fp16)[name = tensor("op_134_cast_fp16")]; - tensor expanded_mask_3_reps_0 = const()[name = tensor("expanded_mask_3_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_134_cast_fp16)[name = tensor("expanded_mask_3_cast_fp16")]; - tensor input_3_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor tensor_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor("tensor_5_cast_fp16")]; - tensor input_5_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor tensor_7_pad_type_0 = const()[name = tensor("tensor_7_pad_type_0"), val = tensor("custom")]; - tensor tensor_7_pad_0 = const()[name = tensor("tensor_7_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_7_strides_0 = const()[name = tensor("tensor_7_strides_0"), val = tensor([2, 2])]; - tensor tensor_7_groups_0 = const()[name = tensor("tensor_7_groups_0"), val = tensor(256)]; - tensor tensor_7_dilations_0 = const()[name = tensor("tensor_7_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_2_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5312)))]; - tensor module_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9984)))]; - tensor tensor_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16, x = input_5_cast_fp16)[name = tensor("tensor_7_cast_fp16")]; - tensor var_154_promoted_to_fp16 = const()[name = tensor("op_154_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_155_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_154_promoted_to_fp16)[name = tensor("op_155_cast_fp16")]; - tensor var_156_promoted_to_fp16 = const()[name = tensor("op_156_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_157_cast_fp16 = add(x = var_155_cast_fp16, y = var_156_promoted_to_fp16)[name = tensor("op_157_cast_fp16")]; - tensor var_158_promoted_to_fp16 = const()[name = tensor("op_158_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_159_cast_fp16 = sub(x = var_157_cast_fp16, y = var_158_promoted_to_fp16)[name = tensor("op_159_cast_fp16")]; - tensor var_21_promoted_1_to_fp16 = const()[name = tensor("op_21_promoted_1_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_1_cast_fp16 = floor_div(x = var_159_cast_fp16, y = var_21_promoted_1_to_fp16)[name = tensor("floor_div_1_cast_fp16")]; - tensor var_161_promoted_to_fp16 = const()[name = tensor("op_161_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_161_promoted_to_fp16)[name = tensor("current_lengths_5_cast_fp16")]; - tensor cast_6_dtype_0 = const()[name = tensor("cast_6_dtype_0"), val = tensor("int32")]; - tensor expand_dims_2 = const()[name = tensor("expand_dims_2"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375]])]; - tensor var_170_axes_0 = const()[name = tensor("op_170_axes_0"), val = tensor([1])]; - tensor current_lengths_5_cast_fp16_to_int32 = cast(dtype = cast_6_dtype_0, x = current_lengths_5_cast_fp16)[name = tensor("cast_177")]; - tensor var_170 = expand_dims(axes = var_170_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = tensor("op_170")]; - tensor time_mask_5 = less(x = expand_dims_2, y = var_170)[name = tensor("time_mask_5")]; - tensor var_172_axes_0 = const()[name = tensor("op_172_axes_0"), val = tensor([-1])]; - tensor var_172 = expand_dims(axes = var_172_axes_0, x = time_mask_5)[name = tensor("op_172")]; - tensor var_174_reps_0 = const()[name = tensor("op_174_reps_0"), val = tensor([1, 1, 20])]; - tensor var_174 = tile(reps = var_174_reps_0, x = var_172)[name = tensor("op_174")]; - tensor var_180_axes_0 = const()[name = tensor("op_180_axes_0"), val = tensor([1])]; - tensor cast_7_to_fp16_dtype_0 = const()[name = tensor("cast_7_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_174_to_fp16 = cast(dtype = cast_7_to_fp16_dtype_0, x = var_174)[name = tensor("cast_176")]; - tensor var_180_cast_fp16 = expand_dims(axes = var_180_axes_0, x = var_174_to_fp16)[name = tensor("op_180_cast_fp16")]; - tensor expanded_mask_7_reps_0 = const()[name = tensor("expanded_mask_7_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_180_cast_fp16)[name = tensor("expanded_mask_7_cast_fp16")]; - tensor input_7_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor tensor_9_pad_type_0 = const()[name = tensor("tensor_9_pad_type_0"), val = tensor("valid")]; - tensor tensor_9_strides_0 = const()[name = tensor("tensor_9_strides_0"), val = tensor([1, 1])]; - tensor tensor_9_pad_0 = const()[name = tensor("tensor_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor tensor_9_dilations_0 = const()[name = tensor("tensor_9_dilations_0"), val = tensor([1, 1])]; - tensor tensor_9_groups_0 = const()[name = tensor("tensor_9_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_3_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_3_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10560)))]; - tensor module_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141696)))]; - tensor tensor_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("tensor_9_cast_fp16")]; - tensor input_9_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; - tensor tensor_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor("tensor_11_cast_fp16")]; - tensor input_11_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_11_cast_fp16")]; - tensor tensor_13_pad_type_0 = const()[name = tensor("tensor_13_pad_type_0"), val = tensor("custom")]; - tensor tensor_13_pad_0 = const()[name = tensor("tensor_13_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_13_strides_0 = const()[name = tensor("tensor_13_strides_0"), val = tensor([2, 2])]; - tensor tensor_13_groups_0 = const()[name = tensor("tensor_13_groups_0"), val = tensor(256)]; - tensor tensor_13_dilations_0 = const()[name = tensor("tensor_13_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_5_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_5_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142272)))]; - tensor module_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146944)))]; - tensor tensor_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("tensor_13_cast_fp16")]; - tensor var_215_promoted_to_fp16 = const()[name = tensor("op_215_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_216_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_215_promoted_to_fp16)[name = tensor("op_216_cast_fp16")]; - tensor var_217_promoted_to_fp16 = const()[name = tensor("op_217_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_218_cast_fp16 = add(x = var_216_cast_fp16, y = var_217_promoted_to_fp16)[name = tensor("op_218_cast_fp16")]; - tensor var_219_promoted_to_fp16 = const()[name = tensor("op_219_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_220_cast_fp16 = sub(x = var_218_cast_fp16, y = var_219_promoted_to_fp16)[name = tensor("op_220_cast_fp16")]; - tensor var_21_promoted_2_to_fp16 = const()[name = tensor("op_21_promoted_2_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_2_cast_fp16 = floor_div(x = var_220_cast_fp16, y = var_21_promoted_2_to_fp16)[name = tensor("floor_div_2_cast_fp16")]; - tensor var_222_promoted_to_fp16 = const()[name = tensor("op_222_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_222_promoted_to_fp16)[name = tensor("current_lengths_cast_fp16")]; - tensor cast_8_dtype_0 = const()[name = tensor("cast_8_dtype_0"), val = tensor("int32")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187]])]; - tensor var_231_axes_0 = const()[name = tensor("op_231_axes_0"), val = tensor([1])]; - tensor current_lengths_cast_fp16_to_int32 = cast(dtype = cast_8_dtype_0, x = current_lengths_cast_fp16)[name = tensor("cast_175")]; - tensor var_231 = expand_dims(axes = var_231_axes_0, x = current_lengths_cast_fp16_to_int32)[name = tensor("op_231")]; - tensor time_mask = less(x = expand_dims_3, y = var_231)[name = tensor("time_mask")]; - tensor var_233_axes_0 = const()[name = tensor("op_233_axes_0"), val = tensor([-1])]; - tensor var_233 = expand_dims(axes = var_233_axes_0, x = time_mask)[name = tensor("op_233")]; - tensor var_235_reps_0 = const()[name = tensor("op_235_reps_0"), val = tensor([1, 1, 10])]; - tensor var_235 = tile(reps = var_235_reps_0, x = var_233)[name = tensor("op_235")]; - tensor var_241_axes_0 = const()[name = tensor("op_241_axes_0"), val = tensor([1])]; - tensor cast_9_to_fp16_dtype_0 = const()[name = tensor("cast_9_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_235_to_fp16 = cast(dtype = cast_9_to_fp16_dtype_0, x = var_235)[name = tensor("cast_174")]; - tensor var_241_cast_fp16 = expand_dims(axes = var_241_axes_0, x = var_235_to_fp16)[name = tensor("op_241_cast_fp16")]; - tensor expanded_mask_13_reps_0 = const()[name = tensor("expanded_mask_13_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_241_cast_fp16)[name = tensor("expanded_mask_13_cast_fp16")]; - tensor input_13_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("input_13_cast_fp16")]; - tensor tensor_15_pad_type_0 = const()[name = tensor("tensor_15_pad_type_0"), val = tensor("valid")]; - tensor tensor_15_strides_0 = const()[name = tensor("tensor_15_strides_0"), val = tensor([1, 1])]; - tensor tensor_15_pad_0 = const()[name = tensor("tensor_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor tensor_15_dilations_0 = const()[name = tensor("tensor_15_dilations_0"), val = tensor([1, 1])]; - tensor tensor_15_groups_0 = const()[name = tensor("tensor_15_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_6_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_6_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147520)))]; - tensor module_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278656)))]; - tensor tensor_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16, x = input_13_cast_fp16)[name = tensor("tensor_15_cast_fp16")]; - tensor input_15_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; - tensor tensor_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor("tensor_cast_fp16")]; - tensor x_3_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("x_3_cast_fp16")]; - tensor var_275_perm_0 = const()[name = tensor("op_275_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_276 = const()[name = tensor("op_276"), val = tensor([1, 188, -1])]; - tensor var_275_cast_fp16 = transpose(perm = var_275_perm_0, x = x_3_cast_fp16)[name = tensor("transpose_206")]; - tensor input_17_cast_fp16 = reshape(shape = var_276, x = var_275_cast_fp16)[name = tensor("input_17_cast_fp16")]; - tensor module_pre_encode_out_weight_to_fp16 = const()[name = tensor("module_pre_encode_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279232)))]; - tensor module_pre_encode_out_bias_to_fp16 = const()[name = tensor("module_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2900736)))]; - tensor linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16, x = input_17_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor cast_12_dtype_0 = const()[name = tensor("cast_12_dtype_0"), val = tensor("int32")]; - tensor var_314_axes_0 = const()[name = tensor("op_314_axes_0"), val = tensor([-1])]; - tensor encoder_length = cast(dtype = cast_12_dtype_0, x = current_lengths_cast_fp16)[name = tensor("cast_173")]; - tensor var_314 = expand_dims(axes = var_314_axes_0, x = encoder_length)[name = tensor("op_314")]; - tensor pad_mask_1 = less(x = expand_dims_3, y = var_314)[name = tensor("pad_mask_1")]; - tensor var_316_axes_0 = const()[name = tensor("op_316_axes_0"), val = tensor([1])]; - tensor var_316 = expand_dims(axes = var_316_axes_0, x = pad_mask_1)[name = tensor("op_316")]; - tensor var_317 = const()[name = tensor("op_317"), val = tensor([1, 188, 1])]; - tensor pad_mask_for_att_mask_1 = tile(reps = var_317, x = var_316)[name = tensor("pad_mask_for_att_mask_1")]; - tensor var_319_perm_0 = const()[name = tensor("op_319_perm_0"), val = tensor([0, 2, 1])]; - tensor var_319 = transpose(perm = var_319_perm_0, x = pad_mask_for_att_mask_1)[name = tensor("transpose_205")]; - tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_319)[name = tensor("pad_mask_for_att_mask")]; - tensor const_63 = const()[name = tensor("const_63"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; - tensor att_mask = logical_and(x = pad_mask_for_att_mask, y = const_63)[name = tensor("att_mask")]; - tensor mask_9 = logical_not(x = att_mask)[name = tensor("mask_9")]; - tensor pad_mask = logical_not(x = pad_mask_1)[name = tensor("pad_mask")]; - tensor input_21_axes_0 = const()[name = tensor("input_21_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2901824)))]; - tensor module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2902912)))]; - tensor var_9_to_fp16 = const()[name = tensor("op_9_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = tensor("input_21_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2904000)))]; - tensor module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001216)))]; - tensor linear_1_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16, x = input_21_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor("input_25_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5005376)))]; - tensor module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7102592)))]; - tensor linear_2_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16, x = input_25_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor var_352_to_fp16 = const()[name = tensor("op_352_to_fp16"), val = tensor(0x1p-1)]; - tensor var_353_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_352_to_fp16)[name = tensor("op_353_cast_fp16")]; - tensor input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_353_cast_fp16)[name = tensor("input_31_cast_fp16")]; - tensor query_1_axes_0 = const()[name = tensor("query_1_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7103680)))]; - tensor module_layers_0_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7104768)))]; - tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("query_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7105856)))]; - tensor module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7630208)))]; - tensor linear_3_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_q_bias_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_3_cast_fp16")]; - tensor var_370 = const()[name = tensor("op_370"), val = tensor([1, -1, 8, 64])]; - tensor q_1_cast_fp16 = reshape(shape = var_370, x = linear_3_cast_fp16)[name = tensor("q_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7631296)))]; - tensor module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8155648)))]; - tensor linear_4_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_k_bias_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_4_cast_fp16")]; - tensor var_375 = const()[name = tensor("op_375"), val = tensor([1, -1, 8, 64])]; - tensor k_1_cast_fp16 = reshape(shape = var_375, x = linear_4_cast_fp16)[name = tensor("k_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8156736)))]; - tensor module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8681088)))]; - tensor linear_5_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_v_bias_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_5_cast_fp16")]; - tensor var_380 = const()[name = tensor("op_380"), val = tensor([1, -1, 8, 64])]; - tensor v_1_cast_fp16 = reshape(shape = var_380, x = linear_5_cast_fp16)[name = tensor("v_1_cast_fp16")]; - tensor value_3_perm_0 = const()[name = tensor("value_3_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8682176)))]; - tensor var_392_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = tensor("op_392_cast_fp16")]; - tensor module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8683264)))]; - tensor var_394_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = tensor("op_394_cast_fp16")]; - tensor q_with_bias_v_1_perm_0 = const()[name = tensor("q_with_bias_v_1_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_7_transpose_x_0 = const()[name = tensor("x_7_transpose_x_0"), val = tensor(false)]; - tensor x_7_transpose_y_0 = const()[name = tensor("x_7_transpose_y_0"), val = tensor(false)]; - tensor var_396_to_fp16 = const()[name = tensor("op_396_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8684352)))]; - tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_394_cast_fp16)[name = tensor("transpose_203")]; - tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = var_396_to_fp16)[name = tensor("x_7_cast_fp16")]; - tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_9_mode_0 = const()[name = tensor("x_9_mode_0"), val = tensor("constant")]; - tensor const_70_to_fp16 = const()[name = tensor("const_70_to_fp16"), val = tensor(0x0p+0)]; - tensor x_9_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = tensor("x_9_cast_fp16")]; - tensor var_404 = const()[name = tensor("op_404"), val = tensor([1, 8, -1, 188])]; - tensor x_11_cast_fp16 = reshape(shape = var_404, x = x_9_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor var_408_begin_0 = const()[name = tensor("op_408_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_408_end_0 = const()[name = tensor("op_408_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_408_end_mask_0 = const()[name = tensor("op_408_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_408_cast_fp16 = slice_by_index(begin = var_408_begin_0, end = var_408_end_0, end_mask = var_408_end_mask_0, x = x_11_cast_fp16)[name = tensor("op_408_cast_fp16")]; - tensor var_409 = const()[name = tensor("op_409"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_1_cast_fp16 = reshape(shape = var_409, x = var_408_cast_fp16)[name = tensor("matrix_bd_1_cast_fp16")]; - tensor matrix_ac_1_transpose_x_0 = const()[name = tensor("matrix_ac_1_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_1_transpose_y_0 = const()[name = tensor("matrix_ac_1_transpose_y_0"), val = tensor(false)]; - tensor transpose_51_perm_0 = const()[name = tensor("transpose_51_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_52_perm_0 = const()[name = tensor("transpose_52_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = k_1_cast_fp16)[name = tensor("transpose_201")]; - tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_392_cast_fp16)[name = tensor("transpose_202")]; - tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_51, y = transpose_52)[name = tensor("matrix_ac_1_cast_fp16")]; - tensor matrix_bd_3_begin_0 = const()[name = tensor("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_3_end_0 = const()[name = tensor("matrix_bd_3_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_3_end_mask_0 = const()[name = tensor("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = tensor("matrix_bd_3_cast_fp16")]; - tensor var_418_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = tensor("op_418_cast_fp16")]; - tensor _inversed_scores_1_y_0_to_fp16 = const()[name = tensor("_inversed_scores_1_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_1_cast_fp16 = mul(x = var_418_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = tensor("_inversed_scores_1_cast_fp16")]; - tensor mask_11_axes_0 = const()[name = tensor("mask_11_axes_0"), val = tensor([1])]; - tensor mask_11 = expand_dims(axes = mask_11_axes_0, x = mask_9)[name = tensor("mask_11")]; - tensor var_12_to_fp16 = const()[name = tensor("op_12_to_fp16"), val = tensor(-0x1.388p+13)]; - tensor scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_11)[name = tensor("scores_3_cast_fp16")]; - tensor var_424_cast_fp16 = softmax(axis = var_23, x = scores_3_cast_fp16)[name = tensor("op_424_cast_fp16")]; - tensor var_11_to_fp16 = const()[name = tensor("op_11_to_fp16"), val = tensor(0x0p+0)]; - tensor input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_424_cast_fp16, cond = mask_11)[name = tensor("input_33_cast_fp16")]; - tensor x_13_transpose_x_0 = const()[name = tensor("x_13_transpose_x_0"), val = tensor(false)]; - tensor x_13_transpose_y_0 = const()[name = tensor("x_13_transpose_y_0"), val = tensor(false)]; - tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = tensor("transpose_204")]; - tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_3_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_428_perm_0 = const()[name = tensor("op_428_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_429 = const()[name = tensor("op_429"), val = tensor([1, -1, 512])]; - tensor var_428_cast_fp16 = transpose(perm = var_428_perm_0, x = x_13_cast_fp16)[name = tensor("transpose_200")]; - tensor input_35_cast_fp16 = reshape(shape = var_429, x = var_428_cast_fp16)[name = tensor("input_35_cast_fp16")]; - tensor module_layers_0_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9068416)))]; - tensor module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9592768)))]; - tensor linear_7_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_out_bias_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16, x = input_35_cast_fp16)[name = tensor("linear_7_cast_fp16")]; - tensor input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = tensor("input_39_cast_fp16")]; - tensor x_17_axes_0 = const()[name = tensor("x_17_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9593856)))]; - tensor module_layers_0_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9594944)))]; - tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("x_17_cast_fp16")]; - tensor input_41_perm_0 = const()[name = tensor("input_41_perm_0"), val = tensor([0, 2, 1])]; - tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; - tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([1])]; - tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0])]; - tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1])]; - tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9596032)))]; - tensor module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10644672)))]; - tensor input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = tensor("transpose_199")]; - tensor input_43_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv1_bias_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16, x = input_41_cast_fp16)[name = tensor("input_43_cast_fp16")]; - tensor x_19_split_num_splits_0 = const()[name = tensor("x_19_split_num_splits_0"), val = tensor(2)]; - tensor x_19_split_axis_0 = const()[name = tensor("x_19_split_axis_0"), val = tensor(1)]; - tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = tensor("x_19_split_cast_fp16")]; - tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = tensor("x_19_split_1_sigmoid_cast_fp16")]; - tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = tensor("x_19_cast_fp16")]; - tensor var_453_axes_0 = const()[name = tensor("op_453_axes_0"), val = tensor([1])]; - tensor var_453 = expand_dims(axes = var_453_axes_0, x = pad_mask)[name = tensor("op_453")]; - tensor input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_453)[name = tensor("input_45_cast_fp16")]; - tensor input_47_pad_0 = const()[name = tensor("input_47_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_47_mode_0 = const()[name = tensor("input_47_mode_0"), val = tensor("constant")]; - tensor const_73_to_fp16 = const()[name = tensor("const_73_to_fp16"), val = tensor(0x0p+0)]; - tensor input_47_cast_fp16 = pad(constant_val = const_73_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = tensor("input_47_cast_fp16")]; - tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("valid")]; - tensor input_49_groups_0 = const()[name = tensor("input_49_groups_0"), val = tensor(512)]; - tensor input_49_strides_0 = const()[name = tensor("input_49_strides_0"), val = tensor([1])]; - tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([0, 0])]; - tensor input_49_dilations_0 = const()[name = tensor("input_49_dilations_0"), val = tensor([1])]; - tensor const_234_to_fp16 = const()[name = tensor("const_234_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10646784)))]; - tensor const_235_to_fp16 = const()[name = tensor("const_235_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10656064)))]; - tensor input_51_cast_fp16 = conv(bias = const_235_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_234_to_fp16, x = input_47_cast_fp16)[name = tensor("input_51_cast_fp16")]; - tensor input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = tensor("input_53_cast_fp16")]; - tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("valid")]; - tensor x_21_strides_0 = const()[name = tensor("x_21_strides_0"), val = tensor([1])]; - tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0])]; - tensor x_21_dilations_0 = const()[name = tensor("x_21_dilations_0"), val = tensor([1])]; - tensor x_21_groups_0 = const()[name = tensor("x_21_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10657152)))]; - tensor module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11181504)))]; - tensor x_21_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv2_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_layers_0_conv_pointwise_conv2_weight_to_fp16, x = input_53_cast_fp16)[name = tensor("x_21_cast_fp16")]; - tensor input_55_perm_0 = const()[name = tensor("input_55_perm_0"), val = tensor([0, 2, 1])]; - tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = tensor("transpose_198")]; - tensor input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; - tensor input_59_axes_0 = const()[name = tensor("input_59_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11182592)))]; - tensor module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11183680)))]; - tensor input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("input_59_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11184768)))]; - tensor module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13281984)))]; - tensor linear_8_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("linear_8_cast_fp16")]; - tensor input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = tensor("input_63_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13286144)))]; - tensor module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15383360)))]; - tensor linear_9_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16, x = input_63_cast_fp16)[name = tensor("linear_9_cast_fp16")]; - tensor var_495_to_fp16 = const()[name = tensor("op_495_to_fp16"), val = tensor(0x1p-1)]; - tensor var_496_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_495_to_fp16)[name = tensor("op_496_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_496_cast_fp16)[name = tensor("input_69_cast_fp16")]; - tensor input_71_axes_0 = const()[name = tensor("input_71_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15384448)))]; - tensor module_layers_0_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15385536)))]; - tensor input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = tensor("input_71_cast_fp16")]; - tensor input_73_axes_0 = const()[name = tensor("input_73_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15386624)))]; - tensor module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15387712)))]; - tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = tensor("input_73_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15388800)))]; - tensor module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17486016)))]; - tensor linear_10_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16, x = input_73_cast_fp16)[name = tensor("linear_10_cast_fp16")]; - tensor input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor("input_77_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17490176)))]; - tensor module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19587392)))]; - tensor linear_11_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16, x = input_77_cast_fp16)[name = tensor("linear_11_cast_fp16")]; - tensor var_526_to_fp16 = const()[name = tensor("op_526_to_fp16"), val = tensor(0x1p-1)]; - tensor var_527_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_526_to_fp16)[name = tensor("op_527_cast_fp16")]; - tensor input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_527_cast_fp16)[name = tensor("input_83_cast_fp16")]; - tensor query_3_axes_0 = const()[name = tensor("query_3_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19588480)))]; - tensor module_layers_1_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19589568)))]; - tensor query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = tensor("query_3_cast_fp16")]; - tensor module_layers_1_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19590656)))]; - tensor module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20115008)))]; - tensor linear_12_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_q_bias_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_12_cast_fp16")]; - tensor var_544 = const()[name = tensor("op_544"), val = tensor([1, -1, 8, 64])]; - tensor q_7_cast_fp16 = reshape(shape = var_544, x = linear_12_cast_fp16)[name = tensor("q_7_cast_fp16")]; - tensor module_layers_1_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20116096)))]; - tensor module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20640448)))]; - tensor linear_13_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_k_bias_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_13_cast_fp16")]; - tensor var_549 = const()[name = tensor("op_549"), val = tensor([1, -1, 8, 64])]; - tensor k_5_cast_fp16 = reshape(shape = var_549, x = linear_13_cast_fp16)[name = tensor("k_5_cast_fp16")]; - tensor module_layers_1_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20641536)))]; - tensor module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21165888)))]; - tensor linear_14_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_v_bias_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_14_cast_fp16")]; - tensor var_554 = const()[name = tensor("op_554"), val = tensor([1, -1, 8, 64])]; - tensor v_3_cast_fp16 = reshape(shape = var_554, x = linear_14_cast_fp16)[name = tensor("v_3_cast_fp16")]; - tensor value_5_perm_0 = const()[name = tensor("value_5_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21166976)))]; - tensor var_566_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = tensor("op_566_cast_fp16")]; - tensor module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21168064)))]; - tensor var_568_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = tensor("op_568_cast_fp16")]; - tensor q_with_bias_v_3_perm_0 = const()[name = tensor("q_with_bias_v_3_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_29_transpose_x_0 = const()[name = tensor("x_29_transpose_x_0"), val = tensor(false)]; - tensor x_29_transpose_y_0 = const()[name = tensor("x_29_transpose_y_0"), val = tensor(false)]; - tensor var_570_to_fp16 = const()[name = tensor("op_570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21169152)))]; - tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_568_cast_fp16)[name = tensor("transpose_196")]; - tensor x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = var_570_to_fp16)[name = tensor("x_29_cast_fp16")]; - tensor x_31_pad_0 = const()[name = tensor("x_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_31_mode_0 = const()[name = tensor("x_31_mode_0"), val = tensor("constant")]; - tensor const_80_to_fp16 = const()[name = tensor("const_80_to_fp16"), val = tensor(0x0p+0)]; - tensor x_31_cast_fp16 = pad(constant_val = const_80_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = tensor("x_31_cast_fp16")]; - tensor var_578 = const()[name = tensor("op_578"), val = tensor([1, 8, -1, 188])]; - tensor x_33_cast_fp16 = reshape(shape = var_578, x = x_31_cast_fp16)[name = tensor("x_33_cast_fp16")]; - tensor var_582_begin_0 = const()[name = tensor("op_582_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_582_end_0 = const()[name = tensor("op_582_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_582_end_mask_0 = const()[name = tensor("op_582_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_582_cast_fp16 = slice_by_index(begin = var_582_begin_0, end = var_582_end_0, end_mask = var_582_end_mask_0, x = x_33_cast_fp16)[name = tensor("op_582_cast_fp16")]; - tensor var_583 = const()[name = tensor("op_583"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_5_cast_fp16 = reshape(shape = var_583, x = var_582_cast_fp16)[name = tensor("matrix_bd_5_cast_fp16")]; - tensor matrix_ac_3_transpose_x_0 = const()[name = tensor("matrix_ac_3_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_3_transpose_y_0 = const()[name = tensor("matrix_ac_3_transpose_y_0"), val = tensor(false)]; - tensor transpose_53_perm_0 = const()[name = tensor("transpose_53_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_54_perm_0 = const()[name = tensor("transpose_54_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = k_5_cast_fp16)[name = tensor("transpose_194")]; - tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = var_566_cast_fp16)[name = tensor("transpose_195")]; - tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_53, y = transpose_54)[name = tensor("matrix_ac_3_cast_fp16")]; - tensor matrix_bd_7_begin_0 = const()[name = tensor("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_7_end_0 = const()[name = tensor("matrix_bd_7_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_7_end_mask_0 = const()[name = tensor("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = tensor("matrix_bd_7_cast_fp16")]; - tensor var_592_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = tensor("op_592_cast_fp16")]; - tensor _inversed_scores_5_y_0_to_fp16 = const()[name = tensor("_inversed_scores_5_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_5_cast_fp16 = mul(x = var_592_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = tensor("_inversed_scores_5_cast_fp16")]; - tensor scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_11)[name = tensor("scores_7_cast_fp16")]; - tensor var_598_cast_fp16 = softmax(axis = var_23, x = scores_7_cast_fp16)[name = tensor("op_598_cast_fp16")]; - tensor input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_598_cast_fp16, cond = mask_11)[name = tensor("input_85_cast_fp16")]; - tensor x_35_transpose_x_0 = const()[name = tensor("x_35_transpose_x_0"), val = tensor(false)]; - tensor x_35_transpose_y_0 = const()[name = tensor("x_35_transpose_y_0"), val = tensor(false)]; - tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = tensor("transpose_197")]; - tensor x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_5_cast_fp16)[name = tensor("x_35_cast_fp16")]; - tensor var_602_perm_0 = const()[name = tensor("op_602_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_603 = const()[name = tensor("op_603"), val = tensor([1, -1, 512])]; - tensor var_602_cast_fp16 = transpose(perm = var_602_perm_0, x = x_35_cast_fp16)[name = tensor("transpose_193")]; - tensor input_87_cast_fp16 = reshape(shape = var_603, x = var_602_cast_fp16)[name = tensor("input_87_cast_fp16")]; - tensor module_layers_1_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21553216)))]; - tensor module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22077568)))]; - tensor linear_16_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_out_bias_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16, x = input_87_cast_fp16)[name = tensor("linear_16_cast_fp16")]; - tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = tensor("input_91_cast_fp16")]; - tensor x_39_axes_0 = const()[name = tensor("x_39_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22078656)))]; - tensor module_layers_1_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22079744)))]; - tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = tensor("x_39_cast_fp16")]; - tensor input_93_perm_0 = const()[name = tensor("input_93_perm_0"), val = tensor([0, 2, 1])]; - tensor input_95_pad_type_0 = const()[name = tensor("input_95_pad_type_0"), val = tensor("valid")]; - tensor input_95_strides_0 = const()[name = tensor("input_95_strides_0"), val = tensor([1])]; - tensor input_95_pad_0 = const()[name = tensor("input_95_pad_0"), val = tensor([0, 0])]; - tensor input_95_dilations_0 = const()[name = tensor("input_95_dilations_0"), val = tensor([1])]; - tensor input_95_groups_0 = const()[name = tensor("input_95_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22080832)))]; - tensor module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23129472)))]; - tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = tensor("transpose_192")]; - tensor input_95_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv1_bias_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16, x = input_93_cast_fp16)[name = tensor("input_95_cast_fp16")]; - tensor x_41_split_num_splits_0 = const()[name = tensor("x_41_split_num_splits_0"), val = tensor(2)]; - tensor x_41_split_axis_0 = const()[name = tensor("x_41_split_axis_0"), val = tensor(1)]; - tensor x_41_split_cast_fp16_0, tensor x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = tensor("x_41_split_cast_fp16")]; - tensor x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = tensor("x_41_split_1_sigmoid_cast_fp16")]; - tensor x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = tensor("x_41_cast_fp16")]; - tensor input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_453)[name = tensor("input_97_cast_fp16")]; - tensor input_99_pad_0 = const()[name = tensor("input_99_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_99_mode_0 = const()[name = tensor("input_99_mode_0"), val = tensor("constant")]; - tensor const_83_to_fp16 = const()[name = tensor("const_83_to_fp16"), val = tensor(0x0p+0)]; - tensor input_99_cast_fp16 = pad(constant_val = const_83_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = tensor("input_99_cast_fp16")]; - tensor input_101_pad_type_0 = const()[name = tensor("input_101_pad_type_0"), val = tensor("valid")]; - tensor input_101_groups_0 = const()[name = tensor("input_101_groups_0"), val = tensor(512)]; - tensor input_101_strides_0 = const()[name = tensor("input_101_strides_0"), val = tensor([1])]; - tensor input_101_pad_0 = const()[name = tensor("input_101_pad_0"), val = tensor([0, 0])]; - tensor input_101_dilations_0 = const()[name = tensor("input_101_dilations_0"), val = tensor([1])]; - tensor const_236_to_fp16 = const()[name = tensor("const_236_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23131584)))]; - tensor const_237_to_fp16 = const()[name = tensor("const_237_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23140864)))]; - tensor input_103_cast_fp16 = conv(bias = const_237_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_236_to_fp16, x = input_99_cast_fp16)[name = tensor("input_103_cast_fp16")]; - tensor input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = tensor("input_105_cast_fp16")]; - tensor x_43_pad_type_0 = const()[name = tensor("x_43_pad_type_0"), val = tensor("valid")]; - tensor x_43_strides_0 = const()[name = tensor("x_43_strides_0"), val = tensor([1])]; - tensor x_43_pad_0 = const()[name = tensor("x_43_pad_0"), val = tensor([0, 0])]; - tensor x_43_dilations_0 = const()[name = tensor("x_43_dilations_0"), val = tensor([1])]; - tensor x_43_groups_0 = const()[name = tensor("x_43_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23141952)))]; - tensor module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23666304)))]; - tensor x_43_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv2_bias_to_fp16, dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = module_layers_1_conv_pointwise_conv2_weight_to_fp16, x = input_105_cast_fp16)[name = tensor("x_43_cast_fp16")]; - tensor input_107_perm_0 = const()[name = tensor("input_107_perm_0"), val = tensor([0, 2, 1])]; - tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = tensor("transpose_191")]; - tensor input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = tensor("input_109_cast_fp16")]; - tensor input_111_axes_0 = const()[name = tensor("input_111_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23667392)))]; - tensor module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23668480)))]; - tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("input_111_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23669568)))]; - tensor module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25766784)))]; - tensor linear_17_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16, x = input_111_cast_fp16)[name = tensor("linear_17_cast_fp16")]; - tensor input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = tensor("input_115_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25770944)))]; - tensor module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27868160)))]; - tensor linear_18_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16, x = input_115_cast_fp16)[name = tensor("linear_18_cast_fp16")]; - tensor var_669_to_fp16 = const()[name = tensor("op_669_to_fp16"), val = tensor(0x1p-1)]; - tensor var_670_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_669_to_fp16)[name = tensor("op_670_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_670_cast_fp16)[name = tensor("input_121_cast_fp16")]; - tensor input_123_axes_0 = const()[name = tensor("input_123_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27869248)))]; - tensor module_layers_1_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27870336)))]; - tensor input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("input_123_cast_fp16")]; - tensor input_125_axes_0 = const()[name = tensor("input_125_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27871424)))]; - tensor module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27872512)))]; - tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor("input_125_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27873600)))]; - tensor module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29970816)))]; - tensor linear_19_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16, x = input_125_cast_fp16)[name = tensor("linear_19_cast_fp16")]; - tensor input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor("input_129_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29974976)))]; - tensor module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32072192)))]; - tensor linear_20_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("linear_20_cast_fp16")]; - tensor var_700_to_fp16 = const()[name = tensor("op_700_to_fp16"), val = tensor(0x1p-1)]; - tensor var_701_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_700_to_fp16)[name = tensor("op_701_cast_fp16")]; - tensor input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_701_cast_fp16)[name = tensor("input_135_cast_fp16")]; - tensor query_5_axes_0 = const()[name = tensor("query_5_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32073280)))]; - tensor module_layers_2_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32074368)))]; - tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = tensor("query_5_cast_fp16")]; - tensor module_layers_2_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32075456)))]; - tensor module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32599808)))]; - tensor linear_21_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_q_bias_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_21_cast_fp16")]; - tensor var_718 = const()[name = tensor("op_718"), val = tensor([1, -1, 8, 64])]; - tensor q_13_cast_fp16 = reshape(shape = var_718, x = linear_21_cast_fp16)[name = tensor("q_13_cast_fp16")]; - tensor module_layers_2_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32600896)))]; - tensor module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33125248)))]; - tensor linear_22_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_k_bias_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_22_cast_fp16")]; - tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, -1, 8, 64])]; - tensor k_9_cast_fp16 = reshape(shape = var_723, x = linear_22_cast_fp16)[name = tensor("k_9_cast_fp16")]; - tensor module_layers_2_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33126336)))]; - tensor module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33650688)))]; - tensor linear_23_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_v_bias_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_23_cast_fp16")]; - tensor var_728 = const()[name = tensor("op_728"), val = tensor([1, -1, 8, 64])]; - tensor v_5_cast_fp16 = reshape(shape = var_728, x = linear_23_cast_fp16)[name = tensor("v_5_cast_fp16")]; - tensor value_7_perm_0 = const()[name = tensor("value_7_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33651776)))]; - tensor var_740_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = tensor("op_740_cast_fp16")]; - tensor module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33652864)))]; - tensor var_742_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = tensor("op_742_cast_fp16")]; - tensor q_with_bias_v_5_perm_0 = const()[name = tensor("q_with_bias_v_5_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_51_transpose_x_0 = const()[name = tensor("x_51_transpose_x_0"), val = tensor(false)]; - tensor x_51_transpose_y_0 = const()[name = tensor("x_51_transpose_y_0"), val = tensor(false)]; - tensor var_744_to_fp16 = const()[name = tensor("op_744_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33653952)))]; - tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_742_cast_fp16)[name = tensor("transpose_189")]; - tensor x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = var_744_to_fp16)[name = tensor("x_51_cast_fp16")]; - tensor x_53_pad_0 = const()[name = tensor("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_53_mode_0 = const()[name = tensor("x_53_mode_0"), val = tensor("constant")]; - tensor const_90_to_fp16 = const()[name = tensor("const_90_to_fp16"), val = tensor(0x0p+0)]; - tensor x_53_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = tensor("x_53_cast_fp16")]; - tensor var_752 = const()[name = tensor("op_752"), val = tensor([1, 8, -1, 188])]; - tensor x_55_cast_fp16 = reshape(shape = var_752, x = x_53_cast_fp16)[name = tensor("x_55_cast_fp16")]; - tensor var_756_begin_0 = const()[name = tensor("op_756_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_756_end_0 = const()[name = tensor("op_756_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_756_end_mask_0 = const()[name = tensor("op_756_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_756_cast_fp16 = slice_by_index(begin = var_756_begin_0, end = var_756_end_0, end_mask = var_756_end_mask_0, x = x_55_cast_fp16)[name = tensor("op_756_cast_fp16")]; - tensor var_757 = const()[name = tensor("op_757"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_9_cast_fp16 = reshape(shape = var_757, x = var_756_cast_fp16)[name = tensor("matrix_bd_9_cast_fp16")]; - tensor matrix_ac_5_transpose_x_0 = const()[name = tensor("matrix_ac_5_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_5_transpose_y_0 = const()[name = tensor("matrix_ac_5_transpose_y_0"), val = tensor(false)]; - tensor transpose_55_perm_0 = const()[name = tensor("transpose_55_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_56_perm_0 = const()[name = tensor("transpose_56_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = k_9_cast_fp16)[name = tensor("transpose_187")]; - tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_740_cast_fp16)[name = tensor("transpose_188")]; - tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_55, y = transpose_56)[name = tensor("matrix_ac_5_cast_fp16")]; - tensor matrix_bd_11_begin_0 = const()[name = tensor("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_11_end_0 = const()[name = tensor("matrix_bd_11_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_11_end_mask_0 = const()[name = tensor("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = tensor("matrix_bd_11_cast_fp16")]; - tensor var_766_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = tensor("op_766_cast_fp16")]; - tensor _inversed_scores_9_y_0_to_fp16 = const()[name = tensor("_inversed_scores_9_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_9_cast_fp16 = mul(x = var_766_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = tensor("_inversed_scores_9_cast_fp16")]; - tensor scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_11)[name = tensor("scores_11_cast_fp16")]; - tensor var_772_cast_fp16 = softmax(axis = var_23, x = scores_11_cast_fp16)[name = tensor("op_772_cast_fp16")]; - tensor input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_772_cast_fp16, cond = mask_11)[name = tensor("input_137_cast_fp16")]; - tensor x_57_transpose_x_0 = const()[name = tensor("x_57_transpose_x_0"), val = tensor(false)]; - tensor x_57_transpose_y_0 = const()[name = tensor("x_57_transpose_y_0"), val = tensor(false)]; - tensor value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = tensor("transpose_190")]; - tensor x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_7_cast_fp16)[name = tensor("x_57_cast_fp16")]; - tensor var_776_perm_0 = const()[name = tensor("op_776_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_777 = const()[name = tensor("op_777"), val = tensor([1, -1, 512])]; - tensor var_776_cast_fp16 = transpose(perm = var_776_perm_0, x = x_57_cast_fp16)[name = tensor("transpose_186")]; - tensor input_139_cast_fp16 = reshape(shape = var_777, x = var_776_cast_fp16)[name = tensor("input_139_cast_fp16")]; - tensor module_layers_2_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34038016)))]; - tensor module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34562368)))]; - tensor linear_25_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_out_bias_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("linear_25_cast_fp16")]; - tensor input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = tensor("input_143_cast_fp16")]; - tensor x_61_axes_0 = const()[name = tensor("x_61_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34563456)))]; - tensor module_layers_2_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34564544)))]; - tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = tensor("x_61_cast_fp16")]; - tensor input_145_perm_0 = const()[name = tensor("input_145_perm_0"), val = tensor([0, 2, 1])]; - tensor input_147_pad_type_0 = const()[name = tensor("input_147_pad_type_0"), val = tensor("valid")]; - tensor input_147_strides_0 = const()[name = tensor("input_147_strides_0"), val = tensor([1])]; - tensor input_147_pad_0 = const()[name = tensor("input_147_pad_0"), val = tensor([0, 0])]; - tensor input_147_dilations_0 = const()[name = tensor("input_147_dilations_0"), val = tensor([1])]; - tensor input_147_groups_0 = const()[name = tensor("input_147_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34565632)))]; - tensor module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35614272)))]; - tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = tensor("transpose_185")]; - tensor input_147_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16, x = input_145_cast_fp16)[name = tensor("input_147_cast_fp16")]; - tensor x_63_split_num_splits_0 = const()[name = tensor("x_63_split_num_splits_0"), val = tensor(2)]; - tensor x_63_split_axis_0 = const()[name = tensor("x_63_split_axis_0"), val = tensor(1)]; - tensor x_63_split_cast_fp16_0, tensor x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = tensor("x_63_split_cast_fp16")]; - tensor x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = tensor("x_63_split_1_sigmoid_cast_fp16")]; - tensor x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = tensor("x_63_cast_fp16")]; - tensor input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_453)[name = tensor("input_149_cast_fp16")]; - tensor input_151_pad_0 = const()[name = tensor("input_151_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_151_mode_0 = const()[name = tensor("input_151_mode_0"), val = tensor("constant")]; - tensor const_93_to_fp16 = const()[name = tensor("const_93_to_fp16"), val = tensor(0x0p+0)]; - tensor input_151_cast_fp16 = pad(constant_val = const_93_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = tensor("input_151_cast_fp16")]; - tensor input_153_pad_type_0 = const()[name = tensor("input_153_pad_type_0"), val = tensor("valid")]; - tensor input_153_groups_0 = const()[name = tensor("input_153_groups_0"), val = tensor(512)]; - tensor input_153_strides_0 = const()[name = tensor("input_153_strides_0"), val = tensor([1])]; - tensor input_153_pad_0 = const()[name = tensor("input_153_pad_0"), val = tensor([0, 0])]; - tensor input_153_dilations_0 = const()[name = tensor("input_153_dilations_0"), val = tensor([1])]; - tensor const_238_to_fp16 = const()[name = tensor("const_238_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35616384)))]; - tensor const_239_to_fp16 = const()[name = tensor("const_239_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35625664)))]; - tensor input_155_cast_fp16 = conv(bias = const_239_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_238_to_fp16, x = input_151_cast_fp16)[name = tensor("input_155_cast_fp16")]; - tensor input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = tensor("input_157_cast_fp16")]; - tensor x_65_pad_type_0 = const()[name = tensor("x_65_pad_type_0"), val = tensor("valid")]; - tensor x_65_strides_0 = const()[name = tensor("x_65_strides_0"), val = tensor([1])]; - tensor x_65_pad_0 = const()[name = tensor("x_65_pad_0"), val = tensor([0, 0])]; - tensor x_65_dilations_0 = const()[name = tensor("x_65_dilations_0"), val = tensor([1])]; - tensor x_65_groups_0 = const()[name = tensor("x_65_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35626752)))]; - tensor module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36151104)))]; - tensor x_65_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv2_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_layers_2_conv_pointwise_conv2_weight_to_fp16, x = input_157_cast_fp16)[name = tensor("x_65_cast_fp16")]; - tensor input_159_perm_0 = const()[name = tensor("input_159_perm_0"), val = tensor([0, 2, 1])]; - tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = tensor("transpose_184")]; - tensor input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = tensor("input_161_cast_fp16")]; - tensor input_163_axes_0 = const()[name = tensor("input_163_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36152192)))]; - tensor module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36153280)))]; - tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("input_163_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36154368)))]; - tensor module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38251584)))]; - tensor linear_26_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16, x = input_163_cast_fp16)[name = tensor("linear_26_cast_fp16")]; - tensor input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = tensor("input_167_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38255744)))]; - tensor module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40352960)))]; - tensor linear_27_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16, x = input_167_cast_fp16)[name = tensor("linear_27_cast_fp16")]; - tensor var_843_to_fp16 = const()[name = tensor("op_843_to_fp16"), val = tensor(0x1p-1)]; - tensor var_844_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_843_to_fp16)[name = tensor("op_844_cast_fp16")]; - tensor input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_844_cast_fp16)[name = tensor("input_173_cast_fp16")]; - tensor input_175_axes_0 = const()[name = tensor("input_175_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40354048)))]; - tensor module_layers_2_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40355136)))]; - tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = tensor("input_175_cast_fp16")]; - tensor input_177_axes_0 = const()[name = tensor("input_177_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40356224)))]; - tensor module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40357312)))]; - tensor input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor("input_177_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40358400)))]; - tensor module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42455616)))]; - tensor linear_28_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16, x = input_177_cast_fp16)[name = tensor("linear_28_cast_fp16")]; - tensor input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = tensor("input_181_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42459776)))]; - tensor module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44556992)))]; - tensor linear_29_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16, x = input_181_cast_fp16)[name = tensor("linear_29_cast_fp16")]; - tensor var_874_to_fp16 = const()[name = tensor("op_874_to_fp16"), val = tensor(0x1p-1)]; - tensor var_875_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_874_to_fp16)[name = tensor("op_875_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_875_cast_fp16)[name = tensor("input_187_cast_fp16")]; - tensor query_7_axes_0 = const()[name = tensor("query_7_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44558080)))]; - tensor module_layers_3_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44559168)))]; - tensor query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = tensor("query_7_cast_fp16")]; - tensor module_layers_3_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44560256)))]; - tensor module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45084608)))]; - tensor linear_30_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_q_bias_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_30_cast_fp16")]; - tensor var_892 = const()[name = tensor("op_892"), val = tensor([1, -1, 8, 64])]; - tensor q_19_cast_fp16 = reshape(shape = var_892, x = linear_30_cast_fp16)[name = tensor("q_19_cast_fp16")]; - tensor module_layers_3_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45085696)))]; - tensor module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45610048)))]; - tensor linear_31_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_k_bias_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_31_cast_fp16")]; - tensor var_897 = const()[name = tensor("op_897"), val = tensor([1, -1, 8, 64])]; - tensor k_13_cast_fp16 = reshape(shape = var_897, x = linear_31_cast_fp16)[name = tensor("k_13_cast_fp16")]; - tensor module_layers_3_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45611136)))]; - tensor module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46135488)))]; - tensor linear_32_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_v_bias_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_32_cast_fp16")]; - tensor var_902 = const()[name = tensor("op_902"), val = tensor([1, -1, 8, 64])]; - tensor v_7_cast_fp16 = reshape(shape = var_902, x = linear_32_cast_fp16)[name = tensor("v_7_cast_fp16")]; - tensor value_9_perm_0 = const()[name = tensor("value_9_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46136576)))]; - tensor var_914_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = tensor("op_914_cast_fp16")]; - tensor module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46137664)))]; - tensor var_916_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = tensor("op_916_cast_fp16")]; - tensor q_with_bias_v_7_perm_0 = const()[name = tensor("q_with_bias_v_7_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_73_transpose_x_0 = const()[name = tensor("x_73_transpose_x_0"), val = tensor(false)]; - tensor x_73_transpose_y_0 = const()[name = tensor("x_73_transpose_y_0"), val = tensor(false)]; - tensor var_918_to_fp16 = const()[name = tensor("op_918_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46138752)))]; - tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_916_cast_fp16)[name = tensor("transpose_182")]; - tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = var_918_to_fp16)[name = tensor("x_73_cast_fp16")]; - tensor x_75_pad_0 = const()[name = tensor("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_75_mode_0 = const()[name = tensor("x_75_mode_0"), val = tensor("constant")]; - tensor const_100_to_fp16 = const()[name = tensor("const_100_to_fp16"), val = tensor(0x0p+0)]; - tensor x_75_cast_fp16 = pad(constant_val = const_100_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = tensor("x_75_cast_fp16")]; - tensor var_926 = const()[name = tensor("op_926"), val = tensor([1, 8, -1, 188])]; - tensor x_77_cast_fp16 = reshape(shape = var_926, x = x_75_cast_fp16)[name = tensor("x_77_cast_fp16")]; - tensor var_930_begin_0 = const()[name = tensor("op_930_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_930_end_0 = const()[name = tensor("op_930_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_930_end_mask_0 = const()[name = tensor("op_930_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_930_cast_fp16 = slice_by_index(begin = var_930_begin_0, end = var_930_end_0, end_mask = var_930_end_mask_0, x = x_77_cast_fp16)[name = tensor("op_930_cast_fp16")]; - tensor var_931 = const()[name = tensor("op_931"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_13_cast_fp16 = reshape(shape = var_931, x = var_930_cast_fp16)[name = tensor("matrix_bd_13_cast_fp16")]; - tensor matrix_ac_7_transpose_x_0 = const()[name = tensor("matrix_ac_7_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_7_transpose_y_0 = const()[name = tensor("matrix_ac_7_transpose_y_0"), val = tensor(false)]; - tensor transpose_57_perm_0 = const()[name = tensor("transpose_57_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_58_perm_0 = const()[name = tensor("transpose_58_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = k_13_cast_fp16)[name = tensor("transpose_180")]; - tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_914_cast_fp16)[name = tensor("transpose_181")]; - tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_57, y = transpose_58)[name = tensor("matrix_ac_7_cast_fp16")]; - tensor matrix_bd_15_begin_0 = const()[name = tensor("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_15_end_0 = const()[name = tensor("matrix_bd_15_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_15_end_mask_0 = const()[name = tensor("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = tensor("matrix_bd_15_cast_fp16")]; - tensor var_940_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = tensor("op_940_cast_fp16")]; - tensor _inversed_scores_13_y_0_to_fp16 = const()[name = tensor("_inversed_scores_13_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_13_cast_fp16 = mul(x = var_940_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = tensor("_inversed_scores_13_cast_fp16")]; - tensor scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_11)[name = tensor("scores_15_cast_fp16")]; - tensor var_946_cast_fp16 = softmax(axis = var_23, x = scores_15_cast_fp16)[name = tensor("op_946_cast_fp16")]; - tensor input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_946_cast_fp16, cond = mask_11)[name = tensor("input_189_cast_fp16")]; - tensor x_79_transpose_x_0 = const()[name = tensor("x_79_transpose_x_0"), val = tensor(false)]; - tensor x_79_transpose_y_0 = const()[name = tensor("x_79_transpose_y_0"), val = tensor(false)]; - tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = tensor("transpose_183")]; - tensor x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_9_cast_fp16)[name = tensor("x_79_cast_fp16")]; - tensor var_950_perm_0 = const()[name = tensor("op_950_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_951 = const()[name = tensor("op_951"), val = tensor([1, -1, 512])]; - tensor var_950_cast_fp16 = transpose(perm = var_950_perm_0, x = x_79_cast_fp16)[name = tensor("transpose_179")]; - tensor input_191_cast_fp16 = reshape(shape = var_951, x = var_950_cast_fp16)[name = tensor("input_191_cast_fp16")]; - tensor module_layers_3_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46522816)))]; - tensor module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47047168)))]; - tensor linear_34_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_out_bias_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16, x = input_191_cast_fp16)[name = tensor("linear_34_cast_fp16")]; - tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = tensor("input_195_cast_fp16")]; - tensor x_83_axes_0 = const()[name = tensor("x_83_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47048256)))]; - tensor module_layers_3_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47049344)))]; - tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = tensor("x_83_cast_fp16")]; - tensor input_197_perm_0 = const()[name = tensor("input_197_perm_0"), val = tensor([0, 2, 1])]; - tensor input_199_pad_type_0 = const()[name = tensor("input_199_pad_type_0"), val = tensor("valid")]; - tensor input_199_strides_0 = const()[name = tensor("input_199_strides_0"), val = tensor([1])]; - tensor input_199_pad_0 = const()[name = tensor("input_199_pad_0"), val = tensor([0, 0])]; - tensor input_199_dilations_0 = const()[name = tensor("input_199_dilations_0"), val = tensor([1])]; - tensor input_199_groups_0 = const()[name = tensor("input_199_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47050432)))]; - tensor module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48099072)))]; - tensor input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = tensor("transpose_178")]; - tensor input_199_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv1_bias_to_fp16, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16, x = input_197_cast_fp16)[name = tensor("input_199_cast_fp16")]; - tensor x_85_split_num_splits_0 = const()[name = tensor("x_85_split_num_splits_0"), val = tensor(2)]; - tensor x_85_split_axis_0 = const()[name = tensor("x_85_split_axis_0"), val = tensor(1)]; - tensor x_85_split_cast_fp16_0, tensor x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = tensor("x_85_split_cast_fp16")]; - tensor x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = tensor("x_85_split_1_sigmoid_cast_fp16")]; - tensor x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = tensor("x_85_cast_fp16")]; - tensor input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_453)[name = tensor("input_201_cast_fp16")]; - tensor input_203_pad_0 = const()[name = tensor("input_203_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_203_mode_0 = const()[name = tensor("input_203_mode_0"), val = tensor("constant")]; - tensor const_103_to_fp16 = const()[name = tensor("const_103_to_fp16"), val = tensor(0x0p+0)]; - tensor input_203_cast_fp16 = pad(constant_val = const_103_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = tensor("input_203_cast_fp16")]; - tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("valid")]; - tensor input_205_groups_0 = const()[name = tensor("input_205_groups_0"), val = tensor(512)]; - tensor input_205_strides_0 = const()[name = tensor("input_205_strides_0"), val = tensor([1])]; - tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([0, 0])]; - tensor input_205_dilations_0 = const()[name = tensor("input_205_dilations_0"), val = tensor([1])]; - tensor const_240_to_fp16 = const()[name = tensor("const_240_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48101184)))]; - tensor const_241_to_fp16 = const()[name = tensor("const_241_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48110464)))]; - tensor input_207_cast_fp16 = conv(bias = const_241_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_240_to_fp16, x = input_203_cast_fp16)[name = tensor("input_207_cast_fp16")]; - tensor input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = tensor("input_209_cast_fp16")]; - tensor x_87_pad_type_0 = const()[name = tensor("x_87_pad_type_0"), val = tensor("valid")]; - tensor x_87_strides_0 = const()[name = tensor("x_87_strides_0"), val = tensor([1])]; - tensor x_87_pad_0 = const()[name = tensor("x_87_pad_0"), val = tensor([0, 0])]; - tensor x_87_dilations_0 = const()[name = tensor("x_87_dilations_0"), val = tensor([1])]; - tensor x_87_groups_0 = const()[name = tensor("x_87_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48111552)))]; - tensor module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48635904)))]; - tensor x_87_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv2_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16, x = input_209_cast_fp16)[name = tensor("x_87_cast_fp16")]; - tensor input_211_perm_0 = const()[name = tensor("input_211_perm_0"), val = tensor([0, 2, 1])]; - tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = tensor("transpose_177")]; - tensor input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = tensor("input_213_cast_fp16")]; - tensor input_215_axes_0 = const()[name = tensor("input_215_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48636992)))]; - tensor module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48638080)))]; - tensor input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = tensor("input_215_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48639168)))]; - tensor module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50736384)))]; - tensor linear_35_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16, x = input_215_cast_fp16)[name = tensor("linear_35_cast_fp16")]; - tensor input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = tensor("input_219_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50740544)))]; - tensor module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52837760)))]; - tensor linear_36_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16, x = input_219_cast_fp16)[name = tensor("linear_36_cast_fp16")]; - tensor var_1017_to_fp16 = const()[name = tensor("op_1017_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1018_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1017_to_fp16)[name = tensor("op_1018_cast_fp16")]; - tensor input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_1018_cast_fp16)[name = tensor("input_225_cast_fp16")]; - tensor input_227_axes_0 = const()[name = tensor("input_227_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52838848)))]; - tensor module_layers_3_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52839936)))]; - tensor input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = tensor("input_227_cast_fp16")]; - tensor input_229_axes_0 = const()[name = tensor("input_229_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52841024)))]; - tensor module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52842112)))]; - tensor input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = tensor("input_229_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52843200)))]; - tensor module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54940416)))]; - tensor linear_37_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16, x = input_229_cast_fp16)[name = tensor("linear_37_cast_fp16")]; - tensor input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = tensor("input_233_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54944576)))]; - tensor module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57041792)))]; - tensor linear_38_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16, x = input_233_cast_fp16)[name = tensor("linear_38_cast_fp16")]; - tensor var_1048_to_fp16 = const()[name = tensor("op_1048_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1049_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1048_to_fp16)[name = tensor("op_1049_cast_fp16")]; - tensor input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_1049_cast_fp16)[name = tensor("input_239_cast_fp16")]; - tensor query_9_axes_0 = const()[name = tensor("query_9_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57042880)))]; - tensor module_layers_4_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57043968)))]; - tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = tensor("query_9_cast_fp16")]; - tensor module_layers_4_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57045056)))]; - tensor module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57569408)))]; - tensor linear_39_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_q_bias_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_39_cast_fp16")]; - tensor var_1066 = const()[name = tensor("op_1066"), val = tensor([1, -1, 8, 64])]; - tensor q_25_cast_fp16 = reshape(shape = var_1066, x = linear_39_cast_fp16)[name = tensor("q_25_cast_fp16")]; - tensor module_layers_4_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57570496)))]; - tensor module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58094848)))]; - tensor linear_40_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_k_bias_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_40_cast_fp16")]; - tensor var_1071 = const()[name = tensor("op_1071"), val = tensor([1, -1, 8, 64])]; - tensor k_17_cast_fp16 = reshape(shape = var_1071, x = linear_40_cast_fp16)[name = tensor("k_17_cast_fp16")]; - tensor module_layers_4_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58095936)))]; - tensor module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58620288)))]; - tensor linear_41_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_v_bias_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_41_cast_fp16")]; - tensor var_1076 = const()[name = tensor("op_1076"), val = tensor([1, -1, 8, 64])]; - tensor v_9_cast_fp16 = reshape(shape = var_1076, x = linear_41_cast_fp16)[name = tensor("v_9_cast_fp16")]; - tensor value_11_perm_0 = const()[name = tensor("value_11_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58621376)))]; - tensor var_1088_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1088_cast_fp16")]; - tensor module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58622464)))]; - tensor var_1090_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1090_cast_fp16")]; - tensor q_with_bias_v_9_perm_0 = const()[name = tensor("q_with_bias_v_9_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_95_transpose_x_0 = const()[name = tensor("x_95_transpose_x_0"), val = tensor(false)]; - tensor x_95_transpose_y_0 = const()[name = tensor("x_95_transpose_y_0"), val = tensor(false)]; - tensor var_1092_to_fp16 = const()[name = tensor("op_1092_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58623552)))]; - tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1090_cast_fp16)[name = tensor("transpose_175")]; - tensor x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = var_1092_to_fp16)[name = tensor("x_95_cast_fp16")]; - tensor x_97_pad_0 = const()[name = tensor("x_97_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_97_mode_0 = const()[name = tensor("x_97_mode_0"), val = tensor("constant")]; - tensor const_110_to_fp16 = const()[name = tensor("const_110_to_fp16"), val = tensor(0x0p+0)]; - tensor x_97_cast_fp16 = pad(constant_val = const_110_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = tensor("x_97_cast_fp16")]; - tensor var_1100 = const()[name = tensor("op_1100"), val = tensor([1, 8, -1, 188])]; - tensor x_99_cast_fp16 = reshape(shape = var_1100, x = x_97_cast_fp16)[name = tensor("x_99_cast_fp16")]; - tensor var_1104_begin_0 = const()[name = tensor("op_1104_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1104_end_0 = const()[name = tensor("op_1104_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1104_end_mask_0 = const()[name = tensor("op_1104_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1104_cast_fp16 = slice_by_index(begin = var_1104_begin_0, end = var_1104_end_0, end_mask = var_1104_end_mask_0, x = x_99_cast_fp16)[name = tensor("op_1104_cast_fp16")]; - tensor var_1105 = const()[name = tensor("op_1105"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_17_cast_fp16 = reshape(shape = var_1105, x = var_1104_cast_fp16)[name = tensor("matrix_bd_17_cast_fp16")]; - tensor matrix_ac_9_transpose_x_0 = const()[name = tensor("matrix_ac_9_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_9_transpose_y_0 = const()[name = tensor("matrix_ac_9_transpose_y_0"), val = tensor(false)]; - tensor transpose_59_perm_0 = const()[name = tensor("transpose_59_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_60_perm_0 = const()[name = tensor("transpose_60_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = k_17_cast_fp16)[name = tensor("transpose_173")]; - tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = var_1088_cast_fp16)[name = tensor("transpose_174")]; - tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_59, y = transpose_60)[name = tensor("matrix_ac_9_cast_fp16")]; - tensor matrix_bd_19_begin_0 = const()[name = tensor("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_19_end_0 = const()[name = tensor("matrix_bd_19_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_19_end_mask_0 = const()[name = tensor("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = tensor("matrix_bd_19_cast_fp16")]; - tensor var_1114_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = tensor("op_1114_cast_fp16")]; - tensor _inversed_scores_17_y_0_to_fp16 = const()[name = tensor("_inversed_scores_17_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_17_cast_fp16 = mul(x = var_1114_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = tensor("_inversed_scores_17_cast_fp16")]; - tensor scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_11)[name = tensor("scores_19_cast_fp16")]; - tensor var_1120_cast_fp16 = softmax(axis = var_23, x = scores_19_cast_fp16)[name = tensor("op_1120_cast_fp16")]; - tensor input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_1120_cast_fp16, cond = mask_11)[name = tensor("input_241_cast_fp16")]; - tensor x_101_transpose_x_0 = const()[name = tensor("x_101_transpose_x_0"), val = tensor(false)]; - tensor x_101_transpose_y_0 = const()[name = tensor("x_101_transpose_y_0"), val = tensor(false)]; - tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = tensor("transpose_176")]; - tensor x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_11_cast_fp16)[name = tensor("x_101_cast_fp16")]; - tensor var_1124_perm_0 = const()[name = tensor("op_1124_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1125 = const()[name = tensor("op_1125"), val = tensor([1, -1, 512])]; - tensor var_1124_cast_fp16 = transpose(perm = var_1124_perm_0, x = x_101_cast_fp16)[name = tensor("transpose_172")]; - tensor input_243_cast_fp16 = reshape(shape = var_1125, x = var_1124_cast_fp16)[name = tensor("input_243_cast_fp16")]; - tensor module_layers_4_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59007616)))]; - tensor module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59531968)))]; - tensor linear_43_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_out_bias_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16, x = input_243_cast_fp16)[name = tensor("linear_43_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = tensor("input_247_cast_fp16")]; - tensor x_105_axes_0 = const()[name = tensor("x_105_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59533056)))]; - tensor module_layers_4_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59534144)))]; - tensor x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = tensor("x_105_cast_fp16")]; - tensor input_249_perm_0 = const()[name = tensor("input_249_perm_0"), val = tensor([0, 2, 1])]; - tensor input_251_pad_type_0 = const()[name = tensor("input_251_pad_type_0"), val = tensor("valid")]; - tensor input_251_strides_0 = const()[name = tensor("input_251_strides_0"), val = tensor([1])]; - tensor input_251_pad_0 = const()[name = tensor("input_251_pad_0"), val = tensor([0, 0])]; - tensor input_251_dilations_0 = const()[name = tensor("input_251_dilations_0"), val = tensor([1])]; - tensor input_251_groups_0 = const()[name = tensor("input_251_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59535232)))]; - tensor module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60583872)))]; - tensor input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = tensor("transpose_171")]; - tensor input_251_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv1_bias_to_fp16, dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16, x = input_249_cast_fp16)[name = tensor("input_251_cast_fp16")]; - tensor x_107_split_num_splits_0 = const()[name = tensor("x_107_split_num_splits_0"), val = tensor(2)]; - tensor x_107_split_axis_0 = const()[name = tensor("x_107_split_axis_0"), val = tensor(1)]; - tensor x_107_split_cast_fp16_0, tensor x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = tensor("x_107_split_cast_fp16")]; - tensor x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = tensor("x_107_split_1_sigmoid_cast_fp16")]; - tensor x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = tensor("x_107_cast_fp16")]; - tensor input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_453)[name = tensor("input_253_cast_fp16")]; - tensor input_255_pad_0 = const()[name = tensor("input_255_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_255_mode_0 = const()[name = tensor("input_255_mode_0"), val = tensor("constant")]; - tensor const_113_to_fp16 = const()[name = tensor("const_113_to_fp16"), val = tensor(0x0p+0)]; - tensor input_255_cast_fp16 = pad(constant_val = const_113_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = tensor("input_255_cast_fp16")]; - tensor input_257_pad_type_0 = const()[name = tensor("input_257_pad_type_0"), val = tensor("valid")]; - tensor input_257_groups_0 = const()[name = tensor("input_257_groups_0"), val = tensor(512)]; - tensor input_257_strides_0 = const()[name = tensor("input_257_strides_0"), val = tensor([1])]; - tensor input_257_pad_0 = const()[name = tensor("input_257_pad_0"), val = tensor([0, 0])]; - tensor input_257_dilations_0 = const()[name = tensor("input_257_dilations_0"), val = tensor([1])]; - tensor const_242_to_fp16 = const()[name = tensor("const_242_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60585984)))]; - tensor const_243_to_fp16 = const()[name = tensor("const_243_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60595264)))]; - tensor input_259_cast_fp16 = conv(bias = const_243_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_242_to_fp16, x = input_255_cast_fp16)[name = tensor("input_259_cast_fp16")]; - tensor input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = tensor("input_261_cast_fp16")]; - tensor x_109_pad_type_0 = const()[name = tensor("x_109_pad_type_0"), val = tensor("valid")]; - tensor x_109_strides_0 = const()[name = tensor("x_109_strides_0"), val = tensor([1])]; - tensor x_109_pad_0 = const()[name = tensor("x_109_pad_0"), val = tensor([0, 0])]; - tensor x_109_dilations_0 = const()[name = tensor("x_109_dilations_0"), val = tensor([1])]; - tensor x_109_groups_0 = const()[name = tensor("x_109_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60596352)))]; - tensor module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61120704)))]; - tensor x_109_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv2_bias_to_fp16, dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16, x = input_261_cast_fp16)[name = tensor("x_109_cast_fp16")]; - tensor input_263_perm_0 = const()[name = tensor("input_263_perm_0"), val = tensor([0, 2, 1])]; - tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = tensor("transpose_170")]; - tensor input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = tensor("input_265_cast_fp16")]; - tensor input_267_axes_0 = const()[name = tensor("input_267_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61121792)))]; - tensor module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61122880)))]; - tensor input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor("input_267_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61123968)))]; - tensor module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63221184)))]; - tensor linear_44_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16, x = input_267_cast_fp16)[name = tensor("linear_44_cast_fp16")]; - tensor input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = tensor("input_271_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63225344)))]; - tensor module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65322560)))]; - tensor linear_45_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16, x = input_271_cast_fp16)[name = tensor("linear_45_cast_fp16")]; - tensor var_1191_to_fp16 = const()[name = tensor("op_1191_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1192_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1191_to_fp16)[name = tensor("op_1192_cast_fp16")]; - tensor input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1192_cast_fp16)[name = tensor("input_277_cast_fp16")]; - tensor input_279_axes_0 = const()[name = tensor("input_279_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65323648)))]; - tensor module_layers_4_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65324736)))]; - tensor input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = tensor("input_279_cast_fp16")]; - tensor input_281_axes_0 = const()[name = tensor("input_281_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65325824)))]; - tensor module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65326912)))]; - tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = tensor("input_281_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65328000)))]; - tensor module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67425216)))]; - tensor linear_46_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16, x = input_281_cast_fp16)[name = tensor("linear_46_cast_fp16")]; - tensor input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = tensor("input_285_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67429376)))]; - tensor module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69526592)))]; - tensor linear_47_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16, x = input_285_cast_fp16)[name = tensor("linear_47_cast_fp16")]; - tensor var_1222_to_fp16 = const()[name = tensor("op_1222_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1223_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1222_to_fp16)[name = tensor("op_1223_cast_fp16")]; - tensor input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1223_cast_fp16)[name = tensor("input_291_cast_fp16")]; - tensor query_11_axes_0 = const()[name = tensor("query_11_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69527680)))]; - tensor module_layers_5_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69528768)))]; - tensor query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = tensor("query_11_cast_fp16")]; - tensor module_layers_5_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69529856)))]; - tensor module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70054208)))]; - tensor linear_48_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_q_bias_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_48_cast_fp16")]; - tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1, -1, 8, 64])]; - tensor q_31_cast_fp16 = reshape(shape = var_1240, x = linear_48_cast_fp16)[name = tensor("q_31_cast_fp16")]; - tensor module_layers_5_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70055296)))]; - tensor module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70579648)))]; - tensor linear_49_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_k_bias_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_49_cast_fp16")]; - tensor var_1245 = const()[name = tensor("op_1245"), val = tensor([1, -1, 8, 64])]; - tensor k_21_cast_fp16 = reshape(shape = var_1245, x = linear_49_cast_fp16)[name = tensor("k_21_cast_fp16")]; - tensor module_layers_5_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70580736)))]; - tensor module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71105088)))]; - tensor linear_50_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_v_bias_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_50_cast_fp16")]; - tensor var_1250 = const()[name = tensor("op_1250"), val = tensor([1, -1, 8, 64])]; - tensor v_11_cast_fp16 = reshape(shape = var_1250, x = linear_50_cast_fp16)[name = tensor("v_11_cast_fp16")]; - tensor value_13_perm_0 = const()[name = tensor("value_13_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71106176)))]; - tensor var_1262_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1262_cast_fp16")]; - tensor module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71107264)))]; - tensor var_1264_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1264_cast_fp16")]; - tensor q_with_bias_v_11_perm_0 = const()[name = tensor("q_with_bias_v_11_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_117_transpose_x_0 = const()[name = tensor("x_117_transpose_x_0"), val = tensor(false)]; - tensor x_117_transpose_y_0 = const()[name = tensor("x_117_transpose_y_0"), val = tensor(false)]; - tensor var_1266_to_fp16 = const()[name = tensor("op_1266_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71108352)))]; - tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1264_cast_fp16)[name = tensor("transpose_168")]; - tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = var_1266_to_fp16)[name = tensor("x_117_cast_fp16")]; - tensor x_119_pad_0 = const()[name = tensor("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_119_mode_0 = const()[name = tensor("x_119_mode_0"), val = tensor("constant")]; - tensor const_120_to_fp16 = const()[name = tensor("const_120_to_fp16"), val = tensor(0x0p+0)]; - tensor x_119_cast_fp16 = pad(constant_val = const_120_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = tensor("x_119_cast_fp16")]; - tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([1, 8, -1, 188])]; - tensor x_121_cast_fp16 = reshape(shape = var_1274, x = x_119_cast_fp16)[name = tensor("x_121_cast_fp16")]; - tensor var_1278_begin_0 = const()[name = tensor("op_1278_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1278_end_0 = const()[name = tensor("op_1278_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1278_end_mask_0 = const()[name = tensor("op_1278_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1278_cast_fp16 = slice_by_index(begin = var_1278_begin_0, end = var_1278_end_0, end_mask = var_1278_end_mask_0, x = x_121_cast_fp16)[name = tensor("op_1278_cast_fp16")]; - tensor var_1279 = const()[name = tensor("op_1279"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1279, x = var_1278_cast_fp16)[name = tensor("matrix_bd_21_cast_fp16")]; - tensor matrix_ac_11_transpose_x_0 = const()[name = tensor("matrix_ac_11_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_11_transpose_y_0 = const()[name = tensor("matrix_ac_11_transpose_y_0"), val = tensor(false)]; - tensor transpose_61_perm_0 = const()[name = tensor("transpose_61_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_62_perm_0 = const()[name = tensor("transpose_62_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = k_21_cast_fp16)[name = tensor("transpose_166")]; - tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_1262_cast_fp16)[name = tensor("transpose_167")]; - tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_61, y = transpose_62)[name = tensor("matrix_ac_11_cast_fp16")]; - tensor matrix_bd_23_begin_0 = const()[name = tensor("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_23_end_0 = const()[name = tensor("matrix_bd_23_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_23_end_mask_0 = const()[name = tensor("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = tensor("matrix_bd_23_cast_fp16")]; - tensor var_1288_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = tensor("op_1288_cast_fp16")]; - tensor _inversed_scores_21_y_0_to_fp16 = const()[name = tensor("_inversed_scores_21_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_21_cast_fp16 = mul(x = var_1288_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = tensor("_inversed_scores_21_cast_fp16")]; - tensor scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_11)[name = tensor("scores_23_cast_fp16")]; - tensor var_1294_cast_fp16 = softmax(axis = var_23, x = scores_23_cast_fp16)[name = tensor("op_1294_cast_fp16")]; - tensor input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1294_cast_fp16, cond = mask_11)[name = tensor("input_293_cast_fp16")]; - tensor x_123_transpose_x_0 = const()[name = tensor("x_123_transpose_x_0"), val = tensor(false)]; - tensor x_123_transpose_y_0 = const()[name = tensor("x_123_transpose_y_0"), val = tensor(false)]; - tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = tensor("transpose_169")]; - tensor x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_13_cast_fp16)[name = tensor("x_123_cast_fp16")]; - tensor var_1298_perm_0 = const()[name = tensor("op_1298_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1299 = const()[name = tensor("op_1299"), val = tensor([1, -1, 512])]; - tensor var_1298_cast_fp16 = transpose(perm = var_1298_perm_0, x = x_123_cast_fp16)[name = tensor("transpose_165")]; - tensor input_295_cast_fp16 = reshape(shape = var_1299, x = var_1298_cast_fp16)[name = tensor("input_295_cast_fp16")]; - tensor module_layers_5_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71492416)))]; - tensor module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72016768)))]; - tensor linear_52_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_out_bias_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16, x = input_295_cast_fp16)[name = tensor("linear_52_cast_fp16")]; - tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = tensor("input_299_cast_fp16")]; - tensor x_127_axes_0 = const()[name = tensor("x_127_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72017856)))]; - tensor module_layers_5_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72018944)))]; - tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = tensor("x_127_cast_fp16")]; - tensor input_301_perm_0 = const()[name = tensor("input_301_perm_0"), val = tensor([0, 2, 1])]; - tensor input_303_pad_type_0 = const()[name = tensor("input_303_pad_type_0"), val = tensor("valid")]; - tensor input_303_strides_0 = const()[name = tensor("input_303_strides_0"), val = tensor([1])]; - tensor input_303_pad_0 = const()[name = tensor("input_303_pad_0"), val = tensor([0, 0])]; - tensor input_303_dilations_0 = const()[name = tensor("input_303_dilations_0"), val = tensor([1])]; - tensor input_303_groups_0 = const()[name = tensor("input_303_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72020032)))]; - tensor module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73068672)))]; - tensor input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = tensor("transpose_164")]; - tensor input_303_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv1_bias_to_fp16, dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16, x = input_301_cast_fp16)[name = tensor("input_303_cast_fp16")]; - tensor x_129_split_num_splits_0 = const()[name = tensor("x_129_split_num_splits_0"), val = tensor(2)]; - tensor x_129_split_axis_0 = const()[name = tensor("x_129_split_axis_0"), val = tensor(1)]; - tensor x_129_split_cast_fp16_0, tensor x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = tensor("x_129_split_cast_fp16")]; - tensor x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = tensor("x_129_split_1_sigmoid_cast_fp16")]; - tensor x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = tensor("x_129_cast_fp16")]; - tensor input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_453)[name = tensor("input_305_cast_fp16")]; - tensor input_307_pad_0 = const()[name = tensor("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_307_mode_0 = const()[name = tensor("input_307_mode_0"), val = tensor("constant")]; - tensor const_123_to_fp16 = const()[name = tensor("const_123_to_fp16"), val = tensor(0x0p+0)]; - tensor input_307_cast_fp16 = pad(constant_val = const_123_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = tensor("input_307_cast_fp16")]; - tensor input_309_pad_type_0 = const()[name = tensor("input_309_pad_type_0"), val = tensor("valid")]; - tensor input_309_groups_0 = const()[name = tensor("input_309_groups_0"), val = tensor(512)]; - tensor input_309_strides_0 = const()[name = tensor("input_309_strides_0"), val = tensor([1])]; - tensor input_309_pad_0 = const()[name = tensor("input_309_pad_0"), val = tensor([0, 0])]; - tensor input_309_dilations_0 = const()[name = tensor("input_309_dilations_0"), val = tensor([1])]; - tensor const_244_to_fp16 = const()[name = tensor("const_244_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73070784)))]; - tensor const_245_to_fp16 = const()[name = tensor("const_245_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73080064)))]; - tensor input_311_cast_fp16 = conv(bias = const_245_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_244_to_fp16, x = input_307_cast_fp16)[name = tensor("input_311_cast_fp16")]; - tensor input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = tensor("input_313_cast_fp16")]; - tensor x_131_pad_type_0 = const()[name = tensor("x_131_pad_type_0"), val = tensor("valid")]; - tensor x_131_strides_0 = const()[name = tensor("x_131_strides_0"), val = tensor([1])]; - tensor x_131_pad_0 = const()[name = tensor("x_131_pad_0"), val = tensor([0, 0])]; - tensor x_131_dilations_0 = const()[name = tensor("x_131_dilations_0"), val = tensor([1])]; - tensor x_131_groups_0 = const()[name = tensor("x_131_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73081152)))]; - tensor module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73605504)))]; - tensor x_131_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv2_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_layers_5_conv_pointwise_conv2_weight_to_fp16, x = input_313_cast_fp16)[name = tensor("x_131_cast_fp16")]; - tensor input_315_perm_0 = const()[name = tensor("input_315_perm_0"), val = tensor([0, 2, 1])]; - tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = tensor("transpose_163")]; - tensor input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = tensor("input_317_cast_fp16")]; - tensor input_319_axes_0 = const()[name = tensor("input_319_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73606592)))]; - tensor module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73607680)))]; - tensor input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor("input_319_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73608768)))]; - tensor module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75705984)))]; - tensor linear_53_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16, x = input_319_cast_fp16)[name = tensor("linear_53_cast_fp16")]; - tensor input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = tensor("input_323_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75710144)))]; - tensor module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77807360)))]; - tensor linear_54_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16, x = input_323_cast_fp16)[name = tensor("linear_54_cast_fp16")]; - tensor var_1365_to_fp16 = const()[name = tensor("op_1365_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1366_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1365_to_fp16)[name = tensor("op_1366_cast_fp16")]; - tensor input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1366_cast_fp16)[name = tensor("input_329_cast_fp16")]; - tensor input_331_axes_0 = const()[name = tensor("input_331_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77808448)))]; - tensor module_layers_5_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77809536)))]; - tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("input_331_cast_fp16")]; - tensor input_333_axes_0 = const()[name = tensor("input_333_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77810624)))]; - tensor module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77811712)))]; - tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("input_333_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77812800)))]; - tensor module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79910016)))]; - tensor linear_55_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16, x = input_333_cast_fp16)[name = tensor("linear_55_cast_fp16")]; - tensor input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = tensor("input_337_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79914176)))]; - tensor module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82011392)))]; - tensor linear_56_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16, x = input_337_cast_fp16)[name = tensor("linear_56_cast_fp16")]; - tensor var_1396_to_fp16 = const()[name = tensor("op_1396_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1397_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1396_to_fp16)[name = tensor("op_1397_cast_fp16")]; - tensor input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1397_cast_fp16)[name = tensor("input_343_cast_fp16")]; - tensor query_13_axes_0 = const()[name = tensor("query_13_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82012480)))]; - tensor module_layers_6_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82013568)))]; - tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = tensor("query_13_cast_fp16")]; - tensor module_layers_6_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82014656)))]; - tensor module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82539008)))]; - tensor linear_57_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_q_bias_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_57_cast_fp16")]; - tensor var_1414 = const()[name = tensor("op_1414"), val = tensor([1, -1, 8, 64])]; - tensor q_37_cast_fp16 = reshape(shape = var_1414, x = linear_57_cast_fp16)[name = tensor("q_37_cast_fp16")]; - tensor module_layers_6_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82540096)))]; - tensor module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83064448)))]; - tensor linear_58_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_k_bias_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_58_cast_fp16")]; - tensor var_1419 = const()[name = tensor("op_1419"), val = tensor([1, -1, 8, 64])]; - tensor k_25_cast_fp16 = reshape(shape = var_1419, x = linear_58_cast_fp16)[name = tensor("k_25_cast_fp16")]; - tensor module_layers_6_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83065536)))]; - tensor module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83589888)))]; - tensor linear_59_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_v_bias_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_59_cast_fp16")]; - tensor var_1424 = const()[name = tensor("op_1424"), val = tensor([1, -1, 8, 64])]; - tensor v_13_cast_fp16 = reshape(shape = var_1424, x = linear_59_cast_fp16)[name = tensor("v_13_cast_fp16")]; - tensor value_15_perm_0 = const()[name = tensor("value_15_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83590976)))]; - tensor var_1436_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1436_cast_fp16")]; - tensor module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83592064)))]; - tensor var_1438_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1438_cast_fp16")]; - tensor q_with_bias_v_13_perm_0 = const()[name = tensor("q_with_bias_v_13_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_139_transpose_x_0 = const()[name = tensor("x_139_transpose_x_0"), val = tensor(false)]; - tensor x_139_transpose_y_0 = const()[name = tensor("x_139_transpose_y_0"), val = tensor(false)]; - tensor var_1440_to_fp16 = const()[name = tensor("op_1440_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83593152)))]; - tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1438_cast_fp16)[name = tensor("transpose_161")]; - tensor x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = var_1440_to_fp16)[name = tensor("x_139_cast_fp16")]; - tensor x_141_pad_0 = const()[name = tensor("x_141_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_141_mode_0 = const()[name = tensor("x_141_mode_0"), val = tensor("constant")]; - tensor const_130_to_fp16 = const()[name = tensor("const_130_to_fp16"), val = tensor(0x0p+0)]; - tensor x_141_cast_fp16 = pad(constant_val = const_130_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = tensor("x_141_cast_fp16")]; - tensor var_1448 = const()[name = tensor("op_1448"), val = tensor([1, 8, -1, 188])]; - tensor x_143_cast_fp16 = reshape(shape = var_1448, x = x_141_cast_fp16)[name = tensor("x_143_cast_fp16")]; - tensor var_1452_begin_0 = const()[name = tensor("op_1452_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1452_end_0 = const()[name = tensor("op_1452_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1452_end_mask_0 = const()[name = tensor("op_1452_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1452_cast_fp16 = slice_by_index(begin = var_1452_begin_0, end = var_1452_end_0, end_mask = var_1452_end_mask_0, x = x_143_cast_fp16)[name = tensor("op_1452_cast_fp16")]; - tensor var_1453 = const()[name = tensor("op_1453"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1453, x = var_1452_cast_fp16)[name = tensor("matrix_bd_25_cast_fp16")]; - tensor matrix_ac_13_transpose_x_0 = const()[name = tensor("matrix_ac_13_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_13_transpose_y_0 = const()[name = tensor("matrix_ac_13_transpose_y_0"), val = tensor(false)]; - tensor transpose_63_perm_0 = const()[name = tensor("transpose_63_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_64_perm_0 = const()[name = tensor("transpose_64_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = k_25_cast_fp16)[name = tensor("transpose_159")]; - tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_1436_cast_fp16)[name = tensor("transpose_160")]; - tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_63, y = transpose_64)[name = tensor("matrix_ac_13_cast_fp16")]; - tensor matrix_bd_27_begin_0 = const()[name = tensor("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_27_end_0 = const()[name = tensor("matrix_bd_27_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_27_end_mask_0 = const()[name = tensor("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = tensor("matrix_bd_27_cast_fp16")]; - tensor var_1462_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = tensor("op_1462_cast_fp16")]; - tensor _inversed_scores_25_y_0_to_fp16 = const()[name = tensor("_inversed_scores_25_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_25_cast_fp16 = mul(x = var_1462_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = tensor("_inversed_scores_25_cast_fp16")]; - tensor scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_11)[name = tensor("scores_27_cast_fp16")]; - tensor var_1468_cast_fp16 = softmax(axis = var_23, x = scores_27_cast_fp16)[name = tensor("op_1468_cast_fp16")]; - tensor input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1468_cast_fp16, cond = mask_11)[name = tensor("input_345_cast_fp16")]; - tensor x_145_transpose_x_0 = const()[name = tensor("x_145_transpose_x_0"), val = tensor(false)]; - tensor x_145_transpose_y_0 = const()[name = tensor("x_145_transpose_y_0"), val = tensor(false)]; - tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = tensor("transpose_162")]; - tensor x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_15_cast_fp16)[name = tensor("x_145_cast_fp16")]; - tensor var_1472_perm_0 = const()[name = tensor("op_1472_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1473 = const()[name = tensor("op_1473"), val = tensor([1, -1, 512])]; - tensor var_1472_cast_fp16 = transpose(perm = var_1472_perm_0, x = x_145_cast_fp16)[name = tensor("transpose_158")]; - tensor input_347_cast_fp16 = reshape(shape = var_1473, x = var_1472_cast_fp16)[name = tensor("input_347_cast_fp16")]; - tensor module_layers_6_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83977216)))]; - tensor module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84501568)))]; - tensor linear_61_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_out_bias_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16, x = input_347_cast_fp16)[name = tensor("linear_61_cast_fp16")]; - tensor input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = tensor("input_351_cast_fp16")]; - tensor x_149_axes_0 = const()[name = tensor("x_149_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84502656)))]; - tensor module_layers_6_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84503744)))]; - tensor x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = tensor("x_149_cast_fp16")]; - tensor input_353_perm_0 = const()[name = tensor("input_353_perm_0"), val = tensor([0, 2, 1])]; - tensor input_355_pad_type_0 = const()[name = tensor("input_355_pad_type_0"), val = tensor("valid")]; - tensor input_355_strides_0 = const()[name = tensor("input_355_strides_0"), val = tensor([1])]; - tensor input_355_pad_0 = const()[name = tensor("input_355_pad_0"), val = tensor([0, 0])]; - tensor input_355_dilations_0 = const()[name = tensor("input_355_dilations_0"), val = tensor([1])]; - tensor input_355_groups_0 = const()[name = tensor("input_355_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84504832)))]; - tensor module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85553472)))]; - tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = tensor("transpose_157")]; - tensor input_355_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv1_bias_to_fp16, dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16, x = input_353_cast_fp16)[name = tensor("input_355_cast_fp16")]; - tensor x_151_split_num_splits_0 = const()[name = tensor("x_151_split_num_splits_0"), val = tensor(2)]; - tensor x_151_split_axis_0 = const()[name = tensor("x_151_split_axis_0"), val = tensor(1)]; - tensor x_151_split_cast_fp16_0, tensor x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = tensor("x_151_split_cast_fp16")]; - tensor x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = tensor("x_151_split_1_sigmoid_cast_fp16")]; - tensor x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = tensor("x_151_cast_fp16")]; - tensor input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_453)[name = tensor("input_357_cast_fp16")]; - tensor input_359_pad_0 = const()[name = tensor("input_359_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_359_mode_0 = const()[name = tensor("input_359_mode_0"), val = tensor("constant")]; - tensor const_133_to_fp16 = const()[name = tensor("const_133_to_fp16"), val = tensor(0x0p+0)]; - tensor input_359_cast_fp16 = pad(constant_val = const_133_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = tensor("input_359_cast_fp16")]; - tensor input_361_pad_type_0 = const()[name = tensor("input_361_pad_type_0"), val = tensor("valid")]; - tensor input_361_groups_0 = const()[name = tensor("input_361_groups_0"), val = tensor(512)]; - tensor input_361_strides_0 = const()[name = tensor("input_361_strides_0"), val = tensor([1])]; - tensor input_361_pad_0 = const()[name = tensor("input_361_pad_0"), val = tensor([0, 0])]; - tensor input_361_dilations_0 = const()[name = tensor("input_361_dilations_0"), val = tensor([1])]; - tensor const_246_to_fp16 = const()[name = tensor("const_246_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85555584)))]; - tensor const_247_to_fp16 = const()[name = tensor("const_247_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85564864)))]; - tensor input_363_cast_fp16 = conv(bias = const_247_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_246_to_fp16, x = input_359_cast_fp16)[name = tensor("input_363_cast_fp16")]; - tensor input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; - tensor x_153_pad_type_0 = const()[name = tensor("x_153_pad_type_0"), val = tensor("valid")]; - tensor x_153_strides_0 = const()[name = tensor("x_153_strides_0"), val = tensor([1])]; - tensor x_153_pad_0 = const()[name = tensor("x_153_pad_0"), val = tensor([0, 0])]; - tensor x_153_dilations_0 = const()[name = tensor("x_153_dilations_0"), val = tensor([1])]; - tensor x_153_groups_0 = const()[name = tensor("x_153_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85565952)))]; - tensor module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86090304)))]; - tensor x_153_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv2_bias_to_fp16, dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = module_layers_6_conv_pointwise_conv2_weight_to_fp16, x = input_365_cast_fp16)[name = tensor("x_153_cast_fp16")]; - tensor input_367_perm_0 = const()[name = tensor("input_367_perm_0"), val = tensor([0, 2, 1])]; - tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = tensor("transpose_156")]; - tensor input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = tensor("input_369_cast_fp16")]; - tensor input_371_axes_0 = const()[name = tensor("input_371_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86091392)))]; - tensor module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86092480)))]; - tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor("input_371_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86093568)))]; - tensor module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88190784)))]; - tensor linear_62_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16, x = input_371_cast_fp16)[name = tensor("linear_62_cast_fp16")]; - tensor input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = tensor("input_375_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88194944)))]; - tensor module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90292160)))]; - tensor linear_63_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16, x = input_375_cast_fp16)[name = tensor("linear_63_cast_fp16")]; - tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1540_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1539_to_fp16)[name = tensor("op_1540_cast_fp16")]; - tensor input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1540_cast_fp16)[name = tensor("input_381_cast_fp16")]; - tensor input_383_axes_0 = const()[name = tensor("input_383_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90293248)))]; - tensor module_layers_6_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90294336)))]; - tensor input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = tensor("input_383_cast_fp16")]; - tensor input_385_axes_0 = const()[name = tensor("input_385_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90295424)))]; - tensor module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90296512)))]; - tensor input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = tensor("input_385_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90297600)))]; - tensor module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92394816)))]; - tensor linear_64_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16, x = input_385_cast_fp16)[name = tensor("linear_64_cast_fp16")]; - tensor input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = tensor("input_389_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92398976)))]; - tensor module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94496192)))]; - tensor linear_65_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16, x = input_389_cast_fp16)[name = tensor("linear_65_cast_fp16")]; - tensor var_1570_to_fp16 = const()[name = tensor("op_1570_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1571_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1570_to_fp16)[name = tensor("op_1571_cast_fp16")]; - tensor input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1571_cast_fp16)[name = tensor("input_395_cast_fp16")]; - tensor query_15_axes_0 = const()[name = tensor("query_15_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94497280)))]; - tensor module_layers_7_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94498368)))]; - tensor query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = tensor("query_15_cast_fp16")]; - tensor module_layers_7_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94499456)))]; - tensor module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95023808)))]; - tensor linear_66_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_q_bias_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_66_cast_fp16")]; - tensor var_1588 = const()[name = tensor("op_1588"), val = tensor([1, -1, 8, 64])]; - tensor q_43_cast_fp16 = reshape(shape = var_1588, x = linear_66_cast_fp16)[name = tensor("q_43_cast_fp16")]; - tensor module_layers_7_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95024896)))]; - tensor module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95549248)))]; - tensor linear_67_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_k_bias_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_67_cast_fp16")]; - tensor var_1593 = const()[name = tensor("op_1593"), val = tensor([1, -1, 8, 64])]; - tensor k_29_cast_fp16 = reshape(shape = var_1593, x = linear_67_cast_fp16)[name = tensor("k_29_cast_fp16")]; - tensor module_layers_7_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95550336)))]; - tensor module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96074688)))]; - tensor linear_68_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_v_bias_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_68_cast_fp16")]; - tensor var_1598 = const()[name = tensor("op_1598"), val = tensor([1, -1, 8, 64])]; - tensor v_15_cast_fp16 = reshape(shape = var_1598, x = linear_68_cast_fp16)[name = tensor("v_15_cast_fp16")]; - tensor value_17_perm_0 = const()[name = tensor("value_17_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96075776)))]; - tensor var_1610_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1610_cast_fp16")]; - tensor module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96076864)))]; - tensor var_1612_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1612_cast_fp16")]; - tensor q_with_bias_v_15_perm_0 = const()[name = tensor("q_with_bias_v_15_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_161_transpose_x_0 = const()[name = tensor("x_161_transpose_x_0"), val = tensor(false)]; - tensor x_161_transpose_y_0 = const()[name = tensor("x_161_transpose_y_0"), val = tensor(false)]; - tensor var_1614_to_fp16 = const()[name = tensor("op_1614_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96077952)))]; - tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1612_cast_fp16)[name = tensor("transpose_154")]; - tensor x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = var_1614_to_fp16)[name = tensor("x_161_cast_fp16")]; - tensor x_163_pad_0 = const()[name = tensor("x_163_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_163_mode_0 = const()[name = tensor("x_163_mode_0"), val = tensor("constant")]; - tensor const_140_to_fp16 = const()[name = tensor("const_140_to_fp16"), val = tensor(0x0p+0)]; - tensor x_163_cast_fp16 = pad(constant_val = const_140_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = tensor("x_163_cast_fp16")]; - tensor var_1622 = const()[name = tensor("op_1622"), val = tensor([1, 8, -1, 188])]; - tensor x_165_cast_fp16 = reshape(shape = var_1622, x = x_163_cast_fp16)[name = tensor("x_165_cast_fp16")]; - tensor var_1626_begin_0 = const()[name = tensor("op_1626_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1626_end_0 = const()[name = tensor("op_1626_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1626_end_mask_0 = const()[name = tensor("op_1626_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1626_cast_fp16 = slice_by_index(begin = var_1626_begin_0, end = var_1626_end_0, end_mask = var_1626_end_mask_0, x = x_165_cast_fp16)[name = tensor("op_1626_cast_fp16")]; - tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1627, x = var_1626_cast_fp16)[name = tensor("matrix_bd_29_cast_fp16")]; - tensor matrix_ac_15_transpose_x_0 = const()[name = tensor("matrix_ac_15_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_15_transpose_y_0 = const()[name = tensor("matrix_ac_15_transpose_y_0"), val = tensor(false)]; - tensor transpose_65_perm_0 = const()[name = tensor("transpose_65_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_66_perm_0 = const()[name = tensor("transpose_66_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = k_29_cast_fp16)[name = tensor("transpose_152")]; - tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = var_1610_cast_fp16)[name = tensor("transpose_153")]; - tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_65, y = transpose_66)[name = tensor("matrix_ac_15_cast_fp16")]; - tensor matrix_bd_31_begin_0 = const()[name = tensor("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_31_end_0 = const()[name = tensor("matrix_bd_31_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_31_end_mask_0 = const()[name = tensor("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = tensor("matrix_bd_31_cast_fp16")]; - tensor var_1636_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = tensor("op_1636_cast_fp16")]; - tensor _inversed_scores_29_y_0_to_fp16 = const()[name = tensor("_inversed_scores_29_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_29_cast_fp16 = mul(x = var_1636_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = tensor("_inversed_scores_29_cast_fp16")]; - tensor scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_11)[name = tensor("scores_31_cast_fp16")]; - tensor var_1642_cast_fp16 = softmax(axis = var_23, x = scores_31_cast_fp16)[name = tensor("op_1642_cast_fp16")]; - tensor input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1642_cast_fp16, cond = mask_11)[name = tensor("input_397_cast_fp16")]; - tensor x_167_transpose_x_0 = const()[name = tensor("x_167_transpose_x_0"), val = tensor(false)]; - tensor x_167_transpose_y_0 = const()[name = tensor("x_167_transpose_y_0"), val = tensor(false)]; - tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = tensor("transpose_155")]; - tensor x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_17_cast_fp16)[name = tensor("x_167_cast_fp16")]; - tensor var_1646_perm_0 = const()[name = tensor("op_1646_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1647 = const()[name = tensor("op_1647"), val = tensor([1, -1, 512])]; - tensor var_1646_cast_fp16 = transpose(perm = var_1646_perm_0, x = x_167_cast_fp16)[name = tensor("transpose_151")]; - tensor input_399_cast_fp16 = reshape(shape = var_1647, x = var_1646_cast_fp16)[name = tensor("input_399_cast_fp16")]; - tensor module_layers_7_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96462016)))]; - tensor module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96986368)))]; - tensor linear_70_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_out_bias_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16, x = input_399_cast_fp16)[name = tensor("linear_70_cast_fp16")]; - tensor input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = tensor("input_403_cast_fp16")]; - tensor x_171_axes_0 = const()[name = tensor("x_171_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96987456)))]; - tensor module_layers_7_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96988544)))]; - tensor x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = tensor("x_171_cast_fp16")]; - tensor input_405_perm_0 = const()[name = tensor("input_405_perm_0"), val = tensor([0, 2, 1])]; - tensor input_407_pad_type_0 = const()[name = tensor("input_407_pad_type_0"), val = tensor("valid")]; - tensor input_407_strides_0 = const()[name = tensor("input_407_strides_0"), val = tensor([1])]; - tensor input_407_pad_0 = const()[name = tensor("input_407_pad_0"), val = tensor([0, 0])]; - tensor input_407_dilations_0 = const()[name = tensor("input_407_dilations_0"), val = tensor([1])]; - tensor input_407_groups_0 = const()[name = tensor("input_407_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96989632)))]; - tensor module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98038272)))]; - tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = tensor("transpose_150")]; - tensor input_407_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv1_bias_to_fp16, dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16, x = input_405_cast_fp16)[name = tensor("input_407_cast_fp16")]; - tensor x_173_split_num_splits_0 = const()[name = tensor("x_173_split_num_splits_0"), val = tensor(2)]; - tensor x_173_split_axis_0 = const()[name = tensor("x_173_split_axis_0"), val = tensor(1)]; - tensor x_173_split_cast_fp16_0, tensor x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = tensor("x_173_split_cast_fp16")]; - tensor x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = tensor("x_173_split_1_sigmoid_cast_fp16")]; - tensor x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = tensor("x_173_cast_fp16")]; - tensor input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_453)[name = tensor("input_409_cast_fp16")]; - tensor input_411_pad_0 = const()[name = tensor("input_411_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_411_mode_0 = const()[name = tensor("input_411_mode_0"), val = tensor("constant")]; - tensor const_143_to_fp16 = const()[name = tensor("const_143_to_fp16"), val = tensor(0x0p+0)]; - tensor input_411_cast_fp16 = pad(constant_val = const_143_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = tensor("input_411_cast_fp16")]; - tensor input_413_pad_type_0 = const()[name = tensor("input_413_pad_type_0"), val = tensor("valid")]; - tensor input_413_groups_0 = const()[name = tensor("input_413_groups_0"), val = tensor(512)]; - tensor input_413_strides_0 = const()[name = tensor("input_413_strides_0"), val = tensor([1])]; - tensor input_413_pad_0 = const()[name = tensor("input_413_pad_0"), val = tensor([0, 0])]; - tensor input_413_dilations_0 = const()[name = tensor("input_413_dilations_0"), val = tensor([1])]; - tensor const_248_to_fp16 = const()[name = tensor("const_248_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98040384)))]; - tensor const_249_to_fp16 = const()[name = tensor("const_249_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98049664)))]; - tensor input_415_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_248_to_fp16, x = input_411_cast_fp16)[name = tensor("input_415_cast_fp16")]; - tensor input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = tensor("input_417_cast_fp16")]; - tensor x_175_pad_type_0 = const()[name = tensor("x_175_pad_type_0"), val = tensor("valid")]; - tensor x_175_strides_0 = const()[name = tensor("x_175_strides_0"), val = tensor([1])]; - tensor x_175_pad_0 = const()[name = tensor("x_175_pad_0"), val = tensor([0, 0])]; - tensor x_175_dilations_0 = const()[name = tensor("x_175_dilations_0"), val = tensor([1])]; - tensor x_175_groups_0 = const()[name = tensor("x_175_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98050752)))]; - tensor module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98575104)))]; - tensor x_175_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv2_bias_to_fp16, dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = module_layers_7_conv_pointwise_conv2_weight_to_fp16, x = input_417_cast_fp16)[name = tensor("x_175_cast_fp16")]; - tensor input_419_perm_0 = const()[name = tensor("input_419_perm_0"), val = tensor([0, 2, 1])]; - tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = tensor("transpose_149")]; - tensor input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = tensor("input_421_cast_fp16")]; - tensor input_423_axes_0 = const()[name = tensor("input_423_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98576192)))]; - tensor module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98577280)))]; - tensor input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = tensor("input_423_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98578368)))]; - tensor module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100675584)))]; - tensor linear_71_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor("linear_71_cast_fp16")]; - tensor input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = tensor("input_427_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100679744)))]; - tensor module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102776960)))]; - tensor linear_72_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16, x = input_427_cast_fp16)[name = tensor("linear_72_cast_fp16")]; - tensor var_1713_to_fp16 = const()[name = tensor("op_1713_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1714_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1713_to_fp16)[name = tensor("op_1714_cast_fp16")]; - tensor input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1714_cast_fp16)[name = tensor("input_433_cast_fp16")]; - tensor input_435_axes_0 = const()[name = tensor("input_435_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102778048)))]; - tensor module_layers_7_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102779136)))]; - tensor input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = tensor("input_435_cast_fp16")]; - tensor input_437_axes_0 = const()[name = tensor("input_437_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102780224)))]; - tensor module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102781312)))]; - tensor input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = tensor("input_437_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102782400)))]; - tensor module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104879616)))]; - tensor linear_73_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16, x = input_437_cast_fp16)[name = tensor("linear_73_cast_fp16")]; - tensor input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = tensor("input_441_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104883776)))]; - tensor module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106980992)))]; - tensor linear_74_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16, x = input_441_cast_fp16)[name = tensor("linear_74_cast_fp16")]; - tensor var_1744_to_fp16 = const()[name = tensor("op_1744_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1745_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1744_to_fp16)[name = tensor("op_1745_cast_fp16")]; - tensor input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1745_cast_fp16)[name = tensor("input_447_cast_fp16")]; - tensor query_17_axes_0 = const()[name = tensor("query_17_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106982080)))]; - tensor module_layers_8_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106983168)))]; - tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = tensor("query_17_cast_fp16")]; - tensor module_layers_8_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106984256)))]; - tensor module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107508608)))]; - tensor linear_75_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_q_bias_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_75_cast_fp16")]; - tensor var_1762 = const()[name = tensor("op_1762"), val = tensor([1, -1, 8, 64])]; - tensor q_49_cast_fp16 = reshape(shape = var_1762, x = linear_75_cast_fp16)[name = tensor("q_49_cast_fp16")]; - tensor module_layers_8_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107509696)))]; - tensor module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108034048)))]; - tensor linear_76_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_k_bias_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_76_cast_fp16")]; - tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, -1, 8, 64])]; - tensor k_33_cast_fp16 = reshape(shape = var_1767, x = linear_76_cast_fp16)[name = tensor("k_33_cast_fp16")]; - tensor module_layers_8_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108035136)))]; - tensor module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108559488)))]; - tensor linear_77_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_v_bias_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_77_cast_fp16")]; - tensor var_1772 = const()[name = tensor("op_1772"), val = tensor([1, -1, 8, 64])]; - tensor v_17_cast_fp16 = reshape(shape = var_1772, x = linear_77_cast_fp16)[name = tensor("v_17_cast_fp16")]; - tensor value_19_perm_0 = const()[name = tensor("value_19_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108560576)))]; - tensor var_1784_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1784_cast_fp16")]; - tensor module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108561664)))]; - tensor var_1786_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1786_cast_fp16")]; - tensor q_with_bias_v_17_perm_0 = const()[name = tensor("q_with_bias_v_17_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_183_transpose_x_0 = const()[name = tensor("x_183_transpose_x_0"), val = tensor(false)]; - tensor x_183_transpose_y_0 = const()[name = tensor("x_183_transpose_y_0"), val = tensor(false)]; - tensor var_1788_to_fp16 = const()[name = tensor("op_1788_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108562752)))]; - tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1786_cast_fp16)[name = tensor("transpose_147")]; - tensor x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = var_1788_to_fp16)[name = tensor("x_183_cast_fp16")]; - tensor x_185_pad_0 = const()[name = tensor("x_185_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_185_mode_0 = const()[name = tensor("x_185_mode_0"), val = tensor("constant")]; - tensor const_150_to_fp16 = const()[name = tensor("const_150_to_fp16"), val = tensor(0x0p+0)]; - tensor x_185_cast_fp16 = pad(constant_val = const_150_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = tensor("x_185_cast_fp16")]; - tensor var_1796 = const()[name = tensor("op_1796"), val = tensor([1, 8, -1, 188])]; - tensor x_187_cast_fp16 = reshape(shape = var_1796, x = x_185_cast_fp16)[name = tensor("x_187_cast_fp16")]; - tensor var_1800_begin_0 = const()[name = tensor("op_1800_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1800_end_0 = const()[name = tensor("op_1800_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1800_end_mask_0 = const()[name = tensor("op_1800_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1800_cast_fp16 = slice_by_index(begin = var_1800_begin_0, end = var_1800_end_0, end_mask = var_1800_end_mask_0, x = x_187_cast_fp16)[name = tensor("op_1800_cast_fp16")]; - tensor var_1801 = const()[name = tensor("op_1801"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_33_cast_fp16 = reshape(shape = var_1801, x = var_1800_cast_fp16)[name = tensor("matrix_bd_33_cast_fp16")]; - tensor matrix_ac_17_transpose_x_0 = const()[name = tensor("matrix_ac_17_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_17_transpose_y_0 = const()[name = tensor("matrix_ac_17_transpose_y_0"), val = tensor(false)]; - tensor transpose_67_perm_0 = const()[name = tensor("transpose_67_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_68_perm_0 = const()[name = tensor("transpose_68_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = k_33_cast_fp16)[name = tensor("transpose_145")]; - tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_1784_cast_fp16)[name = tensor("transpose_146")]; - tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_67, y = transpose_68)[name = tensor("matrix_ac_17_cast_fp16")]; - tensor matrix_bd_35_begin_0 = const()[name = tensor("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_35_end_0 = const()[name = tensor("matrix_bd_35_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_35_end_mask_0 = const()[name = tensor("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = tensor("matrix_bd_35_cast_fp16")]; - tensor var_1810_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = tensor("op_1810_cast_fp16")]; - tensor _inversed_scores_33_y_0_to_fp16 = const()[name = tensor("_inversed_scores_33_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_33_cast_fp16 = mul(x = var_1810_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = tensor("_inversed_scores_33_cast_fp16")]; - tensor scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_11)[name = tensor("scores_35_cast_fp16")]; - tensor var_1816_cast_fp16 = softmax(axis = var_23, x = scores_35_cast_fp16)[name = tensor("op_1816_cast_fp16")]; - tensor input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1816_cast_fp16, cond = mask_11)[name = tensor("input_449_cast_fp16")]; - tensor x_189_transpose_x_0 = const()[name = tensor("x_189_transpose_x_0"), val = tensor(false)]; - tensor x_189_transpose_y_0 = const()[name = tensor("x_189_transpose_y_0"), val = tensor(false)]; - tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = tensor("transpose_148")]; - tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_19_cast_fp16)[name = tensor("x_189_cast_fp16")]; - tensor var_1820_perm_0 = const()[name = tensor("op_1820_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1821 = const()[name = tensor("op_1821"), val = tensor([1, -1, 512])]; - tensor var_1820_cast_fp16 = transpose(perm = var_1820_perm_0, x = x_189_cast_fp16)[name = tensor("transpose_144")]; - tensor input_451_cast_fp16 = reshape(shape = var_1821, x = var_1820_cast_fp16)[name = tensor("input_451_cast_fp16")]; - tensor module_layers_8_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108946816)))]; - tensor module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109471168)))]; - tensor linear_79_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_out_bias_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16, x = input_451_cast_fp16)[name = tensor("linear_79_cast_fp16")]; - tensor input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = tensor("input_455_cast_fp16")]; - tensor x_193_axes_0 = const()[name = tensor("x_193_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109472256)))]; - tensor module_layers_8_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109473344)))]; - tensor x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = tensor("x_193_cast_fp16")]; - tensor input_457_perm_0 = const()[name = tensor("input_457_perm_0"), val = tensor([0, 2, 1])]; - tensor input_459_pad_type_0 = const()[name = tensor("input_459_pad_type_0"), val = tensor("valid")]; - tensor input_459_strides_0 = const()[name = tensor("input_459_strides_0"), val = tensor([1])]; - tensor input_459_pad_0 = const()[name = tensor("input_459_pad_0"), val = tensor([0, 0])]; - tensor input_459_dilations_0 = const()[name = tensor("input_459_dilations_0"), val = tensor([1])]; - tensor input_459_groups_0 = const()[name = tensor("input_459_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109474432)))]; - tensor module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110523072)))]; - tensor input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = tensor("transpose_143")]; - tensor input_459_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv1_bias_to_fp16, dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16, x = input_457_cast_fp16)[name = tensor("input_459_cast_fp16")]; - tensor x_195_split_num_splits_0 = const()[name = tensor("x_195_split_num_splits_0"), val = tensor(2)]; - tensor x_195_split_axis_0 = const()[name = tensor("x_195_split_axis_0"), val = tensor(1)]; - tensor x_195_split_cast_fp16_0, tensor x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = tensor("x_195_split_cast_fp16")]; - tensor x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = tensor("x_195_split_1_sigmoid_cast_fp16")]; - tensor x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = tensor("x_195_cast_fp16")]; - tensor input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_453)[name = tensor("input_461_cast_fp16")]; - tensor input_463_pad_0 = const()[name = tensor("input_463_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_463_mode_0 = const()[name = tensor("input_463_mode_0"), val = tensor("constant")]; - tensor const_153_to_fp16 = const()[name = tensor("const_153_to_fp16"), val = tensor(0x0p+0)]; - tensor input_463_cast_fp16 = pad(constant_val = const_153_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = tensor("input_463_cast_fp16")]; - tensor input_465_pad_type_0 = const()[name = tensor("input_465_pad_type_0"), val = tensor("valid")]; - tensor input_465_groups_0 = const()[name = tensor("input_465_groups_0"), val = tensor(512)]; - tensor input_465_strides_0 = const()[name = tensor("input_465_strides_0"), val = tensor([1])]; - tensor input_465_pad_0 = const()[name = tensor("input_465_pad_0"), val = tensor([0, 0])]; - tensor input_465_dilations_0 = const()[name = tensor("input_465_dilations_0"), val = tensor([1])]; - tensor const_250_to_fp16 = const()[name = tensor("const_250_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110525184)))]; - tensor const_251_to_fp16 = const()[name = tensor("const_251_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110534464)))]; - tensor input_467_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_250_to_fp16, x = input_463_cast_fp16)[name = tensor("input_467_cast_fp16")]; - tensor input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = tensor("input_469_cast_fp16")]; - tensor x_197_pad_type_0 = const()[name = tensor("x_197_pad_type_0"), val = tensor("valid")]; - tensor x_197_strides_0 = const()[name = tensor("x_197_strides_0"), val = tensor([1])]; - tensor x_197_pad_0 = const()[name = tensor("x_197_pad_0"), val = tensor([0, 0])]; - tensor x_197_dilations_0 = const()[name = tensor("x_197_dilations_0"), val = tensor([1])]; - tensor x_197_groups_0 = const()[name = tensor("x_197_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110535552)))]; - tensor module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111059904)))]; - tensor x_197_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv2_bias_to_fp16, dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = module_layers_8_conv_pointwise_conv2_weight_to_fp16, x = input_469_cast_fp16)[name = tensor("x_197_cast_fp16")]; - tensor input_471_perm_0 = const()[name = tensor("input_471_perm_0"), val = tensor([0, 2, 1])]; - tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = tensor("transpose_142")]; - tensor input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = tensor("input_473_cast_fp16")]; - tensor input_475_axes_0 = const()[name = tensor("input_475_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111060992)))]; - tensor module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111062080)))]; - tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = tensor("input_475_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111063168)))]; - tensor module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113160384)))]; - tensor linear_80_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16, x = input_475_cast_fp16)[name = tensor("linear_80_cast_fp16")]; - tensor input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = tensor("input_479_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113164544)))]; - tensor module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115261760)))]; - tensor linear_81_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16, x = input_479_cast_fp16)[name = tensor("linear_81_cast_fp16")]; - tensor var_1887_to_fp16 = const()[name = tensor("op_1887_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1888_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1887_to_fp16)[name = tensor("op_1888_cast_fp16")]; - tensor input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1888_cast_fp16)[name = tensor("input_485_cast_fp16")]; - tensor input_487_axes_0 = const()[name = tensor("input_487_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115262848)))]; - tensor module_layers_8_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115263936)))]; - tensor input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = tensor("input_487_cast_fp16")]; - tensor input_489_axes_0 = const()[name = tensor("input_489_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115265024)))]; - tensor module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115266112)))]; - tensor input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = tensor("input_489_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115267200)))]; - tensor module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117364416)))]; - tensor linear_82_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16, x = input_489_cast_fp16)[name = tensor("linear_82_cast_fp16")]; - tensor input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = tensor("input_493_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117368576)))]; - tensor module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119465792)))]; - tensor linear_83_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16, x = input_493_cast_fp16)[name = tensor("linear_83_cast_fp16")]; - tensor var_1918_to_fp16 = const()[name = tensor("op_1918_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1919_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1918_to_fp16)[name = tensor("op_1919_cast_fp16")]; - tensor input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1919_cast_fp16)[name = tensor("input_499_cast_fp16")]; - tensor query_19_axes_0 = const()[name = tensor("query_19_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119466880)))]; - tensor module_layers_9_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119467968)))]; - tensor query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = tensor("query_19_cast_fp16")]; - tensor module_layers_9_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119469056)))]; - tensor module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119993408)))]; - tensor linear_84_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_q_bias_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_84_cast_fp16")]; - tensor var_1936 = const()[name = tensor("op_1936"), val = tensor([1, -1, 8, 64])]; - tensor q_55_cast_fp16 = reshape(shape = var_1936, x = linear_84_cast_fp16)[name = tensor("q_55_cast_fp16")]; - tensor module_layers_9_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119994496)))]; - tensor module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120518848)))]; - tensor linear_85_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_k_bias_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_85_cast_fp16")]; - tensor var_1941 = const()[name = tensor("op_1941"), val = tensor([1, -1, 8, 64])]; - tensor k_37_cast_fp16 = reshape(shape = var_1941, x = linear_85_cast_fp16)[name = tensor("k_37_cast_fp16")]; - tensor module_layers_9_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120519936)))]; - tensor module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121044288)))]; - tensor linear_86_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_v_bias_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_86_cast_fp16")]; - tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([1, -1, 8, 64])]; - tensor v_19_cast_fp16 = reshape(shape = var_1946, x = linear_86_cast_fp16)[name = tensor("v_19_cast_fp16")]; - tensor value_21_perm_0 = const()[name = tensor("value_21_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121045376)))]; - tensor var_1958_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1958_cast_fp16")]; - tensor module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121046464)))]; - tensor var_1960_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1960_cast_fp16")]; - tensor q_with_bias_v_19_perm_0 = const()[name = tensor("q_with_bias_v_19_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_205_transpose_x_0 = const()[name = tensor("x_205_transpose_x_0"), val = tensor(false)]; - tensor x_205_transpose_y_0 = const()[name = tensor("x_205_transpose_y_0"), val = tensor(false)]; - tensor var_1962_to_fp16 = const()[name = tensor("op_1962_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121047552)))]; - tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1960_cast_fp16)[name = tensor("transpose_140")]; - tensor x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = var_1962_to_fp16)[name = tensor("x_205_cast_fp16")]; - tensor x_207_pad_0 = const()[name = tensor("x_207_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_207_mode_0 = const()[name = tensor("x_207_mode_0"), val = tensor("constant")]; - tensor const_160_to_fp16 = const()[name = tensor("const_160_to_fp16"), val = tensor(0x0p+0)]; - tensor x_207_cast_fp16 = pad(constant_val = const_160_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = tensor("x_207_cast_fp16")]; - tensor var_1970 = const()[name = tensor("op_1970"), val = tensor([1, 8, -1, 188])]; - tensor x_209_cast_fp16 = reshape(shape = var_1970, x = x_207_cast_fp16)[name = tensor("x_209_cast_fp16")]; - tensor var_1974_begin_0 = const()[name = tensor("op_1974_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1974_end_0 = const()[name = tensor("op_1974_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1974_end_mask_0 = const()[name = tensor("op_1974_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1974_cast_fp16 = slice_by_index(begin = var_1974_begin_0, end = var_1974_end_0, end_mask = var_1974_end_mask_0, x = x_209_cast_fp16)[name = tensor("op_1974_cast_fp16")]; - tensor var_1975 = const()[name = tensor("op_1975"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1975, x = var_1974_cast_fp16)[name = tensor("matrix_bd_37_cast_fp16")]; - tensor matrix_ac_19_transpose_x_0 = const()[name = tensor("matrix_ac_19_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_19_transpose_y_0 = const()[name = tensor("matrix_ac_19_transpose_y_0"), val = tensor(false)]; - tensor transpose_69_perm_0 = const()[name = tensor("transpose_69_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_70_perm_0 = const()[name = tensor("transpose_70_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = k_37_cast_fp16)[name = tensor("transpose_138")]; - tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1958_cast_fp16)[name = tensor("transpose_139")]; - tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_69, y = transpose_70)[name = tensor("matrix_ac_19_cast_fp16")]; - tensor matrix_bd_39_begin_0 = const()[name = tensor("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_39_end_0 = const()[name = tensor("matrix_bd_39_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_39_end_mask_0 = const()[name = tensor("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = tensor("matrix_bd_39_cast_fp16")]; - tensor var_1984_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = tensor("op_1984_cast_fp16")]; - tensor _inversed_scores_37_y_0_to_fp16 = const()[name = tensor("_inversed_scores_37_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_37_cast_fp16 = mul(x = var_1984_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = tensor("_inversed_scores_37_cast_fp16")]; - tensor scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_11)[name = tensor("scores_39_cast_fp16")]; - tensor var_1990_cast_fp16 = softmax(axis = var_23, x = scores_39_cast_fp16)[name = tensor("op_1990_cast_fp16")]; - tensor input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1990_cast_fp16, cond = mask_11)[name = tensor("input_501_cast_fp16")]; - tensor x_211_transpose_x_0 = const()[name = tensor("x_211_transpose_x_0"), val = tensor(false)]; - tensor x_211_transpose_y_0 = const()[name = tensor("x_211_transpose_y_0"), val = tensor(false)]; - tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = tensor("transpose_141")]; - tensor x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_21_cast_fp16)[name = tensor("x_211_cast_fp16")]; - tensor var_1994_perm_0 = const()[name = tensor("op_1994_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1995 = const()[name = tensor("op_1995"), val = tensor([1, -1, 512])]; - tensor var_1994_cast_fp16 = transpose(perm = var_1994_perm_0, x = x_211_cast_fp16)[name = tensor("transpose_137")]; - tensor input_503_cast_fp16 = reshape(shape = var_1995, x = var_1994_cast_fp16)[name = tensor("input_503_cast_fp16")]; - tensor module_layers_9_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121431616)))]; - tensor module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121955968)))]; - tensor linear_88_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_out_bias_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16, x = input_503_cast_fp16)[name = tensor("linear_88_cast_fp16")]; - tensor input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = tensor("input_507_cast_fp16")]; - tensor x_215_axes_0 = const()[name = tensor("x_215_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121957056)))]; - tensor module_layers_9_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121958144)))]; - tensor x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = tensor("x_215_cast_fp16")]; - tensor input_509_perm_0 = const()[name = tensor("input_509_perm_0"), val = tensor([0, 2, 1])]; - tensor input_511_pad_type_0 = const()[name = tensor("input_511_pad_type_0"), val = tensor("valid")]; - tensor input_511_strides_0 = const()[name = tensor("input_511_strides_0"), val = tensor([1])]; - tensor input_511_pad_0 = const()[name = tensor("input_511_pad_0"), val = tensor([0, 0])]; - tensor input_511_dilations_0 = const()[name = tensor("input_511_dilations_0"), val = tensor([1])]; - tensor input_511_groups_0 = const()[name = tensor("input_511_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121959232)))]; - tensor module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123007872)))]; - tensor input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = tensor("transpose_136")]; - tensor input_511_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv1_bias_to_fp16, dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16, x = input_509_cast_fp16)[name = tensor("input_511_cast_fp16")]; - tensor x_217_split_num_splits_0 = const()[name = tensor("x_217_split_num_splits_0"), val = tensor(2)]; - tensor x_217_split_axis_0 = const()[name = tensor("x_217_split_axis_0"), val = tensor(1)]; - tensor x_217_split_cast_fp16_0, tensor x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = tensor("x_217_split_cast_fp16")]; - tensor x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = tensor("x_217_split_1_sigmoid_cast_fp16")]; - tensor x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = tensor("x_217_cast_fp16")]; - tensor input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_453)[name = tensor("input_513_cast_fp16")]; - tensor input_515_pad_0 = const()[name = tensor("input_515_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_515_mode_0 = const()[name = tensor("input_515_mode_0"), val = tensor("constant")]; - tensor const_163_to_fp16 = const()[name = tensor("const_163_to_fp16"), val = tensor(0x0p+0)]; - tensor input_515_cast_fp16 = pad(constant_val = const_163_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = tensor("input_515_cast_fp16")]; - tensor input_517_pad_type_0 = const()[name = tensor("input_517_pad_type_0"), val = tensor("valid")]; - tensor input_517_groups_0 = const()[name = tensor("input_517_groups_0"), val = tensor(512)]; - tensor input_517_strides_0 = const()[name = tensor("input_517_strides_0"), val = tensor([1])]; - tensor input_517_pad_0 = const()[name = tensor("input_517_pad_0"), val = tensor([0, 0])]; - tensor input_517_dilations_0 = const()[name = tensor("input_517_dilations_0"), val = tensor([1])]; - tensor const_252_to_fp16 = const()[name = tensor("const_252_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123009984)))]; - tensor const_253_to_fp16 = const()[name = tensor("const_253_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123019264)))]; - tensor input_519_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_252_to_fp16, x = input_515_cast_fp16)[name = tensor("input_519_cast_fp16")]; - tensor input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = tensor("input_521_cast_fp16")]; - tensor x_219_pad_type_0 = const()[name = tensor("x_219_pad_type_0"), val = tensor("valid")]; - tensor x_219_strides_0 = const()[name = tensor("x_219_strides_0"), val = tensor([1])]; - tensor x_219_pad_0 = const()[name = tensor("x_219_pad_0"), val = tensor([0, 0])]; - tensor x_219_dilations_0 = const()[name = tensor("x_219_dilations_0"), val = tensor([1])]; - tensor x_219_groups_0 = const()[name = tensor("x_219_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123020352)))]; - tensor module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123544704)))]; - tensor x_219_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv2_bias_to_fp16, dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = module_layers_9_conv_pointwise_conv2_weight_to_fp16, x = input_521_cast_fp16)[name = tensor("x_219_cast_fp16")]; - tensor input_523_perm_0 = const()[name = tensor("input_523_perm_0"), val = tensor([0, 2, 1])]; - tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = tensor("transpose_135")]; - tensor input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = tensor("input_525_cast_fp16")]; - tensor input_527_axes_0 = const()[name = tensor("input_527_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123545792)))]; - tensor module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123546880)))]; - tensor input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = tensor("input_527_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123547968)))]; - tensor module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125645184)))]; - tensor linear_89_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16, x = input_527_cast_fp16)[name = tensor("linear_89_cast_fp16")]; - tensor input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = tensor("input_531_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125649344)))]; - tensor module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127746560)))]; - tensor linear_90_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16, x = input_531_cast_fp16)[name = tensor("linear_90_cast_fp16")]; - tensor var_2061_to_fp16 = const()[name = tensor("op_2061_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2062_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2061_to_fp16)[name = tensor("op_2062_cast_fp16")]; - tensor input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_2062_cast_fp16)[name = tensor("input_537_cast_fp16")]; - tensor input_539_axes_0 = const()[name = tensor("input_539_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127747648)))]; - tensor module_layers_9_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127748736)))]; - tensor input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = tensor("input_539_cast_fp16")]; - tensor input_541_axes_0 = const()[name = tensor("input_541_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127749824)))]; - tensor module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127750912)))]; - tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = tensor("input_541_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127752000)))]; - tensor module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129849216)))]; - tensor linear_91_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16, x = input_541_cast_fp16)[name = tensor("linear_91_cast_fp16")]; - tensor input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = tensor("input_545_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129853376)))]; - tensor module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131950592)))]; - tensor linear_92_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16, x = input_545_cast_fp16)[name = tensor("linear_92_cast_fp16")]; - tensor var_2092_to_fp16 = const()[name = tensor("op_2092_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2093_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2092_to_fp16)[name = tensor("op_2093_cast_fp16")]; - tensor input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_2093_cast_fp16)[name = tensor("input_551_cast_fp16")]; - tensor query_21_axes_0 = const()[name = tensor("query_21_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131951680)))]; - tensor module_layers_10_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131952768)))]; - tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = tensor("query_21_cast_fp16")]; - tensor module_layers_10_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131953856)))]; - tensor module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132478208)))]; - tensor linear_93_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_q_bias_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_93_cast_fp16")]; - tensor var_2110 = const()[name = tensor("op_2110"), val = tensor([1, -1, 8, 64])]; - tensor q_61_cast_fp16 = reshape(shape = var_2110, x = linear_93_cast_fp16)[name = tensor("q_61_cast_fp16")]; - tensor module_layers_10_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132479296)))]; - tensor module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133003648)))]; - tensor linear_94_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_k_bias_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_94_cast_fp16")]; - tensor var_2115 = const()[name = tensor("op_2115"), val = tensor([1, -1, 8, 64])]; - tensor k_41_cast_fp16 = reshape(shape = var_2115, x = linear_94_cast_fp16)[name = tensor("k_41_cast_fp16")]; - tensor module_layers_10_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133004736)))]; - tensor module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133529088)))]; - tensor linear_95_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_v_bias_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_95_cast_fp16")]; - tensor var_2120 = const()[name = tensor("op_2120"), val = tensor([1, -1, 8, 64])]; - tensor v_21_cast_fp16 = reshape(shape = var_2120, x = linear_95_cast_fp16)[name = tensor("v_21_cast_fp16")]; - tensor value_23_perm_0 = const()[name = tensor("value_23_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133530176)))]; - tensor var_2132_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2132_cast_fp16")]; - tensor module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133531264)))]; - tensor var_2134_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2134_cast_fp16")]; - tensor q_with_bias_v_21_perm_0 = const()[name = tensor("q_with_bias_v_21_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_227_transpose_x_0 = const()[name = tensor("x_227_transpose_x_0"), val = tensor(false)]; - tensor x_227_transpose_y_0 = const()[name = tensor("x_227_transpose_y_0"), val = tensor(false)]; - tensor var_2136_to_fp16 = const()[name = tensor("op_2136_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133532352)))]; - tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2134_cast_fp16)[name = tensor("transpose_133")]; - tensor x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = var_2136_to_fp16)[name = tensor("x_227_cast_fp16")]; - tensor x_229_pad_0 = const()[name = tensor("x_229_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_229_mode_0 = const()[name = tensor("x_229_mode_0"), val = tensor("constant")]; - tensor const_170_to_fp16 = const()[name = tensor("const_170_to_fp16"), val = tensor(0x0p+0)]; - tensor x_229_cast_fp16 = pad(constant_val = const_170_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = tensor("x_229_cast_fp16")]; - tensor var_2144 = const()[name = tensor("op_2144"), val = tensor([1, 8, -1, 188])]; - tensor x_231_cast_fp16 = reshape(shape = var_2144, x = x_229_cast_fp16)[name = tensor("x_231_cast_fp16")]; - tensor var_2148_begin_0 = const()[name = tensor("op_2148_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2148_end_0 = const()[name = tensor("op_2148_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2148_end_mask_0 = const()[name = tensor("op_2148_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2148_cast_fp16 = slice_by_index(begin = var_2148_begin_0, end = var_2148_end_0, end_mask = var_2148_end_mask_0, x = x_231_cast_fp16)[name = tensor("op_2148_cast_fp16")]; - tensor var_2149 = const()[name = tensor("op_2149"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_41_cast_fp16 = reshape(shape = var_2149, x = var_2148_cast_fp16)[name = tensor("matrix_bd_41_cast_fp16")]; - tensor matrix_ac_21_transpose_x_0 = const()[name = tensor("matrix_ac_21_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_21_transpose_y_0 = const()[name = tensor("matrix_ac_21_transpose_y_0"), val = tensor(false)]; - tensor transpose_71_perm_0 = const()[name = tensor("transpose_71_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_72_perm_0 = const()[name = tensor("transpose_72_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = k_41_cast_fp16)[name = tensor("transpose_131")]; - tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = var_2132_cast_fp16)[name = tensor("transpose_132")]; - tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_71, y = transpose_72)[name = tensor("matrix_ac_21_cast_fp16")]; - tensor matrix_bd_43_begin_0 = const()[name = tensor("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_43_end_0 = const()[name = tensor("matrix_bd_43_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_43_end_mask_0 = const()[name = tensor("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = tensor("matrix_bd_43_cast_fp16")]; - tensor var_2158_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = tensor("op_2158_cast_fp16")]; - tensor _inversed_scores_41_y_0_to_fp16 = const()[name = tensor("_inversed_scores_41_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_41_cast_fp16 = mul(x = var_2158_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = tensor("_inversed_scores_41_cast_fp16")]; - tensor scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_11)[name = tensor("scores_43_cast_fp16")]; - tensor var_2164_cast_fp16 = softmax(axis = var_23, x = scores_43_cast_fp16)[name = tensor("op_2164_cast_fp16")]; - tensor input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_2164_cast_fp16, cond = mask_11)[name = tensor("input_553_cast_fp16")]; - tensor x_233_transpose_x_0 = const()[name = tensor("x_233_transpose_x_0"), val = tensor(false)]; - tensor x_233_transpose_y_0 = const()[name = tensor("x_233_transpose_y_0"), val = tensor(false)]; - tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = tensor("transpose_134")]; - tensor x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_23_cast_fp16)[name = tensor("x_233_cast_fp16")]; - tensor var_2168_perm_0 = const()[name = tensor("op_2168_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2169 = const()[name = tensor("op_2169"), val = tensor([1, -1, 512])]; - tensor var_2168_cast_fp16 = transpose(perm = var_2168_perm_0, x = x_233_cast_fp16)[name = tensor("transpose_130")]; - tensor input_555_cast_fp16 = reshape(shape = var_2169, x = var_2168_cast_fp16)[name = tensor("input_555_cast_fp16")]; - tensor module_layers_10_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133916416)))]; - tensor module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134440768)))]; - tensor linear_97_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_out_bias_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16, x = input_555_cast_fp16)[name = tensor("linear_97_cast_fp16")]; - tensor input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = tensor("input_559_cast_fp16")]; - tensor x_237_axes_0 = const()[name = tensor("x_237_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134441856)))]; - tensor module_layers_10_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134442944)))]; - tensor x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = tensor("x_237_cast_fp16")]; - tensor input_561_perm_0 = const()[name = tensor("input_561_perm_0"), val = tensor([0, 2, 1])]; - tensor input_563_pad_type_0 = const()[name = tensor("input_563_pad_type_0"), val = tensor("valid")]; - tensor input_563_strides_0 = const()[name = tensor("input_563_strides_0"), val = tensor([1])]; - tensor input_563_pad_0 = const()[name = tensor("input_563_pad_0"), val = tensor([0, 0])]; - tensor input_563_dilations_0 = const()[name = tensor("input_563_dilations_0"), val = tensor([1])]; - tensor input_563_groups_0 = const()[name = tensor("input_563_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134444032)))]; - tensor module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135492672)))]; - tensor input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = tensor("transpose_129")]; - tensor input_563_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv1_bias_to_fp16, dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16, x = input_561_cast_fp16)[name = tensor("input_563_cast_fp16")]; - tensor x_239_split_num_splits_0 = const()[name = tensor("x_239_split_num_splits_0"), val = tensor(2)]; - tensor x_239_split_axis_0 = const()[name = tensor("x_239_split_axis_0"), val = tensor(1)]; - tensor x_239_split_cast_fp16_0, tensor x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = tensor("x_239_split_cast_fp16")]; - tensor x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = tensor("x_239_split_1_sigmoid_cast_fp16")]; - tensor x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = tensor("x_239_cast_fp16")]; - tensor input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_453)[name = tensor("input_565_cast_fp16")]; - tensor input_567_pad_0 = const()[name = tensor("input_567_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_567_mode_0 = const()[name = tensor("input_567_mode_0"), val = tensor("constant")]; - tensor const_173_to_fp16 = const()[name = tensor("const_173_to_fp16"), val = tensor(0x0p+0)]; - tensor input_567_cast_fp16 = pad(constant_val = const_173_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = tensor("input_567_cast_fp16")]; - tensor input_569_pad_type_0 = const()[name = tensor("input_569_pad_type_0"), val = tensor("valid")]; - tensor input_569_groups_0 = const()[name = tensor("input_569_groups_0"), val = tensor(512)]; - tensor input_569_strides_0 = const()[name = tensor("input_569_strides_0"), val = tensor([1])]; - tensor input_569_pad_0 = const()[name = tensor("input_569_pad_0"), val = tensor([0, 0])]; - tensor input_569_dilations_0 = const()[name = tensor("input_569_dilations_0"), val = tensor([1])]; - tensor const_254_to_fp16 = const()[name = tensor("const_254_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135494784)))]; - tensor const_255_to_fp16 = const()[name = tensor("const_255_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135504064)))]; - tensor input_571_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_254_to_fp16, x = input_567_cast_fp16)[name = tensor("input_571_cast_fp16")]; - tensor input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = tensor("input_573_cast_fp16")]; - tensor x_241_pad_type_0 = const()[name = tensor("x_241_pad_type_0"), val = tensor("valid")]; - tensor x_241_strides_0 = const()[name = tensor("x_241_strides_0"), val = tensor([1])]; - tensor x_241_pad_0 = const()[name = tensor("x_241_pad_0"), val = tensor([0, 0])]; - tensor x_241_dilations_0 = const()[name = tensor("x_241_dilations_0"), val = tensor([1])]; - tensor x_241_groups_0 = const()[name = tensor("x_241_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135505152)))]; - tensor module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136029504)))]; - tensor x_241_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv2_bias_to_fp16, dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = module_layers_10_conv_pointwise_conv2_weight_to_fp16, x = input_573_cast_fp16)[name = tensor("x_241_cast_fp16")]; - tensor input_575_perm_0 = const()[name = tensor("input_575_perm_0"), val = tensor([0, 2, 1])]; - tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = tensor("transpose_128")]; - tensor input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = tensor("input_577_cast_fp16")]; - tensor input_579_axes_0 = const()[name = tensor("input_579_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136030592)))]; - tensor module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136031680)))]; - tensor input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = tensor("input_579_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136032768)))]; - tensor module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138129984)))]; - tensor linear_98_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16, x = input_579_cast_fp16)[name = tensor("linear_98_cast_fp16")]; - tensor input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = tensor("input_583_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138134144)))]; - tensor module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140231360)))]; - tensor linear_99_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16, x = input_583_cast_fp16)[name = tensor("linear_99_cast_fp16")]; - tensor var_2235_to_fp16 = const()[name = tensor("op_2235_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2236_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2235_to_fp16)[name = tensor("op_2236_cast_fp16")]; - tensor input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_2236_cast_fp16)[name = tensor("input_589_cast_fp16")]; - tensor input_591_axes_0 = const()[name = tensor("input_591_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140232448)))]; - tensor module_layers_10_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140233536)))]; - tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = tensor("input_591_cast_fp16")]; - tensor input_593_axes_0 = const()[name = tensor("input_593_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140234624)))]; - tensor module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140235712)))]; - tensor input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = tensor("input_593_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140236800)))]; - tensor module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142334016)))]; - tensor linear_100_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16, x = input_593_cast_fp16)[name = tensor("linear_100_cast_fp16")]; - tensor input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = tensor("input_597_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142338176)))]; - tensor module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144435392)))]; - tensor linear_101_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16, x = input_597_cast_fp16)[name = tensor("linear_101_cast_fp16")]; - tensor var_2266_to_fp16 = const()[name = tensor("op_2266_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2267_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2266_to_fp16)[name = tensor("op_2267_cast_fp16")]; - tensor input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2267_cast_fp16)[name = tensor("input_603_cast_fp16")]; - tensor query_23_axes_0 = const()[name = tensor("query_23_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144436480)))]; - tensor module_layers_11_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144437568)))]; - tensor query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = tensor("query_23_cast_fp16")]; - tensor module_layers_11_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144438656)))]; - tensor module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144963008)))]; - tensor linear_102_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_q_bias_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_102_cast_fp16")]; - tensor var_2284 = const()[name = tensor("op_2284"), val = tensor([1, -1, 8, 64])]; - tensor q_67_cast_fp16 = reshape(shape = var_2284, x = linear_102_cast_fp16)[name = tensor("q_67_cast_fp16")]; - tensor module_layers_11_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144964096)))]; - tensor module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145488448)))]; - tensor linear_103_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_k_bias_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_103_cast_fp16")]; - tensor var_2289 = const()[name = tensor("op_2289"), val = tensor([1, -1, 8, 64])]; - tensor k_45_cast_fp16 = reshape(shape = var_2289, x = linear_103_cast_fp16)[name = tensor("k_45_cast_fp16")]; - tensor module_layers_11_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145489536)))]; - tensor module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146013888)))]; - tensor linear_104_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_v_bias_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_104_cast_fp16")]; - tensor var_2294 = const()[name = tensor("op_2294"), val = tensor([1, -1, 8, 64])]; - tensor v_23_cast_fp16 = reshape(shape = var_2294, x = linear_104_cast_fp16)[name = tensor("v_23_cast_fp16")]; - tensor value_25_perm_0 = const()[name = tensor("value_25_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146014976)))]; - tensor var_2306_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2306_cast_fp16")]; - tensor module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146016064)))]; - tensor var_2308_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2308_cast_fp16")]; - tensor q_with_bias_v_23_perm_0 = const()[name = tensor("q_with_bias_v_23_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_249_transpose_x_0 = const()[name = tensor("x_249_transpose_x_0"), val = tensor(false)]; - tensor x_249_transpose_y_0 = const()[name = tensor("x_249_transpose_y_0"), val = tensor(false)]; - tensor var_2310_to_fp16 = const()[name = tensor("op_2310_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146017152)))]; - tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2308_cast_fp16)[name = tensor("transpose_126")]; - tensor x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = var_2310_to_fp16)[name = tensor("x_249_cast_fp16")]; - tensor x_251_pad_0 = const()[name = tensor("x_251_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_251_mode_0 = const()[name = tensor("x_251_mode_0"), val = tensor("constant")]; - tensor const_180_to_fp16 = const()[name = tensor("const_180_to_fp16"), val = tensor(0x0p+0)]; - tensor x_251_cast_fp16 = pad(constant_val = const_180_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = tensor("x_251_cast_fp16")]; - tensor var_2318 = const()[name = tensor("op_2318"), val = tensor([1, 8, -1, 188])]; - tensor x_253_cast_fp16 = reshape(shape = var_2318, x = x_251_cast_fp16)[name = tensor("x_253_cast_fp16")]; - tensor var_2322_begin_0 = const()[name = tensor("op_2322_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2322_end_0 = const()[name = tensor("op_2322_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2322_end_mask_0 = const()[name = tensor("op_2322_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2322_cast_fp16 = slice_by_index(begin = var_2322_begin_0, end = var_2322_end_0, end_mask = var_2322_end_mask_0, x = x_253_cast_fp16)[name = tensor("op_2322_cast_fp16")]; - tensor var_2323 = const()[name = tensor("op_2323"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2323, x = var_2322_cast_fp16)[name = tensor("matrix_bd_45_cast_fp16")]; - tensor matrix_ac_23_transpose_x_0 = const()[name = tensor("matrix_ac_23_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_23_transpose_y_0 = const()[name = tensor("matrix_ac_23_transpose_y_0"), val = tensor(false)]; - tensor transpose_73_perm_0 = const()[name = tensor("transpose_73_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_74_perm_0 = const()[name = tensor("transpose_74_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = k_45_cast_fp16)[name = tensor("transpose_124")]; - tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_2306_cast_fp16)[name = tensor("transpose_125")]; - tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_73, y = transpose_74)[name = tensor("matrix_ac_23_cast_fp16")]; - tensor matrix_bd_47_begin_0 = const()[name = tensor("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_47_end_0 = const()[name = tensor("matrix_bd_47_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_47_end_mask_0 = const()[name = tensor("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = tensor("matrix_bd_47_cast_fp16")]; - tensor var_2332_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = tensor("op_2332_cast_fp16")]; - tensor _inversed_scores_45_y_0_to_fp16 = const()[name = tensor("_inversed_scores_45_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_45_cast_fp16 = mul(x = var_2332_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = tensor("_inversed_scores_45_cast_fp16")]; - tensor scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_11)[name = tensor("scores_47_cast_fp16")]; - tensor var_2338_cast_fp16 = softmax(axis = var_23, x = scores_47_cast_fp16)[name = tensor("op_2338_cast_fp16")]; - tensor input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2338_cast_fp16, cond = mask_11)[name = tensor("input_605_cast_fp16")]; - tensor x_255_transpose_x_0 = const()[name = tensor("x_255_transpose_x_0"), val = tensor(false)]; - tensor x_255_transpose_y_0 = const()[name = tensor("x_255_transpose_y_0"), val = tensor(false)]; - tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = tensor("transpose_127")]; - tensor x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_25_cast_fp16)[name = tensor("x_255_cast_fp16")]; - tensor var_2342_perm_0 = const()[name = tensor("op_2342_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2343 = const()[name = tensor("op_2343"), val = tensor([1, -1, 512])]; - tensor var_2342_cast_fp16 = transpose(perm = var_2342_perm_0, x = x_255_cast_fp16)[name = tensor("transpose_123")]; - tensor input_607_cast_fp16 = reshape(shape = var_2343, x = var_2342_cast_fp16)[name = tensor("input_607_cast_fp16")]; - tensor module_layers_11_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146401216)))]; - tensor module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146925568)))]; - tensor linear_106_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_out_bias_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16, x = input_607_cast_fp16)[name = tensor("linear_106_cast_fp16")]; - tensor input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = tensor("input_611_cast_fp16")]; - tensor x_259_axes_0 = const()[name = tensor("x_259_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146926656)))]; - tensor module_layers_11_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146927744)))]; - tensor x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = tensor("x_259_cast_fp16")]; - tensor input_613_perm_0 = const()[name = tensor("input_613_perm_0"), val = tensor([0, 2, 1])]; - tensor input_615_pad_type_0 = const()[name = tensor("input_615_pad_type_0"), val = tensor("valid")]; - tensor input_615_strides_0 = const()[name = tensor("input_615_strides_0"), val = tensor([1])]; - tensor input_615_pad_0 = const()[name = tensor("input_615_pad_0"), val = tensor([0, 0])]; - tensor input_615_dilations_0 = const()[name = tensor("input_615_dilations_0"), val = tensor([1])]; - tensor input_615_groups_0 = const()[name = tensor("input_615_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146928832)))]; - tensor module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147977472)))]; - tensor input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = tensor("transpose_122")]; - tensor input_615_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv1_bias_to_fp16, dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16, x = input_613_cast_fp16)[name = tensor("input_615_cast_fp16")]; - tensor x_261_split_num_splits_0 = const()[name = tensor("x_261_split_num_splits_0"), val = tensor(2)]; - tensor x_261_split_axis_0 = const()[name = tensor("x_261_split_axis_0"), val = tensor(1)]; - tensor x_261_split_cast_fp16_0, tensor x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = tensor("x_261_split_cast_fp16")]; - tensor x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = tensor("x_261_split_1_sigmoid_cast_fp16")]; - tensor x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = tensor("x_261_cast_fp16")]; - tensor input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_453)[name = tensor("input_617_cast_fp16")]; - tensor input_619_pad_0 = const()[name = tensor("input_619_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_619_mode_0 = const()[name = tensor("input_619_mode_0"), val = tensor("constant")]; - tensor const_183_to_fp16 = const()[name = tensor("const_183_to_fp16"), val = tensor(0x0p+0)]; - tensor input_619_cast_fp16 = pad(constant_val = const_183_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = tensor("input_619_cast_fp16")]; - tensor input_621_pad_type_0 = const()[name = tensor("input_621_pad_type_0"), val = tensor("valid")]; - tensor input_621_groups_0 = const()[name = tensor("input_621_groups_0"), val = tensor(512)]; - tensor input_621_strides_0 = const()[name = tensor("input_621_strides_0"), val = tensor([1])]; - tensor input_621_pad_0 = const()[name = tensor("input_621_pad_0"), val = tensor([0, 0])]; - tensor input_621_dilations_0 = const()[name = tensor("input_621_dilations_0"), val = tensor([1])]; - tensor const_256_to_fp16 = const()[name = tensor("const_256_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147979584)))]; - tensor const_257_to_fp16 = const()[name = tensor("const_257_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147988864)))]; - tensor input_623_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_256_to_fp16, x = input_619_cast_fp16)[name = tensor("input_623_cast_fp16")]; - tensor input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = tensor("input_625_cast_fp16")]; - tensor x_263_pad_type_0 = const()[name = tensor("x_263_pad_type_0"), val = tensor("valid")]; - tensor x_263_strides_0 = const()[name = tensor("x_263_strides_0"), val = tensor([1])]; - tensor x_263_pad_0 = const()[name = tensor("x_263_pad_0"), val = tensor([0, 0])]; - tensor x_263_dilations_0 = const()[name = tensor("x_263_dilations_0"), val = tensor([1])]; - tensor x_263_groups_0 = const()[name = tensor("x_263_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147989952)))]; - tensor module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148514304)))]; - tensor x_263_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv2_bias_to_fp16, dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = module_layers_11_conv_pointwise_conv2_weight_to_fp16, x = input_625_cast_fp16)[name = tensor("x_263_cast_fp16")]; - tensor input_627_perm_0 = const()[name = tensor("input_627_perm_0"), val = tensor([0, 2, 1])]; - tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = tensor("transpose_121")]; - tensor input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = tensor("input_629_cast_fp16")]; - tensor input_631_axes_0 = const()[name = tensor("input_631_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148515392)))]; - tensor module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148516480)))]; - tensor input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = tensor("input_631_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148517568)))]; - tensor module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150614784)))]; - tensor linear_107_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16, x = input_631_cast_fp16)[name = tensor("linear_107_cast_fp16")]; - tensor input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = tensor("input_635_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150618944)))]; - tensor module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152716160)))]; - tensor linear_108_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16, x = input_635_cast_fp16)[name = tensor("linear_108_cast_fp16")]; - tensor var_2409_to_fp16 = const()[name = tensor("op_2409_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2410_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2409_to_fp16)[name = tensor("op_2410_cast_fp16")]; - tensor input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2410_cast_fp16)[name = tensor("input_641_cast_fp16")]; - tensor input_643_axes_0 = const()[name = tensor("input_643_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152717248)))]; - tensor module_layers_11_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152718336)))]; - tensor input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = tensor("input_643_cast_fp16")]; - tensor input_645_axes_0 = const()[name = tensor("input_645_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152719424)))]; - tensor module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152720512)))]; - tensor input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = tensor("input_645_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152721600)))]; - tensor module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154818816)))]; - tensor linear_109_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16, x = input_645_cast_fp16)[name = tensor("linear_109_cast_fp16")]; - tensor input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = tensor("input_649_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154822976)))]; - tensor module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156920192)))]; - tensor linear_110_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16, x = input_649_cast_fp16)[name = tensor("linear_110_cast_fp16")]; - tensor var_2440_to_fp16 = const()[name = tensor("op_2440_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2441_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2440_to_fp16)[name = tensor("op_2441_cast_fp16")]; - tensor input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2441_cast_fp16)[name = tensor("input_655_cast_fp16")]; - tensor query_25_axes_0 = const()[name = tensor("query_25_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156921280)))]; - tensor module_layers_12_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156922368)))]; - tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = tensor("query_25_cast_fp16")]; - tensor module_layers_12_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156923456)))]; - tensor module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157447808)))]; - tensor linear_111_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_q_bias_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_111_cast_fp16")]; - tensor var_2458 = const()[name = tensor("op_2458"), val = tensor([1, -1, 8, 64])]; - tensor q_73_cast_fp16 = reshape(shape = var_2458, x = linear_111_cast_fp16)[name = tensor("q_73_cast_fp16")]; - tensor module_layers_12_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157448896)))]; - tensor module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157973248)))]; - tensor linear_112_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_k_bias_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_112_cast_fp16")]; - tensor var_2463 = const()[name = tensor("op_2463"), val = tensor([1, -1, 8, 64])]; - tensor k_49_cast_fp16 = reshape(shape = var_2463, x = linear_112_cast_fp16)[name = tensor("k_49_cast_fp16")]; - tensor module_layers_12_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157974336)))]; - tensor module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158498688)))]; - tensor linear_113_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_v_bias_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_113_cast_fp16")]; - tensor var_2468 = const()[name = tensor("op_2468"), val = tensor([1, -1, 8, 64])]; - tensor v_25_cast_fp16 = reshape(shape = var_2468, x = linear_113_cast_fp16)[name = tensor("v_25_cast_fp16")]; - tensor value_27_perm_0 = const()[name = tensor("value_27_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158499776)))]; - tensor var_2480_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2480_cast_fp16")]; - tensor module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158500864)))]; - tensor var_2482_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2482_cast_fp16")]; - tensor q_with_bias_v_25_perm_0 = const()[name = tensor("q_with_bias_v_25_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_271_transpose_x_0 = const()[name = tensor("x_271_transpose_x_0"), val = tensor(false)]; - tensor x_271_transpose_y_0 = const()[name = tensor("x_271_transpose_y_0"), val = tensor(false)]; - tensor var_2484_to_fp16 = const()[name = tensor("op_2484_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158501952)))]; - tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2482_cast_fp16)[name = tensor("transpose_119")]; - tensor x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = var_2484_to_fp16)[name = tensor("x_271_cast_fp16")]; - tensor x_273_pad_0 = const()[name = tensor("x_273_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_273_mode_0 = const()[name = tensor("x_273_mode_0"), val = tensor("constant")]; - tensor const_190_to_fp16 = const()[name = tensor("const_190_to_fp16"), val = tensor(0x0p+0)]; - tensor x_273_cast_fp16 = pad(constant_val = const_190_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = tensor("x_273_cast_fp16")]; - tensor var_2492 = const()[name = tensor("op_2492"), val = tensor([1, 8, -1, 188])]; - tensor x_275_cast_fp16 = reshape(shape = var_2492, x = x_273_cast_fp16)[name = tensor("x_275_cast_fp16")]; - tensor var_2496_begin_0 = const()[name = tensor("op_2496_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2496_end_0 = const()[name = tensor("op_2496_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2496_end_mask_0 = const()[name = tensor("op_2496_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2496_cast_fp16 = slice_by_index(begin = var_2496_begin_0, end = var_2496_end_0, end_mask = var_2496_end_mask_0, x = x_275_cast_fp16)[name = tensor("op_2496_cast_fp16")]; - tensor var_2497 = const()[name = tensor("op_2497"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_49_cast_fp16 = reshape(shape = var_2497, x = var_2496_cast_fp16)[name = tensor("matrix_bd_49_cast_fp16")]; - tensor matrix_ac_25_transpose_x_0 = const()[name = tensor("matrix_ac_25_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_25_transpose_y_0 = const()[name = tensor("matrix_ac_25_transpose_y_0"), val = tensor(false)]; - tensor transpose_75_perm_0 = const()[name = tensor("transpose_75_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_76_perm_0 = const()[name = tensor("transpose_76_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = k_49_cast_fp16)[name = tensor("transpose_117")]; - tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_2480_cast_fp16)[name = tensor("transpose_118")]; - tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_75, y = transpose_76)[name = tensor("matrix_ac_25_cast_fp16")]; - tensor matrix_bd_51_begin_0 = const()[name = tensor("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_51_end_0 = const()[name = tensor("matrix_bd_51_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_51_end_mask_0 = const()[name = tensor("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = tensor("matrix_bd_51_cast_fp16")]; - tensor var_2506_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = tensor("op_2506_cast_fp16")]; - tensor _inversed_scores_49_y_0_to_fp16 = const()[name = tensor("_inversed_scores_49_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_49_cast_fp16 = mul(x = var_2506_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = tensor("_inversed_scores_49_cast_fp16")]; - tensor scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_11)[name = tensor("scores_51_cast_fp16")]; - tensor var_2512_cast_fp16 = softmax(axis = var_23, x = scores_51_cast_fp16)[name = tensor("op_2512_cast_fp16")]; - tensor input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2512_cast_fp16, cond = mask_11)[name = tensor("input_657_cast_fp16")]; - tensor x_277_transpose_x_0 = const()[name = tensor("x_277_transpose_x_0"), val = tensor(false)]; - tensor x_277_transpose_y_0 = const()[name = tensor("x_277_transpose_y_0"), val = tensor(false)]; - tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = tensor("transpose_120")]; - tensor x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_27_cast_fp16)[name = tensor("x_277_cast_fp16")]; - tensor var_2516_perm_0 = const()[name = tensor("op_2516_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2517 = const()[name = tensor("op_2517"), val = tensor([1, -1, 512])]; - tensor var_2516_cast_fp16 = transpose(perm = var_2516_perm_0, x = x_277_cast_fp16)[name = tensor("transpose_116")]; - tensor input_659_cast_fp16 = reshape(shape = var_2517, x = var_2516_cast_fp16)[name = tensor("input_659_cast_fp16")]; - tensor module_layers_12_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158886016)))]; - tensor module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159410368)))]; - tensor linear_115_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_out_bias_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16, x = input_659_cast_fp16)[name = tensor("linear_115_cast_fp16")]; - tensor input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = tensor("input_663_cast_fp16")]; - tensor x_281_axes_0 = const()[name = tensor("x_281_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159411456)))]; - tensor module_layers_12_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159412544)))]; - tensor x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = tensor("x_281_cast_fp16")]; - tensor input_665_perm_0 = const()[name = tensor("input_665_perm_0"), val = tensor([0, 2, 1])]; - tensor input_667_pad_type_0 = const()[name = tensor("input_667_pad_type_0"), val = tensor("valid")]; - tensor input_667_strides_0 = const()[name = tensor("input_667_strides_0"), val = tensor([1])]; - tensor input_667_pad_0 = const()[name = tensor("input_667_pad_0"), val = tensor([0, 0])]; - tensor input_667_dilations_0 = const()[name = tensor("input_667_dilations_0"), val = tensor([1])]; - tensor input_667_groups_0 = const()[name = tensor("input_667_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159413632)))]; - tensor module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160462272)))]; - tensor input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = tensor("transpose_115")]; - tensor input_667_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv1_bias_to_fp16, dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16, x = input_665_cast_fp16)[name = tensor("input_667_cast_fp16")]; - tensor x_283_split_num_splits_0 = const()[name = tensor("x_283_split_num_splits_0"), val = tensor(2)]; - tensor x_283_split_axis_0 = const()[name = tensor("x_283_split_axis_0"), val = tensor(1)]; - tensor x_283_split_cast_fp16_0, tensor x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = tensor("x_283_split_cast_fp16")]; - tensor x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = tensor("x_283_split_1_sigmoid_cast_fp16")]; - tensor x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = tensor("x_283_cast_fp16")]; - tensor input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_453)[name = tensor("input_669_cast_fp16")]; - tensor input_671_pad_0 = const()[name = tensor("input_671_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_671_mode_0 = const()[name = tensor("input_671_mode_0"), val = tensor("constant")]; - tensor const_193_to_fp16 = const()[name = tensor("const_193_to_fp16"), val = tensor(0x0p+0)]; - tensor input_671_cast_fp16 = pad(constant_val = const_193_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = tensor("input_671_cast_fp16")]; - tensor input_673_pad_type_0 = const()[name = tensor("input_673_pad_type_0"), val = tensor("valid")]; - tensor input_673_groups_0 = const()[name = tensor("input_673_groups_0"), val = tensor(512)]; - tensor input_673_strides_0 = const()[name = tensor("input_673_strides_0"), val = tensor([1])]; - tensor input_673_pad_0 = const()[name = tensor("input_673_pad_0"), val = tensor([0, 0])]; - tensor input_673_dilations_0 = const()[name = tensor("input_673_dilations_0"), val = tensor([1])]; - tensor const_258_to_fp16 = const()[name = tensor("const_258_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160464384)))]; - tensor const_259_to_fp16 = const()[name = tensor("const_259_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160473664)))]; - tensor input_675_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_258_to_fp16, x = input_671_cast_fp16)[name = tensor("input_675_cast_fp16")]; - tensor input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = tensor("input_677_cast_fp16")]; - tensor x_285_pad_type_0 = const()[name = tensor("x_285_pad_type_0"), val = tensor("valid")]; - tensor x_285_strides_0 = const()[name = tensor("x_285_strides_0"), val = tensor([1])]; - tensor x_285_pad_0 = const()[name = tensor("x_285_pad_0"), val = tensor([0, 0])]; - tensor x_285_dilations_0 = const()[name = tensor("x_285_dilations_0"), val = tensor([1])]; - tensor x_285_groups_0 = const()[name = tensor("x_285_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160474752)))]; - tensor module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160999104)))]; - tensor x_285_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv2_bias_to_fp16, dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = module_layers_12_conv_pointwise_conv2_weight_to_fp16, x = input_677_cast_fp16)[name = tensor("x_285_cast_fp16")]; - tensor input_679_perm_0 = const()[name = tensor("input_679_perm_0"), val = tensor([0, 2, 1])]; - tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = tensor("transpose_114")]; - tensor input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = tensor("input_681_cast_fp16")]; - tensor input_683_axes_0 = const()[name = tensor("input_683_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161000192)))]; - tensor module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161001280)))]; - tensor input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = tensor("input_683_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161002368)))]; - tensor module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163099584)))]; - tensor linear_116_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16, x = input_683_cast_fp16)[name = tensor("linear_116_cast_fp16")]; - tensor input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = tensor("input_687_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163103744)))]; - tensor module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165200960)))]; - tensor linear_117_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16, x = input_687_cast_fp16)[name = tensor("linear_117_cast_fp16")]; - tensor var_2583_to_fp16 = const()[name = tensor("op_2583_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2584_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2583_to_fp16)[name = tensor("op_2584_cast_fp16")]; - tensor input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2584_cast_fp16)[name = tensor("input_693_cast_fp16")]; - tensor input_695_axes_0 = const()[name = tensor("input_695_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165202048)))]; - tensor module_layers_12_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165203136)))]; - tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = tensor("input_695_cast_fp16")]; - tensor input_697_axes_0 = const()[name = tensor("input_697_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165204224)))]; - tensor module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165205312)))]; - tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = tensor("input_697_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165206400)))]; - tensor module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167303616)))]; - tensor linear_118_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16, x = input_697_cast_fp16)[name = tensor("linear_118_cast_fp16")]; - tensor input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = tensor("input_701_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167307776)))]; - tensor module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169404992)))]; - tensor linear_119_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16, x = input_701_cast_fp16)[name = tensor("linear_119_cast_fp16")]; - tensor var_2614_to_fp16 = const()[name = tensor("op_2614_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2615_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2614_to_fp16)[name = tensor("op_2615_cast_fp16")]; - tensor input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2615_cast_fp16)[name = tensor("input_707_cast_fp16")]; - tensor query_27_axes_0 = const()[name = tensor("query_27_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169406080)))]; - tensor module_layers_13_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169407168)))]; - tensor query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = tensor("query_27_cast_fp16")]; - tensor module_layers_13_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169408256)))]; - tensor module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169932608)))]; - tensor linear_120_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_q_bias_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_120_cast_fp16")]; - tensor var_2632 = const()[name = tensor("op_2632"), val = tensor([1, -1, 8, 64])]; - tensor q_79_cast_fp16 = reshape(shape = var_2632, x = linear_120_cast_fp16)[name = tensor("q_79_cast_fp16")]; - tensor module_layers_13_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169933696)))]; - tensor module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170458048)))]; - tensor linear_121_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_k_bias_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_121_cast_fp16")]; - tensor var_2637 = const()[name = tensor("op_2637"), val = tensor([1, -1, 8, 64])]; - tensor k_53_cast_fp16 = reshape(shape = var_2637, x = linear_121_cast_fp16)[name = tensor("k_53_cast_fp16")]; - tensor module_layers_13_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170459136)))]; - tensor module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170983488)))]; - tensor linear_122_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_v_bias_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_122_cast_fp16")]; - tensor var_2642 = const()[name = tensor("op_2642"), val = tensor([1, -1, 8, 64])]; - tensor v_27_cast_fp16 = reshape(shape = var_2642, x = linear_122_cast_fp16)[name = tensor("v_27_cast_fp16")]; - tensor value_29_perm_0 = const()[name = tensor("value_29_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170984576)))]; - tensor var_2654_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2654_cast_fp16")]; - tensor module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170985664)))]; - tensor var_2656_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2656_cast_fp16")]; - tensor q_with_bias_v_27_perm_0 = const()[name = tensor("q_with_bias_v_27_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_293_transpose_x_0 = const()[name = tensor("x_293_transpose_x_0"), val = tensor(false)]; - tensor x_293_transpose_y_0 = const()[name = tensor("x_293_transpose_y_0"), val = tensor(false)]; - tensor var_2658_to_fp16 = const()[name = tensor("op_2658_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170986752)))]; - tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2656_cast_fp16)[name = tensor("transpose_112")]; - tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = var_2658_to_fp16)[name = tensor("x_293_cast_fp16")]; - tensor x_295_pad_0 = const()[name = tensor("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_295_mode_0 = const()[name = tensor("x_295_mode_0"), val = tensor("constant")]; - tensor const_200_to_fp16 = const()[name = tensor("const_200_to_fp16"), val = tensor(0x0p+0)]; - tensor x_295_cast_fp16 = pad(constant_val = const_200_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = tensor("x_295_cast_fp16")]; - tensor var_2666 = const()[name = tensor("op_2666"), val = tensor([1, 8, -1, 188])]; - tensor x_297_cast_fp16 = reshape(shape = var_2666, x = x_295_cast_fp16)[name = tensor("x_297_cast_fp16")]; - tensor var_2670_begin_0 = const()[name = tensor("op_2670_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2670_end_0 = const()[name = tensor("op_2670_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2670_end_mask_0 = const()[name = tensor("op_2670_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2670_cast_fp16 = slice_by_index(begin = var_2670_begin_0, end = var_2670_end_0, end_mask = var_2670_end_mask_0, x = x_297_cast_fp16)[name = tensor("op_2670_cast_fp16")]; - tensor var_2671 = const()[name = tensor("op_2671"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2671, x = var_2670_cast_fp16)[name = tensor("matrix_bd_53_cast_fp16")]; - tensor matrix_ac_27_transpose_x_0 = const()[name = tensor("matrix_ac_27_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_27_transpose_y_0 = const()[name = tensor("matrix_ac_27_transpose_y_0"), val = tensor(false)]; - tensor transpose_77_perm_0 = const()[name = tensor("transpose_77_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_78_perm_0 = const()[name = tensor("transpose_78_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = k_53_cast_fp16)[name = tensor("transpose_110")]; - tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = var_2654_cast_fp16)[name = tensor("transpose_111")]; - tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_77, y = transpose_78)[name = tensor("matrix_ac_27_cast_fp16")]; - tensor matrix_bd_55_begin_0 = const()[name = tensor("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_55_end_0 = const()[name = tensor("matrix_bd_55_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_55_end_mask_0 = const()[name = tensor("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = tensor("matrix_bd_55_cast_fp16")]; - tensor var_2680_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = tensor("op_2680_cast_fp16")]; - tensor _inversed_scores_53_y_0_to_fp16 = const()[name = tensor("_inversed_scores_53_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_53_cast_fp16 = mul(x = var_2680_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = tensor("_inversed_scores_53_cast_fp16")]; - tensor scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_11)[name = tensor("scores_55_cast_fp16")]; - tensor var_2686_cast_fp16 = softmax(axis = var_23, x = scores_55_cast_fp16)[name = tensor("op_2686_cast_fp16")]; - tensor input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2686_cast_fp16, cond = mask_11)[name = tensor("input_709_cast_fp16")]; - tensor x_299_transpose_x_0 = const()[name = tensor("x_299_transpose_x_0"), val = tensor(false)]; - tensor x_299_transpose_y_0 = const()[name = tensor("x_299_transpose_y_0"), val = tensor(false)]; - tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = tensor("transpose_113")]; - tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_29_cast_fp16)[name = tensor("x_299_cast_fp16")]; - tensor var_2690_perm_0 = const()[name = tensor("op_2690_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2691 = const()[name = tensor("op_2691"), val = tensor([1, -1, 512])]; - tensor var_2690_cast_fp16 = transpose(perm = var_2690_perm_0, x = x_299_cast_fp16)[name = tensor("transpose_109")]; - tensor input_711_cast_fp16 = reshape(shape = var_2691, x = var_2690_cast_fp16)[name = tensor("input_711_cast_fp16")]; - tensor module_layers_13_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171370816)))]; - tensor module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171895168)))]; - tensor linear_124_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_out_bias_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16, x = input_711_cast_fp16)[name = tensor("linear_124_cast_fp16")]; - tensor input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = tensor("input_715_cast_fp16")]; - tensor x_303_axes_0 = const()[name = tensor("x_303_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171896256)))]; - tensor module_layers_13_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171897344)))]; - tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = tensor("x_303_cast_fp16")]; - tensor input_717_perm_0 = const()[name = tensor("input_717_perm_0"), val = tensor([0, 2, 1])]; - tensor input_719_pad_type_0 = const()[name = tensor("input_719_pad_type_0"), val = tensor("valid")]; - tensor input_719_strides_0 = const()[name = tensor("input_719_strides_0"), val = tensor([1])]; - tensor input_719_pad_0 = const()[name = tensor("input_719_pad_0"), val = tensor([0, 0])]; - tensor input_719_dilations_0 = const()[name = tensor("input_719_dilations_0"), val = tensor([1])]; - tensor input_719_groups_0 = const()[name = tensor("input_719_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171898432)))]; - tensor module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172947072)))]; - tensor input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = tensor("transpose_108")]; - tensor input_719_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv1_bias_to_fp16, dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16, x = input_717_cast_fp16)[name = tensor("input_719_cast_fp16")]; - tensor x_305_split_num_splits_0 = const()[name = tensor("x_305_split_num_splits_0"), val = tensor(2)]; - tensor x_305_split_axis_0 = const()[name = tensor("x_305_split_axis_0"), val = tensor(1)]; - tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = tensor("x_305_split_cast_fp16")]; - tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = tensor("x_305_split_1_sigmoid_cast_fp16")]; - tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = tensor("x_305_cast_fp16")]; - tensor input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_453)[name = tensor("input_721_cast_fp16")]; - tensor input_723_pad_0 = const()[name = tensor("input_723_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_723_mode_0 = const()[name = tensor("input_723_mode_0"), val = tensor("constant")]; - tensor const_203_to_fp16 = const()[name = tensor("const_203_to_fp16"), val = tensor(0x0p+0)]; - tensor input_723_cast_fp16 = pad(constant_val = const_203_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = tensor("input_723_cast_fp16")]; - tensor input_725_pad_type_0 = const()[name = tensor("input_725_pad_type_0"), val = tensor("valid")]; - tensor input_725_groups_0 = const()[name = tensor("input_725_groups_0"), val = tensor(512)]; - tensor input_725_strides_0 = const()[name = tensor("input_725_strides_0"), val = tensor([1])]; - tensor input_725_pad_0 = const()[name = tensor("input_725_pad_0"), val = tensor([0, 0])]; - tensor input_725_dilations_0 = const()[name = tensor("input_725_dilations_0"), val = tensor([1])]; - tensor const_260_to_fp16 = const()[name = tensor("const_260_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172949184)))]; - tensor const_261_to_fp16 = const()[name = tensor("const_261_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172958464)))]; - tensor input_727_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_260_to_fp16, x = input_723_cast_fp16)[name = tensor("input_727_cast_fp16")]; - tensor input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = tensor("input_729_cast_fp16")]; - tensor x_307_pad_type_0 = const()[name = tensor("x_307_pad_type_0"), val = tensor("valid")]; - tensor x_307_strides_0 = const()[name = tensor("x_307_strides_0"), val = tensor([1])]; - tensor x_307_pad_0 = const()[name = tensor("x_307_pad_0"), val = tensor([0, 0])]; - tensor x_307_dilations_0 = const()[name = tensor("x_307_dilations_0"), val = tensor([1])]; - tensor x_307_groups_0 = const()[name = tensor("x_307_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172959552)))]; - tensor module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173483904)))]; - tensor x_307_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv2_bias_to_fp16, dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = module_layers_13_conv_pointwise_conv2_weight_to_fp16, x = input_729_cast_fp16)[name = tensor("x_307_cast_fp16")]; - tensor input_731_perm_0 = const()[name = tensor("input_731_perm_0"), val = tensor([0, 2, 1])]; - tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = tensor("transpose_107")]; - tensor input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = tensor("input_733_cast_fp16")]; - tensor input_735_axes_0 = const()[name = tensor("input_735_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173484992)))]; - tensor module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173486080)))]; - tensor input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = tensor("input_735_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173487168)))]; - tensor module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175584384)))]; - tensor linear_125_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16, x = input_735_cast_fp16)[name = tensor("linear_125_cast_fp16")]; - tensor input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = tensor("input_739_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175588544)))]; - tensor module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177685760)))]; - tensor linear_126_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16, x = input_739_cast_fp16)[name = tensor("linear_126_cast_fp16")]; - tensor var_2757_to_fp16 = const()[name = tensor("op_2757_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2758_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2757_to_fp16)[name = tensor("op_2758_cast_fp16")]; - tensor input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2758_cast_fp16)[name = tensor("input_745_cast_fp16")]; - tensor input_747_axes_0 = const()[name = tensor("input_747_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177686848)))]; - tensor module_layers_13_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177687936)))]; - tensor input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = tensor("input_747_cast_fp16")]; - tensor input_749_axes_0 = const()[name = tensor("input_749_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177689024)))]; - tensor module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177690112)))]; - tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = tensor("input_749_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177691200)))]; - tensor module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179788416)))]; - tensor linear_127_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16, x = input_749_cast_fp16)[name = tensor("linear_127_cast_fp16")]; - tensor input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = tensor("input_753_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179792576)))]; - tensor module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181889792)))]; - tensor linear_128_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16, x = input_753_cast_fp16)[name = tensor("linear_128_cast_fp16")]; - tensor var_2788_to_fp16 = const()[name = tensor("op_2788_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2789_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2788_to_fp16)[name = tensor("op_2789_cast_fp16")]; - tensor input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2789_cast_fp16)[name = tensor("input_759_cast_fp16")]; - tensor query_29_axes_0 = const()[name = tensor("query_29_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181890880)))]; - tensor module_layers_14_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181891968)))]; - tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = tensor("query_29_cast_fp16")]; - tensor module_layers_14_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181893056)))]; - tensor module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182417408)))]; - tensor linear_129_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_q_bias_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_129_cast_fp16")]; - tensor var_2806 = const()[name = tensor("op_2806"), val = tensor([1, -1, 8, 64])]; - tensor q_85_cast_fp16 = reshape(shape = var_2806, x = linear_129_cast_fp16)[name = tensor("q_85_cast_fp16")]; - tensor module_layers_14_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182418496)))]; - tensor module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182942848)))]; - tensor linear_130_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_k_bias_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_130_cast_fp16")]; - tensor var_2811 = const()[name = tensor("op_2811"), val = tensor([1, -1, 8, 64])]; - tensor k_57_cast_fp16 = reshape(shape = var_2811, x = linear_130_cast_fp16)[name = tensor("k_57_cast_fp16")]; - tensor module_layers_14_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182943936)))]; - tensor module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183468288)))]; - tensor linear_131_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_v_bias_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_131_cast_fp16")]; - tensor var_2816 = const()[name = tensor("op_2816"), val = tensor([1, -1, 8, 64])]; - tensor v_29_cast_fp16 = reshape(shape = var_2816, x = linear_131_cast_fp16)[name = tensor("v_29_cast_fp16")]; - tensor value_31_perm_0 = const()[name = tensor("value_31_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183469376)))]; - tensor var_2828_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2828_cast_fp16")]; - tensor module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183470464)))]; - tensor var_2830_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2830_cast_fp16")]; - tensor q_with_bias_v_29_perm_0 = const()[name = tensor("q_with_bias_v_29_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_315_transpose_x_0 = const()[name = tensor("x_315_transpose_x_0"), val = tensor(false)]; - tensor x_315_transpose_y_0 = const()[name = tensor("x_315_transpose_y_0"), val = tensor(false)]; - tensor var_2832_to_fp16 = const()[name = tensor("op_2832_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183471552)))]; - tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2830_cast_fp16)[name = tensor("transpose_105")]; - tensor x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = var_2832_to_fp16)[name = tensor("x_315_cast_fp16")]; - tensor x_317_pad_0 = const()[name = tensor("x_317_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_317_mode_0 = const()[name = tensor("x_317_mode_0"), val = tensor("constant")]; - tensor const_210_to_fp16 = const()[name = tensor("const_210_to_fp16"), val = tensor(0x0p+0)]; - tensor x_317_cast_fp16 = pad(constant_val = const_210_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = tensor("x_317_cast_fp16")]; - tensor var_2840 = const()[name = tensor("op_2840"), val = tensor([1, 8, -1, 188])]; - tensor x_319_cast_fp16 = reshape(shape = var_2840, x = x_317_cast_fp16)[name = tensor("x_319_cast_fp16")]; - tensor var_2844_begin_0 = const()[name = tensor("op_2844_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2844_end_0 = const()[name = tensor("op_2844_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2844_end_mask_0 = const()[name = tensor("op_2844_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2844_cast_fp16 = slice_by_index(begin = var_2844_begin_0, end = var_2844_end_0, end_mask = var_2844_end_mask_0, x = x_319_cast_fp16)[name = tensor("op_2844_cast_fp16")]; - tensor var_2845 = const()[name = tensor("op_2845"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_57_cast_fp16 = reshape(shape = var_2845, x = var_2844_cast_fp16)[name = tensor("matrix_bd_57_cast_fp16")]; - tensor matrix_ac_29_transpose_x_0 = const()[name = tensor("matrix_ac_29_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_29_transpose_y_0 = const()[name = tensor("matrix_ac_29_transpose_y_0"), val = tensor(false)]; - tensor transpose_79_perm_0 = const()[name = tensor("transpose_79_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_80_perm_0 = const()[name = tensor("transpose_80_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = k_57_cast_fp16)[name = tensor("transpose_103")]; - tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_2828_cast_fp16)[name = tensor("transpose_104")]; - tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_79, y = transpose_80)[name = tensor("matrix_ac_29_cast_fp16")]; - tensor matrix_bd_59_begin_0 = const()[name = tensor("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_59_end_0 = const()[name = tensor("matrix_bd_59_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_59_end_mask_0 = const()[name = tensor("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = tensor("matrix_bd_59_cast_fp16")]; - tensor var_2854_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = tensor("op_2854_cast_fp16")]; - tensor _inversed_scores_57_y_0_to_fp16 = const()[name = tensor("_inversed_scores_57_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_57_cast_fp16 = mul(x = var_2854_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = tensor("_inversed_scores_57_cast_fp16")]; - tensor scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_11)[name = tensor("scores_59_cast_fp16")]; - tensor var_2860_cast_fp16 = softmax(axis = var_23, x = scores_59_cast_fp16)[name = tensor("op_2860_cast_fp16")]; - tensor input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2860_cast_fp16, cond = mask_11)[name = tensor("input_761_cast_fp16")]; - tensor x_321_transpose_x_0 = const()[name = tensor("x_321_transpose_x_0"), val = tensor(false)]; - tensor x_321_transpose_y_0 = const()[name = tensor("x_321_transpose_y_0"), val = tensor(false)]; - tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = tensor("transpose_106")]; - tensor x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_31_cast_fp16)[name = tensor("x_321_cast_fp16")]; - tensor var_2864_perm_0 = const()[name = tensor("op_2864_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2865 = const()[name = tensor("op_2865"), val = tensor([1, -1, 512])]; - tensor var_2864_cast_fp16 = transpose(perm = var_2864_perm_0, x = x_321_cast_fp16)[name = tensor("transpose_102")]; - tensor input_763_cast_fp16 = reshape(shape = var_2865, x = var_2864_cast_fp16)[name = tensor("input_763_cast_fp16")]; - tensor module_layers_14_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183855616)))]; - tensor module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184379968)))]; - tensor linear_133_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_out_bias_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16, x = input_763_cast_fp16)[name = tensor("linear_133_cast_fp16")]; - tensor input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = tensor("input_767_cast_fp16")]; - tensor x_325_axes_0 = const()[name = tensor("x_325_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184381056)))]; - tensor module_layers_14_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184382144)))]; - tensor x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = tensor("x_325_cast_fp16")]; - tensor input_769_perm_0 = const()[name = tensor("input_769_perm_0"), val = tensor([0, 2, 1])]; - tensor input_771_pad_type_0 = const()[name = tensor("input_771_pad_type_0"), val = tensor("valid")]; - tensor input_771_strides_0 = const()[name = tensor("input_771_strides_0"), val = tensor([1])]; - tensor input_771_pad_0 = const()[name = tensor("input_771_pad_0"), val = tensor([0, 0])]; - tensor input_771_dilations_0 = const()[name = tensor("input_771_dilations_0"), val = tensor([1])]; - tensor input_771_groups_0 = const()[name = tensor("input_771_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184383232)))]; - tensor module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185431872)))]; - tensor input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = tensor("transpose_101")]; - tensor input_771_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv1_bias_to_fp16, dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16, x = input_769_cast_fp16)[name = tensor("input_771_cast_fp16")]; - tensor x_327_split_num_splits_0 = const()[name = tensor("x_327_split_num_splits_0"), val = tensor(2)]; - tensor x_327_split_axis_0 = const()[name = tensor("x_327_split_axis_0"), val = tensor(1)]; - tensor x_327_split_cast_fp16_0, tensor x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = tensor("x_327_split_cast_fp16")]; - tensor x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = tensor("x_327_split_1_sigmoid_cast_fp16")]; - tensor x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = tensor("x_327_cast_fp16")]; - tensor input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_453)[name = tensor("input_773_cast_fp16")]; - tensor input_775_pad_0 = const()[name = tensor("input_775_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_775_mode_0 = const()[name = tensor("input_775_mode_0"), val = tensor("constant")]; - tensor const_213_to_fp16 = const()[name = tensor("const_213_to_fp16"), val = tensor(0x0p+0)]; - tensor input_775_cast_fp16 = pad(constant_val = const_213_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = tensor("input_775_cast_fp16")]; - tensor input_777_pad_type_0 = const()[name = tensor("input_777_pad_type_0"), val = tensor("valid")]; - tensor input_777_groups_0 = const()[name = tensor("input_777_groups_0"), val = tensor(512)]; - tensor input_777_strides_0 = const()[name = tensor("input_777_strides_0"), val = tensor([1])]; - tensor input_777_pad_0 = const()[name = tensor("input_777_pad_0"), val = tensor([0, 0])]; - tensor input_777_dilations_0 = const()[name = tensor("input_777_dilations_0"), val = tensor([1])]; - tensor const_262_to_fp16 = const()[name = tensor("const_262_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185433984)))]; - tensor const_263_to_fp16 = const()[name = tensor("const_263_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185443264)))]; - tensor input_779_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_262_to_fp16, x = input_775_cast_fp16)[name = tensor("input_779_cast_fp16")]; - tensor input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = tensor("input_781_cast_fp16")]; - tensor x_329_pad_type_0 = const()[name = tensor("x_329_pad_type_0"), val = tensor("valid")]; - tensor x_329_strides_0 = const()[name = tensor("x_329_strides_0"), val = tensor([1])]; - tensor x_329_pad_0 = const()[name = tensor("x_329_pad_0"), val = tensor([0, 0])]; - tensor x_329_dilations_0 = const()[name = tensor("x_329_dilations_0"), val = tensor([1])]; - tensor x_329_groups_0 = const()[name = tensor("x_329_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185444352)))]; - tensor module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185968704)))]; - tensor x_329_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv2_bias_to_fp16, dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = module_layers_14_conv_pointwise_conv2_weight_to_fp16, x = input_781_cast_fp16)[name = tensor("x_329_cast_fp16")]; - tensor input_783_perm_0 = const()[name = tensor("input_783_perm_0"), val = tensor([0, 2, 1])]; - tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = tensor("transpose_100")]; - tensor input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = tensor("input_785_cast_fp16")]; - tensor input_787_axes_0 = const()[name = tensor("input_787_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185969792)))]; - tensor module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185970880)))]; - tensor input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = tensor("input_787_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185971968)))]; - tensor module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188069184)))]; - tensor linear_134_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16, x = input_787_cast_fp16)[name = tensor("linear_134_cast_fp16")]; - tensor input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = tensor("input_791_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188073344)))]; - tensor module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190170560)))]; - tensor linear_135_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16, x = input_791_cast_fp16)[name = tensor("linear_135_cast_fp16")]; - tensor var_2931_to_fp16 = const()[name = tensor("op_2931_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2932_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2931_to_fp16)[name = tensor("op_2932_cast_fp16")]; - tensor input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2932_cast_fp16)[name = tensor("input_797_cast_fp16")]; - tensor input_799_axes_0 = const()[name = tensor("input_799_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190171648)))]; - tensor module_layers_14_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190172736)))]; - tensor input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = tensor("input_799_cast_fp16")]; - tensor input_801_axes_0 = const()[name = tensor("input_801_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190173824)))]; - tensor module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190174912)))]; - tensor input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = tensor("input_801_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190176000)))]; - tensor module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192273216)))]; - tensor linear_136_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16, x = input_801_cast_fp16)[name = tensor("linear_136_cast_fp16")]; - tensor input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = tensor("input_805_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192277376)))]; - tensor module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194374592)))]; - tensor linear_137_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16, x = input_805_cast_fp16)[name = tensor("linear_137_cast_fp16")]; - tensor var_2962_to_fp16 = const()[name = tensor("op_2962_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2963_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2962_to_fp16)[name = tensor("op_2963_cast_fp16")]; - tensor input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2963_cast_fp16)[name = tensor("input_811_cast_fp16")]; - tensor query_31_axes_0 = const()[name = tensor("query_31_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194375680)))]; - tensor module_layers_15_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194376768)))]; - tensor query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = tensor("query_31_cast_fp16")]; - tensor module_layers_15_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194377856)))]; - tensor module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194902208)))]; - tensor linear_138_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_q_bias_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_138_cast_fp16")]; - tensor var_2980 = const()[name = tensor("op_2980"), val = tensor([1, -1, 8, 64])]; - tensor q_91_cast_fp16 = reshape(shape = var_2980, x = linear_138_cast_fp16)[name = tensor("q_91_cast_fp16")]; - tensor module_layers_15_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194903296)))]; - tensor module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195427648)))]; - tensor linear_139_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_k_bias_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_139_cast_fp16")]; - tensor var_2985 = const()[name = tensor("op_2985"), val = tensor([1, -1, 8, 64])]; - tensor k_61_cast_fp16 = reshape(shape = var_2985, x = linear_139_cast_fp16)[name = tensor("k_61_cast_fp16")]; - tensor module_layers_15_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195428736)))]; - tensor module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195953088)))]; - tensor linear_140_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_v_bias_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_140_cast_fp16")]; - tensor var_2990 = const()[name = tensor("op_2990"), val = tensor([1, -1, 8, 64])]; - tensor v_31_cast_fp16 = reshape(shape = var_2990, x = linear_140_cast_fp16)[name = tensor("v_31_cast_fp16")]; - tensor value_33_perm_0 = const()[name = tensor("value_33_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195954176)))]; - tensor var_3002_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3002_cast_fp16")]; - tensor module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195955264)))]; - tensor var_3004_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3004_cast_fp16")]; - tensor q_with_bias_v_31_perm_0 = const()[name = tensor("q_with_bias_v_31_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_337_transpose_x_0 = const()[name = tensor("x_337_transpose_x_0"), val = tensor(false)]; - tensor x_337_transpose_y_0 = const()[name = tensor("x_337_transpose_y_0"), val = tensor(false)]; - tensor var_3006_to_fp16 = const()[name = tensor("op_3006_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195956352)))]; - tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3004_cast_fp16)[name = tensor("transpose_98")]; - tensor x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = var_3006_to_fp16)[name = tensor("x_337_cast_fp16")]; - tensor x_339_pad_0 = const()[name = tensor("x_339_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_339_mode_0 = const()[name = tensor("x_339_mode_0"), val = tensor("constant")]; - tensor const_220_to_fp16 = const()[name = tensor("const_220_to_fp16"), val = tensor(0x0p+0)]; - tensor x_339_cast_fp16 = pad(constant_val = const_220_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = tensor("x_339_cast_fp16")]; - tensor var_3014 = const()[name = tensor("op_3014"), val = tensor([1, 8, -1, 188])]; - tensor x_341_cast_fp16 = reshape(shape = var_3014, x = x_339_cast_fp16)[name = tensor("x_341_cast_fp16")]; - tensor var_3018_begin_0 = const()[name = tensor("op_3018_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3018_end_0 = const()[name = tensor("op_3018_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3018_end_mask_0 = const()[name = tensor("op_3018_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3018_cast_fp16 = slice_by_index(begin = var_3018_begin_0, end = var_3018_end_0, end_mask = var_3018_end_mask_0, x = x_341_cast_fp16)[name = tensor("op_3018_cast_fp16")]; - tensor var_3019 = const()[name = tensor("op_3019"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_61_cast_fp16 = reshape(shape = var_3019, x = var_3018_cast_fp16)[name = tensor("matrix_bd_61_cast_fp16")]; - tensor matrix_ac_31_transpose_x_0 = const()[name = tensor("matrix_ac_31_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_31_transpose_y_0 = const()[name = tensor("matrix_ac_31_transpose_y_0"), val = tensor(false)]; - tensor transpose_81_perm_0 = const()[name = tensor("transpose_81_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_82_perm_0 = const()[name = tensor("transpose_82_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = k_61_cast_fp16)[name = tensor("transpose_96")]; - tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_3002_cast_fp16)[name = tensor("transpose_97")]; - tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_81, y = transpose_82)[name = tensor("matrix_ac_31_cast_fp16")]; - tensor matrix_bd_63_begin_0 = const()[name = tensor("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_63_end_0 = const()[name = tensor("matrix_bd_63_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_63_end_mask_0 = const()[name = tensor("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = tensor("matrix_bd_63_cast_fp16")]; - tensor var_3028_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = tensor("op_3028_cast_fp16")]; - tensor _inversed_scores_61_y_0_to_fp16 = const()[name = tensor("_inversed_scores_61_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_61_cast_fp16 = mul(x = var_3028_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = tensor("_inversed_scores_61_cast_fp16")]; - tensor scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_11)[name = tensor("scores_63_cast_fp16")]; - tensor var_3034_cast_fp16 = softmax(axis = var_23, x = scores_63_cast_fp16)[name = tensor("op_3034_cast_fp16")]; - tensor input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_3034_cast_fp16, cond = mask_11)[name = tensor("input_813_cast_fp16")]; - tensor x_343_transpose_x_0 = const()[name = tensor("x_343_transpose_x_0"), val = tensor(false)]; - tensor x_343_transpose_y_0 = const()[name = tensor("x_343_transpose_y_0"), val = tensor(false)]; - tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = tensor("transpose_99")]; - tensor x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_33_cast_fp16)[name = tensor("x_343_cast_fp16")]; - tensor var_3038_perm_0 = const()[name = tensor("op_3038_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3039 = const()[name = tensor("op_3039"), val = tensor([1, -1, 512])]; - tensor var_3038_cast_fp16 = transpose(perm = var_3038_perm_0, x = x_343_cast_fp16)[name = tensor("transpose_95")]; - tensor input_815_cast_fp16 = reshape(shape = var_3039, x = var_3038_cast_fp16)[name = tensor("input_815_cast_fp16")]; - tensor module_layers_15_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196340416)))]; - tensor module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196864768)))]; - tensor linear_142_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_out_bias_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16, x = input_815_cast_fp16)[name = tensor("linear_142_cast_fp16")]; - tensor input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = tensor("input_819_cast_fp16")]; - tensor x_347_axes_0 = const()[name = tensor("x_347_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196865856)))]; - tensor module_layers_15_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196866944)))]; - tensor x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = tensor("x_347_cast_fp16")]; - tensor input_821_perm_0 = const()[name = tensor("input_821_perm_0"), val = tensor([0, 2, 1])]; - tensor input_823_pad_type_0 = const()[name = tensor("input_823_pad_type_0"), val = tensor("valid")]; - tensor input_823_strides_0 = const()[name = tensor("input_823_strides_0"), val = tensor([1])]; - tensor input_823_pad_0 = const()[name = tensor("input_823_pad_0"), val = tensor([0, 0])]; - tensor input_823_dilations_0 = const()[name = tensor("input_823_dilations_0"), val = tensor([1])]; - tensor input_823_groups_0 = const()[name = tensor("input_823_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196868032)))]; - tensor module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197916672)))]; - tensor input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = tensor("transpose_94")]; - tensor input_823_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv1_bias_to_fp16, dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16, x = input_821_cast_fp16)[name = tensor("input_823_cast_fp16")]; - tensor x_349_split_num_splits_0 = const()[name = tensor("x_349_split_num_splits_0"), val = tensor(2)]; - tensor x_349_split_axis_0 = const()[name = tensor("x_349_split_axis_0"), val = tensor(1)]; - tensor x_349_split_cast_fp16_0, tensor x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = tensor("x_349_split_cast_fp16")]; - tensor x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = tensor("x_349_split_1_sigmoid_cast_fp16")]; - tensor x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = tensor("x_349_cast_fp16")]; - tensor input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_453)[name = tensor("input_825_cast_fp16")]; - tensor input_827_pad_0 = const()[name = tensor("input_827_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_827_mode_0 = const()[name = tensor("input_827_mode_0"), val = tensor("constant")]; - tensor const_223_to_fp16 = const()[name = tensor("const_223_to_fp16"), val = tensor(0x0p+0)]; - tensor input_827_cast_fp16 = pad(constant_val = const_223_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = tensor("input_827_cast_fp16")]; - tensor input_829_pad_type_0 = const()[name = tensor("input_829_pad_type_0"), val = tensor("valid")]; - tensor input_829_groups_0 = const()[name = tensor("input_829_groups_0"), val = tensor(512)]; - tensor input_829_strides_0 = const()[name = tensor("input_829_strides_0"), val = tensor([1])]; - tensor input_829_pad_0 = const()[name = tensor("input_829_pad_0"), val = tensor([0, 0])]; - tensor input_829_dilations_0 = const()[name = tensor("input_829_dilations_0"), val = tensor([1])]; - tensor const_264_to_fp16 = const()[name = tensor("const_264_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197918784)))]; - tensor const_265_to_fp16 = const()[name = tensor("const_265_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197928064)))]; - tensor input_831_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_264_to_fp16, x = input_827_cast_fp16)[name = tensor("input_831_cast_fp16")]; - tensor input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = tensor("input_833_cast_fp16")]; - tensor x_351_pad_type_0 = const()[name = tensor("x_351_pad_type_0"), val = tensor("valid")]; - tensor x_351_strides_0 = const()[name = tensor("x_351_strides_0"), val = tensor([1])]; - tensor x_351_pad_0 = const()[name = tensor("x_351_pad_0"), val = tensor([0, 0])]; - tensor x_351_dilations_0 = const()[name = tensor("x_351_dilations_0"), val = tensor([1])]; - tensor x_351_groups_0 = const()[name = tensor("x_351_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197929152)))]; - tensor module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198453504)))]; - tensor x_351_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv2_bias_to_fp16, dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = module_layers_15_conv_pointwise_conv2_weight_to_fp16, x = input_833_cast_fp16)[name = tensor("x_351_cast_fp16")]; - tensor input_835_perm_0 = const()[name = tensor("input_835_perm_0"), val = tensor([0, 2, 1])]; - tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = tensor("transpose_93")]; - tensor input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = tensor("input_837_cast_fp16")]; - tensor input_839_axes_0 = const()[name = tensor("input_839_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198454592)))]; - tensor module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198455680)))]; - tensor input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = tensor("input_839_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198456768)))]; - tensor module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200553984)))]; - tensor linear_143_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16, x = input_839_cast_fp16)[name = tensor("linear_143_cast_fp16")]; - tensor input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = tensor("input_843_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200558144)))]; - tensor module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202655360)))]; - tensor linear_144_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16, x = input_843_cast_fp16)[name = tensor("linear_144_cast_fp16")]; - tensor var_3105_to_fp16 = const()[name = tensor("op_3105_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3106_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3105_to_fp16)[name = tensor("op_3106_cast_fp16")]; - tensor input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_3106_cast_fp16)[name = tensor("input_849_cast_fp16")]; - tensor input_851_axes_0 = const()[name = tensor("input_851_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202656448)))]; - tensor module_layers_15_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202657536)))]; - tensor input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = tensor("input_851_cast_fp16")]; - tensor input_853_axes_0 = const()[name = tensor("input_853_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202658624)))]; - tensor module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202659712)))]; - tensor input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = tensor("input_853_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202660800)))]; - tensor module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204758016)))]; - tensor linear_145_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16, x = input_853_cast_fp16)[name = tensor("linear_145_cast_fp16")]; - tensor input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = tensor("input_857_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204762176)))]; - tensor module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206859392)))]; - tensor linear_146_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16, x = input_857_cast_fp16)[name = tensor("linear_146_cast_fp16")]; - tensor var_3136_to_fp16 = const()[name = tensor("op_3136_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3137_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3136_to_fp16)[name = tensor("op_3137_cast_fp16")]; - tensor input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_3137_cast_fp16)[name = tensor("input_863_cast_fp16")]; - tensor query_axes_0 = const()[name = tensor("query_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206860480)))]; - tensor module_layers_16_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206861568)))]; - tensor query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = tensor("query_cast_fp16")]; - tensor module_layers_16_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206862656)))]; - tensor module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207387008)))]; - tensor linear_147_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_q_bias_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_147_cast_fp16")]; - tensor var_3154 = const()[name = tensor("op_3154"), val = tensor([1, -1, 8, 64])]; - tensor q_97_cast_fp16 = reshape(shape = var_3154, x = linear_147_cast_fp16)[name = tensor("q_97_cast_fp16")]; - tensor module_layers_16_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207388096)))]; - tensor module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207912448)))]; - tensor linear_148_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_k_bias_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_148_cast_fp16")]; - tensor var_3159 = const()[name = tensor("op_3159"), val = tensor([1, -1, 8, 64])]; - tensor k_65_cast_fp16 = reshape(shape = var_3159, x = linear_148_cast_fp16)[name = tensor("k_65_cast_fp16")]; - tensor module_layers_16_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207913536)))]; - tensor module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208437888)))]; - tensor linear_149_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_v_bias_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_149_cast_fp16")]; - tensor var_3164 = const()[name = tensor("op_3164"), val = tensor([1, -1, 8, 64])]; - tensor v_cast_fp16 = reshape(shape = var_3164, x = linear_149_cast_fp16)[name = tensor("v_cast_fp16")]; - tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208438976)))]; - tensor var_3176_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3176_cast_fp16")]; - tensor module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208440064)))]; - tensor var_3178_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3178_cast_fp16")]; - tensor q_with_bias_v_perm_0 = const()[name = tensor("q_with_bias_v_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_359_transpose_x_0 = const()[name = tensor("x_359_transpose_x_0"), val = tensor(false)]; - tensor x_359_transpose_y_0 = const()[name = tensor("x_359_transpose_y_0"), val = tensor(false)]; - tensor var_3180_to_fp16 = const()[name = tensor("op_3180_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208441152)))]; - tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_3178_cast_fp16)[name = tensor("transpose_91")]; - tensor x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_cast_fp16, y = var_3180_to_fp16)[name = tensor("x_359_cast_fp16")]; - tensor x_361_pad_0 = const()[name = tensor("x_361_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_361_mode_0 = const()[name = tensor("x_361_mode_0"), val = tensor("constant")]; - tensor const_230_to_fp16 = const()[name = tensor("const_230_to_fp16"), val = tensor(0x0p+0)]; - tensor x_361_cast_fp16 = pad(constant_val = const_230_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = tensor("x_361_cast_fp16")]; - tensor var_3188 = const()[name = tensor("op_3188"), val = tensor([1, 8, -1, 188])]; - tensor x_363_cast_fp16 = reshape(shape = var_3188, x = x_361_cast_fp16)[name = tensor("x_363_cast_fp16")]; - tensor var_3192_begin_0 = const()[name = tensor("op_3192_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3192_end_0 = const()[name = tensor("op_3192_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3192_end_mask_0 = const()[name = tensor("op_3192_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3192_cast_fp16 = slice_by_index(begin = var_3192_begin_0, end = var_3192_end_0, end_mask = var_3192_end_mask_0, x = x_363_cast_fp16)[name = tensor("op_3192_cast_fp16")]; - tensor var_3193 = const()[name = tensor("op_3193"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_65_cast_fp16 = reshape(shape = var_3193, x = var_3192_cast_fp16)[name = tensor("matrix_bd_65_cast_fp16")]; - tensor matrix_ac_transpose_x_0 = const()[name = tensor("matrix_ac_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_transpose_y_0 = const()[name = tensor("matrix_ac_transpose_y_0"), val = tensor(false)]; - tensor transpose_83_perm_0 = const()[name = tensor("transpose_83_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_84_perm_0 = const()[name = tensor("transpose_84_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = k_65_cast_fp16)[name = tensor("transpose_89")]; - tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = var_3176_cast_fp16)[name = tensor("transpose_90")]; - tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_83, y = transpose_84)[name = tensor("matrix_ac_cast_fp16")]; - tensor matrix_bd_begin_0 = const()[name = tensor("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_end_0 = const()[name = tensor("matrix_bd_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_end_mask_0 = const()[name = tensor("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_65_cast_fp16)[name = tensor("matrix_bd_cast_fp16")]; - tensor var_3202_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = tensor("op_3202_cast_fp16")]; - tensor _inversed_scores_65_y_0_to_fp16 = const()[name = tensor("_inversed_scores_65_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_65_cast_fp16 = mul(x = var_3202_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = tensor("_inversed_scores_65_cast_fp16")]; - tensor scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_11)[name = tensor("scores_cast_fp16")]; - tensor var_3208_cast_fp16 = softmax(axis = var_23, x = scores_cast_fp16)[name = tensor("op_3208_cast_fp16")]; - tensor input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_3208_cast_fp16, cond = mask_11)[name = tensor("input_865_cast_fp16")]; - tensor x_365_transpose_x_0 = const()[name = tensor("x_365_transpose_x_0"), val = tensor(false)]; - tensor x_365_transpose_y_0 = const()[name = tensor("x_365_transpose_y_0"), val = tensor(false)]; - tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = tensor("transpose_92")]; - tensor x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_cast_fp16)[name = tensor("x_365_cast_fp16")]; - tensor var_3212_perm_0 = const()[name = tensor("op_3212_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3213 = const()[name = tensor("op_3213"), val = tensor([1, -1, 512])]; - tensor var_3212_cast_fp16 = transpose(perm = var_3212_perm_0, x = x_365_cast_fp16)[name = tensor("transpose_88")]; - tensor input_867_cast_fp16 = reshape(shape = var_3213, x = var_3212_cast_fp16)[name = tensor("input_867_cast_fp16")]; - tensor module_layers_16_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208825216)))]; - tensor module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209349568)))]; - tensor linear_151_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_out_bias_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16, x = input_867_cast_fp16)[name = tensor("linear_151_cast_fp16")]; - tensor input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = tensor("input_871_cast_fp16")]; - tensor x_369_axes_0 = const()[name = tensor("x_369_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209350656)))]; - tensor module_layers_16_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209351744)))]; - tensor x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = tensor("x_369_cast_fp16")]; - tensor input_873_perm_0 = const()[name = tensor("input_873_perm_0"), val = tensor([0, 2, 1])]; - tensor input_875_pad_type_0 = const()[name = tensor("input_875_pad_type_0"), val = tensor("valid")]; - tensor input_875_strides_0 = const()[name = tensor("input_875_strides_0"), val = tensor([1])]; - tensor input_875_pad_0 = const()[name = tensor("input_875_pad_0"), val = tensor([0, 0])]; - tensor input_875_dilations_0 = const()[name = tensor("input_875_dilations_0"), val = tensor([1])]; - tensor input_875_groups_0 = const()[name = tensor("input_875_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209352832)))]; - tensor module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210401472)))]; - tensor input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = tensor("transpose_87")]; - tensor input_875_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv1_bias_to_fp16, dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16, x = input_873_cast_fp16)[name = tensor("input_875_cast_fp16")]; - tensor x_371_split_num_splits_0 = const()[name = tensor("x_371_split_num_splits_0"), val = tensor(2)]; - tensor x_371_split_axis_0 = const()[name = tensor("x_371_split_axis_0"), val = tensor(1)]; - tensor x_371_split_cast_fp16_0, tensor x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = tensor("x_371_split_cast_fp16")]; - tensor x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = tensor("x_371_split_1_sigmoid_cast_fp16")]; - tensor x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = tensor("x_371_cast_fp16")]; - tensor input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_453)[name = tensor("input_877_cast_fp16")]; - tensor input_879_pad_0 = const()[name = tensor("input_879_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_879_mode_0 = const()[name = tensor("input_879_mode_0"), val = tensor("constant")]; - tensor const_233_to_fp16 = const()[name = tensor("const_233_to_fp16"), val = tensor(0x0p+0)]; - tensor input_879_cast_fp16 = pad(constant_val = const_233_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = tensor("input_879_cast_fp16")]; - tensor input_881_pad_type_0 = const()[name = tensor("input_881_pad_type_0"), val = tensor("valid")]; - tensor input_881_groups_0 = const()[name = tensor("input_881_groups_0"), val = tensor(512)]; - tensor input_881_strides_0 = const()[name = tensor("input_881_strides_0"), val = tensor([1])]; - tensor input_881_pad_0 = const()[name = tensor("input_881_pad_0"), val = tensor([0, 0])]; - tensor input_881_dilations_0 = const()[name = tensor("input_881_dilations_0"), val = tensor([1])]; - tensor const_266_to_fp16 = const()[name = tensor("const_266_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210403584)))]; - tensor const_267_to_fp16 = const()[name = tensor("const_267_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210412864)))]; - tensor input_883_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_266_to_fp16, x = input_879_cast_fp16)[name = tensor("input_883_cast_fp16")]; - tensor input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = tensor("input_885_cast_fp16")]; - tensor x_373_pad_type_0 = const()[name = tensor("x_373_pad_type_0"), val = tensor("valid")]; - tensor x_373_strides_0 = const()[name = tensor("x_373_strides_0"), val = tensor([1])]; - tensor x_373_pad_0 = const()[name = tensor("x_373_pad_0"), val = tensor([0, 0])]; - tensor x_373_dilations_0 = const()[name = tensor("x_373_dilations_0"), val = tensor([1])]; - tensor x_373_groups_0 = const()[name = tensor("x_373_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210413952)))]; - tensor module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210938304)))]; - tensor x_373_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv2_bias_to_fp16, dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = module_layers_16_conv_pointwise_conv2_weight_to_fp16, x = input_885_cast_fp16)[name = tensor("x_373_cast_fp16")]; - tensor input_887_perm_0 = const()[name = tensor("input_887_perm_0"), val = tensor([0, 2, 1])]; - tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = tensor("transpose_86")]; - tensor input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = tensor("input_889_cast_fp16")]; - tensor input_891_axes_0 = const()[name = tensor("input_891_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210939392)))]; - tensor module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210940480)))]; - tensor input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = tensor("input_891_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210941568)))]; - tensor module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213038784)))]; - tensor linear_152_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16, x = input_891_cast_fp16)[name = tensor("linear_152_cast_fp16")]; - tensor input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = tensor("input_895_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213042944)))]; - tensor module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215140160)))]; - tensor linear_153_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16, x = input_895_cast_fp16)[name = tensor("linear_153_cast_fp16")]; - tensor var_3279_to_fp16 = const()[name = tensor("op_3279_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3280_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3279_to_fp16)[name = tensor("op_3280_cast_fp16")]; - tensor input_cast_fp16 = add(x = input_889_cast_fp16, y = var_3280_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor audio_signal_axes_0 = const()[name = tensor("audio_signal_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215141248)))]; - tensor module_layers_16_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215142336)))]; - tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("audio_signal_cast_fp16")]; - tensor obj_1_perm_0 = const()[name = tensor("obj_1_perm_0"), val = tensor([0, 2, 1])]; - tensor obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_1_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = tensor("transpose_85")]; - tensor encoder_output = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = tensor("cast_172")]; - } -> (encoder_output, encoder_length); -} \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/weights/weight.bin b/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/weights/weight.bin deleted file mode 100644 index c5bffa9667270bb21c093e55898f4fb2e299e426..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Encoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:cecf7994b2758397d992802a4f6e5d656e3a1aeb7bbedc2aa430b1316d62474c -size 215143424 diff --git 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sha256:0800e3bdf4ecb1bd46fd27e1826d33125cd574f9ae1e15dd9ff70ea42944ca2d -size 476 diff --git a/models/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/metadata.json b/models/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/metadata.json deleted file mode 100644 index fe49b70e7e98f7ec9524130b0027886d862f6f8a..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/metadata.json +++ /dev/null @@ -1,102 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M joint + decision head (split, softmax, argmax)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 188 × 1)", - "shortDescription" : "", - "shape" : "[1, 188, 1]", - "name" : "token_id", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 188 × 1)", - 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"10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 512 × 188)", - "shortDescription" : "", - "shape" : "[1, 512, 188]", - "name" : "encoder", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.version" : "8.3.0", - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0" - }, - "generatedClassName" : "parakeet_joint_decision", - "method" : "predict" - } -] \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/model.mil b/models/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/model.mil deleted file mode 100644 index edfc5050f47ffbe1f5344799cef5dc0b21c392e0..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/model.mil +++ /dev/null @@ -1,58 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor decoder, tensor encoder) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_to_fp16_dtype_0 = const()[name = tensor("encoder_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_to_fp16_dtype_0 = const()[name = tensor("decoder_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488)))]; - tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = tensor("cast_6")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656832)))]; - tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476096)))]; - tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = tensor("cast_5")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; - tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; - tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; - tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1477440)))]; - tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2795904)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 188, 1, 1025])]; - tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; - tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 188, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; - tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; - tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; - tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; - tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; - tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; - tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; - tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; - tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_4")]; - tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; - tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; - tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; - tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; - tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; - tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; - tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_3")]; - } -> (token_id, token_prob, duration); -} \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/weights/weight.bin b/models/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid 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a/models/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1af2cb9bcc13eec83ce006e4f1c2cf158393745cd9187428333fbcb6917da244 -size 535 diff --git a/models/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/metadata.json b/models/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/metadata.json deleted file mode 100644 index b96db3615aa1d5e110c61c1f4694def827cb0a1f..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/metadata.json +++ /dev/null @@ -1,123 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M single-step joint decision (current frame)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_id", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_prob", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "duration", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1 × 64)", - "shortDescription" : "", - "shape" : "[1, 1, 1, 64]", - "name" : "top_k_ids", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 1 × 64)", - "shortDescription" : "", - "shape" : "[1, 1, 1, 64]", - "name" : "top_k_logits", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.reduceArgmax" : 2, - "Ios17.linear" : 3, - "Ios17.transpose" : 2, - "Ios17.sliceByIndex" : 2, - "Ios17.add" : 1, - "Ios17.topk" : 1, - "Ios16.relu" : 1, - "Ios16.softmax" : 1, - "Ios17.expandDims" : 3, - "Ios17.squeeze" : 1, - "Ios17.cast" : 6, - "Ios17.gatherAlongAxis" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32, UInt16)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 512 × 1)", - "shortDescription" : "", - "shape" : "[1, 512, 1]", - "name" : "encoder_step", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder_step", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "generatedClassName" : "parakeet_joint_decision_single_step", - "method" : "predict" - } -] \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/model.mil b/models/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/model.mil deleted file mode 100644 index 42bd4ac5def601f37ece871ff09bd6242de025aa..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/model.mil +++ /dev/null @@ -1,69 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor decoder_step, tensor encoder_step) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_step_to_fp16_dtype_0 = const()[name = tensor("encoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_step_to_fp16_dtype_0 = const()[name = tensor("decoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488)))]; - tensor encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor("cast_9")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656832)))]; - tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476096)))]; - tensor decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor("cast_8")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; - tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; - tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; - tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1477440)))]; - tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2795904)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 1, 1, 1025])]; - tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; - tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 1, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; - tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; - tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; - tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; - tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; - tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; - tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; - tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; - tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_7")]; - tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; - tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; - tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; - tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; - tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; - tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; - tensor var_72 = const()[name = tensor("op_72"), val = tensor(64)]; - tensor var_76_axis_0 = const()[name = tensor("op_76_axis_0"), val = tensor(-1)]; - tensor var_76_ascending_0 = const()[name = tensor("op_76_ascending_0"), val = tensor(false)]; - tensor var_76_sort_0 = const()[name = tensor("op_76_sort_0"), val = tensor(true)]; - tensor var_76_return_indices_0 = const()[name = tensor("op_76_return_indices_0"), val = tensor(true)]; - tensor var_76_cast_fp16_cast_int16_output_indices_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_output_indices_dtype_0"), val = tensor("uint16")]; - tensor var_76_cast_fp16_cast_int16_0, tensor var_76_cast_fp16_cast_int16_1 = topk(ascending = var_76_ascending_0, axis = var_76_axis_0, k = var_72, output_indices_dtype = var_76_cast_fp16_cast_int16_output_indices_dtype_0, return_indices = var_76_return_indices_0, sort = var_76_sort_0, x = token_logits_cast_fp16)[name = tensor("op_76_cast_fp16_cast_int16")]; - tensor var_76_cast_fp16_cast_int16_1_to_int32_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_1_to_int32_dtype_0"), val = tensor("int32")]; - tensor var_76_cast_fp16_0_to_fp32_dtype_0 = const()[name = tensor("op_76_cast_fp16_0_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor top_k_logits = cast(dtype = var_76_cast_fp16_0_to_fp32_dtype_0, x = var_76_cast_fp16_cast_int16_0)[name = tensor("cast_4")]; - tensor top_k_ids = cast(dtype = var_76_cast_fp16_cast_int16_1_to_int32_dtype_0, x = var_76_cast_fp16_cast_int16_1)[name = tensor("cast_5")]; - tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_6")]; - } -> (token_id, token_prob, duration, top_k_ids, top_k_logits); -} \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/weights/weight.bin b/models/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b3f771cb65b190f1873e39629676ed79b65a8361522f451b37bdba8b1106e6ff -size 2798028 diff --git a/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin b/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index d34457003aea6648633a14e7cd6134edc9c30f17..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a1ac15543fbb9301fba5f018b147e44d767479dec352aaa91dfe7bcf65949693 -size 243 diff --git a/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/coremldata.bin b/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/coremldata.bin deleted file mode 100644 index cd81f47cc6672a441cbe9556757a30ffc0ff0bc7..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4940877938cc1b6d8830bbdd68ac8a49377cc57d75b61308883da5235b6a1914 -size 439 diff --git a/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/metadata.json b/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/metadata.json deleted file mode 100644 index 6087d5e76f7dd060cf4f135f1f3ae0bbbeb2f30a..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/metadata.json +++ /dev/null @@ -1,112 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M preprocessor (15 s window)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32)", - "shortDescription" : "", - "shape" : "[]", - "name" : "mel_features", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Range1d" : 3, - "Ios17.equal" : 1, - "Ios17.notEqual" : 1, - "Ios17.reshape" : 2, - "Identity" : 1, - "Ios17.matmul" : 1, - "Select" : 6, - "Ios17.expandDims" : 12, - "Ios17.add" : 3, - "Tile" : 2, - "Ios17.sliceByIndex" : 3, - "Ios16.reduceSum" : 4, - "Shape" : 4, - "Ios17.gather" : 4, - "Ios17.logicalNot" : 1, - "Pad" : 1, - "Ios17.log" : 1, - "Ios17.less" : 2, - "Ios17.sub" : 4, - "Ios17.conv" : 2, - "Ios17.pow" : 2, - "Ios17.cast" : 10, - "Ios17.concat" : 3, - "Stack" : 1, - "Ios17.floorDiv" : 1, - "Ios17.realDiv" : 4, - "Ios17.sqrt" : 1, - "Ios17.greaterEqual" : 1, - "Ios17.mul" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32, UInt16)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "dataType" : "Float32", - "hasShapeFlexibility" : "1", - "isOptional" : "0", - "shapeFlexibility" : "1 × 1...240000", - "shapeRange" : "[[1, 1], [1, 240000]]", - "formattedType" : "MultiArray (Float32 1 × 1)", - "type" : "MultiArray", - "shape" : "[1, 1]", - "name" : "audio", - "shortDescription" : "" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "audio_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "generatedClassName" : "parakeet_preprocessor", - "method" : "predict" - } -] \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/model.mil b/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/model.mil deleted file mode 100644 index ae325b6071a8ee5dad74a2e516e60889d89771d8..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/model.mil +++ /dev/null @@ -1,191 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor audio, tensor audio_length) [FlexibleShapeInformation = tuple, dict, tensor>>, tuple, dict, list, ?>>>>((("DefaultShapes", {{"audio", [1, 1]}}), ("RangeDims", {{"audio", [[1, 1], [1, 240000]]}})))] { - tensor var_9 = const()[name = tensor("op_9"), val = tensor(1)]; - tensor var_10 = const()[name = tensor("op_10"), val = tensor(160)]; - tensor var_12 = const()[name = tensor("op_12"), val = tensor(0)]; - tensor var_34 = const()[name = tensor("op_34"), val = tensor(512)]; - tensor var_35 = add(x = audio_length, y = var_34)[name = tensor("op_35")]; - tensor var_36 = const()[name = tensor("op_36"), val = tensor(512)]; - tensor var_37 = sub(x = var_35, y = var_36)[name = tensor("op_37")]; - tensor floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor("floor_div_0")]; - tensor var_40 = equal(x = audio_length, y = var_12)[name = tensor("op_40")]; - tensor var_41 = const()[name = tensor("op_41"), val = tensor([0])]; - tensor mel_length = select(a = var_41, b = floor_div_0, cond = var_40)[name = tensor("seq_len")]; - tensor audio_to_fp16_dtype_0 = const()[name = tensor("audio_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = tensor("cast_27")]; - tensor var_43_shape_cast_fp16 = shape(x = audio_to_fp16)[name = tensor("op_43_shape_cast_fp16")]; - tensor gather_0_axis_0 = const()[name = tensor("gather_0_axis_0"), val = tensor(0)]; - tensor gather_0_batch_dims_0 = const()[name = tensor("gather_0_batch_dims_0"), val = tensor(0)]; - tensor gather_0_validate_indices_0 = const()[name = tensor("gather_0_validate_indices_0"), val = tensor(false)]; - tensor var_43_shape_cast_fp16_to_int16_dtype_0 = const()[name = tensor("op_43_shape_cast_fp16_to_int16_dtype_0"), val = tensor("int16")]; - tensor select_0_to_uint16 = const()[name = tensor("select_0_to_uint16"), val = tensor(1)]; - tensor var_43_shape_cast_fp16_to_int16 = cast(dtype = var_43_shape_cast_fp16_to_int16_dtype_0, x = var_43_shape_cast_fp16)[name = tensor("cast_26")]; - tensor gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_43_shape_cast_fp16_to_int16)[name = tensor("gather_0_cast_uint16")]; - tensor gather_0_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_0_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_0 = const()[name = tensor("const_0"), val = tensor(0)]; - tensor const_1 = const()[name = tensor("const_1"), val = tensor(1)]; - tensor gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = tensor("cast_25")]; - tensor var_44 = range_1d(end = gather_0_cast_uint16_to_int32, start = const_0, step = const_1)[name = tensor("op_44")]; - tensor var_45_axes_0 = const()[name = tensor("op_45_axes_0"), val = tensor([0])]; - tensor var_45 = expand_dims(axes = var_45_axes_0, x = var_44)[name = tensor("op_45")]; - tensor var_46_axes_0 = const()[name = tensor("op_46_axes_0"), val = tensor([1])]; - tensor var_46 = expand_dims(axes = var_46_axes_0, x = audio_length)[name = tensor("op_46")]; - tensor timemask = less(x = var_45, y = var_46)[name = tensor("timemask")]; - tensor var_49_begin_0 = const()[name = tensor("op_49_begin_0"), val = tensor([0, 0])]; - tensor var_49_end_0 = const()[name = tensor("op_49_end_0"), val = tensor([1, 1])]; - tensor var_49_end_mask_0 = const()[name = tensor("op_49_end_mask_0"), val = tensor([true, false])]; - tensor var_49_squeeze_mask_0 = const()[name = tensor("op_49_squeeze_mask_0"), val = tensor([false, true])]; - tensor var_49_cast_fp16 = slice_by_index(begin = var_49_begin_0, end = var_49_end_0, end_mask = var_49_end_mask_0, squeeze_mask = var_49_squeeze_mask_0, x = audio_to_fp16)[name = tensor("op_49_cast_fp16")]; - tensor var_50_axes_0 = const()[name = tensor("op_50_axes_0"), val = tensor([1])]; - tensor var_50_cast_fp16 = expand_dims(axes = var_50_axes_0, x = var_49_cast_fp16)[name = tensor("op_50_cast_fp16")]; - tensor var_52_begin_0 = const()[name = tensor("op_52_begin_0"), val = tensor([0, 1])]; - tensor var_52_end_0 = const()[name = tensor("op_52_end_0"), val = tensor([1, 0])]; - tensor var_52_end_mask_0 = const()[name = tensor("op_52_end_mask_0"), val = tensor([true, true])]; - tensor var_52_cast_fp16 = slice_by_index(begin = var_52_begin_0, end = var_52_end_0, end_mask = var_52_end_mask_0, x = audio_to_fp16)[name = tensor("op_52_cast_fp16")]; - tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 0])]; - tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([1, -1])]; - tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, false])]; - tensor var_54_cast_fp16 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = audio_to_fp16)[name = tensor("op_54_cast_fp16")]; - tensor var_55_to_fp16 = const()[name = tensor("op_55_to_fp16"), val = tensor(0x1.f0cp-1)]; - tensor var_56_cast_fp16 = mul(x = var_54_cast_fp16, y = var_55_to_fp16)[name = tensor("op_56_cast_fp16")]; - tensor var_57_cast_fp16 = sub(x = var_52_cast_fp16, y = var_56_cast_fp16)[name = tensor("op_57_cast_fp16")]; - tensor x_3_interleave_0 = const()[name = tensor("x_3_interleave_0"), val = tensor(false)]; - tensor x_3_cast_fp16 = concat(axis = var_9, interleave = x_3_interleave_0, values = (var_50_cast_fp16, var_57_cast_fp16))[name = tensor("x_3_cast_fp16")]; - tensor var_60 = logical_not(x = timemask)[name = tensor("op_60")]; - tensor var_16_to_fp16 = const()[name = tensor("op_16_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_60)[name = tensor("input_1_cast_fp16")]; - tensor concat_1x = const()[name = tensor("concat_1x"), val = tensor([1, 1, -1])]; - tensor input_3_cast_fp16 = reshape(shape = concat_1x, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; - tensor input_5_mode_0 = const()[name = tensor("input_5_mode_0"), val = tensor("constant")]; - tensor const_3_to_fp16 = const()[name = tensor("const_3_to_fp16"), val = tensor(0x0p+0)]; - tensor input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor concat_2x = const()[name = tensor("concat_2x"), val = tensor([1, -1])]; - tensor input_cast_fp16 = reshape(shape = concat_2x, x = input_5_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([160])]; - tensor expand_dims_4_axes_0 = const()[name = tensor("expand_dims_4_axes_0"), val = tensor([1])]; - tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = tensor("expand_dims_4_cast_fp16")]; - tensor conv_0_pad_type_0 = const()[name = tensor("conv_0_pad_type_0"), val = tensor("valid")]; - tensor conv_0_pad_0 = const()[name = tensor("conv_0_pad_0"), val = tensor([0, 0])]; - tensor conv_0_dilations_0 = const()[name = tensor("conv_0_dilations_0"), val = tensor([1])]; - tensor conv_0_groups_0 = const()[name = tensor("conv_0_groups_0"), val = tensor(1)]; - tensor expand_dims_1_to_fp16 = const()[name = tensor("expand_dims_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_0_cast_fp16")]; - tensor conv_1_pad_type_0 = const()[name = tensor("conv_1_pad_type_0"), val = tensor("valid")]; - tensor conv_1_pad_0 = const()[name = tensor("conv_1_pad_0"), val = tensor([0, 0])]; - tensor conv_1_dilations_0 = const()[name = tensor("conv_1_dilations_0"), val = tensor([1])]; - tensor conv_1_groups_0 = const()[name = tensor("conv_1_groups_0"), val = tensor(1)]; - tensor expand_dims_2_to_fp16 = const()[name = tensor("expand_dims_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263296)))]; - tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_1_cast_fp16")]; - tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(-1)]; - tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor("stack_0_cast_fp16")]; - tensor var_19_promoted_to_fp16 = const()[name = tensor("op_19_promoted_to_fp16"), val = tensor(0x1p+1)]; - tensor var_75_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_19_promoted_to_fp16)[name = tensor("op_75_cast_fp16")]; - tensor var_77_axes_0 = const()[name = tensor("op_77_axes_0"), val = tensor([-1])]; - tensor var_77_keep_dims_0 = const()[name = tensor("op_77_keep_dims_0"), val = tensor(false)]; - tensor var_77_cast_fp16 = reduce_sum(axes = var_77_axes_0, keep_dims = var_77_keep_dims_0, x = var_75_cast_fp16)[name = tensor("op_77_cast_fp16")]; - tensor x_11_cast_fp16 = identity(x = var_77_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor x_13_transpose_x_0 = const()[name = tensor("x_13_transpose_x_0"), val = tensor(false)]; - tensor x_13_transpose_y_0 = const()[name = tensor("x_13_transpose_y_0"), val = tensor(false)]; - tensor const_4_to_fp16 = const()[name = tensor("const_4_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526528)))]; - tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_4_to_fp16, y = x_11_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_84_to_fp16 = const()[name = tensor("op_84_to_fp16"), val = tensor(0x1p-24)]; - tensor var_85_cast_fp16 = add(x = x_13_cast_fp16, y = var_84_to_fp16)[name = tensor("op_85_cast_fp16")]; - tensor x_15_epsilon_0 = const()[name = tensor("x_15_epsilon_0"), val = tensor(0x1p-149)]; - tensor x_15_cast_fp16 = log(epsilon = x_15_epsilon_0, x = var_85_cast_fp16)[name = tensor("x_15_cast_fp16")]; - tensor var_87_shape_cast_fp16 = shape(x = x_15_cast_fp16)[name = tensor("op_87_shape_cast_fp16")]; - tensor gather_5 = const()[name = tensor("gather_5"), val = tensor(1)]; - tensor gather_6_axis_0 = const()[name = tensor("gather_6_axis_0"), val = tensor(0)]; - tensor gather_6_batch_dims_0 = const()[name = tensor("gather_6_batch_dims_0"), val = tensor(0)]; - tensor gather_6_validate_indices_0 = const()[name = tensor("gather_6_validate_indices_0"), val = tensor(false)]; - tensor var_87_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_87_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor select_6_to_uint16 = const()[name = tensor("select_6_to_uint16"), val = tensor(2)]; - tensor var_87_shape_cast_fp16_to_uint16 = cast(dtype = var_87_shape_cast_fp16_to_uint16_dtype_0, x = var_87_shape_cast_fp16)[name = tensor("cast_24")]; - tensor gather_6_cast_uint16 = gather(axis = gather_6_axis_0, batch_dims = gather_6_batch_dims_0, indices = select_6_to_uint16, validate_indices = gather_6_validate_indices_0, x = var_87_shape_cast_fp16_to_uint16)[name = tensor("gather_6_cast_uint16")]; - tensor gather_6_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_6_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_5 = const()[name = tensor("const_5"), val = tensor(0)]; - tensor const_6 = const()[name = tensor("const_6"), val = tensor(1)]; - tensor gather_6_cast_uint16_to_int32 = cast(dtype = gather_6_cast_uint16_to_int32_dtype_0, x = gather_6_cast_uint16)[name = tensor("cast_23")]; - tensor var_89 = range_1d(end = gather_6_cast_uint16_to_int32, start = const_5, step = const_6)[name = tensor("op_89")]; - tensor var_90_axes_0 = const()[name = tensor("op_90_axes_0"), val = tensor([0])]; - tensor var_90 = expand_dims(axes = var_90_axes_0, x = var_89)[name = tensor("op_90")]; - tensor concat_3_axis_0 = const()[name = tensor("concat_3_axis_0"), val = tensor(0)]; - tensor concat_3_interleave_0 = const()[name = tensor("concat_3_interleave_0"), val = tensor(false)]; - tensor concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (gather_5, gather_6_cast_uint16_to_int32))[name = tensor("concat_3")]; - tensor shape_8 = shape(x = var_90)[name = tensor("shape_8")]; - tensor real_div_0 = real_div(x = concat_3, y = shape_8)[name = tensor("real_div_0")]; - tensor time_steps = tile(reps = real_div_0, x = var_90)[name = tensor("time_steps")]; - tensor var_93_axes_0 = const()[name = tensor("op_93_axes_0"), val = tensor([1])]; - tensor var_93 = expand_dims(axes = var_93_axes_0, x = mel_length)[name = tensor("op_93")]; - tensor valid_mask = less(x = time_steps, y = var_93)[name = tensor("valid_mask")]; - tensor var_95_axes_0 = const()[name = tensor("op_95_axes_0"), val = tensor([1])]; - tensor var_95 = expand_dims(axes = var_95_axes_0, x = valid_mask)[name = tensor("op_95")]; - tensor var_96_cast_fp16 = select(a = x_15_cast_fp16, b = var_16_to_fp16, cond = var_95)[name = tensor("op_96_cast_fp16")]; - tensor x_mean_numerator_axes_0 = const()[name = tensor("x_mean_numerator_axes_0"), val = tensor([2])]; - tensor x_mean_numerator_keep_dims_0 = const()[name = tensor("x_mean_numerator_keep_dims_0"), val = tensor(false)]; - tensor x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_96_cast_fp16)[name = tensor("x_mean_numerator_cast_fp16")]; - tensor x_mean_denominator_axes_0 = const()[name = tensor("x_mean_denominator_axes_0"), val = tensor([1])]; - tensor x_mean_denominator_keep_dims_0 = const()[name = tensor("x_mean_denominator_keep_dims_0"), val = tensor(false)]; - tensor cast_6_to_fp16_dtype_0 = const()[name = tensor("cast_6_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor valid_mask_to_fp16 = cast(dtype = cast_6_to_fp16_dtype_0, x = valid_mask)[name = tensor("cast_22")]; - tensor x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor("x_mean_denominator_cast_fp16")]; - tensor var_101_axes_0 = const()[name = tensor("op_101_axes_0"), val = tensor([1])]; - tensor var_101_cast_fp16 = expand_dims(axes = var_101_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor("op_101_cast_fp16")]; - tensor x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_101_cast_fp16)[name = tensor("x_mean_cast_fp16")]; - tensor var_104_axes_0 = const()[name = tensor("op_104_axes_0"), val = tensor([2])]; - tensor var_104_cast_fp16 = expand_dims(axes = var_104_axes_0, x = x_mean_cast_fp16)[name = tensor("op_104_cast_fp16")]; - tensor var_105_cast_fp16 = sub(x = x_15_cast_fp16, y = var_104_cast_fp16)[name = tensor("op_105_cast_fp16")]; - tensor var_106_cast_fp16 = select(a = var_105_cast_fp16, b = var_16_to_fp16, cond = var_95)[name = tensor("op_106_cast_fp16")]; - tensor var_19_promoted_1_to_fp16 = const()[name = tensor("op_19_promoted_1_to_fp16"), val = tensor(0x1p+1)]; - tensor var_107_cast_fp16 = pow(x = var_106_cast_fp16, y = var_19_promoted_1_to_fp16)[name = tensor("op_107_cast_fp16")]; - tensor var_109_axes_0 = const()[name = tensor("op_109_axes_0"), val = tensor([2])]; - tensor var_109_keep_dims_0 = const()[name = tensor("op_109_keep_dims_0"), val = tensor(false)]; - tensor var_109_cast_fp16 = reduce_sum(axes = var_109_axes_0, keep_dims = var_109_keep_dims_0, x = var_107_cast_fp16)[name = tensor("op_109_cast_fp16")]; - tensor var_111_to_fp16 = const()[name = tensor("op_111_to_fp16"), val = tensor(0x1p+0)]; - tensor var_112_cast_fp16 = sub(x = var_101_cast_fp16, y = var_111_to_fp16)[name = tensor("op_112_cast_fp16")]; - tensor var_113_cast_fp16 = real_div(x = var_109_cast_fp16, y = var_112_cast_fp16)[name = tensor("op_113_cast_fp16")]; - tensor x_std_1_cast_fp16 = sqrt(x = var_113_cast_fp16)[name = tensor("x_std_1_cast_fp16")]; - tensor var_115_cast_fp16 = not_equal(x = x_std_1_cast_fp16, y = x_std_1_cast_fp16)[name = tensor("op_115_cast_fp16")]; - tensor x_std_3_cast_fp16 = select(a = var_16_to_fp16, b = x_std_1_cast_fp16, cond = var_115_cast_fp16)[name = tensor("x_std_3_cast_fp16")]; - tensor var_25_to_fp16 = const()[name = tensor("op_25_to_fp16"), val = tensor(0x1.5p-17)]; - tensor x_std_cast_fp16 = add(x = x_std_3_cast_fp16, y = var_25_to_fp16)[name = tensor("x_std_cast_fp16")]; - tensor var_120_axes_0 = const()[name = tensor("op_120_axes_0"), val = tensor([2])]; - tensor var_120_cast_fp16 = expand_dims(axes = var_120_axes_0, x = x_std_cast_fp16)[name = tensor("op_120_cast_fp16")]; - tensor x_cast_fp16 = real_div(x = var_105_cast_fp16, y = var_120_cast_fp16)[name = tensor("x_cast_fp16")]; - tensor var_122_shape_cast_fp16 = shape(x = x_cast_fp16)[name = tensor("op_122_shape_cast_fp16")]; - tensor gather_7_axis_0 = const()[name = tensor("gather_7_axis_0"), val = tensor(0)]; - tensor gather_7_batch_dims_0 = const()[name = tensor("gather_7_batch_dims_0"), val = tensor(0)]; - tensor gather_7_validate_indices_0 = const()[name = tensor("gather_7_validate_indices_0"), val = tensor(false)]; - tensor var_122_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_122_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor select_7_to_uint16 = const()[name = tensor("select_7_to_uint16"), val = tensor(2)]; - tensor var_122_shape_cast_fp16_to_uint16 = cast(dtype = var_122_shape_cast_fp16_to_uint16_dtype_0, x = var_122_shape_cast_fp16)[name = tensor("cast_21")]; - tensor gather_7_cast_uint16 = gather(axis = gather_7_axis_0, batch_dims = gather_7_batch_dims_0, indices = select_7_to_uint16, validate_indices = gather_7_validate_indices_0, x = var_122_shape_cast_fp16_to_uint16)[name = tensor("gather_7_cast_uint16")]; - tensor gather_7_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_7_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_7 = const()[name = tensor("const_7"), val = tensor(0)]; - tensor const_8 = const()[name = tensor("const_8"), val = tensor(1)]; - tensor gather_7_cast_uint16_to_int32 = cast(dtype = gather_7_cast_uint16_to_int32_dtype_0, x = gather_7_cast_uint16)[name = tensor("cast_20")]; - tensor mask_1 = range_1d(end = gather_7_cast_uint16_to_int32, start = const_7, step = const_8)[name = tensor("mask_1")]; - tensor gather_8_axis_0 = const()[name = tensor("gather_8_axis_0"), val = tensor(0)]; - tensor gather_8_batch_dims_0 = const()[name = tensor("gather_8_batch_dims_0"), val = tensor(0)]; - tensor gather_8_validate_indices_0 = const()[name = tensor("gather_8_validate_indices_0"), val = tensor(false)]; - tensor select_8_to_uint16 = const()[name = tensor("select_8_to_uint16"), val = tensor(0)]; - tensor gather_8_cast_uint16 = gather(axis = gather_8_axis_0, batch_dims = gather_8_batch_dims_0, indices = select_8_to_uint16, validate_indices = gather_8_validate_indices_0, x = var_122_shape_cast_fp16_to_uint16)[name = tensor("gather_8_cast_uint16")]; - tensor gather_8_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_8_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor concat_4_axis_0 = const()[name = tensor("concat_4_axis_0"), val = tensor(0)]; - tensor concat_4_interleave_0 = const()[name = tensor("concat_4_interleave_0"), val = tensor(false)]; - tensor gather_8_cast_uint16_to_int32 = cast(dtype = gather_8_cast_uint16_to_int32_dtype_0, x = gather_8_cast_uint16)[name = tensor("cast_19")]; - tensor concat_4 = concat(axis = concat_4_axis_0, interleave = concat_4_interleave_0, values = (gather_8_cast_uint16_to_int32, var_9))[name = tensor("concat_4")]; - tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0])]; - tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = tensor("expand_dims_0")]; - tensor var_126 = tile(reps = concat_4, x = expand_dims_0)[name = tensor("op_126")]; - tensor mask = greater_equal(x = var_126, y = var_93)[name = tensor("mask")]; - tensor var_129_axes_0 = const()[name = tensor("op_129_axes_0"), val = tensor([1])]; - tensor var_129 = expand_dims(axes = var_129_axes_0, x = mask)[name = tensor("op_129")]; - tensor cast_15_to_fp16 = const()[name = tensor("cast_15_to_fp16"), val = tensor(0x0p+0)]; - tensor processed_signal_cast_fp16 = select(a = cast_15_to_fp16, b = x_cast_fp16, cond = var_129)[name = tensor("processed_signal_cast_fp16")]; - tensor processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("processed_signal_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor mel_features = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = tensor("cast_18")]; - } -> (mel_features, mel_length); -} \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/weights/weight.bin b/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/weights/weight.bin deleted file mode 100644 index d0c6ac87e5f78315662149af9ab4ea3658497dbe..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c062338de852a26607ce4101f74e6895de3a4134a57b07232bd72bfc6f1d7f1a -size 567712 diff --git a/models/parakeet-ctc-110m-coreml/config.json b/models/parakeet-ctc-110m-coreml/config.json deleted file mode 100644 index aaa7174ca014092519980c51451ac7ca3832a352..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/config.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "bos_token_id": 1, - "eos_token_id": 2, - "nemo_model_type": "parakeet", - "pad_token_id": 0, - "vocab_size": 1024, -} diff --git a/models/parakeet-ctc-110m-coreml/metadata.json b/models/parakeet-ctc-110m-coreml/metadata.json deleted file mode 100644 index 6db872ae8a7d463c95dc180210ace0fce019f4a6..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/metadata.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "model_id": "nvidia/parakeet-tdt_ctc-110m", - "model_type": "hybrid_rnnt_ctc", - "sample_rate": 16000, - "max_audio_seconds": 15.0, - "max_audio_samples": 240000, - "max_symbol_steps": 1, - "vocab_size": 1024, - "joint_extra_outputs": 5, - "encoder_dim": 512, - "decoder_dim": 640, - "decoder_hidden": 640, - "decoder_layers": 1, - "blank_id": 1024, - "checkpoint": { - "type": "pretrained", - "model_id": "nvidia/parakeet-tdt_ctc-110m" - }, - "coreml": { - "compute_units": "CPU_ONLY", - "compute_precision": "FLOAT32" - }, - "components": { - "preprocessor": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "path": "parakeet_preprocessor.mlpackage" - }, - "encoder": { - "inputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_encoder.mlpackage" - }, - "ctc_head": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ] - }, - "outputs": { - "log_probs": [ - 1, - 188, - 1025 - ] - }, - "path": "parakeet_ctc_head.mlpackage" - }, - "mel_encoder": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_mel_encoder.mlpackage" - }, - "decoder": { - "inputs": { - "targets": [ - 1, - 1 - ], - "target_length": [ - 1 - ], - "h_in": [ - 1, - 1, - 640 - ], - "c_in": [ - 1, - 1, - 640 - ] - }, - "outputs": { - "decoder": [ - 1, - 640, - 1 - ], - "h_out": [ - 1, - 1, - 640 - ], - "c_out": [ - 1, - 1, - 640 - ] - }, - "path": "parakeet_decoder.mlpackage" - }, - "joint": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "logits": [ - 1, - 188, - 1, - 1030 - ] - }, - "path": "parakeet_joint.mlpackage" - }, - "joint_decision": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 188, - 1 - ], - "token_prob": [ - 1, - 188, - 1 - ], - "duration": [ - 1, - 188, - 1 - ] - }, - "path": "parakeet_joint_decision.mlpackage" - }, - "joint_decision_single_step": { - "inputs": { - "encoder_step": [ - 1, - 512, - 1 - ], - "decoder_step": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 1, - 1 - ], - "token_prob": [ - 1, - 1, - 1 - ], - "duration": [ - 1, - 1, - 1 - ], - "top_k_ids": [ - 1, - 1, - 1, - 64 - ], - "top_k_logits": [ - 1, - 1, - 1, - 64 - ] - }, - "path": "parakeet_joint_decision_single_step.mlpackage" - } - } -} \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/preprocessor_config.json b/models/parakeet-ctc-110m-coreml/preprocessor_config.json deleted file mode 100644 index 54683c2afb1e841dfe59d03ad92c060a6cf50c0d..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/preprocessor_config.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "feature_extractor_type": "ParakeetFeatureExtractor", - "feature_size": 80, - "hop_length": 160, - "n_fft": 512, - "padding_side": "right", - "padding_value": 0.0, - "preemphasis": 0.97, - "processor_class": "ParakeetProcessor", - "return_attention_mask": true, - "sampling_rate": 16000, - "win_length": 400 -} diff --git a/models/parakeet-ctc-110m-coreml/special_tokens_map.json b/models/parakeet-ctc-110m-coreml/special_tokens_map.json deleted file mode 100644 index 815fbdc56abc60a0f29e51ff2373c998d7779805..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/special_tokens_map.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "pad_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "unk_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - } -} diff --git a/models/parakeet-ctc-110m-coreml/tokenizer.json b/models/parakeet-ctc-110m-coreml/tokenizer.json deleted file mode 100644 index 21e87f185d71820f5089456aa9c79c97c0a0ce8b..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/tokenizer.json +++ /dev/null @@ -1,2456 +0,0 @@ -{ - "version": "1.0", - "truncation": null, - "padding": null, - "added_tokens": [ - 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MAIBNDACA1x0AgMwEAQDKCgCAdxsAgKelAADWCgCAo40AAMwUAgCAuQAAgbkAAKeFAAALDACAgmUAAIcbAICMNQAA9gsAgMzsHADN/AMAjxsAgK6tAADcCgCAlxsAgMzABgDN0AYAsL0BAMyQBwDiCgCAgckBAMwYHQDNIAIAhBEAAO4KAIDNuAYAzKwGAKcbAIDoCgCAgSkAALcbAICvGwCAo+0BAMxAHQDNEAIAvxsAgMcbAICBCQAAzxsAgMxAHQDN0AIAqNkBABcMAIDMkAcAzBwBAMxgBgDNZAYA9AoAgB8KAIDXGwCAkSkBAAALAICBzR8A3xsAgPoKAIDvGwCA5xsAgMzEBgDNwAYAgTEAAIDZAAAiCgCAJQoAgIK5AQCDRQEAgLkBAIG5AQCGXQEA9x0AgIRdAQDvHQCAzcAAAMzwAACIARwAiXkBAAceAICPVQEAjGEBAP8dAICB3R4AgRUfAJ8bAICBXR8AjIEfAIdBHwDMGAMAzWgDAIBNHwCBpR8AKAoAgIOpHwCMFR8AjNEeACsKAICHtR8AgJUfAIGZHwCBEQAAg70fAICFHwCBiR8A9xsAgIQ9AACeDACAiZkfAP8bAICIBQAACQsAgAccAICADQAAgf0AAA8cAICj2R8Ao3keAKOFAAAPCwCArTUfAKdhHgCnqR8ApAwAgIQNAACqDACAozUfAC4KAICtiR8AhHEAAKchHwCxPR4AsYUfAJgMAIDnHQCAFQsAgLoLAIDMtBwAzbAcAFMMAICxQR8AWQwAgJ8LAIAfHgCAFx4AgC8eAIAnHgCAgLkeAIG5HgCCIQEAgzUBAIRhAQA3HgCAhokBAIe9AQCIkQEAiekBAN8dAICL/QEAjOUBAIINAAAPHgCAj90BAIO5AQCRrQEAgb0BAIC9AQCAoQEAgaEBAPwLAIACDACAhD0AABccAICJlQEAm4EBAIHNHgCAzR4AzPwCAM3wAgCB5QAAHxwAgIHtAACjpQAAzJABAM1cAgCHHQAAHgsAgKj5AAAnHACAKgsAgF8MAIBlDACALxwAgIQFAAA3HACAo9UAACQLAIA/HACAgVEAAMz0AQDN0AEAMAsAgIc9AABXHACANgsAgEccAIBCCwCAhwUAAF8cAIBPHACAh/EDAIHZAwCBmQMAgZEAAGccAIB3DACAjPkDAMwkAQCHuQMAgfkDADwLAIDMZAIAgskDAIyZAwBvHACAh9EDAI+RAwCB3QYAkfUDAMwABADN7AMAh2UAAB8dAIBOCwCAdxwAgH0MAIBICwCAzBgBAIg5AACHHACAfxwAgMxcAwCMJQAAMQoAgMwsAQCx/QAAozkDADQKAIA3CgCApxwAgKdZAwDMdAMAiAkAAKNRAwCvHACAYAsAgINtDQCnnQAApq0AAKOdAACxDQMAzCgBANgLAICntQAAprUAAMwLAIDMMAEAgdUHAMYLAIDMKAEA0gsAgEceAIBmCwCArYkAAGwLAICAzQEAgd0BAMxEAQDNnB4AhPUBAMALAIDMWAEAzUwBAIDtAQCB/QEAg7UAAGsMAICM3QEAcQwAgMwIHgCM8QYAzDgBAM08AQBXHgCAiREAAIEFBgBPHgCAZx4AgF8eAIBvHgCAgz0AAIAhAACBOQAAgDkAAIEhAAA/HgCAjxwAgMwoAQCB2QYAcgsAgIH9BgDMJAEAnxwAgJccAIC3HACAgCEBAIE1AQCjBQAAvxwAgMccAIDPHACAzIwFAM1AAgC3HAMAeAsAgIfNBwDfHACA1xwAgCMdAIDNiAAAzJAAAIzdBQCjhQAAGQoAgMzgAgDnHACAiNUHAIFNAACATQAAVAsAgO8cAIBaCwCAkTkHADoKAICIxQcAqAsAgIrJBwD3HACAmz0AAIflBwB3HgCAgYUHAICFBwA9CgCAgvkHAILVBgCDRQAAgMkGAIHdBgCG4QYAfgsAgIRRAACPHgCAipUGAIuZBgCIeQAAiZ0GALAMAICPWQcAjG0HAP8cAIDMgAMAzSQCALARBwBACgCAhx4AgCcdAIB/HgCAhwsAgICNAACBnQAAzOwDAM3oBAAHHQCAjQsAgKNJBwCTCwCADx0AgKO9BwAXHQCAGwAAgOoHAIALAACApKUHAOsEAICKBQCAAwAAgKhhBwDfDQCAZQAAgMgDAIAeCQCArWkHAIAtAQCBPQEAgl0BAINRAQCEYQEAuAQAgKwEAICHYQEAiK0BAIm1AQCKvQEAjykVALwFAIAgDACAzHgCAM3YBQCB3QEAgXEAAOcLAICC/QEAhBkAACYMAICH7QEAIwwAgMw0BADNMAQA6gsAgJ9pFQApDACAjMkBAM34BADM8AIAsUkBACEHAICB1QAAoxUBAKCZFQB2CACARgcAgIT1AADMKAQAzSwEAMYIAICveQEAqH0BADcNAICqaQEAVQkAgLQlAQC1KQEAowkBAAUMAIDqBgCA7gYAgLIFAQCzPQEAvPUAAL39AAC+2QAAOwgAgLgBAQC5AQEAugEBADwHAIBDBwCAhgwAALOdAwCyiQMAtggAgIC9AwBsBwCAbwcAgBUJAIDkBgCA5wYAgDgIAICJhQMAzOQHAL+hAwAIDACA2gwAgIxlAADN5AwAzCQMAIlBAACIVQAAi0UAAIpFAACFtQMAhLUDAIeVAwCGgQMABA0AgAcNAIAKDQCAmCwAABMAAICmyAAAzYwGAMyoBgCFaQAAFwAAgDEAAIBpAACAzPADAAcAAIA1AACA1AwAgLGVAAArDQCAs5UAALKVAAA7DQCAPg0AgEYNAIBBDQCANA0AgHUAAICmBgCAJQAAgJsJAIAjIQCAv1UDAEkNAIAfIQCAGyEAgGcgAIC4bAAAlGUNAJIAAgCcrQEAnaUBAJqJAQCbiQEAmJkBAJmJAQDMIAYAzQQGAMxABgDNXAYAzDwHAM04BwDMvAcAhXUAAIABDwCBDQ8AbyAAgLqZAQCFBQAAdyAAgF8gAIC+hQEAgSkPAIAlDwBrIACAgiEPAIUpAAC0pQEAhREAAHMgAICziQ8AsoUPALHJAQCwAQwAt4EPALbtAQC17QEAtO0BAIFlAQCAZQEAg2EBALi1DwDMPAsAhHkBAIDhDwCB3Q8AeyAAgGMgAIDMyAQAzbgEAIWtAACFFQAAJyEAgD8hAIDM6BkAzbQZAKRdAQBMDQCAok0CAKPxDwCgVQEAod0PAIIIAIBxCQCAPgkAgPMeAIBvCQCA+x4AgHoJAID3HgCAtAgAgJMNAACzHgCA/x4AgITVDACF6Q4AlGkAAIfdDgC7HgCAmbQCAMMeAIDLHgCAtx4AgEMhAIC/HgCAn3QBAMceAICRGA0AgI0OAIGBDgCGhQ4AlYwDAISJDgCXRAIAghEAAKm4AACA0QAAge0AAM8eAIBPDQCA6x4AgIVZDwCDiQAAoTQNAIFFDgCASQ4A7x4AgKU0AQCFYQ8AzPAUACMfAIC5xAUAzMgDAM3cAwCA3QAAgcEAACsfAIC/kAUAhREAALHsBwCA9QAAgcEAAKcgAIC1jAYAMx8AgLdABgCA3Q4AgekOAMwoAgDNtAIAgM0OAIH5DgCFKQAAg4UBAIB1AQCBsQEAgPEBAIHVAQCvIACAOx8AgIUFAAC3IACAgJkBAIG9AQCCfQAAk9UBAJThAQCFDQAAnyAAgCcfAICACQAAgRkAAC8fAICTrQEAlC0AAKsgAICFDQAANx8AgIUFAACzIACAPx8AgIUpAACCGQAAhTUAAIDxAACB4QAAuyAAgKMgAIBHIQCAhQUAAGchAICDdQEAgO0BAIEpAQDM8AEAzbABAFINAIBjIQCAXyEAgKkNAIBjHwCAax8AgIA9AACBDQAAcx8AgHsfAICALQAAgR0AAIIVAABnHwCAzSwBAG8fAIB3HwCAfx8AgIjFAwCrIQCAzJACAM28AgCE7QMAVQ0AgIb5AwCjHwCAgIEDAIH9AwCAPQAAgTUAAIFJAACAQQAAzdwBAIJBAACrHwCApx8AgK8fAIDNMAEAlJ0DAJMhAIDN8AEAzAwBAIG5AwCAxQMAg6EDAJOlAwCArQAAgdUAAICdAACBqQAAjyEAgFgNAICBwQAAgMkAAIC1AACBgQAAiyEAgINpBADMcAMAzbQDAIchAIDNPAEArA0AgJMBBADNjAIAzPQCAIANAACBNQAAlNkGANcfAIDbHwCA3x8AgMwIAQDNHAEAgREAAIApAACvIQCAghkAAICRAQCBkQEAzWgFAMyUAgDMEAkAzSgWAMxYDgDNeA4AzBQNAM3YCgDMKAwAzYwNAMzgFwDM4AoAzDgLAM30CACFEQAAWw0AgIBRBwCBUQcA5yAAgM2QDgCFBQAA7yAAgMzYDgDN7AEA9yAAgM0ADgCFGQAAzfAPAM08DgDNVA4AzGgBAM1sAQDfIACAZAgAgJSZBwDMwDsAgGEBAIHZAACFKQAAzWQOAMx4AQDNfAEAga0HAICtBwCFZQAAgp0HAIBRAQCBUQEAlOEHAM3AAACEeQEAk8UHAIZhAQDrIACAiCEBAIUNAADzIACAzRgBAMzYAADNtAAAgN0HAIHNBwCfHwCAhQkAANMfAID7IACAAyAAgOMgAIALIACAEyAAgBsgAIAPIACAByAAgLMhAIAXIACAHyAAgMy4AgDNHAMAgGUAAIF1AACCfQAAIyAAgIUJAACFQQAAByEAgK8NAICAmQYAgSEHAIUZAACDfQAADyEAgIVZAAADIQCA/yAAgIDNAACB2QAAkx4AgIURAACE6QAAmx4AgIblAABHIACAgDUAAIENAACjHgCAhR0AAE8gAICrHgCAhQUAAFcgAICAVQAAgW0AAIJ9AACTRQAAlA0AAIUNAAA/IACAlx4AgIAJAACBEQAAnx4AgIUdAABLIACApx4AgIUFAABTIACAgOkBAIHxAQCCBQAArx4AgIUJAACFCQAAWyAAgEMgAICAbQEAgXkBAIIZAACDpQEAEyEAgIV1AACFBQAAFyEAgAshAIAnIACAzMgCAM3cAgCyDQCA0x4AgIA5AACBOQAA2x4AgOMeAIDXHgCA3x4AgIAdAACBDQAA5x4AgCsgAICAxQAAgdUAAM3AAADMJAIAgNUAAIHFAACFOQAAg8kAACshAIC1DQCAgNUAAIEJAACFBQAAMyEAgAMfAICHIACAgAkAAIERAAALHwCAk5kAAJS5AAATHwCAhWUAAIU9AACPIACAk10AABsfAICFEQAAzXAFAMx0BQCUATwAlyAAgH8gAIDNKAEAiyAAgJMgAICFGQAAmyAAgIMgAIA7IQCALyEAgC8gAICFJQAAhTkAAMz4AgDNxAMAzTwBALgNAICBlQMAgI0DAM3EAQCCpQMAhVEAAIVJAADMKAEAzSwBAM04AQDMPAEAgGk+AIFpPgBPIQCASyEAgM04PADMVDwAgdE8AJOdPgDMSAEAzcgCAM00AQBTIQCAlLk+AF4NAICAoT4AgaE+AIKhPgCIjTwAWyEAgIWtAACALQAAgSEAAIXVPwCbHwCAgO0AAIHxAACGpQAASx8AgISpAADNJAEAzSgBAFMfAICI+T4AhfE/AFsfAIBPHwCAhcU/AM0wAQDNEAEAzfQGAIDdAQCB6QEAzbwGAM1wBgDM4AYAzVwBAMxoBgDNkAYAzWQGAM14BgDMrAcAzagHAMzoBwDNyAcAgk0/AIP9AgCANQIAgekCAFcfAIBfHwCAgAU9AIV9AQBXIQCAMyAAgM0UAQAvDgCAge0BAIDhAQDNPAEAgs0BAM0sAQCCdQEAgW0BAIBZAQCAZQEAgcUAAIsfAIDNJAEAzTgBAILxAACB+QAAgFkBAIApAACBcQAAzBgBAM18AQDNLAEAkx8AgIEdAACAHQAAjx8AgJcfAIB3IQCAzSQBAMzkPQDNXA8AzegAAMwMAQCA1QEAgckBAIKZAACD5T8ADx8AgBcfAIAfHwCANyEAgCkOAIB7IQCAQx8AgDcgAIBHHwCAMg4AgIBNPwCBQT8Agx8AgG8hAICHHwCAayEAgIAlPwCBKT8Ak5E/AIN9AAAsDgCAlEEAAMzYAgDNrAIAcyEAgJNVAACACQAAgR0AALsNAICDIQCAlEEAALMfAICAnQAAgaEAAIAdAACBEQAAhKUAALsfAICGpQAAwx8AgIjxAACC0QAAgdkAAIDNAACAJQAAgSkAAIIFAADLHwCAtx8AgL8fAIDHHwCAk7EAAJQRAADPHwCAgB0AAIEVAACAJQAAgS0AAII9AAB/IQCAgO0AAIHRAACCFQAAg4EAAIHQPQA7IACAzCACAM3cAQCFeAIAlyEAgDUOAICfIQCAiRgDAOMfAICALQAAgTUAAIAJAACBbQAA6x8AgMcgAICRsQAAkKkAAJPdOwCSAQQAlaUAAJSVOwDzHwCAlqEAAIUJAACTQQAAzyAAgPsfAICFBQAA1yAAgJT1AAC/IACAgLkAAIHdAACC5QAA5x8AgO8fAICF6QAAgAkAAIE1AACFBQAAyyAAgPcfAICFHQAA0yAAgP8fAICFBQAA2yAAgLHBBQCwxQMAwyAAgLLFAwC12QUAtM0DAKMhAICFOQAAuf0DAKchAICbIQCAwQ0AgNMNAIAdDgCABx8AgAsOAIDZDQCAzIgCABEOAIDN4D4AzZABAMwkAQB2DQCAlA0AgEcOAICDDgCAgLEAAM3UPgDN5D4AiQ4AgMy8PgDNuD4AgNEDAIHtAwCC/QMAhmkAAEQOAICFnQMAzTwBAD4OAIDM6AIAzTw/AIjlAADNGAEAjw4AgIhBAABBDgCAfQ4AgM0sAQCbDgCAgNUAAKEOAICG4QAAhukAAE0OAIDNJAEApw4AgM0QAQCI0QAAiCkAAMz4AgBTDgCAzfgCAMwkAQCtDgCAhS0DAMygPgDNbD4AgNUDAIHNAwCCAQMAg/kDAMxkAwDNzAIASg4AgM0kAQDMDAIAzQgCAIERAADMnAMAzLA+AM20PgDMxD4AzcA+AMyAPgDNuD4Asw4AgMyEAgDMmD8AzVA+AMwgPgDNoD4AzQw/AM0wPwDNeD8AzQQ/AIhZAADFDgCAzfgBAMzEAQBQDgCAyw4AgNEOAIDMFAIAzAgBAM3IAQCIBQAA1w4AgN0OAIDMKAIAvw4AgIgNAACG0QAAgB0BAITNAACI9QAAzDwCAIQ1AQDMRAIAhikBAIYOAICIZQEAjA4AgKdEBQBoDgCAi+0AAIjtAACBDQAAiCUAAIZlAADMcAIAzXQCAMwwAgDN2AUAYg4AgJIOAICAOQAAZQ4AgMzgBQCADgCAzCgBAM0UAQCGJQAAiFUAAA4OAICGhDAAyg0AgIDVBwCG/QcAng4AgMwkAgCIPQAApA4AgHEOAICIPQAAqg4AgMxIAgDNeAIAVg4AgLAOAICXwAUAlnAFAJUYBQCAaQAAk1gFAIE5AACIZQAAkPg8AIZZAACeqAUAhEUAAG4OAIDM1AIAmrQFAIBdAACYrAUAp+wEAIgRAADM2AIAzdwCAKO8BAC2DgCAzGACAMgOAIB0DgCAzg4AgK0IBADUDgCAq/QEAMwsAgCIBQAA2g4AgLfoAwC2HAQAtSgEAMwAAgCzKAQAi3kAAIh9AACwdAQAhkEAAL6kAwCEdQAAiB0AAOAOAIC6TAMAzNwDALj8AwCDqAIAiA0AAMIOAICIFQAAh5QCAMw4AgBrDgCAzAQCAIvcAgCPDQAAdw4AgI8ZAADMIAIAeg4AgI3wAgCIdQAAmCADAJksAwCVDgCAmg0AgMxMAgCWcAMAzCQCAIg9AACYDgCAzCwCAIgFAAC5DgCAzCQCAIgNAAC8DgCAh/UAAKjUAwCpxAMA4w4AgNlgAgDYDwCA2w8AgOEPAICUNQAAkzEAANloAgDeDwCA2UwCAJQFAADkDwCAlSEAAJQpAABWEACAehYAgEkXAIDYFgCA2WACAD0XAIC12AMAtPADAJQ1AADZWAIAYBcAgJQFAADZVAIAlA0AADcXAIDgdAEAisgAALwVAACIyAAA4IACAI0XAICBoAAApOwCAKTIAgCoXAAAvA0AAJ8XAIDghAIAvAUAAKMXAICk+AIA4PQCALDMAwCV0AAAYxcAgLPgAwCmyAIAp2ACAJLYAABqFwCAvsEAAHEXAICXwQAAeBcAgH8XAICGFwCAzXg/AMy8PwC+gA0AkRcAgLx4DAC9gA0AuvQMALtUDAC49AwAmBcAgLwXAIC3uAwAwBcAgLWMDACyoAMAs6AMAKcXAICxQAMArnACAK9kAwC4BQMArUgDAK4XAIC1FwCAqEQDAKnYAwDgFwCAp9gDAKRoAgCliAMAtjUDALc9AwCSyAIAtT0DAJldAQCYTQEAm2UBAJppAQCdZQEAnGUBAJ+FAQCemQEAh5wCAL6tAACWpQAAl70AAMw0BQDNjDcAzLg4AM2sOACflQEAth0AAJ2ZAQCc9QEAs7EBAK54AgDnFwCAxBcAgJk9AADLFwCAmxkAAJoJAADSFwCA2RcAgOBIAgCeCQAArFwCAK30AgAAGACA/BcAgAQYAIDuFwCAh2ADAPUXAICvVAIAvhEAAJcFAAAIGACA4KwCAAwYAICG+AMAh+wDAOC0AgAUGACAr0gCAK6QAgDgPAIAvg0AABAYAICXGQAA4NgCAIaEAwCWEQAAvwAMAJ1tAACcYQAAGBgAgLFMAgCzUAIAlQ0AABwYAICGnAMA4MgCALMEAgCCBQAAKBgAgLNQAgCVDQAALBgAgCAYAIAkGACA4LQCAIaMAwCH3AMAvg0AAJVpAACWeQAAMBgAgLToAgC1UAIAlwUAADgYAIDg1AIAtPQCAL4ZAADgoAIANBgAgODUAgCZjAMAt9QCAIoFAAA8GACAQBgAgIoVAAC3NAIAjx0AAEQYAIBIGACAswUAAEwYAICzBQAAYRgAgJwJAACdCQAAUxgAgFoYAICMBQAAaBgAgHMYAIB6GACAgRgAgJ9JAACIGACAjxgAgGwYAICWGACAnRgAgN8YAIDVGACA8BgAgOYYAICkGACAg8kBAIH5AQCyGACAuRgAgMAYAIDHGACAzhgAgKsYAICAtAIApYgDAOEIAgCuHQAA9xgAgLwJAACN9QEA+xgAgOEAAgCSlQEA45QQAJNFAACXiQEAhRQAAId4AQCGAAQAVzoAgFs6AIBfOgCAYzoAgGc6AICdeQAA74xoAJyhAQBrOgCAbzoAgKKZAABzOgCAdzoAgHs6AIB/OgCAp4kAAIM6AICHOgCAqUkBAIs6AICsqQAAjzoAgJM6AICXOgCAsyUBAJs6AICfOgCAozoAgLchAQC2OQEAtTEBAKc6AICrOgCAufkAALkRAQC4GQEArzoAgLM6AIC3OgCAuzoAgICwAQCEiAIAvzoAgIPIAQCEVAMAhFwEAMM6AICEXAUAgN0DAIEtAACCMQAAvjwCAMs6AIDPOgCAh4gDAIacBACzLQMA0zoAgNc6AIC+AAQAvhwFALbRAwC12QMA2zoAgLv5AwC68QMAmljTAYTgBwC/xQMAvtkDAL3dAwC83QMAvgAYAKUFAwCmDQMA3zoAgIQcGADjOgCA5zoAgKPxAwCsAQMArQEDAK4FAwCvGQMArKQbAq3cGgKqLQMAqyUDAL5MGQC+SBoA6zoAgL6AGwC04BoCtdQdArYwHgLvCAIA7zoAgOGgAQC6OBoC4/gCALoAAAC9ZBwCvvQcAr8AEAKRBNMBkOT2AeBEAQCSCD4C8zoAgPc6AID7OgCA/zoAgL6sHAADOwCABzsAgAs7AIAPOwCAEzsAgBc7AIAbOwCAgbBtAICAAQCDHFIAgth3AIUgmgCEkL4AhwjPAIaM5gCJbDcBiOAsAYsYfgGK2BMBjeClAYzwWgGP/OsBjliPAbDVFwCxAWgAso1rALOdawC0SWsAtZVvAB87AIDgcAEAIzsAgCc7AIArOwCALzsAgIAZAACBGQAAggUAADM7AIA7OwCAoaUCAKJJBwCjQQcApEEGAKXVGwCm3RsAp8EaAKgBHACp4R8AqkkfAKsBEACs9RMAra0TAK4BFACv+RcAqDEGAKkxBgCqTQYAq0UGAKxNBgCtmQYAro0GAK+FBgCGgAMAhxgDAD87AIBDOwCARzsAgEs7AIBPOwCAUzsAgLhtBwC5dQcAun0HALt1BwC8bQcAvc0HAL75BwC/+QcAsKkGALGFBgCyeQcAs3kHALRpBwC1aQcAtl0HALdVBwDHOgCAs8EGAFc7AIA3OwCAth0GAFs7AIBfOwCAtcEGALppBgC7RQYAYzsAgGc7AIC+qQcAv6kHALypBwC9qQcAo4UGAGs7AIBvOwCAczsAgHc7AICmWQYApYUGAHs7AICrAQYAqi0GAH87AICDOwCAr+0HAK7tBwCt7QcArO0HAKjBBgCpLQEAqiUBAKs9AQCsJQEArS0BAK4lAQCvlQEAhzsAgIs7AICPOwCAkzsAgJc7AICCvQAAgb0AAIC9AAC4nQEAua0BALqlAQC7bQAAvHUAAL19AAC+dQAAv20AALD1AQCx/QEAssEBALPBAQC0tQEAtb0BALa1AQC3rQEAmzsAgJ87AICjOwCAs6EBAKc7AIC1oQEAtqEBAKs7AICGgAEAh8QBALo9AQC7NQEAvBkBAL0ZAQC+fQEAv3UBAKPtAQCvOwCAszsAgLc7AIC7OwCApu0BAKXtAQC/OwCAq3kBAKpxAQDDOwCAxzsAgK85AQCuMQEArVUBAKxVAQDLOwCAzzsAgNM7AIDXOwCA2zsAgOGsAQDfOwCA42AGAOM7AIDnOwCA6zsAgO9UBgDvOwCA8zsAgL60GgD3OwCA+zsAgP87AICGaBwAh4wDAAM8AIAHPACACzwAgA88AICAOQAAgTkAAIIFAAATPACAGzwAgB88AIAjPACAJzwAgKgdAwCpQQMAqkEDAKtBAwCsQQMArUkDAK5xAwCvcQMAhCAdACs8AIAvPACAMzwAgDc8AIA7PACAPzwAgEM8AIC46QAAufUAALr9AAC78QAAvJEAAL2RAAC+iQAAv4kAALDhAACx4QAAsuEAALPhAAC04QAAte0AALbZAAC32QAA4wwHAOEgBwDhMAEA4wgHAEc8AIBLPACATzwAgFM8AIBXPACAWzwAgF88AIBjPACA75gHAGc8AIBrPACA74gHALOJAgBvPACAczwAgL6AGgB3PACAtokCALWJAgB7PACAu2UBALplAQB/PACAgzwAgL9pAQC+ZQEAvXUBALx1AQC3PQYAtj0GALU9BgC0IQYAszUGALI1BgCxAQYAsAkGAL9ZBgC+UQYAvVkGALxNBgC7bQYAunkGALlxBgC4eQYAgJ0AAIGtAACCpQAAizwAgI88AICTPACAlzwAgJs8AICvcQYArmkGAK1tBgCsbQYAq4EGAKqZBgCpkQYAqJkGABc8AICHPACAnzwAgKPFHQCjPACApcUdAKbFHQCnPACAhgADAIdkAwCqKR4AqykeAKw5HgCtOR4ArikeAK8lHgCzOR4AqzwAgK88AICzPACAtzwAgLb9HgC1/R4AuzwAgLvZHgC60R4AvzwAgMM8AIC/aR8AvmEfAL1pHwC8wR4AqPEeAKnxHgCq8R4Aq/EeAKw1HgCtPR4ArjUeAK8tHgDHPACAyzwAgM88AIDTPACA1zwAgNs8AIDfPACA4zwAgLjlHwC57R8AuuUfALv5HwC86R8AvZEfAL6RHwC/jR8AsFUeALFdHgCyVR4As/0fALTlHwC17R8AtuUfALfdHwCjeR8A5zwAgOs8AIDvPACA8zwAgKa9HwClvR8A9zwAgKuZHwCqkR8AhogAAIdMAQCvKR4AriEeAK0pHgCsgR8AgEkAAIFJAACCWQAAs5keAPs8AIC1iR4AtlEBAP88AIADPQCABz0AgLotAQC7JQEAvD0BAL0lAQC+JQEAvxUBAKhNHgCpVR4Aql0eAKtVHgCsTR4ArZ0BAK6JAQCvgQEAhKwBAAs9AIAPPQCAEz0AgBc9AIAbPQCAHz0AgCM9AIC4ZQEAuW0BALplAQC7fQEAvGUBAL1tAQC+ZQEAv9kAALClAQCxrQEAsqUBALO9AQC0rQEAtZ0BALaVAQC3XQEAo9UdACc9AIArPQCALz0AgDM9AICmHQIApcUdADc9AICraQIAqmECADs9AIA/PQCAr1kCAK5pAgCtaQIArHECAEM9AIBHPQCASz0AgE89AIBTPQCAVz0AgFs9AIBfPQCAgDkAAIE5AACCBQAAYz0AgGs9AIBvPQCAh0ADAIZcBACETAQAcz0AgHc9AICEBAUA4yABAHs9AIDhqAEAfz0AgO+UGgCDPQCAhz0AgIs9AICPPQCAkz0AgJc9AICbPQCAs6EDAJ89AICjPQCApz0AgKs9AIC2fQMAtX0DAK89AIC7WQMAulEDALM9AIC3PQCAv/0AAL79AAC9/QAAvEEDAKhRAgCpWQIAqmkCAKtpAgCstQIArb0CAK61AgCvrQIAhKgHALs9AIC/PQCAwz0AgIKpAADHPQCAgKkAAIGpAAC4aQEAuWkBALoJAQC7CQEAvBkBAL0ZAQC+CQEAvwkBALDVAgCx3QIAstUCALNpAQC0eQEAtXkBALZpAQC3YQEA4bgBAOHUHwDjOB8A4wwbAMs9AIDPPQCA0z0AgNs9AIDfPQCA4z0AgOc9AIDrPQCAvjwJAO89AIDvhBsA74QbAKOhAgDzPQCAhugEAIe8BQD3PQCApn0CAKV9AgD7PQCAq1kCAKpRAgD/PQCAAz4AgK/9AQCu/QEArf0BAKxBAgCzhQYA1z0AgAc+AIALPgCADz4AgLaJBgC1jQYAEz4AgLuRBgC6iQYAFz4AgBs+AIC/9QYAvokGAL2BBgC8iQYAHz4AgCM+AIAnPgCAKz4AgC8+AIAzPgCANz4AgO+EHQA7PgCA4QAEAD8+AIDj/AQAgBEAAIEdAACCBQAAQz4AgKjxBgCp8QYAqg0GAKsFBgCsBQYArQkGAK49BgCvNQYARz4AgEs+AICGiAAAhxADAE8+AIBTPgCAVz4AgFs+AIC4EQYAuRkGALohBgC7IQYAvPUHAL39BwC+9QcAv+kHALBNBgCxVQYAsl0GALNVBgC0TQYAtTEGALYxBgC3MQYAo4UHAF8+AIBjPgCAZz4AgGs+AICmiQcApY0HAG8+AICrkQcAqokHAHM+AIB3PgCAr/UHAK6JBwCtgQcArIkHAHs+AICz4QYAfz4AgIM+AIC25QYAhz4AgIs+AIC18QYAur0GALuNBgCPPgCAkz4AgL59AQC/ZQEAvJUGAL11AQCoHQYAqSUGAKotBgCrJQYArD0GAK0hBgCuXQYAr00GAJc+AICbPgCAnz4AgKM+AICnPgCAgrkDAIGxAwCAuQMAuO0BALmFAQC6jQEAu4UBALydAQC9hQEAvo0BAL+FAQCwPQYAsQ0GALIFBgCz5QEAtP0BALXlAQC25QEAt9UBAKOlBQCrPgCArz4AgLM+AIC7PgCApqEFAKW1BQC/PgCAq8kFAKr5BQCGCAwAhxwDAK8hAgCuOQIArTECAKzRBQDDPgCAs/ECAMc+AIDLPgCAtlUDAM8+AIDTPgCAteECALpxAwC7eQMA1z4AgNs+AIC+MQMAvz0DALxRAwC9UQMAqCUCAKk1AgCqPQIAqzUCAKwtAgCtkQMArpEDAK+RAwDfPgCA4z4AgOc+AIDrPgCArAAAAO8+AIDzPgCA9z4AgLiZAwC5rQMAuqUDALttAwC8dQMAvX0DAL51AwC/bQMAsPEDALH5AwCywQMAs8EDALSxAwC1vQMAtrUDALepAwD7PgCA/z4AgAM/AIAHPwCACz8AgA8/AIATPwCA76gaAL5oDADhlAEAFz8AgOMcBgCADQAAgXEAAIJxAAAbPwCAo/UDAB8/AIAjPwCAhEwCACs/AICmUQIApeUDAC8/AICrfQIAqnUCAIbIDACHLA0ArzkCAK41AgCtVQIArFUCAOFQBgAzPwCA4xQHAITADAA3PwCAOz8AgD8/AIBDPwCARz8AgEs/AIBPPwCAUz8AgFc/AIBbPwCA73gbAL74DwBfPwCAYz8AgGc/AICzjQEAaz8AgLWZAQC2jQEAbz8AgGc9AIBzPwCAuoUBALtNAQC8VQEAvV0BAL5VAQC/SQEAo0EOACc/AIB3PwCAez8AgH8/AICmQQ4ApVUOAIM/AICrgQ4AqkkOAIc/AICLPwCAr4UOAK6ZDgCtkQ4ArJkOAIBtAACBCQAAgh0AAI8/AIDvGAkAkz8AgJc/AICbPwCA4zwNAJ8/AIDhWAwAoz8AgIbQAACHvAMApz8AgKs/AICokQ4AqZkOAKrJDgCrxQ4ArN0OAK3BDgCuwQ4Ar/UOAIToAACvPwCAsz8AgLc/AIC7PwCAvz8AgMM/AIDHPwCAuMEPALnBDwC6wQ8Au8EPALzBDwC9wQ8AvsEPAL/1DwCwjQ4AsUUOALJNDgCzRQ4AtF0OALVBDgC2QQ4At0EOAKhRDgCpWQ4Aqo0OAKudDgCshQ4ArY0OAK6FDgCvvQ4Ayz8AgM8/AIDTPwCA1z8AgNs/AIDfPwCA4z8AgOc/AIC4kQ4AuZkOALqtDgC7RQEAvF0BAL1FAQC+RQEAv3UBALDFDgCxzQ4AssUOALPdDgC0xQ4AtbUOALa9DgC3tQ4AswUOAOs/AIDvPwCA8z8AgPc/AIC2DQ4AtQ0OAPs/AIC7CQ4AugEOAP8/AIADQACAv3EOAL4BDgC9CQ4AvBEOAIJtAACjQQ4AgFUAAIFlAACmSQ4AC0AAgA9AAIClSQ4AqkUOAKtNDgCGSAAAh3gAAK5FDgCvNQ4ArFUOAK1NDgCoXQIAqWECAKplAgCrdQIArG0CAK2xAgCusQIAr7ECAITsBAATQACAF0AAgBtAAIAfQACAI0AAgCdAAIArQACAuHEDALlxAwC6cQMAu3EDALzVAwC93QMAvtUDAL/NAwCw0QIAsdECALLRAgCz0QIAtFEDALVRAwC2UQMAt1EDAC9AAICz6QIAM0AAgL6ABAC2NQIAN0AAgDtAAIC14QIAuhECALsRAgA/QACAQ0AAgL6RAwC/kQMAvAECAL0BAgBHQACAS0AAgKOlAgBPQACApa0CAFNAAIBXQACApnkCAFtAAIBfQACAq10CAKpdAgCtTQIArE0CAK/dAwCu3QMAqNUCAKndAgCqLQEAqyUBAKw9AQCtJQEAri0BAK8lAQBjQACAZ0AAgGtAAIBvQACAc0AAgHtAAIB/QACAg0AAgLiFAQC5iQEAup0BALuVAQC8sQEAvbEBAL55AAC/eQAAsF0BALHlAQCy4QEAs/kBALTpAQC13QEAttUBALe9AQDh8A4Ah0AAgOMUDgCLQACAgb0AAIC9AACPQACAgq0AAIYABACH7AUAk0AAgJdAAICbQACAn0AAgO9gDgCjQACAp0AAgKtAAICFXH0Ar0AAgLNAAIDjZAEAt0AAgOG0AQC7QACA76AOAL9AAIC3PgCAhPgFAMNAAIDHQACAy0AAgLMlBgB3QACAz0AAgNNAAIDXQACAtiUGALU1BgDbQACAu6EGALoZBgDfQACA40AAgL+ZBgC+rQYAva0GALy1BgCCbQAA7zAEAIBVAACBZQAAvlwDAOdAAICG+AAAh2wDAOtAAIDvQACA80AAgPdAAID7QACA40QEAP9AAIDhjAcAo6UGAANBAIAHQQCAC0EAgA9BAICmpQYApbUGABNBAICrIQYAqpkGABdBAIAbQQCArxkGAK4tBgCtLQYArDUGAB9BAICz+QcAI0EAgCdBAIC2SQcAK0EAgC9BAIC1UQcAulEHALtRBwAzQQCAN0EAgL41BwC/OQcAvEUHAL09BwCoNQYAqT0GAKo1BgCriQYArJ0GAK2NBgCusQYAr7EGADtBAIA/QQCAQ0EAgEdBAICADQAAgbEAAIKxAABLQQCAuKEGALmtBgC6vQYAu7UGALytBgC9XQEAvlUBAL9NAQCw0QYAsdEGALLVBgCzrQYAtLUGALW5BgC2qQYAt6UGAKO9BgBPQQCAU0EAgISEAgC+kAEApg0GAKUVBgBbQQCAqxUGAKoVBgCGCAAAh3wBAK99BgCucQYArXkGAKwBBgBfQQCAs60BAGNBAIBnQQCAtqkBAGtBAIBvQQCAta0BALptAQC7dQEAc0EAgHdBAIC+XQEAvzUBALxlAQC9VQEAqGECAKlhAgCqYQIAq2ECAKxhAgCtbQIArp0CAK+VAgB7QQCAf0EAgINBAICHQQCAi0EAgI9BAICTQQCAl0EAgLiVAgC5nQIAuqECALuhAgC8cQMAvXEDAL5xAwC/cQMAsO0CALH1AgCy9QIAs8UCALTdAgC1tQIAtrECALexAgCbQQCAn0EAgKNBAICj5QIAp0EAgKXlAgCm4QIAq0EAgK9BAICzQQCAqiUCAKs9AgCsLQIArR0CAK4VAgCvfQIAt0EAgLtBAIC/QQCAhEB8AIAVAACBHQAAggUAAMNBAIC+7HwAy0EAgIZIfQCHCAMAz0EAgNNBAIDXQQCA20EAgKidAgCpxQIAqsECAKvBAgCsxQIArc0CAK7xAgCv8QIA30EAgONBAIDnQQCA60EAgMkAAADvQQCA80EAgPdBAIC4wQEAucEBALrBAQC73QEAvM0BAL31AQC+/QEAv50BALBBAQCxQQEAskEBALNBAQC0QQEAtUEBALZBAQC3QQEA4TgGAPtBAIDjaAYA/0EAgANCAIAHQgCAC0IAgISUfQC+rHwAD0IAgBNCAIAXQgCAvrh/ABtCAIDvEAEAH0IAgCNCAIAnQgCAK0IAgC9CAIDhkAEAM0IAgONEAAA7QgCAgS0AAIAtAADvgAAAgjkAAD9CAIBDQgCAB0AAgEdCAIDhsH8Ax0EAgOPUfABLQgCAN0IAgE9CAICGuAAAh9QCAFNCAIBXQgCAW0IAgF9CAIBjQgCAZ0IAgO8gfABrQgCAs4l9AG9CAIBzQgCAd0IAgHtCAIC2jX0AtY19AH9CAIC7RX4AukV+AINCAICHQgCAv0V+AL5FfgC9VX4AvFV+AKNJfQCLQgCAj0IAgJNCAICXQgCApk19AKVNfQCbQgCAq4V+AKqFfgCfQgCAo0IAgK+FfgCuhX4ArZV+AKyVfgCCbQAAszF+AIBVAACBZQAAtvF/AITcAwCnQgCAtSF+ALrNfwC70X8AhgAEAIfUAAC+dX8Av3l/ALzBfwC9wX8AqOV/AKn1fwCq/X8Aq/V/AKztfwCtNX4Arj1+AK81fgCrQgCAr0IAgLNCAIC3QgCAu0IAgL9CAIDDQgCAx0IAgLjZfgC54X4AuuF+ALvhfgC85X4Avel+AL6ZfgC/mX4AsE1+ALFRfgCyUX4As1F+ALT1fgC1+X4Atul+ALfpfgCjdX8Ay0IAgM9CAIDTQgCA10IAgKa1fgClZX8A20IAgKuVfgCqiX4A30IAgONCAICvPX4ArjF+AK2FfgCshX4A50IAgLMxfgDrQgCA70IAgLbFAQDzQgCA90IAgLXRAQC6yQEAu8kBAPtCAID/QgCAvs0BAL+xAQC8yQEAvckBAKjdfQCp9X0Aqv19AKvxfQCsHQIArQECAK45AgCvOQIAA0MAgAdDAIALQwCAD0MAgIIFAAATQwCAgBEAAIERAAC4EQIAuRkCALohAgC7IQIAvNUCAL3dAgC+1QIAv80CALBJAgCxSQIAslkCALNZAgC0TQIAtTECALYxAgC3MQIAvgADAKNxfQCEiAIAvoAEAKaFAgAbQwCAH0MAgKWRAgCqiQIAq4kCAIYoBACHDAMAro0CAK/xAgCsiQIArYkCACNDAICEyAMAhcwFALPlAwAnQwCAteUDALbtAwArQwCAL0MAgDNDAIC6bQMAu2UDALx9AwC9ZQMAvmUDAL9VAwA3QwCAO0MAgL8ABACjJQIAP0MAgKUlAgCmLQIAQ0MAgEdDAIBLQwCAqq0CAKulAgCsvQIAraUCAK6lAgCvlQIAT0MAgFNDAIBXQwCAW0MAgF9DAIDjzAMAY0MAgOGsAQBnQwCA7xwDAGtDAIBvQwCAc0MAgHdDAIB7QwCAf0MAgOFwfwBXQQCA4wR+AINDAICLQwCA4ZQBAI9DAIDjWAEAgNkAAIHZAACCJQAA7+R+AJNDAICXQwCA7+B+AJtDAICzAQEAn0MAgIboBwCHLAQAo0MAgLY1AQC1BQEAp0MAgLvxAAC64QAAq0MAgK9DAIC/sQAAvtEAAL3ZAAC84QAAF0MAgIdDAICzQwCAt0MAgKEBBACgEQQAoxkAAKLFBACotQYAqb0GAKrpBgCr/QYArO0GAK3VBgCu3QYArz0HALBFBwCxVQcAslUHALNtBwC0dQcAtRUHALYdBwC3FQcAuC0HALk1BwC6MQcAuw0HALwZBwC9GQcAvgkHAL8JBwCjQQYAu0MAgL9DAIDDQwCAx0MAgKZ1BgClRQYAy0MAgKuxBwCqoQcAj8ltAM9DAICv8QcArpEHAK2ZBwCsoQcAld11AJTBdACXzXAAli1zAJFdaACQVWgAk9l0AJJNaQCd5XgAnB17AJ9tBwCeuXgAmR1/AJhVcACboXwAmvl8AIJhbACDhWkA00MAgNdDAICGEXUAhxF1AISVaQCFjWgAij10AIvFcgDbQwCA30MAgI7dfgCPMX0AjD1xAI2dcQCSGX0Ak716AONDAIDvkAkAltUGAJdRBQCUXXkAlQl5AJpxBQCbvQUA50MAgOtDAIDvQwCA4agFAJx5AQDjuAgAoYUBAPNDAICjqQ0AogEMAKUBCACkOQ0Ap6kJAKa9CQCppRUAqAEUAKsBFACq/RUArbkRAKyxEQCvARwArqEQALH9HACw5R0As+kZALIBGAC1ASQAtH0ZAIQUAAC+FAAAgI0AAIGVAACCbQAA+0MAgIZQDwCHZAAA/0MAgANEAIC61QcAu90HALjBBwC5wQcAvjEEAL8xBAC88QcAvfEHALKtBwCztQcAsK0HALGlBwC2nQcAt/UHALSlBwC1lQcAqmkHAKtpBwCoaQcAqWkHAK5pBwCvaQcArGkHAK1pBwAHRACAC0QAgA9EAIATRACAF0QAgBtEAIAfRACAI0QAgKgRBQCpHQUAqjkFAKs5BQCsLQUArVEFAK5JBQCvQQUAJ0QAgCtEAIAvRACAM0QAgDdEAIA7RACAP0QAgENEAIC4XQIAuWkCALrBAwC7wQMAvPkDAL35AwC+kQMAv7UDALAJBQCxCQUAsuECALPhAgC0dQIAtX0CALZ1AgC3bQIAs7EEAIQAAgC+BA0AR0QAgEtEAIC20QQAtaUEAE9EAIC7zQQAus0EAFNEAIBXRACAv7kDAL6xAwC9NQMAvDUDAFtEAICj9QQAX0QAgGNEAICmlQQAa0QAgG9EAICl4QQAqokEAKuJBACHqA0AhswMAK71AwCv/QMArHEDAK1xAwDhUAYA4TQHAONAAADjWAcAgNEAAIHdAACC1QAAc0QAgHdEAIB7RACAf0QAgINEAICHRACAi0QAgO+cAADvyAcAj0QAgJNEAICzNQIAl0QAgLW1AQCbRACAn0QAgLa1AQC+7AwAo0QAgLuRAQC6mQEAvVEBALyJAQC/UQEAvlkBAKjtDQCp/Q0AqvUNAKttDgCsdQ4ArX0OAK51DgCvbQ4AZ0QAgKdEAICrRACAr0QAgLNEAIC3RACAu0QAgL9EAIC49Q4Auf0OALr1DgC7QQ8AvEEPAL1JDwC+cQ8Av3EPALAVDgCxHQ4AshUOALPNDgC01Q4Atd0OALbVDgC3zQ4Ao30NAMNEAIDHRACAy0QAgM9EAICm/Q4Apf0OANNEAICr2Q4AqtEOAISoAgDXRACArxkOAK4RDgCtGQ4ArMEOAIBNAACBVQAAglUAALNRDwDbRACAtXEPALZxDwDfRACAhuAAAIcEAwC6XQ8Auy0PALw1DwC9OQ8Avi0PAL8lDwCoVQ4AqV0OAKqVDgCrrQ4ArLUOAK29DgCutQ4Ar60OAONEAIDnRACA60QAgO9EAIDzRACA90QAgPtEAID/RACAuGkBALlpAQC6eQEAu3kBALxpAQC9aQEAvt0BAL/VAQCw1Q4AsaUOALKtDgCzoQ4AtKUOALWtDgC2nQ4At1kBAKMdDgADRQCAB0UAgPdDAIALRQCApj0OAKU9DgAPRQCAq2EOAKoRDgATRQCAF0UAgK9pDgCuYQ4ArXUOAKx5DgAbRQCAH0UAgCNFAIAnRQCAK0UAgC9FAIAzRQCAN0UAgIANAACBFQAAgh0AADtFAIA/RQCAQ0UAgIR4AQC+FAAA4xQPAEtFAIDh4A0AhAADAIawBACHFAMAT0UAgFNFAIBXRQCAW0UAgF9FAIBjRQCA78APAGdFAIBrRQCAb0UAgHNFAIB3RQCAe0UAgLNtAwB/RQCAtX0DALZ1AwCDRQCAh0UAgItFAIC6UQMAu1EDALz1AwC9/QMAvukDAL/hAwCPRQCAk0UAgJdFAICbRQCAn0UAgKNFAICnRQCAq0UAgKhxAgCpeQIAqokDAKuJAwCsmQMArZkDAK6JAwCviQMAsPkDALH5AwCyTQMAs0UDALRBAwC1SQMAtnEDALdxAwC4IQMAuSEDALohAwC7IQMAvCEDAL0hAwC+IQMAvyEDAICdAQCBEQAAghEAAIQEBQDvFAAAr0UAgLNFAIC+EAUA48gAALtFAIDh0AEAv0UAgMNFAIDHRQCAy0UAgM9FAICqeQIAq3kCAIboBACHYAUArsECAK/JAgCs3QIArdUCANNFAICjRQIA10UAgNtFAICmXQIA30UAgONFAIClVQIA50UAgOtFAIDvRQCA80UAgPdFAID7RQCA/0UAgO+EDgC+rAQA4dAOAANGAIDjFAEAB0YAgAtGAIAPRgCAE0YAgLPdAQAXRgCAG0YAgB9GAIAjRgCAtv0BALX9AQArRgCAu90BALrdAQCE4AQAL0YAgL+hAQC+vQEAvb0BALy9AQCoBQYAqR0GAKoVBgCrLQYArDUGAK09BgCuNQYArykGALdFAICC9QcAgeUHAIDlBwAnRgCAM0YAgIYcAACHsAMAuCUGALnFBgC6zQYAu8UGALzdBgC9xQYAvs0GAL/FBgCwWQYAsVkGALIpBgCzKQYAtDkGALUlBgC2JQYAtx0GAKOdBgA3RgCAO0YAgD9GAIBDRgCApr0GAKW9BgBHRgCAq50GAKqdBgBLRgCAT0YAgK/hBgCu/QYArf0GAKz9BgBTRgCAs/UHAFdGAIBbRgCAtu0HAF9GAIBjRgCAteUHALqNBwC7kQcAZ0YAgGtGAIC+dQcAv30HALyBBwC9fQcAqCUGAKkpBgCqOQYAqzkGAKwpBgCtKQYArnkGAK91BgBvRgCAc0YAgHdGAIB7RgCAf0YAgINGAICHRgCAi0YAgLjVBgC53QYAuuEGALv9BgC85QYAve0GAL7lBgC/mQYAsA0GALERBgCyEQYAs+0GALT1BgC1/QYAtvUGALftBgCjsQYAgi0AAIEVAACAsQAAR0UAgKapBgCloQYAj0YAgKvVBgCqyQYAk0YAgL5oAQCvOQYArjEGAK05BgCsxQYAm0YAgLPxAQCGaAAAh3wBALZdAQCfRgCAo0YAgLVVAQC6SQEAu0kBAKdGAICrRgCAvj0BAL8hAQC8OQEAvTUBAK9GAICzRgCAhAQDAL6AHAC3RgCA4RwGALtGAIDjAAYAvwguAL9GAIDDRgCA78gHAMdGAIDLRgCAz0YAgNNGAIDXRgCA20YAgKN9AgDfRgCApdkCAONGAIDnRgCAptECAOtGAIDvRgCAq8UCAKrFAgCtuQIArLUCAK+tAgCusQIAqW0FAKhZBQCrDQIAqrkCAK0dAgCsHQIArwUCAK4NAgC+aB0A80YAgPdGAID7RgCAgB0AAIEJAACCmQEA/0YAgLnhAwC4KQIAu+EDALrpAwC94QMAvPkDAL/hAwC+6QMAsU0CALBNAgCzIQIAsi0CALUlAgC0OQIAtxECALYlAgCowQIAqdECAKrRAgCr5QIArP0CAK0VAQCuHQEArw0BAANHAIALRwCAD0cAgBNHAIAXRwCAG0cAgB9HAIAjRwCAuAUBALkJAQC6HQEAuxUBALwxAQC9MQEAvv0BAL/1AQCweQEAsUEBALJBAQCzXQEAtEUBALVNAQC2RQEAtz0BAIagHQCHxB0AJ0cAgO/YAAArRwCAL0cAgDNHAIDvxAYAhGwcAOH0BgA3RwCA47AGADtHAIDhlAEAP0cAgONEBgCzGQIAQ0cAgEdHAIBLRwCAhewsALbVAQC1NQIAT0cAgLvFAQC6/QEAU0cAgFdHAIC/yQEAvsEBAL3JAQC81QEAo9kdAAdHAIBbRwCAX0cAgGNHAICmFR4ApfUdAGdHAICrBR4Aqj0eAGtHAIBvRwCArwkeAK4BHgCtCR4ArBUeAIBpAACBaQAAggUAAHNHAIB3RwCAe0cAgIcQAwCGfAMAf0cAgINHAICHRwCAi0cAgI9HAICTRwCAl0cAgJtHAICopR8Aqa0fAKqlHwCrvR8ArKUfAK2tHwCupR8ArxUfAJ9HAICjRwCAp0cAgKtHAICvRwCAs0cAgLdHAIC7RwCAuA0fALkZHwC6IR8AuyEfALzZAAC92QAAvskAAL/BAACwcR8AsXEfALJxHwCzRR8AtEEfALVNHwC2PR8AtzUfALMtHgC/RwCAw0cAgMdHAIDLRwCAti0eALUtHgDPRwCAu7UeALq1HgDTRwCA10cAgL+JHgC+hR4AvZEeALylHgCCKQAAo2keAIAdAACBFQAApmkeANtHAIDfRwCApWkeAKrxHgCr8R4A40cAgITgAQCuwR4Ar80eAKzhHgCt1R4AqNUBAKnlAQCq7QEAq+UBAKz9AQCt5QEAru0BAK/lAQC+oAEAl0YAgOdHAIDrRwCAhhAAAId0AQDvRwCA80cAgLh9AQC5wQAAusEAALvBAAC8wQAAvckAAL7xAAC/8QAAsJ0BALFFAQCyTQEAs0UBALRdAQC1RQEAtk0BALdFAQD3RwCA+0cAgP9HAIADSACAB0gAgO80AgDv7B4AC0gAgOHwHQDj4AIA4zAeAOGEAQAPSACAE0gAgBdIAIAbSACAsyUCAJQAAAAfSACAI0gAgCdIAIC2JQIAtTUCACtIAIC7wQIAuhkCAC9IAIAzSACAv8ECAL7ZAgC90QIAvNkCADdIAIA7SACAP0gAgKPpAgBDSACApfkCAKbpAgBHSACAS0gAgE9IAICq1QIAqw0CAKwVAgCtHQIArhUCAK8NAgCAYQAAgWEAAIIFAABTSACAW0gAgIQABAC+FAQAX0gAgIbABACHUAMAY0gAgGdIAIBrSACAb0gAgHNIAIB3SACAqK0CAKm9AgCqtQIAqw0BAKwVAQCtHQEArhUBAK8NAQCE7AQAe0gAgH9IAICDSACAh0gAgItIAICPSACAk0gAgLgdAQC5LQEAuiUBALvNAQC81QEAvd0BAL7JAQC/wQEAsH0BALFVAQCyXQEAs1UBALRNAQC1PQEAtjUBALctAQDhGB4Al0gAgOM4HgCbSACAn0gAgKNIAICnSACAq0gAgK9IAICzSACAvmAEALdIAICBdQAAgHUAAO/gHwCCbQAAu0gAgL9IAICG6AQAh3wFAMNIAIDhkAEAy0gAgOOgAADPSACA00gAgNdIAIDvtAAA20gAgN9IAIDjSACA50gAgLUFBgBXSACAx0gAgLYFBgDrSACA70gAgLOlBQDzSACAvRkGALwRBgC/YQYAvhEGAPdIAID7SACAuwkGALohBgCj/QUA/0gAgANJAIAHSQCAC0kAgKZdBgClXQYAD0kAgKtRBgCqeQYAE0kAgBdJAICvOQYArkkGAK1BBgCsSQYAqFEGAKlZBgCqYQYAq2EGAKxhBgCtYQYArmEGAK9hBgAbSQCAH0kAgCNJAIAnSQCAgA0AAIGxAQCCsQEAK0kAgLhNBwC5VQcAul0HALtVBwC8TQcAvXUHAL59BwC/cQcAsMUHALHNBwCyxQcAs90HALTFBwC1zQcAtsUHALd5BwCz6QcAL0kAgDNJAICEwAEAvtgBALbhBwC16QcAN0kAgLsJBgC6AQYAhogAAIesAQC/CQYAvgEGAL0JBgC8EQYAO0kAgKOtBwA/SQCAQ0kAgKalBwBHSQCAS0kAgKWtBwCqRQYAq00GAE9JAIBTSQCArkUGAK9NBgCsVQYArU0GAKhZBgCpZQYAqm0GAKtlBgCsYQYArWEGAK5hBgCvYQYAhKwBAFdJAIBbSQCAX0kAgGNJAIBnSQCAa0kAgG9JAIC4kQEAuZkBALqhAQC7oQEAvHEBAL1xAQC+cQEAv3EBALDxAQCx8QEAsvUBALPdAQC0xQEAtbEBALaxAQC3sQEAs+UFAHNJAIB3SQCAe0kAgH9JAIC24QUAtekFAINJAIC7NQIAujUCAIdJAICLSQCAv3UCAL4BAgC9CQIAvCECAI9JAICjoQUAk0kAgJdJAICmpQUAm0kAgJ9JAIClrQUAqnECAKtxAgCjSQCAvigDAK5FAgCvMQIArGUCAK1NAgCA1QAAgd0AAILhAACrSQCA4yABAK9JAIDhqAEAs0kAgO80AgC3SQCAhggMAIdoAwCsAAAAu0kAgL9JAIDDSQCAs40DAMdJAIDLSQCAhIAMAM9JAIC2vQMAtYEDANNJAIC7TQMAuk0DANdJAIDbSQCAv00DAL5NAwC9TQMAvE0DAKhBAgCpTQIAqkUCAKtZAgCsSQIArX0CAK51AgCvuQIAvmgNAN9JAIDjSQCA50kAgIRsDADrSQCA70kAgPNJAIC4TQEAuVUBALpVAQC7ZQEAvH0BAL0VAQC+EQEAvxEBALDJAgCxyQIAstkCALPZAgC0yQIAtckCALZ9AQC3dQEA4XgHAOOYAADjuAYA4VwGAPdJAID7SQCA/0kAgANKAIAHSgCAC0oAgA9KAIATSgCA7AAAAO9cAADv6AYAG0oAgIFpAACAYQAAo4UCAIJhAACliQIAH0oAgCNKAICmtQIAhkAMAIfEDACrRQIAqkUCAK1FAgCsRQIAr0UCAK5FAgCojQ4AqZEOAKqVDgCrqQ4ArKUOAK2tDgCupQ4Ar9kOABdKAIAnSgCAK0oAgC9KAIAzSgCAN0oAgDtKAIA/SgCAuHUPALl9DwC6dQ8Au90PALzFDwC9zQ8AvsUPAL/9DwCwqQ4AsbUOALK1DgCzhQ4AtJ0OALVRDwC2UQ8At1EPALMdDgBDSgCAR0oAgEtKAIBPSgCAti0OALUtDgBTSgCAu3EOALptDgBXSgCAW0oAgL+VDwC+WQ4AvVEOALxhDgBfSgCAo1kOAGNKAIBnSgCApmkOAGtKAIBvSgCApWkOAKopDgCrNQ4Ac0oAgHdKAICuHQ4Ar9EPAKwlDgCtFQ4AqL0OAKnRDgCq0Q4AqykBAKw5AQCtOQEArikBAK8pAQCADQAAgRUAAIIdAAB7SgCAf0oAgINKAIC+dAIAh0oAgLjtAQC5hQEAuoEBALuBAQC8hQEAvY0BAL6xAQC/sQEAsFkBALFZAQCy7QEAs+UBALT9AQC15QEAtuUBALfVAQCLSgCAtqkBALWhAQCPSgCAs0kOAJNKAICGOAAAh9wBAL8xAQC+KQEAvSEBALwpAQC7jQEAuo0BAKdJAICXSgCAoxkOAJtKAICfSgCAo0oAgKdKAICm+QEApfEBAKtKAICr3QEAqt0BAK9KAICzSgCAr2EBAK55AQCtcQEArHkBALdKAIDv3A8Au0oAgL9KAIDDSgCAx0oAgMtKAIDPSgCA00oAgNdKAIDbSgCA30oAgONKAIDj6A4A50oAgOGMDgCAEQAAgREAAIIRAACEQAIA60oAgO9KAIDzSgCAvhADAIbABACHRAMA+0oAgP9KAIADSwCAB0sAgAtLAIAPSwCA7yQCABNLAIAXSwCAG0sAgB9LAIAjSwCAJ0sAgCtLAICE7AQAL0sAgDNLAIA3SwCA4+wCADtLAIDhOAEAP0sAgLNVAwBDSwCAR0sAgEtLAIBPSwCAth0DALUdAwBTSwCAuwkDALo5AwBXSwCAW0sAgL/9AAC+/QAAvfkAALwRAwCogQIAqYkCAKqdAgCrsQIArNUCAK3dAgCu1QIAr80CAIDNAQCBCQAAghkAAF9LAIBjSwCAa0sAgL5wBQBvSwCAuFkBALlZAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9lAQCwvQIAsY0CALKFAgCzbQEAtHkBALV5AQC2aQEAt2kBAIYgBACHCAUAc0sAgHdLAIB7SwCAf0sAgINLAIDvXAAAhOwEAOFcDgCHSwCA44wOAItLAICPSwCAk0sAgJdLAICjVQIAm0sAgJ9LAICjSwCAp0sAgKYdAgClHQIAq0sAgKsJAgCqOQIAr0sAgLNLAICv/QEArv0BAK35AQCsEQIAqGkGAKlpBgCqeQYAq3kGAKxpBgCtaQYArp0GAK+VBgBnSwCAt0sAgLtLAIC/SwCAw0sAgMdLAIDLSwCAz0sAgLj1BgC5+QYAuo0GALuFBgC8nQYAvYUGAL6FBgC/tQYAsO0GALH1BgCy/QYAs/UGALTtBgC10QYAttEGALfRBgCz8QYAghUAAIG1AACAtQAA00sAgLbpBgC14QYAvtQDALsxBgC6KQYA10sAgNtLAIC/FQYAvikGAL0hBgC8KQYA30sAgKO1BgCGyAAAh8gAAKatBgDjSwCA50sAgKWlBgCqbQYAq3UGAOtLAIDvSwCArm0GAK9RBgCsbQYArWUGAKg1BgCpOQYAqoEGAKuBBgCsgQYArYEGAK6BBgCvtQYA80sAgPdLAID7SwCA/0sAgANMAIAHTACAC0wAgA9MAIC4nQYAua0GALqlBgC7aQEAvHkBAL15AQC+aQEAv2kBALDRBgCx0QYAstEGALPRBgC0tQYAtb0GALa1BgC3rQYAswkGABNMAIAXTACAG0wAgB9MAIC2AQYAtQkGACNMAIC7FQYAuhUGACdMAIArTACAv3kGAL5xBgC9BQYAvAUGAC9MAICjTQYAM0wAgPdKAICmRQYAN0wAgDtMAIClTQYAqlEGAKtRBgA/TACAQ0wAgK41BgCvPQYArEEGAK1BBgCB6QMAgN0DAISIAwCC4QMAhrA8AIeIAgC+VAMAS0wAgE9MAIBTTACAV0wAgFtMAIBfTACAY0wAgGdMAIBrTACA4/AGAG9MAIDhMAYAhAA8AHNMAIB3TACAe0wAgH9MAICDTACAhTQ9AIdMAICLTACA77AHAI9MAICTTACAl0wAgJtMAICfTACAo0wAgL7EPACnTACAgp0BAIGdAQCAnQEAqA0CAKllAgCqfQIAq3UCAKxZAgCtWQIArpkDAK+ZAwCw6QMAsekDALL5AwCz+QMAtOkDALXpAwC2XQMAt1UDALhtAwC5dQMAunUDALtFAwC8XQMAvTUDAL4xAwC/KQMAq0wAgK9MAICzTACAu0wAgOFgAwDv9AMA40QCAL9MAIDDTACA4zwDAO/0NwDh/AEAx0wAgMtMAIDPTACA00wAgIZkPwCHaD0AhTQhALOZAwDXTACAtb0DALa1AwDbTACA30wAgONMAIC6QQIAu0ECALxBAgC9QQIAvkECAL9BAgDnTACA60wAgO9MAIDzTACA90wAgPtMAID/TACA7/gBAIRoPADhPAYAA00AgOMcBgAHTQCAC00AgA9NAIATTQCAoxUDABdNAIAbTQCAH00AgCNNAICmOQMApTEDACtNAICrzQIAqs0CAL5kPgAvTQCAr80CAK7NAgCtzQIArM0CAKgdPgCpJT4Aqi0+AKslPgCsPT4ArSU+AK4tPgCvJT4At0wAgIL1PwCB5T8AgOU/ACdNAIAzTQCAhgAEAIecAwC4LT4AuTE+ALoxPgC7MT4AvNE+AL3RPgC+0T4Av80+ALBdPgCxIT4Asjk+ALM5PgC0KT4AtSk+ALYZPgC3FT4As6U+ADdNAIA7TQCAP00AgENNAIC2pT4AtbU+AEdNAIC75T4Aupk+AEtNAIBPTQCAv+0+AL7tPgC97T4AvO0+AFNNAICj4T4AV00AgFtNAICm4T4AX00AgGNNAICl8T4Aqt0+AKuhPgBnTQCAa00AgK6pPgCvqT4ArKk+AK2pPgCPBSUAsyU+AG9NAIBzTQCAtik+AHdNAIB7TQCAtSk+ALp9PgC7RT4Af00AgINNAIC+tT4Av70+ALxdPgC9vT4An304AJ5lOQCd8TgAnFE0AJtZNQCaUTUAmfEwAJgNMQCXZTEAlsEwAJVZLQCUTS0Ak+EsAJLZKQCRWSkAkPEoALSlGQC13RgAh00AgIQIAACwkRUAsQEVALIBGACzvRkAgA0AAIGtAwCCpQMAi00AgKNhAACiHT0AoZk9AKBxPACkxQUApUEEAKYBCACn4QkAR0wAgKH1AQCi6QEAo90FAKwBEACtxREArtkRAK85EACoZQgAqQEMAKrZDQCrCQ0AijEuAIuhMwCPTQCAk00AgI65MwCPETYAjB0yAI1NMgCCJSYAg6krAL5kAwCEYAQAhqEvAIcVLgCEGSoAhZEqAJphPgCb7T4AhsgEAIfcAwCbTQCA4Vw+AJyJAwDjAD4Akmk2AJN5NwCfTQCA7xg+AJZNOwCXuT8AlME7AJVdOgCpnT0AqIk9AKu5PQCqrT0Arak9AKyhPQCvyT0ArqE9AL7oBACjTQCAp00AgKtNAICvTQCAs00AgLdNAIC7TQCAuVk9ALhRPQC7eT0AumU9AL1pPQC8YT0Avx09AL5hPQCxgT0AsLk9ALNpPQCyiT0AtXk9ALRxPQC3aT0AtnE9AKMhPAC/TQCAw00AgMdNAIDLTQCApi08AKUtPADPTQCAq0E8AKp5PADTTQCA100AgK+5PACusTwArbk8AKxZPADbTQCA300AgLN9AwDjTQCAtdkDAOdNAIDrTQCAttEDAO9NAIDzTQCAu8UDALrFAwC9uQMAvLUDAL+tAwC+sQMA900AgPtNAID/TQCA71wDAIAVAACBHQAAgjEAAO+MPgCE7AQA4fw+AANOAIDjHD4AC04AgOGUAQAPTgCA4yAAAKP1AwATTgCAh+gEAIZsBAAXTgCAplkDAKVRAwAbTgCAq00DAKpNAwAfTgCAI04AgK8lAwCuOQMArTEDAKw9AwCXTQCAB04AgCdOAIArTgCAL04AgDNOAIA3TgCAO04AgKhxBgCpTQYAqo0GAKuFBgCsnQYArYUGAK6NBgCvhQYAsP0GALFBBwCyQQcAs0EHALRBBwC1SQcAtnEHALdxBwC4IQcAuSEHALolBwC7OQcAvCkHAL0VBwC+HQcAv/0HALMlBgA/TgCAQ04AgEdOAIBLTgCAtiUGALU1BgBPTgCAu6UHALoZBgBTTgCAV04AgL+tBwC+pQcAvbUHALy1BwBbTgCAo2EGAF9OAIBjTgCApmEGAGdOAIBrTgCApXEGAKpdBgCr4QcAb04AgHNOAICu4QcAr+kHAKzxBwCt8QcAqLEGAKm9BgCqzQYAq90GAKzNBgCt/QYArvUGAK8VAQCA+QEAgc0BAILFAQC+ZAIAhpAAAIcAAQB7TgCAf04AgLjRAQC52QEAuuEBALvhAQC8kQEAvZ0BAL6VAQC/iQEAsG0BALF1AQCyfQEAs3UBALRtAQC18QEAtvEBALfxAQCzRQYAd04AgINOAICHTgCAi04AgLZ9BgC1RQYAj04AgLuxAQC6qQEAk04AgJdOAIC/NQEAvqkBAL2hAQC8qQEAm04AgKMBBgCfTgCAo04AgKY5BgCnTgCAq04AgKUBBgCq7QEAq/UBAK9OAICzTgCAru0BAK9xAQCs7QEAreUBAOEoAQC3TgCA41ACALtOAIC/TgCAw04AgMdOAIDLTgCAz04AgNNOAIDXTgCA204AgIFxAACAGQAA75wCAIJ5AADfTgCA404AgITIAgCzxQMA604AgLXFAwC2xQMAvhADAIbADACHRAwAuqkDALulAwC8vQMAvaEDAL6hAwC/lQMArhEGAK8ZBgCsAQYArQEGAKqlBgCrEQYAqEU5AKlxOQDvTgCA804AgPdOAID7TgCA/04AgANPAIAHTwCAC08AgL7tBwC/TQcAvNEHAL3lBwC63QcAu8EHALg1BgC51QcAtjkGALcNBgC0JQYAtTkGALIxBgCzPQYAsFEGALFRBgCoOQIAqTkCAKqBAgCrgQIArIECAK2JAgCusQIAr7ECAIRsDQAPTwCAvmANABNPAIAXTwCAG08AgB9PAIAjTwCAuE0BALlVAQC6XQEAu1UBALxNAQC9dQEAvn0BAL91AQCwoQIAsa0CALKlAgCzuQIAtKkCALWdAgC2lQIAt3kBAOFUBgDh1AcA4zgGAOOwBwAnTwCAK08AgC9PAIAzTwCAhOQMADdPAIA7TwCAP08AgENPAIBHTwCA72wAAO/kBwCjSQIAS08AgE9PAIBTTwCAW08AgKZJAgClSQIAX08AgKspAgCqJQIAhkgMAIfcDACvGQIAri0CAK0tAgCsMQIAqFEOAKmlDgCqrQ4Aq6UOAKy9DgCtpQ4Arq0OAK+lDgCA5Q8Age0PAILlDwBXTwCAY08AgGdPAIBrTwCAb08AgLjVDwC53Q8AutUPALvpDwC8+Q8AvfkPAL7pDwC/6Q8AsN0OALFBDwCyRQ8As10PALRFDwC1TQ8AtkUPALftDwCzJQ4Ac08AgHdPAIB7TwCAf08AgLYlDgC1NQ4Ag08AgLuFDwC6GQ4Ah08AgItPAIC/iQ8AvoEPAL2JDwC8kQ8Aj08AgKNhDgCTTwCAl08AgKZhDgCbTwCAn08AgKVxDgCqXQ4Aq8EPAKNPAICnTwCArsUPAK/NDwCs1Q8Arc0PAKjRDgCp2Q4AqjkBAKs5AQCsKQEArSkBAK6dAQCvlQEAq08AgK9PAICzTwCAt08AgIANAACBtQAAgr0AALtPAIC4lQEAuZ0BALqhAQC7oQEAvHEAAL1xAAC+cQAAv3EAALDtAQCx9QEAsvUBALPFAQC03QEAtbUBALaxAQC3sQEAv08AgMNPAICzuQEAvsACALWpAQDHTwCAy08AgLahAQCGgAEAh8QBALs5AQC6IQEAvRkBALwpAQC/eQEAvhEBAKPxAQDPTwCA504AgNNPAIDXTwCApukBAKXhAQDbTwCAq3EBAKppAQDfTwCA408AgK8xAQCuWQEArVEBAKxhAQDnTwCA608AgO9PAIDzTwCA4agBAPdPAIDjQAIA+08AgL8oFQD/TwCA73QCAANQAIAHUACAC1AAgA9QAIATUACAF1AAgON0DwCEiAMA4TQOABtQAIAfUACAI1AAgCdQAICADQAAgRUAAIIRAAArUACAL1AAgO+kDwAzUACAO1AAgKgZAwCpQQMAqkUDAKtdAwCsTQMArX0DAK51AwCvnQAAhaQVAL58AwCGCAQAhxwDAD9QAIBDUACAR1AAgEtQAIC49QAAuf0AALr1AAC7jQAAvIEAAL2BAAC+gQAAv4EAALDlAACx7QAAsuUAALP5AAC07QAAtdEAALbVAAC3zQAAT1AAgFNQAIBXUACAs8ECAFtQAIC1yQIAtvECAF9QAIBjUACAZ1AAgLotAQC7JQEAvD0BAL0hAQC+JQEAvxkBAKapAgCESAIAa1AAgKWRAgBvUACAo5kCAHNQAIB3UACArn0BAK9BAQCsZQEArXkBAKp1AQCrfQEAe1AAgH9QAICDUACAh1AAgItQAICPUACA7+QAAJNQAICXUACAm1AAgOMQDgCfUACA4VgOAKNQAICALQAAgREAAIIVAAC+sAUAs3UBAKtQAICHFAUAhmwEAK9QAIC21QAAtWUBALNQAIC7/QAAuvUAALdQAIC7UACAv6EAAL69AAC93QAAvN0AAKh9BgCptQYAqr0GAKu1BgCsrQYArRUHAK4dBwCvFQcAp1AAgL9QAIDDUACAx1AAgMtQAIDPUACA01AAgNdQAIC4OQcAuTkHALrJBwC7yQcAvNkHAL3ZBwC+zQcAv8UHALBxBwCxeQcAskkHALNJBwC0OQcAtSUHALYhBwC3IQcAozUGANtQAIDfUACA41AAgOdQAICmlQcApSUGAOtQAICrvQcAqrUHAO9QAIDzUACAr+EHAK79BwCtnQcArJ0HAPdQAID7UACA/1AAgANRAIAHUQCAgj0AAIE9AACAPQAAC1EAgA9RAIATUQCAhKADAL6kAwAXUQCAhvgAAIfgAACoxQYAqdUGAKrVBgCr5QYArP0GAK0xAQCuMQEArzEBABtRAIAfUQCAI1EAgCdRAIArUQCAL1EAgDNRAIA3UQCAuN0BALntAQC65QEAu40BALyVAQC9nQEAvpUBAL+NAQCwUQEAsVEBALJRAQCzUQEAtPUBALX9AQC29QEAt+0BALNdBgA7UQCAP1EAgENRAIBHUQCAtrEBALV1BgBLUQCAu5UBALqVAQBPUQCAU1EAgL85AQC+MQEAvYUBALyFAQClLQYAV1EAgFtRAICm6QEAX1EAgGNRAICjBQYAZ1EAgK3dAQCs3QEAr2EBAK5pAQBrUQCAN1AAgKvNAQCqzQEAb1EAgHNRAICExAMAvwD0AHdRAICCPQAAgT0AAIA9AAB7UQCAf1EAgINRAIC+YAMAi1EAgI9RAICTUQCAl1EAgIbgHACHAAMA7wwHAJtRAICfUQCAo1EAgKdRAICrUQCAr1EAgLNRAIC3UQCAu1EAgOHABgC/UQCA4ywHAMNRAIDHUQCAy1EAgM9RAIDTUQCA11EAgNtRAIDfUQCA41EAgKiBAwCpgQMAqoEDAKuBAwCsgQMArYEDAK6BAwCvgQMAsEUDALFNAwCyRQMAs10DALRNAwC1fQMAtnUDALcZAwC4KQMAuTUDALo9AwC7MQMAvAEDAL31AAC+/QAAv+0AALMpAgDnUQCA61EAgO9RAIDzUQCAtiECALUpAgCEUB0Au6kCALqhAgD7UQCA/1EAgL+ZAgC+qQIAvakCALyxAgCBTQAAgE0AAO+cAwCCXQAAhvAcAId4HQC+EB0AA1IAgAdSAIALUgCAD1IAgBNSAIDhkAEAF1IAgONgAwAbUgCAH1IAgCNSAIAnUgCAK1IAgC9SAIAzUgCAN1IAgO+UAQCE7BwA4XAGADtSAIDjUAEAP1IAgENSAIBHUgCAS1IAgKPpAgBPUgCAU1IAgFdSAIBbUgCApuECAKXpAgBfUgCAq2kCAKphAgBjUgCAvqgcAK9ZAgCuaQIArWkCAKxxAgCoMR4AqTEeAKoxHgCrMR4ArF0eAK1FHgCuTR4Ar0UeAPdRAICCzR8AgfUfAID9HwBnUgCAa1IAgIYcAACH+AMAuMUeALnNHgC6xR4Au90eALzFHgC9zR4AvsUeAL9ZHwCwPR4AsQUeALINHgCzBR4AtB0eALUBHgC2BR4At/0eALO5HgBvUgCAc1IAgHdSAIB7UgCAtsUeALXVHgB/UgCAu8EeALr5HgCDUgCAh1IAgL/FHgC+2R4AvdEeALzZHgCLUgCAo/0eAI9SAICTUgCApoEeAJdSAICbUgCApZEeAKq9HgCrhR4An1IAgKNSAICunR4Ar4EeAKydHgCtlR4AqCkeAKkpHgCqVR4Aq20eAKx1HgCtfR4ArnUeAK9pHgCnUgCAq1IAgK9SAICzUgCAt1IAgLtSAIC/UgCAw1IAgLjpHgC59R4Auv0eALv1HgC87R4AvZEeAL6RHgC/kR4AsB0eALHlHgCy7R4As+UeALT9HgC15R4Atu0eALflHgCz3R4Ax1IAgMtSAIDPUgCA01IAgLb9HgC1/R4AhFgBALshHgC62R4AvigAANtSAIC/IR4AvjkeAL0xHgC8OR4AgU0AAIBNAACjlR4Agl0AAKW1HgDXUgCA31IAgKa1HgCHUQCA41IAgKtpHgCqkR4ArXkeAKxxHgCvaR4ArnEeAIYABACHRAMAs4ECAOdSAIC1gQIA61IAgO9SAIC2gQIAiAAAAPNSAIC74QIAuu0CAL3lAgC8+QIAv9ECAL7lAgD3UgCA+1IAgIREAwC+jAMA4UgCAP9SAIDjAAIA7/wfAANTAIDhPB4A79wCAONgHwAHUwCAC1MAgA9TAIATUwCAqQUCAKixAgCrBQIAqgUCAK0NAgCsBQIArzUCAK41AgCEbAUAF1MAgBtTAIAfUwCAI1MAgCdTAIArUwCAL1MAgLnpAwC44QMAu/kDALrhAwC96QMAvOEDAL9dAwC+4QMAsSkCALAlAgCzPQIAsiECALUZAgC0LQIAt9kDALYRAgAzUwCAN1MAgDtTAICjhQMAP1MAgKWFAwCmhQMAQ1MAgEtTAIBPUwCAqukDAKvlAwCs/QMAreEDAK7hAwCv1QMAgEkAAIFVAACCVQAAo6kCAL6YBAClQQEApkEBAFNTAICG4AUAh+AFAKotAQCrOQEArBEBAK0FAQCuDQEArwUBAFdTAIBbUwCAX1MAgO/cAABjUwCAZ1MAgGtTAIDviB4AhCwHAOHsHgBvUwCA4xweAHNTAIDhlAEAd1MAgOMwAACzJQIAhWDmAHtTAIB/UwCAg1MAgLbNAQC1zQEAh1MAgLu1AQC6oQEAi1MAgI9TAIC/iQEAvoEBAL2JAQC8nQEAR1MAgJNTAICXUwCAm1MAgJ9TAICjUwCAp1MAgKtTAICoAQcAqQEHAKp1BwCrrQcArLUHAK29BwCuqQcAr6kHALDZBwCx7QcAsvkHALP1BwC0mQcAtZkHALaJBwC3gQcAuIkHALmJBwC6bQAAu2UAALx9AAC9ZQAAvm0AAL9lAACBCQAAgJkAAK9TAICCHQAAs1MAgLdTAIC7UwCAv1MAgKgNBQCpfQUAqk0FAKuhBgCspQYAra0GAK6dBgCv/QYAsIUGALGRBgCyqQYAs70GALSlBgC1rQYAtqUGALd5BgC4SQYAuUkGALpZBgC7WQYAvEkGAL1JBgC++QcAv/kHALNdBgDDUwCAhigCAIcsAQDHUwCAtp0GALWdBgDLUwCAu4kGALq9BgDPUwCA01MAgL/9BgC+/QYAvYEGALyNBgDXUwCAoxkGANtTAIDfUwCAptkGAONTAIDnUwCApdkGAKr5BgCrzQYA61MAgO9TAICuuQYAr7kGAKzJBgCtxQYAqBkBAKkZAQCqjQAAq50AAKyNAACtvQAArrUAAK/dAADzUwCA91MAgPtTAID/UwCAA1QAgAdUAIALVACAD1QAgLhpAAC5aQAAunkAALt5AAC8aQAAvWkAAL7dAwC/1QMAsKkAALGpAACyvQAAs7UAALSZAAC1mQAAtlkAALdZAAC+LAIAE1QAgBdUAIAbVACAH1QAgCNUAIArVACAL1QAgIAtAACBNQAAgj0AADNUAICGkAwAh+gCADdUAIA7VACAs0UDAD9UAIBDVACAR1QAgEtUAIC2fQMAtUUDAE9UAIC7LQMAui0DAFNUAIBXVACAvx0DAL4dAwC9IQMAvCkDAKvNAwCqzQMAW1QAgF9UAICv/QMArv0DAK3BAwCsyQMAo6UDAGNUAIBnVACAa1QAgG9UAICmnQMApaUDAHNUAIB3VACAe1QAgH9UAICDVACAh1QAgII9AACBPQAAgD0AAItUAICPVACAk1QAgIRgAwCG0AwAhzADAJtUAICfVACAvkQCAKNUAICnVACAq1QAgOEAAACvVACA46gGALNUAICE7AwAt1QAgO/QAwC7VACAv1QAgMNUAIDHVACAy1QAgLNtAQDPVACA01QAgNdUAIDbVACAthEBALVlAQDfVACAuz0BALo1AQDjVACA51QAgL/9AQC+/QEAvRUBALwVAQDrVACA4fwGAO9UAIDjPAcA81QAgPdUAID7VACA/1QAgANVAIC+bAwAC1UAgA9VAIATVQCAF1UAgBtVAIDvFAYAgV0AAIBdAACj5QEAgm0AAKXtAQAfVQCAI1UAgKaZAQCHqAwAhuQMAKu1AQCqvQEArZ0BAKydAQCvdQEArnUBAKgZDgCpGQ4AqiUOAKs1DgCsLQ4ArVEOAK5RDgCvUQ4Al1QAgAdVAIAnVQCAK1UAgC9VAIAzVQCAN1UAgDtVAIC47Q4AufUOALr1DgC7jQ4AvJUOAL2dDgC+lQ4Av40OALAxDgCxOQ4AsgEOALMBDgC0+Q4AtfkOALbdDgC31Q4AqHkOAKl5DgCqjQ8Aq4UPAKydDwCtgQ8AroUPAK+5DwA/VQCAQ1UAgEdVAIBLVQCAT1UAgFNVAIBXVQCAW1UAgLiRDwC5mQ8AuqEPALuhDwC8UQ8AvV0PAL5JDwC/SQ8AsM0PALHVDwCy3Q8As9UPALTNDwC1sQ8AtrEPALexDwCzBQ4AX1UAgGNVAIBnVQCAa1UAgLYBDgC1FQ4Ab1UAgLsRDgC6CQ4Ac1UAgISgAQC/dQ4AvgkOAL0BDgC8CQ4AgmkAAKNBDgCAWQAAgVEAAKZFDgC+WAEAd1UAgKVRDgCqTQ4Aq1UOAIbIAACHrAEArk0OAK8xDgCsTQ4ArUUOAHtVAIB/VQCAg1UAgIdVAICLVQCAj1UAgCdUAICTVQCAqAkOAKkJDgCqGQ4AqxkOAKwJDgCtYQ4ArmEOAK+VAQCw7QEAsfUBALL9AQCz9QEAtO0BALV1AQC2fQEAt3UBALhNAQC5VQEAul0BALtVAQC8TQEAvfEAAL7xAAC/8QAAl1UAgJtVAICfVQCAo1UAgKdVAIDj6A4Aq1UAgOE0DgC+AAQA79wPAK9VAICzVQCAt1UAgLtVAIC/VQCAw1UAgLPxDQDHVQCAy1UAgM9VAIDTVQCAtoENALXhDQDXVQCAu1ECALpJAgDbVQCA31UAgL/RAgC+SQIAvUECALxJAgCjMQ0A41UAgISIAwDrVQCA71UAgKZBDQClIQ0A81UAgKuRAgCqiQIA91UAgPtVAICvEQIArokCAK2BAgCsiQIAgKkAAIGpAACCTQAA/1UAgOFkEgDjTAIA4wgLAOGsAQADVgCA7zwCAO8YFgAHVgCAhlAGAIdIAwALVgCAD1YAgKiBAgCpgQIAqoECAKuBAgCsgQIArYECAK6FAgCvHQEAE1YAgBdWAIAbVgCAH1YAgCNWAIAnVgCAK1YAgIS4BQC4dQEAuX0BALp1AQC7CQEAvBkBAL0ZAQC+CQEAvwEBALBlAQCxbQEAsmUBALN9AQC0aQEAtV0BALZVAQC3TQEAL1YAgDNWAIA3VgCAO1YAgD9WAIBDVgCA7zQAAO/ADgDhXA4A4UwPAOOUAADjnA4AR1YAgIJlAACBfQAAgH0AAEtWAIBPVgCAvsQHALNFAgBTVgCAtUUCALZNAgBbVgCAhkAGAIeQBAC67QEAu+UBALz9AQC95QEAvuEBAL/VAQCflQgAngUIAJ3dDQCcPQwAmzEMAJr1DQCZ7RAAmD0QAJfVEQCWsRUAlQUUAJTlFQCTtRkAkjEYAJE5GACQDRwAj2EcAOdVAICz1QYAX1YAgLX9BgBXVgCAY1YAgLaRBgBnVgCAa1YAgLuVBgC6lQYAvVUHALxVBwC/VQcAvlUHAG9WAIBzVgCAqo0GAKuFBgCsnQYArYUGAK6BBgCvtQYAhKgAAHdWAIB7VgCAoyUFAH9WAIClJQUApi0FAINWAICHVgCAi1YAgI9WAICTVgCAl1YAgJtWAICfVgCAo1YAgKdWAICrVgCAr1YAgLNWAICjqQUAotEEAKHZBACgZQUAgiEdAIM1HQC3VgCAu1YAgIaVGACH3RQAhBkZAIUZGQCKDRUAi7EUAL9WAIDDVgCAjsURAI/VDACMzRAAjR0RAJJhDQCTdQ0AvkwAAMtWAICWxQkAl80EAJSNDACVXQkAmkEFAJtBBQCGyP8Ah0wAAIFZAACAeQAAnCEEAIJRAAChxQEAz1YAgKMB/ACi2QEApRX9AKS1/QCnufkApgH4AKkJ+AColfkAqwX1AKqt9QCtsfEArAHwAK8d8ACurfEAseHtALAB7ACzAegAsv3sALVd6QC09ekA01YAgNdWAIDbVgCA31YAgONWAIDnVgCA61YAgO9WAIDzVgCA91YAgKiNBACplQQAqpUEAKulBACsvQQArdkEAK75BACv8QQAhGz8APtWAID/VgCAA1cAgAdXAIALVwCAD1cAgBNXAIC4eQUAucUFALrNBQC7xQUAvN0FAL3FBQC+zQUAv+0FALCZBACxmQQAskkFALNJBQC0WQUAtVkFALZJBQC3SQUAox0EAL7M/AAXVwCAG1cAgB9XAICmWQQApTUEACNXAICrXQQAql0EACdXAIArVwCAr50FAK6dBQCtnQUArJ0FAC9XAICznQIAM1cAgDtXAIC2UQIAP1cAgENXAIC1uQIAukkCALtVAgCGSP0Ah8D8AL41AgC/PQIAvEUCAL09AgCo3QQAqUkDAKpRAwCrbQMArHUDAK2VAwCunQMAr7kDAICNAQCB5QEAguEBAEdXAIBLVwCAT1cAgFNXAIBXVwCAuJUDALmdAwC6lQMAu60DALy1AwC9vQMAvrUDAL9VAgCwyQMAsdUDALLVAwCzrQMAtLUDALW9AwC2tQMAt60DAFtXAIBfVwCAo9EDAGNXAICl9QMAZ1cAgGtXAICmHQMAb1cAgHNXAICrGQMAqgUDAK1xAwCsCQMAr3EDAK55AwDhKAcAd1cAgOPkBgB7VwCA4SgGAH9XAIDjaAEAg1cAgIdXAICLVwCA71gAAI9XAICTVwCAl1cAgO/IBgCbVwCAqE39AKmB/QCq0f0Aq9H9AKzx/QCt8f0ArvH9AK/x/QA3VwCAghEAAIEZAACA0f8An1cAgKNXAICEdAMAvnQDALh1/gC5ff4AunX+ALvF/gC83f4AvcX+AL7F/gC/9f4AsJH9ALGR/QCykf0As5H9ALRV/gC1Xf4AtlX+ALdN/gCzWf0Ap1cAgIasAACHRAMAq1cAgLZx/QC1ef0Ar1cAgLtV/QC6Vf0As1cAgLdXAIC/mf4AvpH+AL1F/QC8Rf0Au1cAgKMd/QC/VwCAw1cAgKY1/QDHVwCAy1cAgKU9/QCqEf0AqxH9AM9XAIDTVwCArtX+AK/d/gCsAf0ArQH9AKjN/wCp0f8AqtH/AKsh/gCsIf4ArSH+AK4h/gCvIf4A11cAgNtXAIDfVwCA41cAgOdXAIDrVwCA71cAgPNXAIC4jf4AuZH+ALqV/gC7rf4AvLX+AL25/gC+qf4Av6n+ALDh/gCx4f4AsuX+ALP5/gC06f4AtdX+ALbd/gC3uf4As1n/APdXAIDHVgCA+1cAgP9XAIC2of4Atan+AANYAIC7Jf4AuiX+AAdYAIALWACAvxH+AL4t/gC9Lf4AvDH+AIIZAACjHf8AgGUAAIEZAACm5f4AD1gAgBNYAICl7f4AqmH+AKth/gCEZAEAviAAAK5p/gCvVf4ArHX+AK1p/gAbWACA4zT+AB9YAIDhfP0AhrAEAIcIAwAjWACAJ1gAgCtYAIAvWACAhCQDAIQkBAAzWACA70j+ADdYAIA7WACAs+kCAD9YAIC+RAQAvkAFAENYAIC2nQIAtZkCAEdYAIC7iQIAur0CAEtYAIBPWACAv1kDAL5RAwC9WQMAvJECAKkdAgCoFQIAqyUCAKolAgCtWQIArFUCAK9NAgCuUQIAvmQGAFNYAIBXWACAW1gAgF9YAIBjWACAZ1gAgGtYAIC5+QMAuPEDALtNAwC68QMAvUEDALxZAwC/cQMAvkEDALEJAgCwPQIAs8kDALIBAgC12QMAtNEDALfJAwC20QMA4ZABAG9YAIDj8AAAc1gAgHdYAICCPQAAgT0AAIA9AAB7WACAf1gAgINYAICLWACAj1gAgJNYAIDvLAAAl1gAgKPpAwCbWACAhugEAIdgBQCfWACApp0DAKWZAwCjWACAq4kDAKq9AwCnWACAq1gAgK9ZAgCuUQIArVkCAKyRAwCvWACAs1gAgLdYAIC7WACAv1gAgMNYAIDHWACA71gBAISgBADhVP8Ay1gAgOOEAQDPWACA01gAgNdYAIDbWACAs9kBAN9YAICFzBkA41gAgOdYAIC28QEAtfkBAOtYAIC7pQEAutkBAO9YAIDzWACAv50BAL6dAQC9pQEAvK0BAKgBBgCpDQYAqhEGAKsRBgCsMQYArTEGAK4pBgCvJQYAh1gAgILJBwCBwQcAgPEHAPdYAID7WACAhhwAAIf8AwC47QYAufUGALr9BgC79QYAvO0GAL1RBwC+VQcAv00HALBdBgCxIQYAsjkGALMxBgC0GQYAtRkGALbdBgC31QYAo5kGAP9YAIADWQCAB1kAgAtZAICmsQYApbkGAA9ZAICr5QYAqpkGABNZAIAXWQCAr90GAK7dBgCt5QYArO0GABtZAICz8QcAH1kAgCNZAIC2gQcAJ1kAgCtZAIC1mQcAuo0HALtlBwAvWQCAM1kAgL59BwC/ZQcAvH0HAL11BwCoLQYAqTUGAKo9BgCrMQYArFUGAK1FBgCuRQYAr3UGADdZAIA7WQCAP1kAgENZAIBHWQCAS1kAgE9ZAIBTWQCAuOkGALn1BgC6/QYAu/UGALztBgC9kQYAvpUGAL+NBgCwDQYAseUGALLtBgCz5QYAtP0GALXlBgC27QYAt+UGAKO1BgBXWQCAW1kAgF9ZAIBjWQCApsUGAKXdBgAXWACAqyEGAKrJBgBnWQCAa1kAgK8hBgCuOQYArTEGAKw5BgCASQAAgUkAAIJZAACzRQEAb1kAgLVFAQC2RQEAc1kAgIZAAACHZAAAuikBALslAQC8PQEAvSEBAL4hAQC/FQEAd1kAgHtZAICEBAMAvgAMAOMoBgDv4AIA4RAGAH9ZAIDvkAYA4zwCAINZAIDh1AEAh1kAgItZAICPWQCAk1kAgJdZAICbWQCAo8ECAJ9ZAIClwQIAo1kAgKdZAICmwQIAq1kAgK9ZAICroQIAqq0CAK2lAgCsuQIAr5ECAK6lAgCpBQIAqLECAKsFAgCqBQIArQ0CAKwFAgCvNQIArjUCAISoDACzWQCAt1kAgLtZAIC/WQCAw1kAgMdZAIDLWQCAuekDALjhAwC7+QMAuuEDAL3pAwC84QMAv10DAL7hAwCxKQIAsCUCALM9AgCyIQIAtRkCALQtAgC32QMAthECAKitAgCp1QIAqtUCAKsNAQCsFQEArQkBAK4xAQCvLQEAz1kAgNNZAIDbWQCA31kAgONZAIDnWQCA61kAgO9ZAIC4IQEAuSEBALrtAQC75QEAvP0BAL3lAQC+7QEAv+UBALBVAQCxXQEAslUBALMtAQC0NQEAtTkBALYtAQC3JQEAgD0BAIGlAACCrQAA79QHAPNZAID3WQCA+1kAgO8oBwC+LAwA4fQGAP9ZAIDjkAcAA1oAgOGUAQAHWgCA4wwGALMdAgALWgCAh0QNAIZMDQAPWgCAtskBALXdAQATWgCAu9kBALrRAQAXWgCAG1oAgL+9AQC+sQEAvbkBALzBAQDXWQCAH1oAgCNaAIAnWgCAK1oAgC9aAIAzWgCAN1oAgKgJDwCpCQ8AqhkPAKsZDwCsCQ8ArQkPAK6pDwCvqQ8AsNkPALHtDwCy+Q8As/UPALSVDwC1hQ8AtoUPALe1DwC4jQ8AuWEAALphAAC7YQAAvGEAAL1hAAC+YQAAv2EAAKNdDQCCLQAAgRUAAIAdAAA7WgCApokOAKWdDgA/WgCAq5kOAKqRDgBDWgCAR1oAgK/9DgCu8Q4ArfkOAKyBDgBLWgCAs/UPAIboAwCHvAMAtu0PAE9aAIBTWgCAteUPALp5DwC7TQ8AV1oAgFtaAIC+NQ8AvyUPALxJDwC9RQ8AozEOAF9aAIBjWgCAZ1oAgGtaAICmKQ4ApSEOAG9aAICriQ4Aqr0OAHNaAIB3WgCAr+EOAK7xDgCtgQ4ArI0OAHtaAIB/WgCAg1oAgIdaAICLWgCAj1oAgJNaAICXWgCAm1oAgJ9aAICjWgCAp1oAgIANAACB1QAAgt0AAKtaAICoQQEAqVEBAKpRAQCrZQEArH0BAK2RAACukQAAr5EAAK9aAICzWgCAhGQBAL5kAQCGkAEAh4QAALtaAIC/WgCAuJEAALmRAAC6kQAAu5EAALyxAAC9sQAAvrEAAL+xAACw8QAAsfkAALLBAACzwQAAtLEAALWxAAC2sQAAt7EAALPZAgDDWgCAvnADAL5EBADHWgCAthEDALX1AgDLWgCAuz0DALo1AwDPWgCA01oAgL91AwC+dQMAvRUDALwVAwDXWgCAo50CANtaAIDfWgCAplUDAONaAIDnWgCApbECAKpxAwCreQMA61oAgO9aAICuMQMArzEDAKxRAwCtUQMAqDkDAKk5AwCqjQAAq50AAKyNAACtvQAArrUAAK/dAADzWgCA91oAgPtaAID/WgCAA1sAgAdbAIALWwCAD1sAgLhpAAC5aQAAunkAALt5AAC8aQAAvWkAAL7ZAQC/2QEAsKkAALGpAACyvQAAs7UAALSZAAC1mQAAtlkAALdZAAATWwCAF1sAgBtbAIAfWwCA70QAACNbAICGmAUAh+QCAOOYAACEqAIA4fgBACtbAICAOQAAgTkAAIItAAAvWwCAs0UBADNbAIA3WwCAO1sAgD9bAIC2fQEAtUUBAENbAIC7LQEAui0BAEdbAIBLWwCAvx0BAL4dAQC9IQEAvCkBAE9bAIDhUA4AU1sAgOM8DwBXWwCAW1sAgF9bAIBjWwCAZ1sAgGtbAIDjAAAAb1sAgHNbAIB3WwCAhPQFAO/kDgCuqQEAr6kBAKydAQCtlQEAqpkBAKuZAQB7WwCAf1sAgKbJAQCDWwCAh1sAgKXxAQCC/QcAo/EBAID9BwCB9QcAJ1sAgItbAICPWwCAk1sAgJdbAICbWwCAhrgDAIeQAwCoDQcAqRkHAKptBwCrZQcArH0HAK1lBwCuZQcAr1UHALAtBwCxxQcAssEHALPdBwC0xQcAtc0HALbFBwC3/QcAuMUHALnJBwC62QcAu9kHALypBwC9qQcAvp0HAL+VBwCzxQcAn1sAgKNbAICnWwCAq1sAgLbFBwC11QcAr1sAgLshBwC6yQcAs1sAgLdbAIC/KQcAviEHAL0pBwC8NQcAu1sAgKOBBwC/WwCAw1sAgKaBBwDHWwCAy1sAgKWRBwCqjQcAq2UHAM9bAIDTWwCArmUHAK9tBwCscQcArW0HAKgVAQCpgQEAqoEBAKuBAQCsgQEArYkBAK6xAQCvsQEA11sAgNtbAIDfWwCA41sAgOdbAIDrWwCA71sAgPNbAIC4ZQAAuW0AALplAAC7fQAAvGUAAL1tAAC+ZQAAv90AALChAQCxrQEAsqUBALO5AQC0qQEAtZ0BALaVAQC3XQAA91sAgIIdAACBHQAAgB0AAPtbAID/WwCAA1wAgL5YAQCErAIAB1wAgIcIAQCGjAEAC1wAgLdaAIAPXACAE1wAgLNJAQAXXACAG1wAgB9cAIAjXACAtkkBALVJAQAnXACAuykBALolAQArXACAL1wAgL8ZAQC+LQEAvS0BALwxAQC+2AMAM1wAgO/4BgA3XACAO1wAgD9cAIDv4AIAQ1wAgOGUAQBHXACA43QCAEtcAIDhmAUAT1wAgOMMBwBTXACAV1wAgFtcAICjwQIAhIwDAKXBAgBfXACAY1wAgKbBAgBnXACAa1wAgKuhAgCqrQIAraUCAKy5AgCvkQIArqUCAKgxAwCpPQMAqjUDAKtJAwCsWQMArVkDAK5JAwCvQQMAgMUAAIEJAACCGQAAb1wAgHNcAIB7XACAh2wDAIYcHAC47QAAufEAALr1AAC7jQAAvJUAAL2BAAC+gQAAv70AALAJAwCxCQMAsu0AALPhAAC04QAAteEAALblAAC32QAAf1wAgINcAICHXACAs7ECAItcAIC13QIAttUCAI9cAICTXACAl1wAgLrBAgC7wQIAvDUBAL05AQC+KQEAvykBAKaNAgCbXACAn1wAgKWFAgCjXACAo+kCAKdcAICrXACArnEBAK9xAQCsbQEArWEBAKqZAgCrmQIAr1wAgLNcAIC3XACA4YQGALtcAIDjJAYAv1wAgOGUAQDDXACA4ywAAL7oHQDHXACAy1wAgO/IAACE/B0AvvAcAM9cAIDvSAcA01wAgNdcAIDbXACA31wAgIEdAACAHQAA41wAgIIFAACGQBwAh8QcAOtcAIDvXACA81wAgPdcAID7XACA/1wAgKi1HgCpBR8Aqg0fAKsFHwCsAR8ArQkfAK45HwCvOR8A51wAgANdAIAHXQCAC10AgA9dAIATXQCAF10AgBtdAIC4yR8AudUfALrRHwC76R8AvPkfAL3tHwC+mR8Av5kfALAlHwCxLR8AsjkfALM1HwC0LR8AtQ0fALYFHwC3/R8As4UfAB9dAIAjXQCAJ10AgCtdAIC2iR8AtYkfAC9dAIC76R8AuuEfADNdAIA3XQCAv8kfAL7pHwC94R8AvO0fADtdAICjwR8AP10AgENdAICmzR8AR10AgEtdAIClzR8AqqUfAKutHwBPXQCAU10AgK6tHwCvjR8ArKkfAK2lHwCo6R4AqekeAKr5HgCr+R4ArOkeAK3pHgCuPQEArzUBAID5AQCBzQEAgsUBAIRgAgBXXQCAW10AgIdoAQCGnAAAuNEBALnZAQC64QEAu+EBALyRAQC9nQEAvpUBAL+JAQCwTQEAsVUBALJdAQCzVQEAtE0BALXxAQC28QEAt/EBALNxHgBfXQCAY10AgGddAIBrXQCAtmkeALVhHgBvXQCAu5EBALqJAQBzXQCAd10AgL81AQC+iQEAvYEBALyJAQB7XQCAd1wAgKM5HgB/XQCApSkeAINdAICHXQCApiEeAItdAICPXQCAq9kBAKrBAQCtyQEArMEBAK99AQCuwQEAk10AgJddAICbXQCAn10AgKNdAICnXQCAq10AgK9dAICzXQCAt10AgLtdAIC/XQCAw10AgMtdAIDPXQCAvnADAOHkHgCESAIA4+gfAIQABACAeQAAgXkAAIJpAADTXQCAhsAEAIdEAwDXXQCA210AgN9dAIDjXQCA7yAfAOddAIDrXQCA710AgPNdAIDvSAIA910AgPtdAID/XQCAA14AgL7oBAAHXgCAC14AgA9eAIATXgCA4ZABABdeAIDj6AIAs0kDABteAIAfXgCAI14AgCdeAIC2SQMAtUkDACteAIC7LQMAuiUDAC9eAIAzXgCAvxUDAL4VAwC9IQMAvCkDAKg1AgCpgQIAqoECAKuBAgCsgQIArYkCAK6xAgCvsQIAgP0BAIHNAQCCxQEAO14AgIaQBACHBAUAP14AgIRwBAC4SQEAuUkBALpZAQC7WQEAvEkBAL1JAQC+eQEAv3kBALChAgCxqQIAsr0CALO1AgC0kQIAtZECALZ5AQC3eQEAQ14AgEdeAIBLXgCAT14AgFNeAIBXXgCAW14AgO/QHgC+6AQA4VweAF9eAIDjkAAAY14AgGdeAIBrXgCAb14AgKNJAgBzXgCAd14AgHteAIB/XgCApkkCAKVJAgCDXgCAqy0CAKolAgCHXgCAi14AgK8VAgCuFQIArSECAKwpAgCoNQYAqT0GAKpVBgCrZQYArH0GAK1lBgCubQYAr2EGADdeAICPXgCAk14AgJdeAICADQAAgbEAAIKxAACbXgCAuOkGALnpBgC6+QYAu/UGALyVBgC9nQYAvpUGAL+NBgCw4QYAseEGALLhBgCz/QYAtOUGALXtBgC25QYAt9kGALPdBgCfXgCAo14AgKdeAICrXgCAtuUGALX1BgCvXgCAuyUGALolBgCGmAAAh6wAAL8pBgC+IQYAvSkGALw1BgCzXgCAo5kGALdeAIC7XgCApqEGAL9eAIDDXgCApbEGAKphBgCrYQYAx14AgMteAICuZQYAr20GAKxxBgCtbQYAqC0GAKk9BgCqiQYAq4kGAKyZBgCtmQYArokGAK+JBgDPXgCA014AgNdeAIDbXgCA314AgONeAIDnXgCA614AgLiNBgC5lQYAupUGALulBgC8vQYAvXEBAL5xAQC/cQEAsPkGALHNBgCy2QYAs9kGALTJBgC1yQYAtr0GALe1BgCzAQYA714AgPNeAID3XgCA+14AgLYZBgC1EQYA/14AgLsJBgC6PQYAA18AgAdfAIC/DQYAvg0GAL0NBgC8DQYAC18AgKNFBgDHXQCAD18AgKZdBgATXwCAhFgAAKVVBgCqeQYAq00GAL5oAQAXXwCArkkGAK9JBgCsSQYArUkGAIDBAwCByQMAgt0DAKPNAgAbXwCApdkCAKbNAgAfXwCAhoANAIeUAwCqxQIAqw0DAKwVAwCtHQMArhUDAK8NAwDhnBcA4xgGAOMUAwDhNAYA7xgCACNfAIAnXwCAK18AgOPQAgAvXwCA4VACADNfAIA3XwCA7ywGAO/kJQA7XwCArE0CAK1RAgCuUQIAr2UCAKgBAgCpCQIAqlkCAKtVAgCE7A0AP18AgENfAIBHXwCAvvgNAEtfAIBPXwCAU18AgLxRAwC9WQMAvmEDAL9hAwC47QMAuVEDALpRAwC7UQMAtM0DALXVAwC23QMAt9UDALAdAgCx1QMAst0DALPVAwDjyAAAV18AgOG4AQBbXwCAhFQPAF9fAIBjXwCAZ18AgKHpAgCgFQYAo6UDAKINAwDvIAAAa18AgG9fAIBzXwCAd18AgHtfAICFNCYAs40DAH9fAIC1mQMAto0DAINfAICGwA8Ah5QNALqFAwC7TQIAvFUCAL1dAgC+VQIAv00CAItfAICPXwCAk18AgJdfAICbXwCAn18AgI/d6wDvxAYAvuAPAOGMBgCjXwCA44AGAID1AACB5QAAguUAAKdfAICZbR8AmMUfAJvJGwCaeRoAnXUaAJzFGwCf+QcAnhkGAJFpFgCQsesAk20XAJLNFwCV0RMAlGkSAJdREgCWzRMAg1XkAIJB5ACHXwCAq18AgIeNHQCGkRgAhTkYAISVGQCLERwAigUcAK9fAICzXwCAj4UVAI6ZEACNORAAjJUdAJNRFACSRRQAt18AgLtfAICXYQkAlnUIAJWdCQCU+RUAm0EMAJqtDQC/XwCAw18AgMdfAIDLXwCAz18AgJzxDAChbQ0A018AgKMBBACihQAApZkEAKSRBACnGTgApsUFAKkJOACoKTgAq4k8AKoBPACtATAArB08AK8pMACunTAAseE0ALABNACzASgAsv00ALXZKAC00SgA118AgNtfAIDfXwCA418AgOdfAIDrXwCAgB0AAIEJAACC2QEA718AgKgRDwCpGQ8Aql0PAKtVDwCsTQ8ArXEPAK51DwCvbQ8A818AgPtfAICGiAAAhxABAP9fAIADYACAB2AAgAtgAIC4TQ4AuVEOALpRDgC7UQ4AvGUOAL1tDgC+ZQ4Avx0OALAdDwCxwQ8AssEPALPBDwC0xQ8Atc0PALbFDwC3eQ4As9UPAA9gAIATYACAF2AAgBtgAIC28Q8AtcUPAB9gAIC7BQ8AutkPACNgAIAnYACAvwkPAL4BDwC9FQ8AvBUPACtgAICjkQ8AL2AAgDNgAICmtQ8AN2AAgDtgAIClgQ8Aqp0PAKtBDwA/YACAQ2AAgK5FDwCvTQ8ArFEPAK1RDwCogQ0AqYENAKqBDQCrgQ0ArIENAK2BDQCusQ0Ar6ENAEdgAIBLYACAT2AAgFNgAIBXYACAgrkAAIG9AACAvQAAuDUCALk9AgC6zQIAu5UCALyNAgC9tQIAvr0CAL+1AgCwbQIAsU0CALJFAgCzJQIAtD0CALUdAgC2FQIAtw0CAFtgAIBfYACAswENAGNgAIC1AQ0Aa2AAgISUAwC2CQ0AviwEAG9gAIC7gQIAuqECAL35AgC8mQIAv9ECAL7xAgBzYACAd2AAgHtgAICjRQ0Af2AAgKVFDQCmTQ0Ag2AAgIbgBACHpAQAquUCAKvFAgCs3QIArb0CAK61AgCvlQIAqCUCAKk1AgCqPQIAqzUCAKwtAgCtkQIArpECAK+RAgCHYACAi2AAgI9gAICTYACAzAAAAJdgAICbYACAn2AAgLiZAgC5rQIAuqUCALttAQC8dQEAvX0BAL51AQC/bQEAsPECALH5AgCywQIAs8ECALSxAgC1vQIAtrUCALepAgCjYACA44QOAKdgAIDh9A4Aq2AAgK9gAICzYACAt2AAgIQgBQC7YACAv2AAgMNgAIDHYACA7+wOAMtgAIDPYACAs/UCANNgAICG6AQAh4wEAL5cBAC2UQIAteUCANtgAIC7fQIAunUCAN9gAIDjYACAvzkCAL41AgC9VQIAvFUCAKM1BQBnYACA12AAgOdgAIDrYACAppEFAKUlBQDvYACAq70FAKq1BQDzYACA92AAgK/5BQCu9QUArZUFAKyVBQCA+QcAgfkHAIKNBwCzjQYA+2AAgLWdBgC2iQYA/2AAgANhAIAHYQCAuk0HALtFBwC8XQcAvUEHAL5BBwC/QQcAC2EAgA9hAID3XwCAE2EAgBdhAIAbYQCAH2EAgCNhAICoNQYAqQEGAKppBgCraQYArHkGAK1lBgCuZQYAr50HALDlBwCx7QcAsuUHALP5BwC06QcAtekHALZZBwC3VQcAuHEHALlxBwC6cQcAu3EHALxVBwC9XQcAvlUHAL9NBwCjwQcAJ2EAgCthAIAvYQCAM2EAgKbFBwCl0QcAN2EAgKsJBgCqAQYAO2EAgD9hAICvDQYArg0GAK0NBgCsEQYAgGkAAIFpAACCBQAAQ2EAgL6YAQCEmAEAR2EAgEthAICGADwAh8QBAE9hAIBTYQCAV2EAgFthAIBfYQCAY2EAgKhdBgCpbQYAqmUGAKuBAQCsgQEArYkBAK6xAQCvsQEAZ2EAgGthAIBvYQCAc2EAgHdhAIB7YQCAf2EAgINhAIC4VQEAuV0BALpVAQC7yQAAvNkAAL3ZAAC+yQAAv8EAALCxAQCxuQEAsokBALOJAQC0cQEAtXEBALZ1AQC3bQEAs+0FAIdhAICLYQCAj2EAgJNhAIC2CQIAtQkCAJdhAIC7fQIAunUCAJthAICfYQCAv7UCAL61AgC9XQIAvF0CAL5gAgCjqQUAo2EAgKdhAICmTQIAq2EAgK9hAIClTQIAqjECAKs5AgCzYQCAhOADAK7xAgCv8QIArBkCAK0ZAgC+iDwAu2EAgKotAwCrJQMArD0DAK0lAwCuLQMAryUDAID1AACB/QAAgsEAAKPBAwC/YQCApcEDAKbBAwDDYQCAhmA8AIdUAwDHYQCAy2EAgM9hAIDjqAIA02EAgOGkAQDXYQCA71wCANthAIDfYQCA42EAgOdhAIDrYQCA72EAgPNhAIDjjAcA92EAgOE8BAD7YQCA/2EAgANiAIAHYgCAhCACAAtiAIAPYgCAE2IAgBdiAIDvbAcAG2IAgB9iAICzLQIAhEQ9ACNiAIArYgCAL2IAgLYtAgC1LQIAM2IAgLvJAgC6wQIAN2IAgDtiAIC/yQIAvsECAL3JAgC80QIA4XgHAOPAAADjOAYA4VwGAICpAACBqQAAgtEAAD9iAIBDYgCAR2IAgL6kPABLYgCAT2IAgO8cAADvkAYAU2IAgIZgPACHBD0AV2IAgLNxAQBbYgCAtRkBALYJAQBfYgCAY2IAgGdiAIC6AQEAuwEBALwBAQC9AQEAvgEBAL8BAQCohT4AqbU+AKq1PgCrxT4ArN0+AK3FPgCuwT4Ar/0+AGtiAIBvYgCAc2IAgHdiAIB7YgCAf2IAgINiAICHYgCAuFE/ALlRPwC6UT8Au1E/ALx1PwC9fT8AvnU/AL9tPwCwiT4AsYk+ALKZPgCzmT4AtIk+ALWJPgC2eT8At3U/ALdhAICjOT4Ai2IAgCdiAICmQT4Aj2IAgJNiAIClUT4Aqkk+AKtJPgCXYgCAm2IAgK5JPgCvST4ArEk+AK1JPgCASQAAgVEAAIJRAACzkT8An2IAgLW5PwC2RT8Ao2IAgIZAAACHBAMAukU/ALtdPwC8TT8AvT0/AL4pPwC/IT8AqE0+AKlVPgCqVT4Aq2U+AKx9PgCtiT4Arrk+AK+5PgCnYgCAq2IAgK9iAICzYgCAt2IAgLtiAIC/YgCAw2IAgLhhAQC5YQEAumEBALthAQC8YQEAvWEBAL5hAQC/YQEAsM0+ALHVPgCy1T4As6U+ALShPgC1qT4Atpk+ALeZPgCj3T4Ax2IAgMtiAIDPYgCA02IAgKYJPgCl9T4A12IAgKsRPgCqCT4A22IAgN9iAICvbT4ArmU+AK1xPgCsAT4A42IAgOdiAIDrYgCA72IAgPNiAID3YgCA+2IAgP9iAICAOQAAgTkAAIIFAAADYwCAvrgBAIS4AQALYwCAD2MAgKitAgCp1QIAqtUCAKstAwCsNQMArT0DAK41AwCvLQMAE2MAgBdjAIAbYwCAH2MAgCNjAIAnYwCAK2MAgC9jAIC46QMAuekDALqJAwC7iQMAvJkDAL2ZAwC+iQMAv4kDALBVAwCxXQMAslUDALPpAwC0+QMAtfkDALbpAwC34QMAs10CADNjAICGKAQAh8wDADdjAIC2vQMAtb0DADtjAIC7mQMAupEDAD9jAIBDYwCAvz0DAL49AwC9PQMAvIEDAIUAFACjGQIAR2MAgEtjAICm+QMAT2MAgFNjAICl+QMAqtUDAKvdAwBXYwCAW2MAgK55AwCveQMArMUDAK15AwDjVD4A4dw/AOHQPgDjPD4AX2MAgO8cAABjYwCAZ2MAgGtjAIDjwAAAb2MAgOHUAQDvYD4Ac2MAgHtjAIDvRD8AgGEAAIFtAACCfQAAhAAFAIbwBACHnAUAvhAFAH9jAICDYwCAh2MAgItjAICPYwCAk2MAgJdjAICbYwCAn2MAgLiJPQC5iT0Aupk9ALuRPQC8uT0Avbk9AL7RPQC/0T0AsAU+ALENPgCyBT4Asx0+ALQFPgC1DT4AtgU+ALe5PQConT4Aqa0+AKqlPgCrvT4ArKU+AK2tPgCupT4Ar30+AISsBAC+rAQAo2MAgKdjAICrYwCAr2MAgLNjAIC3YwCAqPkFAKn5BQCqKQYAqykGAKw5BgCtOQYArikGAK8pBgB3YwCAu2MAgL9jAIDDYwCAx2MAgMtjAIDPYwCA02MAgLiNBgC5kQYAupEGALulBgC8vQYAvUUHAL5BBwC/QQcAsFkGALFZBgCy7QYAs/0GALTtBgC13QYAttUGALe1BgCzoQYA12MAgNtjAIDfYwCA42MAgLa5BgC1sQYA62MAgLudBgC6nQYA52MAgAdjAIC/GQYAvikGAL0pBgC8OQYAglEAAKPlBgCAQQAAgUEAAKb9BgDvYwCA82MAgKX1BgCq2QYAq9kGAIZIAACHbAAArm0GAK9dBgCsfQYArW0GAKg5BgCpWQYAqmkGAKtpBgCseQYArXkGAK5pBgCvaQYA92MAgPtjAID/YwCAA2QAgAdkAIALZACAD2QAgBNkAIC4ZQEAuW0BALplAQC7fQEAvGUBAL1tAQC+ZQEAv9kBALAZBgCxGQYAsoEGALOBBgC0gQYAtYEGALaBBgC3gQYAs+EGABdkAIAbZACAH2QAgCNkAIC2+QYAtfEGACdkAIC73QYAut0GACtkAIAvZACAv0UGAL5FBgC9VQYAvFUGADNkAICjpQYAN2QAgDtkAICmvQYAP2QAgENkAICltQYAqpkGAKuZBgBHZACAS2QAgK4BBgCvAQYArBEGAK0RBgConQIAqdECAKrRAgCrLQMArDUDAK09AwCuNQMAry0DAE9kAIBTZACAvmQCAFtkAIBfZACAY2QAgGdkAIBrZACAuOkDALnpAwC6iQMAu4UDALydAwC9gQMAvoEDAL+1AwCwVQMAsV0DALJVAwCz6QMAtPkDALX5AwC26QMAt+EDAIBtAwCBpQAAgq0AALNVAgBvZACAtbEDALaxAwBzZACAhOACAHdkAIC6nQMAu5UDALyNAwC9MQMAvjEDAL8xAwCjGQIAe2QAgIVwaQB/ZACAg2QAgKb9AwCl/QMAh2QAgKvZAwCq0QMAhkgMAIe8AwCvfQMArn0DAK19AwCswQMAi2QAgI9kAICTZACAl2QAgO+wBgDvxAMAm2QAgJ9kAIDjfAYA45QDAOG4BwDh3AEAo2QAgKdkAICrZACAr2QAgLNkAIC3ZACAhEQCAL5YDQCADQAAgTUAAII9AAC7ZACAv2QAgMNkAICGyAwAh1wNAMtkAIDPZACA02QAgNdkAIDbZACA32QAgONkAIDnZACA62QAgO9kAIDzZACA74AGAISsDQDh7AYA92QAgONcBgD7ZACA/2QAgANlAIAHZQCAs/UBAAtlAIAPZQCAE2UAgBdlAIC2RQEAteUBABtlAIC7LQEAuiEBAB9lAIAjZQCAv/UAAL71AAC9JQEAvC0BAKgtDgCpNQ4Aqj0OAKs1DgCsLQ4ArYUOAK6FDgCvuQ4Ax2QAgCdlAIArZQCAL2UAgIAZAACBGQAAggUAADNlAIC4WQ8AuVkPALp5DwC7eQ8AvGkPAL1pDwC+GQ8AvxkPALClDgCxqQ4AsrkOALOxDgC0cQ8AtXEPALZxDwC3cQ8Apb0OAL6IAwA7ZQCAph0OADdlAIA/ZQCAo60OAENlAICtfQ4ArHUOAK+tDwCurQ8AV2QAgEdlAICrdQ4AqnkOALO5DwBLZQCAhmgAAIcMAwBPZQCAtlEPALVZDwBTZQCAu3UPALp1DwBXZQCAW2UAgL9FDwC+RQ8AvVEPALxlDwCocQ4AqXEOAKpxDgCrcQ4ArJEOAK2RDgCukQ4Ar5EOAF9lAIBjZQCAZ2UAgGtlAIBvZQCAc2UAgHdlAIB7ZQCAuIUOALmNDgC6hQ4Au50OALyNDgC9vQ4AvrUOAL95AQCw8Q4AsfEOALLxDgCzxQ4AtMEOALXBDgC2wQ4At8EOAKP5DgB/ZQCAg2UAgIdlAICLZQCAphEOAKUZDgCPZQCAqzUOAKo1DgCTZQCAl2UAgK8FDgCuBQ4ArREOAKwlDgCADQAAgRUAAIIdAACbZQCAn2UAgKNlAICElAEAvpQBAIZABwCH5AAAq2UAgK9lAICzZQCAt2UAgLtlAIC/ZQCAqIkCAKmRAgCqlQIAq7kCAKzVAgCtxQIArsUCAK/1AgDDZQCAx2UAgMtlAIDPZQCAvnwDANNlAIDXZQCA22UAgLh9AwC5wQMAusEDALvBAwC8wQMAvckDAL7xAwC/8QMAsI0CALFFAwCyTQMAs0UDALRdAwC1RQMAtk0DALdFAwCzHQIA32UAgONlAIDnZQCA62UAgLZFAgC1XQIA72UAgLuBAwC6SQIA82UAgPdlAIC/gQMAvpkDAL2RAwC8mQMA+2UAgKNZAgD/ZQCAA2YAgKYBAgAHZgCAC2YAgKUZAgCqDQIAq8UDAA9mAIATZgCArt0DAK/FAwCs3QMArdUDAIDZAQCB7QEAguUBAO+4DgAbZgCA4cQBAISYAgDj1AAAH2YAgL7sBAAjZgCA7wgAACdmAIDhxA8AK2YAgONkDgCGAAUAh2gFAC9mAICzvQIAM2YAgLWtAgC2pQIAN2YAgDtmAIA/ZgCAukEBALtBAQC8RQEAvU0BAL5FAQC/+QEAQ2YAgEdmAIBLZgCAT2YAgFNmAIBXZgCAW2YAgO/gAQCEbAQA4dQOAF9mAIDjHA4AY2YAgGdmAIBrZgCAb2YAgKMxAgBzZgCAhCQHAHdmAIB7ZgCApikCAKUhAgB/ZgCAq80BAKrNAQCDZgCAi2YAgK91AQCuyQEArcEBAKzJAQCo6QUAqekFAKr5BQCr+QUArOkFAK3pBQCuOQYArzkGABdmAICCzQcAgfUHAID9BwCHZgCAj2YAgIYYAwCHkAMAuNEGALnZBgC64QYAu+EGALyRBgC9nQYAvpUGAL+JBgCwSQYAsUkGALJdBgCzVQYAtE0GALXxBgC28QYAt/EGALDhBwCx4QcAsgkHALMJBwC0GQcAtRkHALYJBwC3CQcAuDkHALkNBwC6GQcAuxkHALwJBwC9CQcAvn0HAL9xBwCTZgCAp2UAgJdmAICbZgCAn2YAgKNmAICnZgCAq2YAgKjxBwCpxQcAqsEHAKvdBwCsyQcArb0HAK6pBwCvoQcAsykGAK9mAICzZgCAt2YAgLtmAIC2XQYAtSEGAL9mAIC7RQYAukUGAMNmAIDHZgCAv70GAL69BgC9vQYAvL0GAMtmAICjbQYAz2YAgNNmAICmGQYA12YAgNtmAIClZQYAqgEGAKsBBgDfZgCA42YAgK75BgCv+QYArPkGAK35BgCobQYAqbEBAKpJAQCrRQEArF0BAK1FAQCuTQEAr0UBAOdmAICCHQAAgR0AAIAdAADrZgCA72YAgPNmAIC+VAEAuIEAALmNAAC6hQAAu5kAALyJAAC9vQAAvrUAAL99AACwPQEAseEAALLhAACz4QAAtOEAALXpAAC20QAAt9EAALsFAwC62QIAhiwCAIcsAwC/DQMAvgUDAL0VAwC8FQMAs+ECAPtmAID/ZgCAhCwDAANnAIC25QIAtfUCAAdnAICqnQIAq0EDAAtnAIAPZwCArkEDAK9JAwCsUQMArVEDABNnAICjpQIAF2cAgBtnAICmoQIAH2cAgCNnAIClsQIAqakAAKihAACrtQAAqr0AAK3dAACs3QAAr/EAAK79AAC+LBwAJ2cAgCtnAIAvZwCAM2cAgDdnAIA7ZwCAP2cAgLl9AAC4fQAAu80BALrNAQC93QEAvN0BAL/NAQC+zQEAsZUAALCJAACzTQAAspUAALVdAAC0XQAAt00AALZNAABDZwCAR2cAgEtnAIBPZwCAU2cAgFdnAIBbZwCAX2cAgIA5AACBOQAAggUAAGNnAIBrZwCAb2cAgIf4AgCGfB0A4bgEAL7IHADjQAYAc2cAgHdnAIB7ZwCAf2cAgINnAICHZwCAi2cAgI9nAICTZwCAl2cAgJtnAIDvsAcAn2cAgKNnAICnZwCAq2cAgO/IAACvZwCAs2cAgLdnAIDvQAYAu2cAgOH8BgC/ZwCA4xwGAMNnAIDhlAEAx2cAgONkBgCAEQAAgRkAAIIpAACz/QEAy2cAgLWdAQC2lQEAz2cAgNNnAICEbB0AuoUBALuZAQC8iQEAvVEBAL5RAQC/UQEAozEeAGdnAIDXZwCA22cAgN9nAICmWR4ApVEeAONnAICrVR4AqkkeAIYIAwCHbAMAr50eAK6dHgCtnR4ArEUeAOdnAICzCR8A62cAgO9nAIC2CR8A82cAgPdnAIC1CR8AugUfALsNHwD7ZwCA/2cAgL4FHwC/CR8AvBUfAL0NHwCw5R8Ase0fALLlHwCz/R8AtOUfALXpHwC2GR8AtxkfALgpHwC5NR8Auj0fALs1HwC8ER8AvR0fAL4JHwC/BR8AA2gAgAdoAID3ZgCAC2gAgA9oAIATaACAF2gAgBtoAICo0R8AqdEfAKqlHwCrvR8ArKUfAK2tHwCupR8Ar50fAKNNHgAfaACAI2gAgCdoAIAraACApk0eAKVNHgAvaACAq0keAKpBHgAzaACAN2gAgK9NHgCuQR4ArUkeAKxRHgCADQAAgRUAAIIdAAA7aACAP2gAgENoAICEtAEAvrQBAL/oAQBLaACAhkgHAIc0AACEvAYAT2gAgFNoAIC+tAYAqI0BAKmVAQCqlQEAq80BAKzZAQCt2QEArs0BAK/FAQBXaACAW2gAgF9oAIBjaACAZ2gAgGtoAIBvaACAc2gAgLgdAQC5wQAAusEAALvBAAC8wQAAvckAAL7xAAC/8QAAsIkBALGJAQCyKQEAsykBALQ9AQC1JQEAti0BALclAQC7bQIAum0CAHdoAIB7aACAv8ECAL7ZAgC93QIAvN0CALM9AgB/aACAg2gAgIdoAICE/AYAtnkCALVxAgCLaACAqikCAKspAgCPaACAk2gAgK6dAgCvhQIArJkCAK2ZAgCXaACAo3kCAJtoAICfaACApj0CAKNoAICnaACApTUCAIJtJwCDjSoAhqgFAIdsAwCGmS4Ah80vAIQRLgCFmS4AiiESAIspEgCraACAr2gAgI6RFgCPHRYAjBESAI0RFgCScRoAk+UaALNoAIDvlHYAlvEeAJflHgCUSRoAlRkeAJopAgCb4QIAu2gAgL9oAIDDaACA4SASAJzxAgDjIBYAnyEfAJ7BHwCdmRsAnC0bAJuhGwCavRcAmTkXAJixFwCXiRMAlqkTAJWpEwCUdS4AkzkvAJIxLwCRsS8AkDUrAI+tJgDjeB8A0gAAAOFcHwCCmQEAx2gAgIDxAQCB8QEAvqgHAMtoAIDPaACA02gAgIS8BgDvLB8A12gAgNtoAIDhpB4A48wAAON8HgDhvAEA32gAgONoAIDnaACAhJwGAOtoAIC+bAYA72gAgPNoAID3aACA7xAAAO8EHgD7aACA/2gAgANpAIAHaQCAC2kAgA9pAIATaQCAF2kAgBtpAICAPQAAgQkAAILJBwAfaQCAo/kDAKLxAwChMQMAoM0fALBJcQCxAXwAsgl8ALMhfQC0AXgAtRV4AEdoAIC3aACAI2kAgL4oDgCGDAAAh4wDACdpAIAraQCAL2kAgDNpAIA3aQCAoV0AAKJVAACjfQAApAEMAKUVDACm9QwApwEIAKghCACpxQgAqgF0AKsJdACsAXQArR11AK55cACveXAAqOUFAKnxBQCq8QUAqy0FAKw1BQCtPQUArjUFAK8tBQA7aQCAP2kAgENpAIBHaQCAS2kAgE9pAIBTaQCAV2kAgLj9BgC5jQYAuoUGALutBgC8uQYAvbkGAL6tBgC/pQYAsFUFALFdBQCyVQUAs+UGALT9BgC10QYAttEGALfRBgCzeQQAW2kAgF9pAIBjaQCAZ2kAgLa9BAC1vQQAa2kAgLuZBAC6kQQAb2kAgHNpAIC/FQcAvjkHAL0xBwC8gQQAd2kAgKM9BAB7aQCAf2kAgKb5BACDaQCAh2kAgKX5BACq1QQAq90EAItpAICPaQCArn0HAK9RBwCsxQQArXUHAKhpBwCpaQcAqnkHAKvZBgCs9QYArf0GAK71BgCv5QYAgMkAAIHJAACCBQAAk2kAgIZwDwCHNAAAm2kAgJ9pAIC4fQYAuQUGALoNBgC7BQYAvB0GAL0FBgC+DQYAvwUGALCdBgCxdQYAsn0GALN1BgC0UQYAtV0GALZVBgC3TQYAs/EEAKNpAICnaQCAq2kAgK9pAIC2fQUAtX0FALNpAIC7sQUAulkFALdpAIC7aQCAv5kFAL6VBQC9oQUAvKkFAL9pAICjtQQAw2kAgMdpAICmOQUAy2kAgM9pAIClOQUAqh0FAKv1BQDTaQCA12kAgK7RBQCv3QUArO0FAK3lBQCpuQIAqLECAKvJAgCqsQIArTUCAKw1AgCvNQIArjUCANtpAIDfaQCA42kAgOdpAIDraQCA72kAgPNpAID3aQCAuekDALjZAwC7iQMAuuEDAL2dAwC8nQMAv4EDAL6JAwCxVQIAsFUCALNVAgCyVQIAtfkDALTxAwC36QMAtvEDALM9AwD7aQCA/2kAgANqAIALagCAtrEDALW5AwAPagCAu5UDALqVAwCGiAwAh6ANAL85AgC+MQIAvYUDALyFAwATagCAo3kDABdqAIAbagCApvUDAB9qAIAjagCApf0DAKrRAwCr0QMAJ2oAgCtqAICudQIAr30CAKzBAwCtwQMAgIUAAIGNAACChQAA79AGAOOwBwDj9AQA4QgHAOHsBADvOAYA7yAEAL6kDAAvagCAM2oAgOGEAQA3agCA49wGADtqAIA/agCAhMANALPJAQBDagCAtdkBALbJAQBHagCAS2oAgE9qAIC6xQEAu60BALy5AQC9uQEAvq0BAL+lAQCwLQ4AsUUOALJBDgCzQQ4AtEUOALVNDgC2cQ4At3EOALiBDgC5gQ4AuoEOALuBDgC8gQ4AvYEOAL6BDgC/gQ4AB2oAgFNqAIBXagCAW2oAgJdpAIBfagCAY2oAgGdqAICo2Q0AqdkNAKptDgCrZQ4ArH0OAK1lDgCuZQ4Ar1UOAKOFDgCCLQAAgRUAAIAdAABragCApoUOAKWVDgBvagCAq+EOAKqJDgBzagCAd2oAgK/pDgCu4Q4ArfUOAKz1DgB7agCAs4UPAIZoAACHHAMAtoUPAH9qAICDagCAtZEPALqNDwC7SQ8Ah2oAgItqAIC+MQ8AvzEPALxJDwC9RQ8AqBEOAKkZDgCqSQ4Aq0UOAKxdDgCtQQ4ArkEOAK91DgCPagCAk2oAgJdqAICbagCAn2oAgKNqAICnagCAq2oAgLihDgC5oQ4Aug0BALsFAQC8HQEAvQEBAL4BAQC/AQEAsA0OALHJDgCy2Q4As9UOALSxDgC1sQ4AtqkOALehDgCjwQ4Ar2oAgLNqAIC3agCAu2oAgKbBDgCl1Q4Av2oAgKsNDgCqyQ4Aw2oAgMdqAICvdQ4ArnUOAK0BDgCsDQ4Ay2oAgM9qAIDTagCA12oAgIANAACBNQAAgj0AANtqAIDfagCA42oAgISEAQC+hAEAhjAHAIf4AADragCA72oAgKjBAgCp0QIAqtECAKvlAgCs/QIArTUDAK49AwCvNQMA82oAgPdqAID7agCA/2oAgANrAIAHawCAC2sAgA9rAIC40QMAudkDALrhAwC74QMAvJEDAL2RAwC+kQMAv5EDALBNAwCxVQMAsl0DALNVAwC0TQMAtfEDALbxAwC38QMAu7EDALqpAwATawCAvoQDAL8VAwC+qQMAvaEDALypAwCzeQIAF2sAgBtrAIAfawCAI2sAgLaVAwC1VQIAJ2sAgKrtAwCr9QMAK2sAgC9rAICu7QMAr1EDAKztAwCt5QMAM2sAgKM9AgA3awCAO2sAgKbRAwA/awCAQ2sAgKURAgBHawCAgiEAAIEVAACAFQAA7wQAAISUAgBLawCAT2sAgOPYAABTawCA4fgBAFtrAIBfawCAY2sAgGdrAIBrawCAhmAFAIcIBQBvawCAs20BAHNrAIC1fQEAtnUBAHdrAIB7awCAf2sAgLpRAQC7UQEAvPkBAL3RAQC+0QEAv9EBAINrAICjpQEAh2sAgItrAICmvQEAj2sAgJNrAICltQEAqpkBAKuZAQCXawCAm2sAgK4ZAQCvGQEArDEBAK0ZAQCfawCA4fQOAKNrAIDjFA4A9AAAAOF8DACnawCA41AKAKtrAICvawCAviAEAO8wDQCzawCAt2sAgIQ0BADvrA4AsDkGALE5BgCygQYAs6kGALS5BgC1uQYAtqkGALehBgC46QYAuekGALrJBgC7xQYAvN0GAL3BBgC+wQYAvz0HAFdrAICCHQAAgR0AAIAdAAC7awCAv2sAgMNrAIDnagCAqJkFAKmZBQCqSQYAq0kGAKxZBgCtWQYArkkGAK9JBgCorQcAqbUHAKq9BwCrtQcArK0HAK3dBwCuyQcAr8EHAMdrAIDLawCAhogDAIcQAwDPawCA02sAgNdrAIDbawCAuG0HALkFBwC6AQcAuxUHALwxBwC9MQcAvikHAL8pBwCwgQcAsYEHALJpBwCzZQcAtH0HALVhBwC2YQcAt1UHALM1BgDfawCA42sAgOdrAIDrawCAtl0GALUlBgDvawCAu0UGALpFBgDzawCA92sAgL+lBgC+uQYAvbEGALy9BgD7awCAo3EGAP9rAIADbACAphkGAAdsAIALbACApWEGAKoBBgCrAQYAD2wAgBNsAICu/QYAr+EGAKz5BgCt9QYAqCUBAKk1AQCqPQEAqzUBAKwtAQCtkQAArpEAAK+RAAAXbACAG2wAgB9sAIAjbACAJ2wAgIK9AwCBvQMAgL0DALiZAAC5rQAAuqUAALttAAC8dQAAvX0AAL51AAC/bQAAsPEAALH5AACywQAAs8EAALSxAAC1vQAAtrUAALepAAArbACAL2wAgDNsAICEgAIAvhwCADtsAICG+HwAh8wCAISsAwA/bACAQ2wAgEdsAIBLbACAT2wAgFNsAIBXbACAs/UCAFtsAIBfbACAkgAAAGNsAIC2UQMAteUCAGdsAIC7fQMAunUDAGtsAIBvbACAvzkDAL41AwC9VQMAvFUDAKM1AgBzbACAd2wAgHtsAIB/bACAppEDAKUlAgCDbACAq70DAKq1AwCHbACAi2wAgK/5AwCu9QMArZUDAKyVAwC+wAMAj2wAgJNsAICXbACAgA0AAIE1AACCPQAAm2wAgJ9sAICjbACAhsh8AIcAAwCrbACAr2wAgLNsAIC3bACAu2wAgL9sAIDDbACAx2wAgMtsAIDPbACA02wAgO/0AwCE7HwA4ZQBANdsAIDjMAMA22wAgN9sAIDjbACA52wAgLNpAQDrbACA72wAgPNsAID3bACAtmEBALVpAQD7bACAuykBALohAQD/bACAA20AgL8dAQC+HQEAvSUBALwtAQAHbQCAC20AgA9tAICjpQEAE20AgKWlAQCmrQEAvlR8AIaAfACH7HwAqu0BAKvlAQCs4QEArekBAK7RAQCv0QEAG20AgOGcBgCEBH8A4yQGAOPUBgAfbQCA4TAEACNtAIDvlAcAgnUAAIFhAACAaQAAJ20AgCttAIAvbQCA7+wGALiNfgC5lX4AupV+ALulfgC8vX4AvdF+AL7RfgC/0X4AsGV+ALFtfgCyeX4As3F+ALRZfgC1WX4Atr1+ALe1fgCoVX4AqWF+AKphfgCrYX4ArGF+AK1hfgCuYX4Ar2F+ADNtAICnbACAN2wAgDdtAIAXbQCAO20AgD9tAIBDbQCAqHF+AKlxfgCqcX4Aq3F+AKyRfwCtkX8ArpF/AK+RfwBHbQCAS20AgE9tAIBTbQCAV20AgFttAIBfbQCAY20AgLiFfwC5jX8AuoV/ALudfwC8jX8Avb1/AL61fwC/XX8AsPF/ALHxfwCy8X8As8V/ALTBfwC1wX8AtsF/ALfBfwCz+X8AZ20AgGttAIBvbQCAc20AgLYRfgC1GX4Ad20AgLs1fgC6NX4Ae20AgH9tAIC/BX4AvgV+AL0RfgC8JX4AghUAAKO9fwCAYQAAgWEAAKZVfgCDbQCAvpABAKVdfgCqcX4Aq3F+AIdtAICLbQCArkF+AK9BfgCsYX4ArVV+AKhBfgCpUX4AqlV+AKt9fgCsZX4ArW1+AK75AQCv8QEAhgAAAIc0AQCPbQCAk20AgJdtAICbbQCAn20AgKNtAIC4dQEAuX0BALp1AQC7yQAAvNkAAL3ZAAC+yQAAv8EAALCVAQCxnQEAspUBALNNAQC0VQEAtV0BALZVAQC3TQEAs919AKdtAICrbQCAr20AgLNtAIC27X0Ate19ALdtAIC7WQIAulECALttAIC/bQCAv5kCAL6RAgC9mQIAvEECAMNtAICjmX0Ax20AgMttAICmqX0Az20AgNNtAIClqX0AqhUCAKsdAgDXbQCA220AgK7VAgCv3QIArAUCAK3dAgDfbQCA420AgOdtAIDrbQCAgB0AAIEJAACCOQAA720AgPNtAIC+AAQA+20AgP9tAIADbgCAB24AgAtuAIAPbgCAhIwDABNuAICHCAMAhuwEABduAIDviAIAG24AgB9uAICEbAQA4zQCACNuAIDhVAEAJ24AgCtuAIAvbgCAM24AgKhtAgCprQIAqqUCAKu9AgCspQIAra0CAK6lAgCvGQEAvqwEADduAIA7bgCAP24AgENuAIBHbgCAS24AgE9uAIC4DQEAuREBALoRAQC7JQEAvD0BAL3VAQC+3QEAv9UBALBpAQCxaQEAsnkBALNxAQC0WQEAtVkBALY5AQC3NQEAsy0CAFNuAIBXbgCAW24AgF9uAIC2LQIAtS0CAGNuAIC7rQEAuq0BAGtuAIBvbgCAv50BAL6dAQC9pQEAvK0BAIBNAACBVQAAglUAAO9sAABzbgCA7+x/AO+8fgB3bgCA4RB/AOPUfwDj2H4A4ex/AHtuAIDhTH4Af24AgOMkfgD3bQCAZ24AgKsFBgCqBQYArQ0GAKwFBgCvNQYArjUGAIYAAwCHKAMAo4UFAINuAIClhQUAh24AgItuAICmhQUAs/EGAI9uAICTbgCAl24AgJtuAIC26QYAteEGAJ9uAIC7vQYAur0GAKNuAICnbgCAv4kGAL6BBgC9iQYAvJUGAKgpBgCpKQYAqjkGAKs5BgCsKQYArSkGAK5dBgCvTQYAq24AgK9uAICzbgCAt24AgLtuAIC/bgCAw24AgMduAIC46QcAuekHALr5BwC7+QcAvOkHAL3pBwC+XQcAv1UHALA5BgCxOQYAsgEGALMdBgC0BQYAtQ0GALYFBgC32QcAo7EHAIItAACBFQAAgB0AAMtuAICmqQcApaEHAM9uAICr/QcAqv0HANNuAICEpAIAr8kHAK7BBwCtyQcArNUHAL7MAQCzlQYA124AgNtuAIC2qQYA324AgONuAIC1rQYAulkBALshAQCGyAAAhwwBAL4hAQC/KQEAvDEBAL0xAQCoKQYAqSkGAKpZBgCrUQYArGEGAK1tBgCutQEAr6kBAITgAQDnbgCA624AgO9uAIDzbgCA924AgPtuAID/bgCAuGEBALlhAQC6YQEAu2EBALxhAQC9YQEAvmEBAL9hAQCw2QEAsaEBALKhAQCzoQEAtKEBALWpAQC2kQEAt5EBAKPRBQADbwCAB28AgAtvAIAPbwCApu0FAKXpBQATbwCAq2UCAKodAgAXbwCAG28AgK9tAgCuZQIArXUCAKx1AgAfbwCAI28AgCdvAIArbwCAL28AgDNvAIA3bwCAO28AgIA9AACBCQAAghkAAD9vAIBDbwCAS28AgL48AwBPbwCAhgAMAIcUAwBTbwCAs9UDAFdvAIC1PQMAtjUDAFtvAIBfbwCAv4wKALoRAwC7EQMAvLUAAL29AAC+tQAAv60AAGNvAIDjdAEAZ28AgOG8AQBrbwCAb28AgHNvAIB3bwCAe28AgH9vAICDbwCAh28AgItvAIDvdAIAj28AgJNvAICoTQIAqVECAKpRAgCrqQIArLkCAK25AgCuqQIAr6kCAIRsDQCXbwCAm28AgJ9vAICjbwCAp28AgKtvAIC+dA0AuG0BALkFAQC6DQEAuwUBALwdAQC9BQEAvg0BAL8FAQCw2QIAsdkCALJtAQCzZQEAtH0BALVlAQC2ZQEAt1UBAOG4AQDhUAcA47QAAON8BwCAqQAAgQkAAII5AACvbwCAs28AgLtvAIC/bwCAw28AgO4AAADHbwCA7wAAAO9kBgCGYAwAh+QMAKORAgDLbwCApXkCAM9vAIDTbwCApnECANdvAIDbbwCAq1UCAKpVAgCt+QEArPEBAK/pAQCu8QEAt28AgEdvAIDfbwCA428AgOdvAIDrbwCA728AgPNvAICoVQ4AqVkOAKqhDgCrvQ4ArK0OAK2VDgCu+Q4Ar/UOALCRDgCxkQ4AspEOALORDgC0sQ4AtbEOALaxDgC3sQ4AuJEOALmdDgC6lQ4Au0kPALxZDwC9WQ8AvkkPAL9JDwCzCQ4A928AgPtvAID/bwCAA3AAgLY1DgC1BQ4AB3AAgLt1DgC6dQ4AC3AAgA9wAIC/VQ4AvlUOAL1lDgC8ZQ4AE3AAgKNNDgAXcACAG3AAgKZxDgAfcACAI3AAgKVBDgCqMQ4AqzEOAISkAwC+pAMArhEOAK8RDgCsIQ4ArSEOAKilDgCprQ4AqqUOAKu5DgCs3Q4ArcEOAK7BDgCv/Q4AgO0BAIHxAQCC8QEAJ3AAgIaQAQCHtAEAK3AAgC9wAIC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALCFDgCxbQEAsmUBALN9AQC0ZQEAtW0BALZlAQC3+QEAsy0OADNwAIA3cACAO3AAgD9wAIC2QQ4AtVUOAENwAIC7qQEAukEOAEdwAIBLcACAv6kBAL6hAQC9qQEAvLEBAE9wAICjaQ4AU3AAgFdwAICmBQ4AW3AAgF9wAIClEQ4AqgUOAKvtAQBjcACAZ3AAgK7lAQCv7QEArPUBAK3tAQCoOQMAqTkDAKqNAwCrhQMArJ0DAK2FAwCuhQMAr7UDAGtwAIBvcACAc3AAgHdwAIB7cACAf3AAgINwAICHcACAuGEAALlhAAC6YQAAu2EAALxhAAC9YQAAvmEAAL9hAACwzQMAsaUDALKhAwCzoQMAtKUDALWtAwC2kQMAt5EDAIANAACBEQAAghEAAItwAIDv9AIAj3AAgJNwAIC+HAMA4xQCAISIAgDhgAEAm3AAgJ9wAICjcACAh8gDAIY8BAC7AQMAumkDAKdwAICrcACAvwkDAL4BAwC9FQMAvBUDALNlAwCvcACAs3AAgLdwAIC7cACAtmUDALV1AwC/cACAw3AAgMdwAIDLcACAo4kCAM9wAIClmQIApokCANNwAICELAIA13AAgKqFAgCr7QIArPkCAK35AgCu7QIAr+UCANtwAIDfcACAvkQFAIRMBQDjcACA53AAgOtwAIDvcACA83AAgPdwAID7cACA/3AAgIAZAACBGQAAggUAAANxAIDhGA8A4VwOAOO4DgDjdAEAC3EAgA9xAIATcQCAF3EAgIYABACHZAUAG3EAgB9xAIAjcQCAJ3EAgO98DgDvqAEAs3UBACtxAIAvcQCAM3EAgDdxAIC2MQEAtRUBADtxAIC7HQEAuhUBAD9xAIBDcQCAv+EAAL79AAC9/QAAvP0AAAdxAIBHcQCAS3EAgE9xAICXcACAU3EAgFdxAIBbcQCAqI0GAKmVBgCqnQYAq+UGAKz9BgCt0QYArtEGAK/RBgCwsQYAsbkGALJJBwCzSQcAtFkHALVFBwC2RQcAt3kHALghBwC5IQcAujkHALs5BwC8KQcAvSkHAL4ZBwC/GQcAozUGAF9xAIBjcQCAZ3EAgGtxAICmcQYApVUGAG9xAICrXQYAqlUGAHNxAIC+oAMAr6EHAK69BwCtvQcArL0HAIBRAACBWQAAgmEAALNVBwCF9AAAtX0HALZ1BwB3cQCAhgAcAIfkAQC6LQcAuyUHALw9BwC9JQcAviUHAL8VBwCokQYAqZEGAKqRBgCrkQYArLkGAK25BgCuqQYAr6kGAHtxAIB/cQCAg3EAgIdxAICiIQEAozUBAKA5BQChEQQAuEkBALlJAQC6XQEAu1UBALxNAQC90QEAvtEBAL/RAQCwpQYAsa0GALKlBgCzvQYAtK0GALWdBgC2lQYAt3kBAKMZBgCPnXkAi3EAgI9xAICTcQCApjkGAKUxBgCXcQCAq2kGAKphBgCbcQCAn3EAgK9ZBgCuaQYArWkGAKxxBgCeiQgAn8EFAJzJCQCdyQkAmqENAJu9DACYsQ0AmbkNAJahcQCXRXEAlEV1AJWxcQCSoXUAk7V1AJDleQCRzXkAil1yAItFcgCjcQCAvoAcAI51DgCPZQ4AjLlyAI11DgCCOXoAgzl6AKdxAICrcQCAhnF2AIeZdgCECXoAhW12AJptBwCbVQIAr3EAgLNxAIC3cQCA4ZAAAJxZAgDjCBoAkgkPAJNlCgC7cQCA7zgWAJZ1BgCXdQYAlH0KAJU1CwCpjRYAqIUWAKsBEACqMRYArXESAKy1EgCvuS4ArgEsAKF9AgC/cQCAo6EeAKKpHgClsRoApPUfAKflGwCmsRoAhMwDAIRMHADDcQCAx3EAgMtxAIDPcQCA03EAgNdxAICxASgAsNkuALONKgCy6SoAtfUmALQBJACEcB0A23EAgID9AQCBFQAAgh0AAL6AHADfcQCA43EAgIe4AgCGPB0A63EAgO9xAIDzcQCA93EAgPtxAID/cQCAA3IAgAdyAIALcgCAD3IAgBNyAIAXcgCA44ADABtyAIDhoAEAH3IAgO+UAwAjcgCAJ3IAgCtyAIAvcgCAM3IAgDdyAIA7cgCAP3IAgOE8BgBDcgCA49AGAEdyAIDhMAcAS3IAgOOsBgCAOQAAgRUAAIIdAADvHAYAT3IAgFNyAIC+uB8A7+gBALPpAgBbcgCAh8QcAIbsHABfcgCAtlkCALVRAgBjcgCAu00CALpNAgBncgCAa3IAgL+5AQC+2QEAvdEBALz1AQCjKR0A53EAgFdyAIBvcgCAc3IAgKaZHQClkR0Ad3IAgKuNHQCqjR0Ae3IAgH9yAICveR4ArhkeAK0RHgCsNR4Ag3IAgLNtHwCHcgCAi3IAgLZlHwCPcgCAk3IAgLVtHwC6IR8AuyEfAJdyAICbcgCAviUfAL8pHwC8MR8AvTEfAKihHwCpoR8AqqEfAKuhHwCsoR8AraEfAK6hHwCvoR8An3IAgKNyAICncgCAq3IAgK9yAICzcgCAt3IAgLtyAIC4rR8AubUfALq9HwC7tR8AvK0fAL1VHwC+UR8Av00fALChHwCxoR8AsqEfALOhHwC0pR8AtakfALadHwC3lR8AoykeAIIZAACBGQAAgLEBAL9yAICmIR4ApSkeAMNyAICrZR4AqmUeAIaIAACH/AEAr20eAK5hHgCtdR4ArHUeAMdyAICzmR4Ay3IAgM9yAIC2XQEA03IAgNdyAIC1sR4AukkBALtJAQDbcgCA33IAgL49AQC/IQEAvDkBAL01AQCoRR4AqVUeAKpVHgCrZR4ArH0eAK2ZAQCuiQEAr4EBAISsAADjcgCA53IAgOtyAIDvcgCA83IAgPdyAID7cgCAuK0BALllAQC6bQEAu2UBALx9AQC9ZQEAvm0BAL9lAQCwyQEAsckBALKpAQCzpQEAtL0BALWhAQC2oQEAt5UBALhpHAC5oRwAusEcALvBHAC8wRwAvcEcAL7BHAC/wRwAsIkfALGJHwCyIRwAswUcALQdHAC1fRwAtnUcALdtHACoYR8AqWEfAKphHwCrYR8ArNkfAK3ZHwCuyR8Ar8EfAP9yAIADcwCAB3MAgAtzAIAPcwCAE3MAgBdzAIAbcwCAH3MAgCNzAIC+AAQAo1EdACdzAICleR0AppUCACtzAIAvcwCAM3MAgKqBAgCrgQIArPECAK39AgCu9QIAr+kCADtzAIDh9AEAP3MAgON8AQCATQAAgXUAAIJ9AABDcwCAhsAEAIekBABHcwCAS3MAgE9zAIBTcwCAV3MAgO+MAgCoSQIAqUkCAKpdAgCrVQIArHkCAK15AgCuvQIAr7UCAISgBQBbcwCAX3MAgGNzAIC+vAQAZ3MAgGtzAIBvcwCAuC0BALk1AQC6PQEAuzUBALwtAQC91QEAvt0BAL/NAQCwzQIAsdUCALLdAgCz1QIAtM0CALUVAQC2HQEAtxUBAOGEHgDjbB8A41wfAOFYHgBzcwCAd3MAgHtzAIB/cwCAg3MAgIdzAICLcwCAj3MAgOkAAADv9B4A70weAJNzAICzlQIAl3MAgJtzAICfcwCAo3MAgLa5AgC1sQIAq3MAgLtRAgC6SQIAhsgEAIesBAC/kQEAvkkCAL1BAgC8SQIAN3MAgKNRBQCvcwCAp3MAgKZ9BQCzcwCAt3MAgKV1BQCqjQUAq5UFALtzAIC/cwCAro0FAK9VBgCsjQUArYUFAICJBwCBiQcAgpkHALORBgDDcwCAtbkGALapBgDHcwCAy3MAgM9zAIC6TQcAu0UHALxdBwC9QQcAvkEHAL9BBwCoQQYAqU0GAKpVBgCrZQYArH0GAK1lBgCubQYAr2UGANNzAIDXcwCA23MAgN9zAIDjcwCA53MAgOtzAIDvcwCAuFkHALlZBwC6aQcAu2kHALx5BwC9eQcAvmUHAL8ZBwCwxQcAsc0HALLFBwCz2QcAtMkHALXJBwC2aQcAt2kHAKPdBwDzcwCA93MAgPtzAID/cwCApuUHAKX1BwADdACAqwkGAKoBBgAHdACAC3QAgK8NBgCuDQYArQ0GAKwRBgCAbQAAgQkAAIIZAAAPdACAE3QAgISYAQC+kAEAF3QAgIbAAACH5AEAG3QAgB90AIAjdACAJ3QAgCt0AIAvdACAqF0GAKmNAQCqnQEAq5UBAKy5AQCtuQEArskBAK/BAQCEoAAAM3QAgDd0AIA7dACAP3QAgEN0AIBHdACAS3QAgLh5AQC5eQEAus0AALvFAAC83QAAvcUAAL7FAAC/9QAAsIEBALGBAQCySQEAs0kBALRZAQC1WQEAtkkBALdJAQCzFQIAT3QAgFN0AIBXdACAW3QAgLY5AgC1MQIAX3QAgLtFAgC6RQIAY3QAgGd0AIC/nQIAvp0CAL2dAgC8nQIAhXw+AKNRAgBrdACAb3QAgKZ9AgBzdACAd3QAgKV1AgCqAQIAqwECAHt0AIB/dACArtkCAK/ZAgCs2QIArdkCAIDpAACB6QAAggUAAIN0AIC+AAwAi3QAgIeoAwCGvAwAj3QAgJN0AICXdACAm3QAgJ90AICjdACAp3QAgKt0AICvdACAs3QAgLd0AIC7dACA42ABAL90AIDhoAEAw3QAgO+IAgDHdACAy3QAgM90AIDTdACA13QAgNt0AIDfdACAqGkCAKlpAgCqeQIAq3kCAKxpAgCtaQIArr0CAK+1AgC+rAwA43QAgOd0AIDrdACAgB0AAIEJAACCqQAA73QAgLhRAQC5WQEAumEBALthAQC8GQEAvRkBAL4NAQC/BQEAsM0CALHVAgCy3QIAs9UCALTNAgC1cQEAtnEBALdxAQDjxAAA4XwHAOF4BgDjvAYA83QAgIQYDQCGuAwAhzwNAL4sDwD7dACA/3QAgAN1AIDvEAAAB3UAgAt1AIDvdAYAD3UAgBN1AIAXdQCAs70CABt1AIC1rQIAtqUCAB91AIAjdQCAJ3UAgLpFAgC7XQIAvEUCAL1NAgC+RQIAv/kBAId0AIClfQ0ApnUNAPd0AIArdQCAL3UAgDN1AICjbQ0ArJUNAK2dDQCulQ0ArykOADd1AIA7dQCAqpUNAKuNDQCz5Q4AP3UAgEN1AIBHdQCAS3UAgLblDgC19Q4AT3UAgLuhDgC62Q4AU3UAgFd1AIC/pQ4AvrkOAL2xDgC8uQ4AqBUOAKklDgCqLQ4AqyUOAKw9DgCtJQ4Ari0OAK8lDgCADQAAgRUAAIIdAABbdQCAX3UAgGN1AICEMAMAZ3UAgLgpDgC5KQ4AujkOALs5DgC8KQ4AvSkOAL79DwC/9Q8AsF0OALElDgCyLQ4AsyUOALQ9DgC1IQ4AtiUOALcZDgCjpQ8Aa3UAgIYoAQCHTAEAb3UAgKalDwCltQ8Ac3UAgKvhDwCqmQ8Ad3UAgHt1AICv5Q8ArvkPAK3xDwCs+Q8Af3UAgLPpDgCDdQCAh3UAgLaRDgCLdQCAj3UAgLXlDgC6sQ4Au7kOAJN1AICXdQCAvmEBAL9hAQC8mQ4AvZkOAKglDgCpLQ4AqiUOAKs5DgCsKQ4ArVUOAK5dDgCvVQ4Am3UAgJ91AICjdQCAp3UAgKt1AICvdQCAs3UAgLd1AIC49QEAuYEBALqBAQC7gQEAvIEBAL2JAQC+sQEAv7EBALAxDgCxOQ4AsgkOALMJDgC04QEAteEBALbhAQC3zQEAo60NALt1AIC/dQCAw3UAgMd1AICm1Q0ApaENAMt1AICr/Q0AqvUNAM91AIDTdQCAryUCAK4lAgCt3Q0ArN0NAIBdAACBbQAAgmUAALNRAwC+nAMAtXkDALYZAwDbdQCAhOACAN91AIC6PQMAuzUDALwZAwC9GQMAvtkDAL/ZAwCohQMAqZUDAKqVAwCrpQMArL0DAK3VAwCu0QMAr9EDAIYABACHNAMAv6AzAON1AIDndQCA63UAgO91AIDzdQCAuHEDALlxAwC6cQMAu3EDALzVAAC93QAAvtUAAL/NAACwtQMAsb0DALKBAwCzgQMAtFEDALVRAwC2UQMAt1EDAO+oAwD3dQCA+3UAgP91AICEHAIAA3YAgAd2AIALdgCAviwFAA92AIATdgCAF3YAgONAAwAbdgCA4SgAAB92AICjXQIAI3YAgCd2AIArdgCAL3YAgKYVAgCldQIAM3YAgKs5AgCqMQIAN3YAgDt2AICv1QIArtUCAK0VAgCsFQIA4ygBAOEADwDhCA4A4wgOAID9AACBCQAAgjkAAD92AIBDdgCAS3YAgE92AIBTdgCA7+gOAFd2AIBbdgCA72QOALNtAQBfdgCAhugEAIcMBQBjdgCAtm0BALVtAQBndgCAu+0AALrtAABrdgCAb3YAgL/VAAC+6QAAveEAALzpAACoXQYAqWEGAKqlBgCrvQYArKUGAK2tBgCupQYArxkHAEd2AIBzdgCAd3YAgHt2AIB/dgCAg3YAgId2AICLdgCAuHUHALl5BwC6DQcAuwUHALwdBwC9BQcAvgUHAL81BwCwaQcAsWkHALJ9BwCzdQcAtG0HALVRBwC2UQcAt1EHAKMtBgCPdgCAk3YAgJd2AICbdgCApi0GAKUtBgCfdgCAq60HAKqtBwCjdgCAp3YAgK+VBwCuqQcAraEHAKypBwCADQAAgRUAAIIdAACrdgCAr3YAgLN2AICEVAMAvlwAALd2AIC7dgCAhugAAIdMAwC/dgCAw3YAgMd2AIDLdgCAz3YAgOMEBADTdgCA4bQFANd2AIDbdgCA33YAgON2AIDndgCA63YAgO92AIDzdgCA93YAgO/sBAD7dgCA/3YAgLPtBgADdwCAB3cAgAt3AIAPdwCAtpEGALXhBgATdwCAu40GALqNBgAXdwCAG3cAgL9BAQC+WQEAvVEBALxZAQCoJQYAqS0GAKolBgCrOQYArCkGAK1RBgCuSQYAr0EGAIDNAACBCQAAghkAAB93AIAjdwCAhCwBAL40AAArdwCAuP0BALlBAQC6QQEAu0EBALxBAQC9SQEAvnEBAL9xAQCwCQYAsQkGALLNAQCzxQEAtN0BALXFAQC2zQEAt8UBAIagPACHRAMAL3cAgKOhBQAzdwCApa0FAKbdBQA3dwCAO3cAgL4oPACqwQUAq8EFAKwVAgCtHQIArhUCAK8NAgC2QQMAP3cAgEN3AIC1sQIAR3cAgLOhAgBLdwCAT3cAgL5FAwC/TQMAvHUDAL1NAwC6ZQMAu20DAFN3AIBXdwCAW3cAgF93AIDXdQCAY3cAgGd3AIBrdwCAb3cAgHN3AICoRQIAqVUCAKpdAgCrVQIArE0CAK21AwCusQMAr60DALDVAwCx3QMAstUDALPtAwC09QMAtf0DALb1AwC37QMAuNkDALnZAwC6rQMAu6UDALy9AwC9pQMAvqUDAL+VAwCj9QMAd3cAgHt3AIB/dwCAg3cAgKYVAgCl5QMAh3cAgKs5AgCqMQIAi3cAgI93AICvGQIArhECAK0ZAgCsIQIAgGkAAIFpAACCBQAAk3cAgJt3AICfdwCAo3cAgO8cAACEbAIA4ZQBAKd3AIDjyAAAq3cAgK93AICGWDwAh1A9ALN3AIC3dwCAu3cAgISEPQC/dwCAw3cAgMd3AIDvuAEAvmw8AOF0BgDLdwCA42QBAM93AIDTdwCA13cAgNt3AICz0QEA33cAgON3AIDndwCA63cAgLaRAQC1+QEA73cAgLu9AQC6vQEA83cAgPd3AIC/dQEAvnUBAL2FAQC8hQEAqL09AKkNPgCqGT4AqxE+AKwxPgCtUT4ArlE+AK9NPgCXdwCAgh0AAIEdAACAHQAA+3cAgP93AIADeACAB3gAgLjVPgC53T4AutU+ALtJPwC8WT8AvVk/AL5JPwC/QT8AsDk+ALE5PgCyET4AsxE+ALTxPgC18T4AtvU+ALftPgCjkT4AC3gAgIYoAACHwAMAD3gAgKbRPgCluT4AE3gAgKv9PgCq/T4AF3gAgBt4AICvNT4ArjU+AK3FPgCsxT4AH3gAgLOdPwAjeACAJ3gAgLalPwAreACAL3gAgLWtPwC6aT8Au3U/ADN4AIA3eACAvlk/AL9FPwC8bT8AvWU/ADt4AIA/eACAQ3gAgEd4AIDjYDwAS3gAgOEAPQBPeACA7/w9AFN4AIBXeACAW3gAgF94AIBjeACAZ3gAgGt4AICjGT4AghkAAIEZAACAcQAAb3gAgKYhPgClKT4Ac3gAgKvxPgCq7T4AhCQBAL4kAQCvwT4Art0+AK3hPgCs6T4AqNE+AKnRPgCq0T4Aq+U+AKzhPgCt4T4Arhk+AK8ZPgCGAAAAh4QAAHt4AIB/eACAg3gAgId4AICLeACAj3gAgLh9PgC5AT4AugE+ALsBPgC8AT4AvQk+AL4xPgC/MT4AsGk+ALF1PgCyfT4As3U+ALRZPgC1RT4Atk0+ALdFPgCohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAJN4AICXeACAm3gAgL8k5gGfeACAo3gAgKd4AICreACAuFUDALlZAwC6bQMAu2UDALx9AwC9ZQMAvm0DAL9lAwCwtQIAsb0CALKBAgCzgQIAtHEDALVxAwC2cQMAt3EDALMdAgCveACAs3gAgLd4AICEiAMAtlUCALU1AgAndwCAu3kCALpxAgC7eACAv3gAgL+1AwC+tQMAvVUCALxVAgDDeACAo1kCAMd4AIDLeACAphECAM94AIDTeACApXECAKo1AgCrPQIA13gAgNt4AICu8QMAr/EDAKwRAgCtEQIAqKkCAKmpAgCquQIAq7kCAKypAgCtqQIArjkBAK85AQCAzQEAgQkAAIIZAADfeACA43gAgL64BQDreACA73gAgLjpAQC56QEAuokBALuFAQC8nQEAvYEBAL6BAQC/tQEAsEkBALFVAQCyXQEAs1UBALRNAQC18QEAtvEBALfxAQDvFAAA83gAgIaoBQCH3AUA93gAgIRYBAD7eACA78Q+AP94AIDhxD4AA3kAgOMwPgDjyAAAB3kAgOEoAQALeQCAtn0CAA95AIATeQCAtXUCABd5AICzZQIAG3kAgB95AIC+3QEAv2EBALzdAQC91QEAutkBALvFAQAjeQCAJ3kAgKOxBQDneACAK3kAgC95AIAzeQCApqkFAKWhBQA3eQCAqxEGAKoNBgA7eQCAP3kAgK+1BgCuCQYArQEGAKwJBgBDeQCAR3kAgEt5AIBPeQCAgBkAAIEZAACCBQAAU3kAgL5sAwBXeQCAhsgAAIccAwBbeQCAX3kAgGN5AIBneQCAqLkHAKm5BwCqDQcAqx0HAKwJBwCtNQcArjEHAK8pBwCEqAMAa3kAgG95AIBzeQCAd3kAgHt5AIB/eQCAg3kAgLjJAAC5yQAAutkAALvRAAC8+QAAvfkAAL6ZAAC/mQAAsF0HALEhBwCyIQcAsz0HALQpBwC1KQcAtgEHALcBBwCzhQYAh3kAgIt5AICPeQCAk3kAgLa1BgC1gQYAl3kAgLvlBgC6mQYAm3kAgJ95AIC/7QYAvu0GAL3pBgC89QYAo3kAgKd5AICreQCAr3kAgLN5AIC3eQCAu3kAgO+QBAC/eQCA4dwGAMN5AIDj7AUAgCkAAIEVAACCEQAAvnwBAKMFBgDLeQCAhigAAIdMAQDPeQCApjUGAKUBBgDTeQCAq2UGAKoZBgDXeQCA23kAgK9tBgCubQYArWkGAKx1BgDfeQCAs70BAON5AIDneQCAtnkBAOt5AIDveQCAtXkBALpVAQC7XQEA83kAgPd5AIC++QAAv/kAALxFAQC9+QAAqHECAKlxAgCqcQIAq3ECAKy1AgCtvQIArrUCAK+tAgCE7AwA+3kAgP95AIADegCAB3oAgAt6AIAPegCAE3oAgLhpAwC5aQMAugkDALsJAwC8GQMAvRkDAL4JAwC/CQMAsNUCALHdAgCy1QIAs2kDALR5AwC1eQMAtmkDALdhAwAXegCAG3oAgB96AICj9QIAI3oAgKUxAgCmMQIAJ3oAgCt6AIAvegCAqh0CAKsVAgCsDQIArbEDAK6xAwCvsQMAgGEAAIFhAACCBQAAM3oAgIbwDACHYAMAvhAMADt6AIB3eACAP3oAgEN6AIBHegCAS3oAgE96AIBTegCAV3oAgKiFAgCplQIAqpUCAKulAgCsvQIArdUCAK7RAgCv0QIAW3oAgF96AIBjegCAZ3oAgGt6AIBvegCAc3oAgHd6AIC4dQEAuX0BALp1AQC7zQEAvNUBAL3dAQC+yQEAv8EBALC1AgCxvQIAsoECALOBAgC0VQEAtV0BALZVAQC3TQEA4RAGAIRIDADjDAYAe3oAgISYDAB/egCAg3oAgId6AICLegCAj3oAgJN6AICXegCAgXUAAIB1AADvIAEAgnUAAJt6AICfegCAo3oAgL7ADACFtA4A4RACAO9cAADjABYA4ZABAKt6AIDjWAEA7zwHAK96AICzegCAhgAIAIe4DACznQ0AN3oAgLd6AIC7egCAv3oAgLbVDQC1tQ0Aw3oAgLv5DQC68Q0Ax3oAgMt6AIC/GQ4AvhEOAL3VDQC81Q0Az3oAgKPZDQDTegCA13oAgKaRDQDbegCA33oAgKXxDQCqtQ0Aq70NAON6AIDnegCArlUOAK9dDgCskQ0ArZENAKhdDgCpYQ4AqmEOAKthDgCsYQ4ArWEOAK5hDgCvYQ4A63oAgO96AIDzegCA93oAgPt6AID/egCAA3sAgAd7AIC4TQ8AuVEPALpRDwC7UQ8AvHEPAL1xDwC+cQ8Av3EPALDBDwCxwQ8AssEPALPBDwC0wQ8AtcEPALbBDwC3wQ8As+kPAAt7AIC+gAEAD3sAgKd6AIC24Q8AtekPABN7AIC7BQ4AugUOABt7AIAXewCAvwUOAL4FDgC9FQ4AvBUOAIFNAACAQQAA72gNAIJRAACG8AcAh9QBAB97AIAjewCAJ3sAgIRwAQArewCAL3sAgOHgDgAzewCA40gNADd7AICjaQ8AO3sAgD97AIBDewCAR3sAgKZhDwClaQ8AS3sAgKuFDgCqhQ4AT3sAgFN7AICvhQ4AroUOAK2VDgCslQ4AV3sAgLMxDgBbewCAX3sAgLbBAQBjewCAZ3sAgLXRAQC6zQEAu6UBAGt7AIBvewCAvqUBAL+tAQC8sQEAvbEBAI/dJgCj8Q0Ac3sAgHd7AICmAQIAe3sAgH97AIClEQIAqg0CAKtlAgCDewCAviAEAK5lAgCvbQIArHECAK1xAgCfoQwAnnkKAJ1pCgCc0QgAm7E2AJp1NgCZ0TQAmOEyAJdtMgCWZTIAlTU/AJRhPgCTcT4AkjU7AJFxOgCQeToAgJUAAIGdAACCoQAAi3sAgO9EAgDhdA8Aj3sAgOMcDwDj1AEAk3sAgOHgAQDvXAEAo7UCAKJBAACh3Q4AoLkOALWpAwCXewCAhMAEALahAwCG8AUAh+QEALOFAwCbewCAvXEDALxpAwC/QQMAvnEDAJ97AIDHeQCAu3EDALp5AwCC3ScAgwE7AL6EBwC+wAYAhhE/AIcZPwCEETsAhV06AIp9PgCLJTMAo3sAgKd7AICOuTUAjxU3AIw1MwCNgTMAkqE3AJPZCQC+xBkAq3sAgJaxDQCXUQ8AlHkLAJVhCwCaBQ8Am5EBAK97AICzewCAt3sAgN0AAACcfQMAu3sAgOFIDwC/ewCA4xwOAMN7AIDHewCAy3sAgM97AIDTewCAsUEXALChFwCzqesBsgHoAbUB7AG0EesB74wOANd7AICpxR8AqAEcAKsBEACqkR8ArdkTAKzREwCv2RcArgUTAKHxAgDbewCAo8kHAKLBAgClARgApGUHAKehGwCm+RsAqCkFAKldBQCqVQUAq20FAKx5BQCteQUArm0FAK9hBQCHewCA33sAgON7AIDnewCAgA0AAIGxAACCsQAA63sAgLiJBQC5iQUAup0FALuVBQC8uQUAvbkFAL5RBgC/UQYAsOUFALHtBQCy5QUAs/0FALTtBQC13QUAttUFALe9BQCj3QUA73sAgPN7AICEDAAA93sAgKb5BQCl8QUA+3sAgKspBQCqIQUAhpgAAIegAACvGQUArikFAK0pBQCsMQUA/3sAgLNhBgADfACAB3wAgLYhBgALfACAD3wAgLUBBgC6rQcAu40HABN8AIAXfACAvo0HAL9xBwC8lQcAvY0HAL65BQC/uQUAvLkFAL25BQC6uQUAu7kFALi5BQC5uQUAtkkFALdJBQC0fQUAtXUFALJ5BQCzeQUAsBUFALF9BQCuXQUAr20FAKxFBQCtXQUAqqUKAKtdBQCovQoAqa0KABt8AIAffACAI3wAgCd8AIArfACAL3wAgDN8AIA3fACAqA0HAKkdBwCqLQcAq0kHAKxNBwCtZQcArrEGAK+xBgA7fACAP3wAgEN8AIBHfACAS3wAgE98AIBTfACAV3wAgLhVBgC5XQYAulUGALtxBgC8NQYAvfEBAL7xAQC/8QEAsK0GALGNBgCyhQYAs50GALSNBgC1cQYAtnUGALdtBgCjpQQAgi0AAIEVAACAHQAAW3wAgKblBAClxQQAX3wAgKtJBQCqaQUAY3wAgGt8AICvtQUArkkFAK1JBQCsUQUAhmAcAIcIAwBvfACAs4UCAHN8AIC1gQIAtoECAHd8AIB7fACAf3wAgLoJAwC7CQMAvBkDAL0ZAwC+CQMAvwkDAKxVAgCtXQIArmECAK9hAgCoDQIAqVUCAKpRAgCrUQIAhKwDAIN8AICHfACAi3wAgIT8HQCPfACAk3wAgJd8AIC8cQMAvXEDAL5xAwC/cQMAuHEDALlxAwC6cQMAu3EDALSRAwC1kQMAtpEDALeRAwCwkQMAsZEDALKRAwCzkQMAm3wAgJ98AICjfACAp3wAgKt8AIDhpAEAr3wAgOOAAQC+aBwAs3wAgLd8AIDv2AYAu3wAgL98AIDDfACAx3wAgKOJAwCCLQAAgRUAAIAdAADLfACApo0DAKWNAwDPfACAqwUCAKoFAgDTfACA23wAgK8FAgCuBQIArRUCAKwVAgCGIBwAh8QdAN98AIDjfACA53wAgOt8AIDvfACA72wGAPN8AIDhbAcA93wAgON0BwD7fACA/3wAgAN9AIAHfQCAs5EBAAt9AIAPfQCAE30AgBd9AIC2sQEAtbkBABt9AIC7VQEAukkBAB99AIAjfQCAv/UAAL71AAC9RQEAvEUBAKNRHgDXfACAJ30AgCt9AIAvfQCApnEeAKV5HgAzfQCAq5UeAKqJHgA3fQCAO30AgK81HwCuNR8ArYUeAKyFHgCAbQAAgRUAAIIdAADv/BkAP30AgEN9AIBHfQCAS30AgIbAAACHrAMAT30AgFN9AIBXfQCA4SwcAFt9AIDjzBwAqK0eAKnNHgCq2R4Aq9EeAKzxHgCt8R4Arj0eAK81HgCE7AAAX30AgGN9AIBnfQCAa30AgG99AIBzfQCAd30AgLjRHwC53R8Auu0fALvlHwC84R8AveEfAL7hHwC/4R8AsE0eALFRHgCyUR4As1EeALTxHwC18R8AtvEfALfxHwCobR4AqY0eAKqFHgCrnR4ArIUeAK2NHgCuuR4Ar7UeAHt9AIB/fQCAg30AgId9AICLfQCAj30AgJN9AICXfQCAuJ0eALmtHgC6pR4Au0UBALxdAQC9RQEAvkUBAL91AQCw0R4AsdEeALLRHgCz0R4AtLUeALW9HgC2tR4At60eALMNHgCbfQCAn30AgKN9AICnfQCAtg0eALUNHgCrfQCAuxUeALoVHgCvfQCAs30AgL95HgC+cR4AvQUeALwFHgCCbQAAo0keAIBVAACBZQAApkkeAL6cAQC7fQCApUkeAKpRHgCrUR4Ah3wAAIZMAACuNR4Arz0eAKxBHgCtQR4AqF0CAKltAgCqZQIAq30CAKxpAgCtsQIArrECAK+xAgCE7AQAv30AgMN9AIDHfQCAy30AgM99AIDTfQCA130AgLhxAwC5cQMAunEDALtxAwC81QMAvd0DAL7VAwC/zQMAsNECALHRAgCy0QIAs9ECALRRAwC1UQMAtlEDALdRAwCz7QIA230AgN99AIC+gAQA430AgLYxAgC14QIA530AgLsVAgC6FQIA630AgO99AIC/lQMAvpUDAL0FAgC8BQIA830AgKOpAgD3fQCA+30AgKZ1AgD/fQCAA34AgKWlAgCqUQIAq1ECAAd+AIALfgCArtEDAK/RAwCsQQIArUECAKjZAgCpIQEAqiEBAKshAQCsIQEArSEBAK4hAQCvIQEAD34AgBN+AIAXfgCAviAEABt+AIAffgCAI34AgCt+AIC4jQEAuZEBALqRAQC7pQEAvL0BAL11AAC+fQAAv3UAALDlAQCx7QEAsvkBALPxAQC02QEAtdkBALa5AQC3tQEA4RgeAC9+AIDjKB8AM34AgIGlAACApQAAN34AgIKlAACGAAQAh/QFADt+AIA/fgCAQ34AgEd+AIDvYB4AS34AgE9+AIBTfgCAhfD0AVd+AIBbfgCA42QBAF9+AIDhpAEAY34AgO/IAABnfgCAa34AgGd8AICE/AUAb34AgHN+AICzKQYAJ34AgHd+AIB7fgCAf34AgLYhBgC1KQYAg34AgLupBgC6oQYAh34AgIt+AIC/nQYAvp0GAL2lBgC8rQYA4bQHAI9+AIDjeAQAk34AgIB9AACBEQAAghUAAJd+AICGwAAAh1gDAJt+AICffgCAo34AgKd+AIDvDAQAq34AgKOpBgCvfgCAs34AgLd+AIC7fgCApqEGAKWpBgC/fgCAqykGAKohBgDDfgCAx34AgK8dBgCuHQYArSUGAKwtBgDLfgCAs0kHAM9+AIDTfgCAtn0HANd+AIDbfgCAtXUHALpdBwC7JQcA334AgON+AIC+IQcAvy0HALw9BwC9MQcAqD0GAKmBBgCqhQYAq5UGAKy5BgCtuQYArqkGAK+pBgDnfgCA634AgO9+AIDzfgCA934AgIK5AACBsQAAgLkAALitBgC5vQYAurUGALtFAQC8XQEAvUUBAL5FAQC/dQEAsN0GALGlBgCyrQYAs6EGALShBgC1rQYAtpkGALeVBgCjDQYA+34AgP9+AIADfwCAhJgCAKY5BgClMQYAvpwBAKthBgCqGQYAhggAAId8AQCvaQYArmUGAK11BgCseQYAC38AgLO1AQAPfwCAE38AgLZVAQAXfwCAG38AgLWhAQC6cQEAu3kBAB9/AIAjfwCAvjEBAL89AQC8UQEAvVEBAKhpAgCpaQIAqnkCAKt5AgCsbQIArZECAK6RAgCvkQIAJ38AgCt/AIAvfwCAM38AgDd/AIA7fwCAP38AgEN/AIC4mQIAua0CALqlAgC7bQMAvHUDAL19AwC+dQMAv20DALDxAgCx+QIAssECALPBAgC0sQIAtb0CALa1AgC3qQIAR38AgEt/AIBPfwCAo/0CAFN/AICl6QIAph0CAFd/AIBbfwCAX38AgKo5AgCrMQIArBkCAK0ZAgCueQIAr3UCAGN/AIBnfwCAa38AgIQADACAGQAAgQkAAII5AABvfwCAc38AgHt/AIB/fwCAvuAMAIN/AICHfwCAhlgNAIcMAwCowQIAqc0CAKrFAgCr2QIArMkCAK39AgCu9QIArz0BAIt/AICPfwCAk38AgJd/AICbfwCAn38AgKN/AIC+MAwAuMUBALnNAQC62QEAu9EBALzxAQC98QEAvpkBAL+ZAQCwRQEAsU0BALJFAQCzXQEAtEUBALVNAQC2RQEAt/0BAOE4BgCnfwCA42wGAKt/AICvfwCAs38AgLd/AIC7fwCAhKgNAL9/AIDDfwCAx38AgL6wDwDLfwCA72wGAM9/AIDTfwCAt30AgNd/AIDbfwCA41AAAN9/AIDhoAEA438AgO+EAADrfwCAhyANAIZMDwCAPQAAgSEAAIIlAADvfwCAs80NAHd/AIDnfwCA838AgPd/AIC2/Q0AtcENAPt/AIC7CQ4AugEOAP9/AIADgACAvwkOAL4BDgC9CQ4AvBEOAAeAAIDjmAwAC4AAgOH8DwAPgACAE4AAgBeAAIAbgACAH4AAgCOAAIAngACAK4AAgC+AAIDvYAwAM4AAgDeAAICjTQ0AO4AAgD+AAIBDgACAR4AAgKZ9DQClQQ0AS4AAgKuJDgCqgQ4AT4AAgFOAAICviQ4AroEOAK2JDgCskQ4Agm0AALM1DgCAVQAAgWUAALb1DwCE3AMAV4AAgLX9DwC60Q8Au9EPAIYABACH3AAAvn0PAL9lDwC8wQ8AvXkPAKjlDwCp7Q8AqvkPAKv5DwCsMQ4ArTEOAK4xDgCvMQ4AW4AAgF+AAIBjgACAZ4AAgGuAAIBvgACAc4AAgHeAAIC43Q4AueEOALrhDgC74Q4AvOUOAL3pDgC+mQ4Av5UOALBRDgCxUQ4AslEOALPpDgC0/Q4AteUOALbtDgC35Q4Ao3EPAHuAAIB/gACAg4AAgIeAAICmsQ4ApbkOAIuAAICrlQ4AqpUOAI+AAICTgACAryEOAK45DgCtPQ4ArIUOAJeAAICzyQEAm4AAgJ+AAIC2+QEAo4AAgKeAAIC1wQEAuqkBALu1AQCrgACAr4AAgL6tAQC/lQEAvK0BAL2lAQCo5Q0AqfkNAKoFAgCrHQIArA0CAK09AgCuNQIAr10CALOAAIC3gACAu4AAgL+AAICAGQAAgRkAAIIFAADDgACAuC0CALk1AgC6MQIAuzECALzVAgC93QIAvtUCAL/NAgCwKQIAsTUCALI9AgCzNQIAtC0CALUVAgC2HQIAtxUCAMuAAICEnAIAz4AAgKOBAgDTgACApYkCAKaxAgDXgACAhiAEAIfUAwCq4QIAq/0CAKzlAgCt7QIAruUCAK/dAgC29QMAvkQDAIWM/QG1/QMA24AAgLP9AwDfgACA44AAgL59AwC/TQMAvGUDAL19AwC6dQMAu30DAOeAAIDrgACA74AAgPOAAICEBAIAoyUCAPeAAIClJQIApi0CAPuAAID/gACAA4EAgKqtAgCrpQIArL0CAK2lAgCupQIAr5UCAAeBAIALgQCAD4EAgBOBAIAXgQCA48ADABuBAIDhrAEAH4EAgO9YAwAjgQCAJ4EAgIANAACB5QAAgu0AACuBAIDhYA8A40ABAOM4DgDheA4AL4EAgDOBAIC+lAUAO4EAgIYABACHZAUAP4EAgEOBAIBHgQCA7/wOAO98DgBLgQCAs1EBAE+BAIAHfwCAU4EAgFeBAIC2DQEAtQkBAFuBAIC74QAAuhkBAF+BAIBjgQCAv9EAAL7pAAC96QAAvPkAAMeAAIA3gQCAZ4EAgGuBAIBvgQCAc4EAgHeBAIB7gQCAqKEGAKmtBgCquQYAq7EGAKzhBgCt7QYAruUGAK/FBgCwvQYAsUUHALJNBwCzXQcAtE0HALV1BwC2fQcAtx0HALglBwC5LQcAuiUHALs9BwC8KQcAvRUHAL4RBwC/EQcAoxEGAH+BAICDgQCAh4EAgIuBAICmTQYApUkGAI+BAICroQcAqlkGAJOBAICXgQCAr5EHAK6pBwCtqQcArLkHAIANAACBFQAAgh0AAJuBAICfgQCAo4EAgISUAwC+lAMAp4EAgKuBAICGyAAAh4wAAK+BAICzgQCAt4EAgLuBAIConQYAqa0GAKqlBgCrvQYArK0GAK3RBgCu1QYAr80GAL+BAIDDgQCAx4EAgMuBAIDPgQCA04EAgNeBAIDbgQCAuF0BALnBAQC6wQEAu8EBALzBAQC9yQEAvvEBAL/xAQCwvQYAsY0GALKFBgCzZQEAtH0BALVlAQC2bQEAt2UBALMtBgDfgQCA44EAgOeBAIDrgQCAtlEGALUlBgDvgQCAu0kGALp5BgDzgQCA94EAgL+hAQC+uQEAvbEBALxRBgD7gQCAo2kGAP+BAIADggCAphUGAAeCAIALggCApWEGAKo9BgCrDQYAD4IAgBOCAICu/QEAr+UBAKwVBgCt9QEAutUHALvdBwC4wQcAucEHAL4xBAC/MQQAvPEHAL3xBwCyrQcAs7UHALCtBwCxpQcAtp0HALf1BwC0pQcAtZUHAKppBwCraQcAqGkHAKlpBwCuaQcAr2kHAKxpBwCtaQcAgLkDAIGNAwCChQMAhKgDAIZQ/AGHCAMAvjQDABuCAICoZQIAqXUCAKp9AgCrdQIArG0CAK21AwCuvQMAr7UDAB+CAIAjggCAJ4IAgCuCAIAvggCAM4IAgDeCAIA7ggCAuFEDALlZAwC6YQMAu2EDALwRAwC9HQMAvhUDAL8JAwCwzQMAsdUDALLdAwCz1QMAtM0DALVxAwC2cQMAt3EDAD+CAIBDggCAs/0DAEeCAIC17QMAS4IAgE+CAIC2PQIAU4IAgFeCAIC7GQIAugECAL0JAgC8AQIAv70CAL4BAgBbggCAX4IAgITE/QG+wPwBY4IAgGeCAIBrggCA79wDAG+CAIDhlAEAc4IAgOMQAwB3ggCAgu0AAIHtAACA7QAA4TgGAOE8BwDjQAEA45QGAHuCAIB/ggCAg4IAgIuCAICGgPwBh+j9AY+CAICTggCAl4IAgJuCAIDvnAEA79wGAKM1AwCfggCAo4IAgKeCAICrggCApvUCAKUlAwCvggCAq9ECAKrJAgCzggCAt4IAgK91AgCuyQIArcECAKzJAgCHggCAu4IAgL+CAIDDggCA76T9AceCAIDLggCAz4IAgON4/QHTggCA4UD8AdeCAIDbggCA34IAgOOCAIDnggCAs+X+AYItAACBFQAAgB0AAOuCAIC25f4BtfX+Ae+CAIC7Yf8Butn+AfOCAICE5AMAv2n/Ab5h/wG9df8BvHn/Aaj9/gGpJf4Bqi3+Aasl/gGsPf4BrSX+Aa4t/gGvJf4BviwAAPeCAICGiAAAh+wAAPuCAID/ggCAA4MAgAeDAIC4gf8BuYH/AbqZ/wG7mf8BvIn/Ab21/wG+sf8Bv63/AbBd/gGx5f8Bsu3/AbPh/wG05f8Bte3/AbbZ/wG32f8Bo6X/AQuDAIAPgwCAE4MAgBeDAICmpf8BpbX/ARuDAICrIf4Bqpn/AR+DAIAjgwCAryn+Aa4h/gGtNf4BrDn+ASeDAICz6f4BK4MAgC+DAIC2lf4BM4MAgDeDAIC16f4BurH+Abu5/gE7gwCAP4MAgL51AQC/fQEAvJH+Ab2R/gGoHf4BqS3+Aaol/gGrPf4BrCX+Aa1R/gGuUf4Br1H+AUODAIBHgwCAS4MAgE+DAIBTgwCAV4MAgFuDAIBfgwCAuNkBALnZAQC67QEAu+EBALzhAQC94QEAvuEBAL/hAQCwMf4BsTn+AbIB/gGzAf4BtPUBALX9AQC29QEAt+kBAKOt/QFjgwCAvkwDAGuDAIBvgwCAptH9AaWt/QFzgwCAq/39Aar1/QF3gwCAe4MAgK85AgCuMQIArdX9AazV/QGA+QMAgfkDAIJNAACFdCAAf4MAgITYAwCE1AQAg4MAgIZABACHVAMAh4MAgIuDAICPgwCAk4MAgJeDAIC+8AUAqDECAKkxAgCqMQIAqzECAKyVAwCtnQMArpUDAK+NAwCbgwCAn4MAgKODAICngwCAhHwHAKuDAICvgwCAs4MAgLipAwC5qQMAumkDALtpAwC8eQMAvXkDAL5pAwC/aQMAsP0DALHNAwCyxQMAs60DALS5AwC1uQMAtq0DALelAwC3gwCAu4MAgL+DAIDDgwCAx4MAgMuDAIDv6AMAz4MAgOGQAQDTgwCA42wDANuDAICAJQAAgSkAAIIdAADfgwCAs/kDAOODAICGaAcAh1wFAOeDAIC2XQIAtV0CAOuDAIC7SQIAunkCAO+DAIDzgwCAvz0CAL49AgC9OQIAvFECAPeDAIDhPP4BvkAGAOPwAQD7gwCA/4MAgAOEAIAHhACAC4QAgA+EAIAThACAF4IAgBeEAIAbhACAH4QAgO/kAQAjhACAJ4QAgKNxAwArhACApdUCAC+EAIAzhACAptUCADeEAIA7hACAq8ECAKrxAgCtsQIArNkCAK+1AgCutQIA4dz8AdeDAIDjUAQA74gEAID1BwCBCQAAgj0AAD+EAICEJAEAQ4QAgEeEAIBLhACAT4QAgOFMBADv5BwA43QEALNdBgBThACAhgAMAIfgAwBXhACAtgUGALV1BgBbhACAuxEGALoJBgBfhACAY4QAgL/VBgC+1QYAvQEGALwJBgCojQYAqZUGAKqVBgCrpQYArL0GAK3FBgCuxQYAr/UGAGeEAIBrhACAb4QAgHOEAIB3hACAe4QAgH+EAICDhACAuHUGALl9BgC6dQYAu80HALzVBwC93QcAvtUHAL/NBwCwjQYAsZUGALKdBgCzlQYAtFEGALVRBgC2UQYAt1EGAKMdBwCPFewBh4QAgIuEAICPhACApkUHAKU1BwCThACAq1EHAKpJBwCXhACAm4QAgK+VBwCulQcArUEHAKxJBwCeRfkBn6X5AZyR/QGdTfkBmlX9AZtd/QGYBfEBmZX+AZal8gGXYfEBlG31AZU19QGS4ekBk4X2AZBV7AGRXekBsbEdALClHQCziRkAskEcALUBJAC09RkAn4QAgKOEAICnhACAgqkDAIGhAwCAaQAAohUFAKMFAgCgFQYAob0FAKHFAQCrhACAo80NAKLlAQClAQgApN0NAKfRCQCm2QkAqQEUAKilCACrxRQAqs0VAK3REQCsARAArwEcAK51EQCCEe8BgynvAa+EAICzhACAhuH1AYcR9gGEOeoBhY3qAYp59gGL4fEBvqQMALuEAICO+f0BjzH+AYw98gGNYfIBkkn+AZOd/gGHCAwAhmwMAJax+gGX+QUAlFn6AZVZ+gGaYQYAm8EGAL+EAIDDhACAx4QAgMuEAICcyQEAz4QAgKitBQCpuQUAqs0FAKvdBQCszQUArf0FAK71BQCvHQUA04QAgNeEAIDbhACA34QAgOOEAIDnhACA64QAgO+EAIC4dQUAuX0FALoJBQC7CQUAvB0FAL0BBQC+AQUAvz0FALBxBQCxcQUAsnEFALNxBQC0UQUAtVEFALZRBQC3TQUAs0UEAPOEAID3hACA+4QAgP+EAIC2fQQAtUUEAAOFAIC7tQQAurUEAAeFAIALhQCAv5UEAL6VBAC9pQQAvKUEAA+FAICjAQQAE4UAgBeFAICmOQQAG4UAgB+FAIClAQQAqvEEAKvxBAAjhQCAhOwNAK7RBACv0QQArOEEAK3hBADh0AYAhAwMAOMoBwC+AAwAK4UAgO9EAwCGuAwAhywNAC+FAIDjlAEAM4UAgOH8AQBngwCAN4UAgO/IBgA7hQCAP4UAgEOFAICzjQMAR4UAgLWNAwBLhQCAT4UAgLa1AwBThQCAV4UAgLtBAwC6SQMAvUEDALxZAwC/QQMAvkkDAKNFDAC3hACAJ4UAgFuFAIBfhQCApn0MAKVFDABjhQCAq4kMAKqBDABnhQCAa4UAgK+JDACugQwArYkMAKyRDACAFQ8AgR0PAIIhDwCzIQ4Ab4UAgLUhDgC2JQ4Ac4UAgHeFAIB7hQCAusEOALvBDgC8wQ4AvcEOAL7BDgC/wQ4AqK0OAKntDgCq5Q4Aq/0OAKzlDgCt6Q4ArjkOAK85DgB/hQCAg4UAgIeFAICLhQCAgB0AAIEJAACCvQEAj4UAgLjNDwC51Q8AutUPALvlDwC8/Q8AvZUPAL6RDwC/kQ8AsEkOALFJDgCyWQ4As1kOALRJDgC1SQ4Atv0PALf1DwCjbQ8Ak4UAgL6EAQCbhQCAn4UAgKZpDwClbQ8Ao4UAgKuNDwCqjQ8AhogAAIdsAQCvjQ8Aro0PAK2NDwCsjQ8Ap4UAgLPtDgCrhQCAr4UAgLaRDgCzhQCAt4UAgLXhDgC6tQ4Au70OALuFAIC/hQCAvn0BAL9lAQC8mQ4AvZkOAKgRDgCpJQ4AqiEOAKs5DgCsLQ4ArVUOAK5dDgCvUQ4AhKgAAMOFAIDHhQCAy4UAgM+FAIDThQCA14UAgNuFAIC47QEAuZUBALqVAQC7rQEAvLUBAL11AQC+fQEAv3UBALA1DgCxPQ4AsgkOALMJDgC0/QEAteUBALblAQC31QEAo6kNAN+FAIDjhQCA54UAgOuFAICm1Q0ApaUNAO+FAICr+Q0AqvENAPOFAID3hQCAryECAK45AgCt3Q0ArN0NAIANAACBFQAAgh0AAPuFAID/hQCAA4YAgIeQAwCGfAQAvuwEAAuGAIAPhgCAE4YAgBeGAIAbhgCAH4YAgCOGAICyLQ4AszUOALAtDgCxJQ4Ati0OALedDwC0LQ4AtSUOALq9DwC7jQ8AuKUPALm9DwC+LQ8AvxUPALyVDwC9JQ8AJ4YAgCuGAIAvhgCAM4YAgDeGAIA7hgCAP4YAgEOGAICqpQ4Aq7UOAKjFDgCp3Q4Arp0OAK9VDgCspQ4ArZUOAKgNAgCpFQIAqhUCAKtNAgCsWQIArVkCAK5NAgCvRQIAhKgFAEeGAIBLhgCAT4YAgIS4BABThgCAV4YAgFuGAIC4/QIAuUEBALpBAQC7QQEAvEEBAL1JAQC+cQEAv3EBALAJAgCxCQIAss0CALPFAgC03QIAtcUCALbNAgC3xQIA4dQPAOMQDgDj9A4A4QwOAF+GAIBjhgCAZ4YAgGuGAIBvhgCAc4YAgL4kBAB7hgCA7AAAAO9EAADvzA4Af4YAgIJlAACz2QIAgFUAAIFtAAC2nQIAg4YAgIeGAIC1lQIAuokCALuJAgCGqAQAh+AEAL5dAgC/RQIAvF0CAL1VAgCjHQUAB4YAgHeGAICLhgCAj4YAgKZZBQClUQUAk4YAgKtNBQCqTQUAl4YAgJuGAICvgQUArpkFAK2RBQCsmQUAn4YAgLMpBgCjhgCAp4YAgLYpBgCrhgCAr4YAgLUpBgC6pQYAu60GALOGAIC3hgCAvqUGAL+tBgC8tQYAva0GAKjlBgCp7QYAquUGAKv9BgCs5QYAre0GAK7lBgCvXQYAu4YAgL+GAIDDhgCAx4YAgMuGAIDPhgCA04YAgNeGAIC46QcAuekHALr9BwC79QcAvO0HAL1FBwC+TQcAv0UHALAlBgCxLQYAsiUGALM9BgC0JQYAtS0GALYlBgC32QcAo20HAIItAACBFQAAgB0AANuGAICmbQcApW0HAN+GAICr6QcAquEHAOOGAIC+oAEAr+kHAK7hBwCt6QcArPEHAOeGAICzkQYAhugAAIcsAQC2QQEA64YAgO+GAIC1UQEAuk0BALslAQDzhgCA94YAgL4lAQC/LQEAvDEBAL0xAQCwrQEAscUBALLBAQCzwQEAtMUBALXNAQC28QEAt/EBALgBAQC5AQEAugEBALsBAQC8AQEAvQEBAL4BAQC/AQEA+4YAgP+GAIADhwCAB4cAgJeFAIALhwCAD4cAgBOHAICoTQYAqVkGAKo9BgCrNQYArP0BAK3lAQCu5QEAr9UBAKPVBQAXhwCAG4cAgB+HAIAjhwCApgUCAKUVAgAnhwCAq2ECAKoJAgArhwCAL4cAgK9pAgCuYQIArXUCAKx1AgAzhwCAN4cAgDuHAIA/hwCAQ4cAgOFkBQBHhwCA4+wFAIARAACBEQAAghEAAO/0BgBLhwCAT4cAgFOHAIC+MAMAhMQCAFuHAICz4QMAhMAcALVRAwBfhwCAY4cAgLZZAwBnhwCAa4cAgLtxAwC6eQMAvbUAALxpAwC/tQAAvrUAAG+HAIDhlAEAc4cAgONcAgCGcBwAh0QDAHeHAIB7hwCAf4cAgIOHAICHhwCAi4cAgI+HAICThwCAl4cAgO94AgCoVQIAqV0CAKphAgCrYQIArNECAK3RAgCu0QIAr9ECAJuHAICfhwCAo4cAgKeHAICrhwCAr4cAgLOHAIC3hwCAuGkBALlpAQC6CQEAuwkBALwZAQC9GQEAvgkBAL8FAQCwtQIAsb0CALK1AgCzaQEAtHkBALV5AQC2aQEAt2EBAOHEBwDjpAYA47gGAOF8BgCADQAAgTUAAII9AAC7hwCAv4cAgMOHAIC+4B0Ay4cAgM+HAIDvYAAA7+gGANOHAICjqQIA14cAgNuHAIDfhwCA44cAgKYRAgClGQIA54cAgKs5AgCqMQIAhkgcAIfMHACv/QEArv0BAK39AQCsIQIAqIUeAKmRHgCqkR4Aq60eAKy1HgCt1R4ArtEeAK/FHgDHhwCA64cAgO+HAIDzhwCA94cAgPuHAID/hwCAA4gAgLhhHwC5YR8AumEfALthHwC8YR8AvWEfAL5hHwC/YR8AsL0eALGFHgCyjR4As4UeALSdHgC1hR4Ato0eALeFHgCzGR4AB4gAgAuIAIAPiACAE4gAgLZVHgC1PR4AF4gAgLtBHgC6eR4AG4gAgB+IAIC/QR4AvlkeAL1RHgC8WR4AI4gAgKNdHgAniACAK4gAgKYRHgAviACAM4gAgKV5HgCqPR4AqwUeAISkAwC+qAMArh0eAK8FHgCsHR4ArRUeAKitHgCptR4AqrUeAKvJHgCs2R4ArdkeAK7JHgCvwR4AgO0BAIHxAQCC8QEAN4gAgIaQAACHdAEAO4gAgD+IAIC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALBFAQCxTQEAskUBALNdAQC0RQEAtU0BALZFAQC3+QEAsz0eAEOIAIBHiACAS4gAgE+IAIC2WR4AtVEeAFOIAIC7iQEAuoEBAFeIAIBbiACAv4kBAL6BAQC9iQEAvJEBAF+IAIBjiACAo3UeAGeIAIClGR4Aa4gAgG+IAICmER4AV4cAgHOIAICrwQEAqskBAK3BAQCs2QEAr8EBAK7JAQB3iACAe4gAgH+IAICDiACAh4gAgIQYAgCLiACAj4gAgJOIAICXiACAm4gAgJ+IAICjiACAq4gAgK+IAIC+cAMAgGkAAIFpAACCeQAAhAAEAIbwBACHdAMAs4gAgO8MHwC3iACA4aweALuIAIDj8B4Av4gAgMOIAIDHiACAy4gAgM+IAIDTiACA14gAgNuIAIDvVAIA34gAgOOIAIDniACA46QCAOuIAIDhgAEA74gAgPOIAID3iACA+4gAgP+IAICzRQMAA4kAgAeJAIALiQCAD4kAgLZFAwC1VQMAE4kAgLshAwC6SQMAvqAEABuJAIC/KQMAviEDAL01AwC8OQMAqDkCAKk5AgCqjQIAq4UCAKydAgCthQIAroUCAK+1AgCA7QEAgfUBAIL1AQAfiQCAhpAEAIcEBQAjiQCAJ4kAgLhFAQC5TQEAukUBALtdAQC8SQEAvUkBAL55AQC/eQEAsM0CALGlAgCyrQIAs6ECALSlAgC1rQIAtp0CALd9AQAriQCAL4kAgDOJAIA3iQCAO4kAgD+JAIBDiQCA74gBAITsBADhVB4AR4kAgONUAQBLiQCAT4kAgFOJAIBXiQCAo0UCAFuJAIBfiQCAY4kAgGeJAICmRQIApVUCAGuJAICrIQIAqkkCAG+JAIBziQCArykCAK4hAgCtNQIArDkCAKg1BgCpPQYAqlEGAKttBgCseQYArWUGAK5tBgCvZQYAF4kAgHeJAIB7iQCAf4kAgIAZAACBGQAAggUAAIOJAIC45QYAuekGALr5BgC7+QYAvOkGAL3pBgC+nQYAv5UGALAdBgCx5QYAsu0GALPlBgC0/QYAteEGALbhBgC34QYAs9kGAL7QAwCHiQCAi4kAgI+JAIC25QYAtfEGAJOJAIC7IQYAutkGAIaYAACHeAMAvyUGAL45BgC9MQYAvDkGAJeJAICjnQYAm4kAgJ+JAICmoQYAo4kAgKeJAICltQYAqp0GAKtlBgCriQCAr4kAgK59BgCvYQYArH0GAK11BgCo7QcAqSkGAKoxBgCrMQYArJEGAK2RBgCukQYAr5EGALOJAIC3iQCAu4kAgL+JAIDDiQCAx4kAgMuJAIDPiQCAuIUGALmNBgC6hQYAu50GALyNBgC9vQYAvrUGAL95AQCw8QYAsfEGALLxBgCzxQYAtMEGALXBBgC2wQYAt8EGALO5BgDTiQCA14kAgNuJAIDfiQCAthEGALUZBgDjiQCAuzUGALo1BgDniQCA64kAgL8FBgC+BQYAvREGALwlBgClQQYA74kAgPOJAICmSQYAgRUAAIB5AACj4QYAghUAAK1JBgCsfQYAr10GAK5dBgCENAEAp4gAgKttBgCqbQYAvswDAPuJAICzlQIA/4kAgLXZAgADigCAB4oAgLbRAgCGgAwAhzgDALvFAgC6xQIAvRUDALwVAwC/FQMAvhUDAAuKAIAPigCA71gGAIRAAwATigCAF4oAgBuKAIAfigCAI4oAgCeKAIArigCAL4oAgOE4BgAzigCA4yQGAL5wDACsSQIArUkCAK5dAgCvVQIAqB0CAKkFAgCqBQIAq10CAISoDAA3igCAO4oAgD+KAIC+vA0AQ4oAgEeKAIBLigCAvE0DAL1VAwC+VQMAv2UDALjpAwC56QMAul0DALtVAwC0yQMAtckDALbZAwC32QMAsBkCALEZAgCy2QMAs9kDAE+KAIDj5AAAU4oAgOG8AQBXigCAgj0AAIE9AACAPQAAW4oAgF+KAIBjigCAa4oAgG+KAIDvzAMAc4oAgHeKAICj3QMAe4oAgIboDACHYA0Af4oAgKaZAwClkQMAg4oAgKuNAwCqjQMAh4oAgIuKAICvXQIArl0CAK1dAgCsXQIAj4oAgJOKAICXigCAm4oAgJ+KAICjigCAp4oAgO/gAQCEvAwA4YwGAKuKAIDjHAYAr4oAgLOKAIC3igCAu4oAgLPVAQC/igCAw4oAgMeKAIDLigCAtpEBALWZAQDPigCAu70BALq9AQDTigCA24oAgL+dAQC+nQEAvZ0BALydAQCoBQ4AqQkOAKodDgCrFQ4ArFEOAK1RDgCuSQ4Ar0kOAGeKAICCzQ8AgfUPAID9DwDXigCA34oAgIYcAACHsAMAuOkOALnpDgC6/Q4Au/UOALztDgC9VQ8AvlEPAL9NDwCwOQ4AsTkOALIJDgCzCQ4AtBkOALUZDgC2DQ4At9kOAKOVDgDjigCA54oAgOuKAIDvigCAptEOAKXZDgDzigCAq/0OAKr9DgD3igCA+4oAgK/dDgCu3Q4Ard0OAKzdDgD/igCAs/0PAAOLAIAHiwCAtoEPAAuLAIAPiwCAtZkPALqNDwC7ZQ8AE4sAgBeLAIC+fQ8Av2UPALx9DwC9dQ8AqC0OAKk1DgCqMQ4AqzEOAKxVDgCtRQ4ArkUOAK91DgAbiwCAH4sAgCOLAIAniwCAK4sAgC+LAIAziwCAN4sAgLjpDgC59Q4Auv0OALv1DgC87Q4AvZEOAL6RDgC/kQ4AsA0OALHlDgCy7Q4As+UOALT9DgC15Q4Atu0OALflDgCjuQ4Agi0AAIEVAACAHQAAO4sAgKbFDgCl3Q4AP4sAgKshDgCqyQ4AQ4sAgL4sAQCvIQ4ArjkOAK0xDgCsOQ4AS4sAgLZVAQC1RQEAR4sAgLNVAQBPiwCAhngAAIdcAAC/OQEAvjEBAL0lAQC8JQEAuzEBALpZAQD3iQCAU4sAgFeLAIBbiwCAhAQDAKOJAgBfiwCApZkCAKaJAgBjiwCAvyg5AGeLAICqhQIAq+0CAKz5AgCt+QIAru0CAK/lAgDjWAIA78AOAOGIAQBriwCAb4sAgHOLAIB3iwCAe4sAgH+LAICDiwCAh4sAgIuLAIDvKAIA4ygOAI+LAIDhRA4AqbUCAKhpDQCrAQIAqgkCAK0BAgCsGQIArzECAK4BAgC+AAQAk4sAgJeLAICbiwCAn4sAgKOLAICniwCAq4sAgLnlAwC45QMAu+UDALrlAwC95QMAvOUDAL/lAwC+5QMAsSECALBJAgCzJQIAsiUCALUpAgC0IQIAtxUCALYVAgCowQIAqdECAKr1AgCrDQEArBUBAK0FAQCuBQEArzkBAK+LAICziwCAu4sAgL+LAIDDiwCAx4sAgMuLAIDPiwCAuC0BALk9AQC67QEAu+UBALz9AQC95QEAvu0BAL/lAQCwLQEAsTUBALI9AQCzNQEAtC0BALUVAQC2HQEAtxUBAIA9AQCBpQAAgq0AAO/YAACGsAUAh9gFANOLAIDv1A8AhGwEAOH0DgDXiwCA4xwPANuLAIDhlAEA34sAgOMMDgCzPQIA44sAgOeLAIDriwCA74sAgLbFAQC13QEA84sAgLuxAQC6qQEA94sAgPuLAIC/kQEAvqkBAL2hAQC8qQEAt4sAgP+LAICqRQYAq10GAKxFBgCtTQYArkUGAK99BgADjACAB4wAgAuMAICj0QUAD4wAgKUxBgCmKQYAE4wAgBeMAICCHQAAgR0AAIAdAAAbjACAH4wAgCOMAIC+lAMAJ4wAgCuMAICGSAMAh8wDAC+MAIAzjACAN4wAgDuMAICoqQcAqakHAKq5BwCruQcArKkHAK2pBwCuAQcArzUHAD+MAIBDjACAR4wAgEuMAIBPjACAU4wAgFeMAIBbjACAuC0HALnBAAC66QAAu+kAALz5AAC95QAAvuUAAL+dAACwUQcAsV0HALItBwCzJQcAtD0HALUlBwC2JQcAtxUHALMxBgBfjACAY4wAgGeMAIBrjACAtikGALUhBgBvjACAu5kGALqVBgBzjACAd4wAgL/hBgC++QYAvfEGALz5BgB7jACAo3UGAH+MAICDjACApm0GAIeMAICLjACApWUGAKrRBgCr3QYAj4wAgJOMAICuvQYAr6UGAKy9BgCttQYAqOUBAKn1AQCq/QEAq/UBAKztAQCtNQEArj0BAK81AQCA+QAAgc0AAILFAACEYAEAvngBAJuMAICHrAAAhpABALjRAAC52QAAuuEAALvhAAC8kQAAvZ0AAL6VAAC/iQAAsE0BALFVAQCyXQEAs1UBALRNAQC18QAAtvEAALfxAACzdQIAn4wAgKOMAICnjACAq4wAgLa1AgC1ZQIAr4wAgLuRAgC6iQIAs4wAgLeMAIC/NQMAvokCAL2BAgC8iQIAu4wAgKMxAgC/jACAhMADAKbxAgDDjACAx4wAgKUhAgCqzQIAq9UCAMuMAIDPjACArs0CAK9xAwCszQIArcUCAKuNAACqjQAAqY0AAKg5AwCvvQAArr0AAK2FAACsjQAAqgAAAKsAAADTjACA14wAgNuMAIDfjACA44wAgOeMAIC7fQAAun0AALl9AAC4fQAAv90BAL7dAQC93QEAvN0BALO5AACysQAAsaEAALCtAAC3XQAAtl0AALWVAAC0lQAA64wAgO+MAIDzjACA94wAgIE1AACADQAA+4wAgII1AAC+rD0A/4wAgAONAICFaD0AC40AgA+NAICGODwAh8ACALNJAQATjQCA0AAAABeNAIAbjQCAtkkBALVJAQAfjQCAuykBALolAQAjjQCAJ40AgL8dAQC+HQEAvSEBALwpAQDjNDYA4QwGAOGwAgDjPAYAK40AgC+NAIAzjQCAN40AgIQsPwC+oD8AO40AgD+NAIDvfDcAQ40AgEeNAIDvGAEAS40AgE+NAICGaD4Ah8w/AFONAIBXjQCAW40AgO+UAABfjQCA4ZQBAGONAIDjUAAAZ40AgILpPwCB6T8AgPE/AKMJPgCPASQAB40AgGuNAIBvjQCApgk+AKUJPgBzjQCAq2k+AKplPgB3jQCAe40AgK9dPgCuXT4ArWE+AKxpPgCeYTgAn3U4AJzBNACdtTkAmqU1AJt1NACYeTAAmXExAJYhLQCXhTEAlG0sAJVlLACSeSgAk6UtAJBRJACReSgAsQ0UALAFFACzARgAslUUALV5GAC0tRgAf40AgIONAICHjQCAi40AgI+NAICTjQCAotE8AKMlAQCgdTkAob08AKHJAACXjQCAowEEAKLlAAClHQQApPUEAKf5CACmAQgAqQEMAKhtCACrzQwAqs0MAK3REACsARAAr9URAK7ZEACCBSUAgy0lAJuNAICfjQCAhsEsAIcRLQCEHSkAhRUpAIopLQCLZSwAo40AgKeNAICOHTAAj8E0AIzZMACNHTEAkmE1AJPNNQCrjQCAr40AgJZhOQCXmTgAlKE4AJV9OQCaYT0AmwU9ALONAIC3jQCAu40AgL+NAICc6QAAw40AgMeNAIDLjQCAz40AgNONAICXjACA140AgNuNAIDfjQCAqJE+AKmRPgCq7T4Aq+E+AKzhPgCt6T4ArtE+AK/RPgCwUT4AsVE+ALJRPgCzUT4AtHk+ALV5PgC2bT4At2U+ALghPgC5IT4Aujk+ALs5PgC8KT4AvRU+AL4RPgC/DT4AgJkDAIGZAwCCBQAA440AgL5UAwDhsD0A640AgONAPgCEOAIA740AgPONAIDv9D8A940AgPuNAICGmAQAhxwDALMFPQCECAQA/40AgAOOAIAHjgCAtgk9ALUJPQALjgCAu/U9ALr1PQAPjgCAE44AgL/dPQC+3T0AveU9ALzlPQAXjgCAG44AgKPNPQC+xAQApcE9AB+OAIAjjgCApsE9ACeOAIArjgCAqz09AKo9PQCtLT0ArC09AK8VPQCuFT0AtmkCAC+OAIAzjgCAtWkCADeOAICzSQIAO44AgD+OAIC+qQMAv6kDALzBAwC9wQMAuvkDALv5AwBDjgCAR44AgKgtAwCpnQMAqpUDAKutAwCstQMArb0DAK61AwCv2QMAgA0AAIEVAACCHQAAS44AgE+OAIBTjgCAh7QFAIacBAC4MQIAuTECALo1AgC7zQIAvNUCAL3dAgC+1QIAv8kCALBpAgCxaQIAskECALNBAgC0OQIAtTkCALYRAgC3EQIAW44AgOM0PgBfjgCA4aw+AGOOAIDvfAMAZ44AgGuOAIBvjgCA45QDAHOOAIDhfD4Ad44AgO/oPgB7jgCAf44AgIOOAICHjgCAo1UDAIuOAICldQMAj44AgJOOAICmdQMAl44AgJuOAICr5QIAquUCAK3dAgCs3QIAr7UCAK61AgCoGQYAqSEGAKohBgCrPQYArCUGAK1dBgCuVQYAr00GAFeOAICfjgCAo44AgKeOAICrjgCAr44AgLOOAIC3jgCAuOUGALmBBgC6gQYAu50GALyJBgC9iQYAvqEGAL+hBgCwPQYAsQ0GALIFBgCz7QYAtPUGALXhBgC24QYAt90GALOpBgCCLQAAgRUAAIAdAAC7jgCAtt0GALWtBgC/jgCAu8kGALr5BgDDjgCAhOADAL8lBgC+MQYAvTkGALzRBgC+iAMAo+0GAOeNAIDHjgCAppkGAMuOAIDPjgCApekGAKq9BgCrjQYAhkgAAIdsAACudQYAr2EGAKyVBgCtfQYAqIEGAKmNBgCqmQYAq5UGAKyNBgCttQYArrEGAK+tBgDTjgCA144AgNuOAIDfjgCA444AgOeOAIDrjgCA744AgLilBgC5YQEAumEBALthAQC8YQEAvWEBAL5hAQC/YQEAsNkGALHZBgCyqQYAs6kGALS9BgC1oQYAtqEGALedBgCzEQYA844AgPeOAID7jgCA/44AgLY1BgC1BQYAA48AgLsdBgC6HQYAB48AgAuPAIC/ZQYAvnkGAL19BgC8fQYAD48AgKNVBgATjwCAF48AgKZxBgAbjwCAH48AgKVBBgCqWQYAq1kGACOPAIAnjwCArj0GAK8hBgCsOQYArTkGAKjVAgCp3QIAqikDAKspAwCsOQMArTkDAK4pAwCvKQMAK48AgC+PAIAzjwCAO48AgD+PAIBDjwCAvrgDAEePAIC47QMAuYUDALqBAwC7gQMAvIUDAL2NAwC+sQMAv7EDALBZAwCxWQMAsu0DALPlAwC0/QMAteUDALblAwC31QMAgKEAAIGhAACCoQAAvoAMAEuPAICEmAIAT48AgFOPAICGAAwAh/QDAFePAIBbjwCAX48AgGOPAIBnjwCAhLADALPhAwBrjwCAb48AgHOPAIB3jwCAtvkDALXxAwB7jwCAu90DALrdAwB/jwCAg48AgL9hAwC+eQMAvXEDALx5AwCHjwCAi48AgI+PAICjLQIAk48AgKU9AgCmNQIAl48AgJuPAICfjwCAqhECAKsRAgCstQIArb0CAK61AgCvrQIA48QDAOMQBwDhuAEA4WwHAIBxAACBcQAAggUAAKOPAICGwAwAh1QNAKuPAICvjwCA77ADAO8ABwCzjwCAt48AgLuPAIC/jwCAw48AgMePAIDLjwCAz48AgNOPAIDvpAEAhKANAOGABgDXjwCA4xABANuPAIDfjwCA448AgOePAICz9QEA648AgO+PAIDzjwCA948AgLZNAQC1SQEA+48AgLtRAQC6SQEA/48AgAOQAIC/OQEAvjEBAL1BAQC8SQEAqC0OAKk1DgCqPQ4AqzEOAKyBDgCtjQ4AroUOAK+1DgCnjwCAB5AAgAuQAIAPkACAgBkAAIEZAACCBQAAE5AAgLidDgC5rQ4AuqUOALtNDwC8VQ8AvV0PAL5JDwC/QQ8AsM0OALHVDgCy3Q4As9UOALS1DgC1vQ4AtrUOALetDgCjtQ4AvogDABeQAIAbkACAH5AAgKYNDgClCQ4AI5AAgKsRDgCqCQ4AhggAAIdsAwCveQ4ArnEOAK0BDgCsCQ4AJ5AAgCuQAIAvkACAs7UPADOQAIC1VQ8Atl0PADePAIA3kACAO5AAgLp5DwC7eQ8AvGkPAL1dDwC+SQ8Av0kPAKhpDgCpaQ4AqnEOAKtxDgCskQ4ArZEOAK6RDgCvkQ4AP5AAgEOQAIBHkACAS5AAgE+QAIBTkACAV5AAgFuQAIC4hQ4AuY0OALqFDgC7nQ4AvI0OAL29DgC+tQ4Av3kBALDxDgCx8Q4AsvEOALPFDgC0wQ4AtcEOALbBDgC3wQ4Ao/kOAF+QAIBjkACAZ5AAgGuQAICmEQ4ApRkOAG+QAICrNQ4AqjUOAHOQAIB3kACArwUOAK4FDgCtEQ4ArCUOAIANAACBFQAAgh0AAHuQAIB/kACAg5AAgISUAQC+lAEAhkAHAIf0AACLkACAj5AAgJOQAICXkACAm5AAgJ+QAICojQIAqZUCAKqVAgCrzQIArNUCAK3dAgCuyQIAr/0CAKOQAICnkACAq5AAgK+QAIC/ABQAs5AAgLeQAIC7kACAuH0DALnBAwC6wQMAu8EDALzBAwC9yQMAvvEDAL/xAwCwhQIAsUUDALJNAwCzRQMAtF0DALVFAwC2TQMAt0UDALMdAgC/kACAw5AAgMeQAIDLkACAtl0CALVdAgDPkACAu4EDALpBAgDTkACA15AAgL+BAwC+mQMAvZEDALyZAwDbkACAo1kCAN+QAIDjkACAphkCAOeQAIDrkACApRkCAKoFAgCrxQMA75AAgPOQAICu3QMAr8UDAKzdAwCt1QMA+5AAgOPMAACEBAIA4bwBAIDJAQCB/QEAgvUBAL4QBQD/kACAvigEAAORAIAHkQCAC5EAgO8QAAAPkQCAE5EAgIbgBACH9AIAF5EAgBuRAIDj/A8AH5EAgOHgDwAjkQCA7xQPACeRAIArkQCAL5EAgDORAIA3kQCAO5EAgD+RAIBDkQCAR5EAgEuRAIBPkQCAU5EAgFeRAIBbkQCA7+ABAIUEEgDh3A4AX5EAgOMcDgCAKQAAgR0AAIIFAABjkQCAszECAGuRAICEzAUAb5EAgHORAIC2KQIAtSECAHeRAIC7zQEAus0BAHuRAIB/kQCAv3UBAL7JAQC9wQEAvMkBAKjpBQCp6QUAqvkFAKv5BQCs6QUArekFAK45BgCvOQYA95AAgGeRAICGiAAAhwADAIORAICHkQCAi5EAgI+RAIC40QYAudkGALrhBgC74QYAvJEGAL2dBgC+lQYAv4kGALBJBgCxSQYAsl0GALNVBgC0TQYAtfEGALbxBgC38QYAo3EFAJORAICXkQCAm5EAgJ+RAICmaQUApWEFAKORAICrjQYAqo0GAKeRAICrkQCArzUGAK6JBgCtgQYArIkGAK+RAICzkQCAs+EHALeRAIC14QcAu5EAgL+RAIC25QcAh5AAgMORAIC7vQcAuqEHAL2VBwC8qQcAv5UHAL6VBwCoAQYAqSUGAKohBgCrIQYArCEGAK0tBgCuJQYAr1UGAMeRAICCHQAAgR0AAIAdAADLkQCAz5EAgNORAIC+MAEAuDkGALk5BgC6yQYAu8kGALzZBgC92QYAvskGAL/JBgCwLQYAsTEGALI1BgCzCQYAtBkGALUZBgC2CQYAtwkGAKOpBgCEjAIAhigfAIdEAQDbkQCApq0GAKWpBgDfkQCAq/UGAKrpBgDjkQCA55EAgK/dBgCu3QYArd0GAKzhBgDrkQCAsxUGAO+RAIDzkQCAtj0GAPeRAID7kQCAtTUGALrZAQC72QEA/5EAgAOSAIC+fQEAv2UBALx9AQC9dQEAqMUFAKnJBQCq2QUAq9EFAKz5BQCt+QUArikCAK8pAgAHkgCAC5IAgA+SAIATkgCAjAAAABeSAIAbkgCAH5IAgLjtAgC5hQIAuo0CALuBAgC8hQIAvY0CAL69AgC/fQMAsFkCALFZAgCy7QIAs+UCALT9AgC15QIAtuUCALfVAgCjUQUAI5IAgCeSAIArkgCAL5IAgKZ5BQClcQUAM5IAgKudAgCqnQIAN5IAgDuSAICvIQIArjkCAK0xAgCsOQIAghEAAD+SAICAZQAAgQkAAEOSAIC+mAMAS5IAgE+SAICEJAMAU5IAgIdoAwCGjBwAV5IAgFuSAIBfkgCAY5IAgGeSAIBrkgCAs6ECAITAHAC10QIAb5IAgHOSAIC21QIAd5IAgHuSAIC7wQIAuvUCAL0RAQC82QIAvxEBAL4ZAQB/kgCAg5IAgIeSAICLkgCAj5IAgJOSAICXkgCA77gGAJuSAIDhnAQAn5IAgON0BgCjkgCAp5IAgKuSAICvkgCAgPkAAIH5AACCBQAAs5IAgL5YHACEWB8A71wAAO9ABgDhkAEA4fwGAOM8AADjdAYAu5IAgL+SAICGmBwAh/QcAKNpAgC+DB8Aw5IAgMeSAIDLkgCAph0CAKUZAgDPkgCAqwkCAKo9AgDTkgCA15IAgK/ZAQCu0QEArdkBAKwRAgCokR0AqZkdAKqhHQCroR0ArNEdAK3dHQCu1R0Ar8kdAEeSAIC3kgCA25IAgN+SAIDjkgCA55IAgOuSAIDvkgCAuHkeALl5HgC6zR4Au8UeALzdHgC9xR4AvsUeAL/1HgCwuR0AsY0dALKFHQCzTR4AtFUeALVdHgC2VR4At0keALjNHwC51R8Aut0fALvVHwC88R8Avf0fAL7pHwC/6R8AsKUfALGxHwCysR8As40fALSVHwC19R8Atv0fALf1HwCoGR4AqRkeAKotHgCrPR4ArCUeAK0tHgCuJR4Ar90fAPOSAID3kgCA+5IAgP+SAIADkwCA15EAgAeTAIALkwCAs+UfAA+TAIATkwCAF5MAgBuTAIC27R8Ate0fAB+TAIC7NR4AuiEeACOTAIAnkwCAv3EeAL4RHgC9GR4AvCUeAIJpAACjoR8AgFkAAIFRAACmqR8AK5MAgC+TAIClqR8AqmUeAKtxHgCGAAQAh+wBAK5VHgCvNR4ArGEeAK1dHgCoMR4AqTEeAKpBHgCrQR4ArEEeAK1JHgCucR4Ar3EeADOTAIA3kwCAO5MAgD+TAIBDkwCAR5MAgEuTAIBPkwCAuCkBALkpAQC6OQEAuzUBALwtAQC90QAAvtEAAL/RAACwyQEAsckBALLZAQCz2QEAtMkBALXJAQC2GQEAtxkBALPJHQBTkwCAV5MAgFuTAIBfkwCAtskdALXJHQBjkwCAuw0CALoNAgBnkwCAa5MAgL8NAgC+DQIAvQ0CALwNAgBvkwCAo40dAHOTAIB3kwCApo0dAHuTAIB/kwCApY0dAKpJAgCrSQIAg5MAgIeTAICuSQIAr0kCAKxJAgCtSQIAgA0AAIERAACCEQAAi5MAgO/MAgCPkwCAk5MAgISQAgDjLAIAvigDAOHYAQCbkwCAhhAEAIfUAwCfkwCAo5MAgLNhAwCnkwCAq5MAgK+TAICzkwCAtnkDALVxAwC3kwCAu10DALpdAwC7kwCAv5MAgL/hAAC++QAAvfEAALz5AACjoQIAw5MAgMeTAIDLkwCAz5MAgKa5AgClsQIA05MAgKudAgCqnQIA15MAgNuTAICvIQEArjkBAK0xAQCsOQEA35MAgOOTAIDvZB8A55MAgOuTAIDvkwCA85MAgPeTAICADQAAgREAAIIVAAD7kwCA4eAcAP+TAIDjiB8AA5QAgISAAgC+jAUAh0gFAIYsBAALlACAD5QAgO+kHgDv9B4A4QAeAOFQHwDjLB4A47AeABOUAIAXlACAG5QAgB+UAIAjlACAJ5QAgISEBACzcQEAK5QAgLUdAQC2FQEAL5QAgDOUAIA3lACAugEBALsBAQC89QAAvf0AAL71AAC/7QAAqK0GAKm9BgCqtQYAq8kGAKzZBgCt2QYArskGAK/BBgA7lACAP5QAgEOUAIBHlACAS5QAgE+UAIBTlACAV5QAgLhtBwC5BQcAug0HALsBBwC8AQcAvQEHAL4BBwC/AQcAsIkGALGJBgCybQcAs2UHALR9BwC1ZQcAtmUHALdVBwCXkwCAozkGAFuUAIAHlACApl0GAF+UAIBjlACApVUGAKpJBgCrSQYAZ5QAgGuUAICuvQcAr6UHAKy9BwCttQcAgG0AAIEJAACCGQAAb5QAgHOUAIC+nAMAd5QAgHuUAICGQAAAh2AAAH+UAICDlACAh5QAgIuUAICPlACAk5QAgKiRBgCpkQYAqrkGAKu5BgCsqQYArakGAK7ZBgCv2QYAl5QAgJuUAICflACAo5QAgKeUAICrlACAr5QAgLOUAIC4cQEAuXEBALpxAQC7cQEAvNkBAL3BAQC+wQEAv/UBALCxBgCxuQYAsokGALOJBgC0UQEAtVEBALZRAQC3UQEAszEGALeUAIC7lACAv5QAgMOUAIC2KQYAtSEGAMeUAIC7fQYAunUGAMuUAIDPlACAv5UBAL6VAQC9XQYAvF0GANOUAICjdQYA15QAgNuUAICmbQYA35QAgOOUAIClZQYAqjEGAKs5BgCErAEAvqABAK7RAQCv0QEArBkGAK0ZBgCo3QIAqe0CAKrlAgCr/QIArOUCAK3tAgCu5QIArz0DAOuUAIDvlACA85QAgL5kDAD3lACA+5QAgP+UAIADlQCAuMkDALnJAwC62QMAu9EDALz5AwC9+QMAvpkDAL+VAwCwRQMAsU0DALJFAwCzXQMAtEUDALVNAwC2RQMAt/kDAIFVAwCASQMAs2UCAIJVAwC1ZQIAB5UAgAuVAIC2ZQIAhgAMAIfkAwC7gQMAuokDAL2BAwC8mQMAv4EDAL6JAwCjLQIAD5UAgBOVAIAXlQCAG5UAgKYtAgClLQIAH5UAgKvJAwCqwQMAI5UAgCeVAICvyQMArsEDAK3JAwCs0QMA49gGAOGsBwDhnAYA45wGACuVAICEWA0AL5UAgDOVAIA3lQCAO5UAgD+VAIBDlQCA7xwBAEeVAIBLlQCA70AGAIB5AACBFQAAghEAAIQADABPlQCA46wAAFOVAIDhpAEAW5UAgO9wAACGyAwAh6QNAF+VAIBjlQCAZ5UAgGuVAIC6yQUAu8kFALilBQC5zQUAvvkFAL/5BQC8zQUAvcUFALKlBQCzrQUAsBEGALERBgC2rQUAt50FALS1BQC1rQUAqmEGAKthBgConQYAqZUGAK5hBgCvYQYArHEGAK1xBgBvlQCAc5UAgHeVAIB7lQCAf5UAgIOVAIC+sAwAh5UAgKghDgCpIQ4AqiEOAKs9DgCsJQ4ArS0OAK4lDgCviQ4AV5UAgIuVAICPlQCAk5UAgJeVAICblQCAn5UAgKOVAIC4UQ8AuV0PALpVDwC7bQ8AvHUPAL19DwC+dQ8Av2kPALD5DgCxoQ4AsqEOALOhDgC0oQ4AtakOALaRDgC3kQ4As6kOAKeVAIDnlACAq5UAgK+VAIC2rQ4Ata0OALOVAIC7ZQ4Auj0OALeVAIC7lQCAv20OAL5lDgC9dQ4AvHUOAIIZAACj7Q4AgGUAAIEZAACm6Q4Av5UAgMOVAICl6Q4AqnkOAKshDgDHlQCAy5UAgK4hDgCvKQ4ArDEOAK0xDgCoYQ4AqXUOAKp9DgCrdQ4ArG0OAK31DgCu/Q4Ar/UOAIaAAQCHpAEAz5UAgNOVAIDXlQCA25UAgN+VAIDjlQCAuHUBALl9AQC6dQEAu8kBALzdAQC9xQEAvsUBAL/1AQCwjQ4AsZUOALKdDgCzkQ4AtFUBALVdAQC2VQEAt00BALP1DgDnlQCA65UAgO+VAIDzlQCAtnUOALXlDgD3lQCAu1EOALpJDgD7lQCA/5UAgL+ZAQC+kQEAvUUOALxJDgADlgCAo7EOAAeWAIALlgCApjEOAA+WAIATlgCApaEOAKoNDgCrFQ4AF5YAgBuWAICu1QEAr90BAKwNDgCtAQ4AqO0CAKktAwCqJQMAqz0DAKwlAwCtLQMAriUDAK+ZAwAflgCAI5YAgCeWAIArlgCAL5YAgDOWAIC+dAIAO5YAgLiNAwC5kQMAupEDALulAwC8vQMAvXUAAL59AAC/dQAAsOkDALHpAwCy+QMAs/EDALTZAwC12QMAtrkDALe1AwCArQAAgbUAAIK9AACzoQMAP5YAgLWhAwC2oQMAQ5YAgITgAgBHlgCAuiEDALshAwC8IQMAvSkDAL4RAwC/EQMAo+0DAIXABACFtG8AS5YAgE+WAICm7QMApe0DAFOWAICrbQMAqm0DAIZIBQCHbAMAr10DAK5dAwCtZQMArG0DAFeWAIDjAA4A71hsAOG0DwBblgCAX5YAgGOWAIBnlgCAoakDAKD9DwCjwQMAog0DAOHgAwDv4A8A4+QDAGuWAIBvlgCAc5YAgIQEBAC+BAQAd5YAgO+UAwB7lgCAf5YAgIOWAIDj1AMAh5YAgOFUAACLlgCAj5YAgJOWAICXlgCAgA0AAIEVAACCHQAAm5YAgJ+WAICjlgCAj5EbAO+cDgCE4AcA4dQOAKuWAIDj8A4Ar5YAgLOWAICGGAcAh5AEAJnlFwCY5RcAm+kLAJo5CwCd/QoAnPELAJ9VDwCeXQ8AkSkfAJDNGwCTJR8Aks0fAJXREwCUKRMAlxkXAJZ1EwCM4RAAjSUQAI4tEACP+QwAN5YAgKeWAICKORQAi5UUAITpGACFBRgAhuUYAIfxFAC3lgCAu5YAgIIxHACDFRwAnKkEAL+WAIDDlgCAx5YAgMuWAIDPlgCAmtEEAJt9BACUTQ0AleUIAJblCACXtQgA05YAgNeWAICSWQwAk1kMAKGRAADblgCAowF8AKKZAACluXwApJF8AKeZeACm4X0AqYF5AKiheACriXQAqgF0AK0BcACsWXQAr4VwAK6dcACx4WwAsAFsALMBaACyHWwAtfVoALT1aADflgCA45YAgOeWAIDrlgCA75YAgPOWAID3lgCA+5YAgP+WAIADlwCAqD0HAKmVBwCqlQcAq6kHAKzdBwCtxQcArsUHAK8dBgAHlwCAgh0AAIEdAACAHQAAC5cAgA+XAIATlwCAvmABALgZBgC5GQYAuikGALslBgC8IQYAvSEGAL4hBgC/IQYAsHEGALFxBgCycQYAs3EGALRNBgC1NQYAtj0GALctBgCzHQcAG5cAgIYoAACHqAAAH5cAgLZFBwC1VQcAI5cAgLu1BgC6tQYAJ5cAgCuXAIC/8QYAvokGAL2lBgC8pQYAL5cAgKNZBwAzlwCAN5cAgKYBBwA7lwCAP5cAgKURBwCq8QYAq/EGAEOXAIBHlwCArs0GAK+1BgCs4QYAreEGAKipBQCptQUAqr0FAKs9AgCsJQIArVECAK5RAgCvUQIAS5cAgE+XAIBTlwCAV5cAgIQ8AwBblwCAX5cAgGOXAIC4pQIAua0CALqlAgC7vQIAvKUCAL2tAgC+pQIAv30DALAxAgCxMQIAshkCALMZAgC09QIAta0CALalAgC3nQIAZ5cAgGuXAIBvlwCAszkFAHOXAIC1oQIAtt0CAHeXAIB7lwCAf5cAgLr5AgC7+QIAvMECAL3BAgC+PQIAv2UCAIOXAICmgQIApf0CAIuXAICjZQUAvlh8AIbYfACHnHwArzkCAK5hAgCtnQIArJ0CAKulAgCqpQIAj5cAgJOXAICohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAIGFAQCAhQEAl5cAgILtAQCblwCAn5cAgKOXAICnlwCAuHUBALl9AQC6dQEAu80BALzVAQC93QEAvskBAL/BAQCwtQIAsb0CALKBAgCzgQIAtFEBALVRAQC2UQEAt1EBAKuXAICvlwCAs5cAgLeXAIDhMAYA4WQHAOMoBgDjxAYAhCB9ALuXAIDvbAAA7xgGAL+XAIDDlwCAx5cAgMuXAICzXQIAvkh8AM+XAIDTlwCA15cAgLYVAgC1dQIA25cAgLs5AgC6MQIA35cAgOOXAIC/1QEAvtUBAL0VAgC8FQIAo519AIeXAIDnlwCA65cAgO+XAICm1X0ApbV9APOXAICr+X0AqvF9APeXAID7lwCArxV+AK4VfgCt1X0ArNV9AIBNAACBVQAAglUAALOxfgD/lwCAtWV/ALZtfwADmACAhkADAIcEAwC66X8Au+l/ALz5fwC9+X8Avt1/AL/NfwAHmACAC5gAgBeXAIAPmACAE5gAgBeYAIAbmACAH5gAgKhtfgCpXX4AqlV+AKuFfwCsgX8ArYF/AK6BfwCvgX8AsEF/ALFBfwCyQX8As0F/ALR1fwC1ZX8Atm1/ALdlfwC4XX8AuS1/ALolfwC7PX8AvC1/AL0dfwC+FX8Av/UAAKP9fwAjmACAJ5gAgCuYAIAvmACApiF+AKUpfgAzmACAq6V+AKqlfgA3mACAO5gAgK+BfgCukX4ArbV+AKy1fgA/mACAQ5gAgEeYAIBLmACAT5gAgFOYAIBXmACAW5gAgIA9AACBCQAAghkAAF+YAIBjmACAhLgBAL6wAQBnmACAqK0BAKnVAQCq1QEAqw0BAKwVAQCtGQEArgkBAK8JAQCGAAQAhwQBAGuYAIBvmACAc5gAgHeYAIB7mACAf5gAgLjtAAC5hQAAuo0AALuFAAC8nQAAvYUAAL6NAAC/hQAAsHkBALF5AQCy7QAAs+UAALT9AAC15QAAtuUAALfVAACzXQIAg5gAgIeYAICLmACAj5gAgLaZAgC1nQIAk5gAgLu9AgC6vQIAl5gAgJuYAIC/IQMAvjkDAL0xAwC8OQMAvigDAKMZAgCfmACAo5gAgKbdAgCnmACAq5gAgKXZAgCq+QIAq/kCAK+YAICzmACArn0DAK9lAwCsfQMArXUDAL7IBAC3mACAu5gAgL7EBQC/mACAw5gAgMeYAIDLmACAgD0AAIEJAACCGQAAz5gAgNOYAICEOAMA25gAgN+YAIDveAIA45gAgIZIBACHVAMA55gAgOuYAIDvmACA85gAgPeYAID7mACA/5gAgAOZAIDjVAIAB5kAgOFAAQALmQCAD5kAgOMkfwATmQCA4Zx8ABeZAIAbmQCAH5kAgCOZAICEbAUAJ5kAgCuZAIAvmQCAM5kAgO8YfwA3mQCAO5kAgLPxAgA/mQCAQ5kAgEuZAIBPmQCAtukCALXhAgBTmQCAu3EBALppAQCHoAUAhswEAL85AQC+WQEAvVEBALxhAQDhQH8AV5kAgOM4fgCEwAQAgtkAAO8UAACApQAAgdkAAFuZAIDjwAAAX5kAgOHUAQBjmQCAZ5kAgO+EfgBrmQCAqs0BAKvVAQBvmQCAc5kAgK79AQCvnQEArMUBAK31AQB3mQCAo1UCAHuZAIB/mQCApk0CAIOZAICHmQCApUUCANeYAIBHmQCAi5kAgI+ZAICTmQCAl5kAgJuZAICfmQCAqJkGAKmZBgCq7QYAq/0GAKzlBgCt7QYAruUGAK/dBgCwpQYAsa0GALKlBgCzuQYAtK0GALVVBwC2UQcAt00HALh1BwC5fQcAunUHALtJBwC8WQcAvVkHAL5JBwC/RQcAs0UGAKOZAICnmQCAq5kAgK+ZAIC2TQYAtU0GALOZAIC7SQYAukEGAIYIAACHjAAAv7EHAL5JBgC9TQYAvFEGAIJdAACjAQYAgEUAAIFdAACmCQYAu5kAgL+ZAIClCQYAqgUGAKsNBgDDmQCAx5kAgK4NBgCv9QcArBUGAK0JBgCoTQYAqVUGAKpVBgCriQYArLEGAK29BgCuqQYAr6kGALeZAIDLmQCAz5kAgNOZAIDXmQCA25kAgN+ZAIDjmQCAuEkBALlJAQC6WQEAu1kBALxJAQC9SQEAvt0BAL/VAQCw3QYAsa0GALKlBgCzjQYAtJkGALWZBgC2jQYAt4UGALPdBgDnmQCA65kAgO+ZAIDzmQCAtj0GALU5BgD3mQCAu2kGALoZBgD7mQCA/5kAgL9dBgC+XQYAvVkGALxxBgADmgCAo5kGAAeaAIALmgCApnkGAA+aAIATmgCApX0GAKpdBgCrLQYAF5oAgBuaAICuGQYArxkGAKw1BgCtHQYAqNUCAKndAgCq4QIAq+ECAKw1AwCtPQMArjUDAK8tAwCAzQMAgQkAAIIZAAAfmgCAI5oAgIQYAgC+dAMAK5oAgLjpAwC56QMAuokDALuFAwC8nQMAvYEDAL6BAwC/tQMAsFUDALFdAwCyVQMAs+kDALT5AwC1+QMAtukDALfhAwCGIAwAhxADAC+aAIAzmgCAN5oAgDuaAIA/mgCA71wCAEOaAIDhFAAAR5oAgOOIAgC++AwAS5oAgE+aAIBTmgCAu/kDALrxAwC+gA0AV5oAgL9dAwC+XQMAvV0DALzhAwCzCQIAW5oAgF+aAIBjmgCAZ5oAgLbdAwC13QMAa5oAgKipBgCpqQYAqrkGAKu5BgCsqQYArakGAK4dBQCvFQUAb5oAgHOaAIB3mgCAe5oAgH+aAICDmgCAh5oAgIuaAIC4GQUAuS0FALolBQC7yQUAvNkFAL3FBQC+zQUAv8UFALBtBQCxdQUAsnUFALNFBQC0XQUAtT0FALY1BQC3KQUA4fQGAOFUBwDjFAYA47wGAIEJAACAqQAAj5oAgII5AACE7A0Ak5oAgIeIDACGDAwAm5oAgJ+aAIDvzAcA78QHAKMpAwCjmgCAp5oAgKuaAICvmgCApv0CAKX9AgCzmgCAq9kCAKrRAgC3mgCAu5oAgK99AgCufQIArX0CAKzBAgCoPQ4AqY0OAKqFDgCrnQ4ArIUOAK2NDgCuuQ4Ar7UOAJeaAIC/mgCAw5oAgMeaAIDLmgCAz5oAgNOaAIDXmgCAuL0OALllDwC6bQ8Au2UPALx9DwC9ZQ8Avm0PAL9lDwCw1Q4Asd0OALLVDgCzoQ4AtJUOALWdDgC2lQ4At40OALMNDgDbmgCA35oAgOOaAIDnmgCAtg0OALUNDgDrmgCAuxkOALoRDgDvmgCAJ5oAgL9ZDgC+UQ4AvXUOALwBDgDzmgCAo0kOAPeaAID7mgCApkkOAP+aAIADmwCApUkOAKpVDgCrXQ4AhKQDAAebAICuFQ4Arx0OAKxFDgCtMQ4AqLEOAKmxDgCqzQ4Aq8UOAKzdDgCtxQ4ArsUOAK/1DgCA7QEAgfEBAILxAQALmwCAhpABAIe0AQAPmwCAE5sAgLjFAQC5zQEAusUBALvdAQC8zQEAvf0BAL6ZAQC/lQEAsI0OALFBAQCyQQEAs0EBALRBAQC1QQEAtkEBALdBAQCzRQ4AF5sAgBubAIAfmwCAI5sAgLZFDgC1VQ4AJ5sAgLuFAQC6SQ4AK5sAgC+bAIC/hQEAvoUBAL2VAQC8lQEAM5sAgKMBDgA3mwCAO5sAgKYBDgA/mwCAQ5sAgKURDgCqDQ4Aq8EBAEebAIBLmwCArsEBAK/BAQCs0QEArdEBAKgtAwCpPQMAqjUDAKuJAwCsmQMArZkDAK6JAwCvgQMAT5sAgFObAIBXmwCAW5sAgF+bAIBjmwCAZ5sAgGubAIC4rQMAuWUAALptAAC7ZQAAvH0AAL1lAAC+bQAAv2UAALDJAwCxyQMAsqkDALOlAwC0vQMAtaEDALahAwC3lQMAgL0AAIEJAACCGQAAb5sAgHObAIC+2AMAe5sAgH+bAICErAIAg5sAgIfoAwCGDAQAh5sAgIubAICPmwCAk5sAgLP9AwCXmwCAm5sAgJ+bAICjmwCAtlkDALVRAwCnmwCAu00DALpNAwCrmwCAr5sAgL8lAwC+OQMAvTEDALw9AwCzmwCAt5sAgLubAIC/mwCA71gPAMObAIDHmwCAy5sAgOOQDgDPmwCA4bAPANObAIDXmwCA25sAgN+bAIDjmwCAgHUAAIF9AACCdQAAhBgFAO88AwDrmwCAvhQFAO+bAIDj0AMA85sAgOFAAAD3mwCAhtAEAIdYBQD7mwCA/5sAgAOcAIAHnACAC5wAgA+cAIATnACAF5wAgBucAIDvrA8AhOwEAOEQDgAfnACA41QBACOcAIAnnACAK5wAgC+cAICj/QIAM5wAgDecAIA7nACAP5wAgKZZAgClUQIAQ5wAgKtNAgCqTQIAR5wAgEucAICvJQIArjkCAK0xAgCsPQIAqJkGAKmZBgCqrQYAq70GAKylBgCtrQYArqUGAK/ZBgDnmwCAghEAAIEZAACAwQcAT5wAgFOcAIC+cAMAV5wAgLhJBwC5SQcAul0HALtVBwC8TQcAvXEHAL51BwC/bQcAsKkGALGpBgCyuQYAs7EGALSZBgC1mQYAtnkHALd5BwC1NQYAW5wAgF+cAIC2NQYAhjAAAIdcAwCzPQYAY5wAgL19BgC8dQYAv0UGAL5FBgB3mwCAZ5wAgLt1BgC6dQYAo2UGAGucAIBvnACAc5wAgHecAICmbQYApW0GAHucAICrLQYAqi0GAH+cAICDnACArx0GAK4dBgCtJQYArC0GAKhVBgCpWQYAqm0GAKthBgCsaQYArWkGAK6ZBgCvmQYAh5wAgIucAICPnACAk5wAgJecAICbnACAn5wAgKOcAIC4+QYAufkGALqNBgC7hQYAvJ0GAL2FBgC+hQYAv7UGALDpBgCx6QYAsvkGALP5BgC06QYAtd0GALbJBgC3yQYAs+UGAKecAICrnACAr5wAgLOcAIC26QYAteEGALecAIC7LQYAui0GALucAIC/nACAvxkGAL4tBgC9LQYAvC0GAIIVAACjoQYAgGEAAIFhAACmrQYAw5wAgL6QAQClpQYAqmkGAKtpBgCEpAEAy5wAgK5pBgCvXQYArGkGAK1pBgCohQIAqY0CAKqVAgCruQIArNUCAK3dAgCu1QIAr80CAIaAHACHZAMAz5wAgL5gAwDTnACA15wAgNucAIDfnACAuHUDALl9AwC6dQMAu8kDALzZAwC92QMAvskDAL/BAwCwvQIAsY0CALKFAgCzTQMAtFUDALVdAwC2VQMAt00DALMdAgDjnACAhAgDAOecAIDrnACAtl0CALVdAgDvnACAu0kCALp5AgDznACA95wAgL+ZAwC+kQMAvZkDALxRAgCwAAAAo1kCAPucAID/nACAphkCAAOdAIAHnQCApRkCAKo9AgCrDQIAC50AgA+dAICu1QMAr90DAKwVAgCt3QMAE50AgBedAIAbnQCA76wGAB+dAIAjnQCAJ50AgCudAIC+6BwAL50AgDOdAIA7nQCAP50AgOGABwBDnQCA42AGAIBdAACBYQAAgmEAALN9AQBHnQCAtW0BALZlAQBLnQCAhiAdAIdYHQC6+QEAu/EBALzZAQC92QEAvrEBAL+xAQDvoAAAT50AgFOdAIBXnQCAW50AgF+dAIBjnQCA71wBAIRsHADhzAYAZ50AgOMcBgDjSAAAa50AgOEwAQBvnQCAo/EBAHOdAICFABQAd50AgHudAICm6QEApeEBAH+dAICrfQEAqnUBAIOdAICHnQCArz0BAK49AQCtVQEArFUBAKjtHQCpLR4AqjkeAKs5HgCsKR4ArSkeAK6dHgCvkR4AN50AgIudAICPnQCAk50AgJedAICC+QAAgfEAAID9AAC4qR4AuakeALpJHwC7SR8AvFkfAL1FHwC+TR8Av0UfALDxHgCx+R4AssEeALPBHgC0uR4AtbkeALatHgC3pR4AsBEfALERHwCyER8AsyUfALQlHwC1KR8Atl0fALdRHwC4cR8AuXkfALpBHwC7QR8AvJUAAL2dAAC+lQAAv40AAJudAIDHnACAn50AgKOdAICnnQCAq50AgIb4AwCH0AAAqM0fAKnVHwCq0R8Aq70fAKytHwCtcR8ArnEfAK9xHwCzOR4Ar50AgLOdAIC3nQCAu50AgLaRHgC1RR4Av50AgLu1HgC6tR4Aw50AgMedAIC/jR4AvoEeAL2RHgC8pR4Ay50AgKN9HgDPnQCA050AgKbVHgDXnQCA250AgKUBHgCq8R4Aq/EeAN+dAIDjnQCArsUeAK/JHgCs4R4ArdUeAKhVAQCpgQAAqoEAAKuBAACsgQAArYkAAK6xAACvsQAA550AgOudAIDvnQCA850AgPedAID7nQCA/50AgAOeAIC4ZQAAuW0AALplAAC7fQAAvGUAAL1tAAC+ZQAAv90DALChAACxrQAAsqUAALO5AAC0qQAAtZ0AALaVAAC3XQAAB54AgIIdAACBHQAAgB0AAAueAIAPngCAE54AgL4UAgAbngCAhKgCAB+eAIAjngCAJ54AgCueAIAvngCAjwAAALNJAwAzngCAhugEAIesAgA3ngCAtkkDALVJAwA7ngCAuykDALolAwA/ngCAQ54AgL8ZAwC+LQMAvS0DALwxAwBHngCAo40DAEueAIBPngCApo0DAFOeAIBXngCApY0DAKrhAwCr7QMAW54AgF+eAICu6QMAr90DAKz1AwCt6QMAvoQDAGOeAIBnngCAa54AgG+eAIBzngCAd54AgHueAICAPQAAgQkAAIIZAAB/ngCAg54AgIueAICENAMAj54AgLMtAQCTngCAh8wCAIZMBQCXngCAti0BALUtAQCbngCAu0kBALp5AQCfngCAo54AgL+9AQC+vQEAvbkBALxRAQDheB8Ap54AgOPQHwCrngCAr54AgOGUAQCzngCA42gDALeeAIC7ngCAv54AgO+IAwDDngCAx54AgO+sHwDLngCAz54AgNOeAIDXngCA254AgN+eAIDjngCA554AgO9EHgDrngCA4dweAO+eAIDjHB4A854AgPueAID/ngCAA58AgIFpAACAZQAAo+UBAIJ9AACl5QEAB58AgIQUBACm5QEAvigEAAufAICrgQEAqrEBAK1xAQCsmQEAr3UBAK51AQCoIQYAqS0GAKolBgCrPQYArCUGAK0tBgCuXQYAr00GAIeeAID3ngCAhggDAIeMAwAPnwCAE58AgBefAIAbnwCAuOkGALnpBgC6jQYAu4UGALydBgC9hQYAvo0GAL+FBgCwPQYAsQ0GALIFBgCz7QYAtPkGALX5BgC27QYAt+UGALDNBwCx1QcAstEHALPtBwC09QcAtf0HALbpBwC36QcAuN0HALklBwC6LQcAuyUHALw9BwC9JQcAvi0HAL8lBwAfnwCAI58AgBeeAIAnnwCAK58AgC+fAIAznwCAN58AgKgVBgCpGQYAqu0HAKv9BwCs7QcArd0HAK7VBwCvuQcAswUGADufAIA/nwCAQ58AgEefAIC2PQYAtQUGAEufAIC7cQYAumkGAE+fAIBTnwCAv1kGAL5RBgC9WQYAvGUGAFefAICjQQYAW58AgF+fAICmeQYAY58AgIS0AQClQQYAqi0GAKs1BgC+gAEAa58AgK4VBgCvHQYArCEGAK0dBgCoNQYAqT0GAKo1BgCrWQYArHUGAK2lAQCurQEAr6UBAIDpAACB6QAAgv0AAL8kAQCGMA8Ah+QAAG+fAIBznwCAuMUAALnNAAC6xQAAu90AALzNAAC9/QAAvvUAAL+dAACw3QEAsSUBALItAQCzIQEAtCEBALUhAQC2IQEAtyEBALvBAgC6OQIAd58AgHufAIC/xQIAvsUCAL3VAgC82QIAs50FAH+fAICDnwCAh58AgIwAAAC2BQIAtd0FAIufAICqfQIAq4UCAI+fAICTnwCAroECAK+BAgCsnQIArZECAJefAICj2QUAm58AgJ+fAICmQQIAo58AgKefAIClmQUAgpFqAIORagCrnwCAr58AgIa5FgCH6RcAhBEWAIWZFgCKoRIAi6ESALOfAIC3nwCAjpEeAI9ZHgCMmRMAjREeAJJxGgCT5RoAu58AgO/oJACW8QYAlwUGAJTlGgCVGQYAmikCAJvFAgC/nwCAw58AgMefAIDhKBsAnN0CAOMgDwCfIQcAnsEHAJ01GwCcLRsAm6EbAJr5HwCZOR8AmLEfAJcBEgCWIRMAlSkTAJRRFgCTGRcAkjEXAJGxFwCQKWsAj1FrAOOsBwCEBA0A4RwHAIANAACBNQAAgj0AAMufAIDPnwCA058AgL4gDQDbnwCA358AgO9MBwCGWAwAh2ANAOOfAIDnnwCA658AgO+fAICEXA8A858AgO8IAADvhAYA4ZABAOGwBgDj4AAA42QGAPefAID7nwCA/58AgAOgAIAHoACAC6AAgL4ADwCEQA4AD6AAgBOgAIAXoACAG6AAgB+gAIAjoACAJ6AAgCugAICj1QMAotUDAKExAwCgLQcAZ58AgNefAIAvoACAM6AAgDegAICCmQAAgZEAAICZAACoTQ0AqZ0NAKqVDQCrJQ4ArD0OAK0RDgCuEQ4ArxEOALB9DgCxDQ4AsgUOALMtDgC0OQ4AtTkOALYtDgC3JQ4AuOkOALnpDgC6wQ4Au8EOALy5DgC9nQ4AvpUOAL+NDgCzPQ0AO6AAgD+gAIBDoACAR6AAgLaxDgC1lQ4AS6AAgLvpDgC6mQ4AhogAAIfkAAC/3Q4Avt0OAL3ZDgC88Q4AT6AAgKN5DQC+hAEAhIAGAKb1DgBToACAV6AAgKXRDgCq3Q4Aq60OAFugAIBfoACArpkOAK+ZDgCstQ4ArZ0OALIFNQCzGTQAsG0wALENNQBjoACAZ6AAgLQBKAC1PSkAa6AAgG+gAIBzoACAd6AAgHugAIB/oACAg6AAgIegAICiRQEAo9UBAIugAIChTQEAps0FAKcBOACkAQQApX0FAKoBPACrRT0AqEk5AKnlOQCudTEAr30xAKxdPQCtATAAqO0OAKn1DgCqCQ4AqwkOAKwZDgCtGQ4Arg0OAK8tDgCPoACAk6AAgJegAICboACAn6AAgKOgAICnoACAq6AAgLgdDgC5JQ4Aui0OALslDgC8PQ4Avd0BAL7VAQC/zQEAsFUOALFdDgCyVQ4Asy0OALQ1DgC1JQ4Ati0OALclDgCzgQ0Ar6AAgLOgAIC7oACAv6AAgLaZDQC1kQ0AvlQEALuZDQC6kQ0AhogEAIe8AwC/4Q0AvvENAL35DQC8gQ0AgkkAAKPFDQCA9QMAgUkAAKbdDQDDoACAx6AAgKXVDQCq1Q0Aq90NAMugAIDPoACArrUNAK+lDQCsxQ0Arb0NAKgdAgCpRQIAql0CAKtVAgCseQIArXkCAK6JAwCviQMA06AAgNegAIDboACA36AAgIT8BQDjoACA56AAgOugAIC4iQMAuWUDALptAwC7ZQMAvH0DAL1lAwC+bQMAv2UDALDBAwCxwQMAssEDALPBAwC0wQMAtcEDALbBAwC3wQMA76AAgPOgAID3oACA+6AAgP+gAIDhpAEAA6EAgOPADgC+aAQAB6EAgAuhAIDvHAEAD6EAgBOhAIAXoQCAG6EAgLOVAwAfoQCAI6EAgCuhAIAvoQCAtrkDALWxAwAzoQCAu0UCALpFAgCGqAQAh6QFAL9FAgC+RQIAvVUCALxVAgDh4A4A4SwMAOMIDgDj1A4AgK0AAIHRAACC0QAAN6EAgDuhAIA/oQCAQ6EAgEehAIBLoQCAT6EAgO+IDgDvLA4AoxUDAFOhAICFxCsAV6EAgFuhAICmOQMApTEDAF+hAICrxQIAqsUCAGOhAIBnoQCAr8UCAK7FAgCt1QIArNUCAKgNBgCpFQYAql0GAKtVBgCseQYArXkGAK65BgCvuQYAJ6EAgGuhAIBvoQCAc6EAgHehAIB7oQCAf6EAgIOhAIC4TQcAuVUHALpRBwC7aQcAvHkHAL1lBwC+bQcAv2UHALDJBgCxyQYAst0GALPVBgC0zQYAtXUHALZ9BwC3dQcAs9UGAIehAICLoQCAj6EAgJOhAIC2+QYAtfEGAJehAIC7DQYAug0GAIYIAACHLAAAv7EHAL4JBgC9AQYAvAkGAIJRAACjkQYAgEEAAIFBAACmvQYAm6EAgJ+hAICltQYAqkkGAKtJBgCjoQCAp6EAgK5NBgCv9QcArE0GAK1FBgCwsQYAsbEGALLNBgCzwQYAtMEGALXJBgC28QYAt/EGALgFAQC5DQEAugUBALsdAQC8BQEAvQ0BAL4FAQC/uQEAq6EAgK+hAICzoQCAt6EAgLuhAIC/oQCAt6AAgMOhAICoLQYAqTUGAKo1BgCr8QYArNEGAK3RBgCu0QYAr9EGALPdBgDHoQCAy6EAgM+hAIDToQCAtjEGALU5BgDXoQCAuxUGALoVBgDboQCA36EAgL9tBgC+ZQYAvXUGALx5BgDjoQCAo5kGAOehAIDroQCApnUGAO+hAIDzoQCApX0GAKpRBgCrUQYA96EAgPuhAICuIQYArykGAKw9BgCtMQYAqNUCAKndAgCq4QIAq+ECAKxRAwCtUQMArlEDAK9RAwD/oQCAA6IAgL7sAwALogCAD6IAgBOiAIAXogCAG6IAgLjpAwC56QMAuokDALuFAwC8nQMAvYEDAL6BAwC/tQMAsDEDALExAwCyNQMAs+kDALT5AwC1+QMAtukDALfhAwCAbQMAgaUAAIKtAACzZQIAH6IAgLXVAwC23QMAI6IAgITgAgAnogCAuvkDALv5AwC87QMAvTEDAL4xAwC/MQMAh+wDAIZkPACyAAAAK6IAgC+iAIDjCAQAM6IAgOHsBgA3ogCA7wAGADuiAIA/ogCAQ6IAgEeiAIBLogCAT6IAgFOiAIBXogCAW6IAgF+iAIDjoAMAY6IAgOGoAQBnogCA7/ADAIIdAACBHQAAgB0AAGuiAIBvogCAc6IAgHuiAIC+TD0Af6IAgKOhAwC+QDwApRECAIOiAICHogCAphkCAIRsAgCLogCAqz0CAKo9AgCt9QIArCkCAK/1AgCu9QIAhkA8AIe0PQCPogCAk6IAgJeiAICbogCAn6IAgO9EBgCjogCA4dQGAKeiAIDjDAcAq6IAgK+iAICzogCAt6IAgLP1AQC7ogCAv6IAgMOiAIDHogCAtkUBALXlAQDLogCAuzEBALopAQDPogCA06IAgL8dAQC+HQEAvRkBALwlAQCoLT4AqTU+AKo9PgCrNT4ArC0+AK2FPgCuhT4Ar7k+AHeiAIDXogCA26IAgN+iAICAGQAAgRkAAIIFAADjogCAuLk+ALm5PgC6ST8Au0k/ALxZPwC9WT8Avk0/AL9BPwCwrT4AsbU+ALKxPgCzjT4AtJk+ALWZPgC2iT4At4k+AKO1PgCEjAIA56IAgOuiAIDvogCApgU+AKWlPgDzogCAq3E+AKppPgCGCAAAh2gDAK9dPgCuXT4ArVk+AKxlPgD3ogCAs5E/APuiAID/ogCAtlk/AAOjAIAHowCAtbk/ALp1PwC7fT8AC6MAgA+jAIC+QT8Av0E/ALxZPwC9VT8AsJU+ALGdPgCyqT4As6U+ALShPgC1oT4AtqE+ALehPgC45T4Aue0+ALrlPgC7/T4AvO0+AL3dPgC+1T4AvxkBABOjAIAXowCAG6MAgB+jAIAjowCAB6IAgCejAIArowCAqF0+AKkhPgCqPT4AqzU+AKwVPgCt/T4ArvU+AK/tPgCj1T4AL6MAgDOjAIA3owCAO6MAgKYdPgCl/T4AP6MAgKs5PgCqMT4AQ6MAgEejAICvBT4ArgU+AK0RPgCsHT4AgREAAIANAABLowCAghkAAE+jAIBTowCAhJQBAL4QAACGQAcAhwABAFujAIBfowCAY6MAgGejAIBrowCAb6MAgKiNAgCplQIAqpUCAKvNAgCs2QIArdkCAK7NAgCvxQIAc6MAgHejAIB7owCAf6MAgIwAAACDowCAh6MAgIujAIC4HQMAucEDALrBAwC7wQMAvMEDAL3JAwC+8QMAv/EDALCJAgCxiQIAsikDALMpAwC0OQMAtTkDALYpAwC3JQMAsx0CAI+jAICTowCAl6MAgJujAIC2WQIAtVECAJ+jAIC7TQIAuk0CAKOjAICnowCAv/0DAL79AwC9/QMAvP0DAKujAICvowCAs6MAgLejAIDhDD4Au6MAgOOoPwC/owCAgT0AAIAxAADvUD8Agh0AAMOjAIC++AQAhhgFAIdMAwCEDAIA48wAAMujAIDhvAEAz6MAgNOjAIDXowCA26MAgN+jAICELAUA46MAgOejAIDrowCA7xAAAO+jAIDzowCAo90DAPejAID7owCA/6MAgAOkAICmmQMApZEDAAekAICrjQMAqo0DAAukAIAPpACArz0CAK49AgCtPQIArD0CABOkAIAXpACAG6QAgB+kAIAjpACAJ6QAgCukAIDvKD4AL6QAgOE8PgAzpACA4zgBAIApAACBFQAAghEAADukAICzMQIAvsgEAITABAA/pACAQ6QAgLYpAgC1IQIAR6QAgLvNAQC6zQEAS6QAgE+kAIC/dQEAvskBAL3BAQC8yQEAqOkFAKnpBQCq+QUAq/kFAKzpBQCt6QUArjkGAK85BgDHowCAN6QAgIaIAACHQAMAU6QAgFekAIBbpACAX6QAgLjRBgC52QYAuuEGALvhBgC8kQYAvZEGAL6RBgC/kQYAsEkGALFJBgCyXQYAs1UGALRNBgC18QYAtvEGALfxBgCjcQUAY6QAgGekAIBrpACAb6QAgKZpBQClYQUAc6QAgKuNBgCqjQYAd6QAgHukAICvNQYArokGAK2BBgCsiQYAf6QAgLPRBwCDpACAh6QAgLbxBwCLpACAj6QAgLXBBwC60QcAu90HAJOkAICXpACAvrkHAL+5BwC8xQcAvbkHALhpBgC5aQYAuokGALuJBgC8mQYAvZkGAL6JBgC/iQYAsBEGALEdBgCyFQYAs2kGALR5BgC1eQYAtmkGALdhBgCoSQYAqVUGAKpdBgCrVQYArE0GAK11BgCucQYAr3EGAFejAICCHQAAgR0AAIAdAACbpACAn6QAgKOkAIC+cAEAo5UGAKukAICGKAAAh0gBAK+kAICmtQYApYUGALOkAICrmQYAqpUGALekAIC7pACAr/0GAK79BgCt/QYArIEGAL+kAICzFQYAw6QAgMekAIC2PQYAy6QAgM+kAIC1NQYAutkBALvZAQDTpACA16QAgL59AQC/ZQEAvH0BAL11AQCovQUAqckFAKrZBQCr0QUArPkFAK35BQCuKQIArykCANukAIDfpACA46QAgOekAICMAAAA66QAgO+kAIDzpACAuO0CALmFAgC6gQIAu4ECALyFAgC9jQIAvrECAL+xAgCwWQIAsVkCALLtAgCz5QIAtP0CALXlAgC25QIAt9UCAKNRBQD3pACA+6QAgP+kAIADpQCApnkFAKVxBQAHpQCAq50CAKqdAgALpQCAD6UAgK8hAgCuOQIArTECAKw5AgCBbQAAgG0AABOlAICCBQAAvlwMABulAIAfpQCA79AGAITsAwDhHAUAI6UAgOP8BwAnpQCAK6UAgIbYDACHvAwAqIUCAKmVAgCqlQIAq6UCAKy9AgCt1QIArtECAK/RAgAvpQCAM6UAgDelAIA7pQCAP6UAgEOlAIBHpQCAS6UAgLh1AQC5fQEAunUBALvJAQC82QEAvdkBAL7JAQC/wQEAsLUCALG9AgCygQIAs4ECALRRAQC1UQEAtlEBALdRAQBPpQCAhAQNAFOlAIBXpQCAvhwMAFulAIDvHAAA76AGAOGQAQDhRAcA43AGAOOYBgBfpQCAY6UAgGelAIBrpQCAs10CAG+lAIBzpQCAd6UAgHulAIC2FQIAtXUCAH+lAIC7OQIAujECAIOlAICLpQCAv9UBAL7VAQC9FQIAvBUCAKOdDQAXpQCAh6UAgI+lAICTpQCAptUNAKW1DQCXpQCAq/kNAKrxDQCGCAMAh2ADAK8VDgCuFQ4ArdUNAKzVDQCAkQ8AgZkPAIKhDwCzpQ4Am6UAgLWhDgC2eQ8An6UAgKOlAICnpQCAukUPALtdDwC8RQ8AvU0PAL5FDwC//Q8AqFUOAKldDgCqYQ4Aq30OAKxlDgCttQ8Arr0PAK+1DwCrpQCAr6UAgLOlAIC3pQCAu6UAgL+lAIDDpQCAx6UAgLhVDwC5dQ8Aun0PALt1DwC8bQ8AvREPAL4RDwC/EQ8AsM0PALHVDwCy3Q8As9UPALTNDwC1dQ8AtnEPALdxDwCj6Q8Ay6UAgM+lAIDTpQCA16UAgKY1DgCl7Q8A26UAgKsRDgCqCQ4A36UAgOOlAICvsQ4ArgkOAK0BDgCsCQ4A56UAgIIdAACBHQAAgB0AAOulAIDvpQCA86UAgL6UAQCErAEA96UAgIfgAQCGzAAA+6UAgP+lAIADpgCAp6QAgKhtDgCpiQEAqpkBAKuRAQCswQEArckBAK75AQCv+QEAhKAAAAemAIALpgCAD6YAgBOmAIAXpgCAG6YAgB+mAIC4xQAAuc0AALrFAAC73QAAvM0AAL39AAC+9QAAv50AALBBAQCxQQEAskEBALNBAQC0QQEAtUEBALZBAQC3QQEAsxECACOmAIAnpgCAK6YAgC+mAIC2SQIAtUkCADOmAIC7hQIAuoUCADemAIA7pgCAv4UCAL6FAgC9lQIAvJUCAIU8GgCjVQIAP6YAgEOmAICmDQIAR6YAgEumAIClDQIAqsECAKvBAgBPpgCAU6YAgK7BAgCvwQIArNECAK3RAgCCGQAAV6YAgIAZAACBGQAAW6YAgF+mAIBjpgCAa6YAgL4ABABvpgCAc6YAgHemAIB7pgCAf6YAgIOmAICHpgCA7+gOAIumAICG6AQAh1ADAI+mAICTpgCA74ACAJemAIDhlAEAm6YAgONYAQCfpgCA4wAOAKOmAIDhaA0Ap6YAgKhxAgCpcQIAqnECAKupAgCsuQIArbkCAK6pAgCvqQIAhKwFAKumAICvpgCAs6YAgLemAIC7pgCAv6YAgMOmAIC4bQEAuQ0BALoFAQC7GQEAvAkBAL09AQC+NQEAv9kBALDZAgCx2QIAsm0BALNlAQC0fQEAtWUBALZlAQC3VQEA4WAPAOP0AADjHA4A4bwBAMemAICCOQAAgTEAAIA9AADLpgCAvigEAM+mAIDTpgCAvjwHAO8QAADv0A4A26YAgIbgBACHyAQA36YAgLO1AgDjpgCAtX0CALZ1AgDnpgCA66YAgO+mAIC6UQIAu1ECALz1AQC9/QEAvvUBAL/tAQBnpgCA16YAgKqxBQCrsQUArBUGAK0dBgCuFQYArw0GAPOmAID3pgCA+6YAgKNVBQD/pgCApZ0FAKaVBQADpwCAs+kGAAenAIALpwCAD6cAgBOnAIC24QYAtekGABenAIC7sQYAuqEGABunAIAfpwCAv50GAL6RBgC9pQYAvKkGAKgdBgCpIQYAqiEGAKshBgCsIQYArSEGAK4hBgCvIQYAI6cAgCenAIArpwCAL6cAgDOnAIA3pwCAO6cAgD+nAIC45QcAue0HALrlBwC7/QcAvOUHAL3tBwC+5QcAv00HALAlBgCxNQYAsj0GALMxBgC0FQYAtRkGALYNBgC3AQYAo6kHAIIVAACBtQEAgLUBAEOnAICmoQcApakHAEenAICr8QcAquEHAISgAgBLpwCAr90HAK7RBwCt5QcArOkHAE+nAICzlQYAhugAAIcYAQC2tQYAU6cAgFenAIC1vQYAukkBALtVAQBbpwCAX6cAgL45AQC/OQEAvEUBAL05AQCoPQYAqU0GAKpZBgCrUQYArHEGAK1xBgCuuQEAr7kBAISsAQBjpwCAZ6cAgGunAIBvpwCAc6cAgHenAIB7pwCAuKkBALmpAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9pAQCwyQEAsdUBALLVAQCzqQEAtLkBALW5AQC2qQEAt6EBAKPRBQB/pwCAg6cAgIenAICLpwCApvEFAKX5BQCPpwCAqxECAKoNAgCTpwCAl6cAgK99AgCufQIArX0CAKwBAgCbpwCAn6cAgKOnAICnpwCAgTEAAIANAACrpwCAgjkAAK+nAICzpwCAviQDALunAIC/pwCAw6cAgIbYHACHTAMAx6cAgMunAIDPpwCAhMAcAOMgAQDTpwCA4cgBANenAIDvMAIA26cAgN+nAIDjpwCA56cAgOunAIDvpwCA86cAgLOVAwD3pwCA+6cAgP+nAIADqACAtrkDALWxAwAHqACAu1EDALpJAwALqACAD6gAgL/1AAC+SQMAvUEDALxJAwCoLQIAqUUCAKpdAgCrVQIArHkCAK15AgCuvQIAr7UCAL5oHQATqACAF6gAgBuoAICAHQAAgQkAAIKpAAAfqACAuFEBALlZAQC6YQEAu2EBALwRAQC9EQEAvhEBAL8RAQCwzQIAsdUCALLdAgCz1QIAtM0CALVxAQC2cQEAt3EBAOFYBgDhVAcA47AAAOO8BgAjqACAK6gAgIYYHACHVB0AL6gAgDOoAIA3qACAO6gAgL74HAA/qACA7/AGAO/gBgCjlQIAQ6gAgEeoAIBLqACAT6gAgKa5AgClsQIAU6gAgKtRAgCqSQIAV6gAgFuoAICv9QEArkkCAK1BAgCsSQIAqG0eAKl1HgCqfR4Aq40eAKyVHgCtnR4Aro0eAK+BHgAnqACAX6gAgGOoAIBnqACAa6gAgG+oAIBzqACAd6gAgLiJHgC5iR4AupkeALuRHgC8uR4AvbkeAL59HwC/dR8AsMUeALHNHgCyxR4As90eALTFHgC1zR4AtsUeALe5HgCz9R4Ae6gAgH+oAICDqACAh6gAgLYdHgC1HR4Ai6gAgLsJHgC6AR4Aj6gAgJOoAIC/CR4AvgEeAL0JHgC8ER4Agm0AAKOxHgCAVQAAgWUAAKZZHgCEmAMAv9ABAKVZHgCqRR4Aq00eAIYABACHmAEArkUeAK9NHgCsVR4ArU0eAJuoAICfqACAhCQAAKOoAICnqACAq6gAgLenAICXqACAqLUeAKmFHgCqjR4Aq4UeAKydHgCtgR4Arv0eAK/1HgCwjR4AsZUeALKVHgCzpR4AtL0eALVxAQC2cQEAt3EBALhRAQC5UQEAulEBALtRAQC89QEAvf0BAL71AQC/7QEAsyUeAL4IBwCvqACAs6gAgLeoAIC2IR4AtTUeALuoAIC7cR4AumkeAL+oAIDDqACAv5UBAL5ZHgC9UR4AvGEeAMeoAICjYR4Ay6gAgM+oAICmZR4A06gAgNeoAIClcR4Aqi0eAKs1HgDbqACA36gAgK4dHgCv0QEArCUeAK0VHgDhVBoA46gAgONcCgDnqACA66gAgO+oAIDzqACA96gAgPuoAIC+qAUA/6gAgAOpAICPMSoAC6kAgO/E+wAPqQCAk2EuAJIdLwCR2SoAkEkqAJfZEgCWdRIAlQ0TAJTBLgCbHRsAmkEWAJlJFgCYDRcAn3EeAJ4RGwCdcRoAnHkaAKOhAgCinQMAoZUfAKCJHgDjiAEA4wgeAOFoAADh/B4A79wBAO98HwC1if4AtAH8ALMB+gCylfoAsQH4ALAR9gCv4fYArgH0AK0l8gCs7fIAqwHwAKrpDwCp1Q4AqN0OAKcBDACmyQoApe0KAKQBCACj4QYAovEGAKHlAwATqQCAggErAIMBKwAXqQCAG6kAgIYxLwCHiS8AhIkrAIVFLgCKdRIAiwUTAIYIBQCHbAUAjhEXAI8RFwCMsRMAjV0WAJI9GgCTQRsAhMgFAIQABwCWUR8Al1EfAJRRGwCVORoAmn0eAJt9AgAfqQCAI6kAgIFZAQCAVQEAnFkDAIJRAQC+yAcAJ6kAgCupAIAvqQCAM6kAgDepAIA7qQCA79QeAD+pAIDhJB4AQ6kAgONoAQBHqQCAS6kAgE+pAIBTqQCAu2kCALpZAgBXqQCAW6kAgL8dAgC+HQIAvRkCALxxAgCz7QIAX6kAgGOpAIBnqQCAa6kAgLZ9AgC17QIAb6kAgKMNBQAHqQCAc6kAgHupAIB3qQCApp0FAKUNBQB/qQCAq4kFAKq5BQCGCAMAh3wDAK/9BQCu/QUArfkFAKyRBQCAsQcAgbkHAIJBAACzsQYAg6kAgLVZBwC2MQcAh6kAgIupAICPqQCAuuEHALvhBwC84QcAveEHAL7hBwC/3QcAqLUGAKm5BgCqdQYAq4UHAKydBwCt/QcArvUHAK8ZBwCTqQCAl6kAgJupAICfqQCAo6kAgKepAICrqQCAr6kAgLh1BwC5fQcAunUHALsFBwC8HQcAvTEHAL4xBwC/MQcAsGkHALFpBwCyeQcAs3kHALRpBwC1VQcAtlEHALdNBwCj/QcAs6kAgLepAIC7qQCAv6kAgKZ9BgClFQYAw6kAgKutBgCqrQYAx6kAgMupAICvkQYArq0GAK2tBgCsrQYAz6kAgNOpAIDXqQCA26kAgIAdAACBCQAAgjkAAN+pAIDjqQCA66kAgIbIAACHpAEA76kAgPOpAID3qQCA+6kAgKiNAQCpmQEAqtkBAKvRAQCs8QEArfEBAK45AQCvOQEAhKAAAP+pAIADqgCAB6oAgAuqAIAPqgCAE6oAgBeqAIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+nQAAv5UAALBJAQCxSQEAslkBALNZAQC0SQEAtUkBALb9AAC39QAAugUEALsJBAC44QcAueEHAL4JBAC/CQQAvAkEAL0JBACyjQcAs+UHALC1BwCxhQcAtuUHALftBwC08QcAtfEHAKpNBwCrVQcAqEkHAKlJBwCu3QcAr8UHAKxNBwCt1QcAG6oAgB+qAIAjqgCAJ6oAgCuqAIAvqgCAM6oAgDeqAICz0QIAO6oAgD+qAIC+AAwAQ6oAgLbxAgC1+QIAR6oAgLsNAgC6DQIAS6oAgE+qAIC/DQIAvg0CAL0NAgC8DQIAghUAAKOVAgCAYQAAgWEAAKa1AgBTqgCAW6oAgKW9AgCqSQIAq0kCAIbIDACHrAwArkkCAK9JAgCsSQIArUkCAKhlAgCpdQIAqn0CAKt1AgCsbQIArbECAK6xAgCvsQIAhKANAF+qAIBjqgCAZ6oAgGuqAIBvqgCAc6oAgHeqAIC4MQEAuTEBALoxAQC7MQEAvNUBAL3dAQC+yQEAv8EBALDRAgCx0QIAstECALPRAgC0EQEAtREBALYRAQC3EQEA4bAGAHuqAIDj0AYAhEAPAH+qAIDhpAEAg6oAgOPABgCHqgCAi6oAgI+qAIDv1AYA7AAAAJOqAIDvZAcAl6oAgJuqAICfqgCAo6oAgLO5AgCnqgCAtakCALZ9AgCrqgCAr6oAgLOqAIC6WQIAu1kCALxJAgC9SQIAvpkBAL+ZAQCjdQ0AV6oAgLeqAIC7qgCAv6oAgKaxDQClZQ0Aw6oAgKuVDQCqlQ0AvqQDAMeqAICvVQ4ArlUOAK2FDQCshQ0AgE0AAIFVAACCVQAAs2UPAMuqAIC1ZQ8Atm0PAM+qAICGQAMAhxQDALrtDwC7/Q8AvOkPAL3VDwC+3Q8Av9UPAKhZDgCpoQ8AqqEPAKuhDwCsoQ8AraEPAK6hDwCvoQ8A06oAgNeqAIDbqgCA36oAgOOqAIDnqgCA66oAgO+qAIC4AQ8AuQEPALoBDwC7HQ8AvA0PAL01DwC+PQ8Av9UAALBlDwCxdQ8AsnEPALNNDwC0VQ8AtV0PALZNDwC3QQ8AoykOAPOqAID3qgCA+6oAgP+qAICmIQ4ApSkOAAOrAICrsQ4AqqEOAAerAIALqwCAr5kOAK6RDgCtmQ4ArKUOAA+rAIATqwCAF6sAgBurAIDvJA0AH6sAgCOrAIAnqwCA49AOACurAIDhGA4AL6sAgIAVAACBGQAAggUAADOrAICo0QEAqdkBAKopAQCrKQEArDkBAK05AQCuKQEArykBAL5oAQA7qwCAhsgBAIesAAA/qwCAQ6sAgEerAIBLqwCAuO0AALmFAAC6jQAAu4UAALydAAC9gQAAvoEAAL+BAACwWQEAsVkBALLtAACz5QAAtP0AALXlAAC25QAAt9UAALOhAgBPqwCAU6sAgFerAIBbqwCAtrkCALWxAgBfqwCAu50CALqdAgBjqwCAZ6sAgL8hAwC+OQMAvTEDALw5AwCF+PUAo+UCAGurAIBvqwCApv0CAHOrAIB3qwCApfUCAKrZAgCr2QIAe6sAgH+rAICufQMAr2UDAKx9AwCtdQMAuOkAALnpAAC6aQAAu2kAALx5AAC9ZQAAvm0AAL9lAACwsQAAsbkAALKBAACzgQAAtPkAALX5AAC27QAAt+UAAKhlAwCpdQMAqn0DAKt1AwCsbQMArdEAAK7RAACv0QAAg6sAgIerAICLqwCA56kAgI+rAICTqwCAl6sAgJurAICA/QEAgQkAAIIZAACfqwCAo6sAgL5EAgCrqwCAr6sAgISsAgCzqwCAh/gCAIasBQC3qwCAu6sAgL+rAIDDqwCAs/UCAMerAIDLqwCAz6sAgNOrAIC2UQEAteUCANerAIC7fQEAunUBANurAIDfqwCAvz0BAL49AQC9VQEAvFUBAOFwDwDjqwCA47gOAITABQDvyAAA56sAgOurAIDvqwCA4zwOAPOrAIDh0AEA96sAgIR0BwD7qwCA72gBAP+rAIADrACApXkCAKbNAQAHrACAgCEAAIEhAACC3QcAo2kCAKzJAQCtyQEArqEBAK+hAQALrACAD6wAgKrpAQCr4QEAp6sAgBOsAIC+QAIAF6wAgIYwAwCHMAMAG6wAgB+sAICoOQcAqTkHAKoNBwCrHQcArAUHAK0NBwCuBQcAr3kHALAJBwCxCQcAshkHALMRBwC0OQcAtTkHALbdBwC3yQcAuPkHALn5BwC6zQcAu8EHALzFBwC9yQcAvrkHAL+xBwCzpQcAI6wAgCesAIArrACAL6wAgLatBwC1rQcAM6wAgLvtBwC67QcAN6wAgDusAIC/3QcAvt0HAL3lBwC87QcAP6wAgKPhBwBDrACAR6wAgKbpBwBLrACAT6wAgKXpBwCqqQcAq6kHAFOsAIBXrACArpkHAK+ZBwCsqQcAraEHAFusAIBfrACAY6wAgGesAIBrrACAb6wAgHOsAIB3rACAgREAAIANAAB7rACAghkAAH+sAICDrACAvuQBAIesAICG4AAAhxgBAIusAICPrACAk6wAgJesAICbrACA77AEAJ+sAIDh1AYAo6wAgONcBACnrACAq6wAgK+sAICzrACAqJkBAKmZAQCqDQEAqwUBAKwdAQCtBQEArgUBAK81AQCEiAEAt6wAgLusAIC/rACAw6wAgMesAIDLrACAz6wAgLjBAAC5wQAAusEAALvBAAC8wQAAvcEAAL7BAAC/wQAAsE0BALElAQCyIQEAsyEBALQlAQC1LQEAthEBALcRAQDTrACA16wAgLONAgDbrACAtZ0CAN+sAIDjrACAto0CAOesAIDrrACAu+kCALqBAgC9/QIAvP0CAL/hAgC+6QIA76wAgKbVAgClxQIAvggDAKPVAgCCLQAAgRkAAIB5AACvuQIArrECAK2lAgCspQIAq7ECAKrZAgDzrACA+6wAgO80AgD/rACAhxgDAIYs/AADrQCAB60AgAutAIAPrQCAE60AgBetAIAbrQCAH60AgOMAAQAjrQCA4eABACetAIC6tQMAu70DACutAIAvrQCAvnkDAL95AwC8pQMAvXkDADerAICztQMAM60AgDetAIC2kQMAO60AgD+tAIC1pQMAqEkCAKlJAgCqWQIAq1kCAKxJAgCtdQIArnECAK9tAgC+aP0AvqT/AEOtAIBHrQCAS60AgE+tAIBTrQCAV60AgLj5AgC5+QIAukkBALtJAQC8XQEAvUEBAL5BAQC/fQEAsBUCALEdAgCyFQIAs8kCALTZAgC12QIAtskCALfJAgDjIAYA4bAGAOGAAQDjEAYAgA0AAIE1AACCPQAAW60AgF+tAIBjrQCAa60AgG+tAIDvcAAAc60AgHetAIDvTAEAhIz9AHutAICjmQIAf60AgKWJAgCDrQCAh60AgKa9AgCGwPwAh+T8AKuRAgCqmQIArVUCAKyJAgCvVQIArlUCAKh9/gCpgf4Aqpn+AKuZ/gCsif4ArYn+AK65/gCvuf4AZ60AgIutAICPrQCAk60AgJetAICbrQCAn60AgKOtAIC4tf4Aub3+ALph/wC7Yf8AvGH/AL1h/wC+Yf8Av2H/ALDJ/gCxyf4Ast3+ALPR/gC0uf4Atbn+ALaR/gC3kf4AsxH+AKetAICrrQCAr60AgLOtAIC2Cf4AtQH+ALetAIC7Df4Aug3+ALutAIC/rQCAv33+AL59/gC9Bf4AvAn+AMOtAICjVf4Ax60AgMutAICmTf4Az60AgNOtAIClRf4Aqkn+AKtJ/gCEKAMA160AgK45/gCvOf4ArE3+AK1B/gCAzQEAgdEBAILRAQCzuf4A260AgLXR/gC21f4A360AgIZgAQCHYAEAug0BALsFAQC8HQEAvQUBAL4NAQC/BQEA460AgOetAIDrrQCA760AgPOtAIDhwP0A960AgOOM/AD7rQCA/60AgAOuAIDvtPwAB64AgAuuAIAPrgCAE64AgKgp/gCpKf4Aqj3+AKs1/gCsVf4ArVn+AK5N/gCvRf4AF64AgBuuAIAfrgCAI64AgCeuAIArrgCAL64AgDOuAIC4SQEAuUkBALpZAQC7UQEAvHkBAL15AQC+GQEAvxUBALDFAQCxzQEAssUBALPdAQC0xQEAtc0BALbFAQC3eQEAN64AgDuuAIA/rgCAo7n9AEOuAICl0f0AptX9AITQAwBSrgCAvuACAKoNAgCrBQIArB0CAK0FAgCuDQIArwUCAIFJAACAQQAAowkDAIJdAAClGQMAVq4AgFquAICmEQMAhsAEAIfkAwCrDQMAqg0DAK0BAwCsHQMArwEDAK4JAwCw4QMAseEDALLhAwCz/QMAtOUDALXtAwC25QMAtz0DALgFAwC5DQMAugUDALsdAwC8BQMAvQ0DAL4FAwC/vQAAXq4AgGKuAIBmrgCAaq4AgPesAIBurgCAcq4AgHauAICo8QMAqfkDAKqpAwCrqQMArLkDAK25AwCuqQMAr6UDALNBAgB6rgCAfq4AgIKuAICGrgCAtlkCALVRAgCKrgCAu0UCALpFAgCOrgCAkq4AgL9JAgC+QQIAvUkCALxVAgCWrgCAmq4AgJ6uAICirgCA74wDAKauAICqrgCArq4AgONsAwCyrgCA4VAAALauAIC6rgCAvngFAMKuAICEcAIAgOUAAIHpAACC+QAAxq4AgIawBACHVAUAyq4AgO9A/gDOrgCA4Vz+ANKuAIDjVAEA1q4AgNquAIDergCA4q4AgLOZAQDmrgCA6q4AgO6uAIDyrgCAth0BALUdAQD2rgCAuz0BALo9AQD6rgCA/q4AgL/hAAC++QAAvfEAALz5AACoIQYAqVEGAKpRBgCrzQYArNUGAK3dBgCu1QYAr8kGAL6uAIACrwCABq8AgAqvAIAOrwCAEq8AgBavAIAarwCAuG0HALkFBwC6DQcAuwUHALwdBwC9AQcAvgEHAL8BBwCwuQYAsbkGALJtBwCzZQcAtH0HALVlBwC2ZQcAt1UHAKPZBgAerwCAIq8AgCavAIAqrwCApl0GAKVdBgCEnAIAq30GAKp9BgC+JAMALq8AgK+hBwCuuQcArbEHAKy5BwCASQAAgUkAAIJZAACzVQcAMq8AgLV9BwC2aQcANq8AgIZAAACHVAMAulUHALspBwC8OQcAvTkHAL4pBwC/IQcAo5kGADqvAIA+rwCAQq8AgEavAICmpQYApbEGAEqvAICr5QYAqpkGAE6vAIBSrwCAr+0GAK7lBgCt9QYArPUGAOE4BQBWrwCA4yQEAFqvAIBerwCAYq8AgGavAIBqrwCAbq8AgHKvAIB2rwCAeq8AgH6vAICCrwCA7/QEAIavAICo+QYAqQkGAKoRBgCrLQYArDkGAK0lBgCuLQYAryUGAIqvAICOrwCAkq8AgJavAICAGQAAgRkAAIIFAACarwCAuOUBALntAQC65QEAu/0BALzlAQC97QEAvuUBAL9ZAQCwXQYAsSEGALIhBgCzIQYAtCEGALUpBgC2EQYAtxEGAKjRAgCp2QIAqg0DAKsFAwCsHQMArQUDAK4FAwCvNQMAvmQCAKKvAICmrwCAqq8AgK6vAICyrwCAtq8AgLqvAIC4JQMAuS0DALolAwC7PQMAvCUDAL0pAwC++QMAv/kDALBNAwCxIQMAsiUDALM9AwC0JQMAtS0DALYlAwC3HQMAs4UDAITIAgC+rwCAhAgDAMKvAIC2hQMAtZUDAMavAIC75QMAuokDAIYIDACHnAMAv+kDAL7hAwC96QMAvPEDAIXsCgBHrgCAo80DAMqvAICl3QMAzq8AgNKvAICmzQMA1q8AgNqvAICrrQMAqsEDAK2hAwCsuQMAr6EDAK6pAwDerwCA4q8AgOavAIDqrwCA78gDAO6vAIDyrwCA9q8AgOO0AwD6rwCA4dABAP6vAICADQAAgXUAAIJ9AAACsACABrAAgAqwAICzZQEAvgQCALVlAQASsACAFrAAgLZlAQCGQA0Ah1gNALv1AQC6/QEAvaUBALy5AQC/mQEAvqUBABqwAIAesACAIrAAgIQADAAmsACAKrAAgC6wAIDvzAEAMrAAgOEsBgA2sACA4yABAOwAAAA6sACAPrAAgEKwAIBGsACAo+kBAEqwAIBOsACApukBAFKwAIBWsACApekBAKpxAQCreQEAWrAAgF6wAICuKQEArxUBAKw1AQCtKQEAqCUOAKktDgCqJQ4Aqz0OAKwlDgCtLQ4AriUOAK+VDgAOsACAYrAAgGawAIBqsACAbrAAgIKdAACBnQAAgJ0AALhFDwC5TQ8AukUPALtZDwC8SQ8AvUkPAL59DwC/cQ8AsPEOALH5DgCypQ4As7kOALSpDgC1lQ4Atp0OALd9DwCo1Q8Aqd0PAKoJDwCrCQ8ArBkPAK0FDwCuDQ8ArwUPAHKwAIB2sACAerAAgL6gAwB+sACAgrAAgId4AwCGEAAAuBUPALkdDwC6IQ8AuyEPALz1AAC9/QAAvvUAAL/tAACwQQ8AsU0PALJdDwCzVQ8AtE0PALU1DwC2MQ8AtzEPAIawAIDvsAwAirAAgI6wAICSsACAlrAAgJqwAICesACAorAAgKawAICqsACArrAAgLKwAIDjqA0AtrAAgOGMDQCzwQ4AurAAgL6wAIDCsACAxrAAgLbFDgC10Q4AyrAAgLvJDgC6xQ4AzrAAgNKwAIC/sQ4AvskOAL3BDgC8yQ4AowEOANawAIDasACA3rAAgOKwAICmBQ4ApREOAOawAICrCQ4AqgUOAOqwAICErAIAr3EOAK4JDgCtAQ4ArAkOAIBRAACBWQAAgmEAALPFAAC+zAEAtcUAALbNAADysACAhkAHAIcUAQC6yQAAu8kAALzZAAC92QAAvskAAL/FAACrDQMAqg0DAKkJAwCouQIArw0DAK4NAwCtDQMArA0DAL5gAwD2sACA+rAAgP6wAIACsQCABrEAgAqxAIC+MAUAuykDALoZAwC5GQMAuAEDAL/dAwC+3QMAvd0DALwxAwCzTQMAsk0DALFNAwCwTQMAtzkDALYxAwC1QQMAtE0DAA6xAICmkQMApZkDABKxAICjmQMAFrEAgBqxAIAesQCAr5kDAK6VAwCthQMArIUDAKuVAwCqlQMAnq8AgCKxAIAmsQCAKrEAgC6xAIAysQCANrEAgDqxAIA+sQCAQrEAgEaxAIBKsQCATrEAgFKxAICAHQAAgQkAAIL9AQBWsQCAvwgHAFqxAIBisQCA7yQAAGaxAICElAIAarEAgG6xAICH4AIAhgQFAL4AGABysQCAdrEAgOGQAQB6sQCA44AAAH6xAICCsQCAhrEAgLNlAQCKsQCAtWUBALZtAQCOsQCAkrEAgJaxAIC65QEAu/kBALzpAQC96QEAvsUBAL+9AQCasQCAnrEAgKKxAIC+xBkAprEAgKqxAICusQCA78gBALKxAIDh3A4AtrEAgOMwDgC6sQCAvrEAgMKxAICEMAQAgHkAAIEVAACCFQAAo+UBAMaxAICl5QEApu0BAMqxAICGQAYAh5AHAKplAQCreQEArGkBAK1pAQCuRQEArz0BAKjdBQCpIQYAqiEGAKshBgCsIQYArSEGAK4hBgCvnQYAXrEAgM6xAIDSsQCAhDABANaxAIDasQCA3rEAgOKxAIC4jQYAuZUGALqdBgC7lQYAvI0GAL21BgC+vQYAv7UGALDtBgCx8QYAsvEGALPxBgC0zQYAtbUGALa9BgC3tQYAqIkHAKmVBwCqkQcAq5EHAKy9BwCtpQcArqEHAK/dBwDmsQCA6rEAgO6xAIDysQCA9rEAgPqxAID+sQCAArIAgLhJBwC5VQcAul0HALtVBwC8cQcAvX0HAL5pBwC/aQcAsKUHALGtBwCyuQcAs7EHALSRBwC1kQcAtnkHALd5BwAGsgCACrIAgA6yAIASsgCA78gFAOHACQAWsgCA48AZAOMkBAAasgCA4dAGAO/cKACinQMAoxUBAKAZBQChjQUAs1kGAB6yAIAisgCAJrIAgCqyAIC2ZQYAtXUGAC6yAIC7KQYAuiEGADKyAIA2sgCAvxUGAL4VBgC9JQYAvC0GAKOZBgCPmfwAOrIAgEKyAIBGsgCApqUGAKW1BgBKsgCAq+kGAKrhBgCGKB8Ah5wAAK/VBgCu1QYAreUGAKztBgCebQkAn30HAJwNCwCd7QkAmvENAJs5DQCY5fAAmQ0PAJbh8QCX6fEAlMX1AJUN8wCSHfcAk/H1AJD9+QCR7fkAgh3/AIMB+gBOsgCAUrIAgIYV9gCHOfYAhAn6AIXx9ACKwfAAiyXyAFayAIBasgCAjuEMAI8VDgCMNfIAjQHzAJKtDgCTgQgAXrIAgGKyAICW6QQAl3UGAJR5CgCV8QoAmtEGAJvJAABmsgCAarIAgIEdAwCAHQMAnFkCAIL1AwCrARAAqpUWAKmNFgCojRYAr5UuAK4BLACt/RIArJkSAKOlHgCipR4AoY0CAO6wAICnGRoAppUaAKUBGACknR8AbrIAgHKyAIB2sgCAerIAgH6yAICCsgCAhrIAgIqyAICz5SoAsuUqALGtLwCw5S4AjrIAgJKyAIC1ASQAtBEqAKgpAwCpNQMAqj0DAKs1AwCsLQMArbUDAK69AwCvtQMAlrIAgJqyAICesgCAorIAgIAdAACBCQAAgrkAAKayAIC4TQIAuV0CALptAgC7CQIAvBkCAL0ZAgC+CQIAvwECALDNAwCx1QMAst0DALPVAwC0zQMAtXUCALZ9AgC3dQIAqrIAgITIHQCysgCAvgwfALayAIC6sgCA70gGAO9YBwDhWAYA4ZgGAOOUAQDjAAYAhhAcAId8HQC+9B4AvrIAgMKyAIC2ZQMAtfUDAMayAICz5QMAyrIAgM6yAIDSsgCAv+ECAL5ZAwC9UQMAvFkDALtBAwC6WQMA1rIAgNqyAIA+sgCArrIAgN6yAIDisgCA5rIAgOqyAIDusgCA8rIAgKitHQCptR0AqrUdAKslHgCsPR4ArR0eAK4VHgCvdR4AsA0eALEtHgCyJR4As40eALSVHgC1nR4AtpUeALeNHgC4tR4Aub0eALq1HgC7nR4AvIUeAL1VHwC+XR8Av1UfALMdHQD2sgCA+rIAgP6yAIACswCAtr0eALWVHgAGswCAu8keALrpHgAKswCADrMAgL95HgC+cR4AvXkeALzRHgCCKQAAo1kdAIAdAACBFQAApvkeABKzAIAWswCApdEeAKqtHgCrjR4AGrMAgITgAwCuNR4Arz0eAKyVHgCtPR4AqIkeAKmVHgCqnR4Aq7EeAKzRHgCt2R4Ars0eAK/FHgAeswCAIrMAgIaIAACHbAEAJrMAgCqzAIAuswCAMrMAgLhdAQC5wQEAusEBALvBAQC8wQEAvckBAL7xAQC/8QEAsL0eALGdHgCylR4As2UBALR9AQC1ZQEAtm0BALdlAQCqLR0AqzUdADazAIA6swCAri0dAK+VHACsLR0ArSUdAISMAQCjkR0APrMAgEKzAICmER0ARrMAgEqzAIClgR0As1UeAE6zAIBSswCAVrMAgFqzAIC2GR4AtRkeAF6zAIC7GR4AujkeAGKzAIBmswCAv+EBAL75AQC98QEAvAEeAGqzAIBuswCAcrMAgKOZHQB2swCApdUdAKbVHQB6swCAfrMAgIKzAICq9R0Aq9UdAKzNHQCtPQIArjUCAK8tAgCAZQAAgRUAAIIdAACEAAQAhrMAgIqzAICHcAMAhvwEAJKzAICWswCAmrMAgJ6zAICiswCAprMAgKqzAICuswCAvsgEALKzAIC2swCAurMAgL6zAIDCswCAxrMAgO/cHwDKswCA4ZQBAM6zAIDjHAEA0rMAgNazAIDaswCA3rMAgLt1AwC6aQMAvkgGAOKzAIC/HQMAvh0DAL0dAwC8ZQMAs9UDAOazAIDqswCA7rMAgPKzAIC2fQMAtcUDAIRwBQCoJQIAqTUCAKo9AgCrNQIArC0CAK2dAgCulQIAr7UCAIIVAAD2swCAgNkBAIEJAADEAAAA+rMAgAK0AIAGtACAuKkCALmpAgC6SQEAu0kBALxZAQC9RQEAvkUBAL99AQCwzQIAsdECALLRAgCzqQIAtLkCALW5AgC2qQIAt6ECAOEoHgDhNBwA43QBAOMYHgAKtACADrQAgIa4BACHVAUAhDgHABK0AIAWtACAGrQAgL6sBwAetACA78weAO/IGgCj9QIAIrQAgCa0AIAqtACALrQAgKZdAgCl5QIAMrQAgKtVAgCqSQIANrQAgDq0AICvPQIArj0CAK09AgCsRQIAqGEGAKlhBgCqYQYAq2EGAKxhBgCtYQYArmEGAK9hBgD+swCAPrQAgEK0AIBGtACASrQAgE60AIBStACAVrQAgLjxBgC58QYAuvEGALvxBgC8nQYAvbEGAL6xBgC/sQYAsOUGALHtBgCy5QYAs/0GALTlBgC17QYAttkGALfVBgCz6QYAWrQAgF60AIBitACAZrQAgLbhBgC16QYAarQAgLspBgC6IQYAbrQAgHK0AIC/KQYAviEGAL0pBgC8MQYAgl0AAKOtBgCARQAAgV0AAKalBgB2tACAerQAgKWtBgCqZQYAq20GAIYADACHQAMArmUGAK9tBgCsdQYArW0GAH60AIDvfAUAgrQAgIa0AICKtACAjrQAgJK0AICWtACAmrQAgJ60AICitACAprQAgKq0AIDjaAUArrQAgOF4BQCz0QYAsrQAgLa0AIC6tACAvrQAgLb9BgC1/QYAwrQAgLupBgC6oQYAxrQAgMq0AIC/mQYAvqkGAL2pBgC8sQYAqLkGAKm5BgCqGQYAqxkGAKw1BgCtPQYArjUGAK8pBgDOtACAgh0AAIEdAACAHQAA0rQAgNa0AIDatACA4rQAgLjpAQC56QEAuvkBALv5AQC86QEAvekBAL5dAQC/VQEAsCUGALEtBgCyJQYAsz0GALQtBgC1HQYAthUGALfZAQCGgAwAh+QCAOa0AICjnQUA6rQAgKWxBQCmsQUA7rQAgPK0AID2tACAqu0FAKvlBQCs/QUAreUFAK7lBQCv1QUAtk0DAPq0AICExAMAtUUDAP60AICzjQIAArUAgAa1AIC+SQMAv0kDALxJAwC9SQMAumkDALtpAwAKtQCADrUAgBK1AICmiQMApYEDABa1AICjSQIAGrUAgB61AIAitQCAr40DAK6NAwCtjQMArI0DAKutAwCqrQMAjrMAgCa1AIAqtQCALrUAgIW0PQAytQCANrUAgDq1AIA+tQCAQrUAgIA9AACBCQAAgh0AAEa1AIC+sAMASrUAgIc4AwCG3AwAUrUAgFa1AIBatQCAXrUAgGK1AIDvXAYAZrUAgGq1AIC+6AwA45QGAG61AIDh3AEAcrUAgHa1AIB6tQCAfrUAgLNRAQCCtQCAhrUAgIq1AICOtQCAtnEBALV5AQCStQCAuz0BALo9AQCWtQCAmrUAgL/9AQC+9QEAvQUBALwFAQCetQCAorUAgKa1AICEQAwAqrUAgK61AICytQCA76wHALa1AIDhJAYAurUAgONABwCGkAwAh/wMAMK1AIDGtQCAgFkAAIFlAACCYQAAo90BAMq1AICl9QEApv0BAM61AIDStQCA1rUAgKqxAQCrsQEArIkBAK2JAQCueQEAr3EBAN60AIBOtQCA2rUAgN61AIC+tQCA4rUAgOa1AIDqtQCAqJ0NAKktDgCqOQ4AqzEOAKwRDgCtEQ4Arn0OAK9tDgCwGQ4AsRkOALIxDgCzMQ4AtNEOALXZDgC2zQ4At8UOALj9DgC52Q4AuqkOALupDgC8vQ4AvaUOAL6tDgC/pQ4AqIEPAKmBDwCqgQ8Aq4EPAKyBDwCtjQ8AroUPAK+1DwDutQCA8rUAgPa1AID6tQCA/rUAgAK2AIAGtgCACrYAgLidDwC5rQ8AuqUPALtNDwC8VQ8AvV0PAL5JDwC/SQ8AsNEPALHRDwCy0Q8As9EPALS1DwC1vQ8AtrUPALetDwCzCQ4ADrYAgBK2AIAWtgCAGrYAgLYNDgC1CQ4AHrYAgLsVDgC6FQ4AIrYAgCa2AIC/eQ4AvnEOAL0FDgC8BQ4AghUAAKNNDgCAYQAAgWEAAKZJDgAqtgCAvhABAKVNDgCqUQ4Aq1EOAIQkAQAytgCArjUOAK89DgCsQQ4ArUEOAKg5DgCpOQ4AqlkOAKtRDgCscQ4ArXEOAK6RAQCvkQEAhgAAAIeEAAA2tgCAOrYAgD62AIBCtgCARrYAgEq2AIC4dQEAuX0BALp1AQC7yQAAvNkAAL3ZAAC+yQAAv8EAALD1AQCx/QEAsvUBALNNAQC0VQEAtV0BALZVAQC3TQEAuk0PALtVDwC4TQ8AuUUPAL59DwC/tQ8AvEUPAL11DwCyAQ8AswEPALAxDwCxMQ8AtgEPALcNDwC0EQ8AtREPAKqZDgCrRQ8AqOUOAKmZDgCuQQ8Ar0EPAKxRDwCtUQ8ATrYAgFK2AIBWtgCAWrYAgF62AIBitgCAZrYAgGq2AICzUQ0AbrYAgHK2AIB2tgCAerYAgLZxDQC1eQ0AfrYAgLu5AgC6sQIAgrYAgIa2AIC/GQIAvhECAL0ZAgC8oQIAirYAgKMVDQCOtgCAkrYAgKY1DQCWtgCAmrYAgKU9DQCq9QIAq/0CAIToAwCitgCArlUCAK9dAgCs5QIArV0CAKhtAgCprQIAqqUCAKu9AgCspQIAra0CAK6lAgCvfQEAgO0BAIHxAQCC8QEAvqAFAKa2AICqtgCAh2gFAIYcBQC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALAFAQCxDQEAsgUBALMdAQC0BQEAtQ0BALYFAQC3+QEA4WQPAOGcDwDjFA4A49QPAK62AIDhPA4AsrYAgOPkAAC+rAQAtrYAgLq2AIDvDAAAvrYAgMK2AIDvYA4A77QPAMa2AIDKtgCAhEQEALNhAgDOtgCAtWECALZhAgDStgCA1rYAgNq2AIC6jQEAu4UBALydAQC9hQEAvo0BAL+FAQCjrQUAnrYAgN62AIDitgCA5rYAgKatBQClrQUA6rYAgKtJBgCqQQYA7rYAgPK2AICvSQYArkEGAK1JBgCsUQYA9rYAgPq2AID+tgCAArcAgIAdAACBCQAAgjkAAAa3AIAKtwCADrcAgIbIAACHIAMAErcAgBa3AIAatwCAHrcAgKhtBgCptQcAqr0HAKsdBwCsCQcArTEHAK4xBwCvLQcAhKgDACK3AIAmtwCAKrcAgC63AIAytwCANrcAgDq3AIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+nQAAv5UAALBVBwCxJQcAsi0HALM9BwC0LQcAtRUHALYdBwC39QAAPrcAgOG8BgBCtwCA4/QFAEa3AIBKtwCATrcAgFK3AIBWtwCAWrcAgF63AIBitwCAZrcAgGq3AIButwCA7+gEALN1BgCCLQAAgRUAAIAdAABytwCAtvEGALXBBgB2twCAu6EGALrRBgB6twCAvmwBAL+RBgC+qQYAvakGALy5BgCjtQYAgrcAgIYoAACHTAEAhrcAgKYxBgClAQYAircAgKthBgCqEQYAjrcAgJK3AICvUQYArmkGAK1pBgCseQYAlrcAgLO9AQCatwCAnrcAgLZ5AQCitwCAprcAgLV5AQC6VQEAu10BAKq3AICutwCAvvkAAL/lAAC8RQEAvf0AAKhxAgCpcQIAqnECAKtxAgCstQIArb0CAK61AgCvrQIAhOw8ALK3AIC2twCAurcAgL63AIDCtwCAxrcAgMq3AIC4XQMAuWUDALptAwC7ZQMAvH0DAL1lAwC+bQMAv2UDALDVAgCx3QIAstUCALNtAwC0eQMAtWUDALZtAwC3ZQMALrYAgM63AIDStwCAo/UCANa3AIClMQIApjECANq3AIDetwCA4rcAgKodAgCrFQIArA0CAK21AwCusQMAr60DAIBlAACBCQAAghkAAOa3AIDqtwCA8rcAgL4QPAD2twCAhsA8AIcgAwD6twCA/rcAgAK4AIAGuACACrgAgA64AICohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECABK4AIAWuACAGrgAgB64AIAiuACAJrgAgCq4AIAuuACAuHUBALl9AQC6dQEAu8kBALzZAQC9xQEAvsUBAL/9AQCwtQIAsb0CALKBAgCzgQIAtFUBALVdAQC2VQEAt00BAOGkBgAyuACA41AGAL6APACEHDwAvoA/ADa4AIA6uACAPrgAgEK4AIBGuACASrgAgE64AIBSuACA7+AGAFa4AICBfQAAgHEAAFq4AICCBQAAYrgAgGa4AIDvTAAAargAgOGQAQBuuACA41gBAHK4AIB2uACAergAgIZYPwCH/DwAs509AO63AIBeuACAfrgAgIK4AIC21T0AtbU9AIa4AIC7+T0AuvE9AIq4AICOuACAvxk+AL4RPgC91T0AvNU9AJK4AICj2T0AlrgAgJq4AICmkT0AnrgAgKK4AICl8T0AqrU9AKu9PQCmuACAqrgAgK5VPgCvXT4ArJE9AK2RPQCoVT4AqVk+AKphPgCrYT4ArGE+AK1hPgCuYT4Ar2E+AISoAwCuuACAsrgAgLa4AIC6uACAvrgAgMK4AIDGuACAuEU/ALldPwC6VT8Au20/ALx1PwC9fT8AvnU/AL9tPwCwwT8AscE/ALLBPwCzwT8AtME/ALXBPwC2wT8At8E/AIC5AQCBuQEAggUAAMq4AIDhgD4A0rgAgOMoPQDWuACAhoAAAIcEAQDvCD0A2rgAgN64AIDiuACA5rgAgOq4AICzqT8AzrgAgO64AIDyuACA9rgAgLahPwC1qT8A+rgAgLtFPgC6RT4A/rgAgAK5AIC/RT4AvkU+AL1VPgC8VT4Ao2k/AAa5AIAKuQCADrkAgBK5AICmYT8ApWk/ABa5AICrhT4AqoU+ABq5AIAeuQCAr4U+AK6FPgCtlT4ArJU+ACK5AICzGT4AJrkAgCq5AIC2IT4ALrkAgDK5AIC1MT4AuvEBALv5AQA2uQCAOrkAgL6xAQC/vQEAvNEBAL3RAQCo0T0AqdE9AKrVPQCr6T0ArP09AK3lPQCu7T0ArxECAID5AwCBzQMAgsUDAIQkAwC+AAQAQrkAgIesAwCGvAQAuBkCALktAgC6JQIAu+kCALz5AgC9+QIAvukCAL/pAgCwcQIAsXkCALJBAgCzQQIAtDECALU9AgC2NQIAtykCAKVtPQBGuQCASrkAgKZ9PQBOuQCAfrcAgKNFPQBSuQCArY0CAKyNAgCv4QIAru0CAKwAAABWuQCAq6UCAKqtAgDh+AEAWrkAgOP0AgCEwAQAXrkAgGK5AIBmuQCAarkAgG65AIByuQCAdrkAgHq5AIB+uQCAgrkAgO8wAgCGuQCAqBUCAKkZAgCqJQIAqz0CAKwlAgCtLQIAriUCAK9VAgCKuQCAjrkAgJK5AICWuQCAmrkAgJ65AICEsAQAorkAgLjRAgC52QIAuuECALvhAgC8kQIAvZ0CAL6VAgC/iQIAsC0CALE1AgCyNQIAswUCALQdAgC18QIAtvECALfxAgDheD8A4zQBAOMIPgDhbD4AgQkAAICpAACmuQCAgj0AAKq5AICyuQCAtrkAgL4gBAC6uQCA79g+AO/MPgC+uQCAwrkAgLPpAgCG6AQAh8AEALbpAgDGuQCAyrkAgLXpAgC6rQIAu7UCAM65AIDSuQCAvp0CAL9xAgC8pQIAvZUCAD65AICuuQCA1rkAgNq5AIDeuQCA4rkAgOa5AIDquQCAqBUGAKmhBgCqoQYAq70GAKytBgCtgQYArv0GAK/tBgCwlQYAsZ0GALKVBgCzrQYAtLUGALW9BgC2tQYAt60GALiVBgC5mQYAukkHALtJBwC8WQcAvVkHAL5JBwC/SQcArN0FAK3tBQCu5QUArwkFAO65AIDyuQCAqtUFAKvNBQD2uQCApZEFAKaRBQD6uQCA/rkAgAK6AIAGugCAo5EFALNJBgAKugCADroAgBK6AIAWugCAtmEGALVFBgAaugCAuzkGALoxBgC+ZAAAHroAgL8ZBgC+EQYAvRkGALwhBgCjiQcAgtkBAIHZAQCAwQEAIroAgKahBwClhQcAJroAgKv5BwCq8QcAhggBAId8AQCv2QcArtEHAK3ZBwCs4QcAKroAgLP1BgAuugCAMroAgLaFBgA2ugCAOroAgLWdBgC6jQYAu20BAD66AIBCugCAvmUBAL9tAQC8dQEAvW0BAKglBgCpLQYAqjkGAKsxBgCsUQYArUEGAK5BBgCvdQYARroAgEq6AIBOugCAUroAgFa6AIBaugCAXroAgGK6AIC4VQEAuWUBALplAQC7fQEAvGUBAL1tAQC+HQEAvxUBALANBgCx7QEAsuUBALP9AQC05QEAte0BALblAQC3bQEAo7EFAGa6AIBqugCAvkgDAL5YDACmwQUApdkFAG66AICrKQIAqskFAHK6AIB2ugCArykCAK4hAgCtKQIArDECAHq6AIB+ugCAgroAgIa6AICAGQAAgRkAAIIFAACKugCAhKwDAJK6AICHGAMAhswMAJa6AICaugCAnroAgKK6AICokQMAqZkDAKrJAwCrxQMArN0DAK3BAwCuwQMAr/UDAKa6AICqugCArroAgLK6AIC2ugCAuroAgL66AIDCugCAuH0DALnBAAC6wQAAu9EAALz5AAC9+QAAvpkAAL+ZAACwjQMAsUUDALJNAwCzRQMAtF0DALVFAwC2TQMAt0UDALNBAgDGugCAyroAgL8EDwDOugCAtkECALVVAgDSugCAu4ECALpJAgDWugCA2roAgL+BAgC+mQIAvZECALyZAgDeugCA4roAgOa6AIDqugCA76QDAO66AIDyugCA9roAgOMQAwD6ugCA4VgAAIQgDQCAKQAAgSkAAIIdAAACuwCA4VAGAOGgBwDjoAYA41AHAIWUDAAGuwCA70gbAAq7AIDhJAIADrsAgONwGgASuwCAFrsAgBq7AIDvqAEA7+gGAIagDwCHDA0Ao4kCAB67AIClnQIAIrsAgCa7AICmiQIAKrsAgC67AICrSQIAqoECAK1ZAgCsUQIAr0kCAK5RAgCoZQ4AqXUOAKp9DgCrdQ4ArG0OAK21DgCuvQ4Ar7UOAP66AIAyuwCANrsAgDq7AIA+uwCASbsAgE27AIBRuwCAuF0PALltDwC6ZQ8Auw0PALwVDwC9HQ8AvhUPAL8JDwCwzQ4AsdUOALLdDgCz1Q4AtM0OALVxDwC2cQ8At20PALP1DgBVuwCAWbsAgF27AIBhuwCAtjUOALXlDgBluwCAuxEOALoJDgBpuwCAbbsAgL+1DwC+CQ4AvQEOALwJDgCCFQAAo7EOAIBhAACBYQAApnEOAHG7AIC+EAEApaEOAKpNDgCrVQ4AebsAgIQgAQCuTQ4Ar/EPAKxNDgCtRQ4An0UIAJ4NCQCdDQkAnJkLAJt1NQCaETUAmZk3AJgNMQCXJTEAliUxAJWBPQCUDT0Ak4k/AJIVOACRPTkAkD05AI9lJQDvrA0AhgAEAIegAQB9uwCAgbsAgIW7AIDv6AEAibsAgOE0AgCNuwCA4zQBAJG7AIDjCAwAlbsAgOEIDQChoQEAmbsAgKMJBQCibQMApc0EAKQRBQCnHRkAph0ZAKmhHQCoORkAq+kcAKqpHQCtkREArAEQAK8BFACuUREAsfkVALDlFQCz6WkAsgFoALUBbAC0eWkAnbsAgKG7AICluwCAqbsAgK27AICxuwCAowkDAKIZDQCh/Q0AoP0NAIIlJgCDBToAtbsAgLm7AICGqTwAhzU+AIQdOgCFPTsAiok+AIslMgC9uwCAwbsAgI6xNACPMTYAjD0yAI0tMgCSJTYAk9EIAIREAwC+wAQAlhULAJdVDgCUXQoAlVUKAJplDgCbiQ4AxbsAgMm7AIDNuwCA0bsAgJyBAADVuwCAuLUCALm9AgC6tQIAuwkCALwZAgC9GQIAvgkCAL8BAgCwdQ0AsX0NALJJDQCzSQ0AtJUCALWdAgC2lQIAt40CAKi9DQCpUQ0AqlUNAKtpDQCsfQ0ArWUNAK5tDQCvEQ0AdbsAgILtAQCBHQAAgB0AANm7AIDduwCAjroAgL5wBQCznQwAhIwFAOG7AIDpuwCA7bsAgLalDAC1tQwA8bsAgLv5DAC68QwAhigFAIcgBQC/GQMAvhEDAL3dDAC83QwA9bsAgKPZDAD5uwCA/bsAgKbhDAABvACABbwAgKXxDACqtQwAq70MAAm8AIANvACArlUDAK9dAwCsmQwArZkMABG8AIAVvACAGbwAgB28AIAhvACAJbwAgCm8AIDvvAEALbwAgOF8DgAxvACA41ABADW8AIA5vACAPbwAgEG8AICzlQIARbwAgEm8AIBNvACAUbwAgLa9AgC1uQIAWbwAgLs5AgC6YQIAhsgEAIesBAC/GQIAvhECAL0ZAgC8IQIAo1UFAILVBwCBxQcAgMUHAF28AICmfQUApXkFAGG8AICr+QUAqqEFAGW8AIBpvACAr9kFAK7RBQCt2QUArOEFAG28AICzWQcAcbwAgHW8AIC2HQcAebwAgH28AIC1FQcAugkHALsJBwCBvACAhbwAgL75BwC/+QcAvPkHAL35BwDluwCAVbwAgIm8AICNvACAkbwAgJW8AICZvACAnbwAgKitBwCptQcAqrUHAKvtBwCs+QcArfkHAK7tBwCv5QcAsKkHALGpBwCySQcAs0kHALRZBwC1WQcAtkkHALdJBwC4eQcAuUUHALpBBwC7XQcAvEUHAL1NBwC+RQcAvzkHAKMdBgChvACApbwAgKm8AICtvACAplkGAKVRBgCxvACAq00GAKpNBgC1vACAubwAgK+9BgCuvQYArb0GAKy9BgCAbQAAgQkAAIIZAAC9vACAwbwAgISYAQC+kAEAxbwAgIYAHACHxAEAybwAgM28AIDRvACA1bwAgNm8AIDdvACAqF0GAKmVAQCqlQEAq6UBAKy9AQCt1QEArtEBAK/RAQDhvACA5bwAgOm8AIDtvACA8bwAgPW8AID5vACA/bwAgLhZAQC5WQEAus0AALvFAAC83QAAvcUAAL7FAAC/9QAAsLUBALG9AQCygQEAs4EBALR5AQC1eQEAtmkBALdpAQCzHQIAAb0AgAW9AIC+gBwACb0AgLZVAgC1NQIADb0AgLt5AgC6cQIAEb0AgBW9AIC/vQIAvr0CAL1VAgC8VQIAGb0AgKNZAgAdvQCAIb0AgKYRAgAlvQCAKb0AgKVxAgCqNQIAqz0CAC29AIAxvQCArvkCAK/5AgCsEQIArRECADm9AIA9vQCAvgQdAL4AHgBBvQCARb0AgEm9AIBNvQCAgPkAAIHNAACCxQAAhCADAIawHACHlAMAUb0AgFW9AIBZvQCAXb0AgGG9AIBlvQCA42wCAGm9AIDhoAEAbb0AgO8UAgBxvQCAdb0AgHm9AIB9vQCAgb0AgIW9AICJvQCA4fAGAOE0BgDjTAAA4xgGAI29AICRvQCAlb0AgJm9AICAPQAAgQkAAIIZAACdvQCAob0AgIS8HQDvmAAA7zgHALMxAgDRAAAAh9gdAIZsHACpvQCAtikCALUhAgCtvQCAu80CALrNAgCxvQCAtb0AgL/NAgC+zQIAvc0CALzNAgCyXQYAs2UGALANBgCxVQYAtn0GALedBQC0fQYAtXUGALqNBQC7zQUAuKUFALmFBQC+xQUAv8kFALzVBQC9zQUAub0AgL29AIDBvQCAxb0AgMm9AIDNvQCA0b0AgNW9AICqtQYAq70GAKgBBwCpvQYAroEGAK+NBgCsmQYArZUGAKNxHQDZvQCA3b0AgOG9AIDlvQCApmkdAKVhHQDpvQCAq40dAKqNHQDtvQCA8b0AgK+NHQCujR0ArY0dAKyNHQD1vQCAs9UeAPm9AID9vQCAts0eAAG+AIAFvgCAtcUeALqhHgC7oR4ACb4AgA2+AIC+pR4Av6keALyxHgC9sR4ANb0AgKW9AIARvgCAhAQDAID5AACB+QAAghEAABW+AICoIR4AqSEeAKo5HgCrOR4ArCkeAK0pHgCuAR4ArwEeALABHgCxAR4AsgEeALMBHgC0BR4AtQkeALY9HgC3NR4AuA0eALkVHgC6HR4AuxUeALwNHgC95R8Avu0fAL/lHwCjkR8AGb4AgIYoAQCHSAEAHb4AgKaJHwClgR8AIb4AgKvlHwCq5R8AJb4AgCm+AICv7R8AruEfAK31HwCs9R8ALb4AgLMtHgAxvgCANb4AgLaVHgA5vgCAPb4AgLWdHgC6sR4Au7EeAEG+AIBFvgCAvnUBAL99AQC8oR4AvaEeAKjRHgCp2R4AquEeAKvhHgCsUR4ArVEeAK5RHgCvUR4ASb4AgE2+AIBRvgCAVb4AgFm+AIBdvgCAYb4AgGW+AIC43QEAue0BALrlAQC7jQEAvJkBAL2ZAQC+jQEAv4UBALAxHgCxMR4AsjEeALMxHgC09QEAtf0BALb1AQC37QEAo2kdAGm+AIBtvgCAcb4AgHW+AICm0R0ApdkdAHm+AICr9R0AqvUdAH2+AICBvgCArzkCAK4xAgCt5R0ArOUdAIFpAACAWQAAvgAEAIJhAACJvgCAjb4AgJG+AICVvgCAhOwDAJm+AICHiAMAhuwEAJ2+AIChvgCApb4AgKm+AICohQMAqZUDAKqVAwCrpQMArL0DAK3VAwCu0QMAr9EDAK2+AICxvgCAtb4AgLm+AIC9vgCAwb4AgMW+AIDJvgCAuHEDALlxAwC6cQMAu3EDALzVAAC93QAAvtUAAL/NAACwtQMAsb0DALKBAwCzgQMAtFEDALVRAwC2UQMAt1EDAOFUHgDhrB8A45QBAOMoHgDjYAMAzb4AgOEIAADRvgCA75ADANW+AIDZvgCA3b4AgOG+AIDlvgCA70wfAO9MHwCzXQIA6b4AgO2+AIDxvgCA+b4AgLYVAgC1dQIA/b4AgLs5AgC6MQIAhCQFAL7gBAC/1QIAvtUCAL0VAgC8FQIAuJEdALmZHQC6oR0Au6EdALzRHQC93R0AvtUdAL/JHQCwCR4AsQkeALIZHgCzGR4AtAkeALUJHgC2vR0At7UdAKipHgCpqR4AqrkeAKu5HgCsqR4ArakeAK55HgCveR4AgKUAAIGtAACCpQAAAb8AgIbQBACH+AQABb8AgAm/AICFvgCA9b4AgA2/AIARvwCAFb8AgBm/AIAdvwCAIb8AgKhxBgCpcQYAqnEGAKtxBgCsVQYArUUGAK5NBgCvRQYAsD0GALHlBgCy7QYAs+UGALT9BgC15QYAtu0GALflBgC43QYAuXEHALp1BwC7SQcAvFkHAL1ZBwC+SQcAv0kHALPZBgAlvwCAKb8AgC2/AIAxvwCAtuUGALX9BgA1vwCAuwEGALrZBgA5vwCAPb8AgL8BBgC+GQYAvREGALwZBgBBvwCAo9kFAEW/AIBJvwCAppEFAE2/AIBRvwCApfEFAKq1BQCrvQUAVb8AgFm/AICuUQUAr1EFAKyRBQCtkQUAo1kHAIIZAACBGQAAgOEBAF2/AICmZQcApX0HAGG/AICrgQcAqlkHAISgAgC+rAEAr4EHAK6ZBwCtkQcArJkHAGW/AICzqQYAhugAAIcsAQC2WQEAab8AgG2/AIC1oQYAunUBALt9AQBxvwCAdb8AgL75AQC/+QEAvGUBAL35AQCo0QYAqdkGAKplBgCrdQYArG0GAK2dAQCulQEAr40BAITsAQB5vwCAfb8AgIG/AICFvwCAib8AgI2/AICRvwCAuGkBALlpAQC6CQEAuwUBALwdAQC9AQEAvgEBAL81AQCw9QEAsf0BALL1AQCzaQEAtHkBALV5AQC2aQEAt2EBAJW/AICZvwCAnb8AgKPhBQChvwCApekFAKYRAgClvwCAqb8AgK2/AICqPQIAqzUCAKwtAgCtsQIArrECAK+xAgCxvwCAtb8AgL4EAwCEAAwAub8AgL2/AIDBvwCAxb8AgIANAACBFQAAgh0AAMm/AIDNvwCA0b8AgIdEAwCG3AwAs+kDANm/AIDdvwCA4b8AgOW/AIC2PQMAtT0DAOm/AIC7GQMAuhEDAO2/AIDxvwCAv7kAAL6xAAC9uQAAvAEDAPW/AIDhlAEA+b8AgON8AQD9vwCAAcAAgAXAAIAJwACADcAAgBHAAIAVwACAGcAAgB3AAIAhwACAJcAAgO9MAgCoVQIAqV0CAKphAgCrYQIArLUCAK29AgCutQIAr60CAL5oDQApwACALcAAgDHAAIA1wACAgq0AAIGtAACArQAAuGEBALlhAQC6CQEAuwkBALwBAQC9AQEAvgEBAL8BAQCw1QIAsd0CALLVAgCzbQEAtHUBALV9AQC2aQEAt2EBAOFoBgDh8AcA47AAAOP0BgA5wACAPcAAgEHAAIBJwACATcAAgFHAAIBVwACAWcAAgL78DABdwACA72wAAO8oBgCjqQIAYcAAgIZoDACHBA0AZcAAgKZ9AgClfQIAacAAgKtZAgCqUQIAbcAAgHHAAICv+QEArvEBAK35AQCsQQIAqIUOAKmNDgCqhQ4Aq50OAKyNDgCtvQ4ArrUOAK/dDgBFwACAdcAAgHnAAIB9wACAgcAAgIXAAICJwACAjcAAgLitDgC5tQ4Aur0OALu1DgC8dQ8AvX0PAL51DwC/bQ8AsKkOALG1DgCyvQ4As7UOALStDgC1lQ4Atp0OALeVDgCzDQ4AkcAAgJXAAICZwACAncAAgLY9DgC1BQ4AocAAgLtxDgC6bQ4ApcAAgKnAAIC/UQ4AvmkOAL1hDgC8aQ4AghkAAKNJDgCAZQAAgRkAAKZ5DgCtwACAscAAgKVBDgCqKQ4AqzUOAIS8AwC1wACAri0OAK8VDgCsLQ4ArSUOAKidDgCppQ4Aqq0OAKulDgCsvQ4AraEOAK7dDgCvzQ4AhiABAIdkAQC5wACAvcAAgMHAAIDFwACAycAAgM3AAIC4eQEAuXkBALrNAQC7xQEAvN0BAL3FAQC+xQEAv/UBALC9DgCxjQ4AsoUOALNJAQC0WQEAtVkBALZJAQC3SQEAtS0OANHAAIDVwACAtjkOANnAAIDdwACAsz0OAOHAAIC9hQEAvEkOAL+FAQC+hQEA5cAAgNW/AIC7UQ4AumEOAKNlDgDpwACA7cAAgPHAAID1wACApmEOAKV1DgD5wACAqwkOAKo5DgD9wACAAcEAgK/dAQCu3QEArd0BAKwRDgAFwQCACcEAgO/QDwANwQCAEcEAgBXBAIAZwQCAHcEAgCHBAIC+aAMAKcEAgC3BAIDhVA4AMcEAgONkDgA1wQCAgFkAAIFZAACCaQAAhIwDAIbwBACHFAMAOcEAgD3BAIBBwQCARcEAgEnBAIBNwQCAUcEAgFXBAIBZwQCAXcEAgGHBAIBlwQCAacEAgG3BAIBxwQCAdcEAgHnBAIB9wQCAqIkDAKmJAwCqmQMAq5kDAKyJAwCtiQMArj0DAK81AwCwUQMAsVEDALJVAwCzfQMAtBUDALUdAwC2FQMAtw0DALg9AwC5DQMAugUDALvtAAC89QAAvfkAAL7pAAC/6QAAgcEAgIXBAICJwQCAsz0CAI3BAIC1LQIAtiUCAJHBAIC+aAUAmcEAgLq5AgC7uQIAvK0CAL2FAgC+/QIAv/UCAIBJAACBVQAAglUAAIQABQDvjAMAvhgEAId0BQCG/AQA4zwDAJ3BAIDhUAAAocEAgKXBAICpwQCArcEAgLHBAIC1wQCAucEAgL3BAIDBwQCAxcEAgMnBAIDNwQCA79QOAL4oBgDhdA4A0cEAgONUAQDVwQCA2cEAgN3BAIDhwQCAo/ECAOXBAIDpwQCA7cEAgPHBAICm6QIApeECAPXBAICrdQIAqnUCAPnBAID9wQCArzkCAK4xAgCtSQIArGECAKgpBgCpKQYAqj0GAKsxBgCsSQYArUkGAK55BgCveQYAlcEAgIIVAACBxQcAgMUHAAHCAICEaAMABcIAgAnCAIC4yQYAuckGALrZBgC72QYAvMkGAL3JBgC+WQcAv1kHALAJBgCxCQYAshkGALMZBgC0CQYAtQkGALb5BgC3+QYAs7UGAA3CAICGrAAAh0ADABHCAIC2yQYAtcEGABXCAIC7zQYAus0GABnCAIAdwgCAv80GAL7NBgC9zQYAvM0GACHCAICj8QYAJcIAgCnCAICmjQYALcIAgDHCAIClhQYAqokGAKuJBgA1wgCAOcIAgK6JBgCviQYArIkGAK2JBgCoJQYAqWEGAKplBgCrfQYArGUGAK1tBgCuZQYAr50GAD3CAIBBwgCARcIAgEnCAIBNwgCAUcIAgFXCAIBZwgCAuPUGALn9BgC69QYAu4kGALyZBgC9mQYAvokGAL+BBgCw5QYAse0GALLlBgCz/QYAtOUGALXtBgC20QYAt80GAF3CAIC2/QYAtf0GAGHCAICz/QYAZcIAgGnCAIBtwgCAvzkGAL4xBgC9OQYAvCEGALs5BgC6MQYAJcEAgHHCAICjrQYAgnkAAIFVAACAVQAAhFwBAKatBgClrQYAecIAgKtpBgCqYQYAhkh/AIfkAACvaQYArmEGAK1pBgCscQYAfcIAgO/cBwCBwgCAhcIAgInCAICNwgCAkcIAgJXCAICZwgCAhKADAJ3CAIC/JHkAocIAgONoBwClwgCA4XQGALPRAgCpwgCAvgQDAISAfQCtwgCAtvkCALXxAgCxwgCAu7UCALqpAgC1wgCAucIAgL9RAwC+mQIAvZECALylAgCpBQIAqLkCAKsVAgCqHQIArT0CAKw9AgCvUQIArl0CAL5ofQC9wgCAwcIAgMXCAIDJwgCAzcIAgNHCAIDVwgCAufEDALjpAwC78QMAuvkDAL1RAwC86QMAv00DAL5RAwCxNQIAsCkCALMBAgCyNQIAtdEDALQZAgC30QMAttkDAIIpAACjlQMAgB0AAIEVAACmvQMA2cIAgN3CAICltQMAqu0DAKvxAwDhwgCA6cIAgK7dAwCvFQIArOEDAK3VAwCGYH0Ah3h9ALNBAQCEAH8AtUEBAO3CAIDxwgCAtkkBAPXCAID5wgCAu0EBALpNAQC9SQEAvEUBAL8pAQC+OQEA/cIAgO/cBgABwwCABcMAgAnDAIANwwCAEcMAgO8wBgCELH4A4eAGABXDAIDjiAEAGcMAgON0AAAdwwCA4SwBAKPJAQAhwwCAJcMAgIVweQApwwCApsEBAKXJAQAtwwCAq8kBAKrFAQAxwwCANcMAgK+hAQCusQEArcEBAKzNAQCo3X0AqQV+AKoBfgCrAX4ArAF+AK0BfgCuAX4ArwF+AOXCAIA5wwCAPcMAgEHDAIBFwwCAgp0AAIGdAACAnQAAuC1+ALnhfgC64X4Au+F+ALzhfgC94X4AvuF+AL/hfgCwQX4AsU1+ALJZfgCzVX4AtDV+ALUlfgC2JX4AtxV+AKitfwCp0X8AqtF/AKvtfwCs9X8ArRV/AK4RfwCvEX8AScMAgE3DAIBRwwCAVcMAgIbwAwCHuAAAWcMAgF3DAIC4EX8AuRl/ALohfwC7IX8AvPUAAL39AAC+9QAAv+0AALBxfwCxcX8AsnF/ALNFfwC0QX8AtU1/ALY9fwC3NX8As1l+AGHDAIBlwwCAacMAgG3DAIC2lX4AtX1+AHHDAIC7tX4AurV+AHXDAIB5wwCAv4l+AL6FfgC9kX4AvKV+AH3DAICjHX4AgcMAgIXDAICm0X4AicMAgI3DAIClOX4AqvF+AKvxfgCRwwCAlcMAgK7BfgCvzX4ArOF+AK3VfgCwrQAAscUAALLBAACzwQAAtMUAALXNAAC28QAAt/EAALhhAAC5YQAAumEAALt9AAC8ZQAAvW0AAL5lAAC/vQMAmcMAgJ3DAIChwwCAdcIAgKXDAICpwwCArcMAgLHDAICoWQEAqVkBAKrtAACr5QAArP0AAK3lAACu5QAAr9UAALXDAICCHQAAgR0AAIAdAAC5wwCAvcMAgMHDAIC+VAIAhoAEAIfsAgDJwwCAzcMAgNHDAIDVwwCA2cMAgL54AwDjdH4A3cMAgOG4fQDhwwCA5cMAgOnDAIDtwwCA8cMAgPXDAID5wwCA/cMAgAHEAIDvwH4ABcQAgAnEAIANxACAs4UDABHEAIAVxACAGcQAgB3EAIC2hQMAtZUDACHEAIC74QMAuokDAL4kBgAlxACAv+kDAL7hAwC99QMAvPUDAIIpAACjwQMAgB0AAIEVAACmwQMAKcQAgC3EAICl0QMAqs0DAKulAwAxxACAheAFAK6lAwCvrQMArLEDAK2xAwDh+AMAOcQAgONcHwA9xACA7/QDAEHEAICGPAcAh6wCAON8fgBFxACA4YABAEnEAIBNxACAUcQAgO/kEwBVxACAs3EBAFnEAIBdxACAYcQAgGXEAIC2EQEAtWEBAGnEAIC7OQEAujEBAG3EAIBxxACAvxkBAL4RAQC9GQEAvCEBAHXEAIB5xACAfcQAgIHEAICFxACAicQAgI3EAIDvxH8AkcQAgOH8fgCVxACA4/B/AIANAACBdQAAgn0AAJnEAICdxACAocQAgKP5AQC+AAgApekBAKnEAICtxACAppkBAISoBQCxxACAq7EBAKq5AQCtkQEArKkBAK+RAQCumQEAqCkGAKkpBgCqOQYAqzkGAKwpBgCtUQYArlUGAK9NBgA1xACAhCABALXEAIClxACAo+EBAKKZBAChGQQAoPEFALg5BgC5OQYAus0GALvFBgC83QYAvcUGAL7FBgC/8QYAsDUGALE9BgCyNQYAsw0GALQVBgC1HQYAthUGALcJBgCPoWwAs5EHAIYoAQCHfAMAtqEHALnEAIC9xACAtbEHALrlBwC77QcAwcQAgMXEAIC+7QcAv90HALz1BwC97QcAn/l4AJ7leACdcXkAnCF8AJvxfACaYX0AmZlxAJjZcACX4XAAlnl0AJVtdACUbXQAk61pAJJxaACReWgAkB1uAIIhbQCD5W8AycQAgM3EAICGTWgAh5V1AISZaQCFmWkAiqV1AIu5dQDRxACA1cQAgI5xcACPgXwAjDlxAI05cQCSYX0Ak6l9ANnEAIDdxACAlml5AJeZBACU4XgAlX15AJpBBQCbyQUA4cQAgOXEAIDpxACA7cQAgJypAADxxACAo4ENAKKpAQChqQEA9cQAgKexCQCmAQgApU0NAKSZDQCrkRUAqoUVAKkBFACocQkArx0QAK7pEQCtvREArAEQALMBGACy8RwAscEdALDJHQDFwwCA+cQAgLXhGAC0/RkA/cQAgAHFAIAFxQCACcUAgIAdAACBCQAAgv0DAA3FAICjFQUAEcUAgIaIDACHPAMAGcUAgKYlBQClNQUAHcUAgKtpBQCqYQUAIcUAgCXFAICvWQUArmkFAK1pBQCscQUAKcUAgC3FAICEBAwAMcUAgDXFAIDhbAYAOcUAgOPsewA9xQCAQcUAgEXFAIDvqAYAScUAgE3FAIBRxQCAVcUAgKmNBQCogQUAq60FAKqZBQCtoQUArLkFAK+lBQCuqQUAhGgNAFnFAIBdxQCAYcUAgGXFAIBpxQCAbcUAgL70DAC5SQUAuEEFALtZBQC6QQUAvUkFALxBBQC/cQUAvn0FALGpBQCwoQUAs7kFALKhBQC1mQUAtKkFALd5BQC2kQUAqNUEAKndBACq7QQAqyUDAKyFAwCtjQMArrEDAK+xAwBxxQCAdcUAgHnFAIB9xQCAgBkAAIEZAACCBQAAgcUAgLgxAgC5MQIAujUCALvBAgC8hQIAvbUCAL69AgC/tQIAsGkCALFpAgCyQQIAs0ECALQ5AgC1OQIAthECALcRAgCGoAwAh0wNAInFAICNxQCA76QGAJHFAICVxQCA78wHAOOUAQDhpAYA4TgBAONcBgCZxQCAncUAgKHFAIClxQCAqcUAgK3FAICzLQQAscUAgLVFAwC1xQCAucUAgLZFAwC9xQCAwcUAgLvlAgC65QIAvd0CALzdAgC/tQIAvrUCABXFAICFxQCAxcUAgMnFAIDNxQCA0cUAgNXFAIDZxQCAqDEOAKk5DgCqAQ4AqwEOAKxxDgCtcQ4ArnUOAK9tDgCwGQ4AsSUOALItDgCzJQ4AtCEOALUhDgC2IQ4AtyEOALjFDgC5zQ4AusUOALvdDgC8xQ4Avc0OAL5ZDwC/WQ8As6kOAN3FAIDhxQCA5cUAgOnFAIC20Q4AtdkOAO3FAIC7wQ4Auv0OAPHFAIC+LAAAv8UOAL7FDgC90Q4AvNkOAIJpAACj7Q4AgFkAAIFRAACmlQ4A9cUAgPnFAIClnQ4AqrkOAKuFDgCGyAAAh6wAAK6BDgCvgQ4ArJ0OAK2VDgD9xQCAs5EOAAHGAIAFxgCAtqUOAAnGAIANxgCAta0OALrhDgC74Q4AEcYAgBXGAIC+6Q4Av9UOALz1DgC96Q4Ao6UKABnGAIAdxgCAIcYAgCXGAICmzQ0Apc0NACnGAICrbQwAqm0MAC3GAIAxxgCArz0MAK49DACtVQwArFUMAKgJDgCpCQ4Aqh0OAKsVDgCsIQ4ArSEOAK4hDgCvIQ4ANcYAgDnGAIA9xgCAQcYAgEXGAIBJxgCATcYAgFHGAIC4zQEAudUBALrdAQC71QEAvM0BAL1RAQC+UQEAv1EBALAhDgCxIQ4AsiUOALM5DgC0KQ4AtRUOALYdDgC39QEAVcYAgFnGAIBdxgCAo5kNAGHGAIClpQ0Apq0NAL7cAgCE7AMAacYAgKrpDQCr6Q0ArP0NAK3hDQCu4Q0Ar90NAIBFAACBTQAAglkAAKNFAwBtxgCApUEDAKZBAwBxxgCAhsAEAIcAAwCqLQMAqyUDAKw9AwCtJQMAriUDAK8VAwCoWQIAqYUDAKqBAwCrgQMArIUDAK2NAwCusQMAr7EDAHXGAIB5xgCAfcYAgIHGAICFxgCAicYAgI3GAICRxgCAuGUDALltAwC6ZQMAu30DALxlAwC9bQMAvmUDAL/dAACwpQMAsa0DALKlAwCzvQMAtK0DALWdAwC2lQMAt10DALMJAgCVxgCAmcYAgJ3GAIChxgCAtg0CALUNAgClxgCAu2kCALphAgCpxgCArcYAgL9ZAgC+aQIAvWkCALxxAgCxxgCAtcYAgLnGAIC9xgCA4aABAMHGAIDjaAMAxcYAgIEVAACAFQAA74wDAIIVAADJxgCAzcYAgNHGAIC+cAUA4RgOAOGUDwDjOA8A49QPAISUAgDZxgCA3cYAgOHGAIDlxgCA6cYAgO3GAIDxxgCA9cYAgPnGAIDv7AEA7/gPAIZgBACHBAUAs5UBAITMBQC1dQEA/cYAgAHHAIC2dQEABccAgAnHAIC7UQEAulkBAL31AAC8SQEAv/UAAL71AACoJQYAqVUGAKpVBgCrrQYArLUGAK29BgCutQYAr60GANXGAIANxwCAEccAgBXHAIAZxwCAHccAgCHHAIAlxwCAuGkHALlpBwC6CQcAuwkHALwZBwC9GQcAvg0HAL8BBwCw1QYAsd0GALLVBgCzaQcAtHkHALV5BwC2aQcAt2EHAKPdBgApxwCALccAgDHHAIA1xwCApj0GAKU9BgA5xwCAqxkGAKoRBgA9xwCAQccAgK+9BwCuvQcArb0HAKwBBgCAXQAAgW0AAIJlAACzUQcAvtgDALVxBwC2cQcARccAgIbgAACHFAMAul0HALs5BwC8KQcAvRUHAL4dBwC/2QAAqJUGAKmdBgCqlQYAq60GAKy1BgCtvQYArrUGAK+tBgBJxwCATccAgFHHAIBVxwCAWccAgF3HAIBhxwCAZccAgLhxAQC5cQEAunEBALtxAQC81QEAvd0BAL7VAQC/zQEAsNUGALGxBgCysQYAs40GALSVBgC1UQEAtlEBALdRAQBpxwCAoxkGAG3HAIBxxwCApjkGAGXGAIB1xwCApTkGAKoVBgCrcQYAeccAgH3HAICuVQYAr5EBAKxhBgCtXQYAgccAgIXHAICJxwCAjccAgJHHAICVxwCAmccAgJ3HAIChxwCApccAgKnHAICtxwCAgBkAAIEZAACCBQAAsccAgISAAgC+gAMAhwwDAIasHADhaAYAuccAgOOYBwC9xwCAwccAgMXHAIDvrAcAyccAgM3HAIDRxwCA1ccAgNnHAIDdxwCA4ccAgOXHAICzZQMA6ccAgLVlAwC2bQMA7ccAgPHHAID1xwCAuukDALvlAwC8/QMAve0DAL7RAwC/0QMA+ccAgP3HAIAByACABcgAgAnIAIANyACAEcgAgBXIAICogQMAqYEDAKqBAwCrgQMArIEDAK2BAwCugQMAr4EDALBBAwCxTQMAskUDALNVAwC0eQMAtXkDALYZAwC3GQMAuCkDALkpAwC6OQMAuzkDALwpAwC9KQMAvhkDAL8ZAwCBGQAAgBEAAKMhAgCCLQAApSECABnIAIAdyACApikCACHIAIApyACAq6ECAKqtAgCtqQIArLkCAK+VAgCulQIAhEwCAL5IHQCHZB0AhuwcAONAAwAtyACA4aABADHIAIDvnAMANcgAgDnIAIA9yACAQcgAgEXIAIBJyACATcgAgFHIAIBVyACAWcgAgF3IAIBhyACAZcgAgGnIAIDvtAEAhKgdAOF8BgBtyACA43AGAHHIAIB1yACAecgAgH3IAICz4QEAgcgAgIXIAICJyACAjcgAgLblAQC19QEAkcgAgLuhAQC62QEAvuQcAJnIAIC/rQEAvqUBAL2xAQC8uQEAqBUeAKkZHgCqKR4AqykeAKw9HgCtJR4Ari0eAK8lHgAlyACAgvkfAIH5HwCA4R8AlcgAgJ3IAICGHAAAh7ADALjBHgC5wR4AusEeALvBHgC8wR4AvcEeAL7BHgC/wR4AsF0eALElHgCyLR4AsyUeALQhHgC1KR4AthkeALcZHgCjoR4AocgAgKXIAICpyACArcgAgKalHgCltR4AscgAgKvhHgCqmR4AtcgAgLnIAICv7R4AruUeAK3xHgCs+R4AvcgAgLOZHwDByACAxcgAgLa9HwDJyACAzcgAgLW1HwC6mR8Au5kfANHIAIDVyACAvnkfAL95HwC8eR8AvXkfAKglHgCpUR4AqlUeAKtpHgCseR4ArXkeAK5pHgCvaR4A2cgAgN3IAIDhyACA5cgAgOnIAIDtyACA8cgAgPXIAIC42R4Aue0eALr5HgC7+R4AvOkeAL3pHgC+nR4Av5UeALAZHgCxGR4AsukeALPpHgC0+R4AtfkeALbpHgC36R4Ao90eAIIpAACBFQAAgB0AAPnIAICm+R4ApfEeAP3IAICr3R4Aqt0eALXHAIAByQCArz0eAK49HgCtPR4ArD0eAITIAgCzQQEAvgwBAAnJAIC2QQEADckAgBHJAIC1UQEAuk0BALslAQCGSAAAh1ABAL4lAQC/LQEAvDEBAL0xAQAVyQCAGckAgIQEAwC+gAQAHckAgO+oHwAhyQCAJckAgL8oMQDjdB8AKckAgOE4HgAtyQCAMckAgDXJAIA5yQCAPckAgEHJAICjzQIARckAgKXdAgBJyQCATckAgKbNAgBRyQCAVckAgKupAgCqwQIArb0CAKy9AgCvoQIArqkCAKm1AgCoaR0AqwECAKoJAgCtAQIArBkCAK8xAgCuAQIAhGwFAFnJAIBdyQCAYckAgGXJAICCnQEAgZ0BAICdAQC55QMAuOUDALvlAwC65QMAveUDALzlAwC/5QMAvuUDALEhAgCwSQIAsyUCALIlAgC1KQIAtCECALcVAgC2FQIAqM0CAKnRAgCq0QIAqw0BAKwVAQCtBQEArgEBAK8BAQBpyQCAbckAgHHJAIB5yQCAvvgEAH3JAICByQCAhckAgLgVAQC5HQEAuikBALspAQC89QEAvf0BAL71AQC/7QEAsEkBALFVAQCyXQEAs1UBALRNAQC1NQEAtj0BALcxAQCGoAUAh8gFAInJAIDvvAAAjckAgJHJAICVyQCA74weAIQsBwDh8B4AmckAgOMcHgCdyQCA4ZQBAKHJAIDjbAAAsxkCAKXJAICpyQCArckAgIQACAC2xQEAtd0BALHJAIC70QEAus0BALXJAIC5yQCAv7EBAL7JAQC9wQEAvMkBAKPZBQB1yQCAvckAgMHJAIDFyQCApgUGAKUdBgDJyQCAqxEGAKoNBgDNyQCA0ckAgK9xBgCuCQYArQEGAKwJBgDVyQCAgh0AAIEdAACAHQAA2ckAgN3JAIDhyQCA5ckAgIZAAwCHxAMA6ckAgO3JAIDxyQCA9ckAgPnJAID9yQCAqK0HAKmxBwCqsQcAq7EHAKwZBwCtBQcArg0HAK8FBwABygCABcoAgAnKAIANygCAEcoAgBXKAIAZygCAHcoAgLgtBwC5zQAAusUAALvdAAC8zQAAvf0AAL71AAC/nQAAsEkHALFVBwCyUQcAsykHALQ5BwC1OQcAtiUHALcVBwCzOQYAIcoAgCXKAIApygCALcoAgLaFBgC1kQYAMcoAgLuRBgC6jQYANcoAgDnKAIC//QYAvv0GAL39BgC8hQYAPcoAgKN9BgBBygCARcoAgKbBBgBJygCATcoAgKXVBgCqyQYAq9UGAFHKAIC+bAEArrkGAK+5BgCswQYArbkGAKjpAQCp6QEAqvkBAKv5AQCs6QEArekBAK45AQCvOQEAgPUAAIH9AACCwQAAVcoAgIYQAACHdAEAWcoAgAXJAIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+kQAAv5EAALBJAQCxSQEAslkBALNZAQC0SQEAtUkBALb9AAC39QAA7/QGAF3KAIBhygCAZcoAgO8wAgBpygCAbcoAgHHKAIDj4AcAdcoAgOGAAQB5ygCA4ygGAH3KAIDhyAUAgcoAgLMxAgCFygCAicoAgJYAAACNygCAtikCALUhAgCRygCAu80CALrNAgCVygCAmcoAgL/NAgC+zQIAvc0CALzNAgCdygCAocoAgKXKAICj/QIAqcoAgKXtAgCm5QIArcoAgLHKAIC1ygCAqgECAKsBAgCsAQIArQECAK4BAgCvAQIAgA0AAIEVAACCHQAAucoAgL3KAIDBygCAvlQMAMnKAICGwAwAhyQDAM3KAIDRygCA1coAgNnKAIDdygCA4coAgKi5AgCpAQEAqgEBAKsBAQCsBQEArQ0BAK4FAQCvOQEAhKgNAOXKAIDpygCA7coAgPHKAID1ygCA+coAgP3KAIC4LQEAucUBALrNAQC7xQEAvMEBAL3JAQC++QEAv/kBALBNAQCxUQEAslUBALMpAQC0OQEAtSUBALYlAQC3FQEA4RgGAAHLAIDjOAcABcsAgAnLAIC+WAwADcsAgBHLAICEbA8AFcsAgL5gDwAZywCAHcsAgCHLAIDvcAYAJcsAgIAVAACBGQAAgi0AAITMDwDjYAYAKcsAgOGgAQAtywCA73QAADHLAICGyAwAh/wMADnLAIA9ywCAQcsAgEXLAICjCQ4AxcoAgDXLAIBJywCATcsAgKYNDgClDQ4AUcsAgKsVDgCqCQ4AVcsAgFnLAICvYQ4Arn0OAK19DgCsAQ4AXcsAgLOpDgBhywCAZcsAgLapDgBpywCAbcsAgLWpDgC6SQ8Au0kPAHHLAIB1ywCAvkkPAL9JDwC8SQ8AvUkPAKhdDgCpbQ4AqmUOAKt9DgCsZQ4ArW0OAK5lDgCvuQ8AecsAgH3LAICBywCAhcsAgInLAICNywCAkcsAgJXLAIC4UQ8AuV0PALpVDwC7aQ8AvH0PAL1lDwC+bQ8Av2EPALDJDwCxyQ8AstkPALPZDwC0yQ8AtckPALZ9DwC3cQ8AmcsAgLURDwC2EQ8AncsAgIARAACBGQAAgikAALMVDwC8HQ8AvWEPAL5hDwC/fQ8AocsAgKXLAIC6FQ8AuwkPAKOtDwCpywCAhugAAIfIAQCtywCApq0PAKWtDwCxywCAq00OAKpNDgC1ywCAucsAgK9NDgCuTQ4ArU0OAKxNDgCocQ4AqXEOAKpxDgCrcQ4ArJ0BAK2FAQCuhQEAr7UBAL7sAAC9ywCAwcsAgMXLAIDJywCAzcsAgNHLAIDVywCAuGEBALlhAQC6YQEAu2EBALxhAQC9YQEAvmEBAL9hAQCwzQEAsaUBALKhAQCzoQEAtKUBALWtAQC2kQEAt5EBALP5DQDZywCA3csAgOHLAIDlywCAtgUCALUVAgDpywCAu2ECALoJAgDtywCA8csAgL9pAgC+YQIAvXUCALx1AgD1ywCAo70NAPnLAID9ywCApkECAAHMAIAFzACApVECAKpNAgCrJQIACcwAgA3MAICuJQIAry0CAKwxAgCtMQIAge0AAIDtAADv0AEAgh0AABHMAIAZzACAhjgEAIdQAwAdzACAIcwAgCXMAIApzACA4eABAC3MAIDjZA8AMcwAgDXMAIA5zACAPcwAgLORAwBBzACAtbkDALZ9AwBFzACAScwAgE3MAIC6WQMAu1kDALxJAwC9SQMAvv0AAL/1AACoRQIAqVUCAKpVAgCrZQIArH0CAK2xAgCusQIAr7ECAL5oBQBRzACAVcwAgFnMAIBdzACAYcwAgGXMAIBpzACAuF0BALltAQC6ZQEAuw0BALwZAQC9GQEAvg0BAL8FAQCw0QIAsdECALLRAgCz0QIAtHUBALV9AQC2dQEAt20BAOF4DwDjNA4A47gOAOF8DgBtzACAccwAgHXMAIB5zACAfcwAgIHMAICJzACAjcwAgJHMAIDv5A4A79QOAJXMAICjnQIAgmEAAIFpAACAUQAAhJwFAKZxAgCltQIAmcwAgKtVAgCqVQIAhkgEAIfMBACv+QEArvEBAK1FAgCsRQIAqJUGAKmlBgCqrQYAq6UGAKy9BgCtoQYArqUGAK/dBgCFzACAncwAgKHMAIClzACAqcwAgK3MAICxzACAtcwAgLhtBwC5dQcAun0HALt1BwC8bQcAvcUHAL7NBwC/xQcAsKUGALGtBgCyuQYAs7EGALSRBgC1kQYAtl0HALdVBwCzJQYAucwAgL3MAIDBzACAxcwAgLYhBgC1NQYAycwAgLtpBgC6YQYAzcwAgNHMAIC/VQYAvlUGAL1lBgC8bQYA1cwAgKNhBgDZzACA3cwAgKZlBgDhzACA5cwAgKVxBgCqJQYAqy0GAOnMAIDtzACArhEGAK8RBgCsKQYArSEGAKipBgCpqQYAqrkGAKuxBgCszQYArTEBAK4xAQCvMQEAgMkBAIHJAQCCBQAA8cwAgL54AgCEeAIA9cwAgPnMAIC43QEAue0BALrlAQC7jQEAvJkBAL2ZAQC+jQEAv4UBALBRAQCxUQEAslEBALNRAQC09QEAtf0BALb1AQC37QEAszEGAP3MAICGKAAAh9wBAAHNAIC2sQEAtUUGAAXNAIC7lQEAupUBAAnNAIANzQCAvzkBAL4xAQC9hQEAvIUBABXMAICjdQYAEc0AgBXNAICm9QEAGc0AgB3NAIClAQYAqtEBAKvRAQAhzQCAJc0AgK51AQCvfQEArMEBAK3BAQApzQCALc0AgDHNAIA1zQCAOc0AgD3NAIBBzQCARc0AgEnNAIBNzQCAUc0AgFXNAIBZzQCAXc0AgGHNAIC+cAMAhQA8AOHEBgCERAIA44wHAIBhAACBYQAAgmEAAO9oAwCFRDwA4RACAGnNAIDj2CsAhlA9AIf0AwBtzQCA76QHAHHNAIDvQAIAdc0AgHnNAIB9zQCAgc0AgIXNAICJzQCAhDw8AI3NAICRzQCAlc0AgJnNAIDj7AIAnc0AgOEsAQCzUQMAoc0AgKXNAICpzQCArc0AgLZ5AwC1cQMAsc0AgLs5AwC6MQMAtc0AgLnNAIC/9QAAvvUAAL0VAwC8FQMAqD0CAKmBAgCqmQIAq5ECAKy5AgCtuQIArtECAK/RAgCEqD8Avqg/AL3NAIDBzQCAxc0AgMnNAIDNzQCA0c0AgLhRAQC5UQEAulEBALtRAQC8cQEAvXEBAL5xAQC/cQEAsLUCALG9AgCygQIAs4ECALRxAQC1cQEAtnEBALdxAQCAtQAAgb0AAIK1AADZzQCAhrA/AIfgPADdzQCA71QAAL4sPgDhVAYA4c0AgOOIAADlzQCA6c0AgO3NAIDxzQCAo1ECAPXNAIC/2CYA+c0AgP3NAICmeQIApXECAAHOAICrOQIAqjECAAXOAIAJzgCAr/UBAK71AQCtFQIArBUCAJAtJACRBSgAkg0oAJPZKACUhS0AlTUsAJbFLACXtTEAmAEwAJkVMACalTUAmyk0AJxtNACdmTUAnj04AJ81OABlzQCAttU+ALXFPgDVzQCAs9E+AA3OAIARzgCAFc4AgL/ZPgC+1T4AvcU+ALzFPgC71T4Auuk+ABnOAICPXSQAqeUJAKgVCACrBQwAqg0MAK0BEACsAQwAr0EQAK69EACh4QAAHc4AgKMBBACi4QAApZ0EAKSVBACnuQgApgEIAKD1OQChBT0Aouk8AKP1PQAhzgCAJc4AgCnOAIAtzgCAscEUALABFACzARgAsn0UALXVGAC01RgAMc4AgDXOAICCISUAgyklADnOAIA9zgCAhsUpAIeBLACEGSkAhRkpAIoBLQCL+S0AQc4AgEnOAICOATEAj4k0AIyRMACNHTEAkkU1AJMZNQCG6AcAh+wBAJZZOQCXYTgAlPU0AJVZOQCaoTwAm0U9AE3OAIBRzgCAgX0AAIB9AACcQTwAglUAAKjpPwCp/T8Aqgk/AKsFPwCsHT8ArQU/AK4NPwCvBT8AVc4AgFnOAIBdzgCAYc4AgGXOAIBpzgCAbc4AgHHOAIC4DT8AuRU/ALoVPwC7JT8AvD0/AL39PgC+9T4Av+0+ALB9PwCxQT8AskE/ALNBPwC0QT8AtU0/ALY9PwC3NT8Ao4E8AHXOAIB5zgCAfc4AgIHOAICmhTwApZU8AIXOAICrhTwAqrk8AInOAICNzgCAr4k8AK6FPACtlTwArJU8AITIAwCz7T0Akc4AgJXOAIC26T0Amc4AgJ3OAIC16T0Auq09ALu1PQChzgCApc4AgL6dPQC/IQIAvKU9AL2VPQCoDT0AqR09AKohPQCrPT0ArCU9AK0tPQCuJT0Ar1k9AIANAACBFQAAgh0AAKnOAICtzgCAsc4AgLnOAIC+uAMAuLkCALlhAgC6GQIAuxkCALwJAgC9CQIAviECAL8hAgCwLT0AsTU9ALI1PQCzBT0AtB09ALWhAgC2oQIAt6ECAKOpPAC9zgCAhigFAIfsAgDBzgCApq08AKWtPADFzgCAq/E8AKrpPADJzgCAzc4AgK9lAwCu2TwArdE8AKzhPADRzgCAsykCANXOAIDZzgCAtvkCAN3OAIDhzgCAtfkCALrVAgC73QIA5c4AgOnOAIC+eQEAv3kBALzFAgC9eQEA7c4AgPHOAICj5QIA9c4AgKU1AgD5zgCA/c4AgKY1AgABzwCABc8AgKsRAgCqGQIArbUBAKwJAgCvtQEArrUBAOPwPgDhrD8A4UA+AON8PwAJzwCADc8AgBHPAIAVzwCAgA0AAIERAACCEQAAGc8AgO+oPgAdzwCAIc8AgO8gPgCoLQUAqW0FAKplBQCrrQUArLUFAK29BQCutQUAr60FALXOAICE6AMAvuADACXPAICGEAMAh5gDACnPAIAtzwCAuGkGALlpBgC6AQYAuwEGALwFBgC9DQYAvjEGAL8xBgCw1QUAsd0FALLVBQCzaQYAtHkGALV5BgC2aQYAt2EGAKg5BgCpgQcAqpkHAKuRBwCsuQcArbkHAK7ZBwCv1QcAMc8AgDXPAIBFzgCAOc8AgD3PAIBBzwCARc8AgEnPAIC4VQcAuV0HALppBwC7aQcAvAEHAL0BBwC+AQcAvwEHALCtBwCxsQcAsrEHALOFBwC0nQcAtXUHALZ9BwC3cQcAsxEGAE3PAIBRzwCAVc8AgFnPAIC2OQYAtTEGAF3PAIC7dQYAumkGAGHPAIBlzwCAv7EGAL5ZBgC9UQYAvGUGAGnPAICjVQYAbc8AgHHPAICmfQYAdc8AgHnPAICldQYAqi0GAKsxBgB9zwCAgc8AgK4dBgCv9QYArCEGAK0VBgCouQEAqbkBAKopAQCrKQEArD0BAK0lAQCuLQEAryUBAIXPAICCHQAAgR0AAIAdAACJzwCAjc8AgJHPAIC+cAEAuIEAALmNAAC6hQAAu5kAALyJAAC9vQAAvrUAAL99AACwXQEAseEAALLhAACz4QAAtOEAALXpAAC20QAAt9EAAITIAgCzpQIAhzgDAIYoAgC2oQIAmc8AgJ3PAIC1sQIAup0CALshAwC+bAMAoc8AgL4hAwC/KQMAvDEDAL0xAwCj4QIApc8AgKnPAICtzwCAsc8AgKblAgCl9QIAtc8AgKtlAwCq2QIAuc8AgL3PAICvbQMArmUDAK11AwCsdQMAqZkAAKiRAACrzQAAqqEAAK3dAACs3QAAr8UAAK7NAAC+LA0Awc8AgMXPAIDJzwCAzc8AgNHPAIDVzwCA2c8AgLnBAQC4eQAAu8EBALrJAQC9wQEAvNkBAL/FAQC+xQEAsY0AALCNAACzQQAAskkAALVBAAC0WQAAt0EAALZJAADdzwCA4c8AgOXPAIDpzwCA7c8AgO9QBwDxzwCA9c8AgL74DwDjdAcA+c8AgOF8BACAGQAAgQkAAIJ5AAD9zwCAAdAAgLNpAQAJ0ACAhMQCALYdAQAN0ACAEdAAgLUVAQC6CQEAuwkBAIboDQCH6A0Avt0BAL/FAQC83QEAvdUBABXQAIAZ0ACAHdAAgCHQAIDv1AAAJdAAgCnQAIDvTAEA47ADAOG0BgDhgAEA45gBAC3QAIAx0ACANdAAgDnQAIA90ACAQdAAgKPlAQCEwA0ApZkBAEXQAIBJ0ACAppEBAE3QAIBR0ACAq4UBAKqFAQCtWQEArFEBAK9JAQCuUQEABdAAgFXQAIBZ0ACAXdAAgGHQAIBl0ACAadAAgG3QAICoaQ8AqXEPAKpxDwCrrQ8ArLUPAK29DwCutQ8Ar6kPALDZDwCx9Q8Asv0PALP1DwC07Q8AtZUPALadDwC3iQ8AuLkPALmFDwC6jQ8Au2kAALx5AAC9eQAAvmkAAL9pAACBnQAAgJ0AAHHQAICCBQAAddAAgHnQAIB90ACAgdAAgIaAAwCH9AMAhdAAgInQAICN0ACAkdAAgJXQAICVzwCAs5kPAJnQAICd0ACAodAAgKXQAIC2XQ8AtV0PAKnQAIC7UQ8Aun0PAK3QAICx0ACAvzEPAL5JDwC9QQ8AvEkPAKNZDgC10ACAudAAgL3QAIDB0ACApp0OAKWdDgDF0ACAq5EOAKq9DgDJ0ACAzdAAgK/xDgCuiQ4ArYEOAKyJDgDR0ACA1dAAgNnQAIDd0ACAgBkAAIEZAACCBQAA4dAAgISgAQDl0ACAh+gBAIYABADp0ACA7dAAgPHQAID10ACAqBUBAKkdAQCqFQEAqyUBAKw9AQCtJQEAri0BAK8lAQD50ACA/dAAgAHRAIAF0QCACdEAgA3RAIAR0QCAFdEAgLjJAAC5yQAAutkAALvRAAC8+QAAvfkAAL6ZAAC/mQAAsCUBALEtAQCyJQEAsz0BALQtAQC1HQEAthUBALf5AAAZ0QCAHdEAgCHRAICzkQIAJdEAgLW5AgC2qQIAKdEAgC3RAIAx0QCAuu0CALvlAgC8/QIAveUCAL7lAgC/1QIApvECADXRAIA50QCApeECAD3RAICjyQIAQdEAgEXRAICuvQIAr40CAKylAgCtvQIAqrUCAKu9AgBJ0QCATdEAgID5AACB+QAAggUAAFHRAIC+yAMAhBgDAFnRAIBd0QCAYdEAgGXRAIBp0QCAbdEAgHHRAIB10QCAhhgEAIecAwB50QCAfdEAgIHRAICF0QCAidEAgI3RAIDvsAIAkdEAgOGUAQCV0QCA42wCAJnRAICd0QCAodEAgKXRAICp0QCA79APAK3RAICx0QCAtdEAgLnRAIDhrAEAvdEAgONsAACAMQAAgT0AAIIdAADv9A4A42wOAMHRAIDhLA8AvnAFALM5AgCEDAUAhugEAIdgBQDcAAAAtvECALX5AgDJ0QCAu9UCALrVAgDN0QCA0dEAgL91AQC+dQEAvcUCALzFAgDV0QCA4fQOANnRAIDjUA4A3dEAgOHRAIDl0QCA6dEAgO3RAIDx0QCA9dEAgPnRAID90QCAAdIAgAXSAIDv5A8ApmUCAAnSAIAN0gCApW0CABHSAICjrQIAFdIAgBnSAICu4QEAr+EBAKxRAgCtUQIAqkECAKtBAgAd0gCAIdIAgKiZBgCpmQYAqqkGAKupBgCsuQYArbkGAK6pBgCvqQYAJdIAgIIdAACBHQAAgB0AACnSAIAt0gCAMdIAgL50AwC4rQYAubUGALq9BgC7tQYAvK0GAL1RBwC+UQcAv1EHALChBgCxoQYAsqEGALOhBgC0oQYAtaEGALalBgC3mQYAVdEAgLMlBgCExAMAxdEAgLY9BgA10gCAOdIAgLU1BgC6YQYAu2EGAIYIAACHiAAAvmEGAL9hBgC8cQYAvXEGAKNhBgA90gCAQdIAgEXSAIBJ0gCApnkGAKVxBgBN0gCAqyUGAKolBgBR0gCAVdIAgK8lBgCuJQYArTUGAKw1BgCoXQYAqW0GAKplBgCrjQYArJkGAK2FBgCujQYAr4UGAFnSAIBd0gCAYdIAgGXSAIBp0gCAbdIAgHHSAIB10gCAuIUGALmNBgC6mQYAu5UGALyNBgC9rQYAvqUGAL99AQCw/QYAscUGALLNBgCzxQYAtN0GALXFBgC2zQYAt8UGALPtBgB50gCAfdIAgIHSAICF0gCAtgUGALURBgCJ0gCAuwEGALo5BgCN0gCAkdIAgL8BBgC+GQYAvREGALwZBgCV0gCAo6kGAJnSAICd0gCApkEGAKHSAICElAEApVUGAKp9BgCrRQYAvqABAKnSAICuXQYAr0UGAKxdBgCtVQYAqJkCAKnBAgCqwQIAq8ECAKzBAgCtyQIArvECAK/xAgCB7QMAgO0DAK3SAICC+QMAhpAcAId0AwCx0gCAtdIAgLjFAwC5zQMAusUDALvdAwC8zQMAvf0DAL71AwC/nQMAsEEDALFBAwCyQQMAs0EDALRBAwC1QQMAtkEDALdBAwCzSQIAudIAgL3SAIDB0gCAxdIAgLZJAgC1SQIAydIAgLuFAwC6hQMAzdIAgNHSAIC/hQMAvoUDAL2VAwC8lQMA1dIAgKMNAgDZ0gCA3dIAgKYNAgDh0gCA5dIAgKUNAgCqwQMAq8EDAOnSAIDt0gCArsEDAK/BAwCs0QMArdEDAOOYAQDhpAcA4VgGAONYBgDhoAEA8dIAgOPQAAD10gCA+dIAgP3SAIDvOAAAAdMAgO/0AQAF0wCACdMAgO/4BgCAeQAAgRUAAIIdAACEAB0ADdMAgBHTAIC+EB0AGdMAgIbAHACHrB0AHdMAgCHTAIAl0wCAKdMAgC3TAIAx0wCAu8UFALqhBQC5qQUAuJEFAL/NBQC+zQUAvckFALzVBQCzHQYAsh0GALEdBgCwHQYAt6EFALa9BQC1vQUAtL0FAKu9BgCqvQYAqb0GAKi9BgCvfQYArn0GAK19BgCsfQYANdMAgDnTAIA90wCAQdMAgEXTAIBJ0wCATdMAgFHTAICo7R0AqS0eAKoxHgCrMR4ArJUeAK2dHgCulR4Ar40eABXTAIBV0wCAWdMAgF3TAIBh0wCAZdMAgGnTAIBt0wCAuKkeALmpHgC6XR8Au1EfALxxHwC9cR8AvnUfAL9pHwCw/R4Asc0eALLFHgCzrR4AtLkeALW5HgC2rR4At6UeALO5HgBx0wCAddMAgHnTAICl0gCAth0eALUdHgB90wCAuwkeALo5HgCB0wCAhOADAL99HgC+fR4AvXkeALwRHgCCaQAAo/0eAIBFAACBUQAAplkeAL6cAwCF0wCApVkeAKp9HgCrTR4AhkgAAIdsAACuOR4ArzkeAKxVHgCtPR4AqF0eAKltHgCqZR4Aq30eAKxlHgCtbR4ArmUeAK/9HgCJ0wCAjdMAgJHTAICV0wCAmdMAgJ3TAICh0wCApdMAgLhpAQC5aQEAunkBALt5AQC8aQEAvWkBAL7dAQC/1QEAsIUeALGNHgCyhR4As50eALSFHgC1jR4AtoUeALdZAQCz7R4AqdMAgK3TAICx0wCAtdMAgLbtHgC17R4AudMAgLtJHgC6QR4AvdMAgMHTAIC/SR4AvkEeAL1JHgC8UR4AxdMAgKOpHgDJ0wCAzdMAgKapHgDR0wCA1dMAgKWpHgCqBR4Aqw0eANnTAIDd0wCArgUeAK8NHgCsFR4ArQ0eAKghAwCpIQMAqiEDAKshAwCsIQMArSEDAK4hAwCvIQMA4dMAgOXTAIDp0wCAvmACAO3TAIDx0wCA+dMAgP3TAIC4iQMAuYkDALqdAwC7lQMAvLkDAL25AwC+eQAAv3kAALDlAwCx7QMAsuUDALP9AwC07QMAtd0DALbVAwC3vQMAgKkAAIG1AACCvQAAs6UDAAHUAIC1pQMAtq0DAAXUAICE4AIACdQAgLotAwC7JQMAvD0DAL0lAwC+JQMAvxUDAKPpAwAN1ACAhmgEAIeAAwAR1ACApuEDAKXpAwAV1ACAq2kDAKphAwAZ1ACAHdQAgK9ZAwCuaQMArWkDAKxxAwAh1ACAJdQAgCnUAIAt1ACAMdQAgOE8HwA11ACA40AeADnUAIA91ACAQdQAgO+MHgBF1ACASdQAgE3UAIBR1ACAVdQAgIIlAACBEQAAgB0AAFnUAIDj5AMAXdQAgOGsAQBh1ACA77ADAIRkAgC+YAUAhtAEAIdEBQBp1ACAbdQAgHHUAIB11ACAedQAgH3UAICB1ACAhdQAgInUAIDvsAEAhKQFAOHcHgCN1ACA4xABAJHUAICV1ACAmdQAgJ3UAICzUQEAodQAgKXUAICp1ACArdQAgLYRAQC1fQEAsdQAgLsNAQC6DQEAtdQAgLnUAIC//QAAvv0AAL39AAC8/QAAqDkGAKk5BgCqmQYAq5EGAKy1BgCt0QYArskGAK/BBgBl1ACAvdQAgMHUAIDF1ACAgA0AAIGxAACCsQAAydQAgLhhBwC5YQcAumEHALt9BwC8ZQcAvW0HAL5lBwC/HQcAsIkGALGJBgCyaQcAs2kHALR5BwC1eQcAtmkHALdlBwCjEQYAzdQAgNHUAIC+gAMA1dQAgKZRBgClPQYA2dQAgKtNBgCqTQYAhggAAId8AwCvvQcArr0HAK29BwCsvQcA3dQAgOHUAICzSQcA5dQAgLVZBwDp1ACA7dQAgLZRBwDx1ACA9dMAgLtBBwC6dQcAvUUHALxFBwC/RQcAvkUHAKh5BgCpeQYAqokGAKuJBgCsmQYArZkGAK6JBgCviQYA9dQAgPnUAID91ACAAdUAgAXVAIAJ1QCADdUAgBHVAIC4jQYAuZUGALqVBgC7pQYAvL0GAL1xAQC+cQEAv3EBALD5BgCxzQYAstkGALPZBgC0yQYAtckGALa9BgC3tQYAowEGABXVAIAZ1QCAHdUAgCHVAICmGQYApREGACXVAICrCQYAqj0GACnVAIAt1QCArw0GAK4NBgCtDQYArA0GADHVAIA11QCAOdUAgD3VAICAGQAAgRkAAIIFAABB1QCAhKwBAL6sAQCH6AAAhkwPAEnVAIBN1QCAUdUAgFXVAIConQIAqcUCAKrNAgCrwQIArMUCAK3NAgCu+QIArz0DAFnVAIBd1QCAYdUAgGXVAIC+PAwAadUAgG3VAIBx1QCAuMkDALnJAwC62QMAu9EDALz5AwC9+QMAvpkDAL+ZAwCwRQMAsU0DALJFAwCzXQMAtEUDALVNAwC2RQMAt/kDALNFAgB11QCAedUAgH3VAICB1QCAtk0CALVNAgCF1QCAu4kDALqBAwCJ1QCAjdUAgL+JAwC+gQMAvYkDALyRAwCR1QCAowECAJXVAICZ1QCApgkCAJ3VAICh1QCApQkCAKrFAwCrzQMApdUAgKnVAICuxQMAr80DAKzVAwCtzQMAgO0BAIEVAACCEQAAhAACAK3VAIDhpAEAsdUAgOPsAAC51QCAvdUAgMHVAIDvMAAAxdUAgMnVAIDN1QCA0dUAgIbgDACH9AIA1dUAgNnVAIDd1QCA4dUAgO/MBgDl1QCA4bAHAOnVAIDjEAYA7dUAgPHVAID11QCA+dUAgP3VAIAB1gCABdYAgAnWAIAN1gCAEdYAgBXWAIAZ1gCA7+gBAIUYDwDhzAYAHdYAgOMcBgCAKQAAgR0AAIIFAAAh1gCAszkCAITMDQCGaA8Ah/wMAOHQ0gO28QEAtfkBACnWAIC72QEAutEBAL7kDAAt1gCAv30BAL59AQC9fQEAvMEBAKjxDQCp8Q0AqvENAKvxDQCsMQ4ArTEOAK4xDgCvMQ4AtdUAgCXWAIAx1gCANdYAgDnWAIA91gCAQdYAgEXWAIC46Q4AuekOALqJDgC7hQ4AvJ0OAL2BDgC+gQ4Av7UOALBVDgCxXQ4AslUOALPpDgC0+Q4AtfkOALbpDgC34Q4Ao3kNAEnWAIBN1gCAUdYAgFXWAICmsQ4ApbkOAFnWAICrmQ4AqpEOAF3WAIBh1gCArz0OAK49DgCtPQ4ArIEOAGXWAICz7Q8AadYAgG3WAIC26Q8AcdYAgHXWAIC16Q8Auq0PALu1DwBF1QCAedYAgL6VDwC/mQ8AvK0PAL2hDwCoIQ4AqSEOAKohDgCrPQ4ArCUOAK0tDgCuJQ4Ar1UOAH3WAICB1gCAhdYAgInWAICAHQAAgQkAAIK9AACN1gCAuDkOALk5DgC6yQ4Au8kOALzZDgC92Q4AvskOAL/JDgCwLQ4AsTUOALI9DgCzMQ4AtBUOALUZDgC2CQ4AtwkOAKOpDgCR1gCAhIACAL6AAQCFAAQApq0OAKWtDgCZ1gCAq/EOAKrpDgCGKAcAhxgAAK/dDgCu0Q4AreUOAKzpDgCd1gCAs+0BAKHWAICl1gCAtuUBAKnWAICt1gCAte0BALplAQC7bQEAsdYAgLXWAIC+bQEAv10BALx1AQC9bQEAqN0NAKnpDQCqIQIAqyECAKwhAgCtIQIAriECAK8hAgC51gCAvdYAgMHWAIDF1gCAohECAKMRAgCgqQ4AodUCALiJAgC5iQIAup0CALuVAgC8vQIAvXUDAL59AwC/dQMAsOUCALHtAgCy5QIAs/0CALTtAgC13QIAttUCALe9AgCjqQIAj8UaAMnWAIDN1gCA0dYAgKahAgClqQIA1dYAgKspAgCqIQIA2dYAgN3WAICvGQIArikCAK0pAgCsMQIAniUOAJ/lDgCc6QoAnRUKAJpFFgCbRQoAmFkWAJlRFgCWcRIAl4ETAJRVEgCV7RIAktEeAJPZHgCQtRoAkVUeAISpHwCFJR8AhiUfAIexEwDh1gCA5dYAgIJZGwCDURsAjEUSAI2lFwCOpRcAj7kXAIA5+wHp1gCAijkTAIutEwCUmQsAlaEPAJZpDwCX3Q8A7dYAgO+cDwCSyQsAk30LAJxFAwDjeA4A8dYAgOGYDAD11gCAhHgCAJqRAwCbXQMA4QQAAL6IBQDj3OoD+dYAgP3WAIAB1wCA7+wAAO+MDgDhcA4A4fwOAOMwAADjeA4AgSEAAIA5AADvtO0DgikAALMJAgAJ1wCAhmgEAIcsBQAN1wCAtg0CALUNAgAR1wCAu8UBALrFAQAV1wCAGdcAgL99AQC+fQEAvdUBALzVAQCV1gCABdcAgB3XAIAh1wCAJdcAgCnXAIAt1wCAMdcAgKi9BQCp5QUAquEFAKvhBQCs5QUAre0FAK7RBQCv0QUAsGEGALFhBgCyYQYAs2EGALTZBgC12QYAtskGALfBBgC4yQYAuckGALp5BwC7eQcAvEUHAL0lBwC+EQcAvw0HAKNJBQA11wCAOdcAgD3XAIBB1wCApk0FAKVNBQBF1wCAq4UGAKqFBgBJ1wCATdcAgK89BgCuPQYArZUGAKyVBgBR1wCAVdcAgFnXAIBd1wCAYdcAgGXXAIBp1wCAbdcAgIA5AACBOQAAggUAAHHXAIC+uAMAhLgDAHnXAIB91wCAqMUGAKnVBgCq1QYAq+UGAKz9BgCtHQEArhUBAK8NAQB11wCAgdcAgIaIAQCHHAEAhdcAgInXAICN1wCAkdcAgLjpAQC56QEAuokBALuJAQC8mQEAvZkBAL6JAQC/iQEAsHUBALF9AQCydQEAs+kBALT5AQC1+QEAtukBALfhAQCzXQYAldcAgJnXAICd1wCAhLwBALadAQC1dQYAodcAgLu5AQC6sQEApdcAgKnXAIC/PQEAvj0BAL09AQC8oQEArdcAgKMZBgCx1wCAtdcAgKbZAQC51wCAvdcAgKUxBgCq9QEAq/0BAMHXAIDF1wCArnkBAK95AQCs5QEArXkBAKj5AgCp+QIAqi0DAKs9AwCsJQMArS0DAK4lAwCvmQMAydcAgM3XAIDR1wCA1dcAgIANAACBsQAAgrEAANnXAIC4lQMAuZ0DALqhAwC7oQMAvHEAAL1xAAC+cQAAv3EAALDpAwCx6QMAsvUDALPFAwC03QMAtbUDALaxAwC3sQMAvswDAN3XAIDh1wCA6dcAgO3XAIDx1wCA9dcAgO/kAgD51wCA4ZQBAP3XAIDjLAEAAdgAgAXYAICHGAMAhhz8A7tNAwC6TQMACdgAgA3YAIC/EQMAvnkDAL1xAwC8QQMAs8UDAITo/AMR2ACAFdgAgBnYAIC2zQMAtc0DAB3YAICkAfwDpSX/A6bZ/wOnAfgDIdgAgKEVAwCiHQMAoz0CAKwR9wOtAfADri3zA68B8wOoEfsDqZn7A6oB9AOrHfcDtAHoA7Vl6wO+xPwDhMT8A7AB7AOxVe8Dsk3vA7Nx7gMl2ACAKdgAgC3YAIAx2ACANdgAgDnYAIA92ACAQdgAgOFQBgDhNAQA42wBAOPoBgBF2ACASdgAgE3YAIBR2ACAgDUAAIE9AACCNQAAWdgAgF3YAIBh2ACA77ABAO/ABgCj5QIAZdgAgIbo/AOHfP0DadgAgKbtAgCl7QIAbdgAgKttAgCqbQIAcdgAgHXYAICvMQIArlkCAK1RAgCsYQIAqI3+A6mV/gOqnf4Dq5X+A6yx/gOtvf4Drqn+A6+p/gNV2ACAedgAgH3YAICB2ACAhdgAgInYAICN2ACAkdgAgLgl/wO5Lf8DuiX/A7s9/wO8Jf8DvS3/A74l/wO/zf8DsKn+A7Gp/gOygf4Ds4H+A7SB/gO1if4Dtmn/A7cd/wOV2ACA4SD8A5nYAIDjePwDndgAgKHYAICl2ACAqdgAgK3YAICx2ACAtdgAgLnYAICAHQAAgXEAAIJxAADvDP0Ds1X+A73YAIDB2ACAvkAAAMXYAIC2ff4DtXn+A8nYAIC7Lf4Dui3+A4boAACHrAAAvw3+A74F/gO9Ff4DvBX+A6OV/wPN2ACA0dgAgNXYAIDZ2ACApr3/A6W5/wPd2ACAq+3/A6rt/wPh2ACA5dgAgK/N/wOuxf8DrdX/A6zV/wPp2ACAs/H+A+3YAIDx2ACAto3+A/XYAID52ACAtY3+A7pFAQC7TQEA/dgAgAHZAIC+RQEAv00BALxVAQC9TQEAqC3+A6k1/gOqPf4Dq0n+A6xB/gOtSf4DrnH+A69x/gMF2QCACdkAgA3ZAIAR2QCAFdkAgBnZAIAd2QCAIdkAgLhJAQC5VQEAul0BALtVAQC8TQEAvXUBAL59AQC/dQEAsMUBALHNAQCyxQEAs90BALTFAQC1zQEAtsUBALd9AQCjtf0DJdkAgCnZAICExAMALdkAgKbJ/QOlyf0DMdkAgKsJAgCqAQIAOdkAgL7sAgCvCQIArgECAK0JAgCsEQIAgEkAAIFVAACCVQAAo0UDAD3ZAIClRQMApkUDAEHZAICGwAQAhxQDAKopAwCrJQMArD0DAK0hAwCuIQMArxUDAEXZAIBJ2QCATdkAgFHZAIBV2QCAWdkAgF3ZAIBh2QCAqH0CAKmhAwCqoQMAq6EDAKyhAwCtqQMArpEDAK+RAwCwgQMAsY0DALKFAwCzmQMAtIkDALW9AwC2tQMAt30DALhFAwC5TQMAukUDALtdAwC8RQMAvU0DAL5FAwC/+QAA5dcAgLMNAgBl2QCAadkAgLYNAgBt2QCAcdkAgLUNAgC6YQIAu20CAHXZAIB52QCAvmkCAL9dAgC8dQIAvWkCAH3ZAICB2QCAhdkAgInZAICN2QCA4aQBAJHZAIDjQAMAldkAgJnZAICd2QCA77gDAIAVAACBHQAAggUAAKHZAICEgAIAvsgFAIcYBQCGLAQAqdkAgK3ZAICx2QCA76gBALXZAIDhdP4DudkAgOPw/gO92QCAwdkAgMXZAIDJ2QCAzdkAgNHZAIDV2QCAs5EBANnZAIC1UQEAtlEBAN3ZAIDh2QCA5dkAgLp9AQC7dQEAvG0BAL39AAC+9QAAv+kAAKgpBgCpVQYAqlUGAKuNBgCslQYArZ0GAK6VBgCvjQYApdkAgOnZAIDt2QCA8dkAgPXZAID52QCA/dkAgAHaAIC4bQcAuQUHALoNBwC7BQcAvB0HAL0FBwC+AQcAvz0HALD1BgCx/QYAsvUGALNlBwC0fQcAtWEHALZhBwC3VQcA4xAFAAXaAIDh8AQACdoAgIAdAACBCQAAgjkAAA3aAIAR2gCAhOgDAL7gAwAV2gCA78wFABnaAICHOAAAhhgAAKOdBgAd2gCAIdoAgCXaAIAp2gCApl0GAKVdBgAt2gCAq3kGAKpxBgAx2gCANdoAgK/lBwCu+QcArfEHAKxhBgCokQYAqZEGAKqRBgCrrQYArLkGAK2lBgCurQYAr6UGADnaAIA92gCAQdoAgEXaAIBJ2gCATdoAgFHaAIBV2gCAuGUBALltAQC6ZQEAu30BALxlAQC9bQEAvmUBAL/ZAQCw3QYAsaUGALKtBgCzpQYAtKEGALWpBgC2mQYAt5kGALMZBgBZ2gCAXdoAgGHaAIBl2gCAtiUGALUxBgBp2gCAu2EGALoZBgBt2gCAcdoAgL9tBgC+ZQYAvXEGALx5BgB12gCAo10GAHnaAIB92gCApmEGAIHaAICEmAEApXUGAKpdBgCrJQYAvqQBAInaAICuIQYArykGAKw9BgCtNQYAqcUCAKixAgCrxQIAqsUCAK3NAgCsxQIAr/UCAK71AgCN2gCAkdoAgJXaAICZ2gCAndoAgKHaAICl2gCAqdoAgLnJAwC4wQMAu9kDALrBAwC9+QMAvMkDAL+ZAwC+8QMAsUUDALBFAwCzRQMAskUDALVFAwC0RQMAt0UDALZFAwCASQMAgUkDAIJdAwCzRQIAvtwMALVFAgC2RQIArdoAgIYADACH5AMAuokDALuJAwC8mQMAvZkDAL6JAwC/iQMAowkCALHaAIC12gCAudoAgL3aAICmCQIApQkCAMHaAICrxQMAqsUDAMXaAIDJ2gCAr8UDAK7FAwCt1QMArNUDAM3aAIDR2gCA1doAgDXZAIDvAAAA2doAgN3aAIDh2gCA4+gAAOXaAIDhjAEA6doAgO3aAIDx2gCA+doAgP3aAICAbQAAgXUAAIJ9AACEQAIAhvAMAId4DQAB2wCABdsAgAnbAIAN2wCAEdsAgBXbAIAZ2wCAHdsAgCHbAIAl2wCAKdsAgC3bAIAx2wCANdsAgDnbAIA92wCAQdsAgO/MAQCE7AwA4TAGAEXbAIDjGAEASdsAgE3bAIBR2wCAVdsAgLPlAQBZ2wCAhIQPAF3bAIBh2wCAtuUBALX1AQBp2wCAu30BALrZAQC+oAwAbdsAgL8hAQC+OQEAvTEBALw5AQCo7Q0AqSUOAKotDgCrJQ4ArD0OAK0lDgCuLQ4AryUOAPXaAICC9Q8AgeUPAIDpDwBl2wCAcdsAgIaYAACHDAMAuK0OALlFDwC6TQ8Au0UPALxFDwC9TQ8AvkUPAL95DwCwXQ4AsfkOALKtDgCzpQ4AtL0OALWlDgC2pQ4At5UOAHXbAIDv7AwAedsAgH3bAICB2wCAhdsAgInbAICN2wCAvugAAJHbAICV2wCAmdsAgJ3bAIDj6A0AodsAgOEEDACj5Q4ApdsAgKnbAICt2wCAsdsAgKblDgCl9Q4AtdsAgKt9DgCq2Q4AudsAgL3bAICvIQ4ArjkOAK0xDgCsOQ4AqDkOAKk5DgCqUQ4Aq1EOAKxxDgCtcQ4ArnEOAK9xDgDB2wCAxdsAgMnbAIDN2wCAgBkAAIEZAACCBQAA0dsAgLjRDgC50Q4AutEOALvlDgC84Q4AveEOAL7hDgC/4Q4AsBEOALERDgCyEQ4AsxEOALTxDgC18Q4AtvEOALfxDgCz2Q4A2dsAgIYoAACHuAAA3dsAgLbxDgC1+Q4A4dsAgLvVDgC61Q4A5dsAgOnbAIC/NQ4AvjUOAL3FDgC8xQ4A7dsAgKOdDgDx2wCA9dsAgKa1DgD52wCA/dsAgKW9DgCqkQ4Aq5EOAAHcAIAF3ACArnEOAK9xDgCsgQ4ArYEOAKjdDQCp6Q0Aqj0CAKuNAgCsmQIArZkCAK6JAgCviQIAvqwEAAncAIAN3ACAhCADABHcAIAV3ACAGdwAgB3cAIC4iQIAuYkCALqZAgC7kQIAvLkCAL25AgC+eQMAv3kDALD5AgCx+QIAss0CALPFAgC03QIAtcUCALbBAgC3uQIAs7UCACHcAIAl3ACAKdwAgC3cAIC2GQIAtRECADHcAIC7PQIAuj0CADXcAIA53ACAvwECAL4ZAgC9EQIAvBkCAD3cAICj8QIAQdwAgEncAICmXQIATdwAgFHcAIClVQIAqnkCAKt5AgCGSAUAh6wEAK5dAgCvRQIArF0CAK1VAgCohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAFXcAIBZ3ACAXdwAgGHcAICB8QEAgJkBAIXaAICC9QEAuHkBALl5AQC6zQEAu8UBALzdAQC9xQEAvsUBAL/1AQCwtQIAsb0CALKBAgCzgQIAtFUBALVdAQC2SQEAt0kBAGXcAIBp3ACAbdwAgO/UAQCEEAUAcdwAgHXcAIDvjA4AvuwFAOHsDgB53ACA4xwOAH3cAIDhlAEAgdwAgONkDgCzXQIAhdwAgIncAICN3ACAkdwAgLYVAgC1dQIAldwAgLs5AgC6MQIAmdwAgJ3cAIC/2QEAvtEBAL0VAgC8FQIAo50FAEXcAICh3ACApdwAgKncAICm1QUApbUFAK3cAICr+QUAqvEFALHcAIC13ACArxkGAK4RBgCt1QUArNUFAIBRAACBWQAAgmEAALOVBgC53ACAtXEHALZxBwC93ACAhkADAIdUAwC67QcAu+UHALzlBwC97QcAvtEHAL/NBwDB3ACAxdwAgMncAIDN3ACA0dwAgNXcAIDvQAQA2dwAgOEwBwDd3ACA45QEAOHcAIDl3ACA6dwAgO3cAIDx3ACAoxkGAPXcAID53ACA/dwAgAHdAICm/QcApf0HAAXdAICraQcAqmEHAAndAIAN3QCAr0EHAK5dBwCtYQcArGkHAKjNBwCp0QcAqtEHAKstBgCsNQYArT0GAK41BgCvnQYAEd0AgBXdAIAZ3QCAHd0AgIAZAACBGQAAggUAACHdAIC4iQYAuYkGALqZBgC7kQYAvLkGAL25BgC+UQEAv1EBALDlBgCx7QYAsv0GALP1BgC02QYAtcUGALbBBgC3uQYAqNEBAKnZAQCqCQEAqwkBAKwZAQCtGQEArgkBAK8JAQCEYAEAvnwBAIeoAACGjAEAKd0AgC3dAIAx3QCANd0AgLgJAQC5CQEAuhkBALsRAQC8OQEAvTkBAL75AAC/+QAAsH0BALFBAQCyRQEAs10BALRFAQC1TQEAtkUBALc5AQA53QCAPd0AgEHdAICzjQIARd0AgLWdAgC2lQIASd0AgE3dAIBR3QCAurUCALuJAgC8nQIAvYUCAL6NAgC/hQIAps0CAFXdAIBZ3QCApcUCAF3dAICj1QIAYd0AgGXdAICu1QIAr90CAKzFAgCt3QIAqu0CAKvRAgCE9AMAad0AgKgxAwCpMQMAqjEDAKsxAwCskQAArZEAAK6RAACvjQAAbd0AgHHdAIB13QCAed0AgH3dAICB3QCAhd0AgIndAIC4vQAAuWUAALptAAC7ZQAAvH0AAL1lAAC+bQAAv2UAALD9AACxxQAAss0AALOpAAC0uQAAtaUAALahAAC3oQAAgL0BAIEJAACCGQAAjd0AgJHdAIC+WAIAhxQdAIacHQCEbB0A1dsAgJndAICd3QCAvrwcAKHdAICl3QCAqd0AgLP5AgCt3QCAsd0AgLXdAIC53QCAtlEBALVZAQC+3B8Au0EBALp5AQC93QCAwd0AgL8hAQC+PQEAvT0BALxZAQDhcAcAxd0AgOMIBgDJ3QCA78wAAM3dAIDR3QCA1d0AgOMQAADZ3QCA4dABAN3dAICGkBwAh/QcAO/gBgDh3QCAo3kCAOXdAIDp3QCA7d0AgPHdAICm0QEApdkBAPXdAICrwQEAqvkBAPndAID93QCAr6EBAK69AQCtvQEArNkBAJXdAICCFQAAgeUfAIDlHwAB3gCABd4AgAneAIAN3gCAqAkfAKkJHwCqHR8AqxUfAKwNHwCtcR8ArnEfAK9xHwCwER8AsS0fALIlHwCzyR8AtN0fALXBHwC2wR8At8EfALjFHwC5yR8AutUfALupHwC8uR8AvbkfAL6pHwC/oR8As7UfABHeAIAV3gCAGd4AgB3eAIC20R8AtaUfACHeAIC7yR8AuvUfACXeAIAp3gCAvyUfAL45HwC9PR8AvNEfAC3eAIAx3gCANd4AgDneAIA93gCA4WAfAEHeAIDjtBwARd4AgEneAIBN3gCA7wAdAFHeAIBV3gCAWd4AgF3eAICjNR4AYd4AgGXeAIBp3gCAbd4AgKZRHgClJR4Acd4AgKtJHgCqdR4AhKgCAHXeAICvpR4ArrkeAK29HgCsUR4AgE0AAIFVAACCVQAAs8kBAHneAIC12QEAtskBAH3eAICGoAAAhwQBALrFAQC7rQEAvLUBAL29AQC+tQEAv60BAKiZAQCpmQEAqg0BAKsFAQCsHQEArQUBAK4FAQCvNQEAgd4AgIXeAICJ3gCAjd4AgJHeAICV3gCAmd4AgJ3eAIC4JQEAuS0BALo5AQC7OQEAvCkBAL0pAQC+3QAAv9UAALBNAQCxJQEAsi0BALMlAQC0PQEAtSUBALYhAQC3HQEAod4AgKXeAICp3gCAo4kCAK3eAIClmQIApokCALHeAIC13gCAud4AgKqFAgCr7QIArPUCAK39AgCu9QIAr+0CAL3eAIDB3gCAxd4AgIRAAgDJ3gCAzd4AgNHeAIDV3gCAgA0AAIEVAACCHQAA2d4AgN3eAIDh3gCAh7QDAIbcBAC+zAMA6d4AgO3eAIDx3gCA7+gCAPXeAID53gCA/d4AgOP8AgAB3wCA4dABAAXfAIAJ3wCADd8AgBHfAIAV3wCAs2EDABnfAIAd3wCAId8AgCXfAIC2eQMAtXEDACnfAIC7XQMAul0DAC3fAIAx3wCAv+EAAL79AAC9/QAAvP0AALC5AgCxuQIAsgkBALMJAQC0GQEAtQUBALYFAQC3PQEAuAUBALllAQC6bQEAu2UBALxhAQC9YQEAvmEBAL9hAQCFXAcANd8AgDnfAIA93wCAJd0AgEHfAIBF3wCASd8AgKgxAgCpOQIAqskCAKvJAgCs2QIArdkCAK7JAgCvyQIAhMwFAOGAHgBN3wCA47weAOE4HgBR3wCA46AAAL4QBABZ3wCAXd8AgO8MHgBh3wCAZd8AgGnfAIBt3wCA73QeAKNhAgCCUQAAgUEAAICRAABx3wCApnkCAKVxAgB13wCAq10CAKpdAgCGyAQAhzwFAK/hAQCu/QEArf0BAKz9AQCohQYAqY0GAKqFBgCrmQYArIkGAK2JBgCuvQYAr7EGAFXfAIB53wCAfd8AgIHfAICF3wCAid8AgI3fAICR3wCAuJ0GALmtBgC6pQYAuwkHALwZBwC9GQcAvg0HAL8FBwCw0QYAsdEGALLRBgCz0QYAtLUGALW9BgC2tQYAt60GALMNBgCV3wCAmd8AgJ3fAICh3wCAtgkGALUBBgCl3wCAuxUGALoVBgCp3wCArd8AgL95BgC+cQYAvQUGALwFBgCx3wCA4aAEALXfAIDjXAUAgA0AAIE1AACCPQAAud8AgL3fAIDB3wCAhGADAL5sAAC/8AEAhZAAAMXfAIDvmAUAo40HAIQIAACGAAwAh4wAAMnfAICmiQcApYEHAM3fAICrlQcAqpUHANHfAIDV3wCAr/kHAK7xBwCthQcArIUHANnfAICz6QYA3d8AgOHfAIC26QYA5d8AgOnfAIC16QYAukUBALtNAQDt3wCA8d8AgL5FAQC/TQEAvFUBAL1NAQCoIQYAqSEGAKolBgCrPQYArCUGAK0tBgCuSQYAr0EGAPXfAID53wCA/d8AgAHgAIAF4ACACeAAgA3gAIAR4ACAuEkBALlJAQC6WQEAu1EBALx5AQC9eQEAvhkBAL8VAQCwxQEAsc0BALLFAQCz3QEAtMUBALXNAQC2xQEAt3kBABXgAIAZ4ACAHeAAgKOhBQAh4ACApaEFAKahBQAl4ACAjyHqAyngAICqDQIAqwUCAKwdAgCtBQIArg0CAK8FAgCX7RIAlmUSAJVFEQCUnRYAk3EWAJJVFQCReesDkFnqA59hBgCeNQUAnUUaAJxpGgCbVRkAmkUeAJlZHgCYRR0A4WAAAC3gAIDjTD4AMeAAgKOxAgCi1QEAobUHAKCJBgCxATgAsAk+ALOVOgCyjToAtbUmALQBJADvaDoAvjAMAKnJNgCowTYAqwEwAKrhNwCtzTMArPUyAK/5PgCuATwAoRkCADngAICjbQ4Aom0OAKX1CgCkAQgAp4ULAKaZCgCGAA0Ah0QNAIIJ6wODCesDhDHqA4UVFACGORcAh80XAISgDQA94ACAiiUQAIsNEwCMnRMAjQ0cAI4ZHwCPDR8A5d4AgO8AAwCSbRgAk0kbAJR9GwCVBQQAllkHAJdJBwBB4ACAReAAgJpFBgCbLQAAnFEDAONgAABJ4ACA4WwAAIClAQCBAQEAggUBAL4ADABN4ACAUeAAgFXgAIDviAEAWeAAgOFUBgBd4ACA41QBAGHgAIBl4ACAaeAAgG3gAICz6QIAceAAgHXgAIB54ACAfeAAgLadAgC1mQIAgeAAgLuJAgC6vQIAheAAgIngAIC/WQIAvlECAL1ZAgC8kQIAoykNAI3gAICR4ACAleAAgJngAICmXQ0ApVkNAJ3gAICrSQ0Aqn0NAKHgAICp4ACAr5kNAK6RDQCtmQ0ArFENAIBRAACBWQAAgmEAALMtDwCt4ACAtS0PALbJDwCx4ACAhkADAIcIAwC6yQ8Au8UPALzBDwC9wQ8AvsEPAL/BDwA14ACApeAAgLXgAIC54ACAveAAgMHgAIDF4ACAyeAAgKhFDgCpgQ8AqskPAKvJDwCsyQ8ArSUPAK4tDwCvJQ8AsGEPALFtDwCyeQ8As3kPALRpDwC1aQ8Ath0PALcVDwC4LQ8AuTUPALo1DwC7BQ8AvB0PAL3xAAC+8QAAv/EAAKNhDgDN4ACAhMQBANHgAIDV4ACApoUOAKVhDgDZ4ACAq4kOAKqFDgDd4ACA4eAAgK+NDgCujQ4ArY0OAKyNDgDl4ACA6eAAgO3gAIDx4ACA9eAAgPngAID94ACAAeEAgAXhAICCHQAAgR0AAIAdAAAJ4QCADeEAgBHhAIC+tAEAqK0BAKnVAQCq1QEAqwUBAKwdAQCtBQEArg0BAK8FAQCGgAEAhxgBABnhAIAd4QCAIeEAgCXhAIAp4QCALeEAgLiFAAC5jQAAuoUAALudAAC8hQAAvY0AAL6FAAC/vQAAsH0BALHhAACy5QAAs/0AALTtAAC13QAAttUAALe9AACzXQIAMeEAgDXhAIA54QCAPeEAgLaFAgC1lQIAQeEAgLslAwC6uQIAReEAgEnhAIC/GQMAvikDAL0pAwC8MQMAvswEAKMZAgBN4QCAUeEAgKbBAgBV4QCAWeEAgKXRAgCq/QIAq2EDAF3hAIBh4QCArm0DAK9dAwCsdQMArW0DAKgpAwCpKQMAqjkDAKs5AwCsKQMArSkDAK6dAACvlQAAZeEAgGnhAIBt4QCAceEAgHXhAICCqQEAga0BAICtAQC4mQAAua0AALqlAAC7bQAAvHUAAL19AAC+dQAAv20AALDtAACx9QAAsvUAALPFAAC03QAAtb0AALa1AAC3qQAA4XgBAOEcDgDjEAAA4zwOAHnhAIB94QCAvhQEAIHhAICErAIAieEAgId4BQCGDAUAjeEAgJHhAIDvvAAA70gOALPxAgCV4QCAmeEAgJ3hAICh4QCAtukCALXhAgCl4QCAu3EBALppAQCp4QCAhKAEAL85AQC+WQEAvVEBALxhAQCt4QCAhIwEALHhAICEADgAteEAgLnhAIC94QCAweEAgKqJDgCriQ4AqLkOAKmxDgCu/Q4Ar+EOAKz5DgCt9Q4Asq0OALNlDgCwkQ4AsaUOALZ9DgC3ZQ4AtH0OALV1DgC6XQ4Au+UNALhdDgC5VQ4AvuENAL/pDQC8/Q0AvfUNAKOxBQCF4QCAxeEAgMnhAIDN4QCApqkFAKWhBQDR4QCAqzEGAKopBgDV4QCA2eEAgK95BgCuGQYArREGAKwhBgDd4QCA4eEAgOXhAIDp4QCAgB0AAIEJAACCOQAA7eEAgPHhAID14QCAhsgAAIcMAwD54QCA/eEAgAHiAIAF4gCAqKUHAKm1BwCqvQcAq8kHAKzZBwCt2QcArskHAK/BBwC+oAAACeIAgA3iAIAR4gCAFeIAgBniAIAd4gCAIeIAgLjNAAC51QAAutUAALvlAAC8/QAAvZUAAL6dAAC/lQAAsIkHALFlBwCyYQcAs30HALRlBwC1bQcAtmUHALf1AACzNQYAJeIAgCniAIAt4gCAMeIAgLZZBgC1UQYANeIAgLuhBgC6TQYAOeIAgD3iAIC/qQYAvqEGAL2pBgC8tQYAQeIAgEXiAIDv8AUASeIAgE3iAIBR4gCAVeIAgFniAICAPQAAgQkAAIIdAABd4gCA4cgGAGHiAIDjSAQAZeIAgKO1BgBp4gCAhigAAIdAAQBt4gCAptkGAKXRBgBx4gCAqyEGAKrNBgB14gCAeeIAgK8pBgCuIQYArSkGAKw1BgB94gCAs70BAIHiAICF4gCAtnkBAIniAICN4gCAtXkBALpVAQC7XQEAkeIAgJXiAIC++QAAv/kAALxFAQC9+QAAqHECAKlxAgCqcQIAq3ECAKy1AgCtvQIArrUCAK+tAgC+rDwAmeIAgJ3iAICh4gCApeIAgKniAICt4gCAseIAgLhpAwC5aQMAugkDALsJAwC8HQMAvQUDAL4NAwC/BQMAsNUCALHdAgCy1QIAs2kDALR5AwC1eQMAtmkDALdhAwC14gCAueIAgL3iAICj9QIAweIAgKUxAgCmMQIAxeIAgMniAIDN4gCAqh0CAKsVAgCsDQIArbEDAK6xAwCvsQMA7xgCAIIVAACBbQAAgG0AANHiAIDZ4gCAhvg8AIcYAwDd4gCA4eIAgOXiAIDp4gCA42wHABXhAIDhaAEA7eIAgKiFAgCplQIAqpUCAKulAgCsvQIArdUCAK7RAgCv0QIA8eIAgPXiAID54gCA/eIAgAHjAIAF4wCACeMAgA3jAIC4dQEAuX0BALp1AQC7zQEAvNUBAL3dAQC+yQEAv8EBALC1AgCxvQIAsoECALOBAgC0VQEAtV0BALZVAQC3TQEA4bQGABHjAIDj9AYAFeMAgIQYPQAZ4wCAHeMAgCHjAIAl4wCAKeMAgC3jAIAx4wCANeMAgDnjAIDvWAYAPeMAgIF9AACAcQAAQeMAgIIFAABJ4wCATeMAgO+AAQC+VDwA4ZABAFHjAIDjfAYAVeMAgFnjAIBd4wCAhtg8AIf0PACjnT0A1eIAgEXjAIBh4wCAZeMAgKbVPQCltT0AaeMAgKv5PQCq8T0AbeMAgHHjAICvGT4ArhE+AK3VPQCs1T0AdeMAgLOhPgB54wCAfeMAgLatPgCB4wCAheMAgLWxPgC6ST8Au0k/AInjAICN4wCAvkk/AL9JPwC8ST8AvUk/AKhVPgCpZT4Aqm0+AKtlPgCsfT4ArWk+AK65PwCvuT8AkeMAgJXjAICZ4wCAneMAgKHjAICl4wCAqeMAgK3jAIC4VT8AuV0/ALpVPwC7bT8AvHU/AL19PwC+dT8Av20/ALDJPwCxyT8Astk/ALPZPwC0yT8Atck/ALZ9PwC3cT8AghUAAKPhPwCAsQEAgbEBAKbtPwCx4wCAvtABAKXxPwCqCT4Aqwk+AITkAQC14wCArgk+AK8JPgCsCT4ArQk+ALPdPAC54wCAhugAAIfMAQC94wCAtpU8ALX1PADB4wCAu7k8ALqxPADF4wCAyeMAgL9ZPwC+UT8AvZU8ALyVPACoUT4AqVE+AKptPgCrYT4ArGE+AK1hPgCulQEAr40BAISgAQDN4wCA0eMAgNXjAIDZ4wCA3eMAgOHjAIDl4wCAuKkBALmpAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9pAQCw/QEAsc0BALLFAQCzrQEAtLkBALW5AQC2rQEAt6UBALPlPQDp4wCA7eMAgPHjAID14wCAtuE9ALXpPQD54wCAuwkCALo5AgD94wCAAeQAgL99AgC+fQIAvXkCALwRAgAF5ACAo6E9AAnkAIAN5ACApqU9ABHkAIAV5ACApa09AKp9AgCrTQIAGeQAgB3kAICuOQIArzkCAKxVAgCtPQIAgOkAAIHpAACCHQAAvsADAO/kAgAh5ACAh1QDAIY8BADjEAEAKeQAgOH4AQAt5ACAMeQAgDXkAIA55ACAPeQAgEHkAIBF5ACASeQAgLORAwBN5ACAtbkDALZ9AwBR5ACAVeQAgFnkAIC6WQMAu1kDALxJAwC9SQMAvv0AAL/1AACoRQIAqVUCAKpVAgCrZQIArH0CAK2xAgCusQIAr7ECAIRsBQBd5ACAYeQAgGXkAIBp5ACAbeQAgL5wBQBx5ACAuF0BALltAQC6ZQEAuw0BALwZAQC9GQEAvg0BAL8FAQCw0QIAsdECALLRAgCz0QIAtHUBALV9AQC2dQEAt20BAOFAPwDjvAAA4wg+AOFsPgB15ACAeeQAgH3kAICB5ACAheQAgInkAICN5ACAkeQAgL5sBwDvVAAA75w+AJnkAICjnQIAgmkAAIFhAACAaQAAneQAgKZxAgCltQIAoeQAgKtVAgCqVQIAhsgEAIfsBACv+QEArvEBAK1FAgCsRQIAqKUGAKmpBgCquQYAq7kGAKypBgCtqQYArtkGAK/ZBgCV5ACApeQAgKnkAICt5ACAseQAgLXkAIC55ACAveQAgLhxBwC5cQcAunUHALvdBwC8xQcAvc0HAL7FBwC//QcAsKkGALG1BgCytQYAs40GALSVBgC1UQcAtlEHALdRBwCzMQYAweQAgMXkAIDJ5ACAzeQAgLYpBgC1IQYA0eQAgLtxBgC6bQYA1eQAgNnkAIC/lQcAvlEGAL1ZBgC8YQYA3eQAgKN1BgDh5ACA5eQAgKZtBgDp5ACA7eQAgKVlBgCqKQYAqzUGAPHkAID15ACArhUGAK/RBwCsJQYArR0GAIANAACBFQAAgh0AAPnkAID95ACAAeUAgITcAQAF5QCAhoAAAIcgAQAJ5QCADeUAgBHlAIAV5QCAGeUAgB3lAIAh5QCA43QEACXlAIDhyAUAKeUAgC3lAIAx5QCANeUAgDnlAIA95QCAQeUAgEXlAIBJ5QCA77QEAE3lAIBR5QCAqD0GAKlVBgCqVQYAq6kBAKy5AQCtuQEArqkBAK+pAQCErAEAVeUAgFnlAIBd5QCAYeUAgGXlAIBp5QCAbeUAgLhtAQC5BQEAugEBALsBAQC8BQEAvQ0BAL4xAQC/MQEAsNkBALHZAQCybQEAs2UBALR9AQC1ZQEAtmUBALdVAQCBvQMAgL0DALPVBQCCGQAAtTkCAHHlAIC+VAMAtjECAHnlAIB95QCAuxUCALoVAgC9uQIAvLECAL+pAgC+sQIAgeUAgKZpAgClYQIAhAAMAKONBQCF5QCAhvgMAId8AwCv8QIArukCAK3hAgCs6QIAq00CAKpNAgCJ5QCAjeUAgJHlAICV5QCAmeUAgJ3lAIDjIAEAoeUAgOGgAQCl5QCA70ACAKnlAICt5QCAseUAgLXlAIC55QCAveUAgMHlAICz8QMAxeUAgCXkAIDJ5QCAzeUAgLbpAwC14QMA0eUAgLu1AwC6tQMA1eUAgNnlAIC/lQMAvpUDAL2lAwC8pQMAqCkCAKkpAgCqOQIAqzkCAKwpAgCtKQIArlkCAK9VAgCAzQEAgQkAAIIZAADd5QCA4eUAgL58DQCHtA0AhhwMALgxAgC5PQIAujUCALvpAgC8+QIAvfkCAL7pAgC/6QIAsDECALExAgCyMQIAszECALQRAgC1EQIAthECALcRAgDp5QCA7eUAgPHlAID15QCA+eUAgP3lAIAB5gCA79QGAAXmAIDhVAYACeYAgOOkAACsDBUADeYAgBHmAIAV5gCAo/ECABnmAIAd5gCAIeYAgCXmAICm6QIApeECACnmAICrtQIAqrUCAC3mAIAx5gCAr5UCAK6VAgCtpQIArKUCAKghDgCpIQ4AqkkOAKtZDgCsaQ4ArWkOAK6ZDgCvmQ4A5eUAgDXmAIA55gCAPeYAgEHmAIBF5gCASeYAgE3mAIC49Q4Auf0OALr1DgC7iQ4AvJ0OAL2FDgC+hQ4Av7UOALDpDgCx6Q4Asv0OALPxDgC01Q4Atd0OALbVDgC3zQ4As8EOAIIVAACBtQAAgLUAAFHmAIC26Q4AteEOAL4QAAC7LQ4Aui0OAIRkAwBV5gCAvxkOAL4RDgC9JQ4AvCkOAFnmAICjhQ4AhogAAIdsAwCmrQ4AXeYAgGHmAIClpQ4AqmkOAKtpDgBl5gCAaeYAgK5VDgCvXQ4ArG0OAK1hDgCziQ4AbeYAgHHmAIB15gCAeeYAgLaBDgC1iQ4AfeYAgLuVDgC6jQ4AgeYAgIXmAIC/+Q4AvvEOAL2FDgC8hQ4AieYAgI3mAICR5gCAleYAgOMMDQCZ5gCA4RgNAJ3mAIDvrAwAoeYAgKXmAICp5gCAreYAgLHmAIC15gCAueYAgKgBDgCpAQ4AqgEOAKsBDgCsAQ4ArQEOAK4BDgCvPQ4AgN0AAIEJAACCGQAAveYAgMHmAICEPAEAvnQAAMnmAIC4HQ4AuS0OALolDgC76QEAvPkBAL35AQC+6QEAv+kBALBJDgCxUQ4AslEOALNRDgC0NQ4AtT0OALY1DgC3LQ4Ao4kNAM3mAICGrAQAhzwDANHmAICmgQ0ApYkNANXmAICrlQ0Aqo0NANnmAIDd5gCAr/kNAK7xDQCthQ0ArIUNAOHmAICznQIAhEgDAL5ABAC2VQMA5eYAgOnmAIC1sQIAunEDALt5AwDt5gCA8eYAgL4xAwC/MQMAvFEDAL1RAwCwkQMAsZkDALKhAwCzoQMAtNEDALXRAwC20QMAt9EDALj1AwC5+QMAus0DALvFAwC83QMAvcUDAL7NAwC/xQMA9eYAgPnmAID95gCAAecAgIV8GQAF5wCACecAgHXlAICoIQIAqTECAKoxAgCrBQIArB0CAK3xAwCu8QMAr/EDAA3nAIAR5wCAFecAgBnnAIDvUAAAHecAgCHnAIAl5wCA44QAACnnAIDh+AEALecAgIAVAACBGQAAggUAADHnAICjmQMAOecAgIZoBACHYAUAPecAgKZRAgCltQMAQecAgKt9AgCqdQIARecAgEnnAICvNQIArjUCAK1VAgCsVQIATecAgFHnAIBV5wCAWecAgF3nAIBh5wCAZecAgO/4AQC+bAQA4YAOAGnnAIDjFAEAbecAgHHnAIB15wCAeecAgH3nAICB5wCAhecAgLPdAQCJ5wCAtf0BALb1AQCN5wCAkecAgJXnAIC6sQEAu4UBALydAQC9NQEAvj0BAL81AQCpBQYAqLkFAKsVBgCqHQYArT0GAKw9BgCvTQYArl0GADXnAICCHQAAgR0AAIAdAACZ5wCAnecAgKHnAICl5wCAuUEHALidBgC7QQcAukkHAL1FBwC8WQcAv0UHAL5FBwCxCQYAsD0GALOpBgCyAQYAtbkGALSxBgC3rQYAtrEGAKORBgCEjAIAhigAAIfAAwCp5wCAprkGAKWxBgCt5wCAq8kGAKr9BgCx5wCAtecAgK95BgCucQYArXkGAKzRBgC55wCAs5kHAL3nAIDB5wCAtlEHAMXnAIDJ5wCAtbEHALptBwC7dQcAzecAgNHnAIC+WQcAv0UHALxtBwC9ZQcA1ecAgNnnAIDd5wCA4ecAgOXnAIDp5wCA7ecAgO+oBQDx5wCA4TQFAPXnAIDjdAUA+ecAgP3nAIAB6ACABegAgKMdBgCCLQAAgRUAAIAdAAAJ6ACAptUGAKU1BgAN6ACAq/EGAKrpBgAR6ACAhCgBAK/BBgCu3QYAreEGAKzpBgCoxQYAqdUGAKrVBgCr5QYArP0GAK0VBgCuHQYArxUGAL7sAQAZ6ACAhggAAIcgAAAd6ACAIegAgCXoAIAp6ACAuH0GALkFBgC6DQYAuwUGALwBBgC9CQYAvjkGAL85BgCwbQYAsXUGALJ9BgCzdQYAtFkGALVFBgC2TQYAt0UGAKiRAgCpmQIAqqECAKuhAgCs0QIArd0CAK7VAgCvyQIALegAgDHoAIA16ACAvyweADnoAIA96ACAQegAgEXoAIC4VQMAuV0DALppAwC7ZQMAvGEDAL1hAwC+YQMAv2EDALC5AgCxjQIAsoUCALNtAwC0dQMAtX0DALZ1AwC3bQMASegAgE3oAICzIQIAUegAgLVRAgCEiAMAVegAgLZVAgDF5gCAvigcALtBAgC6dQIAvbEDALxZAgC/sQMAvrkDAKNpAgBZ6ACAXegAgGHoAIBl6ACAph0CAKUZAgBp6ACAqwkCAKo9AgBt6ACAcegAgK/5AwCu8QMArfkDAKwRAgCopQIAqbUCAKq9AgCrtQIArK0CAK01AQCuPQEArzUBAL4sHAB16ACAeegAgH3oAICB6ACAiegAgIdoHQCGHB0AuIUBALmNAQC6hQEAu50BALyNAQC9vQEAvrUBAL95AACwUQEAsVEBALJRAQCzUQEAtPEBALXxAQC29QEAt+UBAO/YAACCtQAAgaUAAIClAACN6ACAkegAgJXoAIDvxAYAmegAgOH0BgCd6ACA4zgBAOPMAACh6ACA4SgBAKXoAICp6ACAtuUBALV1AgCEQBwAs2UCAK3oAICx6ACAtegAgL9lAQC+ZQEAvdUBALzVAQC7xQEAusUBALnoAIC96ACAo7UdAIXoAIDB6ACAxegAgMnoAICmNR4ApaUdAM3oAICrFR4AqhUeANHoAIDV6ACAr7UeAK61HgCtBR4ArAUeANnoAIDd6ACA4egAgOXoAICADQAAgTUAAII9AADp6ACA7egAgPHoAIC1BQAAdxoAgOG0AgCs2AIAtQUAAHsaAICotR8AqRUfAKodHwCrFR8ArDEfAK09HwCuLR8AryEfAOG0AgCs2AIAtQUAAH8aAIDhtAIArNgCALUFAACDGgCAuNEAALnZAAC64QAAu+EAALyRAAC9kQAAvpEAAL+RAACwIR8AsTEfALIxHwCzMR8AtAkfALUJHwC28QAAt/EAAOG0AgCs3AIA71QdALUdAACHGgCA4bwCAKzQAgC1KQAAoyUBAKKRAwChFR0AoA0dAOGAHgCLGgCA47wdAOHEAgCz1R4AtQkAAKzYAgCPGgCA4bwCALb9HgC1+R4ArOACALu1HgC6pR4AtQUAAJMaAIC/jR4Avo0eAL2lHgC8pR4AoxUeAOG8AgCs0AIAtREAAI9pJQCmPR4ApTkeAJcaAICrdR4AqmUeAOG0AgCseAEAr00eAK5NHgCtZR4ArGUeAJvdFACa5RUAmQEXAJjhEACfcR8AnnkZAJ35GQCcARsAk+UtAJIRLwCRbSkAkG0pAJf5EQCW8REAlYUsAJSZLQC1JQAA4ZQCAILxJgCDjSoAhJUqAIXhLACGHS4Ah3kuAKy0AgCbGgCAilUvAIspEgCMORIAjRkTAI7xFACPHRYAtQUAAJ8aAICSVRcAk5EYAJRxGgCV+RoAlvkcAJd9HgCC4AMAlgsAgJpVHgCb2QAAnHUCAIYMAIC2DACAuIkKAKwBBACthQYAroEGAMwQAgDMfAMAuQwAgKMaAIDFDACAyAwAgMsMAIADCwCAgaUyAr8MAIAV6ACAmpUGAJtVIwK8kQYAvbEAAL6RBgC/rQYAuOkGALmVBgC6kQYApxoAgLTBBgC1zQYAts0GALfdBgCw/QYAseUGALKdAACz5QYAhVTHA6saAICH/AAAuAEKALMaAIDsDACAtxoAgIzlDACNpAEAzPACAMQNAIDHDQCAiRQAALgZCgCLDAAAIA4AgFkOAIC8DACAwgwAgBwKAICRwAEAzgwAgLhtCgDRDACA1wwAgN0MAIDgDACA4wwAgLsaAIAuDQCA6QwAgL8aAIDhpB4AMQ0AgONUHgCvJXMAzCgCAPIMAIDvDACA9QwAgPgMAID7DACAzIACAJS4AwD+DACAkhQCAO9gHgCQAAIAAQ0AgA0NAIC48QoAEA0AgKILAIATDQCAiSkLABYNAICvGgCAvDABAL/EAQC+7AEAGQ0AgMzsAgC4xQoAukQBALAJAIAcDQCAygYAgN8GAIDyBgCAIg0AgPoGAIAlDQCACgcAgC0HAIAYBwCA+QcAgC8HAICvDQCAOgcAgLUNAIBKBwCAtXkAAGoHAIC3cSoCdQcAgLFhAAB3BwCAsw0pApAHAIC96QAAowcAgP0HAICwBwCAuRkrAsYHAIC7WRQCIggAgF0JAIA/CACANQ4AgF4IAIA5AACAhAgAgHEAAIDKCACAKwAAgCMJAIA9AACAXwkAgEMAAIBhCQCASAgAgG0IAIBJAACAAwgAgFMAAIB8CQCAWQAAgCgNAIBfAACAuw0iAtYNAIDMFDYCHwAAgL9lAAC+EQAAvW0AAOgHAICAaQEAgXUBAIJxAQCD3SEChGkHAIWBBwCGgQcAh3EBAIihAQCJrQEAirUHAIuNBwCMlQcAjaUBAE8AAICPpQEAkOEBAJHtBwCSsSECk/0HAJSNBwCVUQYAlvEBAJfZAQCY0QEAmXUGAJp9BgCb1QEAnGkGAJ2ZFAKeUQYAn1EGAKB1FAKhuQYAokkBAKOFLQKkIQEApS0BAKZ1FAKntQYAqKERAqlRFAKqlQYAtyEAgMy8NQLNPDUCbQAAgKoDAICsAwCArwMAgMMhAIDKIQCA4SEAgOghAIDJAACADwAAgLihBgC6BgCAtwYAgMwAAIDUIQCAtQMAgN0FAIAYBgCAugUCALvVAgC46QUAuf0FAL7JAgC/5RcCvA0CAL0BAgCy4QUAs+EFALCNBQCxnQUAtuUFALfpBQC09QUAte0FAKo9BQCrwQUAqD0FAKk1BQCuzQUAr/UFAKzNBQCtxQUAoj0FAKMFBQCg1QIAoTkFAKYdBQCnBQUApB0FAKUVBQC/BgCAm8EFAD4GAIBVBgCAnt0FAJ8xBACcUQIAndUFAHIGAICJBgCApAMAgDYiAIDbAACAoAMAgJIHAIDxBwCA9QcAgJMJAIAFCACACQgAgJkLAICXCQCAsgoAgHIHAICOBwCAmgcAgKUHAICtBwCArQkAgAEPAIAYDwCAJQ8AgMwEMwLNsDACzCAzAs3gMALMEDACzGgwAsxYMALNjDACzGgxAs0UMQLM1DECzRQ2AsxwIALN0CcCzDA2AswkMQLMDDwCzWg/AswYPwLNND8CzBg9As3AMgLMRDwCzBg5Asw4MgLNqDICzIgyAs34MwLMfDMCzUAzAswoMwLNCDMCzMghAs0kJgLMrCYCzEA4AsyYJQLNyDoCzBwkAs0QJALMhDsCzag7AsysJQLNvDoCzKw4Asz4JwLM4DgCzXQ4Ai0PAID2BgCAZw0AgI4NAIDNICoCzBwrAqoGAIAyIgCAzKQgAs2gJwLMOCYCygQAgMw4OgLNPDsCzBA5As1gPgLMoAMAvj0NAL3tLALWBACAu1UjAgcJAIC5PSICzwYAgNwHAIClBACApg0AgLIEAIBvBQCA9AYAgL4EAIB1BQCAr70MAK6ZLgKtpQwAwgUAgKvFIgIDBgCAxAQAgCMGAIDQBACAyAUAgCkGAIBdBgCAowEYAqAEAIAaBwCAHQcAgJ9dDACeUQwAnUUMACcHAICbWSECsgcAgLQHAIC3BwCAuwcAgCoHAIDRBwCA0wcAgJMtJgLWBwCAbwgAgHIIAICPBQwAjnEMAI1lDAB8CACAi0UgAmMJAICJNS8CZgkAgGoJAIB/CACAcwkAgHYJAIC9AwCABiIAgIFdDACAYQwAgAABAIEYAACCAAQACiIAgIQQBwCFFAYAhuQIAIc8AgCILAUAiaQFAIoAeAAOIgCAjJAGABIiAIAaIgCAFiIAgLgRAACRxHsAkkh6AJNMeQAiIgCAzOgCAJbwCQC4OQAAkMAJACoiAICS8AkAzPgCAJS0CQC4DQAALiIAgMwcAgC4BQAAOiIAgMzkAgC4HQAAPiIAgEIiAIBJIgCAYCIAgKiMCACp5HsAZyIAgKt8BADM5AIAuA0AAHEiAIDMlAIAdSIAgLGUewC4CQAAuBUAAMz8AgC15AgAeSIAgMzYAgB9IgCAuAUAALroBQC7hAQAvAB8AL30fwC++H0Av/xyAIAJOgKBDToCggE6AoMFOgKEGToChR06AoYROgKHFToCiCk6AoktOgKKIToCiyU6Aow5OgLMhAIAjjE6Ao81OgKJIgCAkekPAIUiAIDMzAIAuBEAAJUiAIDM3AIAl+UPALgpAAC4MQAAzPgCAJkiAIC4HQAAzDwCAJ0iAIDMLAIAzIQCAKEiAIClIgCAzIQCAKQtDwClVQ8Apl0PAKkiAICoqToCqa06ArjRAACtIgCAuDUAALEiAIDMUAMAr7U6AswsAwDMFAMA1SIAgLMFDwC0HQ8AzAADALYJDwC3CQ8Avmh9ALhtAAC4dQAAzEgDALwpDwDZIgCAviUPAMxQAwCH5Q4AzOg6ArilAQC4uQEAzPA1As2kMwLMgCICzXwlAs2UNgLMBCkCzew7AsxkOgK4+QEAuMEBAInVDgCI1Q4Al7EOALgNAAC1IgCAuB0AALkiAIC4DQAAvSIAgMEiAICfaTsC3SIAgOEiAIC4MQAAzMQCAMz4AgDFIgCAySIAgLjlAADNIgCA0SIAgLjlAAC46QAAuOkAAMzMMwLlIgCAuCUAAMzoMwK4IQAA6SIAgO0iAIC4KQAAzNgCALgRAAC3TQ0Atk0NALU1DgC0NQ4AuGEAAPEiAICxGQ8AsCkOAL/1AwC+UQ0AvVkNALw1DAC7XQ0Aul0NALldDQC4XQ0AgL0KAIHFCgCCFQQAg8kKAMx8BQCF3QoAhtUKAIfNCgDMUAUAifEKAIq5CACLDQgAjBEIAI0VCACOtScCj+UKAJBpCACRbQgAknEIAJNtJALMJAUAlR0IAJaFCgDMHAUAzCgFAJk9CACaiQoAmw0IAJwRCACdFQgAzCwFAMwgBQCgZQoAoW0KAKJlCgDMvAUApLEEAMzoAgCmsQQAuEkHAKiBBAAbIwCAqpkIAKtdCgCsuQgArakEAB8jAICvNQgAsNEIALHxBADQAwCAs40IALQpKAK1IQoAtiEKALchCgC4IQsAuSUIAOkDAIC7KQsAvA0dAr3dDwC+MQsAvzELAIDdCgCpoQEAqrEBAAIEAIAbBACAhRkJAIaZCQCHlQkAiOEJAIklJQIuBACAQQQAgFQEAIBnBACAegQAgI0EAICQrQoAkUkFAJJtBQCTYQUAlGEFAJVtBQCWZQUAlxEFAJg1BQCZPQUAmjUFAJsNBQCcFQUAnR0FAJ4VBQCfCQUAoKkJAKH9BQCi9QUAowEFAKQFBQClDQUApgUFAKc9BQCoBQUAqQ0FAKoFBQCrGQUArIkJAK2pBQCutQkAr/0JALABCQCxfQUAsnUFALMBBQC0aQkAtQEFALYFBQC3PQUAuAUFALnhJQK6AQUAuwEFALzRJQK9PQkAvnkJAL9dCQCDMAUAoXgHAPMEAIDdAACApHgHAKVMBwATAQCAHAEAgIt8BAAgAQCAJAEAgIhIBAAoAQCALAEAgDABAIA0AQCA4QAAgOYAAICyDAcAswAHAOsAAIDwAACAtugHALfgBwD1AACA+gAAgLrgBwC7nAcAvIQHAL2QBwD/AACAn9F+AKPMBAAEAQCACQEAgIMABAAOAQCAhXQEAKUgBAAXAQCAiEwEAM0DAIDwBACAjwUAgK8tBwCNsAcArSEHAKwpBwDiBQCAHQYAgEMGAICwZQcAWgYAgHcGAICOBgCA0wMAgOwDAIAFBACAHgQAgDEEAIBEBACAVwQAgGoEAIC8fAQAgt0rAoPlKwKA/QoAgfkrAoaZCQCHmQkAhOEKAIXhCgCKiQkAi4kJAIiJCQCJiQkAjoUJAH0EAICM4QgAjY0JAJK5KwKTQScCkJkrApHFCwCWyQsAl3UnApTFDQCV0SQCmskLAJvZKgKYyQsAmXkHAJAEAID2BACAnP0LAKABAICkAQCAqAEAgKwBAICwAQCAtAEAgLgBAIC8AQCAwAEAgJw9fwCzFX8AqBEJAMQBAIDIAQCAzAEAgNABAICC2H4A1AEAgNgBAIDcAQCA4AEAgOQBAIDoAQCA7AEAgPABAID0AQCA+AEAgPwBAIAAAgCABAIAgDwJAIBTIgCAogYAgKD1VAKh2VQCoulUAqP1dQCk7XUApZ12AKaVdgCnvXYAqIV2AKntfACqwXwAqyF9AKwhfQCtHX0ArhV9AK8NfQCwdX0AsX19ALJ1fQCzRX4AtF1+ALVNfgC2RX4At3l+ALhJfgC5VX4Aul1+ALtVfgC8TX4AvTV+AL49fgC/LX4AlQcAgKwGAIDaBwCArwYAgLRtfwC1EQAAthUAAAkjAIC8oVgCvTF4AA8jAIDTMQCAPzoAgJ8qAIDDKgCAzyoAgN8qAIDnKgCA8yoAgPsqAIADKwCADysAgGorAICCKwCAkisAgKIrAICyKwCAwisAgOIrAIDmKwCA6isAgB4sAICAVX8AgWV/AIJtfwCDdX8AhJV/AIWdfwCGiX8Ah4F/AIiFfwCJjX8AioV/AIvtfwCM9X8Ajf1/AI7pfwCP6X8AkJl/AJGZfwCSqX8Ak6l/AJS5fwCVuX8Alql/AJepfwCYmX8AmVF+AJoZfgCbGX4AnA1+AJ31fgCe/X4An/V+AKANfgChFX4Aoh1+AKMVfgCkDX4ApTl+AKYpfgCnKX4AqBl+AKllfgCqbX4Aq2V+AKx9fgCtZX4Arm1+AK9lfgCwHX4AsSV+ALItfgCzJX4AtD1+ALUlfgC2WXcAt9V1ALj9eQC56XUAuvl1ALvZeQC86XUAvdV1AL7RdQC/2XUAgDF2AIE9dgCCSXYAg0V2AIRBdgCFTXYAhvl0AId9dgCIoQIAiU12AIpZdgCLuXoAjEl2AI2degCOsQIAjx16AJCRVgKRKXYAkoF2AJPNdgCU2XYAlel2AJbJdgCX0VkCmKF2AJllWgKa8XYAm01aApzRdgCdYXoAnoFWAp/VdgCgdX0AoY1aAqI1VwKjCXYApCF2AKUtdgCmiVoCp5laAqi5WgKpdXYAql13AEYsAIBWLACAXiwAgGIsAIBuLACAiiwAgI4sAICmLACAqiwAgLIsAIDCLACAXi0AgHItAICyLQCAxi0AgM4tAIDSLQCA4i0AgAUuAIAxLgCAPS4AgJlpCgBdLgCAaS4AgG0uAIBxLgCAiS4AgI0uAIC5LgCAgvx6AIPoegDFLgCAzS4AgIZwewCHcHsA1S4AgOUuAID0LgCA/C4AgCgvAIAsLwCANC8AgDgvAIBALwCASC8AgJJAfABYLwCAdC8AgJHkewDsLwCAADAAgAQwAICEMACAiDAAgKvUfACo6HwAqeB8AJwwAICgMACAqDAAgLAwAICisHwAuDAAgMQwAID6MACAzEBJAs0ASQLM/EoCzWhLAgoxAIAeMQCAmzEAgKcxAIC3MQCAwzEAgM8xAIDXMQCAsqh8ALOofADbMQCA3zEAgOMxAIDnMQCAtER8ALVYfACAfQcAgYUHAIKZBwCDmQcAhIkHAIWJBwCGvQcAh7UHAIiNBwCJ5QcAiu0HAIvlBwCM/QcAjeUHAI7tBwCP5QcAkJ0HAJGtBwCSpQcAk70HAJSlBwCVVQEAll0BAJdVAQCYbQEAmXUBAJp9AQCbdQEAnG0BAJ1VAQCeXQEAn1UBAKCtAQChtQEAor0BAKO1AQCkrQEApdUBAKbdAQCn1QEAqO0BAKn1AQCq/QEAq/UBAKztAQCt1QEArt0BAK/VAQCwrQEAsbUBALK9AQCztQEAtK0BALVVAQC2XQEAt1UBALhtAQC5dQEAun0BALt1AQC8bQEAvVUBAL5dAQC/VQEAnzIAgOcyAIDzMgCA9zIAgPsyAID/MgCABzMAgAszAIAfMwCAOzMAgEMzAICDMwCAhzMAgI8zAICTMwCAmzMAgJ8zAIDDMwCAxzMAgOMzAIDnMwCA6zMAgO8zAIADNACAJzQAgCs0AIAvNACAUzQAgJM0AICXNACAtzQAgMc0AIDPNACA7zQAgBM1AIBXNQCAXzUAgHM1AIB/NQCAhzUAgI81AICTNQCAlzUAgK81AICzNQCAzzUAgNc1AIDfNQCA4zUAgO81AID3NQCA+zUAgP81AIAHNgCACzYAgKs2AIC/NgCA8zYAgPc2AID/NgCAnpkMACs3AIAzNwCAOzcAgICtAwCBtQMAgr0DAIO1AwCErQMAhdUDAIbdAwCH1QMAiO0DAIn1AwCK/QMAi/UDAIztAwCN1QMAjt0DAI/VAwCQrQMAkbEDAJKxAwCTsQMAlAEMAJVVDgCWXQ4Al1UOAJhtDgCZdQ4Amn0OAJt1DgCcbQ4AnVUOAJ5dDgCfVQ4AoK0OAKG1DgCivQ4Ao7UOAKStDgCl1Q4Apt0OAKfVDgCo7Q4AqfUOAKr9DgCr9Q4ArO0OAK3VDgCu3Q4Ar9UOALCtDgCxtQ4Asr0OALO1DgC0rQ4AtVUOALZdDgC3VQ4AuG0OALl1DgC6fQ4Au3UOALxtDgC9VQ4Avl0OAL9VDgC8aQQAvXUEAL59BAC/dQQAuEUEALlNBAC6RQQAu3kEALRlBAC1bQQAtmUEALd9BACwHQQAsQ0EALIFBACzfQQArFUEAK1dBACuVQQAr2UEAKhVBACpXQQAqlUEAKtNBACkkQUApZEFAKaRBQCnkQUAoJEFAKGRBQCikQUAo5EFAJxRBQCdUQUAnlEFAJ9RBQCYUQUAmVEFAJpRBQCbUQUAlBEFAJURBQCWEQUAlxEFAJBFBwCRTQcAkkUHAJMRBQCMJQcAjS0HAI4lBwCPPQcAiAUHAIkNBwCKBQcAiz0HAIQlBwCFLQcAhiUHAIc9BwCARQcAgU0HAIJFBwCDPQcAfzcAgIM3AICLNwCAjzcAgJM3AIC/NwCAwzcAgMs3AIDfNwCA4zcAgP83AIAHOACACzgAgC84AIBPOACAYzgAgGc4AIBvOACAmzgAgJ84AICvOACA0zgAgN84AIDvOACABzkAgA85AIATOQCAFzkAgBs5AIAnOQCAKzkAgDM5AIBPOQCAUzkAgFc5AIBvOQCAczkAgHs5AICPOQCAkzkAgJc5AICfOQCAozkAgKc5AICrOQCArzkAgL85AIDXOQCA2zkAgOc5AIDrOQCA7zkAgPM5AID7OQCA/zkAgAM6AIAPOgCAFzoAgB86AIAjOgCAKzoAgC86AIAzOgCAOzoAgICtAQCBtQEAgr0BAIO1AQCErQEAhdUBAIbdAQCH1QEAiO0BAIn1AQCK/QEAi/UBAIztAQCN1QEAjt0BAI/VAQCQrQEAkbUBAJK9AQCTtQEAlK0BAJUNAABDOgCAQyMAgHIsAIB2LACAKyQAgIJ4AgCZBQAAuSMAgILQAgC+mAIAgIAAAIGYAACC5AYAg4gEAITUGwCFlBoAhhgfAJ7pAACIxB4AiQAQAIqoEwCLrBEAjAAoAI20KwCOuCoAj7wpAON0dADjoAIA47gCAJkdAAC9IwCAvnQCAJ4JAADv2AIAgmwCAMEjAICZDQAA4wQCAO/ocQDvcAIA79QCAL6MAADjBAMAxSMAgOMcAwDJIwCA4yADAM0jAIDjTAMA0SMAgO/kAwDVIwCA74QDANkjAIDv2AMA3SMAgO/sAwDhIwCA48gDAOPcAwDvoAQA4+gDAEM3AIDjqAMA5SMAgO+kBADXAAAA70wDAOkjAIDjAAQA7yADAO88AwDjeAQA70QDAO0jAIDxIwCA9SMAgPkjAIDvWAQA/SMAgO9cBADjgAQA44AEAAEkAIDjmAQA73QEAAUkAIAJJACADSQAgBEkAIAcJACA72AEAOOIBAAgJACAvQAAgMIAAIA3JACAJCQAgHMpAIAOJQCAcSUAgLQlAIDgJQCA46QEAO9EBAAKJgCAgAlLAoZIAQCe7QIAgnACAL58AgCeEQEAmR0BAIJcAgCPQAEAmSkBAI1sAQC+YAIAi3gBAJ45AQCCGAIAvggCAJfUAgCZUQEAldQCAJ5ZAQCT1AIAvlgCAJHUAgCCVAIAn7gCAJYAAACdqAIAmXEBAJu4AgCeaQEAmbQCAJlZAQCCmAIAppQCAJ6tAQCkeAIAgmgCAJ65AQCheAIAmbEBAK+EAgAvJgCAvlgCAIJwAgC+XAIARCYAgJmNAQCokAIAvnwCAJ7xAQC1sAIAmfEBAL5QAgCykAIAtoUCAJmFAQC4XQkAuYUCALqNAgBMJgCAu/QEAIJcAgCexQEAuPQEAL58AgCeXQYAgmACAJllBgC+cAIAgmwCAJ5xBgCZnQYAvnwCAJ6lBgCCYAIAmakGAL58AgCesQYAghwCAL4UAgCZyQYAvkwCAIJMAgCawQYAntkGAJ/ZBgCCvAIAvrACAJn1BgCC3AUAvogCAJrJBgCe5QYAn9EGAIK4AgCCqAIAmTEGAOP0AgDj/AIAmjkGAJ8lBgCeJQYAniEGAJ8hBgC+GAIAmR0GAJoVBgC+DAIAmXEGAO8kAgDv4AIAmnEGAJ4BBgCfCQYA4xQCAJkVBgDjLAIAgnwCAJ4BBgBoJgCA7/ACAJkFBgC+fAIAng0GAHAmAICCdAIA76wCAIKUAwCb0QcA4/ADAL5QAgCZ6QcAn80HAOM0AgCdXAIAnMkHAJ7FBwDv7AIAgmACAJnFBwC+cAIA78QCAJ7RBwCCEAIA43QCAL5sAgCZpQcAvlQCAJ69BwCZpQcAgmgCAOMMAgCekQcAmZkHAIJoAgDv2AIA78wCAL6IAgCCkAIAvpgCALsEAACeeQcAuQAAAJkpBgC/XAAA4/QCAL0EAACeOQYAs7gDAO8oAgCxeAMAgnwCALc8AACZAQYAtfwDAJ4JBgCrSAMAvgACAOO4AgCZIQYArxwDAIJ8AgCtQAMAvkQCAJ4NBgCCbAIAvpwBAJkxAQCCgAEApqgCAO98AgCCvAEA46QBAOPYAQDj5AEAntECAOPoAQCZ5QIAvnwCAJ7tAgDvwAIAmQkAAL5sAgB4JgCA7wwBAO8QAQDvKAEAnhEAAOO0AQDjHAIAmS0AAL5sAgCCfAIAglACAJ49AADjAAIAmeUAAIomAIC+aAIA7/wCAO+gAgDvwAIAnv0AAJnxAADjAAIAliYAgIKQAgCeJgCAvnwCAJ4ZAADjDAIAglgCAJkNAACCBAIA7+QCALQmAIDv2AIAvnQCAJ4VAACZZQEA42wCAJnJAQCanQEAvmQCAJ7dAQCfgQEA4wwCAIJYAgCZlQEAvrQDAO/0AgCalQEA78QCAIKsAwCwRSoAgvQDAIHIAgDj+AMAvjACAPImAIDj5AMAhMQCAL44AgCGEAIA78ACAIgwAgCeXQAAn1UAAJqxAADvxAIAj3QCAJlBAACePQAAn8UAAIKMAgCSGAIAldgDAAMnAICeyQAAn8kAAJr1AACY+AMAm2ADAJn9AACCqAMAJScAgDAnAICAJwCAlCcAgOPEAgC+IAIAvlACALYnAIDEJwCA4+ACAAwoAICZPQAAnn0AAO/cAgCaHQAAGigAgO/EAgCvrAMAghACALEAHACwlAMAmSkAALJMHACeTQAAn2UAAHcpAIDjaAIAeykAgL50AgCeCQAA7/ACAL08HACCbAIAv80fAJn9HwB/KQCAvnQCAJ4JAADjFAIAgmwCAIMpAICZDQAAvkQCAJ41AACCaAIAmQUAAI8pAIC+cAIA7yUAgJ4VAADv2AIA42wCAP8YAIADGQCA47AdACcaAIAHGQCAKxoAgC8aAIALGQCAMxoAgDcaAIA7GgCA77gCAD8aAIBDGgCA74ACALHFAAAPGQCAs9kAALLFAAC1yQAAtMEAALf5AAC2wQAAuWEAALghAAC7wQAAuskAAL3FAAC82QAAv30AAL7FAAATGQCARxoAgFMZAIAXGQCAGxkAgB8ZAIBnGQCA7xB4A7MZAIDh3E0DtxkAgONYeQO7GQCAvxkAgMMZAIDHGQCAgMkBAIHJAQCC2QEAg9kBAITJAQCFdQIAhgEEAIcdBQCIJQUAiTUFAIo9BQCLbQUAjHUFAI1lBQCObQUAj90BAJCpAQCRtQEAkr0BAJO1AQCUrQEAlVUDAJZdAwCXVQMAmG0DAJl1AwCafQMAm3EDAJxRAwCdUQMAnlEDAJ9NAwCgtQMAob0DAKK1AwCjxQMApMEDAKXBAwCmxQMAp/0DAKjFAwCpzQMAqsUDAKvZAwCsyQMArTUDAK49AwCvNQMAsE0DALFVAwCyXQMAs1UDALRNAwC1dQMAtn0DALd1AwC4TQMAuVUDALpdAwC7VQMAvE0DAL01AwC+PQMAvzEDAMsZAIDPGQCA0xkAgNcZAIDbGQCA3xkAgPAQAgDjGQCA5xkAgOsZAIDvGQCAgpwCAPMZAID3GQCA+xkAgP8ZAICc9TYAnf02AAMaAICRaAIArxkAgEsZAIC6ZdUATxkAgEsaAIBPGgCAUxoAgFcaAIDwSAIAWxoAgF8aAICRkAIAYxoAgFcZAIBnGgCAaxoAgFsZAIBfGQCAYxkAgGsZAIBvGQCAcxkAgHcZAIB7GQCAfxkAgIMZAICHGQCAixkAgI8ZAICTGQCAuo3VAJcZAICbGQCAnxkAgG8aAIBzGgCAoxkAgILEAgCnGQCAqxkAgAcaAIALGgCADxoAgPA4AwDh3NICExoAgONUxgIXGgCAGxoAgB8aAIAjGgCAqyoAgGItAICvKgCAxyoAgLMqAICjMwCAuyoAgO+k4AKjKgCA4xSHAuGQ8wLhjJ0C45D3AuGMqwLhkLYC4wBTA+OAogIjGQCA7S0AgO+0SwPvvLMC7ySLAnYtAIAIAgCA7ySXApEIBwAOAgCAFAIAgBoCAIAgAgCAJgIAgCwCAIAyAgCAOAIAgD4CAIBEAgCASgIAgFACAIBWAgCANAMAgDoDAIDhgHgC4wAeAuMUagLhEBMC4aAPAkADAIDjhA4C7yw/AkYDAIDhUDsC7zQ7AuM8IgJMAwCA7ygfAu8MEgJSAwCAKxkAgC8ZAIBYAwCAXgMAgDMZAIA3GQCAdgMAgIIDAICIAwCAjgMAgJQDAICaAwCAfAMAgGQDAIBtAwCAXAIAgDsZAIA/GQCAdAIAgGgCAIBDGQCARxkAgLwCAIB6AgCAmAIAgGICAICSAgCAbgIAgKQCAIDUAgCA8gIAgOwCAICAUQYAgVEGAIJRBgCDUQYAhHEGAIV9BgCGdQYAh20GAIhVBgCJXQYAiq0HAIuhBwCMoQcAjaEHAI6hBwDgAgCALgMAgMICAICSfRQAkwEUAJTNBwCV9QcAlv0HAJf1BwCYzQcAmdUHAJotFACb2QcAnM0HAJ2RBwCejQcAnykUAJklAQCYJQEAmyUBAJolAQCdJQEAnCUBACcZAICeJQEAkcEGAJDxBgCT1QYAkt0GAJU9AQCUPQEAlyUBAJYlAQCJ5QYAiOUGAIvlBgCK5QYAjeUGAIzlBgCP5QYAjuUGAIHlBgCAHQYAg+UGAILlBgCF5QYAhOUGAIflBgCG5QYAuaUDALilAwC7pQMAuqUDAL2lAwC8pQMAv6UDAL6lAwCxpQMAsKUDALOlAwCypQMAtaUDALSlAwC3pQMAtqUDAKmxAQCoqQEAq7EBAKq5AQCtbQEArKkBAK8dAQCuHQEAoakBAKDZAQCjsQEAoqEBAKWRAQCkqQEAp5EBAKaZAQDOAgCA5gIAgNoCAIAEAwCAsAIAgPgCAIAiAwCACgMAgJ4CAICAAgCAtgIAgMgCAID+AgCAhgIAgCgDAICqAgCAEAMAgIwCAIAWAwCAHAMAgBYtAID4LgCA1zQAgIcHAIAGBQCAFQUAgCQFAIAzBQCAQgUAgEsFAIBUBQCAXQUAgIIMAwBmBQCAkgUAgJsFAICkBQCA40huA6cFAIDhTG4DqgUAgO/0AQOtBQCAVzoAgLdMAIDnVQCAR2gAgHdxAICnegCAB40AgGefAICXqACA/roAgDXEAIBlzQCAldYAgMXfAIBCuwCAS64AgBelAID/KgCAlisAgKcqAIDrKgCATjEAgA4xAIBbNACA4iwAgBMzAICXNwCAbzQAgCosAICfNACAqzMAgB84AIBmKwCAkiwAgAcyAIA3OQCAKisAgLorAICrMQCAyS4AgNYsAIBmLACARS4AgDkuAID7MwCAJisAgLop0ACrNwCAgiwAgNotAICwBQCAswUAgLYFAIDh1D8D4VgaA+PcLwPjUA4D4RTyA+FA0wPjQOoD40DDA7kFAIDlBQCA73jrA+9c8gPoBQCA6wUAgO9E3gPvmCUD4bSLA+E8lwPjfKID45iLA+EwQQDhUKwD4xx/AOOIRgDuBQCA8QUAgO84ewDv4EEA9AUAgPcFAIDvzIoD7yCHA4C1GACByRgAghULAIMtCwCE4Q4AheEOAIbhDgCH/RgAiN0OAImZGgCK6RsAiy0dAIzFGwCN8RsAjn0QAI/hGgCQ6RsAkUUPAJJNDwCTRQ8AlF0PAJVFDwCWTQ8Al0UPAJh9DwCZRQ8Amk0PAJuZGgCcWQ8AnVkPAJ5JDwCfSQ8AoLkPAKG5DwCiyQ8Ao8kPAKS1CwClvQsAprULAKfVDwCo7Q8AqfUPAKr9DwCr9Q8ArO0PAK3VDwCu0Q8Ar9EPALCxDwCxsQ8AsrEPALOxDwC0cQ8AtXEPALZpDwC3aQ8AuAEPALkBDwC6GQ8AuxkPALzxAQC98QEAvvEBAL/xAQD6BQCA/QUAgAAGAIAgBgCA4QQAgIAFAIDTBQCADgYAgDQGAIBLBgCAaAYAgH8GAICWBgCA3QMAgPYDAIAPBACAEgcAgEQIAIBBCACAPwcAgD8kAIB4JACAqSQAgM4kAIC/JgCAyiYAgM4mAIDSJgCA1iYAgDUoAIB0KACAnCgAgKAoAIDFKACAzSgAgOkoAID7KACA/ygAgAMpAIAbKQCANikAgPAUNgBRKQCAHysAgEMkAIBQJACAXSQAgGokAIB8JACAiSQAgJskAICtJACAvSQAgNIkAIDcJACA6iQAgPQkAIABJQCAEiUAgBwlAIB1JQCAfCUAgColAICGJQCAgBEDAIERAwCCEQMAgxEDAIQxAwCFMQMAhjEDAIcxAwCIEQMAiREDAIoRAwCLEQMAjHEDAI1xAwCOcQMAj3EDAJARAwCREQMAkgEEAJMVAwCUDQMAlVUGAJZdBgCXVQYAmG0GAJl1BgCafQYAm3UGAJxtBgCdNQYAnj0GAJ81BgCgzQYAodUGAKLdBgCj1QYApPEDAKXxAwCm8QMAp/EDAKjRAwCp+QYAqikGAKspBgCsOQYArTkGAK7NAwCvxQMAsL0DALFFAwCyTQMAs0UDALRdAwC1RQMAtk0DALdFAwC4fQMAuUUDALpBAwC7fQYAvGUGAL1tBgC+ZQYAv1EDAKkVDwCoAQ8Aq00PAKpNDwCtRQ8ArEUPAK+pDQCusQ0AoXULAKBhCwCj6QsAok0LAKXhCwCk+QsApzkPAKZdCAC5mQ0AuJENALupDQC6kQ0AvbkNALyxDQA3JQCAvrENALHZDQCw0Q0As6kNALLRDQC1uQ0AtLENALepDQC2sQ0APiUAgE4lAIBhJQCAuCUAgMIlAICXJQCApyUAgNYlAICB5Q0AgOUNAIPlDQCC5Q0AheUNAITlDQCH4Q0Ahi0YAJlFDQCYuQ0Am0UNAJpFDQCdSQ0AnEUNAJ9xDQCefQ0AkYENAJC5DQCTgQ0AkokNAJWBDQCUmQ0Al4ENAJaJDQDmJACAJiUAgJMlAIDSJQCA5CUAgA4mAIAzJgCASCYAgPYlAIAAJgCAEiYAgB8mAIA3JgCAVCYAgF4mAIB8JgCAUCYAgGwmAIB0JgCAhiYAgJImAICaJgCAqSYAgOQmAICiJgCAuCYAgK0mAIDDJgCA2iYAgOgmAIAHJwCAFycAgCEnAIBVJwCAmCcAgO0nAIBVKQCAYykAgGcpAIBrKQCA9iYAgDQnAIBEJwCATicAgCknAIBZJwCAaScAgIQnAIB2JwCAnCcAgMgnAIDPJwCArCcAgNknAIDjJwCAuicAgB4oAIAQKACA8ScAgCsoAID4JwCAAigAgDkoAIBGKACAUCgAgFooAIBkKACAeCgAgIUoAICMKACApCgAgKsoAIC4KACA0SgAgNsoAIDtKACABykAgBQpAIAfKQCAKSkAgDopAIBBKQCAWSkAgMMDAIDmBACAhQUAgNgFAIATBgCAOQYAgFAGAIBtBgCAhAYAgJsGAIDjAwCA/AMAgBUEAIAoBACAOwQAgE4EAIBhBACAdAQAgIcEAICaBACAAAUAgA8FAIAeBQCALQUAgDwFAIBmCACAJwgAgMEGAID/BwCAIAkAgOM4EwA2CQCALQgAgDAIAIA0CACAJAcAgOkuAIDXMACA5i0AgMgwAIBSMQCAKgkAgO/kEwAJCQCA4g0AgNIIAICGCACAMQcAgEwHAID8BgCADQgAgJcIAIAtCQCADAkAgOYNAIDyDQCA3ggAgJwIAIAVBwCAiQgAgFUHAID/BgCAqQcAgJckAID2DQCA5QgAgCoIAICfCACAWwgAgBgJAID6DQCA6AgAgBcIAICiCACA6wgAgBoIAIDMCACApQgAgO8IAIAeCACAzwgAgKkIAID6CACAAAkAgIsHAICNCACAWQcAgAMHAIBACQCARAkAgEwJAIA5CQCAGwkAgP4NAID3CACAMAkAgA8JAIDqDQCA1QgAgJEIAIBgBwCAMwkAgBIJAIDuDQCA2AgAgJQIAIBjBwCAsAgAgGYHAIDjQBIA4/ATAOP0EwDj2BMA4wgNAOPoEgDjrBIA42QSAO/EDQDv2A0A7zQSAO9YEgDvQBIA79QSAO/IEgDvIBMA7AcAgMwGAIARCACAFAgAgNgGAIDUBgCAJAgAgAcHAIBqCACADAcAgHkIAIA0BwCANwcAgK0IAIC5CACAvAgAgOPQEADjsBAA46gQAON4EQDjTBAA4zAQAOPoEADj/BAA74QQAO+YEADvLBAA7yAQAO8EEADvCBAA73AQAO9MEADjmBMA4zAQAOMwEADjIBAA43wTAONAEwDjWBMA44ATAO/UEwDvqBMA74ATAO98EwDvRBMA7yQTAO8YEwDv7BAAgO08AIH1PACC/TwAg/U8AITtPACFFT0Ahh09AIcVPQCILT0AiTU9AIo9PQCLNT0AjC09AI0VPQCOHT0AjxU9AJBtPQCRdT0Akn09AJN1PQCUbT0AlRU9AJYdPQCXFT0AmC09AJk1PQCaPT0AmzU9AJwtPQCdFT0Anh09AJ8VPQCg7T0AofU9AKL9PQCj9T0ApO09AKUVPQCmHT0ApxU9AKgtPQCpNT0Aqj09AKs1PQCsLT0ArRU9AK4dPQCvFT0AsG09ALF1PQCyfT0As3U9ALRtPQC1FT0AthE9ALcRPQC4MT0AuTE9ALoxPQC7MT0AvBE9AL0RPQC+ET0AvxE9AIDxPACB/TwAgvU8AIMNPwCEFT8AhR0/AIYVPwCHDT8AiDU/AIk9PwCKNT8Aiw0/AIwVPwCNHT8AjhU/AI8NPwCQdT8AkX0/AJJ1PwCTDT8AlBU/AJUZPwCWCT8Alwk/AJg5PwCZOT8Amgk/AJsJPwCcGT8AnRk/AJ4JPwCfCT8AoPk/AKH5PwCiCT8Aowk/AKQZPwClGT8Apgk/AKcJPwCoOT8AqTk/AKoJPwCrCT8ArBk/AK0ZPwCuCT8Arwk/ALB5PwCxeT8Asgk/ALMJPwC0GT8AtRk/ALYJPwC3CT8AuDk/ALk5PwC6CT8Auwk/ALwZPwC9GT8Avgk/AL8JPwCA+TwAgfk8AIJJPQCDST0AhFk9AIVZPQCGST0Ah0k9AIh5PQCJeT0Aikk9AItJPQCMWT0AjVk9AI5JPQCPST0AkDk9AJE5PQCSAQQAk00GAJRVBgCVXQYAllUGAJdNBgCYdQYAmX0GAJp1BgCbTQYAnFUGAJ1dBgCeVQYAn00GAKC1BgChvQYAorUGAKPNBgCk1QYApd0GAKbVBgCnzQYAqPUGAKn9BgCq9QYAq80GAKzVBgCt3QYArtUGAK/NBgCwtQYAsb0GALK1BgCzTQYAtFUGALVdBgC2VQYAt00GALh1BgC5fQYAunUGALtNBgC8VQYAvV0GAL5VBgC/TQYArH0/AK2lPwCurT8Ar6U/AKh9PwCpZT8Aqm0/AKtlPwCkHT8ApUU/AKZNPwCnRT8AoB0/AKEFPwCiDT8AowU/ALydPwC9pT8Avq0/AL+lPwC4nT8AuYU/ALqNPwC7hT8AtN0/ALWlPwC2rT8At6U/ALDdPwCxxT8Ass0/ALPFPwCMYToAjWE6AI5hOgCPYToAiEE6AIlBOgCKQToAi0E6AIRhOgCFYToAhmE6AIdhOgCAAToAgQE6AIIBOgCDAToAnF04AJ3lPwCe7T8An+U/AJhdOACZRTgAmk04AJtFOACUuTgAlWU4AJZtOACXZTgAkAE6AJEBOgCSAToAkwE6AMMIAIDbCACA4QgAgPMIAIB5BwCAJQkAgHwHAICEBwCAVwkAgKAHAIDOBwCAwAcAgMQGAIDcBACAewUAgM4FAIAJBgCALwYAgEYGAIBjBgCAegYAgJEGAIDXAwCA8AMAgAkEAIAiBACANQQAgEgEAIBbBACAbgQAgIEEAICUBACA+gQAgAkFAIAYBQCAJwUAgDYFAIBFBQCATgUAgFcFAIBgBQCAaQUAgJUFAICeBQCAYAgAgFwOAIBfDgCASzoAgLgJAAC5CQAArwoAgBgLAIBHOgCATzoAgOYMAIBTOgCAHw0AgIc3AID+MACArzcAgGcyAIDLKgCAxiwAgPktAICaMDUAKi0AgPUtAIDkLwCA3zMAgJ80AwBvNQCAnSQpAJzxAQCd8QEAnvEBAJ/xAQCnNgCA4zYAgBc3AIArOACAgzEAgA8yAIC7MgCAUzMAgG82AIBXOACAgzkAgO8qAICaLACAlzEAgN8yAICjNgCA0zkAgKEuAICHMgCAkzYAgCc3AIAYMACAyzUAgO82AIAULwCAEjEAgCcyAIArMwCANzgAgDYrAIDOKwCAOiwAgIAwAICPMQCA2zIAgP8zAICbNgCAszYAgNc3AID/OACAszkAgM85AIA7NACArYwCAHs0AIAzNQCAUzYAgIs4AIBbNwCAqRUBAK4tAIAwLwCAsAAdALEEHgCyABgAswwZALToGgC1AOQAthjlALcc5wC4AOAAuSThALog4gC7AOwAvDTtAL007gC+OO8AvwDoAOs0AICrNQCAvwgAgA8zAICkUAMApQAMAKZYDgCnAAgAqGAKAKmQCwCqaBUAq2wXAKwAEACtdBEArnwSAK8AHABDNACApzcAgPc4AICqLQCAfS4AgIcxAIA7MgCAbzIAgCM1AIBLNQCAtzgAgDYsAIC3NQCA2isAgNYrAICnNACANzUAgGs2AIC/OACAdzcAgBwwAIBnNwCA1yoAgFEuAICILwCAPzMAgL8zAIBaLACASzQAgEYrAIBsLwCAtyoAgIDlAwCB7QMAgi0vAIPhAwCE4QMAheEDAIbhAwCH4S8AiN0vAInZAwCKbS8AiykCAIw5AgCNOQIAjikCAI8lAgCQcQIAkXECAJJxAgCTcQIAlBECAJURAgCWEQIAlxECAJgxAgCZMQIAmjECAJsxAgCcEQIAnRECAJ4RAgCfEQIAoPECAKHxAgCi8QIAo/ECAKQRAgClEQIAphECAKcRAgCoMQIAqTECAKoxAgCrMQIArBECAK0RAgCuEQIArxECALBxAgCxcQIAsnECALNxAgC0TSIAtRUCALYdAgC3FQIAuC0CALk1AgC6PQIAuzUCALwRAgC9EQIAvhECAL8RAgCVuQ4AlLEOAJfJDgCWsQ4AkbkOAJCxDgCTqQ4AkrEOAJ31DgCcZQ0An/UOAJ71DgCZ+Q4AmPEOAJvpDgCa8Q4AhQUOAIQFDgCHyQ4AhgEOAIGhDQCAlSAAg6UNAIKlDQCN+Q4AjPEOAI/JDgCO8Q4AifkOAIjxDgCL6Q4AivEOALWxAQC0qQEAt7EBALa5AQCxvSAAsLUBALOxAQCyuQEAvfEBALzpAQC/8QEAvvkBALnxAQC4iQEAu/EBALr5AQClNQ4ApDUOAKc1DgCmNQ4AoTUOAKA1DgCjNQ4AojUOAK31AQCs9QEAr/UBAK71AQCp9QEAqPUBAKv1AQCq9QEA+zEAgJgwAIAfNQCAriwAgJotAIALNACAczYAgEs3AIDHMQCA8zEAgCwwAIArNgCATDAAgLszAIALKwCAjisAgNIrAIBjMQCACzUAgAM2AIBXNwCAazgAgEIsAID2LACAJC8AgLQwAICLMgCATzQAgKc4AICLOQCA3zkAgPc5AID2MACAszEAgPs3AIDwLgCAzC8AgOgvAIB4MACAezIAgMcyAIB3MwCAmzQAgD81AICjNQCA6zcAgHs2AIATOACAjzgAgPYrAIAiLACACi0AgLcyAIADNwCAEC8AgIAvAIBEMACAvzEAgOc0AIAzMwCAGysAgGYtAIC1LgCAjC8AgIBdAwCB8SgAgmkDAINpAwCEeQMAhXkDAIZpAwCHaQMAiFkDAIlZAwCK1SoAi60DAIy1AwCNvQMAjrUDAI+tAwCQ1QMAkd0DAJLVAwCT7QMAlPUDAJX9AwCW9QMAl+0DAJjVAwCZ3QMAmtUDAJutAwCctQMAnb0DAJ61AwCfrQMAoFUDAKGZAwCiUQMAo1EDAKRxAwClcQMApnEDAKdxAwCoUQMAqVEDAKp1DACrVQMArE0DAK21AQCuvQEAr7UBALDNAQCx1QEAst0BALPVAQC0zQEAtfUBALb9AQC39QEAuM0BALnVAQC63QEAu9UBALzNAQC9tQEAvr0BAL+9DwBPMwCAazMAgHs1AICbNQCAczgAgPM4AIADOQCAPzkAgDorAICPNACAXzgAgNs4AICkLwCA9yoAgF4rAIBVLgCAdS4AgKQwAIDTMgCA2zMAgIc2AIAnOACA5jAAgLM4AIAaLACAMjEAgD4xAIAfMgCAVzIAgFszAIC3MwCANzQAgBs1AIBLOQCA+C8AgMM4AIBOKwCAmS4AgD8yAIDvNwCAXC8AgKwvAIBGMQCAyzgAgP4rAIDmLACAhS4AgM8wAIAiMQCAbzEAgAMyAIBXMwCAyzMAgGc1AIAHNwCAEzcAgOc4AIBqLACAWzIAgOosAIDXMgCAezMAgJc2AIDPOACAl50HAJadBwCVnQcAlJ0HAJOdBwCSnQcAke06AJCZBwCfmQcAnpEHAJ2ZBwCcgQcAm9UKAJqdBwCZnQcAmJ0HAIchBwCGGQcAhREHAIS1JACDBQcAggUHAIEVBwCAFQcAj+EHAI4ZBwCNEQcAjBkHAIsBBwCKGQcAiREHAIgZBwC3/SkAtpUBALWFAQC0hQEAs5UBALKVAQCxZQcAsGUHAL+RAQC+iQEAvYEBALyJAQC7kQEAuqkBALmhAQC4qQEApxkHAKYRBwClGQcApAEHAKMZBwCiEQcAoRkHAKBhBwCvFQcArhUHAK0FBwCsBQcAqxUHAKoVBwCpuSQAqCEHALs5AIDjOQCAOjEAgDcyAIDTNQCA0zQAgPc0AIAnMwCArzIAgHM3AIATKwCAOzYAgAIsAIDyKwCAAC8AgCAwAIADNQCAQS4AgBMyAIDyMACA9zcAgLs4AIAcLwCAbisAgEItAICWLQCA4jAAgN4rAIAvMwCA8zMAgFc0AIBzNACAdzQAgIs0AIALOQCA+zQAgJ82AIBjNwCAFzgAgEM4AIBfOQCAYzkAgGc5AIDLOQCAOzgAgNc4AIA+KwCAYisAgHYrAIAyLACAPiwAgH4sAIAyLQCATi0AgFYtAICSLQCAni0AgIEuAICYLwCAwC8AgMgvAICR5BgA4C8AgIwwAICANQMAgT0DAII1AwCDTQMAhFUDAIVdAwCGVQMAh00DAIjJKwCJcQMAitErAIt1AwCMbQMAjVUDALwwAIDqMACAkCUDAGcxAICSIQMAKzIAgEcyAICVOQMAlikDAJcpAwCYGQMAmRkDAJrpAwCb6QMAnPkDAJ35AwCe4SsAdzIAgKARAwDLMgCAoh0DAOsyAIBfMwCApQ0DAKYFAwA/NACAYzQAgF80AICqCQMAqwkDAKwZAwCtGQMArgkDAK8JAwCweQMAsXkDALIJAwCzCQMAtBkDALUZAwC2CQMAtwkDALg5AwC5OQMAugkDALsJAwC85S0AvR0DAL4VAwC/DQMAvY0fALxhAgC/nR8Avp0fALmRHwC4xQIAu5EfALqZHwC1VR8AtFUfALdVHwC2VR8AsVUfALBVHwCzVR8AslUfAK0dHwCsHR8AZzQAgGs0AICpHR8AqB0fAKsNHwCqDR8ApSEfAKRZHwCn8QIApikfAKFBHwCgWR8Ao0EfAKJJHwCdpR8AnKUfAJ+hHwCeqR8AmYUfAJiFHwCbhR8AmoUfAJWpHwCUoR8AlwUGAJahHwCRqTgAkPkAAJO5HwCSARwAjWEBAIzNOACPgQAAjmkBAIldAQCIUQEAi0UBAIpNAQCFrQEAhKEBAIeVAQCGvQEAgQ0CAIANAgCDxQEAgsUBAIc0AICrNACAvzQAgNs0AIBHNQCATzUAgGM1AICLNQCA2zUAgA82AIB3NgCAHzcAgDc3AIBrNwCAbzcAgLM3AIC3NwCADzgAgOs4AIAvOQCARzkAgJAvAICm6gCA8zUAgL8qAIDKKwCAiisAgDIrAIByKwCAnisAgC4sAIBKLACAHi0AgC4tAIBKLQCApi0AgPEtAID9LQCAGS4AgCkuAIAYLwCAIC8AgFAvAIBwLwCAoC8AgLgvAICoLwCAvC8AgPwvAIBUMACAYDAAgGgwAICQMACAFjEAgCoxAIBrMgCAYzIAgJMyAIAjNACA7zIAgCMzAIBvMwCAizMAgK8zAICAmQEAgZkBAIKpAQCDqQEAhLkBAIW5AQCGqQEAh6kBAIiZAQCJ1RwAipUBAIvVHACM8QEAjfEBAI7xAQCP8QEAkJEBAJEtHACS3RQAk5kBAJSJAQCVhTIAlnkYAJcxGgCYvQEAmYUBAJoVHwCbiQEAnPUfAJ2dAQCelQEAn40BAKDxHAChcQEAonEBAKNxAQCkkQMApZEDAKbtHACnlQMAqK0DAKm1AwCqvQMAq7UDAKytAwCtuQEArpkDAK+ZAwCwbRgAse0DALLVAQCz4QMAtOEDALXhAwC24QMAt+EDALjRAQC5pQMAun0cALupAwC8xQEAvSEXAL6xAwC/xQEA0zMAgNczAID3MwCABzQAgBs0AIAXNACARzQAgMM0AIDzNACAKzUAgFs1AIA/NgCAZzYAgNs2AIAjNwCALzcAgE83AIBTNwCAXzcAgHs3AIDzNwCAIzgAgFs4AIB7OACAxzgAgB85AIA7OQCAmzkAgD3qAIA46gCAauoAgOcpAIAPKgCAEyoAgOzqAIAZ6wCAkesAgCc6AIA3OgCASggAgFUIAIBYCACATQgAgFEIAIBaCQCA9w4AgOgOAIDtDgCA/A4AgPIOAIBRDwCA0A8AgIcPAIA1DwCAYA8AgG0PAIB1DwCAow8AgMgPAIC+DwCAww8AgLAPAIC3DwCABA8AgIBNAQCBRQMAglkBAINZAQCESQEAhUkBAIZ5AQCHVQMAiKkeAIlBAQCKZQMAi0UBAIxhAwCNWQEAjsU7AI9NAQCQNQEAkT0BAJI1AQCTzQEAlNUBAJXdAQCW1QEAl80BAJj1AQCZ/QEACQ8AgA4PAIAbDwCAKA8AgDAPAIA4DwCAQg8AgEcPAIBMDwCAVg8AgFsPAIBjDwCAcA8AgHgPAIB9DwCAgg8AgIoPAICPDwCAmQ8AgJ4PAICmDwCAgzQAgKsPAIDLDwCAPQ8AgCAPAIBoDwCAlA8AgBMPAIDjFgCA7BYAgO8WAID1FgCA6RYAgPIWAIDmFgCAGRcAgBwXAID7FgCAnc0GAPgWAICfwQYA/hYAgAEXAIAKFwCABxcAgJSZBgCVmQYAlukGAJfpBgANFwCABBcAgBMXAICTiQYAEBcAgB8XAIAlFwCAKxcAgCgXAIAuFwCAMRcAgDoXAICEzQYAhdUGAIbZBgA0FwCAgO0GAIHVBgCC3QYAg9UGALwZBwBdFwCAvhUHACIXAIC4GQcAuRkHALoJBwC7CQcAtN0HALUlBwC2LQcARhcAgLDdBwCxxQcAss0HALPFBwCsNQYArT0GAK41BgCvpQcAqDkGAKl9QgCqNQYAqy0GAKQ5BgClOQYApgkGAKcJBgCgIQYAoQFCAKI5QwCjKQYAgKEGAIGhBgBDFwCAg6UGAIS9BgBOFwCAhqkGAIepBgCImQYAieUGAIrtBgCL5QYAjP0GAI3lBgCO7QYAj+UGAJCdBgCRmQYAkqkGAJOtBgCUsQYAlbUGAJa9BgCXuQYAmIUGAJmBBgCagQYAm4UGAJyZBgCdnQYAnpUGAJ+RBgCgbQYAoWkGAKJ5BgCjfQYApGEGAKVlBgCmbQYAp2kGAKhVBgCpUQYAqlEGAKtVBgCsSQYArU0GAK5FBgCvQQYAsD0GALE5BgCyyQEAs80BALTRAQC11QEAttEBALfVAQC46QEAue0BALr5AQC7/QEAvOEBAL3lAQC+7QEAv+kBAIEVAgCAEQIAgxECAIIVAgCFDQIAhAkCAIcpAgCGLQIAiRUCAIgRAgCLEQIAihUCAI1xAgCMdQIAj30CAI55AgCRBQIAkAECAJMBAgCSBQIAlRkCAJQdAgCXFQIAlhECAJktAgCYKQIAmzkCAJo9AgCdIQIAnCUCAJ8tAgCeKQIAodkCAKDdAgCj0QIAotUCAKVpUwKkbVMCp8UCAKbBAgCp/QIAqPkCAKvFAgCqwQIArd0CAKzZAgCvPQIArjkCALEpUwKwLVMCVBcAgEAXAIBRFwCAWhcAgH8WAIDnDwCANxAAgBQQAIAoEACAIxAAgC0QAIAyEACAGRAAgFcXAICEnA4A7A8AgPEPAIAFEACAHhAAgF4QAIBjEACAbxAAgIUQAICUEACAmRAAgKQQAIC+EACA0RAAgNmIUAL1EACAJxEAgCwRAIA0EQCAQxEAgFIRAIBXEQCAXxEAgIIRAICpEQCAtREAgNURAIDaEQCA3xEAgBkSAIAsEgCAOBIAgFASAIDKEgCAIBMAgDkTAIA+EwCAURMAgGITAIB0EwCAeRMAgKATAICoEwCAvRMAgOQTAIDpEwCAQxQAgEgUAIBNFACAWRQAgGUUAIBqFACAchQAgH4UAICYFACAnRQAgNlcUAKlFACAqhQAgK8UAIC0FACAuRQAgL4UAIDRFACAn8kOAJ7NDgCdwV0CnBkNAJsFDQCaHQ0AmRENAJixDACXjQwAlqkMAJWlDACUoQwAk70MANYUAIDyFACADBUAgCYVAIAyFQCAShUAgE8VAIBcFQCAfRUAgKAVAIC6FQCAxhUAgMsVAIDTFQCA9BUAgA4WAIAdFgCAOhYAgD8WAIC/fQ4AvnkOAL11DgC8cQ4Au2kOALptDgC5YQ4AuGkOALdVDgC2UQ4AtVkOALRdDgCzXQ4AslkOALFRDgCwVQ4AryUOAK4hDgCtKQ4ArC0OAKsNDgCqCQ4AqQEOAKgFDgCnNQ4ApjEOAKU9DgCkOQ4AoyEOAKIlDgChNQ4AoDEOAIAFDgCBDQ4AggUOAIP1DwCEAQ4AhQEOAIYBDgCHAQ4AiAEOAIkBDgCKAQ4AiwEOAIwBDgCNAQ4AjgUOAI99DgCQBQ4AkQ0OAJIFDgCTHQ4AlAUOAJUNDgCWBQ4Alz0OAJgFDgCZDQ4AmgUOAJsdDgCcBQ4AnQ0OAJ4FDgCf/Q4AoAUOAKENDgCiBQ4Aox0OAKQFDgClDQ4ApgUOAKc9DgCoBQ4AqQ0OAKoFDgCrHQ4ArAUOAK0NDgCuBQ4Ar30OALAFDgCxDQ4AsgUOALMdDgC0BQ4AtQ0OALYFDgC3OQ4AuAkOALkJDgC6GQ4AuxkOALwJDgC9CQ4Avs0BAL/FAQCAPQIAgUUCAIJNAgCDRQIAhF0CAIVFAgCGTQIAh0UCAIh9AgCJRQIAik0CAItFAgCMXQIAjUUCAI5NAgCPRQIAkD0CAJFBAQCSQQEAk0EBAJRBAQCVQQEAlkEBAJdBAQCYQQEAmUEBAJpBAQCbQQEAnEEBAJ1BAQCeQQEAn0EBAKDBAQChwQEAosEBAKPBAQCkwQEApcEBAKaVDQCnxQEAqFkMAKm1DQCq9QEAq80BAKyRDQCt0QEArp0NAK+VDQCwqQEAsakBALL1DQCzvQEAtJENALWRDQC2rQEAt6UBALitDQC5mQEAurkNALu5DQC8OQ0AvTkNAL4hDQC/IQ0Ap1X8AEcWAIBMFgCAXxYAgGQWAICKFgCAlhYAgKIWAICxFgCAzhYAgNMWAID0EQCABRIAgIIWAICBAACAiwAAgJUAAICfAACAqQAAgLMAAID7DwCAABAAgAoQAIB7EACAgBAAgIoQAIDrEACA8BAAgB0RAIA5EQCAPhEAgEgRAIBXFQCAExYAgBgWAIAwFgCApxYAgKwWAIDEFgCA9g8AgA8QAICPEACAIhEAgN0SAIBFFQCANRYAgGkWAIDJFgCATREAgGoSAIClEgCAuBIAgBcUAIAjFACALxQAgJMTAICYEwCA1xMAgNwTAIADFACACBQAgG8SAIB0EgCAvRIAgIL5CwCD+QsAgO0LAIH5CwCGWQQAh1kEAIQtBACFWQQAiqUHAIutBwCIqQcAiXEEAI5JBACPSQQAjE0EAI2xBwCS1QcAk2UHAJB9BwCR3QcAlnkHAJdRCwCUwQcAlXkHAJptCwCbxQcAmGELAJnxBwCebQsAn1ULAJxtCwCdZQsAorELAKOxCwCgLQcAoaELAKbdCwCnzQsApKULAKU1BwCqxQsAq80LAKj1CwCpzQsArskLAK/JCwCsyQsArckLALJtBwCzTQsAsEkLALFJCwC2RQsAt00LALRVCwC1TQsAukkLALtJCwC4dQsAuUkLAL5JCwC/SQsAvEkLAL1JCwCAwQoAgcEKAILZCgCD2QoAhPkKAIX5CgCG6QoAh+kKAIjZCgCJHQUAihUFAIttBQCMdQUAjYUGAI5pBQCPaQUAkBkFAJEZBQCSIQUAkyEFAJQhBQCVIQUAlu0GAJdZBgCYaQYAmd0GAJp9BgCbdQYAnG0GAJ1VBgCexQYAn3EKAKAhBgChpQoAoi0GAKOxCgCkOQYApdkKAKZZBgCnHQoAqGUGAKltBgCqZQYAq1kKAKxJCgCt8QUArs0FAK8JBgCw4QYAsXkGALIZBgCzGQYAtAkGALUJBgC2OQYAtzkGALgJBgC5CQYAuhkGALsZBgC8CQYAvQkGAL75AwC/+QMAwhIAgMgRAIC8TQEAvUkBALqxCQC7sQkAuDUBALktAQC2XQkAtw0BALRdCQC1VQkAsv0FALOVCQCw8QUAsfkFAK5tAQCvdQEArAkBAK1lAQCqaQEAq2kBAKiRBQCpaQEApk0BAKdVAQCkTQEApUUBAKJtAQCjVQEAoG0BAKFlAQCejQEAn5UBAJyNAQCdhQEAmpEAAJuRAACYYQUAmWEFAJZRBQCXUQUAlEEFAJVBBQCSUQUAk1EFAJD5AQCRYQUAjvkBAI/5AQCMAQUAjfkBAIr9AQCL5QEAiP0BAIn1AQCG/QEAh8UBAIT9AQCF9QEAgv0BAIPlAQCA/QEAgfUBAPBQ/wDNEQCAnBEAgKERAIDkEQCA6REAgCwTAIAxEwCAZxMAgGwTAIB8EgCAiBIAgJsSAICgEgCASxIAgOISAIBdEwCAURAAgKkQAIDDEACAyhAAgNYQAID6EACAAREAgAgRAICHEQCAwREAgLoRAIAxEgCAHhIAgCUSAIBcEgCAVRIAgGMSAIDPEgCAJRMAgI0SAICBEgCAqhIAgLESAIBDEwCAVhMAgH4TAICFEwCAjBMAgK0TAIDCEwCAyRMAgO4TAID8EwCA9RMAgFIUAICDFACAihQAgBEVAIAfFQCAGBUAgPcUAIArFQCANxUAgIIVAICJFQCAmRUAgGEVAIBvFQCApRUAgKwVAIBoFQCAURYAgFgWAID5FQCAABYAgN8VAIDmFQCAKRYAgCIWAIC2FgCAdBAAgLcQAICwEACAlRn/AJQR/wCXKf8AlhH/AJEd/wCQHf8Akwn/AJIR/wCdFf8AnBX/AJ8V/wCeFf8AmRX/AJgR/wCbFf8AmhX/AKUJ/wCkDf8Apxn/AKYB/wChEf8AoOn/AKMd/wCiGf8ArT3/AKw5/wCvDf8Arg3/AKkl/wCoJf8AqyH/AKol/wC1df8AtHX/ALdx/wC2df8AsXn/ALBx/wCzdf8AsnX/AL0t/wC8Kf8Avz3/AL49/wC5Mf8AuEn/ALsx/wC6Of8AgNn+AIHZ/gCC6f4Ag+n+AIT1/gCF/f4AhvH+AIfx/gCIzf4AidX+AIrd/gCL1f4AjM3+AI01AQCOPQEAjzUBAOQQAIDdEACAkkUBAJNdAQCURQEAlU0BAJZFAQCXfQEAmEEBAJlBAQCaQQEAm0EBAJxBAQCdRQEAnk0BAJ9FAQCgvQEAocUAAKLNAACjjQMApJUDAKWdAwCmlQMAp40DAKi5AwCpuQMAqokDAKuJAwCsmQMArZkDAK6JAwCviQMAsPkDALH5AwCyiQMAs4kDALQB/gC1Df4AtpEDALeRAwC4sQMAubEDALqxAwC7sQMAvKkDAL2lAwC+mQMAv5kDABYRAIAPEQCAlREAgGQRAICOEQCAkQQHAD0SAIDWEgCAlBIAgEoTAIAFFQCAPhUAgJsWAICPFgCAvRYAgL8VAICRFACABxYAgNATAIDKFACA2BUAgLMVAID+FACAwxQAgGsRAICuEQCAdhUAgF4UAIBoEACARBIAgO0VAIAZEwCAdxQAgEgQAIA/EACAkBUAgOcSAID8EQCAtBMAgHEWAIDwEgCA9xIAgHIRAIAKEgCApgMAgBMjAIAXIwCAoAYAgMcAAIC1BgCAsSMAgLUjAIC/IQCAuyEAgOYHAIB+CQCAggkAgEcjAICtIwCAOyMAgD8jAIAjIwCAJyMAgCsjAICAaQEAgWkBAIJ5AQCDUQEAhKX8AIWZ/ACGbQEAh2UBAC8jAIAzIwCANyMAgN4HAIDiBwCA0QAAgNcAAICiAwCAqAMAgOAHAIDTAACA1QAAgL0GAIB5AACADRQAgH0AAICHAACAkQAAgBIUAICbAACAHhQAgKUAAIAqFACArwAAgDYUAIC5AACAOxQAgNUPAIBbEACAnhAAgKEQAIAxEQCAXBEAgKYRAIDSEQCA7hEAgPERAID5EQCAExIAgBYSAICwvQEAsUUBALJNAQCzRQEAtF0BALVFAQC2TQEAt0UBALh9AQC5RQEAuk0BALtFAQC8XQEAeRIAgMcSAIA2EwCAqAYAgLMGAIDMlLQAPBAAgHETAICdEwCApRMAgOETAIBAFACAbxQAgKIUAIDbFACAVBUAgNAVAIBEFgCAbhYAgJiNBgCZgbEAhxYAgN4UAIDjFACA6BQAgO0UAIDPAACAkNEGAJHRBgCS0QYAk9EGAJSpkgKVtQYAlr0GAJe1BgDZAACAswMAgOQHAICACQCAASMAgAUjAICHKQCAOyQAgHQkAICTJACApSQAgMokAIDJKACA5SgAgPcoAICOJgCAuCEGALkhBgC6IQYAuyEGALwhBgC9IQYAviEGAL95uACwIbEAsTUGALI9BgCzNQYAtCkGALVFsgC2TbIAtyEGAIC5uQCB+QcAgikGAIMpBgCEOQYAiykAgG8pAICHMQYAiBEGAIn5sACK9bAAi/GwAIztsACN7QcAjuEHAI/lBwCQ8QcAkfEHAJL1sAAvJACAlImTApXpBwCWnQcAl50HAJixBwCZ1bMAmt2zAJuxBwCckQcAnZEHAJ6RBwCfSQYAoLkGAKG5BgCiIbMAo80GAKSRAQClkQEApkGyADMkAICo5QYAqe0GAKrlBgCr/QYAhAkAgIcJAICQCQCAjQkAgLCVBgCxnQYAspUGAIoJAIC0sQYA8iEAgLa9BgC3tQYAuI0GALmVBgC6nQYAu5UGALyNBgC9dQYAvn0GAL91BgCCkaMCg5GjAoCFBQCBnaMChrmjAoeNowKEjaMChbWjAoqVowKLkaMCiLGjAomZowKOPQIAj6UFAIyNowKNMQIAktEFAJPRBQCQ2QUAkd0FAJbJBQCXzQUAlM0FAJXFBQCa/QUAm/kFAJjxBQCZ8QUAntEFAJ/VBQCc5QUAnd0FAKIlBQCjIQUAoCkFAKEpBQCmOQUApz0FAKQ9BQClNQUAqg0FAKsVBQCoAQUAqQEFAK4FBQCvDQUArAkFAK0JBQCyfQUAs3kFALBxBQCxcQUAtiUFALchBQC0ZQUAtSkFALoZBQC7HQUAuB0FALkVBQC+DQUAvwkFALwBBQC9AQUAhPwCAPUiAID6IQCAAiIAgPkiAID9IgCAkZQCAKQQDQCffBwAnmQIAJ08CADZzKACzMCFAsz8hQL2IQCA9egAgJmY1gD66ACAm/jWAP/oAICxNN0ABOkAgNmUoAKynPsApwUAAAnpAIDZhKAChywDAJE4AgAO6QCAjAwCAI20AgDwtAIAgqACABPpAIC++QAAkpACAJcdAAC5+AMAhAgCAPBAAgCRfAIAHekAgBjpAICljNYAIukAgI3IAgDwJAIAsGkAAJK4AgC4tAMAudgMAJa0AgCW/AIArz0AAJFUAgCvCCEAJ+kAgLpFAACRTAIALOkAgL1BAACWuAIArw0AAIsZDACKHQwAiREMAIgZDACPDQwAjgkMAI0FDACMBQwAgzEMAII1DACBOQwAgNELAIclDACGIQwAhS0MAIQtDACbaQwAmm0MAJlhDACYaQwAn30MAJ55DACddQwAnHUMAJNBDACSRQwAkUkMAJBxDACXVQwAllEMAJVdDACUXQwAq7kMAKq9DACpsQwAqLkMAK9J/gCuqQwAraUMAKylDACjkQwAopUMAKGZDACggQwAp4UMAKaBDACljQwApI0MALuZDAC6kQwAuZ0MALidDAC/2YgC8HwNALUcAgC8hQwAs6kMALKtDACxoQwAsDX+ALehDAC2uQwAtbUMALS1DACBwQsA8DgBAIM9CgCCUQ0AhV0KAIRdCgCHmQ0AhiUKAImVvwCIlb8Ai4G/AIoRCgCNcQoAjIULAI+9DQCOgbwAkbWdApCxvACTqZ0CkpGdApWtvACUrbwAl9W/AJbVvwCZxb8AmMW/AJuxnwKa0QsAnam+AJx1DQCfvQsAnnkNAKEBvwCg1QoAo5WxAKKFvQClgb0ApJm9AKeBvQCmqbEAqYm9AKiFvQCrmb0Aqom9AK3xvQCsjb0Ar+m9AK71vQCxIb8AsJ29ALNJyQCyrb0AtaG9ALS9vQC3ob0AtkmxALlpyQC4TbEAu8UKALrNsQC9wQoAvLEKAL8hCwC+dQ0AgP2+AIHVngKCZb4Ag8W+AISRvgCFnb4AhqW+AIeNvgCIrZECieW+AIopkgKLtb4AjBGSAo2VvgCOLbIAj8WeApDpvgCRsbUAkkGSApPxnwKU1b4AleW+AJbhvgCXTZICmGWSApl9kgKaub4Am7EIAJz9DgCdlQgAkRQDAJ/tDgCgFQ4AoT0IAKJ1CACjrQkApCUIAKUNDgCmBdYApwEOAKgBDgCpAQ4AqgEOAKsBDgCsAQ4ArQEOAK4BDgCvAQ4AsNUPALGV0wCyndMAs4XUALSJ0wC1idMAttnWALfZ1gC46dYAuenWALr51gC7+dYAvOnWAL3p1gC+udYAv7nWAIBJ1wCBSdcAglnXAINZ1wCESdcAhXXSAIZ90gCHddIAiE3SAIlV0gCKddcAi63XAIy11wCNvdcAjrXXAI9J0gCQOdIAkTnSAJLJ0gCTydIAlNnSAJXZ0gCWydIAl8nSAJj50gCZ+dIAmsnSAJvF0gCc4dcAnW0OAJ41DgCf4Q4AoNHZAKHB2wCiwdsAo93bAKTF2wClzdsApsXbAKf92wCoJdsAqWXbAKrN2wCrxdsArN3bAK3B2wCuwdsAr3nbALAJ2wCxCdsAshnbALMZ2wC0CdsAtbXYALbB3gC33d8AuOXfALn13wC6/d8Au63fALy13wC9pd8Avq3fAKQgBQCPFd4AjhXeAI0F3gCMBd4AixXeAIoV3gD+IQCAsCUAAIfd3ACG3dwAhd3cAITd3ACD2dwAgtXcADHpAIA26QCAP+kAgEjpAIBV6QCAnAXeAJtt3gCabd4AYukAgG/pAICXVd4AllXeAJVB3gCUSd4Ak1HeAJJp3gB86QCAiekAgKzpAICukQsArZkLAKyBCwCriQAAqp0LAKmhCwComQsAkukAgKZxCQClZQkApBEJAKNlmAKiDQkAoQ0JAKANCQC7vQAAtekAgL3BjAKf6QCAvx0AAL4RAAC9eQAAvF0AAIAFAADC6QCA0AoAgLMMAIBkDQCAag0AgHANAIB8DQCAhWgCAH8NAICH6AMAhiADAJ4tAQCfVQEAgg0AgIUNAICIDQCAlw0AgJ0NAICgDQCAow0AgCYiAIDNDQCA3A0AgJVAHQCURB4Al0gbAJYAGACRLAAAkFQBAJNYHwCSABwAnWASAJxkEwCfiBcAnmwRAJlwGQCYdBoAmwAQAJoAFAACDgCABQ4AgBQOAIAXDgCAIw4AgB4iAIA4DgCAOw4AgLgALAC5rC8AuqguAN0WAIAWFwCA4BYAgLoDAIC3AwCAygMAgO0EAICMBQCA3wUAgBoGAIBABgCAVwYAgHQGAICiuQEAo7kBAKCtAQChpQEAiwYAgDgBAICkgQEAPAEAgICBuACBDboAghW2AIMBugCEAboAhSG2AIYBugCHPboAiAm6AIkBugCKGboAixW6AIxxugCNfboAjmm6AI9lugCQobgAkSW6AJLJzgCTJboAlCG6AJXBtgCWIboAl/W2AJjpzgCZRbYAmrmaApsBugCcuboAnfW6AJ7xugCfwboAoBG6AKEJlQKiSboAo42WAqQJugCltZYCpjm6AKeJtgCoWZoCqQ26AKpdsQCrpZYCrA2bAq0xugCuCboArwW6ALDRlgKxwZYCstGWArMdugC0UbgAtd26ALbFtgC30boAuPG6ALnRtgC68boAu826ALzZugC90boAvsm6AL/FugCfCZcCnvGwAJ2huwCc9ZsCmwW3AJq1uwCZOZcCmIW7AJchlwKW5bsAzMS+AJS9uwCTjbsAkr27AJG5uwCQ9bsAjy27AI6VmwKNabcAjMXPAIv5twCKLbsAic23AIgtuwCHCbsAhuXPAIUJuwCEjbkAgym7AIIluwCBMbsAgD27AL8ptwC+/bsAvR23ALz9uwC7+bsAuhXPALn5uwC4fbkAt/m7ALb1uwC14bsAtO27ALOJuwCyhbsAsZ27ALCVuwCv4bsArt27AK39twCs3bsAq927AKrJtwCp0bsAqF25AKcxuwCm/ZcCpe2XAqT9lwKjSbsAokW7AKF9uwCgQZoCgJmkAIEliAKCqaQAgxmoAEABAICFvaQAhu2vAIcViAKInYUCiaGkAIqZpACLlaQAjCGIAo0xiAKOIYgCj+2kAJDBpgCRTaQAklWoAJNBpACUQaQAlWGoAJZBpACXfaQAmEmkAJlBpACaWaQAm1WkAJwxpACdPaQAnimkAJ8lpACgYaYAoeWkAKIJ0ACj5aQApOGkAKUBqACm4aQApzWoAKgp0ACphagAqnmEAqvBpACseaQArTWkAK4xpACvAaQAsFGkALFJiwKyCaQAs82IArRJpAC19YgCtnmkALfJqAC4GYQCuU2kALodrwC75YgCvE2FAr1xpAC+SaQAv0WkAIARiQKBAYkCghGJAoPdpQCEkacAhR2lAEQBAICHEaUAiDGlAIkRqQCKMaUAkQ0AgEgBAICNEaUAjgmlAI8FpQCQAaUAkQ2lAJIZpQCTFaUAlLGnAEwBAICW2dEAlzWlAJgRpQCZ8akAmhGlAJvFqQCc+dEAUAEAgJ6phQKfEaUAoEmlAKEFpQCiAaUAozGlAKQBpQClGYoCplmlAKediQKoOaUAqYWJAqoJpQCruakArEmFAq0dpQCuTa4Ar7WJArB9hAKxQaUAsnmlALN1pQC0wYkCtdGJArbBiQK3DaUAuGGnALntpQBUAQCAu+GlALzhpQC9wakAvuGlAFgBAICAKaYAgSGmAII5pgCDNaYAhFGmAFwBAICGSaYAzEyHAmABAIBkAQCAiqnSAItFpgCMQaYAjaGqAI5BpgCPlaoAkMnSAGgBAICSmYYCkyGmAJSZpgCV1aYAltGmAJfhpgCY8aYAmemJApqppgCbbYoCnOmmAJ1VigKe2aYAn2mqAKB5hgKhLaYAon2tAKOFigKkLYcCpRGmAKYppgCnJaYAqLGKAqmhigKqsYoCq32mAKwxpACtvaYArqWqAK+xpgCw0aYAsfGqALLRpgCz7aYAtPmmALXxpgC26aYAt+WmALihpgC5raYAurmmALu1pgC8EaQAvZWmAL550gC/laYAl7mnAJa1pwCVjacAlLGGApMZiwKS4awAkbGnAJDlhwKfLacAnjmrAGwBAICcraUAm+GnAJotiwKZPYsCmC2LAof9pwCGzacAhcmnAISFpwCDPacAgoWHAoF5qwCA1dMAj3WrAI7FpwCNSYsCjPWnAItxiwKKtacAifWIAojtpwC37acAtlWHArWpqwC0BdMAszmrALLtpwCxDasAsO2nAL+hiwK+ZacAvSWIAnABAIC7DacAdAEAgLk5pwC4dacAeAEAgKb1pwCl7acAfAEAgIABAICizacAhAEAgIgBAICviacArmXTAIwBAICsDaUAq6mnAKqlpwCpsacAkAEAgICRoACBiY8CgsmgAIMNjAKEiaAAhTWMAoa5oACHCawAiNmAAomNoACK3asAiyWMAoyNgQKNsaAAjomgAI+FoACQUYwCkUGMApJRjAKTnaAAlNGiAJVdoACWRawAl1GgAJhxoACZUawAmnGgAJtNoACcWaAAnVGgAJ5JoACfRaAAoMGgAKHNoACi2aAAo9WgAKRxogCl9aAAphnUAKf1oACo0aAAqTGsAKrRoACrBawArDnUAK2VrACuaYACr9GgALAJoACxRaAAskGgALNxoAC0QaAAtVmPArYZoAC33YwCuHmgALnFjAK6SaAAu/msALwJgAK9XaAAvg2rAL/1jAKAvYACgYGhAIK5oQCDtaEAhAGNAoURjQKGAY0Ch82hAIihowCJLaEAijWtAIshoQCMIaEAjQGtAI4hoQCPHaEAkGmhAJFhoQCSeaEAk3WhAJQRoQCVHaEAlgmhAJcFoQCYgaMAmQWhAJrp1QCbBaEAnAGhAJ3hrQCeAaEAn9WtAKAJ1QChpa0AolmBAqPhoQCkWaEApRWhAKYRoQCnIaEAqDGhAKkpjgKqaaEAq62NAqwpoQCtlY0CrhmhAK+prQCwOYECsW2hALI9qgCzxY0CtG2AArVRoQC2aaEAt2WhALjxjQK54Y0CuvGNArs9oQC8caMAvf2hAL7lrQC/8aEAgBGiAIExrgCCEaIAgy2iAIQ5ogCFMaIAhimiAIclogCIYaIAiW2iAIp5ogCLdaIAjNGgAI1VogCOudYAj1WiAJAxogCR0a4AkjGiAJPlrgCU2dYAlXWuAJaJggKXMaIAmKmiAJnlogCa4aIAm9GiAJzhogCd+Y0CnrmiAJ99jgKgGaIAoaWOAqIpogCjma4ApGmCAqU9ogCmbakAp5WOAqgdgwKpIaIAqhmiAKsVogCsoY4CrbGOAq6hjgKvbaIAsEGgALHNogCy1a4As8GiALTBogC14a4AtsGiALf9ogC4yaIAucGiALrZogC71aIAvLGiAL29ogC+qaIAv6WiAJMVrwCSpaMAkSmPApCVowCXGY8CluGoAJWxowCU5YMCm5mjAJqVowCZraMAmJGCAp/howCeLY8CnT2PApwtjwKD6a8Agj2jAIHdrwCAPaMAhz2jAIaFgwKFea8AhNXXAIvdowCK7aMAiemjAIilowCPcY8CjrWjAI31jAKM7aMAs+mjALIF1wCx6aMAsG2hALc5rwC27aMAtQ2vALTtowC7zaMAunWDArmJrwC4JdcAvw2jAL49owC9OaMAvHWjAKPNowCi2a8AocGjAKBNoQCn8aMAps2jAKXtrwCkzaMAq9mjAKrVowCpzaMAqMWjAK+powCupaMArbGjAKy9owC43YwCueWMArrxrQC78a0AvJmuAL2ZrgC+ua4Av7muALDZrQCx2a0AsqGuALOhrgC0ka4AtZGuALbNrQC3yYwCqMmuAKnJrgCq6a4Aq+muAKylrQCtoYwCroWMAq+9jAKgwa4AocGuAKKdrQCjmYwCpK2MAqWVjAKmga0Ap4GtAJh1rQCZcYwCmlWMApttjAKcaa0AnWmtAJ4RrgCfEa4AkH2MApFFjAKSUa0Ak1GtAJQ5rgCVOa4AlhmuAJcZrgCIwbwCiTm5AopRFQCLURUAYQ0AgJQBAICOLa0AjymMAoDdqwCBdRUAgqkBAIN5FQCE+bwChQW5Aob5ugKHCbkCbQ0AgJgBAIBzDQCAnAEAgHkNAIBLIwCAiw0AgNEGAIC+DQCAywcAgNANAIAPBwCACA4AgJcHAIAaDgCAnQcAgCYOAICGYAIAhYABAIRMOQCABwCAyAcAgE8HAIBSBwCAXQcAgJANAADhBgCAFSQAgOglAIA1LgCAiXg/AIgAPAC6LACA1i0AgD83AIAHKwCA0zAAgL8yAIAOLACAYC8AgKYrAICsMACA+isAgCc1AICbNwCAui0AgPIsAIBzMgCAEDAAgDwwAIAbOACAMDAAgAgwAIB/NACAuzQAgN4sAICvoAEAUzIAgKczAIASLACAPi0AgFM4AICPIwCAUyMAgMwouALNRLgCzIi4As38uALMkIQCzTSFAsywhQLNVIcCzBCCAs1QggLMoIICzYyCAswwgQLNJIECzBiBAs2EgQJdIwCAcSMAgJkjAIB7IwCAoyMAgGcjAICFIwCAZC8AgKo5AwCrOQMAziwAgNsqAIDMWLkCzaS6AqwZAwDTKgCAsHWvALFxjgKyVY4Cs22OArRprwC1aa8AthGsALcRrAC4AawAuQGsAOMqAIDP6QCALisAgEIrAIBKKwCAUisAgIJB2gCDra4AgCmsAIGtrgCGqa4Ah32iAISprgCFSaIAis0DAIs5xACIYdoAic2iAI6hAwCPoQMAjM0DAI3BAwCfua8AnrWvAJ2NrwCcsY4CmxmDAprhpACZsa8AmOWPApc1owCWha8AlQmDApS1rwCTMYMCkvWvAJG1gAKQra8Aj/2vAI7NrwCNya8AjIWvAIs9rwCKhY8CiXmjAIjV2wCHyaMAhh2vAIX9owCEHa8AgxmvAIL12wCBGa8AgJ2tAL+xFgC+qRYAvaEWALzBvgK7tRYAuom5ArmBuQK4sQoAt22sALa9AgC1iRYAtLEWALOpFgCysRYAsUUXALCtAgCv2bkCrs0CAK0xFwCszQIAqyUXAKodrACpKRcAqA0DAMyIgQLNSIECpQUXAKQFFwCjIa8Aou2DAqH9gwKg7YMCgAm4AoE9EQCCIQUAg/29AoT1qACFFb4ChiURAIfxiAKIoREAiaERAIoZBQCL0b0CjDG4Ao09vgKOsREAj7ERAJB5BQCRsb0CknWvAJPdEQCUEQUAlcERAJZRuAKXrb0CmGG+ApmRvQKaabgCm5G9ApyhBACdhRAAnrGrAJ+JEACggQUAoX0QAKKBBQCjlb4CpIEFAKVpEACmnREAp4URAKi9EQCphREAqrEFAKthqwCsnQ0Ara2+Aq6lvgKvmREAsI25ArHtEQCy5REAs/0RALT5pAC14REAtvkFALcxvQK4ya8AucmvALrhuAK71REAvNkFAL0FvQK+HagAv/2+AoA9EACB6YkCgokQAIOJEACEIQQAhem8AoYZuQKHFb8CiKkQAImpEACKEQQAi9m8AowNrgCNpRAAjnkEAI+pEACQSbkCkbW8ApJJvwKTubwClFG5ApWpvAKWiQUAl60RAJipqgCZkREAmmkEAJuVEQCceQQAnW2/Ap5pBACfgREAoIUQAKGdEACilRAAo60QAKSJBAClWaoAprUMAKeFvwKovb8CqYEQAKrluAKrhRAArJ0QAK2FEACukaUAr4kQALDhBACxKbwCsuGuALPhrgC02bkCte0QALbxBAC3LbwCuAWpALnlvwK61RAAuwGJArxxEAC9cRAAvskEAL8BvAKAAboCgQ28AoKBEwCDgRMAhCkHAIXhvwKGJa0Ah40TAIhhBwCJsRMAiiG6AovdvwKMMbwCjcG/Ao45ugKPwb8CkJEGAJG1EgCSgakAk7kSAJRRBwCVrRIAllEHAJdFvAKYcQcAmZkSAJptEwCbdRMAnG0TAJ1VEwCeYQcAn7GpAKCtDwChnbwCopW8AqOpEwCk3bsCpb0TAKa1EwCnrRMAqImmAKmREwCqiQcAq0G/AqyZrQCtma0ArrG6Aq+FEwCw6QcAsTW/ArItqgCzzbwCtO0TALU5igK2WRMAt1kTALjRBwC5Gb8Cuum6ArvlvAK8eRMAvXkTAL7BBwC/Cb8CngG9Ap/xvgKcAbsCnf2+AppRBgCbgRIAmCWsAJmNEgCWGQYAl9G+ApShEgCVoRIAkjG7ApM9vQKQCQYAkcG+Ao7BEgCPwRIAjHUSAI2hiwKKtasAi1W9AohxBgCJrb4Chmm7AoddEgCEQawAhUGsAIJRBgCDmb4CgFGnAIFJEgC+qawAv6msALypBgC9Yb4CurmnALuhEgC4tRIAua0SALbtugK3jRIAtLW9ArWJEgCynQ4As629ArChBgCxcagArt0SAK/lEgCszRIArdUSAKrBBgCrKRMAqNEGAKnFvQKm4QYApx0TAKQhqAClGRMAoiEHAKMFEwCg+bsCoQG+AoKhIwBWKwCAWisAgJMpAIDj6QCAh7EjAHorAIB+KwCAmisAgIsJJADU6QCAiWUkAI6NIwCPLSQAlykAgI0JJACSZSQAhisAgN7pAICRtSMAtisAgJf9IwCUrSMAvisAgBcrAICbeSQAxisAgJmRIwC56wCAn8EtAO4rAICdKdQAoiEjAJ8pAIAGLACAoR0jAAosAICnMSMApKEkABYsAICqiSQAoykAgKi5JACp5SQArg0jAK+tJACsiSQArYkkALLlJABOLACAsOkkALE1IwC2TSMAt30jALQtIwC1RSMAuvUkALv5JABSLACAuREjAL5BLQB6LACAvFUtAIYsAICADSUAgZUiAIKpIgCDoSIAhCklAIUpJQCGoSIAh7kiAIgxJQCJbSUAliwAgIsBJQCMASUAjQElAI6FIgCPJSUAkGElAJG9IgCSbSUAk/kiAJSlIgCVzSIAlsUiAJf1IgCY0SIAmZkiAJp9JQCbcSUAniwAgKIsAIDy6QCAozIAgLYsAIChFSIAoikiAKMhIgC+LACApaklAKYhIgCnOSIAqLElAKntJQD36QCAq4ElAKyBJQCtgSUArgUiAK+lJQCw4SUAsT0iALLtJQCzeSIAtCUiALVNIgC2RSIAt3UiALhRIgC5GSIAuv0lALvxJQDKLACA0iwAgNosAIACLQCAgLkvAIG5LwCCyS8Ag8kvAITZLwCF2S8AhskvAIfJLwCI+S8AifkvAIrZLwDuLACA+iwAgP4sAIAGLQCADi0AgJC1LwCRuS8AklkyAJNVMwCUYTMAlQEzAJYtMwCXtTMAmJ0zAJlxMACaSTAAm0kwAJw1MACdXTEAntUxAJ/JMQCgQTEAoUkxAKJZMQCjVTEApE0xAKV9MQCmZTEAp0k6AKilOwCpqTsAqr07AKuxmgCs0ZYArak7AK6dOwASLQCAsEGUALHNlgCy1ZoAs8GWALTBlgC14ZoAtsGWALf9lgC4yZYAucGWALrZlgC71ZYAvLGWAL29lgC+qZYAv6WWAMUAAACpTScAqiEnAKshJwCsIScArSEnAK6lIACvBScAGi0AgKG1IACiiSAAIi0AgKQJJwAmLQCANi0AgKeZIAA6LQCAubkgAEYtAIC7UScAai0AgG4tAIBSLQCAWi0AgLBBJwCxnSAAsk0nALYtAIC0hSAAte0gALblIAC31SAAiJEnAInNJwCKoScAi6EnAIyhJwCNoScAjiUgAI+FJwCArScAgTUgAIIJIACDASAAfi0AgIWJJwCGASAAhxkgAJhxIACZOSAAmt0nAJvRJwCcfS4AnYHXAJ5pLgCfaS4AkMEnAJEdIACSzScAk1kgAJQFIACVbSAAlmUgAJdVIACA+T0Agfk9AIJJPgCDST4AhFk+AIVZPgCGST4Ah0k+AIh5PgCCLQCAhi0AgHotAICOLQCAii0AgKItAID86QCAkB0+AJEtPgCoSBUAvi0AgMItAIDKLQCA3i0AgAEuAICiAAwAo2QPAKBoAwChbAAApgAUAKd0FwCkAAgApXwLAAHqAIAG6gCADS4AgBEuAIAVLgCACS4AgB0uAICnKQCAqykAgCUuAIAtLgCAC+oAgEkuAIBNLgCAWS4AgBDqAIBhLgCAZS4AgEQvAICvKQCAeS4AgJUuAICRLgCAGuoAgJ0uAIAf6gCAqS4AgKUuAICtLgCAvS4AgMEuAICzKQCAgG0/AIF5PwCCoT8Ag6E/AIShPwCFrcgAhq0/AIelPwCInT8AiSEFAIqVyACLJQUAjD0FAI0lBQCO+cgAjyUFAJBdBQCRZQUAkmEFAJN1BQCUFQUAlfU8AJYRBQCXDQUAmD0FAJkFBQCaAQUAmwnVAJwBBQCdgeUAngEFAJ/5BQCgCQUAoRUFAKJlPACjEQUApDUFAKXt1QCmXcgAp1XIAKjd1QCpYQUAqkEFAKtBwwCsRQUArXnIAK5FBQCvlQQAsLEEALGxBACyvQQAs7kEALSpBAC1qQQAtlkEALdNBAC4SQQAuRUEALodBAC7FQQAvA0EAL3ZBwC+yQcAv8kHAIA5BACBOQQAgrUEAIMtBQCEPQUAhSEFAIYtBQCHmdYAiBkFAIllBQCKYQUAi30FAIzlywCNYQUAjmEFAI9hBQCQdcsAkSkFAJL5BQCTaQIAlHkCAJV5AgCWaQIAl2kCAJhZAgCZWQIAmiUCAJs9AgCcJQIAnfE/AJ4hAgCfIQIAoAECAKEBAgCiAQIAowECAKQBAgClAQIApgECAKcBAgCoAQIAqQECAKoBAgCrBQIArB0CAK0FAgCuDQIAr2HAALB5AgCxeQIAsgkCALMJAgC0GQIAtdnlALYVAgC3DQIAuPXlALkxAgC6MQIAuzECALwRAgC9EQIAvhECAL8RAgCfpT4AnqU+AJ2VPgCclT4Am1XMAJqBPgCZiT4AmJE+AJdF9ACWrT4Ala0+AJTh5wCTvekAkrU+AJFNPgCQReQA0S4AgNkuAIDdLgCA4S4AgLcpAIAk6gCAuykAgAQvAIAILwCADC8AgOvrAIAu6gCA5zUAgIJh+wCBCT4AgBE+ADwvAIC/KQCAUeoAgCPrAIC7ZT4AumU+ALl1PgC4dT4At2HkALah8AC1TT4AtHE+ALNpPgCyYT4AsWk+ALCl7wCvDT4AronwAK3h9ACshfAAqxk+AKrJ9ACpbecAqBk+AKchPgCmWT4ApVE+AKRZPgCjQT4Aos3iAKFVPgCgVT4ATC8AgFQvAIDDKQCAaC8AgHgvAIB8LwCAhC8AgJQvAIDLKQCAxykAgDPqAICcLwCAsC8AgLQvAIDELwCA2C8AgNAvAIDULwCA3C8AgPAvAID0LwCADDAAgBQwAIAkMACAWAAAADgwAIBC6gCANDAAgCgwAIBAMACASDAAgFwwAIBH6gCAZDAAgFgwAIBQMACAzykAgGwwAIB0MACAfDAAgHAwAIDTKQCAlDAAgEzqAIDAMACAAjEAgN4wAIDfKQCA2ykAgNcpAICqKwCArisAgAYxAIDuMACAuzUAgEMqAIAaMQCALjEAgCYxAIBl6gCA4ykAgEIxAIA2MQCAXzEAgICJAQCBiQEAgpkBAIOZAQCEiQEAhYkBAIa5AQCHuQEAiN3EAImNAQCKhQEAi50BAIyFAQCNjQEAjoUBAI8BxwCQgQEAkYEBAJKBAQCTgQEAlIEBAJWBAQCWgQEAl4EBAJj1zACZsdkAmokBAJuJAQCcmQEAnZkBAJ6JAQCfiQEAoHkBAKF5AQCizccAo4kCAKSZAgClmQIApokCAKfh2wCotQIAqb0CAKq1AgCrjQIArJUCAK2dAgCulQIAr40CALD1AgCx/QIAsvUCALONAgC0lQIAtfXAALaRAgC3kQIAuLECALmxAgC6sQIAu7ECALyRAgC9kQIAvpECAL+RAgCYjQ0AmQUCAJoNAgCbBQIAnB0CAJ0FAgCeDQIAnwUCAJBt6QCRSQ0AkkUNAJNdDQCUsQ0AlbUNAJa9DQCXtQ0AiBENAIkRDQCKEQ0AixENAIwxDQCNMQ0AjsnPAI81DQCAkQ4AgZEOAIKRDgCDkQ4AhDENAIUxDQCGMQ0AhzENALjpAgC56QIAuvkCALv5AgC86QIAvekCAL4ZAgC/GQIAsFnHALGpzwCy5QIAs/0CALTlAgC17QIAtuUCALfZAgCoddoAqfECAKrxAgCrPccArO0CAK2VAgCunQIAr5UCAKD9AgChxQIAos0CAKMFxwCk2QIApdkCAKbJAgCnyQIAgAAAAG/qAIBrMQCASjEAgHMxAIB3MQCAezEAgH8xAICLMQCAdOoAgJMxAIDrKQCAnzEAgHnqAICjMQCA7ykAgK8xAIC7MQCAyzEAgH7qAIAV6gCAg+oAgOsxAICI6gCA9zEAgP8xAIDvMQCACzIAgBsyAIAjMgCALzIAgDMyAICN6gCAFzIAgEsyAIBPMgCA8ykAgF8yAICS6gCAQzIAgH8yAICX6gCAnOoAgIMyAICXMgCAjzIAgPcpAICbMgCAqzIAgKcyAICzMgCA2ekAgMMyAICh6gCAzzIAgKvqAIDjMgCAAzMAgLDqAIAXMwCAGzMAgLXqAIC66gCANzMAgEczAID7KQCASzMAgP8pAIBjMwCAZzMAgHMzAIB/MwCAAyoAgJczAIC/6gCAszMAgMTqAIAp6gCAzzMAgMnqAIDO6gCA0+oAgAcqAIALKgCA3eoAgNjqAIDi6gCA5+oAgA80AIATNACAHzQAgBcqAIAbKgCA8eoAgF4AAAAzNACAHyoAgPbqAID76gCAAOsAgKM0AIAjKgCArzQAgLM0AIAF6wCACusAgMs0AIAnKgCAD+sAgN80AIDjNACAKyoAgBTrAID/NACALyoAgA81AIAHNQCAFzUAgB7rAIAvNQCAMyoAgDs1AIBDNQCAUzUAgDcqAIAo6wCALesAgDsqAICADd8AgVUBAIJdAQCDVQEAhE0BAIV1AQCGfQEAh3kBAIgx3wCJod8AikEBAItBAQCMQQEAjUEBAI5FAQCP5cgAkFnfAJHFAQCSzQEAkwHCAJTZAQCV2QEAlt3AAJfNAQCY9QEAmWHdAJrxAQCb8QEAnNEBAJ3RAQCe3QEAn9UBAKAtAQChNQEAoj0BAKM1AQCkLQEApVUBAKZdAQCnVQEAqG0BAKl1AQCqfQEAq3UBAKxtAQCtVQEArl0BAK9VAQCwLQEAsTUBALI9AQCzNQEAtC0BALXRAgC20QIAt9ECALjxAgC58QIAuvXdALv1AgC87QIAvdUCAL7dAgC/1QIAntkFAJ/ZBQCc2QUAndkFAJrZBQCb2QUAmNkFAJnZBQCWmQUAl5kFAJTN3gCVmQUAkp0FAJOFBQCQnQUAkZUFAI7dBQCP5QUAjN0FAI3VBQCKwQUAi7XeAIjRBQCJ0QUAhuEFAIfhBQCEEQUAhREFAIIdxQCDAQUAgAkFAIExwAC+yQIAv8kCALzJAgC9yQIAuqkCALupAgC4ccgAuakCALaNAgC3lQIAtI0CALWFAgCyrQIAs5UCALBN3gCxpQIArtECAK/RAgCsxQIArcECAKrVAgCr3QIAqCUFAKndAgCmFQUApx0FAKQFBQClHQUAohUFAKMdBQCgGQUAoRHeAIAAAAAy6wCAazUAgDfrAIB3NQCAgzUAgDzrAIBB6wCAnzUAgEbrAICnNQCAVuoAgD8qAIC/NQCAwzUAgEcqAIDHNQCAIS4AgEvrAIBQ6wCAW+oAgGDqAIDrNQCAAzgAgEsqAIAXNgCAEzYAgBs2AIAmLACAHzYAgCM2AIAnNgCALzYAgFXrAIAzNgCARzYAgEs2AIA3NgCATzYAgGM2AIBDNgCAVzYAgFs2AIBfNgCAWusAgGTrAIBf6wCATyoAgH82AICDNgCAizYAgHjrAICPNgCAaesAgFMqAIBXKgCAbusAgHPrAIBbKgCArzYAgLc2AIC7NgCAxzYAgMM2AIDPNgCAyzYAgNM2AIDXNgCA3zYAgF8qAIDnNgCA6zYAgGMqAID7NgCAfesAgAs3AIAPNwCAZyoAgBs3AICbKQCAgusAgIfrAIBrKgCAbyoAgEc3AICM6wCAnzcAgKM3AIC7NwCAxzcAgJbrAIDo6QCAXQAAANM3AIDPNwCA2zcAgO3pAIDnNwCAm+sAgKDrAIAzOACAPzgAgEc4AICl6wCASzgAgHc4AICDOACAhzgAgH84AICTOACAlzgAgKrrAICjOACAcyoAgKs4AICv6wCAdyoAgOM4AICxLgCA+zgAgLTrAIC+6wCAeyoAgH8qAIAjOQCAw+sAgIMqAIDI6wCAgBkBAIEZAQCCKQEAgykBAIT99ACFPQEAhjUBAIctAQCIFQEAiR0BAIoVAQCLbQEAjHUBAI19AQCOdQEAj20BAJAJwwCRCcMAkkH5AJMZAQCUCQEAlQkBAJY5AQCXOQEAmAkBAJkJAQCaHQEAmxUBAJwNAQCd9QEAnv0BAJ8twwCgCQEAoQkBAKIZAQCjGQEApAkBAKUJAQCmOQEApzkBAKgJAQCpCQEAqhkBAKsZAQCsCQEArQkBAK55AQCveQEAsAkBALEJAQCyGQEAsxkBALQJAQC1CQEAtjkBALc5AQC4CQEAuQkBALoZAQC7GQEAvAkBAL0JAQC+kcMAv5XDAJyVHQCdnR0AnpUdAJ8lwgCYPdsAmZ0dAJqVHQCbjR0AlFkdAJVZHQCWqR0Al6kdAJBZHQCRWR0AkkkdAJNJHQCMGR0AjRkdAI4pHQCPKR0AiB0dAIkFHQCKDR0Aiy0VAIRdHQCFJR0Ahi0dAIclHQCAXR0AgUUdAIJNHQCDRR0AvIkCAL2JAgC+mQIAv5kCALhtHQC5lQIAup0CALupwAC0ZdcAtVUdALZdHQC3VR0AsFEdALFRHQCyUR0As1EdAKwRHQCtER0ArhEdAK8RHQCoER0AqREdAKoRHQCrER0ApFEdAKVRHQCmUR0Ap1EdAKBRHQChUR0AolEdAKNRHQCAeQAAgXkAAIKJAACDiQAAhJkAAIWZAACGiQAAh4kAAIi5AACJuQAAikXBAIuNAACMlQAAjZ0AAI6VAACPjQAAkPUAAJH9AACS9QAAk40AAJSVAACVnfsAlpEAAJeF+wCYrQAAmbUAAJq9AACbtQAAnJ37AJ2pAABDOQCAzesAgFs5AICHKgCAazkAgHc5AIB/OQCAhzkAgIsqAIDS6wCAtzkAgMM5AICPKgCAkyoAgMc5AIDX6wCAlyoAgNzrAIDh6wCA5usAgJsqAIAHOgCACzoAgBM6AIAbOgCA8OsAgLkAAAC4AAAAuwAAALoAAAC9AAAAvAAAAL8AAAC+AAAAACAAIMyBACDMgwAgzIQAIMyFACDMhgAgzIcAIMyIACDMiMyAACDMiMyBACDMiM2CACDMigAgzIsAIMyTACDMk8yAACDMk8yBACDMk82CACDMlAAgzJTMgAAgzJTMgQAgzJTNggAgzKcAIMyoACDMswAgzYIAIM2FACDZiwAg2YwAINmM2ZEAINmNACDZjdmRACDZjgAg2Y7ZkQAg2Y8AINmP2ZEAINmQACDZkNmRACDZkQAg2ZHZsAAg2ZIAIOOCmQAg44KaACEAISEAIT8AIgAjACQAJQAmACcAKAAoMSkAKDEwKQAoMTEpACgxMikAKDEzKQAoMTQpACgxNSkAKDE2KQAoMTcpACgxOCkAKDE5KQAoMikAKDIwKQAoMykAKDQpACg1KQAoNikAKDcpACg4KQAoOSkAKEEpAChCKQAoQykAKEQpAChFKQAoRikAKEcpAChIKQAoSSkAKEopAChLKQAoTCkAKE0pAChOKQAoTykAKFApAChRKQAoUikAKFMpAChUKQAoVSkAKFYpAChXKQAoWCkAKFkpAChaKQAoYSkAKGIpAChjKQAoZCkAKGUpAChmKQAoZykAKGgpAChpKQAoaikAKGspAChsKQAobSkAKG4pAChvKQAocCkAKHEpAChyKQAocykAKHQpACh1KQAodikAKHcpACh4KQAoeSkAKHopACjhhIApACjhhIIpACjhhIMpACjhhIUpACjhhIYpACjhhIcpACjhhIkpACjhhIspACjhhIwpACjhhI4pACjhhI8pACjhhJApACjhhJEpACjhhJIpACjkuIApACjkuIMpACjkuIkpACjkuZ0pACjkuowpACjkupQpACjku6MpACjkvIEpACjkvJEpACjlhaspACjlha0pACjlirQpACjljYEpACjljZQpACjlkI0pACjlkbwpACjlm5spACjlnJ8pACjlraYpACjml6UpACjmnIgpACjmnIkpACjmnKgpACjmoKopACjmsLQpACjngaspACjnibkpACjnm6MpACjnpL4pACjnpZ0pACjnpa0pACjoh6opACjoh7MpACjosqEpACjos4cpACjph5EpACjqsIApACjrgpgpACjri6QpACjrnbwpACjrp4gpACjrsJQpACjsgqwpACjslYQpACjsmKTsoIQpACjsmKTtm4QpACjsnpApACjso7wpACjssKgpACjsubQpACjtg4ApACjtjIwpACjtlZgpACkAKgArACwALQAuAC4uAC4uLgAvADAAMCwAMC4AMOKBhDMAMOeCuQAxADEsADEuADEwADEwLgAxMOaXpQAxMOaciAAxMOeCuQAxMQAxMS4AMTHml6UAMTHmnIgAMTHngrkAMTIAMTIuADEy5pelADEy5pyIADEy54K5ADEzADEzLgAxM+aXpQAxM+eCuQAxNAAxNC4AMTTml6UAMTTngrkAMTUAMTUuADE15pelADE154K5ADE2ADE2LgAxNuaXpQAxNueCuQAxNwAxNy4AMTfml6UAMTfngrkAMTgAMTguADE45pelADE454K5ADE5ADE5LgAxOeaXpQAxOeeCuQAx4oGEADHigYQxMAAx4oGEMgAx4oGEMwAx4oGENAAx4oGENQAx4oGENgAx4oGENwAx4oGEOAAx4oGEOQAx5pelADHmnIgAMeeCuQAyADIsADIuADIwADIwLgAyMOaXpQAyMOeCuQAyMQAyMeaXpQAyMeeCuQAyMgAyMuaXpQAyMueCuQAyMwAyM+aXpQAyM+eCuQAyNAAyNOaXpQAyNOeCuQAyNQAyNeaXpQAyNgAyNuaXpQAyNwAyN+aXpQAyOAAyOOaXpQAyOQAyOeaXpQAy4oGEMwAy4oGENQAy5pelADLmnIgAMueCuQAzADMsADMuADMwADMw5pelADMxADMx5pelADMyADMzADM0ADM1ADM2ADM3ADM4ADM5ADPigYQ0ADPigYQ1ADPigYQ4ADPml6UAM+aciAAz54K5ADQANCwANC4ANDAANDEANDIANDMANDQANDUANDYANDcANDgANDkANOKBhDUANOaXpQA05pyIADTngrkANQA1LAA1LgA1MAA14oGENgA14oGEOAA15pelADXmnIgANeeCuQA2ADYsADYuADbml6UANuaciAA254K5ADcANywANy4AN+KBhDgAN+aXpQA35pyIADfngrkAOAA4LAA4LgA45pelADjmnIgAOOeCuQA5ADksADkuADnml6UAOeaciAA554K5ADoAOjo9ADsAPAA9AD09AD09PQA+AD8APyEAPz8AQABBAEFVAEHiiJVtAEIAQnEAQwBDRABDby4AQ+KIlWtnAEQAREoARFoARHoARMW9AETFvgBFAEYARkFYAEcAR0IAR0h6AEdQYQBHeQBIAEhQAEhWAEhnAEh6AEkASUkASUlJAElKAElVAElWAElYAEoASwBLQgBLSwBLTQBMAExKAExURABMagBMwrcATQBNQgBNQwBNRABNSHoATVBhAE1SAE1WAE1XAE3OqQBOAE5KAE5qAE5vAE8AUABQSABQUE0AUFBWAFBSAFBURQBQYQBRAFIAUnMAUwBTRABTTQBTUwBTdgBUAFRFTABUSHoAVE0AVQBWAFZJAFZJSQBWSUlJAFbiiJVtAFcAV0MAV1oAV2IAWABYSQBYSUkAWQBaAFsAXABdAF4AXwBgAGEAYS5tLgBhL2MAYS9zAGHKvgBiAGJhcgBjAGMvbwBjL3UAY2FsAGNjAGNkAGNtAGNtMgBjbTMAZABkQgBkYQBkbABkbQBkbTIAZG0zAGR6AGTFvgBlAGVWAGVyZwBmAGZmAGZmaQBmZmwAZmkAZmwAZm0AZwBnYWwAaABoUGEAaGEAaQBpaQBpaWkAaWoAaW4AaXYAaXgAagBrAGtBAGtIegBrUGEAa1YAa1cAa2NhbABrZwBrbABrbQBrbTIAa20zAGt0AGvOqQBsAGxqAGxtAGxuAGxvZwBseABswrcAbQBtMgBtMwBtQQBtVgBtVwBtYgBtZwBtaWwAbWwAbW0AbW0yAG1tMwBtb2wAbXMAbeKIlXMAbeKIlXMyAG4AbkEAbkYAblYAblcAbmoAbm0AbnMAbwBvVgBwAHAubS4AcEEAcEYAcFYAcFcAcGMAcHMAcQByAHJhZAByYWTiiJVzAHJhZOKIlXMyAHMAc3IAc3QAdAB1AHYAdmkAdmlpAHZpaWkAdwB4AHhpAHhpaQB5AHoAewB8AH0AwqIAwqMAwqUAwqYAwqwAwrBDAMKwRgDCtwDDgADDgQDDggDDgwDDhADDhQDDhgDDhwDDiADDiQDDigDDiwDDjADDjQDDjgDDjwDDkQDDkgDDkwDDlADDlQDDlgDDmQDDmgDDmwDDnADDnQDDoADDoQDDogDDowDDpADDpQDDpwDDqADDqQDDqgDDqwDDrADDrQDDrgDDrwDDsADDsQDDsgDDswDDtADDtQDDtgDDuQDDugDDuwDDvADDvQDDvwDEgADEgQDEggDEgwDEhADEhQDEhgDEhwDEiADEiQDEigDEiwDEjADEjQDEjgDEjwDEkgDEkwDElADElQDElgDElwDEmADEmQDEmgDEmwDEnADEnQDEngDEnwDEoADEoQDEogDEowDEpADEpQDEpgDEpwDEqADEqQDEqgDEqwDErADErQDErgDErwDEsADEsQDEtADEtQDEtgDEtwDEuQDEugDEuwDEvADEvQDEvgDFgwDFhADFhQDFhgDFhwDFiADFiwDFjADFjQDFjgDFjwDFkADFkQDFkwDFlADFlQDFlgDFlwDFmADFmQDFmgDFmwDFnADFnQDFngDFnwDFoADFoQDFogDFowDFpADFpQDFqADFqQDFqgDFqwDFrADFrQDFrgDFrwDFsADFsQDFsgDFswDFtADFtQDFtgDFtwDFuADFuQDFugDFuwDFvADFvQDFvgDGjgDGkADGoADGoQDGqwDGrwDGsADHjQDHjgDHjwDHkADHkQDHkgDHkwDHlADHlQDHlgDHlwDHmADHmQDHmgDHmwDHnADHngDHnwDHoADHoQDHogDHowDHpgDHpwDHqADHqQDHqgDHqwDHrADHrQDHrgDHrwDHsADHtADHtQDHuADHuQDHugDHuwDHvADHvQDHvgDHvwDIgADIgQDIggDIgwDIhADIhQDIhgDIhwDIiADIiQDIigDIiwDIjADIjQDIjgDIjwDIkADIkQDIkgDIkwDIlADIlQDIlgDIlwDImADImQDImgDImwDIngDInwDIogDIpgDIpwDIqADIqQDIqgDIqwDIrADIrQDIrgDIrwDIsADIsQDIsgDIswDItwDJkADJkQDJkgDJlADJlQDJmQDJmwDJnADJnwDJoQDJowDJpQDJpgDJqADJqQDJqgDJqwDJrQDJrwDJsADJsQDJsgDJswDJtADJtQDJuADJuQDJuwDKgQDKggDKgwDKiQDKigDKiwDKjADKjQDKkADKkQDKkgDKlQDKnQDKnwDKuQDKvG4AzIAAzIEAzIjMgQDMkwDOhgDOiADOiQDOigDOjADOjgDOjwDOkADOkQDOkgDOkwDOlADOlQDOlgDOlwDOmADOmQDOmgDOmwDOnADOnQDOngDOnwDOoADOoQDOowDOpADOpQDOpgDOpwDOqADOqQDOqgDOqwDOrADOrQDOrgDOrwDOsADOsQDOsgDOswDOtADOtQDOtgDOtwDOuADOuQDOugDOuwDOvADOvEEAzrxGAM68VgDOvFcAzrxnAM68bADOvG0AzrxzAM69AM6+AM6/AM+AAM+BAM+CAM+DAM+EAM+FAM+GAM+HAM+IAM+JAM+KAM+LAM+MAM+NAM+OAM+cAM+dANCAANCBANCDANCHANCMANCNANCOANCZANC5ANC9ANGKANGMANGQANGRANGTANGXANGcANGdANGeANG2ANG3ANOBANOCANOQANORANOSANOTANOWANOXANOaANObANOcANOdANOeANOfANOiANOjANOkANOlANOmANOnANOqANOrANOsANOtANOuANOvANOwANOxANOyANOzANO0ANO1ANO4ANO5ANWl1oIA1bTVpQDVtNWrANW01a0A1bTVtgDVvtW2ANeQANeQ1rcA15DWuADXkNa8ANeQ15wA15EA15HWvADXkda/ANeSANeS1rwA15MA15PWvADXlADXlNa8ANeV1rkA15XWvADXlta8ANeY1rwA15nWtADXmda8ANea1rwA15sA15vWvADXm9a/ANecANec1rwA150A157WvADXoNa8ANeh1rwA16IA16PWvADXpNa8ANek1r8A16bWvADXp9a8ANeoANeo1rwA16nWvADXqda814EA16nWvNeCANep14EA16nXggDXqgDXqta8ANey1rcA2KEA2KIA2KMA2KQA2KUA2KYA2KbYpwDYptisANim2K0A2KbYrgDYptixANim2LIA2KbZhQDYptmGANim2YcA2KbZiADYptmJANim2YoA2KbbhgDYptuHANim24gA2KbbkADYptuVANinANin2YPYqNixANin2YTZhNmHANin2YsA2KfZtADYqADYqNisANio2K0A2KjYrdmKANio2K4A2KjYrtmKANio2LEA2KjYsgDYqNmFANio2YYA2KjZhwDYqNmJANio2YoA2KkA2KoA2KrYrADYqtis2YUA2KrYrNmJANiq2KzZigDYqtitANiq2K3YrADYqtit2YUA2KrYrgDYqtiu2YUA2KrYrtmJANiq2K7ZigDYqtixANiq2LIA2KrZhQDYqtmF2KwA2KrZhditANiq2YXYrgDYqtmF2YkA2KrZhdmKANiq2YYA2KrZhwDYqtmJANiq2YoA2KsA2KvYrADYq9ixANir2LIA2KvZhQDYq9mGANir2YcA2KvZiQDYq9mKANisANis2K0A2KzYrdmJANis2K3ZigDYrNmEINis2YTYp9mE2YcA2KzZhQDYrNmF2K0A2KzZhdmJANis2YXZigDYrNmJANis2YoA2K0A2K3YrADYrdis2YoA2K3ZhQDYrdmF2YkA2K3ZhdmKANit2YkA2K3ZigDYrgDYrtisANiu2K0A2K7ZhQDYrtmJANiu2YoA2K8A2LAA2LDZsADYsQDYsdiz2YjZhADYsdmwANix24zYp9mEANiyANizANiz2KwA2LPYrNitANiz2KzZiQDYs9itANiz2K3YrADYs9iuANiz2K7ZiQDYs9iu2YoA2LPYsQDYs9mFANiz2YXYrADYs9mF2K0A2LPZhdmFANiz2YcA2LPZiQDYs9mKANi0ANi02KwA2LTYrNmKANi02K0A2LTYrdmFANi02K3ZigDYtNiuANi02LEA2LTZhQDYtNmF2K4A2LTZhdmFANi02YcA2LTZiQDYtNmKANi1ANi12K0A2LXYrditANi12K3ZigDYtdiuANi12LEA2LXZhNi52YUA2LXZhNmJANi12YTZiSDYp9mE2YTZhyDYudmE2YrZhyDZiNiz2YTZhQDYtdmE25IA2LXZhQDYtdmF2YUA2LXZiQDYtdmKANi2ANi22KwA2LbYrQDYttit2YkA2LbYrdmKANi22K4A2LbYrtmFANi22LEA2LbZhQDYttmJANi22YoA2LcA2LfYrQDYt9mFANi32YXYrQDYt9mF2YUA2LfZhdmKANi32YkA2LfZigDYuADYuNmFANi5ANi52KwA2LnYrNmFANi52YTZitmHANi52YUA2LnZhdmFANi52YXZiQDYudmF2YoA2LnZiQDYudmKANi6ANi62KwA2LrZhQDYutmF2YUA2LrZhdmJANi62YXZigDYutmJANi62YoA2YDZiwDZgNmOANmA2Y7ZkQDZgNmPANmA2Y/ZkQDZgNmQANmA2ZDZkQDZgNmRANmA2ZIA2YEA2YHYrADZgditANmB2K4A2YHYrtmFANmB2YUA2YHZhdmKANmB2YkA2YHZigDZggDZgtitANmC2YTbkgDZgtmFANmC2YXYrQDZgtmF2YUA2YLZhdmKANmC2YkA2YLZigDZgwDZg9inANmD2KwA2YPYrQDZg9iuANmD2YQA2YPZhQDZg9mF2YUA2YPZhdmKANmD2YkA2YPZigDZhADZhNiiANmE2KMA2YTYpQDZhNinANmE2KwA2YTYrNisANmE2KzZhQDZhNis2YoA2YTYrQDZhNit2YUA2YTYrdmJANmE2K3ZigDZhNiuANmE2K7ZhQDZhNmFANmE2YXYrQDZhNmF2YoA2YTZhwDZhNmJANmE2YoA2YUA2YXYpwDZhdisANmF2KzYrQDZhdis2K4A2YXYrNmFANmF2KzZigDZhditANmF2K3YrADZhdit2YUA2YXYrdmF2K8A2YXYrdmKANmF2K4A2YXYrtisANmF2K7ZhQDZhdiu2YoA2YXZhQDZhdmF2YoA2YXZiQDZhdmKANmGANmG2KwA2YbYrNitANmG2KzZhQDZhtis2YkA2YbYrNmKANmG2K0A2YbYrdmFANmG2K3ZiQDZhtit2YoA2YbYrgDZhtixANmG2LIA2YbZhQDZhtmF2YkA2YbZhdmKANmG2YYA2YbZhwDZhtmJANmG2YoA2YcA2YfYrADZh9mFANmH2YXYrADZh9mF2YUA2YfZiQDZh9mKANmH2bAA2YgA2YjYs9mE2YUA2YjZtADZiQDZidmwANmKANmK2KwA2YrYrNmKANmK2K0A2YrYrdmKANmK2K4A2YrYsQDZitiyANmK2YUA2YrZhdmFANmK2YXZigDZitmGANmK2YcA2YrZiQDZitmKANmK2bQA2a4A2a8A2bEA2bkA2boA2bsA2b4A2b8A2oAA2oMA2oQA2oYA2ocA2ogA2owA2o0A2o4A2pEA2pgA2qEA2qQA2qYA2qkA2q0A2q8A2rEA2rMA2roA2rsA2r4A24AA24EA24IA24UA24YA24cA24fZtADbiADbiQDbiwDbjADbkADbkgDbkwDgpJXgpLwA4KSW4KS8AOCkl+CkvADgpJzgpLwA4KSh4KS8AOCkouCkvADgpKkA4KSr4KS8AOCkr+CkvADgpLEA4KS0AOCmoeCmvADgpqLgprwA4Kav4Ka8AOCniwDgp4wA4KiW4Ki8AOCol+CovADgqJzgqLwA4Kir4Ki8AOCosuCovADgqLjgqLwA4Kyh4Ky8AOCsouCsvADgrYgA4K2LAOCtjADgrpQA4K+KAOCviwDgr4wA4LGIAOCzgADgs4cA4LOIAOCzigDgs4sA4LWKAOC1iwDgtYwA4LeaAOC3nADgt50A4LeeAOC5jeC4sgDguqvgupkA4Lqr4LqhAOC7jeC6sgDgvIsA4L2A4L61AOC9guC+twDgvYzgvrcA4L2R4L63AOC9luC+twDgvZvgvrcA4L2x4L2yAOC9seC9tADgvbHgvoAA4L6Q4L61AOC+kuC+twDgvpzgvrcA4L6h4L63AOC+puC+twDgvqvgvrcA4L6y4L2x4L6AAOC+suC+gADgvrPgvbHgvoAA4L6z4L6AAOGApgDhg5wA4YSAAOGEgQDhhIIA4YSDAOGEhADhhIUA4YSGAOGEhwDhhIgA4YSJAOGEigDhhIsA4YSMAOGEjQDhhI4A4YSPAOGEkADhhJEA4YSSAOGElADhhJUA4YSaAOGEnADhhJ0A4YSeAOGEoADhhKEA4YSiAOGEowDhhKcA4YSpAOGEqwDhhKwA4YStAOGErgDhhK8A4YSyAOGEtgDhhYAA4YWHAOGFjADhhZcA4YWYAOGFmQDhhaAA4YWhAOGFogDhhaMA4YWkAOGFpQDhhaYA4YWnAOGFqADhhakA4YWqAOGFqwDhhawA4YWtAOGFrgDhha8A4YWwAOGFsQDhhbIA4YWzAOGFtADhhbUA4YaEAOGGhQDhhogA4YaRAOGGkgDhhpQA4YaeAOGGoQDhhqoA4YasAOGGrQDhhrAA4YaxAOGGsgDhhrMA4Ya0AOGGtQDhh4cA4YeIAOGHjADhh44A4YeTAOGHlwDhh5kA4YedAOGHnwDhh7EA4YeyAOGshgDhrIgA4ayKAOGsjADhrI4A4aySAOGsuwDhrL0A4a2AAOGtgQDhrYMA4bSCAOG0lgDhtJcA4bScAOG0nQDhtKUA4bW7AOG2hQDhuIAA4biBAOG4ggDhuIMA4biEAOG4hQDhuIYA4biHAOG4iADhuIkA4biKAOG4iwDhuIwA4biNAOG4jgDhuI8A4biQAOG4kQDhuJIA4biTAOG4lADhuJUA4biWAOG4lwDhuJgA4biZAOG4mgDhuJsA4bicAOG4nQDhuJ4A4bifAOG4oADhuKEA4biiAOG4owDhuKQA4bilAOG4pgDhuKcA4bioAOG4qQDhuKoA4birAOG4rADhuK0A4biuAOG4rwDhuLAA4bixAOG4sgDhuLMA4bi0AOG4tQDhuLYA4bi3AOG4uADhuLkA4bi6AOG4uwDhuLwA4bi9AOG4vgDhuL8A4bmAAOG5gQDhuYIA4bmDAOG5hADhuYUA4bmGAOG5hwDhuYgA4bmJAOG5igDhuYsA4bmMAOG5jQDhuY4A4bmPAOG5kADhuZEA4bmSAOG5kwDhuZQA4bmVAOG5lgDhuZcA4bmYAOG5mQDhuZoA4bmbAOG5nADhuZ0A4bmeAOG5nwDhuaAA4bmhAOG5ogDhuaMA4bmkAOG5pQDhuaYA4bmnAOG5qADhuakA4bmqAOG5qwDhuawA4bmtAOG5rgDhua8A4bmwAOG5sQDhubIA4bmzAOG5tADhubUA4bm2AOG5twDhubgA4bm5AOG5ugDhubsA4bm8AOG5vQDhub4A4bm/AOG6gADhuoEA4bqCAOG6gwDhuoQA4bqFAOG6hgDhuocA4bqIAOG6iQDhuooA4bqLAOG6jADhuo0A4bqOAOG6jwDhupAA4bqRAOG6kgDhupMA4bqUAOG6lQDhupYA4bqXAOG6mADhupkA4bqgAOG6oQDhuqIA4bqjAOG6pADhuqUA4bqmAOG6pwDhuqgA4bqpAOG6qgDhuqsA4bqsAOG6rQDhuq4A4bqvAOG6sADhurEA4bqyAOG6swDhurQA4bq1AOG6tgDhurcA4bq4AOG6uQDhuroA4bq7AOG6vADhur0A4bq+AOG6vwDhu4AA4buBAOG7ggDhu4MA4buEAOG7hQDhu4YA4buHAOG7iADhu4kA4buKAOG7iwDhu4wA4buNAOG7jgDhu48A4buQAOG7kQDhu5IA4buTAOG7lADhu5UA4buWAOG7lwDhu5gA4buZAOG7mgDhu5sA4bucAOG7nQDhu54A4bufAOG7oADhu6EA4buiAOG7owDhu6QA4bulAOG7pgDhu6cA4buoAOG7qQDhu6oA4burAOG7rADhu60A4buuAOG7rwDhu7AA4buxAOG7sgDhu7MA4bu0AOG7tQDhu7YA4bu3AOG7uADhu7kA4byAAOG8gQDhvIIA4byDAOG8hADhvIUA4byGAOG8hwDhvIgA4byJAOG8igDhvIsA4byMAOG8jQDhvI4A4byPAOG8kADhvJEA4bySAOG8kwDhvJQA4byVAOG8mADhvJkA4byaAOG8mwDhvJwA4bydAOG8oADhvKEA4byiAOG8owDhvKQA4bylAOG8pgDhvKcA4byoAOG8qQDhvKoA4byrAOG8rADhvK0A4byuAOG8rwDhvLAA4byxAOG8sgDhvLMA4by0AOG8tQDhvLYA4by3AOG8uADhvLkA4by6AOG8uwDhvLwA4by9AOG8vgDhvL8A4b2AAOG9gQDhvYIA4b2DAOG9hADhvYUA4b2IAOG9iQDhvYoA4b2LAOG9jADhvY0A4b2QAOG9kQDhvZIA4b2TAOG9lADhvZUA4b2WAOG9lwDhvZkA4b2bAOG9nQDhvZ8A4b2gAOG9oQDhvaIA4b2jAOG9pADhvaUA4b2mAOG9pwDhvagA4b2pAOG9qgDhvasA4b2sAOG9rQDhva4A4b2vAOG9sADhvbIA4b20AOG9tgDhvbgA4b26AOG9vADhvoAA4b6BAOG+ggDhvoMA4b6EAOG+hQDhvoYA4b6HAOG+iADhvokA4b6KAOG+iwDhvowA4b6NAOG+jgDhvo8A4b6QAOG+kQDhvpIA4b6TAOG+lADhvpUA4b6WAOG+lwDhvpgA4b6ZAOG+mgDhvpsA4b6cAOG+nQDhvp4A4b6fAOG+oADhvqEA4b6iAOG+owDhvqQA4b6lAOG+pgDhvqcA4b6oAOG+qQDhvqoA4b6rAOG+rADhvq0A4b6uAOG+rwDhvrAA4b6xAOG+sgDhvrMA4b60AOG+tgDhvrcA4b64AOG+uQDhvroA4b68AOG/ggDhv4MA4b+EAOG/hgDhv4cA4b+IAOG/igDhv4wA4b+QAOG/kQDhv5IA4b+WAOG/lwDhv5gA4b+ZAOG/mgDhv6AA4b+hAOG/ogDhv6QA4b+lAOG/pgDhv6cA4b+oAOG/qQDhv6oA4b+sAOG/sgDhv7MA4b+0AOG/tgDhv7cA4b+4AOG/ugDhv7wA4oCQAOKAkwDigJQA4oCy4oCyAOKAsuKAsuKAsgDigLLigLLigLLigLIA4oC14oC1AOKAteKAteKAtQDigqkA4oaQAOKGkQDihpIA4oaTAOKGmgDihpsA4oauAOKHjQDih44A4oePAOKIggDiiIQA4oiHAOKIiQDiiIwA4oiRAOKIkgDiiKQA4oimAOKIq+KIqwDiiKviiKviiKsA4oir4oir4oir4oirAOKIruKIrgDiiK7iiK7iiK4A4omBAOKJhADiiYcA4omJAOKJoADiiaIA4omtAOKJrgDiia8A4omwAOKJsQDiibQA4om1AOKJuADiibkA4oqAAOKKgQDiioQA4oqFAOKKiADiiokA4oqsAOKKrQDiiq4A4oqvAOKLoADii6EA4ouiAOKLowDii6oA4ourAOKLrADii60A4pSCAOKWoADil4sA4qaFAOKmhgDiq53MuADitaEA44CBAOOAggDjgIgA44CJAOOAigDjgIsA44CMAOOAjQDjgI4A44CPAOOAkADjgJEA44CSAOOAlADjgJRT44CVAOOAlOS4ieOAlQDjgJTkuozjgJUA44CU5Yud44CVAOOAlOWuieOAlQDjgJTmiZPjgJUA44CU5pWX44CVAOOAlOacrOOAlQDjgJTngrnjgJUA44CU55uX44CVAOOAlQDjgJYA44CXAOOBjADjgY4A44GQAOOBkgDjgZQA44GWAOOBmADjgZoA44GcAOOBngDjgaAA44GiAOOBpQDjgacA44GpAOOBsADjgbEA44GzAOOBtADjgbYA44G3AOOBuQDjgboA44G744GLAOOBvADjgb0A44KI44KKAOOClADjgpkA44KaAOOCngDjgqEA44KiAOOCouODkeODvOODiADjgqLjg6vjg5XjgqEA44Ki44Oz44Oa44KiAOOCouODvOODqwDjgqMA44KkAOOCpOODi+ODs+OCsADjgqTjg7Pjg4EA44KlAOOCpgDjgqbjgqnjg7MA44KnAOOCqADjgqjjgrnjgq/jg7zjg4kA44Ko44O844Kr44O8AOOCqQDjgqoA44Kq44Oz44K5AOOCquODvOODoADjgqsA44Kr44Kk44OqAOOCq+ODqeODg+ODiADjgqvjg63jg6rjg7wA44KsAOOCrOODreODswDjgqzjg7Pjg54A44KtAOOCreODpeODquODvADjgq3jg60A44Kt44Ot44Kw44Op44OgAOOCreODreODoeODvOODiOODqwDjgq3jg63jg6/jg4Pjg4gA44KuAOOCruOCrADjgq7jg4vjg7wA44Ku44Or44OA44O8AOOCrwDjgq/jg6vjgrzjgqTjg60A44Kv44Ot44O844ONAOOCsADjgrDjg6njg6AA44Kw44Op44Og44OI44OzAOOCsQDjgrHjg7zjgrkA44KyAOOCswDjgrPjgrMA44Kz44OIAOOCs+ODq+ODigDjgrPjg7zjg50A44K0AOOCtQDjgrXjgqTjgq/jg6sA44K144Oz44OB44O844OgAOOCtgDjgrcA44K344Oq44Oz44KwAOOCuADjgrkA44K6AOOCuwDjgrvjg7Pjg4EA44K744Oz44OIAOOCvADjgr0A44K+AOOCvwDjg4AA44OA44O844K5AOODgQDjg4IA44ODAOODhADjg4UA44OGAOODhwDjg4fjgrcA44OIAOODiOODswDjg4kA44OJ44OrAOODigDjg4rjg44A44OLAOODjADjg40A44OOAOODjuODg+ODiADjg48A44OP44Kk44OEAOODkADjg5Djg7zjg6zjg6sA44ORAOODkeODvOOCu+ODs+ODiADjg5Hjg7zjg4QA44OSAOODkwDjg5Pjg6sA44OUAOODlOOCouOCueODiOODqwDjg5Tjgq/jg6sA44OU44KzAOODlQDjg5XjgqHjg6njg4Pjg4kA44OV44Kj44O844OIAOODleODqeODswDjg5YA44OW44OD44K344Kn44OrAOODlwDjg5gA44OY44Kv44K/44O844OrAOODmOODq+ODhADjg5kA44OZ44O844K/AOODmgDjg5rjgr0A44Oa44OL44OSAOODmuODs+OCuQDjg5rjg7zjgrgA44ObAOODm+ODswDjg5vjg7zjg6sA44Ob44O844OzAOODnADjg5zjg6vjg4gA44OdAOODneOCpOODs+ODiADjg53jg7Pjg4kA44OeAOODnuOCpOOCr+ODrQDjg57jgqTjg6sA44Oe44OD44OPAOODnuODq+OCrwDjg57jg7Pjgrfjg6fjg7MA44OfAOODn+OCr+ODreODswDjg5/jg6oA44Of44Oq44OQ44O844OrAOODoADjg6EA44Oh44KsAOODoeOCrOODiOODswDjg6Hjg7zjg4jjg6sA44OiAOODowDjg6QA44Ok44O844OJAOODpOODvOODqwDjg6UA44OmAOODpuOCouODswDjg6cA44OoAOODqQDjg6oA44Oq44OD44OI44OrAOODquODqQDjg6sA44Or44OU44O8AOODq+ODvOODluODqwDjg6wA44Os44OgAOODrOODs+ODiOOCsuODswDjg60A44OvAOODr+ODg+ODiADjg7AA44OxAOODsgDjg7MA44O0AOODtwDjg7gA44O5AOODugDjg7sA44O8AOODvgDjkp4A45K5AOOSuwDjk58A45SVAOObrgDjm7wA456BAOOgrwDjoaIA46G8AOOjhwDjo6MA46ScAOOkugDjqK4A46msAOOrpADjrIgA46yZAOOtiQDjrp0A47CYAOOxjgDjtLMA47aWAOO6rADjurgA47ybAOO/vADkgIgA5ICYAOSAuQDkgYYA5IKWAOSDowDkhK8A5IiCAOSIpwDkiqAA5IyBAOSMtADkjZkA5I+VAOSPmQDkkIsA5JGrAOSUqwDklZ0A5JWhAOSVqwDkl5cA5Je5AOSYtQDkmr4A5JuHAOSmlQDkp6YA5KmuAOSptgDkqrIA5KyzAOSvjgDks44A5LOtAOSzuADktZYA5LiAAOS4gQDkuIMA5LiJAOS4igDkuIsA5LiNAOS4mQDkuKYA5LioAOS4rQDkuLIA5Li2AOS4uADkuLkA5Li9AOS4vwDkuYEA5LmZAOS5nQDkuoIA5LqFAOS6hgDkuowA5LqUAOS6oADkuqQA5LquAOS6ugDku4AA5LuMAOS7pADku6TlkowA5LyBAOS8kQDkvaAA5L6AAOS+hgDkvosA5L6uAOS+uwDkvr8A5YCCAOWAqwDlgboA5YKZAOWDjwDlg5oA5YOnAOWEqgDlhL8A5YWAAOWFhQDlhY0A5YWUAOWFpADlhaUA5YWnAOWFqADlhakA5YWrAOWFrQDlhbcA5YaAAOWGggDlho0A5YaSAOWGlQDlhpYA5YaXAOWGmQDlhqQA5YarAOWGrADlhrUA5Ya3AOWHiQDlh4wA5YecAOWHngDlh6AA5Ye1AOWIgADliIMA5YiHAOWIlwDliJ0A5YipAOWIugDliLsA5YmGAOWJjQDlibIA5Ym3AOWKiQDlipsA5YqjAOWKswDlirQA5YuHAOWLiQDli5IA5YueAOWLpADli7UA5Yu5AOWLugDljIUA5YyGAOWMlQDljJcA5YyaAOWMuADljLsA5Yy/AOWNgQDljYQA5Y2FAOWNiQDljZEA5Y2UAOWNmgDljZwA5Y2pAOWNsADljbMA5Y21AOWNvQDljb8A5Y6CAOWOtgDlj4MA5Y+IAOWPigDlj4wA5Y+fAOWPowDlj6UA5Y+rAOWPrwDlj7EA5Y+zAOWQhgDlkIgA5ZCNAOWQjwDlkJ0A5ZC4AOWQuQDlkYIA5ZGIAOWRqADlkp4A5ZKiAOWSvQDlk7YA5ZSQAOWVjwDllZMA5ZWVAOWVowDlloQA5ZaHAOWWmQDllp0A5ZarAOWWswDllrYA5ZeAAOWXggDll6IA5ZiGAOWZkQDlmagA5Zm0AOWblwDlm5sA5Zu5AOWclgDlnJcA5ZyfAOWcsADlnosA5Z+OAOWftADloI0A5aCxAOWgsgDloYAA5aGaAOWhngDloqgA5aKsAOWiswDlo5gA5aOfAOWjqwDlo64A5aOwAOWjsgDlo7cA5aSCAOWkhgDlpIoA5aSVAOWkmgDlpJwA5aSiAOWkpwDlpKfmraMA5aSpAOWlhADlpYgA5aWRAOWllADlpaIA5aWzAOWnmADlp6wA5aibAOWopwDlqaIA5ammAOWqtQDlrIgA5ayoAOWsvgDlrZAA5a2XAOWtpgDlroAA5a6FAOWulwDlr4MA5a+YAOWvpwDlr64A5a+zAOWvuADlr78A5bCGAOWwjwDlsKIA5bC4AOWwvwDlsaAA5bGiAOWxpADlsaUA5bGuAOWxsQDlso0A5bOAAOW0mQDltYMA5bWQAOW1qwDlta4A5bW8AOW2sgDltroA5bebAOW3oQDlt6IA5belAOW3pgDlt7EA5be9AOW3vgDluKgA5bi9AOW5qQDlubIA5bmz5oiQAOW5tADluboA5bm8AOW5vwDluqYA5bqwAOW6swDlurYA5buJAOW7igDlu5IA5buTAOW7mQDlu6wA5bu0AOW7vgDlvIQA5byLAOW8kwDlvKIA5b2QAOW9kwDlvaEA5b2iAOW9qQDlvasA5b2zAOW+iwDlvowA5b6XAOW+mgDlvqkA5b6tAOW/gwDlv40A5b+XAOW/tQDlv7kA5oCSAOaAnADmgbUA5oKBAOaClADmg4cA5oOYAOaDoQDmhIgA5oWEAOaFiADmhYwA5oWOAOaFoADmhagA5oW6AOaGjgDmhpAA5oakAOaGrwDmhrIA5oeeAOaHsgDmh7YA5oiAAOaIiADmiJAA5oibAOaIrgDmiLQA5oi2AOaJiwDmiZMA5omdAOaKlQDmirEA5ouJAOaLjwDmi5MA5ouUAOaLvADmi74A5oyHAOaMvQDmjZAA5o2VAOaNqADmjbsA5o6DAOaOoADmjqkA5o+EAOaPhQDmj6QA5pCcAOaQogDmkZIA5pGpAOaRtwDmkb4A5pKaAOaSnQDmk4QA5pSvAOaUtADmlY8A5pWWAOaVrADmlbgA5paHAOaWlwDmlpkA5pakAOaWsADmlrkA5peFAOaXoADml6IA5pejAOaXpQDmmI7msrsA5piTAOaYoADmmK3lkowA5pmJAOaZtADmmogA5pqRAOaanADmmrQA5puGAOabsADmm7QA5pu4AOacgADmnIgA5pyJAOaclwDmnJsA5pyhAOacqADmnY4A5p2TAOadlgDmnZ4A5p27AOaehQDmnpcA5p+zAOafugDmoJcA5qCfAOagqgDmoKrlvI/kvJrnpL4A5qGSAOaigQDmooUA5qKOAOaiqADmpJQA5qWCAOamowDmp6oA5qiCAOaokwDmqqgA5quTAOarmwDmrIQA5qygAOasoQDmrZQA5q2iAOatowDmrbIA5q23AOatuQDmrp8A5q6uAOauswDmrroA5q67AOaviwDmr40A5q+UAOavmwDmsI8A5rCUAOawtADmsY4A5rGnAOayiADmsr8A5rOMAOazjQDms6UA5rOoAOa0lgDmtJsA5rSeAOa0tADmtL4A5rWBAOa1qQDmtaoA5rW3AOa1uADmtoUA5reLAOa3mgDmt6oA5re5AOa4mgDmuK8A5rmuAOa6gADmupwA5rq6AOa7hwDmu4sA5ruRAOa7mwDmvI8A5ryUAOa8ogDmvKMA5r2uAOa/hgDmv6sA5r++AOeAmwDngJ4A54C5AOeBigDngasA54GwAOeBtwDngb0A54KZAOeCrQDng4gA54OZAOeEoQDnhYUA54WJAOeFrgDnhpwA54eOAOeHkADniJAA54ibAOeIqADniKoA54irAOeItQDniLYA54i7AOeIvwDniYcA54mQAOeJmQDniZsA54miAOeJuQDnioAA54qVAOeKrADniq8A54uAAOeLvADnjKoA5421AOeNugDnjoQA546HAOeOiQDnjosA546lAOeOsgDnj54A55CGAOeQiQDnkKIA55GHAOeRnADnkakA55GxAOeShQDnkokA55KYAOeTigDnk5wA55OmAOeUhgDnlJgA55SfAOeUpADnlKgA55SwAOeUsgDnlLMA55S3AOeUuwDnlL4A55WZAOeVpQDnlbAA55aLAOeWkgDnl6IA55iQAOeYnQDnmJ8A55mCAOeZqQDnmbYA55m9AOeargDnmr8A55uKAOebmwDnm6MA55unAOebrgDnm7QA55yBAOecngDnnJ8A552AAOedigDnnosA556nAOefmwDnn6IA55+zAOehjgDnoasA56KMAOeikQDno4oA56OMAOejuwDnpKoA56S6AOekvADnpL4A56WIAOeliQDnpZAA56WWAOelnQDnpZ4A56WlAOelvwDnpoEA56aNAOemjgDnpo8A56auAOemuADnpr4A56eKAOenmADnp6sA56icAOepgADnqYoA56mPAOeptADnqboA56qBAOeqsQDnq4sA56uuAOeruQDnrKAA566PAOevgADnr4YA56+JAOewvgDnsaAA57GzAOexuwDnspIA57K+AOezkgDns5YA57OjAOezpwDns6gA57O4AOe0gADntJAA57SiAOe0rwDntYIA57WbAOe1owDntqAA57a+AOe3hwDnt7QA57iCAOe4iQDnuLcA57mBAOe5hQDnvLYA57y+AOe9kQDnvbIA5725AOe9ugDnvoUA576KAOe+lQDnvpoA5769AOe/ugDogIEA6ICFAOiAjADogJIA6ICzAOiBhgDogaAA6IGvAOiBsADogb4A6IG/AOiCiQDogosA6IKtAOiCsgDohIMA6IS+AOiHmADoh6MA6IeoAOiHqgDoh60A6IezAOiHvADoiIEA6IiEAOiIjADoiJgA6IibAOiInwDoia4A6ImvAOiJsgDoibgA6Im5AOiKiwDoipEA6IqdAOiKsQDoirMA6Iq9AOiLpQDoi6YA6IydAOiMowDojLYA6I2SAOiNkwDojaMA6I6tAOiOvQDoj4kA6I+KAOiPjADoj5wA6I+nAOiPrwDoj7EA6JC9AOiRiQDokZcA6JOuAOiTsQDok7MA6JO8AOiUlgDolaQA6JeNAOiXugDomIYA6JiSAOiYrQDomL8A6JmNAOiZkADomZwA6JmnAOiZqQDomasA6JqIAOiaqQDom6IA6JyOAOicqADonasA6J25AOiehgDonroA6J+hAOiggQDooJ8A6KGAAOihjADooaAA6KGjAOijggDoo48A6KOXAOijngDoo6EA6KO4AOijugDopJAA6KWBAOilpADopb4A6KaGAOimiwDoppYA6KeSAOinowDoqIAA6KqgAOiqqgDoqr8A6KuLAOirkgDoq5YA6KutAOiruADoq74A6KyBAOisuQDorZgA6K6AAOiuigDosLcA6LGGAOixiADosZUA6LG4AOiynQDosqEA6LKpAOiyqwDos4EA6LOCAOizhwDos4gA6LOTAOi0iADotJsA6LWkAOi1sADotbcA6LazAOi2vADot4sA6LevAOi3sADouqsA6LuKAOi7lADovKYA6LyqAOi8uADovLsA6L2iAOi+mwDovp4A6L6wAOi+tQDovrYA6YCjAOmAuADpgYoA6YGpAOmBsgDpgbwA6YKPAOmCkQDpgpQA6YOOAOmDngDpg7EA6YO9AOmEkQDphJsA6YWJAOmFjQDphaoA6YaZAOmGtADph4YA6YeMAOmHjwDph5EA6Yi0AOmIuADpibYA6Ym8AOmLlwDpi5gA6YyEAOmNigDpj7kA6ZCVAOmVtwDploAA6ZaLAOmWrQDplrcA6ZicAOmYrgDpmYsA6ZmNAOmZtQDpmbgA6Zm8AOmahgDpmqMA6Zq2AOmatwDpmrgA6Zq5AOmbgwDpm6IA6ZujAOmbqADpm7YA6Zu3AOmcowDpnLIA6Z2IAOmdkQDpnZYA6Z2eAOmdogDpnakA6Z+LAOmfmwDpn6AA6Z+tAOmfswDpn78A6aCBAOmghQDpoIsA6aCYAOmgqQDpoLsA6aGeAOmiqADpo5sA6aOfAOmjogDpo68A6aO8AOmkqADppKkA6aaWAOmmmQDppqcA6aasAOmnggDpp7EA6ae+AOmpqgDpqqgA6auYAOmrnwDprJIA6aylAOmsrwDprLIA6ay8AOmtmgDpra8A6bGAAOmxlwDps6UA6bO9AOm1pwDptrQA6be6AOm4ngDpubUA6bm/AOm6lwDpup8A6bqlAOm6uwDpu4MA6buNAOm7jgDpu5EA6bu5AOm7vQDpu74A6byFAOm8jgDpvI8A6byTAOm8lgDpvKAA6by7AOm9gwDpvYoA6b2SAOm+jQDpvo4A6b6cAOm+nwDpvqAA6pynAOqdrwDqrLcA6q2SAOqwgADqsIEA6rCCAOqwgwDqsIQA6rCFAOqwhgDqsIcA6rCIAOqwiQDqsIoA6rCLAOqwjADqsI0A6rCOAOqwjwDqsJAA6rCRAOqwkgDqsJMA6rCUAOqwlQDqsJYA6rCXAOqwmADqsJkA6rCaAOqwmwDqsJwA6rCdAOqwngDqsJ8A6rCgAOqwoQDqsKIA6rCjAOqwpADqsKUA6rCmAOqwpwDqsKgA6rCpAOqwqgDqsKsA6rCsAOqwrQDqsK4A6rCvAOqwsADqsLEA6rCyAOqwswDqsLQA6rC1AOqwtgDqsLcA6rC4AOqwuQDqsLoA6rC7AOqwvADqsL0A6rC+AOqwvwDqsYAA6rGBAOqxggDqsYMA6rGEAOqxhQDqsYYA6rGHAOqxiADqsYkA6rGKAOqxiwDqsYwA6rGNAOqxjgDqsY8A6rGQAOqxkQDqsZIA6rGTAOqxlADqsZUA6rGWAOqxlwDqsZgA6rGZAOqxmgDqsZsA6rGcAOqxnQDqsZ4A6rGfAOqxoADqsaEA6rGiAOqxowDqsaQA6rGlAOqxpgDqsacA6rGoAOqxqQDqsaoA6rGrAOqxrADqsa0A6rGuAOqxrwDqsbAA6rGxAOqxsgDqsbMA6rG0AOqxtQDqsbYA6rG3AOqxuADqsbkA6rG6AOqxuwDqsbwA6rG9AOqxvgDqsb8A6rKAAOqygQDqsoIA6rKDAOqyhADqsoUA6rKGAOqyhwDqsogA6rKJAOqyigDqsosA6rKMAOqyjQDqso4A6rKPAOqykADqspEA6rKSAOqykwDqspQA6rKVAOqylgDqspcA6rKYAOqymQDqspoA6rKbAOqynADqsp0A6rKeAOqynwDqsqAA6rKhAOqyogDqsqMA6rKkAOqypQDqsqYA6rKnAOqyqADqsqkA6rKqAOqyqwDqsqwA6rKtAOqyrgDqsq8A6rKwAOqysQDqsrIA6rKzAOqytADqsrUA6rK2AOqytwDqsrgA6rK5AOqyugDqsrsA6rK8AOqyvQDqsr4A6rK/AOqzgADqs4EA6rOCAOqzgwDqs4QA6rOFAOqzhgDqs4cA6rOIAOqziQDqs4oA6rOLAOqzjADqs40A6rOOAOqzjwDqs5AA6rORAOqzkgDqs5MA6rOUAOqzlQDqs5YA6rOXAOqzmADqs5kA6rOaAOqzmwDqs5wA6rOdAOqzngDqs58A6rOgAOqzoQDqs6IA6rOjAOqzpADqs6UA6rOmAOqzpwDqs6gA6rOpAOqzqgDqs6sA6rOsAOqzrQDqs64A6rOvAOqzsADqs7EA6rOyAOqzswDqs7QA6rO1AOqztgDqs7cA6rO4AOqzuQDqs7oA6rO7AOqzvADqs70A6rO+AOqzvwDqtIAA6rSBAOq0ggDqtIMA6rSEAOq0hQDqtIYA6rSHAOq0iADqtIkA6rSKAOq0iwDqtIwA6rSNAOq0jgDqtI8A6rSQAOq0kQDqtJIA6rSTAOq0lADqtJUA6rSWAOq0lwDqtJgA6rSZAOq0mgDqtJsA6rScAOq0nQDqtJ4A6rSfAOq0oADqtKEA6rSiAOq0owDqtKQA6rSlAOq0pgDqtKcA6rSoAOq0qQDqtKoA6rSrAOq0rADqtK0A6rSuAOq0rwDqtLAA6rSxAOq0sgDqtLMA6rS0AOq0tQDqtLYA6rS3AOq0uADqtLkA6rS6AOq0uwDqtLwA6rS9AOq0vgDqtL8A6rWAAOq1gQDqtYIA6rWDAOq1hADqtYUA6rWGAOq1hwDqtYgA6rWJAOq1igDqtYsA6rWMAOq1jQDqtY4A6rWPAOq1kADqtZEA6rWSAOq1kwDqtZQA6rWVAOq1lgDqtZcA6rWYAOq1mQDqtZoA6rWbAOq1nADqtZ0A6rWeAOq1nwDqtaAA6rWhAOq1ogDqtaMA6rWkAOq1pQDqtaYA6rWnAOq1qADqtakA6rWqAOq1qwDqtawA6rWtAOq1rgDqta8A6rWwAOq1sQDqtbIA6rWzAOq1tADqtbUA6rW2AOq1twDqtbgA6rW5AOq1ugDqtbsA6rW8AOq1vQDqtb4A6rW/AOq2gADqtoEA6raCAOq2gwDqtoQA6raFAOq2hgDqtocA6raIAOq2iQDqtooA6raLAOq2jADqto0A6raOAOq2jwDqtpAA6raRAOq2kgDqtpMA6raUAOq2lQDqtpYA6raXAOq2mADqtpkA6raaAOq2mwDqtpwA6radAOq2ngDqtp8A6ragAOq2oQDqtqIA6rajAOq2pADqtqUA6ramAOq2pwDqtqgA6rapAOq2qgDqtqsA6rasAOq2rQDqtq4A6ravAOq2sADqtrEA6rayAOq2swDqtrQA6ra1AOq2tgDqtrcA6ra4AOq2uQDqtroA6ra7AOq2vADqtr0A6ra+AOq2vwDqt4AA6reBAOq3ggDqt4MA6reEAOq3hQDqt4YA6reHAOq3iADqt4kA6reKAOq3iwDqt4wA6reNAOq3jgDqt48A6reQAOq3kQDqt5IA6reTAOq3lADqt5UA6reWAOq3lwDqt5gA6reZAOq3mgDqt5sA6recAOq3nQDqt54A6refAOq3oADqt6EA6reiAOq3owDqt6QA6relAOq3pgDqt6cA6reoAOq3qQDqt6oA6rerAOq3rADqt60A6reuAOq3rwDqt7AA6rexAOq3sgDqt7MA6re0AOq3tQDqt7YA6re3AOq3uADqt7kA6re6AOq3uwDqt7wA6re9AOq3vgDqt78A6riAAOq4gQDquIIA6riDAOq4hADquIUA6riGAOq4hwDquIgA6riJAOq4igDquIsA6riMAOq4jQDquI4A6riPAOq4kADquJEA6riSAOq4kwDquJQA6riVAOq4lgDquJcA6riYAOq4mQDquJoA6ribAOq4nADquJ0A6rieAOq4nwDquKAA6rihAOq4ogDquKMA6rikAOq4pQDquKYA6rinAOq4qADquKkA6riqAOq4qwDquKwA6ritAOq4rgDquK8A6riwAOq4sQDquLIA6rizAOq4tADquLUA6ri2AOq4twDquLgA6ri5AOq4ugDquLsA6ri8AOq4vQDquL4A6ri/AOq5gADquYEA6rmCAOq5gwDquYQA6rmFAOq5hgDquYcA6rmIAOq5iQDquYoA6rmLAOq5jADquY0A6rmOAOq5jwDquZAA6rmRAOq5kgDquZMA6rmUAOq5lQDquZYA6rmXAOq5mADquZkA6rmaAOq5mwDquZwA6rmdAOq5ngDquZ8A6rmgAOq5oQDquaIA6rmjAOq5pADquaUA6rmmAOq5pwDquagA6rmpAOq5qgDquasA6rmsAOq5rQDqua4A6rmvAOq5sADqubEA6rmyAOq5swDqubQA6rm1AOq5tgDqubcA6rm4AOq5uQDquboA6rm7AOq5vADqub0A6rm+AOq5vwDquoAA6rqBAOq6ggDquoMA6rqEAOq6hQDquoYA6rqHAOq6iADquokA6rqKAOq6iwDquowA6rqNAOq6jgDquo8A6rqQAOq6kQDqupIA6rqTAOq6lADqupUA6rqWAOq6lwDqupgA6rqZAOq6mgDqupsA6rqcAOq6nQDqup4A6rqfAOq6oADquqEA6rqiAOq6owDquqQA6rqlAOq6pgDquqcA6rqoAOq6qQDquqoA6rqrAOq6rADquq0A6rquAOq6rwDqurAA6rqxAOq6sgDqurMA6rq0AOq6tQDqurYA6rq3AOq6uADqurkA6rq6AOq6uwDqurwA6rq9AOq6vgDqur8A6ruAAOq7gQDqu4IA6ruDAOq7hADqu4UA6ruGAOq7hwDqu4gA6ruJAOq7igDqu4sA6ruMAOq7jQDqu44A6ruPAOq7kADqu5EA6ruSAOq7kwDqu5QA6ruVAOq7lgDqu5cA6ruYAOq7mQDqu5oA6rubAOq7nADqu50A6rueAOq7nwDqu6AA6ruhAOq7ogDqu6MA6rukAOq7pQDqu6YA6runAOq7qADqu6kA6ruqAOq7qwDqu6wA6rutAOq7rgDqu68A6ruwAOq7sQDqu7IA6ruzAOq7tADqu7UA6ru2AOq7twDqu7gA6ru5AOq7ugDqu7sA6ru8AOq7vQDqu74A6ru/AOq8gADqvIEA6ryCAOq8gwDqvIQA6ryFAOq8hgDqvIcA6ryIAOq8iQDqvIoA6ryLAOq8jADqvI0A6ryOAOq8jwDqvJAA6ryRAOq8kgDqvJMA6ryUAOq8lQDqvJYA6ryXAOq8mADqvJkA6ryaAOq8mwDqvJwA6rydAOq8ngDqvJ8A6rygAOq8oQDqvKIA6ryjAOq8pADqvKUA6rymAOq8pwDqvKgA6rypAOq8qgDqvKsA6rysAOq8rQDqvK4A6ryvAOq8sADqvLEA6ryyAOq8swDqvLQA6ry1AOq8tgDqvLcA6ry4AOq8uQDqvLoA6ry7AOq8vADqvL0A6ry+AOq8vwDqvYAA6r2BAOq9ggDqvYMA6r2EAOq9hQDqvYYA6r2HAOq9iADqvYkA6r2KAOq9iwDqvYwA6r2NAOq9jgDqvY8A6r2QAOq9kQDqvZIA6r2TAOq9lADqvZUA6r2WAOq9lwDqvZgA6r2ZAOq9mgDqvZsA6r2cAOq9nQDqvZ4A6r2fAOq9oADqvaEA6r2iAOq9owDqvaQA6r2lAOq9pgDqvacA6r2oAOq9qQDqvaoA6r2rAOq9rADqva0A6r2uAOq9rwDqvbAA6r2xAOq9sgDqvbMA6r20AOq9tQDqvbYA6r23AOq9uADqvbkA6r26AOq9uwDqvbwA6r29AOq9vgDqvb8A6r6AAOq+gQDqvoIA6r6DAOq+hADqvoUA6r6GAOq+hwDqvogA6r6JAOq+igDqvosA6r6MAOq+jQDqvo4A6r6PAOq+kADqvpEA6r6SAOq+kwDqvpQA6r6VAOq+lgDqvpcA6r6YAOq+mQDqvpoA6r6bAOq+nADqvp0A6r6eAOq+nwDqvqAA6r6hAOq+ogDqvqMA6r6kAOq+pQDqvqYA6r6nAOq+qADqvqkA6r6qAOq+qwDqvqwA6r6tAOq+rgDqvq8A6r6wAOq+sQDqvrIA6r6zAOq+tADqvrUA6r62AOq+twDqvrgA6r65AOq+ugDqvrsA6r68AOq+vQDqvr4A6r6/AOq/gADqv4EA6r+CAOq/gwDqv4QA6r+FAOq/hgDqv4cA6r+IAOq/iQDqv4oA6r+LAOq/jADqv40A6r+OAOq/jwDqv5AA6r+RAOq/kgDqv5MA6r+UAOq/lQDqv5YA6r+XAOq/mADqv5kA6r+aAOq/mwDqv5wA6r+dAOq/ngDqv58A6r+gAOq/oQDqv6IA6r+jAOq/pADqv6UA6r+mAOq/pwDqv6gA6r+pAOq/qgDqv6sA6r+sAOq/rQDqv64A6r+vAOq/sADqv7EA6r+yAOq/swDqv7QA6r+1AOq/tgDqv7cA6r+4AOq/uQDqv7oA6r+7AOq/vADqv70A6r++AOq/vwDrgIAA64CBAOuAggDrgIMA64CEAOuAhQDrgIYA64CHAOuAiADrgIkA64CKAOuAiwDrgIwA64CNAOuAjgDrgI8A64CQAOuAkQDrgJIA64CTAOuAlADrgJUA64CWAOuAlwDrgJgA64CZAOuAmgDrgJsA64CcAOuAnQDrgJ4A64CfAOuAoADrgKEA64CiAOuAowDrgKQA64ClAOuApgDrgKcA64CoAOuAqQDrgKoA64CrAOuArADrgK0A64CuAOuArwDrgLAA64CxAOuAsgDrgLMA64C0AOuAtQDrgLYA64C3AOuAuADrgLkA64C6AOuAuwDrgLwA64C9AOuAvgDrgL8A64GAAOuBgQDrgYIA64GDAOuBhADrgYUA64GGAOuBhwDrgYgA64GJAOuBigDrgYsA64GMAOuBjQDrgY4A64GPAOuBkADrgZEA64GSAOuBkwDrgZQA64GVAOuBlgDrgZcA64GYAOuBmQDrgZoA64GbAOuBnADrgZ0A64GeAOuBnwDrgaAA64GhAOuBogDrgaMA64GkAOuBpQDrgaYA64GnAOuBqADrgakA64GqAOuBqwDrgawA64GtAOuBrgDrga8A64GwAOuBsQDrgbIA64GzAOuBtADrgbUA64G2AOuBtwDrgbgA64G5AOuBugDrgbsA64G8AOuBvQDrgb4A64G/AOuCgADrgoEA64KCAOuCgwDrgoQA64KFAOuChgDrgocA64KIAOuCiQDrgooA64KLAOuCjADrgo0A64KOAOuCjwDrgpAA64KRAOuCkgDrgpMA64KUAOuClQDrgpYA64KXAOuCmADrgpkA64KaAOuCmwDrgpwA64KdAOuCngDrgp8A64KgAOuCoQDrgqIA64KjAOuCpADrgqUA64KmAOuCpwDrgqgA64KpAOuCqgDrgqsA64KsAOuCrQDrgq4A64KvAOuCsADrgrEA64KyAOuCswDrgrQA64K1AOuCtgDrgrcA64K4AOuCuQDrgroA64K7AOuCvADrgr0A64K+AOuCvwDrg4AA64OBAOuDggDrg4MA64OEAOuDhQDrg4YA64OHAOuDiADrg4kA64OKAOuDiwDrg4wA64ONAOuDjgDrg48A64OQAOuDkQDrg5IA64OTAOuDlADrg5UA64OWAOuDlwDrg5gA64OZAOuDmgDrg5sA64OcAOuDnQDrg54A64OfAOuDoADrg6EA64OiAOuDowDrg6QA64OlAOuDpgDrg6cA64OoAOuDqQDrg6oA64OrAOuDrADrg60A64OuAOuDrwDrg7AA64OxAOuDsgDrg7MA64O0AOuDtQDrg7YA64O3AOuDuADrg7kA64O6AOuDuwDrg7wA64O9AOuDvgDrg78A64SAAOuEgQDrhIIA64SDAOuEhADrhIUA64SGAOuEhwDrhIgA64SJAOuEigDrhIsA64SMAOuEjQDrhI4A64SPAOuEkADrhJEA64SSAOuEkwDrhJQA64SVAOuElgDrhJcA64SYAOuEmQDrhJoA64SbAOuEnADrhJ0A64SeAOuEnwDrhKAA64ShAOuEogDrhKMA64SkAOuEpQDrhKYA64SnAOuEqADrhKkA64SqAOuEqwDrhKwA64StAOuErgDrhK8A64SwAOuEsQDrhLIA64SzAOuEtADrhLUA64S2AOuEtwDrhLgA64S5AOuEugDrhLsA64S8AOuEvQDrhL4A64S/AOuFgADrhYEA64WCAOuFgwDrhYQA64WFAOuFhgDrhYcA64WIAOuFiQDrhYoA64WLAOuFjADrhY0A64WOAOuFjwDrhZAA64WRAOuFkgDrhZMA64WUAOuFlQDrhZYA64WXAOuFmADrhZkA64WaAOuFmwDrhZwA64WdAOuFngDrhZ8A64WgAOuFoQDrhaIA64WjAOuFpADrhaUA64WmAOuFpwDrhagA64WpAOuFqgDrhasA64WsAOuFrQDrha4A64WvAOuFsADrhbEA64WyAOuFswDrhbQA64W1AOuFtgDrhbcA64W4AOuFuQDrhboA64W7AOuFvADrhb0A64W+AOuFvwDrhoAA64aBAOuGggDrhoMA64aEAOuGhQDrhoYA64aHAOuGiADrhokA64aKAOuGiwDrhowA64aNAOuGjgDrho8A64aQAOuGkQDrhpIA64aTAOuGlADrhpUA64aWAOuGlwDrhpgA64aZAOuGmgDrhpsA64acAOuGnQDrhp4A64afAOuGoADrhqEA64aiAOuGowDrhqQA64alAOuGpgDrhqcA64aoAOuGqQDrhqoA64arAOuGrADrhq0A64auAOuGrwDrhrAA64axAOuGsgDrhrMA64a0AOuGtQDrhrYA64a3AOuGuADrhrkA64a6AOuGuwDrhrwA64a9AOuGvgDrhr8A64eAAOuHgQDrh4IA64eDAOuHhADrh4UA64eGAOuHhwDrh4gA64eJAOuHigDrh4sA64eMAOuHjQDrh44A64ePAOuHkADrh5EA64eSAOuHkwDrh5QA64eVAOuHlgDrh5cA64eYAOuHmQDrh5oA64ebAOuHnADrh50A64eeAOuHnwDrh6AA64ehAOuHogDrh6MA64ekAOuHpQDrh6YA64enAOuHqADrh6kA64eqAOuHqwDrh6wA64etAOuHrgDrh68A64ewAOuHsQDrh7IA64ezAOuHtADrh7UA64e2AOuHtwDrh7gA64e5AOuHugDrh7sA64e8AOuHvQDrh74A64e/AOuIgADriIEA64iCAOuIgwDriIQA64iFAOuIhgDriIcA64iIAOuIiQDriIoA64iLAOuIjADriI0A64iOAOuIjwDriJAA64iRAOuIkgDriJMA64iUAOuIlQDriJYA64iXAOuImADriJkA64iaAOuImwDriJwA64idAOuIngDriJ8A64igAOuIoQDriKIA64ijAOuIpADriKUA64imAOuIpwDriKgA64ipAOuIqgDriKsA64isAOuIrQDriK4A64ivAOuIsADriLEA64iyAOuIswDriLQA64i1AOuItgDriLcA64i4AOuIuQDriLoA64i7AOuIvADriL0A64i+AOuIvwDriYAA64mBAOuJggDriYMA64mEAOuJhQDriYYA64mHAOuJiADriYkA64mKAOuJiwDriYwA64mNAOuJjgDriY8A64mQAOuJkQDriZIA64mTAOuJlADriZUA64mWAOuJlwDriZgA64mZAOuJmgDriZsA64mcAOuJnQDriZ4A64mfAOuJoADriaEA64miAOuJowDriaQA64mlAOuJpgDriacA64moAOuJqQDriaoA64mrAOuJrADria0A64muAOuJrwDribAA64mxAOuJsgDribMA64m0AOuJtQDribYA64m3AOuJuADribkA64m6AOuJuwDribwA64m9AOuJvgDrib8A64qAAOuKgQDrioIA64qDAOuKhADrioUA64qGAOuKhwDriogA64qJAOuKigDriosA64qMAOuKjQDrio4A64qPAOuKkADripEA64qSAOuKkwDripQA64qVAOuKlgDripcA64qYAOuKmQDripoA64qbAOuKnADrip0A64qeAOuKnwDriqAA64qhAOuKogDriqMA64qkAOuKpQDriqYA64qnAOuKqADriqkA64qqAOuKqwDriqwA64qtAOuKrgDriq8A64qwAOuKsQDrirIA64qzAOuKtADrirUA64q2AOuKtwDrirgA64q5AOuKugDrirsA64q8AOuKvQDrir4A64q/AOuLgADri4EA64uCAOuLgwDri4QA64uFAOuLhgDri4cA64uIAOuLiQDri4oA64uLAOuLjADri40A64uOAOuLjwDri5AA64uRAOuLkgDri5MA64uUAOuLlQDri5YA64uXAOuLmADri5kA64uaAOuLmwDri5wA64udAOuLngDri58A64ugAOuLoQDri6IA64ujAOuLpADri6UA64umAOuLpwDri6gA64upAOuLqgDri6sA64usAOuLrQDri64A64uvAOuLsADri7EA64uyAOuLswDri7QA64u1AOuLtgDri7cA64u4AOuLuQDri7oA64u7AOuLvADri70A64u+AOuLvwDrjIAA64yBAOuMggDrjIMA64yEAOuMhQDrjIYA64yHAOuMiADrjIkA64yKAOuMiwDrjIwA64yNAOuMjgDrjI8A64yQAOuMkQDrjJIA64yTAOuMlADrjJUA64yWAOuMlwDrjJgA64yZAOuMmgDrjJsA64ycAOuMnQDrjJ4A64yfAOuMoADrjKEA64yiAOuMowDrjKQA64ylAOuMpgDrjKcA64yoAOuMqQDrjKoA64yrAOuMrADrjK0A64yuAOuMrwDrjLAA64yxAOuMsgDrjLMA64y0AOuMtQDrjLYA64y3AOuMuADrjLkA64y6AOuMuwDrjLwA64y9AOuMvgDrjL8A642AAOuNgQDrjYIA642DAOuNhADrjYUA642GAOuNhwDrjYgA642JAOuNigDrjYsA642MAOuNjQDrjY4A642PAOuNkADrjZEA642SAOuNkwDrjZQA642VAOuNlgDrjZcA642YAOuNmQDrjZoA642bAOuNnADrjZ0A642eAOuNnwDrjaAA642hAOuNogDrjaMA642kAOuNpQDrjaYA642nAOuNqADrjakA642qAOuNqwDrjawA642tAOuNrgDrja8A642wAOuNsQDrjbIA642zAOuNtADrjbUA6422AOuNtwDrjbgA6425AOuNugDrjbsA6428AOuNvQDrjb4A642/AOuOgADrjoEA646CAOuOgwDrjoQA646FAOuOhgDrjocA646IAOuOiQDrjooA646LAOuOjADrjo0A646OAOuOjwDrjpAA646RAOuOkgDrjpMA646UAOuOlQDrjpYA646XAOuOmADrjpkA646aAOuOmwDrjpwA646dAOuOngDrjp8A646gAOuOoQDrjqIA646jAOuOpADrjqUA646mAOuOpwDrjqgA646pAOuOqgDrjqsA646sAOuOrQDrjq4A646vAOuOsADrjrEA646yAOuOswDrjrQA6461AOuOtgDrjrcA6464AOuOuQDrjroA6467AOuOvADrjr0A646+AOuOvwDrj4AA64+BAOuPggDrj4MA64+EAOuPhQDrj4YA64+HAOuPiADrj4kA64+KAOuPiwDrj4wA64+NAOuPjgDrj48A64+QAOuPkQDrj5IA64+TAOuPlADrj5UA64+WAOuPlwDrj5gA64+ZAOuPmgDrj5sA64+cAOuPnQDrj54A64+fAOuPoADrj6EA64+iAOuPowDrj6QA64+lAOuPpgDrj6cA64+oAOuPqQDrj6oA64+rAOuPrADrj60A64+uAOuPrwDrj7AA64+xAOuPsgDrj7MA64+0AOuPtQDrj7YA64+3AOuPuADrj7kA64+6AOuPuwDrj7wA64+9AOuPvgDrj78A65CAAOuQgQDrkIIA65CDAOuQhADrkIUA65CGAOuQhwDrkIgA65CJAOuQigDrkIsA65CMAOuQjQDrkI4A65CPAOuQkADrkJEA65CSAOuQkwDrkJQA65CVAOuQlgDrkJcA65CYAOuQmQDrkJoA65CbAOuQnADrkJ0A65CeAOuQnwDrkKAA65ChAOuQogDrkKMA65CkAOuQpQDrkKYA65CnAOuQqADrkKkA65CqAOuQqwDrkKwA65CtAOuQrgDrkK8A65CwAOuQsQDrkLIA65CzAOuQtADrkLUA65C2AOuQtwDrkLgA65C5AOuQugDrkLsA65C8AOuQvQDrkL4A65C/AOuRgADrkYEA65GCAOuRgwDrkYQA65GFAOuRhgDrkYcA65GIAOuRiQDrkYoA65GLAOuRjADrkY0A65GOAOuRjwDrkZAA65GRAOuRkgDrkZMA65GUAOuRlQDrkZYA65GXAOuRmADrkZkA65GaAOuRmwDrkZwA65GdAOuRngDrkZ8A65GgAOuRoQDrkaIA65GjAOuRpADrkaUA65GmAOuRpwDrkagA65GpAOuRqgDrkasA65GsAOuRrQDrka4A65GvAOuRsADrkbEA65GyAOuRswDrkbQA65G1AOuRtgDrkbcA65G4AOuRuQDrkboA65G7AOuRvADrkb0A65G+AOuRvwDrkoAA65KBAOuSggDrkoMA65KEAOuShQDrkoYA65KHAOuSiADrkokA65KKAOuSiwDrkowA65KNAOuSjgDrko8A65KQAOuSkQDrkpIA65KTAOuSlADrkpUA65KWAOuSlwDrkpgA65KZAOuSmgDrkpsA65KcAOuSnQDrkp4A65KfAOuSoADrkqEA65KiAOuSowDrkqQA65KlAOuSpgDrkqcA65KoAOuSqQDrkqoA65KrAOuSrADrkq0A65KuAOuSrwDrkrAA65KxAOuSsgDrkrMA65K0AOuStQDrkrYA65K3AOuSuADrkrkA65K6AOuSuwDrkrwA65K9AOuSvgDrkr8A65OAAOuTgQDrk4IA65ODAOuThADrk4UA65OGAOuThwDrk4gA65OJAOuTigDrk4sA65OMAOuTjQDrk44A65OPAOuTkADrk5EA65OSAOuTkwDrk5QA65OVAOuTlgDrk5cA65OYAOuTmQDrk5oA65ObAOuTnADrk50A65OeAOuTnwDrk6AA65OhAOuTogDrk6MA65OkAOuTpQDrk6YA65OnAOuTqADrk6kA65OqAOuTqwDrk6wA65OtAOuTrgDrk68A65OwAOuTsQDrk7IA65OzAOuTtADrk7UA65O2AOuTtwDrk7gA65O5AOuTugDrk7sA65O8AOuTvQDrk74A65O/AOuUgADrlIEA65SCAOuUgwDrlIQA65SFAOuUhgDrlIcA65SIAOuUiQDrlIoA65SLAOuUjADrlI0A65SOAOuUjwDrlJAA65SRAOuUkgDrlJMA65SUAOuUlQDrlJYA65SXAOuUmADrlJkA65SaAOuUmwDrlJwA65SdAOuUngDrlJ8A65SgAOuUoQDrlKIA65SjAOuUpADrlKUA65SmAOuUpwDrlKgA65SpAOuUqgDrlKsA65SsAOuUrQDrlK4A65SvAOuUsADrlLEA65SyAOuUswDrlLQA65S1AOuUtgDrlLcA65S4AOuUuQDrlLoA65S7AOuUvADrlL0A65S+AOuUvwDrlYAA65WBAOuVggDrlYMA65WEAOuVhQDrlYYA65WHAOuViADrlYkA65WKAOuViwDrlYwA65WNAOuVjgDrlY8A65WQAOuVkQDrlZIA65WTAOuVlADrlZUA65WWAOuVlwDrlZgA65WZAOuVmgDrlZsA65WcAOuVnQDrlZ4A65WfAOuVoADrlaEA65WiAOuVowDrlaQA65WlAOuVpgDrlacA65WoAOuVqQDrlaoA65WrAOuVrADrla0A65WuAOuVrwDrlbAA65WxAOuVsgDrlbMA65W0AOuVtQDrlbYA65W3AOuVuADrlbkA65W6AOuVuwDrlbwA65W9AOuVvgDrlb8A65aAAOuWgQDrloIA65aDAOuWhADrloUA65aGAOuWhwDrlogA65aJAOuWigDrlosA65aMAOuWjQDrlo4A65aPAOuWkADrlpEA65aSAOuWkwDrlpQA65aVAOuWlgDrlpcA65aYAOuWmQDrlpoA65abAOuWnADrlp0A65aeAOuWnwDrlqAA65ahAOuWogDrlqMA65akAOuWpQDrlqYA65anAOuWqADrlqkA65aqAOuWqwDrlqwA65atAOuWrgDrlq8A65awAOuWsQDrlrIA65azAOuWtADrlrUA65a2AOuWtwDrlrgA65a5AOuWugDrlrsA65a8AOuWvQDrlr4A65a/AOuXgADrl4EA65eCAOuXgwDrl4QA65eFAOuXhgDrl4cA65eIAOuXiQDrl4oA65eLAOuXjADrl40A65eOAOuXjwDrl5AA65eRAOuXkgDrl5MA65eUAOuXlQDrl5YA65eXAOuXmADrl5kA65eaAOuXmwDrl5wA65edAOuXngDrl58A65egAOuXoQDrl6IA65ejAOuXpADrl6UA65emAOuXpwDrl6gA65epAOuXqgDrl6sA65esAOuXrQDrl64A65evAOuXsADrl7EA65eyAOuXswDrl7QA65e1AOuXtgDrl7cA65e4AOuXuQDrl7oA65e7AOuXvADrl70A65e+AOuXvwDrmIAA65iBAOuYggDrmIMA65iEAOuYhQDrmIYA65iHAOuYiADrmIkA65iKAOuYiwDrmIwA65iNAOuYjgDrmI8A65iQAOuYkQDrmJIA65iTAOuYlADrmJUA65iWAOuYlwDrmJgA65iZAOuYmgDrmJsA65icAOuYnQDrmJ4A65ifAOuYoADrmKEA65iiAOuYowDrmKQA65ilAOuYpgDrmKcA65ioAOuYqQDrmKoA65irAOuYrADrmK0A65iuAOuYrwDrmLAA65ixAOuYsgDrmLMA65i0AOuYtQDrmLYA65i3AOuYuADrmLkA65i6AOuYuwDrmLwA65i9AOuYvgDrmL8A65mAAOuZgQDrmYIA65mDAOuZhADrmYUA65mGAOuZhwDrmYgA65mJAOuZigDrmYsA65mMAOuZjQDrmY4A65mPAOuZkADrmZEA65mSAOuZkwDrmZQA65mVAOuZlgDrmZcA65mYAOuZmQDrmZoA65mbAOuZnADrmZ0A65meAOuZnwDrmaAA65mhAOuZogDrmaMA65mkAOuZpQDrmaYA65mnAOuZqADrmakA65mqAOuZqwDrmawA65mtAOuZrgDrma8A65mwAOuZsQDrmbIA65mzAOuZtADrmbUA65m2AOuZtwDrmbgA65m5AOuZugDrmbsA65m8AOuZvQDrmb4A65m/AOuagADrmoEA65qCAOuagwDrmoQA65qFAOuahgDrmocA65qIAOuaiQDrmooA65qLAOuajADrmo0A65qOAOuajwDrmpAA65qRAOuakgDrmpMA65qUAOualQDrmpYA65qXAOuamADrmpkA65qaAOuamwDrmpwA65qdAOuangDrmp8A65qgAOuaoQDrmqIA65qjAOuapADrmqUA65qmAOuapwDrmqgA65qpAOuaqgDrmqsA65qsAOuarQDrmq4A65qvAOuasADrmrEA65qyAOuaswDrmrQA65q1AOuatgDrmrcA65q4AOuauQDrmroA65q7AOuavADrmr0A65q+AOuavwDrm4AA65uBAOubggDrm4MA65uEAOubhQDrm4YA65uHAOubiADrm4kA65uKAOubiwDrm4wA65uNAOubjgDrm48A65uQAOubkQDrm5IA65uTAOublADrm5UA65uWAOublwDrm5gA65uZAOubmgDrm5sA65ucAOubnQDrm54A65ufAOuboADrm6EA65uiAOubowDrm6QA65ulAOubpgDrm6cA65uoAOubqQDrm6oA65urAOubrADrm60A65uuAOubrwDrm7AA65uxAOubsgDrm7MA65u0AOubtQDrm7YA65u3AOubuADrm7kA65u6AOubuwDrm7wA65u9AOubvgDrm78A65yAAOucgQDrnIIA65yDAOuchADrnIUA65yGAOuchwDrnIgA65yJAOucigDrnIsA65yMAOucjQDrnI4A65yPAOuckADrnJEA65ySAOuckwDrnJQA65yVAOuclgDrnJcA65yYAOucmQDrnJoA65ybAOucnADrnJ0A65yeAOucnwDrnKAA65yhAOucogDrnKMA65ykAOucpQDrnKYA65ynAOucqADrnKkA65yqAOucqwDrnKwA65ytAOucrgDrnK8A65ywAOucsQDrnLIA65yzAOuctADrnLUA65y2AOuctwDrnLgA65y5AOucugDrnLsA65y8AOucvQDrnL4A65y/AOudgADrnYEA652CAOudgwDrnYQA652FAOudhgDrnYcA652IAOudiQDrnYoA652LAOudjADrnY0A652OAOudjwDrnZAA652RAOudkgDrnZMA652UAOudlQDrnZYA652XAOudmADrnZkA652aAOudmwDrnZwA652dAOudngDrnZ8A652gAOudoQDrnaIA652jAOudpADrnaUA652mAOudpwDrnagA652pAOudqgDrnasA652sAOudrQDrna4A652vAOudsADrnbEA652yAOudswDrnbQA6521AOudtgDrnbcA6524AOuduQDrnboA6527AOudvADrnb0A652+AOudvwDrnoAA656BAOueggDrnoMA656EAOuehQDrnoYA656HAOueiADrnokA656KAOueiwDrnowA656NAOuejgDrno8A656QAOuekQDrnpIA656TAOuelADrnpUA656WAOuelwDrnpgA656ZAOuemgDrnpsA656cAOuenQDrnp4A656fAOueoADrnqEA656iAOueowDrnqQA656lAOuepgDrnqcA656oAOueqQDrnqoA656rAOuerADrnq0A656uAOuerwDrnrAA656xAOuesgDrnrMA6560AOuetQDrnrYA6563AOueuADrnrkA6566AOueuwDrnrwA6569AOuevgDrnr8A65+AAOufgQDrn4IA65+DAOufhADrn4UA65+GAOufhwDrn4gA65+JAOufigDrn4sA65+MAOufjQDrn44A65+PAOufkADrn5EA65+SAOufkwDrn5QA65+VAOuflgDrn5cA65+YAOufmQDrn5oA65+bAOufnADrn50A65+eAOufnwDrn6AA65+hAOufogDrn6MA65+kAOufpQDrn6YA65+nAOufqADrn6kA65+qAOufqwDrn6wA65+tAOufrgDrn68A65+wAOufsQDrn7IA65+zAOuftADrn7UA65+2AOuftwDrn7gA65+5AOufugDrn7sA65+8AOufvQDrn74A65+/AOuggADroIEA66CCAOuggwDroIQA66CFAOughgDroIcA66CIAOugiQDroIoA66CLAOugjADroI0A66COAOugjwDroJAA66CRAOugkgDroJMA66CUAOuglQDroJYA66CXAOugmADroJkA66CaAOugmwDroJwA66CdAOugngDroJ8A66CgAOugoQDroKIA66CjAOugpADroKUA66CmAOugpwDroKgA66CpAOugqgDroKsA66CsAOugrQDroK4A66CvAOugsADroLEA66CyAOugswDroLQA66C1AOugtgDroLcA66C4AOuguQDroLoA66C7AOugvADroL0A66C+AOugvwDroYAA66GBAOuhggDroYMA66GEAOuhhQDroYYA66GHAOuhiADroYkA66GKAOuhiwDroYwA66GNAOuhjgDroY8A66GQAOuhkQDroZIA66GTAOuhlADroZUA66GWAOuhlwDroZgA66GZAOuhmgDroZsA66GcAOuhnQDroZ4A66GfAOuhoADroaEA66GiAOuhowDroaQA66GlAOuhpgDroacA66GoAOuhqQDroaoA66GrAOuhrADroa0A66GuAOuhrwDrobAA66GxAOuhsgDrobMA66G0AOuhtQDrobYA66G3AOuhuADrobkA66G6AOuhuwDrobwA66G9AOuhvgDrob8A66KAAOuigQDrooIA66KDAOuihADrooUA66KGAOuihwDroogA66KJAOuiigDroosA66KMAOuijQDroo4A66KPAOuikADropEA66KSAOuikwDropQA66KVAOuilgDropcA66KYAOuimQDropoA66KbAOuinADrop0A66KeAOuinwDroqAA66KhAOuiogDroqMA66KkAOuipQDroqYA66KnAOuiqADroqkA66KqAOuiqwDroqwA66KtAOuirgDroq8A66KwAOuisQDrorIA66KzAOuitADrorUA66K2AOuitwDrorgA66K5AOuiugDrorsA66K8AOuivQDror4A66K/AOujgADro4EA66OCAOujgwDro4QA66OFAOujhgDro4cA66OIAOujiQDro4oA66OLAOujjADro40A66OOAOujjwDro5AA66ORAOujkgDro5MA66OUAOujlQDro5YA66OXAOujmADro5kA66OaAOujmwDro5wA66OdAOujngDro58A66OgAOujoQDro6IA66OjAOujpADro6UA66OmAOujpwDro6gA66OpAOujqgDro6sA66OsAOujrQDro64A66OvAOujsADro7EA66OyAOujswDro7QA66O1AOujtgDro7cA66O4AOujuQDro7oA66O7AOujvADro70A66O+AOujvwDrpIAA66SBAOukggDrpIMA66SEAOukhQDrpIYA66SHAOukiADrpIkA66SKAOukiwDrpIwA66SNAOukjgDrpI8A66SQAOukkQDrpJIA66STAOuklADrpJUA66SWAOuklwDrpJgA66SZAOukmgDrpJsA66ScAOuknQDrpJ4A66SfAOukoADrpKEA66SiAOukowDrpKQA66SlAOukpgDrpKcA66SoAOukqQDrpKoA66SrAOukrADrpK0A66SuAOukrwDrpLAA66SxAOuksgDrpLMA66S0AOuktQDrpLYA66S3AOukuADrpLkA66S6AOukuwDrpLwA66S9AOukvgDrpL8A66WAAOulgQDrpYIA66WDAOulhADrpYUA66WGAOulhwDrpYgA66WJAOuligDrpYsA66WMAOuljQDrpY4A66WPAOulkADrpZEA66WSAOulkwDrpZQA66WVAOullgDrpZcA66WYAOulmQDrpZoA66WbAOulnADrpZ0A66WeAOulnwDrpaAA66WhAOulogDrpaMA66WkAOulpQDrpaYA66WnAOulqADrpakA66WqAOulqwDrpawA66WtAOulrgDrpa8A66WwAOulsQDrpbIA66WzAOultADrpbUA66W2AOultwDrpbgA66W5AOulugDrpbsA66W8AOulvQDrpb4A66W/AOumgADrpoEA66aCAOumgwDrpoQA66aFAOumhgDrpocA66aIAOumiQDrpooA66aLAOumjADrpo0A66aOAOumjwDrppAA66aRAOumkgDrppMA66aUAOumlQDrppYA66aXAOummADrppkA66aaAOummwDrppwA66adAOumngDrpp8A66agAOumoQDrpqIA66ajAOumpADrpqUA66amAOumpwDrpqgA66apAOumqgDrpqsA66asAOumrQDrpq4A66avAOumsADrprEA66ayAOumswDrprQA66a1AOumtgDrprcA66a4AOumuQDrproA66a7AOumvADrpr0A66a+AOumvwDrp4AA66eBAOunggDrp4MA66eEAOunhQDrp4YA66eHAOuniADrp4kA66eKAOuniwDrp4wA66eNAOunjgDrp48A66eQAOunkQDrp5IA66eTAOunlADrp5UA66eWAOunlwDrp5gA66eZAOunmgDrp5sA66ecAOunnQDrp54A66efAOunoADrp6EA66eiAOunowDrp6QA66elAOunpgDrp6cA66eoAOunqQDrp6oA66erAOunrADrp60A66euAOunrwDrp7AA66exAOunsgDrp7MA66e0AOuntQDrp7YA66e3AOunuADrp7kA66e6AOunuwDrp7wA66e9AOunvgDrp78A66iAAOuogQDrqIIA66iDAOuohADrqIUA66iGAOuohwDrqIgA66iJAOuoigDrqIsA66iMAOuojQDrqI4A66iPAOuokADrqJEA66iSAOuokwDrqJQA66iVAOuolgDrqJcA66iYAOuomQDrqJoA66ibAOuonADrqJ0A66ieAOuonwDrqKAA66ihAOuoogDrqKMA66ikAOuopQDrqKYA66inAOuoqADrqKkA66iqAOuoqwDrqKwA66itAOuorgDrqK8A66iwAOuosQDrqLIA66izAOuotADrqLUA66i2AOuotwDrqLgA66i5AOuougDrqLsA66i8AOuovQDrqL4A66i/AOupgADrqYEA66mCAOupgwDrqYQA66mFAOuphgDrqYcA66mIAOupiQDrqYoA66mLAOupjADrqY0A66mOAOupjwDrqZAA66mRAOupkgDrqZMA66mUAOuplQDrqZYA66mXAOupmADrqZkA66maAOupmwDrqZwA66mdAOupngDrqZ8A66mgAOupoQDrqaIA66mjAOuppADrqaUA66mmAOuppwDrqagA66mpAOupqgDrqasA66msAOuprQDrqa4A66mvAOupsADrqbEA66myAOupswDrqbQA66m1AOuptgDrqbcA66m4AOupuQDrqboA66m7AOupvADrqb0A66m+AOupvwDrqoAA66qBAOuqggDrqoMA66qEAOuqhQDrqoYA66qHAOuqiADrqokA66qKAOuqiwDrqowA66qNAOuqjgDrqo8A66qQAOuqkQDrqpIA66qTAOuqlADrqpUA66qWAOuqlwDrqpgA66qZAOuqmgDrqpsA66qcAOuqnQDrqp4A66qfAOuqoADrqqEA66qiAOuqowDrqqQA66qlAOuqpgDrqqcA66qoAOuqqQDrqqoA66qrAOuqrADrqq0A66quAOuqrwDrqrAA66qxAOuqsgDrqrMA66q0AOuqtQDrqrYA66q3AOuquADrqrkA66q6AOuquwDrqrwA66q9AOuqvgDrqr8A66uAAOurgQDrq4IA66uDAOurhADrq4UA66uGAOurhwDrq4gA66uJAOurigDrq4sA66uMAOurjQDrq44A66uPAOurkADrq5EA66uSAOurkwDrq5QA66uVAOurlgDrq5cA66uYAOurmQDrq5oA66ubAOurnADrq50A66ueAOurnwDrq6AA66uhAOurogDrq6MA66ukAOurpQDrq6YA66unAOurqADrq6kA66uqAOurqwDrq6wA66utAOurrgDrq68A66uwAOursQDrq7IA66uzAOurtADrq7UA66u2AOurtwDrq7gA66u5AOurugDrq7sA66u8AOurvQDrq74A66u/AOusgADrrIEA66yCAOusgwDrrIQA66yFAOushgDrrIcA66yIAOusiQDrrIoA66yLAOusjADrrI0A66yOAOusjwDrrJAA66yRAOuskgDrrJMA66yUAOuslQDrrJYA66yXAOusmADrrJkA66yaAOusmwDrrJwA66ydAOusngDrrJ8A66ygAOusoQDrrKIA66yjAOuspADrrKUA66ymAOuspwDrrKgA66ypAOusqgDrrKsA66ysAOusrQDrrK4A66yvAOussADrrLEA66yyAOusswDrrLQA66y1AOustgDrrLcA66y4AOusuQDrrLoA66y7AOusvADrrL0A66y+AOusvwDrrYAA662BAOutggDrrYMA662EAOuthQDrrYYA662HAOutiADrrYkA662KAOutiwDrrYwA662NAOutjgDrrY8A662QAOutkQDrrZIA662TAOutlADrrZUA662WAOutlwDrrZgA662ZAOutmgDrrZsA662cAOutnQDrrZ4A662fAOutoADrraEA662iAOutowDrraQA662lAOutpgDrracA662oAOutqQDrraoA662rAOutrADrra0A662uAOutrwDrrbAA662xAOutsgDrrbMA6620AOuttQDrrbYA6623AOutuADrrbkA6626AOutuwDrrbwA6629AOutvgDrrb8A666AAOuugQDrroIA666DAOuuhADrroUA666GAOuuhwDrrogA666JAOuuigDrrosA666MAOuujQDrro4A666PAOuukADrrpEA666SAOuukwDrrpQA666VAOuulgDrrpcA666YAOuumQDrrpoA666bAOuunADrrp0A666eAOuunwDrrqAA666hAOuuogDrrqMA666kAOuupQDrrqYA666nAOuuqADrrqkA666qAOuuqwDrrqwA666tAOuurgDrrq8A666wAOuusQDrrrIA666zAOuutADrrrUA6662AOuutwDrrrgA6665AOuuugDrrrsA6668AOuuvQDrrr4A666/AOuvgADrr4EA66+CAOuvgwDrr4QA66+FAOuvhgDrr4cA66+IAOuviQDrr4oA66+LAOuvjADrr40A66+OAOuvjwDrr5AA66+RAOuvkgDrr5MA66+UAOuvlQDrr5YA66+XAOuvmADrr5kA66+aAOuvmwDrr5wA66+dAOuvngDrr58A66+gAOuvoQDrr6IA66+jAOuvpADrr6UA66+mAOuvpwDrr6gA66+pAOuvqgDrr6sA66+sAOuvrQDrr64A66+vAOuvsADrr7EA66+yAOuvswDrr7QA66+1AOuvtgDrr7cA66+4AOuvuQDrr7oA66+7AOuvvADrr70A66++AOuvvwDrsIAA67CBAOuwggDrsIMA67CEAOuwhQDrsIYA67CHAOuwiADrsIkA67CKAOuwiwDrsIwA67CNAOuwjgDrsI8A67CQAOuwkQDrsJIA67CTAOuwlADrsJUA67CWAOuwlwDrsJgA67CZAOuwmgDrsJsA67CcAOuwnQDrsJ4A67CfAOuwoADrsKEA67CiAOuwowDrsKQA67ClAOuwpgDrsKcA67CoAOuwqQDrsKoA67CrAOuwrADrsK0A67CuAOuwrwDrsLAA67CxAOuwsgDrsLMA67C0AOuwtQDrsLYA67C3AOuwuADrsLkA67C6AOuwuwDrsLwA67C9AOuwvgDrsL8A67GAAOuxgQDrsYIA67GDAOuxhADrsYUA67GGAOuxhwDrsYgA67GJAOuxigDrsYsA67GMAOuxjQDrsY4A67GPAOuxkADrsZEA67GSAOuxkwDrsZQA67GVAOuxlgDrsZcA67GYAOuxmQDrsZoA67GbAOuxnADrsZ0A67GeAOuxnwDrsaAA67GhAOuxogDrsaMA67GkAOuxpQDrsaYA67GnAOuxqADrsakA67GqAOuxqwDrsawA67GtAOuxrgDrsa8A67GwAOuxsQDrsbIA67GzAOuxtADrsbUA67G2AOuxtwDrsbgA67G5AOuxugDrsbsA67G8AOuxvQDrsb4A67G/AOuygADrsoEA67KCAOuygwDrsoQA67KFAOuyhgDrsocA67KIAOuyiQDrsooA67KLAOuyjADrso0A67KOAOuyjwDrspAA67KRAOuykgDrspMA67KUAOuylQDrspYA67KXAOuymADrspkA67KaAOuymwDrspwA67KdAOuyngDrsp8A67KgAOuyoQDrsqIA67KjAOuypADrsqUA67KmAOuypwDrsqgA67KpAOuyqgDrsqsA67KsAOuyrQDrsq4A67KvAOuysADrsrEA67KyAOuyswDrsrQA67K1AOuytgDrsrcA67K4AOuyuQDrsroA67K7AOuyvADrsr0A67K+AOuyvwDrs4AA67OBAOuzggDrs4MA67OEAOuzhQDrs4YA67OHAOuziADrs4kA67OKAOuziwDrs4wA67ONAOuzjgDrs48A67OQAOuzkQDrs5IA67OTAOuzlADrs5UA67OWAOuzlwDrs5gA67OZAOuzmgDrs5sA67OcAOuznQDrs54A67OfAOuzoADrs6EA67OiAOuzowDrs6QA67OlAOuzpgDrs6cA67OoAOuzqQDrs6oA67OrAOuzrADrs60A67OuAOuzrwDrs7AA67OxAOuzsgDrs7MA67O0AOuztQDrs7YA67O3AOuzuADrs7kA67O6AOuzuwDrs7wA67O9AOuzvgDrs78A67SAAOu0gQDrtIIA67SDAOu0hADrtIUA67SGAOu0hwDrtIgA67SJAOu0igDrtIsA67SMAOu0jQDrtI4A67SPAOu0kADrtJEA67SSAOu0kwDrtJQA67SVAOu0lgDrtJcA67SYAOu0mQDrtJoA67SbAOu0nADrtJ0A67SeAOu0nwDrtKAA67ShAOu0ogDrtKMA67SkAOu0pQDrtKYA67SnAOu0qADrtKkA67SqAOu0qwDrtKwA67StAOu0rgDrtK8A67SwAOu0sQDrtLIA67SzAOu0tADrtLUA67S2AOu0twDrtLgA67S5AOu0ugDrtLsA67S8AOu0vQDrtL4A67S/AOu1gADrtYEA67WCAOu1gwDrtYQA67WFAOu1hgDrtYcA67WIAOu1iQDrtYoA67WLAOu1jADrtY0A67WOAOu1jwDrtZAA67WRAOu1kgDrtZMA67WUAOu1lQDrtZYA67WXAOu1mADrtZkA67WaAOu1mwDrtZwA67WdAOu1ngDrtZ8A67WgAOu1oQDrtaIA67WjAOu1pADrtaUA67WmAOu1pwDrtagA67WpAOu1qgDrtasA67WsAOu1rQDrta4A67WvAOu1sADrtbEA67WyAOu1swDrtbQA67W1AOu1tgDrtbcA67W4AOu1uQDrtboA67W7AOu1vADrtb0A67W+AOu1vwDrtoAA67aBAOu2ggDrtoMA67aEAOu2hQDrtoYA67aHAOu2iADrtokA67aKAOu2iwDrtowA67aNAOu2jgDrto8A67aQAOu2kQDrtpIA67aTAOu2lADrtpUA67aWAOu2lwDrtpgA67aZAOu2mgDrtpsA67acAOu2nQDrtp4A67afAOu2oADrtqEA67aiAOu2owDrtqQA67alAOu2pgDrtqcA67aoAOu2qQDrtqoA67arAOu2rADrtq0A67auAOu2rwDrtrAA67axAOu2sgDrtrMA67a0AOu2tQDrtrYA67a3AOu2uADrtrkA67a6AOu2uwDrtrwA67a9AOu2vgDrtr8A67eAAOu3gQDrt4IA67eDAOu3hADrt4UA67eGAOu3hwDrt4gA67eJAOu3igDrt4sA67eMAOu3jQDrt44A67ePAOu3kADrt5EA67eSAOu3kwDrt5QA67eVAOu3lgDrt5cA67eYAOu3mQDrt5oA67ebAOu3nADrt50A67eeAOu3nwDrt6AA67ehAOu3ogDrt6MA67ekAOu3pQDrt6YA67enAOu3qADrt6kA67eqAOu3qwDrt6wA67etAOu3rgDrt68A67ewAOu3sQDrt7IA67ezAOu3tADrt7UA67e2AOu3twDrt7gA67e5AOu3ugDrt7sA67e8AOu3vQDrt74A67e/AOu4gADruIEA67iCAOu4gwDruIQA67iFAOu4hgDruIcA67iIAOu4iQDruIoA67iLAOu4jADruI0A67iOAOu4jwDruJAA67iRAOu4kgDruJMA67iUAOu4lQDruJYA67iXAOu4mADruJkA67iaAOu4mwDruJwA67idAOu4ngDruJ8A67igAOu4oQDruKIA67ijAOu4pADruKUA67imAOu4pwDruKgA67ipAOu4qgDruKsA67isAOu4rQDruK4A67ivAOu4sADruLEA67iyAOu4swDruLQA67i1AOu4tgDruLcA67i4AOu4uQDruLoA67i7AOu4vADruL0A67i+AOu4vwDruYAA67mBAOu5ggDruYMA67mEAOu5hQDruYYA67mHAOu5iADruYkA67mKAOu5iwDruYwA67mNAOu5jgDruY8A67mQAOu5kQDruZIA67mTAOu5lADruZUA67mWAOu5lwDruZgA67mZAOu5mgDruZsA67mcAOu5nQDruZ4A67mfAOu5oADruaEA67miAOu5owDruaQA67mlAOu5pgDruacA67moAOu5qQDruaoA67mrAOu5rADrua0A67muAOu5rwDrubAA67mxAOu5sgDrubMA67m0AOu5tQDrubYA67m3AOu5uADrubkA67m6AOu5uwDrubwA67m9AOu5vgDrub8A67qAAOu6gQDruoIA67qDAOu6hADruoUA67qGAOu6hwDruogA67qJAOu6igDruosA67qMAOu6jQDruo4A67qPAOu6kADrupEA67qSAOu6kwDrupQA67qVAOu6lgDrupcA67qYAOu6mQDrupoA67qbAOu6nADrup0A67qeAOu6nwDruqAA67qhAOu6ogDruqMA67qkAOu6pQDruqYA67qnAOu6qADruqkA67qqAOu6qwDruqwA67qtAOu6rgDruq8A67qwAOu6sQDrurIA67qzAOu6tADrurUA67q2AOu6twDrurgA67q5AOu6ugDrursA67q8AOu6vQDrur4A67q/AOu7gADru4EA67uCAOu7gwDru4QA67uFAOu7hgDru4cA67uIAOu7iQDru4oA67uLAOu7jADru40A67uOAOu7jwDru5AA67uRAOu7kgDru5MA67uUAOu7lQDru5YA67uXAOu7mADru5kA67uaAOu7mwDru5wA67udAOu7ngDru58A67ugAOu7oQDru6IA67ujAOu7pADru6UA67umAOu7pwDru6gA67upAOu7qgDru6sA67usAOu7rQDru64A67uvAOu7sADru7EA67uyAOu7swDru7QA67u1AOu7tgDru7cA67u4AOu7uQDru7oA67u7AOu7vADru70A67u+AOu7vwDrvIAA67yBAOu8ggDrvIMA67yEAOu8hQDrvIYA67yHAOu8iADrvIkA67yKAOu8iwDrvIwA67yNAOu8jgDrvI8A67yQAOu8kQDrvJIA67yTAOu8lADrvJUA67yWAOu8lwDrvJgA67yZAOu8mgDrvJsA67ycAOu8nQDrvJ4A67yfAOu8oADrvKEA67yiAOu8owDrvKQA67ylAOu8pgDrvKcA67yoAOu8qQDrvKoA67yrAOu8rADrvK0A67yuAOu8rwDrvLAA67yxAOu8sgDrvLMA67y0AOu8tQDrvLYA67y3AOu8uADrvLkA67y6AOu8uwDrvLwA67y9AOu8vgDrvL8A672AAOu9gQDrvYIA672DAOu9hADrvYUA672GAOu9hwDrvYgA672JAOu9igDrvYsA672MAOu9jQDrvY4A672PAOu9kADrvZEA672SAOu9kwDrvZQA672VAOu9lgDrvZcA672YAOu9mQDrvZoA672bAOu9nADrvZ0A672eAOu9nwDrvaAA672hAOu9ogDrvaMA672kAOu9pQDrvaYA672nAOu9qADrvakA672qAOu9qwDrvawA672tAOu9rgDrva8A672wAOu9sQDrvbIA672zAOu9tADrvbUA6722AOu9twDrvbgA6725AOu9ugDrvbsA6728AOu9vQDrvb4A672/AOu+gADrvoEA676CAOu+gwDrvoQA676FAOu+hgDrvocA676IAOu+iQDrvooA676LAOu+jADrvo0A676OAOu+jwDrvpAA676RAOu+kgDrvpMA676UAOu+lQDrvpYA676XAOu+mADrvpkA676aAOu+mwDrvpwA676dAOu+ngDrvp8A676gAOu+oQDrvqIA676jAOu+pADrvqUA676mAOu+pwDrvqgA676pAOu+qgDrvqsA676sAOu+rQDrvq4A676vAOu+sADrvrEA676yAOu+swDrvrQA6761AOu+tgDrvrcA6764AOu+uQDrvroA6767AOu+vADrvr0A676+AOu+vwDrv4AA67+BAOu/ggDrv4MA67+EAOu/hQDrv4YA67+HAOu/iADrv4kA67+KAOu/iwDrv4wA67+NAOu/jgDrv48A67+QAOu/kQDrv5IA67+TAOu/lADrv5UA67+WAOu/lwDrv5gA67+ZAOu/mgDrv5sA67+cAOu/nQDrv54A67+fAOu/oADrv6EA67+iAOu/owDrv6QA67+lAOu/pgDrv6cA67+oAOu/qQDrv6oA67+rAOu/rADrv60A67+uAOu/rwDrv7AA67+xAOu/sgDrv7MA67+0AOu/tQDrv7YA67+3AOu/uADrv7kA67+6AOu/uwDrv7wA67+9AOu/vgDrv78A7ICAAOyAgQDsgIIA7ICDAOyAhADsgIUA7ICGAOyAhwDsgIgA7ICJAOyAigDsgIsA7ICMAOyAjQDsgI4A7ICPAOyAkADsgJEA7ICSAOyAkwDsgJQA7ICVAOyAlgDsgJcA7ICYAOyAmQDsgJoA7ICbAOyAnADsgJ0A7ICeAOyAnwDsgKAA7IChAOyAogDsgKMA7ICkAOyApQDsgKYA7ICnAOyAqADsgKkA7ICqAOyAqwDsgKwA7ICtAOyArgDsgK8A7ICwAOyAsQDsgLIA7ICzAOyAtADsgLUA7IC2AOyAtwDsgLgA7IC5AOyAugDsgLsA7IC8AOyAvQDsgL4A7IC/AOyBgADsgYEA7IGCAOyBgwDsgYQA7IGFAOyBhgDsgYcA7IGIAOyBiQDsgYoA7IGLAOyBjADsgY0A7IGOAOyBjwDsgZAA7IGRAOyBkgDsgZMA7IGUAOyBlQDsgZYA7IGXAOyBmADsgZkA7IGaAOyBmwDsgZwA7IGdAOyBngDsgZ8A7IGgAOyBoQDsgaIA7IGjAOyBpADsgaUA7IGmAOyBpwDsgagA7IGpAOyBqgDsgasA7IGsAOyBrQDsga4A7IGvAOyBsADsgbEA7IGyAOyBswDsgbQA7IG1AOyBtgDsgbcA7IG4AOyBuQDsgboA7IG7AOyBvADsgb0A7IG+AOyBvwDsgoAA7IKBAOyCggDsgoMA7IKEAOyChQDsgoYA7IKHAOyCiADsgokA7IKKAOyCiwDsgowA7IKNAOyCjgDsgo8A7IKQAOyCkQDsgpIA7IKTAOyClADsgpUA7IKWAOyClwDsgpgA7IKZAOyCmgDsgpsA7IKcAOyCnQDsgp4A7IKfAOyCoADsgqEA7IKiAOyCowDsgqQA7IKlAOyCpgDsgqcA7IKoAOyCqQDsgqoA7IKrAOyCrADsgq0A7IKuAOyCrwDsgrAA7IKxAOyCsgDsgrMA7IK0AOyCtQDsgrYA7IK3AOyCuADsgrkA7IK6AOyCuwDsgrwA7IK9AOyCvgDsgr8A7IOAAOyDgQDsg4IA7IODAOyDhADsg4UA7IOGAOyDhwDsg4gA7IOJAOyDigDsg4sA7IOMAOyDjQDsg44A7IOPAOyDkADsg5EA7IOSAOyDkwDsg5QA7IOVAOyDlgDsg5cA7IOYAOyDmQDsg5oA7IObAOyDnADsg50A7IOeAOyDnwDsg6AA7IOhAOyDogDsg6MA7IOkAOyDpQDsg6YA7IOnAOyDqADsg6kA7IOqAOyDqwDsg6wA7IOtAOyDrgDsg68A7IOwAOyDsQDsg7IA7IOzAOyDtADsg7UA7IO2AOyDtwDsg7gA7IO5AOyDugDsg7sA7IO8AOyDvQDsg74A7IO/AOyEgADshIEA7ISCAOyEgwDshIQA7ISFAOyEhgDshIcA7ISIAOyEiQDshIoA7ISLAOyEjADshI0A7ISOAOyEjwDshJAA7ISRAOyEkgDshJMA7ISUAOyElQDshJYA7ISXAOyEmADshJkA7ISaAOyEmwDshJwA7ISdAOyEngDshJ8A7ISgAOyEoQDshKIA7ISjAOyEpADshKUA7ISmAOyEpwDshKgA7ISpAOyEqgDshKsA7ISsAOyErQDshK4A7ISvAOyEsADshLEA7ISyAOyEswDshLQA7IS1AOyEtgDshLcA7IS4AOyEuQDshLoA7IS7AOyEvADshL0A7IS+AOyEvwDshYAA7IWBAOyFggDshYMA7IWEAOyFhQDshYYA7IWHAOyFiADshYkA7IWKAOyFiwDshYwA7IWNAOyFjgDshY8A7IWQAOyFkQDshZIA7IWTAOyFlADshZUA7IWWAOyFlwDshZgA7IWZAOyFmgDshZsA7IWcAOyFnQDshZ4A7IWfAOyFoADshaEA7IWiAOyFowDshaQA7IWlAOyFpgDshacA7IWoAOyFqQDshaoA7IWrAOyFrADsha0A7IWuAOyFrwDshbAA7IWxAOyFsgDshbMA7IW0AOyFtQDshbYA7IW3AOyFuADshbkA7IW6AOyFuwDshbwA7IW9AOyFvgDshb8A7IaAAOyGgQDshoIA7IaDAOyGhADshoUA7IaGAOyGhwDshogA7IaJAOyGigDshosA7IaMAOyGjQDsho4A7IaPAOyGkADshpEA7IaSAOyGkwDshpQA7IaVAOyGlgDshpcA7IaYAOyGmQDshpoA7IabAOyGnADshp0A7IaeAOyGnwDshqAA7IahAOyGogDshqMA7IakAOyGpQDshqYA7IanAOyGqADshqkA7IaqAOyGqwDshqwA7IatAOyGrgDshq8A7IawAOyGsQDshrIA7IazAOyGtADshrUA7Ia2AOyGtwDshrgA7Ia5AOyGugDshrsA7Ia8AOyGvQDshr4A7Ia/AOyHgADsh4EA7IeCAOyHgwDsh4QA7IeFAOyHhgDsh4cA7IeIAOyHiQDsh4oA7IeLAOyHjADsh40A7IeOAOyHjwDsh5AA7IeRAOyHkgDsh5MA7IeUAOyHlQDsh5YA7IeXAOyHmADsh5kA7IeaAOyHmwDsh5wA7IedAOyHngDsh58A7IegAOyHoQDsh6IA7IejAOyHpADsh6UA7IemAOyHpwDsh6gA7IepAOyHqgDsh6sA7IesAOyHrQDsh64A7IevAOyHsADsh7EA7IeyAOyHswDsh7QA7Ie1AOyHtgDsh7cA7Ie4AOyHuQDsh7oA7Ie7AOyHvADsh70A7Ie+AOyHvwDsiIAA7IiBAOyIggDsiIMA7IiEAOyIhQDsiIYA7IiHAOyIiADsiIkA7IiKAOyIiwDsiIwA7IiNAOyIjgDsiI8A7IiQAOyIkQDsiJIA7IiTAOyIlADsiJUA7IiWAOyIlwDsiJgA7IiZAOyImgDsiJsA7IicAOyInQDsiJ4A7IifAOyIoADsiKEA7IiiAOyIowDsiKQA7IilAOyIpgDsiKcA7IioAOyIqQDsiKoA7IirAOyIrADsiK0A7IiuAOyIrwDsiLAA7IixAOyIsgDsiLMA7Ii0AOyItQDsiLYA7Ii3AOyIuADsiLkA7Ii6AOyIuwDsiLwA7Ii9AOyIvgDsiL8A7ImAAOyJgQDsiYIA7ImDAOyJhADsiYUA7ImGAOyJhwDsiYgA7ImJAOyJigDsiYsA7ImMAOyJjQDsiY4A7ImPAOyJkADsiZEA7ImSAOyJkwDsiZQA7ImVAOyJlgDsiZcA7ImYAOyJmQDsiZoA7ImbAOyJnADsiZ0A7ImeAOyJnwDsiaAA7ImhAOyJogDsiaMA7ImkAOyJpQDsiaYA7ImnAOyJqADsiakA7ImqAOyJqwDsiawA7ImtAOyJrgDsia8A7ImwAOyJsQDsibIA7ImzAOyJtADsibUA7Im2AOyJtwDsibgA7Im5AOyJugDsibsA7Im8AOyJvQDsib4A7Im/AOyKgADsioEA7IqCAOyKgwDsioQA7IqFAOyKhgDsiocA7IqIAOyKiQDsiooA7IqLAOyKjADsio0A7IqOAOyKjwDsipAA7IqRAOyKkgDsipMA7IqUAOyKlQDsipYA7IqXAOyKmADsipkA7IqaAOyKmwDsipwA7IqdAOyKngDsip8A7IqgAOyKoQDsiqIA7IqjAOyKpADsiqUA7IqmAOyKpwDsiqgA7IqpAOyKqgDsiqsA7IqsAOyKrQDsiq4A7IqvAOyKsADsirEA7IqyAOyKswDsirQA7Iq1AOyKtgDsircA7Iq4AOyKuQDsiroA7Iq7AOyKvADsir0A7Iq+AOyKvwDsi4AA7IuBAOyLggDsi4MA7IuEAOyLhQDsi4YA7IuHAOyLiADsi4kA7IuKAOyLiwDsi4wA7IuNAOyLjgDsi48A7IuQAOyLkQDsi5IA7IuTAOyLlADsi5UA7IuWAOyLlwDsi5gA7IuZAOyLmgDsi5sA7IucAOyLnQDsi54A7IufAOyLoADsi6EA7IuiAOyLowDsi6QA7IulAOyLpgDsi6cA7IuoAOyLqQDsi6oA7IurAOyLrADsi60A7IuuAOyLrwDsi7AA7IuxAOyLsgDsi7MA7Iu0AOyLtQDsi7YA7Iu3AOyLuADsi7kA7Iu6AOyLuwDsi7wA7Iu9AOyLvgDsi78A7IyAAOyMgQDsjIIA7IyDAOyMhADsjIUA7IyGAOyMhwDsjIgA7IyJAOyMigDsjIsA7IyMAOyMjQDsjI4A7IyPAOyMkADsjJEA7IySAOyMkwDsjJQA7IyVAOyMlgDsjJcA7IyYAOyMmQDsjJoA7IybAOyMnADsjJ0A7IyeAOyMnwDsjKAA7IyhAOyMogDsjKMA7IykAOyMpQDsjKYA7IynAOyMqADsjKkA7IyqAOyMqwDsjKwA7IytAOyMrgDsjK8A7IywAOyMsQDsjLIA7IyzAOyMtADsjLUA7Iy2AOyMtwDsjLgA7Iy5AOyMugDsjLsA7Iy8AOyMvQDsjL4A7Iy/AOyNgADsjYEA7I2CAOyNgwDsjYQA7I2FAOyNhgDsjYcA7I2IAOyNiQDsjYoA7I2LAOyNjADsjY0A7I2OAOyNjwDsjZAA7I2RAOyNkgDsjZMA7I2UAOyNlQDsjZYA7I2XAOyNmADsjZkA7I2aAOyNmwDsjZwA7I2dAOyNngDsjZ8A7I2gAOyNoQDsjaIA7I2jAOyNpADsjaUA7I2mAOyNpwDsjagA7I2pAOyNqgDsjasA7I2sAOyNrQDsja4A7I2vAOyNsADsjbEA7I2yAOyNswDsjbQA7I21AOyNtgDsjbcA7I24AOyNuQDsjboA7I27AOyNvADsjb0A7I2+AOyNvwDsjoAA7I6BAOyOggDsjoMA7I6EAOyOhQDsjoYA7I6HAOyOiADsjokA7I6KAOyOiwDsjowA7I6NAOyOjgDsjo8A7I6QAOyOkQDsjpIA7I6TAOyOlADsjpUA7I6WAOyOlwDsjpgA7I6ZAOyOmgDsjpsA7I6cAOyOnQDsjp4A7I6fAOyOoADsjqEA7I6iAOyOowDsjqQA7I6lAOyOpgDsjqcA7I6oAOyOqQDsjqoA7I6rAOyOrADsjq0A7I6uAOyOrwDsjrAA7I6xAOyOsgDsjrMA7I60AOyOtQDsjrYA7I63AOyOuADsjrkA7I66AOyOuwDsjrwA7I69AOyOvgDsjr8A7I+AAOyPgQDsj4IA7I+DAOyPhADsj4UA7I+GAOyPhwDsj4gA7I+JAOyPigDsj4sA7I+MAOyPjQDsj44A7I+PAOyPkADsj5EA7I+SAOyPkwDsj5QA7I+VAOyPlgDsj5cA7I+YAOyPmQDsj5oA7I+bAOyPnADsj50A7I+eAOyPnwDsj6AA7I+hAOyPogDsj6MA7I+kAOyPpQDsj6YA7I+nAOyPqADsj6kA7I+qAOyPqwDsj6wA7I+tAOyPrgDsj68A7I+wAOyPsQDsj7IA7I+zAOyPtADsj7UA7I+2AOyPtwDsj7gA7I+5AOyPugDsj7sA7I+8AOyPvQDsj74A7I+/AOyQgADskIEA7JCCAOyQgwDskIQA7JCFAOyQhgDskIcA7JCIAOyQiQDskIoA7JCLAOyQjADskI0A7JCOAOyQjwDskJAA7JCRAOyQkgDskJMA7JCUAOyQlQDskJYA7JCXAOyQmADskJkA7JCaAOyQmwDskJwA7JCdAOyQngDskJ8A7JCgAOyQoQDskKIA7JCjAOyQpADskKUA7JCmAOyQpwDskKgA7JCpAOyQqgDskKsA7JCsAOyQrQDskK4A7JCvAOyQsADskLEA7JCyAOyQswDskLQA7JC1AOyQtgDskLcA7JC4AOyQuQDskLoA7JC7AOyQvADskL0A7JC+AOyQvwDskYAA7JGBAOyRggDskYMA7JGEAOyRhQDskYYA7JGHAOyRiADskYkA7JGKAOyRiwDskYwA7JGNAOyRjgDskY8A7JGQAOyRkQDskZIA7JGTAOyRlADskZUA7JGWAOyRlwDskZgA7JGZAOyRmgDskZsA7JGcAOyRnQDskZ4A7JGfAOyRoADskaEA7JGiAOyRowDskaQA7JGlAOyRpgDskacA7JGoAOyRqQDskaoA7JGrAOyRrADska0A7JGuAOyRrwDskbAA7JGxAOyRsgDskbMA7JG0AOyRtQDskbYA7JG3AOyRuADskbkA7JG6AOyRuwDskbwA7JG9AOyRvgDskb8A7JKAAOySgQDskoIA7JKDAOyShADskoUA7JKGAOyShwDskogA7JKJAOySigDskosA7JKMAOySjQDsko4A7JKPAOySkADskpEA7JKSAOySkwDskpQA7JKVAOySlgDskpcA7JKYAOySmQDskpoA7JKbAOySnADskp0A7JKeAOySnwDskqAA7JKhAOySogDskqMA7JKkAOySpQDskqYA7JKnAOySqADskqkA7JKqAOySqwDskqwA7JKtAOySrgDskq8A7JKwAOySsQDskrIA7JKzAOyStADskrUA7JK2AOyStwDskrgA7JK5AOySugDskrsA7JK8AOySvQDskr4A7JK/AOyTgADsk4EA7JOCAOyTgwDsk4QA7JOFAOyThgDsk4cA7JOIAOyTiQDsk4oA7JOLAOyTjADsk40A7JOOAOyTjwDsk5AA7JORAOyTkgDsk5MA7JOUAOyTlQDsk5YA7JOXAOyTmADsk5kA7JOaAOyTmwDsk5wA7JOdAOyTngDsk58A7JOgAOyToQDsk6IA7JOjAOyTpADsk6UA7JOmAOyTpwDsk6gA7JOpAOyTqgDsk6sA7JOsAOyTrQDsk64A7JOvAOyTsADsk7EA7JOyAOyTswDsk7QA7JO1AOyTtgDsk7cA7JO4AOyTuQDsk7oA7JO7AOyTvADsk70A7JO+AOyTvwDslIAA7JSBAOyUggDslIMA7JSEAOyUhQDslIYA7JSHAOyUiADslIkA7JSKAOyUiwDslIwA7JSNAOyUjgDslI8A7JSQAOyUkQDslJIA7JSTAOyUlADslJUA7JSWAOyUlwDslJgA7JSZAOyUmgDslJsA7JScAOyUnQDslJ4A7JSfAOyUoADslKEA7JSiAOyUowDslKQA7JSlAOyUpgDslKcA7JSoAOyUqQDslKoA7JSrAOyUrADslK0A7JSuAOyUrwDslLAA7JSxAOyUsgDslLMA7JS0AOyUtQDslLYA7JS3AOyUuADslLkA7JS6AOyUuwDslLwA7JS9AOyUvgDslL8A7JWAAOyVgQDslYIA7JWDAOyVhADslYUA7JWGAOyVhwDslYgA7JWJAOyVigDslYsA7JWMAOyVjQDslY4A7JWPAOyVkADslZEA7JWSAOyVkwDslZQA7JWVAOyVlgDslZcA7JWYAOyVmQDslZoA7JWbAOyVnADslZ0A7JWeAOyVnwDslaAA7JWhAOyVogDslaMA7JWkAOyVpQDslaYA7JWnAOyVqADslakA7JWqAOyVqwDslawA7JWtAOyVrgDsla8A7JWwAOyVsQDslbIA7JWzAOyVtADslbUA7JW2AOyVtwDslbgA7JW5AOyVugDslbsA7JW8AOyVvQDslb4A7JW/AOyWgADsloEA7JaCAOyWgwDsloQA7JaFAOyWhgDslocA7JaIAOyWiQDslooA7JaLAOyWjADslo0A7JaOAOyWjwDslpAA7JaRAOyWkgDslpMA7JaUAOyWlQDslpYA7JaXAOyWmADslpkA7JaaAOyWmwDslpwA7JadAOyWngDslp8A7JagAOyWoQDslqIA7JajAOyWpADslqUA7JamAOyWpwDslqgA7JapAOyWqgDslqsA7JasAOyWrQDslq4A7JavAOyWsADslrEA7JayAOyWswDslrQA7Ja1AOyWtgDslrcA7Ja4AOyWuQDslroA7Ja7AOyWvADslr0A7Ja+AOyWvwDsl4AA7JeBAOyXggDsl4MA7JeEAOyXhQDsl4YA7JeHAOyXiADsl4kA7JeKAOyXiwDsl4wA7JeNAOyXjgDsl48A7JeQAOyXkQDsl5IA7JeTAOyXlADsl5UA7JeWAOyXlwDsl5gA7JeZAOyXmgDsl5sA7JecAOyXnQDsl54A7JefAOyXoADsl6EA7JeiAOyXowDsl6QA7JelAOyXpgDsl6cA7JeoAOyXqQDsl6oA7JerAOyXrADsl60A7JeuAOyXrwDsl7AA7JexAOyXsgDsl7MA7Je0AOyXtQDsl7YA7Je3AOyXuADsl7kA7Je6AOyXuwDsl7wA7Je9AOyXvgDsl78A7JiAAOyYgQDsmIIA7JiDAOyYhADsmIUA7JiGAOyYhwDsmIgA7JiJAOyYigDsmIsA7JiMAOyYjQDsmI4A7JiPAOyYkADsmJEA7JiSAOyYkwDsmJQA7JiVAOyYlgDsmJcA7JiYAOyYmQDsmJoA7JibAOyYnADsmJ0A7JieAOyYnwDsmKAA7JihAOyYogDsmKMA7JikAOyYpQDsmKYA7JinAOyYqADsmKkA7JiqAOyYqwDsmKwA7JitAOyYrgDsmK8A7JiwAOyYsQDsmLIA7JizAOyYtADsmLUA7Ji2AOyYtwDsmLgA7Ji5AOyYugDsmLsA7Ji8AOyYvQDsmL4A7Ji/AOyZgADsmYEA7JmCAOyZgwDsmYQA7JmFAOyZhgDsmYcA7JmIAOyZiQDsmYoA7JmLAOyZjADsmY0A7JmOAOyZjwDsmZAA7JmRAOyZkgDsmZMA7JmUAOyZlQDsmZYA7JmXAOyZmADsmZkA7JmaAOyZmwDsmZwA7JmdAOyZngDsmZ8A7JmgAOyZoQDsmaIA7JmjAOyZpADsmaUA7JmmAOyZpwDsmagA7JmpAOyZqgDsmasA7JmsAOyZrQDsma4A7JmvAOyZsADsmbEA7JmyAOyZswDsmbQA7Jm1AOyZtgDsmbcA7Jm4AOyZuQDsmboA7Jm7AOyZvADsmb0A7Jm+AOyZvwDsmoAA7JqBAOyaggDsmoMA7JqEAOyahQDsmoYA7JqHAOyaiADsmokA7JqKAOyaiwDsmowA7JqNAOyajgDsmo8A7JqQAOyakQDsmpIA7JqTAOyalADsmpUA7JqWAOyalwDsmpgA7JqZAOyamgDsmpsA7JqcAOyanQDsmp4A7JqfAOyaoADsmqEA7JqiAOyaowDsmqQA7JqlAOyapgDsmqcA7JqoAOyaqQDsmqoA7JqrAOyarADsmq0A7JquAOyarwDsmrAA7JqxAOyasgDsmrMA7Jq0AOyatQDsmrYA7Jq3AOyauADsmrkA7Jq6AOyauwDsmrwA7Jq9AOyavgDsmr8A7JuAAOybgQDsm4IA7JuDAOybhADsm4UA7JuGAOybhwDsm4gA7JuJAOybigDsm4sA7JuMAOybjQDsm44A7JuPAOybkADsm5EA7JuSAOybkwDsm5QA7JuVAOyblgDsm5cA7JuYAOybmQDsm5oA7JubAOybnADsm50A7JueAOybnwDsm6AA7JuhAOybogDsm6MA7JukAOybpQDsm6YA7JunAOybqADsm6kA7JuqAOybqwDsm6wA7JutAOybrgDsm68A7JuwAOybsQDsm7IA7JuzAOybtADsm7UA7Ju2AOybtwDsm7gA7Ju5AOybugDsm7sA7Ju8AOybvQDsm74A7Ju/AOycgADsnIEA7JyCAOycgwDsnIQA7JyFAOychgDsnIcA7JyIAOyciQDsnIoA7JyLAOycjADsnI0A7JyOAOycjwDsnJAA7JyRAOyckgDsnJMA7JyUAOyclQDsnJYA7JyXAOycmADsnJkA7JyaAOycmwDsnJwA7JydAOycngDsnJ8A7JygAOycoQDsnKIA7JyjAOycpADsnKUA7JymAOycpwDsnKgA7JypAOycqgDsnKsA7JysAOycrQDsnK4A7JyvAOycsADsnLEA7JyyAOycswDsnLQA7Jy1AOyctgDsnLcA7Jy4AOycuQDsnLoA7Jy7AOycvADsnL0A7Jy+AOycvwDsnYAA7J2BAOydggDsnYMA7J2EAOydhQDsnYYA7J2HAOydiADsnYkA7J2KAOydiwDsnYwA7J2NAOydjgDsnY8A7J2QAOydkQDsnZIA7J2TAOydlADsnZUA7J2WAOydlwDsnZgA7J2ZAOydmgDsnZsA7J2cAOydnQDsnZ4A7J2fAOydoADsnaEA7J2iAOydowDsnaQA7J2lAOydpgDsnacA7J2oAOydqQDsnaoA7J2rAOydrADsna0A7J2uAOydrwDsnbAA7J2xAOydsgDsnbMA7J20AOydtQDsnbYA7J23AOyduADsnbkA7J26AOyduwDsnbwA7J29AOydvgDsnb8A7J6AAOyegQDsnoIA7J6DAOyehADsnoUA7J6GAOyehwDsnogA7J6JAOyeigDsnosA7J6MAOyejQDsno4A7J6PAOyekADsnpEA7J6SAOyekwDsnpQA7J6VAOyelgDsnpcA7J6YAOyemQDsnpoA7J6bAOyenADsnp0A7J6eAOyenwDsnqAA7J6hAOyeogDsnqMA7J6kAOyepQDsnqYA7J6nAOyeqADsnqkA7J6qAOyeqwDsnqwA7J6tAOyergDsnq8A7J6wAOyesQDsnrIA7J6zAOyetADsnrUA7J62AOyetwDsnrgA7J65AOyeugDsnrsA7J68AOyevQDsnr4A7J6/AOyfgADsn4EA7J+CAOyfgwDsn4QA7J+FAOyfhgDsn4cA7J+IAOyfiQDsn4oA7J+LAOyfjADsn40A7J+OAOyfjwDsn5AA7J+RAOyfkgDsn5MA7J+UAOyflQDsn5YA7J+XAOyfmADsn5kA7J+aAOyfmwDsn5wA7J+dAOyfngDsn58A7J+gAOyfoQDsn6IA7J+jAOyfpADsn6UA7J+mAOyfpwDsn6gA7J+pAOyfqgDsn6sA7J+sAOyfrQDsn64A7J+vAOyfsADsn7EA7J+yAOyfswDsn7QA7J+1AOyftgDsn7cA7J+4AOyfuQDsn7oA7J+7AOyfvADsn70A7J++AOyfvwDsoIAA7KCBAOygggDsoIMA7KCEAOyghQDsoIYA7KCHAOygiADsoIkA7KCKAOygiwDsoIwA7KCNAOygjgDsoI8A7KCQAOygkQDsoJIA7KCTAOyglADsoJUA7KCWAOyglwDsoJgA7KCZAOygmgDsoJsA7KCcAOygnQDsoJ4A7KCfAOygoADsoKEA7KCiAOygowDsoKQA7KClAOygpgDsoKcA7KCoAOygqQDsoKoA7KCrAOygrADsoK0A7KCuAOygrwDsoLAA7KCxAOygsgDsoLMA7KC0AOygtQDsoLYA7KC3AOyguADsoLkA7KC6AOyguwDsoLwA7KC9AOygvgDsoL8A7KGAAOyhgQDsoYIA7KGDAOyhhADsoYUA7KGGAOyhhwDsoYgA7KGJAOyhigDsoYsA7KGMAOyhjQDsoY4A7KGPAOyhkADsoZEA7KGSAOyhkwDsoZQA7KGVAOyhlgDsoZcA7KGYAOyhmQDsoZoA7KGbAOyhnADsoZ0A7KGeAOyhnwDsoaAA7KGhAOyhogDsoaMA7KGkAOyhpQDsoaYA7KGnAOyhqADsoakA7KGqAOyhqwDsoawA7KGtAOyhrgDsoa8A7KGwAOyhsQDsobIA7KGzAOyhtADsobUA7KG2AOyhtwDsobgA7KG5AOyhugDsobsA7KG8AOyhvQDsob4A7KG/AOyigADsooEA7KKCAOyigwDsooQA7KKFAOyihgDsoocA7KKIAOyiiQDsoooA7KKLAOyijADsoo0A7KKOAOyijwDsopAA7KKRAOyikgDsopMA7KKUAOyilQDsopYA7KKXAOyimADsopkA7KKaAOyimwDsopwA7KKdAOyingDsop8A7KKgAOyioQDsoqIA7KKjAOyipADsoqUA7KKmAOyipwDsoqgA7KKpAOyiqgDsoqsA7KKsAOyirQDsoq4A7KKvAOyisADsorEA7KKyAOyiswDsorQA7KK1AOyitgDsorcA7KK4AOyiuQDsoroA7KK7AOyivADsor0A7KK+AOyivwDso4AA7KOBAOyjggDso4MA7KOEAOyjhQDso4YA7KOHAOyjiADso4kA7KOKAOyjiwDso4wA7KONAOyjjgDso48A7KOQAOyjkQDso5IA7KOTAOyjlADso5UA7KOWAOyjlwDso5gA7KOZAOyjmgDso5sA7KOcAOyjnQDso54A7KOfAOyjoADso6EA7KOiAOyjowDso6QA7KOlAOyjpgDso6cA7KOoAOyjqQDso6oA7KOrAOyjrADso60A7KOuAOyjrwDso7AA7KOxAOyjsgDso7MA7KO0AOyjtQDso7YA7KO3AOyjuADso7kA7KO6AOyjuwDso7wA7KO87J2YAOyjvQDso74A7KO/AOykgADspIEA7KSCAOykgwDspIQA7KSFAOykhgDspIcA7KSIAOykiQDspIoA7KSLAOykjADspI0A7KSOAOykjwDspJAA7KSRAOykkgDspJMA7KSUAOyklQDspJYA7KSXAOykmADspJkA7KSaAOykmwDspJwA7KSdAOykngDspJ8A7KSgAOykoQDspKIA7KSjAOykpADspKUA7KSmAOykpwDspKgA7KSpAOykqgDspKsA7KSsAOykrQDspK4A7KSvAOyksADspLEA7KSyAOykswDspLQA7KS1AOyktgDspLcA7KS4AOykuQDspLoA7KS7AOykvADspL0A7KS+AOykvwDspYAA7KWBAOylggDspYMA7KWEAOylhQDspYYA7KWHAOyliADspYkA7KWKAOyliwDspYwA7KWNAOyljgDspY8A7KWQAOylkQDspZIA7KWTAOyllADspZUA7KWWAOyllwDspZgA7KWZAOylmgDspZsA7KWcAOylnQDspZ4A7KWfAOyloADspaEA7KWiAOylowDspaQA7KWlAOylpgDspacA7KWoAOylqQDspaoA7KWrAOylrADspa0A7KWuAOylrwDspbAA7KWxAOylsgDspbMA7KW0AOyltQDspbYA7KW3AOyluADspbkA7KW6AOyluwDspbwA7KW9AOylvgDspb8A7KaAAOymgQDspoIA7KaDAOymhADspoUA7KaGAOymhwDspogA7KaJAOymigDsposA7KaMAOymjQDspo4A7KaPAOymkADsppEA7KaSAOymkwDsppQA7KaVAOymlgDsppcA7KaYAOymmQDsppoA7KabAOymnADspp0A7KaeAOymnwDspqAA7KahAOymogDspqMA7KakAOympQDspqYA7KanAOymqADspqkA7KaqAOymqwDspqwA7KatAOymrgDspq8A7KawAOymsQDsprIA7KazAOymtADsprUA7Ka2AOymtwDsprgA7Ka5AOymugDsprsA7Ka8AOymvQDspr4A7Ka/AOyngADsp4EA7KeCAOyngwDsp4QA7KeFAOynhgDsp4cA7KeIAOyniQDsp4oA7KeLAOynjADsp40A7KeOAOynjwDsp5AA7KeRAOynkgDsp5MA7KeUAOynlQDsp5YA7KeXAOynmADsp5kA7KeaAOynmwDsp5wA7KedAOynngDsp58A7KegAOynoQDsp6IA7KejAOynpADsp6UA7KemAOynpwDsp6gA7KepAOynqgDsp6sA7KesAOynrQDsp64A7KevAOynsADsp7EA7KeyAOynswDsp7QA7Ke1AOyntgDsp7cA7Ke4AOynuQDsp7oA7Ke7AOynvADsp70A7Ke+AOynvwDsqIAA7KiBAOyoggDsqIMA7KiEAOyohQDsqIYA7KiHAOyoiADsqIkA7KiKAOyoiwDsqIwA7KiNAOyojgDsqI8A7KiQAOyokQDsqJIA7KiTAOyolADsqJUA7KiWAOyolwDsqJgA7KiZAOyomgDsqJsA7KicAOyonQDsqJ4A7KifAOyooADsqKEA7KiiAOyoowDsqKQA7KilAOyopgDsqKcA7KioAOyoqQDsqKoA7KirAOyorADsqK0A7KiuAOyorwDsqLAA7KixAOyosgDsqLMA7Ki0AOyotQDsqLYA7Ki3AOyouADsqLkA7Ki6AOyouwDsqLwA7Ki9AOyovgDsqL8A7KmAAOypgQDsqYIA7KmDAOyphADsqYUA7KmGAOyphwDsqYgA7KmJAOypigDsqYsA7KmMAOypjQDsqY4A7KmPAOypkADsqZEA7KmSAOypkwDsqZQA7KmVAOyplgDsqZcA7KmYAOypmQDsqZoA7KmbAOypnADsqZ0A7KmeAOypnwDsqaAA7KmhAOypogDsqaMA7KmkAOyppQDsqaYA7KmnAOypqADsqakA7KmqAOypqwDsqawA7KmtAOyprgDsqa8A7KmwAOypsQDsqbIA7KmzAOyptADsqbUA7Km2AOyptwDsqbgA7Km5AOypugDsqbsA7Km8AOypvQDsqb4A7Km/AOyqgADsqoEA7KqCAOyqgwDsqoQA7KqFAOyqhgDsqocA7KqIAOyqiQDsqooA7KqLAOyqjADsqo0A7KqOAOyqjwDsqpAA7KqRAOyqkgDsqpMA7KqUAOyqlQDsqpYA7KqXAOyqmADsqpkA7KqaAOyqmwDsqpwA7KqdAOyqngDsqp8A7KqgAOyqoQDsqqIA7KqjAOyqpADsqqUA7KqmAOyqpwDsqqgA7KqpAOyqqgDsqqsA7KqsAOyqrQDsqq4A7KqvAOyqsADsqrEA7KqyAOyqswDsqrQA7Kq1AOyqtgDsqrcA7Kq4AOyquQDsqroA7Kq7AOyqvADsqr0A7Kq+AOyqvwDsq4AA7KuBAOyrggDsq4MA7KuEAOyrhQDsq4YA7KuHAOyriADsq4kA7KuKAOyriwDsq4wA7KuNAOyrjgDsq48A7KuQAOyrkQDsq5IA7KuTAOyrlADsq5UA7KuWAOyrlwDsq5gA7KuZAOyrmgDsq5sA7KucAOyrnQDsq54A7KufAOyroADsq6EA7KuiAOyrowDsq6QA7KulAOyrpgDsq6cA7KuoAOyrqQDsq6oA7KurAOyrrADsq60A7KuuAOyrrwDsq7AA7KuxAOyrsgDsq7MA7Ku0AOyrtQDsq7YA7Ku3AOyruADsq7kA7Ku6AOyruwDsq7wA7Ku9AOyrvgDsq78A7KyAAOysgQDsrIIA7KyDAOyshADsrIUA7KyGAOyshwDsrIgA7KyJAOysigDsrIsA7KyMAOysjQDsrI4A7KyPAOyskADsrJEA7KySAOyskwDsrJQA7KyVAOyslgDsrJcA7KyYAOysmQDsrJoA7KybAOysnADsrJ0A7KyeAOysnwDsrKAA7KyhAOysogDsrKMA7KykAOyspQDsrKYA7KynAOysqADsrKkA7KyqAOysqwDsrKwA7KytAOysrgDsrK8A7KywAOyssQDsrLIA7KyzAOystADsrLUA7Ky2AOystwDsrLgA7Ky5AOysugDsrLsA7Ky8AOysvQDsrL4A7Ky/AOytgADsrYEA7K2CAOytgwDsrYQA7K2FAOythgDsrYcA7K2IAOytiQDsrYoA7K2LAOytjADsrY0A7K2OAOytjwDsrZAA7K2RAOytkgDsrZMA7K2UAOytlQDsrZYA7K2XAOytmADsrZkA7K2aAOytmwDsrZwA7K2dAOytngDsrZ8A7K2gAOytoQDsraIA7K2jAOytpADsraUA7K2mAOytpwDsragA7K2pAOytqgDsrasA7K2sAOytrQDsra4A7K2vAOytsADsrbEA7K2yAOytswDsrbQA7K21AOyttgDsrbcA7K24AOytuQDsrboA7K27AOytvADsrb0A7K2+AOytvwDsroAA7K6BAOyuggDsroMA7K6EAOyuhQDsroYA7K6HAOyuiADsrokA7K6KAOyuiwDsrowA7K6NAOyujgDsro8A7K6QAOyukQDsrpIA7K6TAOyulADsrpUA7K6WAOyulwDsrpgA7K6ZAOyumgDsrpsA7K6cAOyunQDsrp4A7K6fAOyuoADsrqEA7K6iAOyuowDsrqQA7K6lAOyupgDsrqcA7K6oAOyuqQDsrqoA7K6rAOyurADsrq0A7K6uAOyurwDsrrAA7K6xAOyusgDsrrMA7K60AOyutQDsrrYA7K63AOyuuADsrrkA7K66AOyuuwDsrrwA7K69AOyuvgDsrr8A7K+AAOyvgQDsr4IA7K+DAOyvhADsr4UA7K+GAOyvhwDsr4gA7K+JAOyvigDsr4sA7K+MAOyvjQDsr44A7K+PAOyvkADsr5EA7K+SAOyvkwDsr5QA7K+VAOyvlgDsr5cA7K+YAOyvmQDsr5oA7K+bAOyvnADsr50A7K+eAOyvnwDsr6AA7K+hAOyvogDsr6MA7K+kAOyvpQDsr6YA7K+nAOyvqADsr6kA7K+qAOyvqwDsr6wA7K+tAOyvrgDsr68A7K+wAOyvsQDsr7IA7K+zAOyvtADsr7UA7K+2AOyvtwDsr7gA7K+5AOyvugDsr7sA7K+8AOyvvQDsr74A7K+/AOywgADssIEA7LCCAOywgwDssIQA7LCFAOywhgDssIcA7LCIAOywiQDssIoA7LCLAOywjADssI0A7LCOAOywjwDssJAA7LCRAOywkgDssJMA7LCUAOywlQDssJYA7LCXAOywmADssJkA7LCaAOywmwDssJwA7LCdAOywngDssJ8A7LCgAOywoQDssKIA7LCjAOywpADssKUA7LCmAOywpwDssKgA7LCpAOywqgDssKsA7LCsAOywrQDssK4A7LCvAOywsADssLEA7LCyAOywswDssLQA7LC1AOywtgDssLcA7LC4AOywuOqzoADssLkA7LC6AOywuwDssLwA7LC9AOywvgDssL8A7LGAAOyxgQDssYIA7LGDAOyxhADssYUA7LGGAOyxhwDssYgA7LGJAOyxigDssYsA7LGMAOyxjQDssY4A7LGPAOyxkADssZEA7LGSAOyxkwDssZQA7LGVAOyxlgDssZcA7LGYAOyxmQDssZoA7LGbAOyxnADssZ0A7LGeAOyxnwDssaAA7LGhAOyxogDssaMA7LGkAOyxpQDssaYA7LGnAOyxqADssakA7LGqAOyxqwDssawA7LGtAOyxrgDssa8A7LGwAOyxsQDssbIA7LGzAOyxtADssbUA7LG2AOyxtwDssbgA7LG5AOyxugDssbsA7LG8AOyxvQDssb4A7LG/AOyygADssoEA7LKCAOyygwDssoQA7LKFAOyyhgDssocA7LKIAOyyiQDssooA7LKLAOyyjADsso0A7LKOAOyyjwDsspAA7LKRAOyykgDsspMA7LKUAOyylQDsspYA7LKXAOyymADsspkA7LKaAOyymwDsspwA7LKdAOyyngDssp8A7LKgAOyyoQDssqIA7LKjAOyypADssqUA7LKmAOyypwDssqgA7LKpAOyyqgDssqsA7LKsAOyyrQDssq4A7LKvAOyysADssrEA7LKyAOyyswDssrQA7LK1AOyytgDssrcA7LK4AOyyuQDssroA7LK7AOyyvADssr0A7LK+AOyyvwDss4AA7LOBAOyzggDss4MA7LOEAOyzhQDss4YA7LOHAOyziADss4kA7LOKAOyziwDss4wA7LONAOyzjgDss48A7LOQAOyzkQDss5IA7LOTAOyzlADss5UA7LOWAOyzlwDss5gA7LOZAOyzmgDss5sA7LOcAOyznQDss54A7LOfAOyzoADss6EA7LOiAOyzowDss6QA7LOlAOyzpgDss6cA7LOoAOyzqQDss6oA7LOrAOyzrADss60A7LOuAOyzrwDss7AA7LOxAOyzsgDss7MA7LO0AOyztQDss7YA7LO3AOyzuADss7kA7LO6AOyzuwDss7wA7LO9AOyzvgDss78A7LSAAOy0gQDstIIA7LSDAOy0hADstIUA7LSGAOy0hwDstIgA7LSJAOy0igDstIsA7LSMAOy0jQDstI4A7LSPAOy0kADstJEA7LSSAOy0kwDstJQA7LSVAOy0lgDstJcA7LSYAOy0mQDstJoA7LSbAOy0nADstJ0A7LSeAOy0nwDstKAA7LShAOy0ogDstKMA7LSkAOy0pQDstKYA7LSnAOy0qADstKkA7LSqAOy0qwDstKwA7LStAOy0rgDstK8A7LSwAOy0sQDstLIA7LSzAOy0tADstLUA7LS2AOy0twDstLgA7LS5AOy0ugDstLsA7LS8AOy0vQDstL4A7LS/AOy1gADstYEA7LWCAOy1gwDstYQA7LWFAOy1hgDstYcA7LWIAOy1iQDstYoA7LWLAOy1jADstY0A7LWOAOy1jwDstZAA7LWRAOy1kgDstZMA7LWUAOy1lQDstZYA7LWXAOy1mADstZkA7LWaAOy1mwDstZwA7LWdAOy1ngDstZ8A7LWgAOy1oQDstaIA7LWjAOy1pADstaUA7LWmAOy1pwDstagA7LWpAOy1qgDstasA7LWsAOy1rQDsta4A7LWvAOy1sADstbEA7LWyAOy1swDstbQA7LW1AOy1tgDstbcA7LW4AOy1uQDstboA7LW7AOy1vADstb0A7LW+AOy1vwDstoAA7LaBAOy2ggDstoMA7LaEAOy2hQDstoYA7LaHAOy2iADstokA7LaKAOy2iwDstowA7LaNAOy2jgDsto8A7LaQAOy2kQDstpIA7LaTAOy2lADstpUA7LaWAOy2lwDstpgA7LaZAOy2mgDstpsA7LacAOy2nQDstp4A7LafAOy2oADstqEA7LaiAOy2owDstqQA7LalAOy2pgDstqcA7LaoAOy2qQDstqoA7LarAOy2rADstq0A7LauAOy2rwDstrAA7LaxAOy2sgDstrMA7La0AOy2tQDstrYA7La3AOy2uADstrkA7La6AOy2uwDstrwA7La9AOy2vgDstr8A7LeAAOy3gQDst4IA7LeDAOy3hADst4UA7LeGAOy3hwDst4gA7LeJAOy3igDst4sA7LeMAOy3jQDst44A7LePAOy3kADst5EA7LeSAOy3kwDst5QA7LeVAOy3lgDst5cA7LeYAOy3mQDst5oA7LebAOy3nADst50A7LeeAOy3nwDst6AA7LehAOy3ogDst6MA7LekAOy3pQDst6YA7LenAOy3qADst6kA7LeqAOy3qwDst6wA7LetAOy3rgDst68A7LewAOy3sQDst7IA7LezAOy3tADst7UA7Le2AOy3twDst7gA7Le5AOy3ugDst7sA7Le8AOy3vQDst74A7Le/AOy4gADsuIEA7LiCAOy4gwDsuIQA7LiFAOy4hgDsuIcA7LiIAOy4iQDsuIoA7LiLAOy4jADsuI0A7LiOAOy4jwDsuJAA7LiRAOy4kgDsuJMA7LiUAOy4lQDsuJYA7LiXAOy4mADsuJkA7LiaAOy4mwDsuJwA7LidAOy4ngDsuJ8A7LigAOy4oQDsuKIA7LijAOy4pADsuKUA7LimAOy4pwDsuKgA7LipAOy4qgDsuKsA7LisAOy4rQDsuK4A7LivAOy4sADsuLEA7LiyAOy4swDsuLQA7Li1AOy4tgDsuLcA7Li4AOy4uQDsuLoA7Li7AOy4vADsuL0A7Li+AOy4vwDsuYAA7LmBAOy5ggDsuYMA7LmEAOy5hQDsuYYA7LmHAOy5iADsuYkA7LmKAOy5iwDsuYwA7LmNAOy5jgDsuY8A7LmQAOy5kQDsuZIA7LmTAOy5lADsuZUA7LmWAOy5lwDsuZgA7LmZAOy5mgDsuZsA7LmcAOy5nQDsuZ4A7LmfAOy5oADsuaEA7LmiAOy5owDsuaQA7LmlAOy5pgDsuacA7LmoAOy5qQDsuaoA7LmrAOy5rADsua0A7LmuAOy5rwDsubAA7LmxAOy5sgDsubMA7Lm0AOy5tQDsubYA7Lm3AOy5uADsubkA7Lm6AOy5uwDsubwA7Lm9AOy5vgDsub8A7LqAAOy6gQDsuoIA7LqDAOy6hADsuoUA7LqGAOy6hwDsuogA7LqJAOy6igDsuosA7LqMAOy6jQDsuo4A7LqPAOy6kADsupEA7LqSAOy6kwDsupQA7LqVAOy6lgDsupcA7LqYAOy6mQDsupoA7LqbAOy6nADsup0A7LqeAOy6nwDsuqAA7LqhAOy6ogDsuqMA7LqkAOy6pQDsuqYA7LqnAOy6qADsuqkA7LqqAOy6qwDsuqwA7LqtAOy6rgDsuq8A7LqwAOy6sQDsurIA7LqzAOy6tADsurUA7Lq2AOy6twDsurgA7Lq5AOy6ugDsursA7Lq8AOy6vQDsur4A7Lq/AOy7gADsu4EA7LuCAOy7gwDsu4QA7LuFAOy7hgDsu4cA7LuIAOy7iQDsu4oA7LuLAOy7jADsu40A7LuOAOy7jwDsu5AA7LuRAOy7kgDsu5MA7LuUAOy7lQDsu5YA7LuXAOy7mADsu5kA7LuaAOy7mwDsu5wA7LudAOy7ngDsu58A7LugAOy7oQDsu6IA7LujAOy7pADsu6UA7LumAOy7pwDsu6gA7LupAOy7qgDsu6sA7LusAOy7rQDsu64A7LuvAOy7sADsu7EA7LuyAOy7swDsu7QA7Lu1AOy7tgDsu7cA7Lu4AOy7uQDsu7oA7Lu7AOy7vADsu70A7Lu+AOy7vwDsvIAA7LyBAOy8ggDsvIMA7LyEAOy8hQDsvIYA7LyHAOy8iADsvIkA7LyKAOy8iwDsvIwA7LyNAOy8jgDsvI8A7LyQAOy8kQDsvJIA7LyTAOy8lADsvJUA7LyWAOy8lwDsvJgA7LyZAOy8mgDsvJsA7LycAOy8nQDsvJ4A7LyfAOy8oADsvKEA7LyiAOy8owDsvKQA7LylAOy8pgDsvKcA7LyoAOy8qQDsvKoA7LyrAOy8rADsvK0A7LyuAOy8rwDsvLAA7LyxAOy8sgDsvLMA7Ly0AOy8tQDsvLYA7Ly3AOy8uADsvLkA7Ly6AOy8uwDsvLwA7Ly9AOy8vgDsvL8A7L2AAOy9gQDsvYIA7L2DAOy9hADsvYUA7L2GAOy9hwDsvYgA7L2JAOy9igDsvYsA7L2MAOy9jQDsvY4A7L2PAOy9kADsvZEA7L2SAOy9kwDsvZQA7L2VAOy9lgDsvZcA7L2YAOy9mQDsvZoA7L2bAOy9nADsvZ0A7L2eAOy9nwDsvaAA7L2hAOy9ogDsvaMA7L2kAOy9pQDsvaYA7L2nAOy9qADsvakA7L2qAOy9qwDsvawA7L2tAOy9rgDsva8A7L2wAOy9sQDsvbIA7L2zAOy9tADsvbUA7L22AOy9twDsvbgA7L25AOy9ugDsvbsA7L28AOy9vQDsvb4A7L2/AOy+gADsvoEA7L6CAOy+gwDsvoQA7L6FAOy+hgDsvocA7L6IAOy+iQDsvooA7L6LAOy+jADsvo0A7L6OAOy+jwDsvpAA7L6RAOy+kgDsvpMA7L6UAOy+lQDsvpYA7L6XAOy+mADsvpkA7L6aAOy+mwDsvpwA7L6dAOy+ngDsvp8A7L6gAOy+oQDsvqIA7L6jAOy+pADsvqUA7L6mAOy+pwDsvqgA7L6pAOy+qgDsvqsA7L6sAOy+rQDsvq4A7L6vAOy+sADsvrEA7L6yAOy+swDsvrQA7L61AOy+tgDsvrcA7L64AOy+uQDsvroA7L67AOy+vADsvr0A7L6+AOy+vwDsv4AA7L+BAOy/ggDsv4MA7L+EAOy/hQDsv4YA7L+HAOy/iADsv4kA7L+KAOy/iwDsv4wA7L+NAOy/jgDsv48A7L+QAOy/kQDsv5IA7L+TAOy/lADsv5UA7L+WAOy/lwDsv5gA7L+ZAOy/mgDsv5sA7L+cAOy/nQDsv54A7L+fAOy/oADsv6EA7L+iAOy/owDsv6QA7L+lAOy/pgDsv6cA7L+oAOy/qQDsv6oA7L+rAOy/rADsv60A7L+uAOy/rwDsv7AA7L+xAOy/sgDsv7MA7L+0AOy/tQDsv7YA7L+3AOy/uADsv7kA7L+6AOy/uwDsv7wA7L+9AOy/vgDsv78A7YCAAO2AgQDtgIIA7YCDAO2AhADtgIUA7YCGAO2AhwDtgIgA7YCJAO2AigDtgIsA7YCMAO2AjQDtgI4A7YCPAO2AkADtgJEA7YCSAO2AkwDtgJQA7YCVAO2AlgDtgJcA7YCYAO2AmQDtgJoA7YCbAO2AnADtgJ0A7YCeAO2AnwDtgKAA7YChAO2AogDtgKMA7YCkAO2ApQDtgKYA7YCnAO2AqADtgKkA7YCqAO2AqwDtgKwA7YCtAO2ArgDtgK8A7YCwAO2AsQDtgLIA7YCzAO2AtADtgLUA7YC2AO2AtwDtgLgA7YC5AO2AugDtgLsA7YC8AO2AvQDtgL4A7YC/AO2BgADtgYEA7YGCAO2BgwDtgYQA7YGFAO2BhgDtgYcA7YGIAO2BiQDtgYoA7YGLAO2BjADtgY0A7YGOAO2BjwDtgZAA7YGRAO2BkgDtgZMA7YGUAO2BlQDtgZYA7YGXAO2BmADtgZkA7YGaAO2BmwDtgZwA7YGdAO2BngDtgZ8A7YGgAO2BoQDtgaIA7YGjAO2BpADtgaUA7YGmAO2BpwDtgagA7YGpAO2BqgDtgasA7YGsAO2BrQDtga4A7YGvAO2BsADtgbEA7YGyAO2BswDtgbQA7YG1AO2BtgDtgbcA7YG4AO2BuQDtgboA7YG7AO2BvADtgb0A7YG+AO2BvwDtgoAA7YKBAO2CggDtgoMA7YKEAO2ChQDtgoYA7YKHAO2CiADtgokA7YKKAO2CiwDtgowA7YKNAO2CjgDtgo8A7YKQAO2CkQDtgpIA7YKTAO2ClADtgpUA7YKWAO2ClwDtgpgA7YKZAO2CmgDtgpsA7YKcAO2CnQDtgp4A7YKfAO2CoADtgqEA7YKiAO2CowDtgqQA7YKlAO2CpgDtgqcA7YKoAO2CqQDtgqoA7YKrAO2CrADtgq0A7YKuAO2CrwDtgrAA7YKxAO2CsgDtgrMA7YK0AO2CtQDtgrYA7YK3AO2CuADtgrkA7YK6AO2CuwDtgrwA7YK9AO2CvgDtgr8A7YOAAO2DgQDtg4IA7YODAO2DhADtg4UA7YOGAO2DhwDtg4gA7YOJAO2DigDtg4sA7YOMAO2DjQDtg44A7YOPAO2DkADtg5EA7YOSAO2DkwDtg5QA7YOVAO2DlgDtg5cA7YOYAO2DmQDtg5oA7YObAO2DnADtg50A7YOeAO2DnwDtg6AA7YOhAO2DogDtg6MA7YOkAO2DpQDtg6YA7YOnAO2DqADtg6kA7YOqAO2DqwDtg6wA7YOtAO2DrgDtg68A7YOwAO2DsQDtg7IA7YOzAO2DtADtg7UA7YO2AO2DtwDtg7gA7YO5AO2DugDtg7sA7YO8AO2DvQDtg74A7YO/AO2EgADthIEA7YSCAO2EgwDthIQA7YSFAO2EhgDthIcA7YSIAO2EiQDthIoA7YSLAO2EjADthI0A7YSOAO2EjwDthJAA7YSRAO2EkgDthJMA7YSUAO2ElQDthJYA7YSXAO2EmADthJkA7YSaAO2EmwDthJwA7YSdAO2EngDthJ8A7YSgAO2EoQDthKIA7YSjAO2EpADthKUA7YSmAO2EpwDthKgA7YSpAO2EqgDthKsA7YSsAO2ErQDthK4A7YSvAO2EsADthLEA7YSyAO2EswDthLQA7YS1AO2EtgDthLcA7YS4AO2EuQDthLoA7YS7AO2EvADthL0A7YS+AO2EvwDthYAA7YWBAO2FggDthYMA7YWEAO2FhQDthYYA7YWHAO2FiADthYkA7YWKAO2FiwDthYwA7YWNAO2FjgDthY8A7YWQAO2FkQDthZIA7YWTAO2FlADthZUA7YWWAO2FlwDthZgA7YWZAO2FmgDthZsA7YWcAO2FnQDthZ4A7YWfAO2FoADthaEA7YWiAO2FowDthaQA7YWlAO2FpgDthacA7YWoAO2FqQDthaoA7YWrAO2FrADtha0A7YWuAO2FrwDthbAA7YWxAO2FsgDthbMA7YW0AO2FtQDthbYA7YW3AO2FuADthbkA7YW6AO2FuwDthbwA7YW9AO2FvgDthb8A7YaAAO2GgQDthoIA7YaDAO2GhADthoUA7YaGAO2GhwDthogA7YaJAO2GigDthosA7YaMAO2GjQDtho4A7YaPAO2GkADthpEA7YaSAO2GkwDthpQA7YaVAO2GlgDthpcA7YaYAO2GmQDthpoA7YabAO2GnADthp0A7YaeAO2GnwDthqAA7YahAO2GogDthqMA7YakAO2GpQDthqYA7YanAO2GqADthqkA7YaqAO2GqwDthqwA7YatAO2GrgDthq8A7YawAO2GsQDthrIA7YazAO2GtADthrUA7Ya2AO2GtwDthrgA7Ya5AO2GugDthrsA7Ya8AO2GvQDthr4A7Ya/AO2HgADth4EA7YeCAO2HgwDth4QA7YeFAO2HhgDth4cA7YeIAO2HiQDth4oA7YeLAO2HjADth40A7YeOAO2HjwDth5AA7YeRAO2HkgDth5MA7YeUAO2HlQDth5YA7YeXAO2HmADth5kA7YeaAO2HmwDth5wA7YedAO2HngDth58A7YegAO2HoQDth6IA7YejAO2HpADth6UA7YemAO2HpwDth6gA7YepAO2HqgDth6sA7YesAO2HrQDth64A7YevAO2HsADth7EA7YeyAO2HswDth7QA7Ye1AO2HtgDth7cA7Ye4AO2HuQDth7oA7Ye7AO2HvADth70A7Ye+AO2HvwDtiIAA7YiBAO2IggDtiIMA7YiEAO2IhQDtiIYA7YiHAO2IiADtiIkA7YiKAO2IiwDtiIwA7YiNAO2IjgDtiI8A7YiQAO2IkQDtiJIA7YiTAO2IlADtiJUA7YiWAO2IlwDtiJgA7YiZAO2ImgDtiJsA7YicAO2InQDtiJ4A7YifAO2IoADtiKEA7YiiAO2IowDtiKQA7YilAO2IpgDtiKcA7YioAO2IqQDtiKoA7YirAO2IrADtiK0A7YiuAO2IrwDtiLAA7YixAO2IsgDtiLMA7Yi0AO2ItQDtiLYA7Yi3AO2IuADtiLkA7Yi6AO2IuwDtiLwA7Yi9AO2IvgDtiL8A7YmAAO2JgQDtiYIA7YmDAO2JhADtiYUA7YmGAO2JhwDtiYgA7YmJAO2JigDtiYsA7YmMAO2JjQDtiY4A7YmPAO2JkADtiZEA7YmSAO2JkwDtiZQA7YmVAO2JlgDtiZcA7YmYAO2JmQDtiZoA7YmbAO2JnADtiZ0A7YmeAO2JnwDtiaAA7YmhAO2JogDtiaMA7YmkAO2JpQDtiaYA7YmnAO2JqADtiakA7YmqAO2JqwDtiawA7YmtAO2JrgDtia8A7YmwAO2JsQDtibIA7YmzAO2JtADtibUA7Ym2AO2JtwDtibgA7Ym5AO2JugDtibsA7Ym8AO2JvQDtib4A7Ym/AO2KgADtioEA7YqCAO2KgwDtioQA7YqFAO2KhgDtiocA7YqIAO2KiQDtiooA7YqLAO2KjADtio0A7YqOAO2KjwDtipAA7YqRAO2KkgDtipMA7YqUAO2KlQDtipYA7YqXAO2KmADtipkA7YqaAO2KmwDtipwA7YqdAO2KngDtip8A7YqgAO2KoQDtiqIA7YqjAO2KpADtiqUA7YqmAO2KpwDtiqgA7YqpAO2KqgDtiqsA7YqsAO2KrQDtiq4A7YqvAO2KsADtirEA7YqyAO2KswDtirQA7Yq1AO2KtgDtircA7Yq4AO2KuQDtiroA7Yq7AO2KvADtir0A7Yq+AO2KvwDti4AA7YuBAO2LggDti4MA7YuEAO2LhQDti4YA7YuHAO2LiADti4kA7YuKAO2LiwDti4wA7YuNAO2LjgDti48A7YuQAO2LkQDti5IA7YuTAO2LlADti5UA7YuWAO2LlwDti5gA7YuZAO2LmgDti5sA7YucAO2LnQDti54A7YufAO2LoADti6EA7YuiAO2LowDti6QA7YulAO2LpgDti6cA7YuoAO2LqQDti6oA7YurAO2LrADti60A7YuuAO2LrwDti7AA7YuxAO2LsgDti7MA7Yu0AO2LtQDti7YA7Yu3AO2LuADti7kA7Yu6AO2LuwDti7wA7Yu9AO2LvgDti78A7YyAAO2MgQDtjIIA7YyDAO2MhADtjIUA7YyGAO2MhwDtjIgA7YyJAO2MigDtjIsA7YyMAO2MjQDtjI4A7YyPAO2MkADtjJEA7YySAO2MkwDtjJQA7YyVAO2MlgDtjJcA7YyYAO2MmQDtjJoA7YybAO2MnADtjJ0A7YyeAO2MnwDtjKAA7YyhAO2MogDtjKMA7YykAO2MpQDtjKYA7YynAO2MqADtjKkA7YyqAO2MqwDtjKwA7YytAO2MrgDtjK8A7YywAO2MsQDtjLIA7YyzAO2MtADtjLUA7Yy2AO2MtwDtjLgA7Yy5AO2MugDtjLsA7Yy8AO2MvQDtjL4A7Yy/AO2NgADtjYEA7Y2CAO2NgwDtjYQA7Y2FAO2NhgDtjYcA7Y2IAO2NiQDtjYoA7Y2LAO2NjADtjY0A7Y2OAO2NjwDtjZAA7Y2RAO2NkgDtjZMA7Y2UAO2NlQDtjZYA7Y2XAO2NmADtjZkA7Y2aAO2NmwDtjZwA7Y2dAO2NngDtjZ8A7Y2gAO2NoQDtjaIA7Y2jAO2NpADtjaUA7Y2mAO2NpwDtjagA7Y2pAO2NqgDtjasA7Y2sAO2NrQDtja4A7Y2vAO2NsADtjbEA7Y2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- "in e", - "i ne", - "▁her e", - "▁he re", - "▁h ere", - "▁w ould", - "ca use", - "ri ght", - "r ight", - "s o", - "os t", - "o st", - "ur e", - "u re", - "▁ha s", - "▁h as", - "e ct", - "▁th ink", - "▁f e", - "on g", - "o ng", - "▁se e", - "▁s ee", - "▁wh en", - "▁wh o", - "▁we re", - "▁w ere", - "▁real ly", - "▁re ally", - "▁the ir", - "▁w ant", - "on e", - "o ne", - "op le", - "o ple", - "▁the n", - "▁th en", - "▁tim e", - "▁s a", - "a p", - "▁t e", - "▁ te", - "▁H e", - "▁y e", - "c k", - "▁he r", - "▁h er", - "▁ her", - "▁th ing", - "▁t hing", - "▁ thing", - "▁r ight", - "▁ right", - "▁wh ich", - "it t", - "ic e", - "i ce", - "ac t", - "a ct", - "▁pe ople", - "t y", - "▁tw o", - "▁ J", - "▁ im", - "th er", - "t her", - "c i", - "os e", - "o se", - "▁c l", - "▁ qu", - "▁m an", - "▁al so", - "re e", - "r ee", - "▁e n", - "▁ en", - "u d", - "▁h ow", - "re at", - "a k", - "h ing", - "a g", - "▁an y", - "▁ any", - "f f", - "ac e", - "a ce", - "pe r", - "p er", - "▁be cause", - "▁ very", - "ow n", - "o wn", - "▁a d", - "▁ ad", - "▁ac t", - "▁a ct", - "▁ act", - "▁be en", - "▁b een", - "▁no w", - "▁n ow", - "▁a g", - "▁ ag", - "▁int o", - "▁com p", - "ar s", - "ion s", - "i ons", - "ar e", - "a re", - "it e", - "i te", - "i v", - "▁the se", - "▁th ese", - "ay s", - "a ys", - "e p", - "▁Th is", - "▁sh e", - "▁s he", - "an s", - "a h", - "ee n", - "e en", - "▁o ver", - "r y", - "▁l o", - "ag e", - "a ge", - "▁p r", - "▁s p", - "u e", - "▁c o", - "▁ co", - "ic k", - "i ck", - "be r", - "b er", - "▁d id", - "i p", - "ac h", - "a ch", - "▁b ack", - "▁n o", - "▁con t", - "▁co nt", - "▁c ont", - "▁o ther", - "▁ other", - "▁ever y", - "▁e very", - "p t", - "▁ne ed", - "▁h im", - "▁ U", - "▁I n", - "▁wor k", - "ir st", - "▁p art", - "▁loo k", - "▁l ook", - "itt le", - "b le", - "i z", - "▁u n", - "▁ un", - "▁m ake", - "ome t", - "om et", - "nd er", - "n der", - "is h", - "n a", - "▁l ittle", - "▁of f", - "▁o ff", - "▁th an", - "▁go t", - "▁g ot", - "ual ly", - "u ally", - "▁pe r", - "▁p er", - "▁ per", - "▁go od", - "▁g ood", - "▁w ay", - "▁cou ld", - "▁c ould", - "▁a c", - "▁ ac", - "▁im p", - "ab le", - "a ble", - "▁wh ere", - "if f", - "i ff", - "▁Th at", - "▁re s", - "▁r es", - "▁ res", - "ou nt", - "p l", - "an ce", - "a nce", - "▁f irst", - "▁r o", - "▁ ro", - "▁pr e", - "▁p re", - "as s", - "▁sa y", - "▁s ay", - "in t", - "i nt", - "ate d", - "at ed", - "ir e", - "i re", - "uc h", - "u ch", - "as e", - "a se", - "▁some t", - "▁s omet", - "ou nd", - "o und", - "▁do wn", - "▁d own", - "▁d iff", - "se l", - "s el", - "▁g u", - "▁a m", - "▁ am", - "res s", - "r ess", - "▁lo t", - "▁l ot", - "en ce", - "e nce", - "▁d is", - "or m", - "i x", - "▁p o", - "v ing", - "ent y", - "en ty", - "▁ K", - "▁sp e", - "▁s pe", - "un d", - "u nd", - "h e", - "▁m uch", - "▁a r", - "▁ ar", - "rou nd", - "ro und", - "r ound", - "▁a pp", - "c o", - "ar k", - "▁ne w", - "▁n ew", - "ate r", - "at er", - "a ter", - "ul t", - "en d", - "e nd", - "▁ev en", - "▁e ven", - "▁st art", - "ation s", - "at ions", - "rou gh", - "r ough", - "il e", - "i le", - "ft er", - "f ter", - "▁we ll", - "▁w ell", - "b e", - "▁The y", - "▁th ree", - "ig n", - "il d", - "i ld", - "▁sa id", - "ou gh", - "an g", - "a ng", - "▁to o", - "▁t oo", - "ad e", - "▁b l", - "en s", - "▁in c", - "i a", - "▁th ose", - "▁m o", - "▁t ake", - "▁th rough", - "▁f l", - "▁k ind", - "▁thing s", - "▁th ings", - "▁be t", - "▁b et", - "▁on ly", - "▁S t", - "▁le t", - "▁l et", - "ces s", - "c ess", - "▁C h", - "ar y", - "a ry", - "ve l", - "v el", - "▁I f", - "x t", - "oth er", - "ot her", - "o ther", - "a v", - "ic al", - "or d", - "▁ag ain", - "▁somet hing", - "▁some thing", - "on na", - "f ore", - "▁m ay", - "t ing", - "▁b u", - "▁diff ere", - "ur n", - "▁g onna", - "▁do es", - "uc t", - "u ct", - "o g", - "▁tw enty", - "▁g r", - "▁ gr", - "▁Y e", - "w n", - "▁sh ould", - "▁com m", - "▁c omm", - "it ion", - "▁un der", - "▁u nder", - "▁he l", - "▁h el", - "or y", - "o ry", - "▁f o", - "▁us e", - "▁u se", - "▁ use", - "ig h", - "i gh", - "if e", - "▁act ually", - "▁t al", - "▁ca ll", - "▁c all", - "ent s", - "en ts", - "i ous", - "ul l", - "u ll", - "▁The re", - "▁Th ere", - "▁Ye ah", - "▁mo st", - "▁m ost", - "▁k e", - "▁ ke", - "or s", - "ve d", - "v ed", - "y s", - "▁s c", - "▁ha pp", - "op e", - "o pe", - "▁hel p", - "at ch", - "▁Wh at", - "▁re m", - "▁r em", - "pl e", - "p le", - "▁No w", - "▁N ow", - "▁b r", - "oo l", - "o ol", - "ot h", - "o th", - "▁fo ur", - "▁f our", - "sel f", - "▁st r", - "n e", - "th ing", - "t hing", - "▁p ut", - "ia l", - "i al", - "▁g reat", - "a il", - "u b", - "n ing", - "▁s m", - "▁fe el", - "▁f ive", - "od y", - "und red", - "is s", - "an k", - "ge t", - "g et", - "ak ing", - "a king", - "▁man y", - "▁m any", - "▁h undred", - "▁year s", - "▁ye ars", - "▁be ing", - "▁com e", - "▁c ome", - "▁me an", - "il y", - "i ly", - "▁differe nt", - "▁a fter", - "▁se r", - "▁s er", - "▁sh ow", - "f orm", - "f ul", - "o y", - "▁s ix", - "▁v ide", - "▁ V", - "▁it s", - "▁ its", - "▁po int", - "▁d ay", - "▁ day", - "▁de s", - "▁d es", - "on s", - "▁b it", - "▁be l", - "▁b el", - "▁be fore", - "▁a w", - "▁en d", - "▁e nd", - "▁ end", - "▁O h", - "▁st ill", - "at h", - "a th", - "▁lo ng", - "▁l ong", - "▁ '", - "is e", - "i se", - "o b", - "d ay", - "▁ad d", - "f t", - "ve s", - "v es", - "ce s", - "c es", - "ad y", - "▁c r", - "▁ar ound", - "▁a round", - "▁tr y", - "▁t ry", - "le s", - "l es", - "ver s", - "v ers", - "k ay", - "ia n", - "i an", - "ate s", - "at es", - "▁fin d", - "▁f ind", - "w ard", - "▁A s", - "▁e ight", - "li c", - "l ic", - "▁s ame", - "▁po s", - "▁p os", - "▁e m", - "▁ em", - "▁m ade", - "▁su pp", - "▁l ife", - "▁B e", - "pe ct", - "p ect", - "▁de c", - "▁pl ay", - "ang e", - "an ge", - "▁at t", - "▁per s", - "▁p ers", - "w ays", - "▁h igh", - "▁ha nd", - "▁h and", - "▁ne xt", - "▁con s", - "▁c ons", - "▁o wn", - "▁ own", - "▁in v", - "ow er", - "▁in d", - "▁ ind", - "er t", - "n g", - "av e", - "a ve", - "▁ye ar", - "▁b ig", - "at ing", - "a ting", - "▁wor ld", - "▁re l", - "▁r el", - "▁sur e", - "▁su re", - "▁s ure", - "▁tr a", - "▁t ra", - "e w", - "ere d", - "er ed", - "e red", - "▁f in", - "▁We ll", - "▁W ell", - "▁s l", - "▁do ing", - "b s", - "▁se t", - "▁s et", - "▁re c", - "u al", - "ci al", - "c ial", - "▁p h", - "▁ ph", - "er m", - "▁lo ve", - "p h", - "▁re al", - "▁la st", - "▁l ast", - "ic t", - "i ct", - "▁b o", - "▁r a", - "▁ ra", - "ib le", - "i ble", - "▁w r", - "m er", - "▁cou nt", - "▁c ount", - "it ies", - "▁al ways", - "ine t", - "in et", - "ment s", - "m ents", - "u c", - "▁m ight", - "▁int er", - "▁in ter", - "▁vide o", - "g in", - "▁te ll", - "▁t ell", - "▁ne ver", - "▁n ever", - "ven t", - "ve nt", - "v ent", - "▁imp ort", - "ie d", - "i ed", - "▁s y", - "▁H ow", - "ical ly", - "ic ally", - "ough t", - "ou ght", - "▁th ir", - "▁re p", - "▁r ep", - "k s", - "i b", - "▁f am", - "j ect", - "▁b as", - "▁S he", - "▁g ive", - "ake s", - "ak es", - "▁n inet", - "▁re g", - "▁m in", - "▁o p", - "▁ op", - "▁de f", - "▁did n", - "t e", - "▁cou r", - "▁co ur", - "▁c our", - "▁wh y", - "▁en t", - "▁e nt", - "▁ ent", - "▁pl ace", - "▁in s", - "▁ca r", - "▁c ar", - "ath er", - "at her", - "a ther", - "▁pers on", - "ul ar", - "▁ins t", - "▁in st", - "▁pro d", - "▁pr od", - "le ct", - "l ect", - "▁A l", - "▁to day", - "▁be c", - "▁su r", - "▁s ur", - "▁Al l", - "▁A ll", - "▁an other", - "▁bu s", - "▁b us", - "▁ke ep", - "el l", - "e ll", - "es e", - "e se", - "ri end", - "▁qu est", - "▁tal k", - "al s", - "ing s", - "▁mo n", - "▁m on", - "co nd", - "c ond", - "ol d", - "o ld", - "▁ac c", - "▁l a", - "▁n um", - "ide nt", - "id ent", - "▁ch e", - "▁c he", - "in ess", - "i ness", - "▁t urn", - "▁e ar", - "▁N o", - "ous and", - "▁bet ter", - "if ic", - "▁lo o", - "▁l oo", - "▁g l", - "o c", - "▁import ant", - "ite d", - "it ed", - "▁A n", - "▁th ousand", - "il ity", - "ll ow", - "▁use d", - "▁us ed", - "▁g en", - "▁s im", - "l i", - "▁happ en", - "▁U n", - "▁L et", - "a ir", - "oc k", - "o ck", - "ab ly", - "g g", - "▁w atch", - "▁F or", - "▁s w", - "re n", - "r en", - "ut e", - "u te", - "e ver", - "▁po l", - "▁p ol", - "▁sc h", - "▁s ch", - "▁Wh en", - "▁su ch", - "▁s uch", - "▁f if", - "▁h ome", - "▁cl e", - "▁c le", - "▁cont in", - "ous e", - "ou se", - "o use", - "▁f riend", - "ur ing", - "▁O kay", - "g r", - "▁ab le", - "▁a ble", - "▁ able", - "▁st ud", - "▁e ff", - "h ip", - "b ody", - "▁to p", - "▁t op", - "n ess", - "▁exp er", - "▁ex per", - "▁pre t", - "▁pr et", - "▁bo th", - "▁b oth", - "▁don e", - "▁do ne", - "▁d one", - "c ri", - "▁m ark", - "▁wh ile", - "▁o ld", - "▁ old", - "ro s", - "r os", - "on t", - "o nt", - "▁se cond", - "at ive", - "▁th ought", - "▁be st", - "▁b est", - "▁fo und", - "▁f ound", - "ie w", - "i ew", - "▁bel ie", - "▁e ach", - "ere st", - "er est", - "▁tr i", - "▁t ri", - "▁e as", - "▁c a", - "▁ ca", - "▁f act", - "▁car e", - "▁ca re", - "▁c are", - "▁f un", - "at ter", - "ure s", - "ur es", - "u res", - "▁he ad", - "▁le ar", - "▁w ater", - "▁h ard", - "▁fe w", - "▁f ew", - "▁s ide", - "w een", - "▁ex p", - "▁aw ay", - "it s", - "i ts", - "▁ex t", - "▁e xt", - "l ud", - "▁r un", - "▁tr ans", - "in ce", - "i nce", - "▁s k", - "▁op en", - "c us", - "▁bet ween", - "▁call ed", - "▁we e", - "▁w ee", - "▁pret ty", - "as on", - "▁f ar", - "em ber", - "om m", - "▁inter est", - "▁int erest", - "an y", - "ne r", - "n er", - "u ff", - "▁pre s", - "▁pr es", - "▁p res", - "▁c ur", - "▁ch ild", - "e e", - "▁to get", - "▁toget her", - "ol og", - "▁G od", - "on d", - "o nd", - "▁ch ar", - "▁look ing", - "▁loo king", - "st em", - "a z", - "ce nt", - "c ent", - "▁o b", - "▁ ob", - "▁as s", - "▁ ass", - "l and", - "▁does n", - "▁bus iness", - "▁cour se", - "▁te n", - "▁t en", - "▁ ten", - "p s", - "ar ch", - "ce d", - "c ed", - "m s", - "iz e", - "n ce", - "▁re f", - "▁n ame", - "ros s", - "▁gr ow", - "one y", - "▁we nt", - "▁w ent", - "ic s", - "te en", - "t een", - "▁co u", - "▁c ou", - "▁pro b", - "▁pr ob", - "▁re t", - "▁r et", - "▁gu ys", - "▁c ame", - "as h", - "le d", - "l ed", - "▁E ur", - "ue s", - "u es", - "▁ ide", - "g an", - "▁every thing", - "▁get ting", - "▁as k", - "▁co r", - "▁c or", - "▁bu ild", - "▁s ign", - "▁sm all", - "uc k", - "u ck", - "▁e l", - "▁ el", - "▁co l", - "▁c ol", - "▁I s", - "ation al", - "st and", - "c y", - "▁con f", - "d er", - "▁br e", - "▁b re", - "▁ca p", - "▁c ap", - "▁mo d", - "▁m od", - "et s", - "e ts", - "i ke", - "▁num ber", - "▁comp le", - "▁com ple", - "ert ain", - "▁ev er", - "▁e ver", - "▁ ever", - "▁col l", - "▁co ll", - "▁h um", - "▁Eur ope", - "▁cr e", - "▁c re", - "▁me t", - "▁m et", - "▁ex am", - "▁mo ve", - "▁p ass", - "▁le ft", - "▁sy stem", - "▁inc lud", - "▁Th ank", - "ce pt", - "▁w om", - "▁prod uct", - "te n", - "t en", - "▁res t", - "▁re st", - "▁r est", - "▁prob ably", - "▁dr i", - "▁d ri", - "▁D o", - "▁gen er", - "▁any thing", - "▁la r", - "▁l ar", - "▁M y", - "▁sch ool", - "▁le ad", - "▁su b", - "▁s ub", - "▁t y", - "▁ ty", - "▁pl an", - "▁see m", - "▁se em", - "▁who le", - "ire ct", - "ir ect", - "▁li ght", - "▁l ight", - "▁m ust", - "▁mo m", - "▁m om", - "▁op p", - "▁o pp", - "▁supp ort", - "▁fam ily", - "ice s", - "ic es", - "i ces", - "am p", - "▁prob le", - "▁pro ble", - "▁d r", - "re ady", - "▁us ing", - "ens e", - "en se", - "▁pro v", - "us h", - "a x", - "▁p ower", - "▁R e", - "al th", - "▁e v", - "▁st and", - "▁ stand", - "▁w ar", - "t s" - ] - } -} \ No newline at end of file diff --git a/models/parakeet-ctc-110m-coreml/tokenizer_config.json b/models/parakeet-ctc-110m-coreml/tokenizer_config.json deleted file mode 100644 index d9eaa0fb393ab6e3ac6890fe99efb6191cded97c..0000000000000000000000000000000000000000 --- a/models/parakeet-ctc-110m-coreml/tokenizer_config.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "added_tokens_decoder": { - "0": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "1024": { - "content": "", - "lstrip": false, 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a/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/analytics/coremldata.bin b/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 7e6fa4eb79131e62089f252a08b860e723f5114f..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:46de1a6fe2e49d19a2125bc91acf020df7f2aea84ba821532aade8427a440b05 -size 243 diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/coremldata.bin b/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/coremldata.bin deleted file mode 100644 index 79b160cb7e3eb3213fb22cde5fbf6ac7f324c91b..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d200ca07694a347f6d02a3886a062ae839831e094e443222f2e48a14945966a8 -size 554 diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/metadata.json b/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/metadata.json deleted file mode 100644 index e3a08d21a5d1577db2bedd1749ea0ddae5d21b73..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/metadata.json +++ /dev/null @@ -1,123 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet decoder (RNNT prediction network)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 2 × 1 × 640)", - "shortDescription" : "", - "shape" : "[2, 1, 640]", - "name" : "h_out", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 2 × 1 × 640)", - "shortDescription" : "", - "shape" : "[2, 1, 640]", - "name" : "c_out", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Select" : 1, - "Ios17.squeeze" : 4, - "Ios17.gather" : 1, - "Ios17.cast" : 8, - "Ios17.lstm" : 2, - "Split" : 2, - "Ios17.add" : 1, - "Ios17.transpose" : 2, - "Ios17.greaterEqual" : 1, - "Identity" : 1, - "Stack" : 2 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1]", - "name" : "targets", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "target_length", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 2 × 1 × 640)", - "shortDescription" : "", - "shape" : "[2, 1, 640]", - "name" : "h_in", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 2 × 1 × 640)", - "shortDescription" : "", - "shape" : "[2, 1, 640]", - "name" : "c_in", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2025-09-25", - "com.github.apple.coremltools.source" : "torch==2.7.0", - "com.github.apple.coremltools.version" : "9.0b1", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "parakeet_decoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/model.mil b/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/model.mil deleted file mode 100644 index 167cc9db34dd7828e0e61eab40d6a09f8a234d30..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/model.mil +++ /dev/null @@ -1,73 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] -{ - func main(tensor c_in, tensor h_in, tensor target_length, tensor targets) { - tensor y_batch_dims_0 = const()[name = tensor("y_batch_dims_0"), val = tensor(0)]; - tensor y_validate_indices_0 = const()[name = tensor("y_validate_indices_0"), val = tensor(false)]; - tensor module_prediction_embed_weight_to_fp16 = const()[name = tensor("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor targets_to_int16_dtype_0 = const()[name = tensor("targets_to_int16_dtype_0"), val = tensor("int16")]; - tensor cast_1_dtype_0 = const()[name = tensor("cast_1_dtype_0"), val = tensor("int32")]; - tensor greater_equal_0_y_0 = const()[name = tensor("greater_equal_0_y_0"), val = tensor(0)]; - tensor targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor("cast_9")]; - tensor cast_1 = cast(dtype = cast_1_dtype_0, x = targets_to_int16)[name = tensor("cast_8")]; - tensor greater_equal_0 = greater_equal(x = cast_1, y = greater_equal_0_y_0)[name = tensor("greater_equal_0")]; - tensor slice_by_index_0 = const()[name = tensor("slice_by_index_0"), val = tensor(1025)]; - tensor add_2 = add(x = cast_1, y = slice_by_index_0)[name = tensor("add_2")]; - tensor select_0 = select(a = cast_1, b = add_2, cond = greater_equal_0)[name = tensor("select_0")]; - tensor y_cast_fp16_cast_uint16_axis_0 = const()[name = tensor("y_cast_fp16_cast_uint16_axis_0"), val = tensor(0)]; - tensor select_0_to_int16_dtype_0 = const()[name = tensor("select_0_to_int16_dtype_0"), val = tensor("int16")]; - tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor("cast_7")]; - tensor y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = tensor("y_cast_fp16_cast_uint16_cast_uint16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([1, 0, 2])]; - tensor split_0_num_splits_0 = const()[name = tensor("split_0_num_splits_0"), val = tensor(2)]; - tensor split_0_axis_0 = const()[name = tensor("split_0_axis_0"), val = tensor(0)]; - tensor h_in_to_fp16_dtype_0 = const()[name = tensor("h_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor("cast_6")]; - tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = tensor("split_0_cast_fp16")]; - tensor split_1_num_splits_0 = const()[name = tensor("split_1_num_splits_0"), val = tensor(2)]; - tensor split_1_axis_0 = const()[name = tensor("split_1_axis_0"), val = tensor(0)]; - tensor c_in_to_fp16_dtype_0 = const()[name = tensor("c_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor("cast_5")]; - tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = tensor("split_1_cast_fp16")]; - tensor input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; - tensor input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = tensor("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; - tensor input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; - tensor input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = tensor("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; - tensor input_lstm_layer_0_direction_0 = const()[name = tensor("input_lstm_layer_0_direction_0"), val = tensor("forward")]; - tensor input_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_lstm_layer_0_output_sequence_0"), val = tensor(true)]; - tensor input_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; - tensor input_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; - tensor input_lstm_layer_0_activation_0 = const()[name = tensor("input_lstm_layer_0_activation_0"), val = tensor("tanh")]; - tensor concat_1_to_fp16 = const()[name = tensor("concat_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312128)))]; - tensor concat_2_to_fp16 = const()[name = tensor("concat_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4588992)))]; - tensor concat_0_to_fp16 = const()[name = tensor("concat_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7865856)))]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = tensor("transpose_2")]; - tensor input_lstm_layer_0_cast_fp16_0, tensor input_lstm_layer_0_cast_fp16_1, tensor input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor("input_lstm_layer_0_cast_fp16")]; - tensor input_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; - tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = tensor("input_lstm_h0_squeeze_cast_fp16")]; - tensor input_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; - tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = tensor("input_lstm_c0_squeeze_cast_fp16")]; - tensor input_direction_0 = const()[name = tensor("input_direction_0"), val = tensor("forward")]; - tensor input_output_sequence_0 = const()[name = tensor("input_output_sequence_0"), val = tensor(true)]; - tensor input_recurrent_activation_0 = const()[name = tensor("input_recurrent_activation_0"), val = tensor("sigmoid")]; - tensor input_cell_activation_0 = const()[name = tensor("input_cell_activation_0"), val = tensor("tanh")]; - tensor input_activation_0 = const()[name = tensor("input_activation_0"), val = tensor("tanh")]; - tensor concat_4_to_fp16 = const()[name = tensor("concat_4_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7871040)))]; - tensor concat_5_to_fp16 = const()[name = tensor("concat_5_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11147904)))]; - tensor concat_3_to_fp16 = const()[name = tensor("concat_3_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14424768)))]; - tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = tensor("input_cast_fp16")]; - tensor obj_3_axis_0 = const()[name = tensor("obj_3_axis_0"), val = tensor(0)]; - tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = tensor("obj_3_cast_fp16")]; - tensor obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_3_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_axis_0 = const()[name = tensor("obj_axis_0"), val = tensor(0)]; - tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = tensor("obj_cast_fp16")]; - tensor obj_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([1, 2, 0])]; - tensor transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("transpose_0_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor("transpose_1")]; - tensor decoder = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = tensor("cast_2")]; - tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = tensor("cast_3")]; - tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = tensor("cast_4")]; - tensor target_length_tmp = identity(x = target_length)[name = tensor("target_length_tmp")]; - } -> (decoder, h_out, c_out); -} \ No newline at end of file diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/weights/weight.bin b/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/weights/weight.bin deleted file mode 100644 index a895491b78de520abc21d90321ffd90c95483662..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:27d26890221d82322c1092fd99d7b40578e435d5cf4b83c887c42603caf97aba -size 14429952 diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/analytics/coremldata.bin b/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 4738782a8e36389621ff4b3b90dd2a661b35146c..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:42e638870d73f26b332918a3496ce36793fbb413a81cbd3d16ba01328637a105 -size 243 diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/coremldata.bin b/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/coremldata.bin deleted file mode 100644 index e016ed32c20e1563399f15adad451a442644b3d7..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4def7aa848599ad0e17a8b9a982edcdbf33cf92e1f4b798de32e2ca0bc74b030 -size 485 diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/metadata.json b/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/metadata.json deleted file mode 100644 index c87f9eb24e76b8e518dc1fbead46c8c0029d3925..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/metadata.json +++ /dev/null @@ -1,106 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "enc6bit-palettize quantized - encoder", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1024 × 188)", - "shortDescription" : "", - "shape" : "[1, 1024, 188]", - "name" : "encoder", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "encoder_length", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Mixed (Float16, Palettized (6 bits))", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.floor" : 3, - "Ios17.logicalAnd" : 2, - "Ios17.reshape" : 145, - "Ios16.softmax" : 24, - "Ios17.matmul" : 72, - "Ios17.transpose" : 172, - "Split" : 24, - "Ios17.expandDims" : 5, - "Select" : 72, - "Ios17.add" : 174, - "Tile" : 1, - "Ios17.sliceByIndex" : 48, - "Ios16.sigmoid" : 24, - "Pad" : 48, - "Ios17.logicalNot" : 2, - "Ios17.layerNorm" : 120, - "Ios16.silu" : 72, - "Ios17.less" : 1, - "Ios16.constexprLutToDense" : 294, - "Ios17.conv" : 77, - "Ios16.relu" : 3, - "Ios17.linear" : 193, - "Ios17.cast" : 4, - "Ios17.mul" : 99 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 128 × 1501)", - "shortDescription" : "", - "shape" : "[1, 128, 1501]", - "name" : "mel", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2025-09-25", - "com.github.apple.coremltools.source" : "torch==2.7.0", - "com.github.apple.coremltools.version" : "9.0b1", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "parakeet_encoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/model.mil b/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/model.mil deleted file mode 100644 index 93d1301ccde7c2ca447af6c01ab05bfda5a5dd2c..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/model.mil +++ /dev/null @@ -1,3404 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})] -{ - func main(tensor mel, tensor mel_length) { - tensor var_30 = const()[name = tensor("op_30"), val = tensor(-1)]; - tensor x_1_perm_0 = const()[name = tensor("x_1_perm_0"), val = tensor([0, 2, 1])]; - tensor mel_to_fp16_dtype_0 = const()[name = tensor("mel_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_86_to_fp16_dtype_0 = const()[name = tensor("op_86_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_87_promoted_to_fp16 = const()[name = tensor("op_87_promoted_to_fp16"), val = tensor(-0x1p+0)]; - tensor mel_length_to_fp16 = cast(dtype = var_86_to_fp16_dtype_0, x = mel_length)[name = tensor("cast_3")]; - tensor var_88_cast_fp16 = add(x = mel_length_to_fp16, y = var_87_promoted_to_fp16)[name = tensor("op_88_cast_fp16")]; - tensor _inversed_90_y_0_to_fp16 = const()[name = tensor("_inversed_90_y_0_to_fp16"), val = tensor(0x1p-1)]; - tensor _inversed_90_cast_fp16 = mul(x = var_88_cast_fp16, y = _inversed_90_y_0_to_fp16)[name = tensor("_inversed_90_cast_fp16")]; - tensor var_91_to_fp16 = const()[name = tensor("op_91_to_fp16"), val = tensor(0x1p+0)]; - tensor lengths_1_cast_fp16 = add(x = _inversed_90_cast_fp16, y = var_91_to_fp16)[name = tensor("lengths_1_cast_fp16")]; - tensor lengths_3_cast_fp16 = floor(x = lengths_1_cast_fp16)[name = tensor("lengths_3_cast_fp16")]; - tensor var_95_promoted_to_fp16 = const()[name = tensor("op_95_promoted_to_fp16"), val = tensor(-0x1p+0)]; - tensor var_96_cast_fp16 = add(x = lengths_3_cast_fp16, y = var_95_promoted_to_fp16)[name = tensor("op_96_cast_fp16")]; - tensor _inversed_98_y_0_to_fp16 = const()[name = tensor("_inversed_98_y_0_to_fp16"), val = tensor(0x1p-1)]; - tensor _inversed_98_cast_fp16 = mul(x = var_96_cast_fp16, y = _inversed_98_y_0_to_fp16)[name = tensor("_inversed_98_cast_fp16")]; - tensor var_99_to_fp16 = const()[name = tensor("op_99_to_fp16"), val = tensor(0x1p+0)]; - tensor lengths_7_cast_fp16 = add(x = _inversed_98_cast_fp16, y = var_99_to_fp16)[name = tensor("lengths_7_cast_fp16")]; - tensor lengths_9_cast_fp16 = floor(x = lengths_7_cast_fp16)[name = tensor("lengths_9_cast_fp16")]; - tensor var_103_promoted_to_fp16 = const()[name = tensor("op_103_promoted_to_fp16"), val = tensor(-0x1p+0)]; - tensor var_104_cast_fp16 = add(x = lengths_9_cast_fp16, y = var_103_promoted_to_fp16)[name = tensor("op_104_cast_fp16")]; - tensor _inversed_106_y_0_to_fp16 = const()[name = tensor("_inversed_106_y_0_to_fp16"), val = tensor(0x1p-1)]; - tensor _inversed_106_cast_fp16 = mul(x = var_104_cast_fp16, y = _inversed_106_y_0_to_fp16)[name = tensor("_inversed_106_cast_fp16")]; - tensor var_107_to_fp16 = const()[name = tensor("op_107_to_fp16"), val = tensor(0x1p+0)]; - tensor lengths_13_cast_fp16 = add(x = _inversed_106_cast_fp16, y = var_107_to_fp16)[name = tensor("lengths_13_cast_fp16")]; - tensor lengths_cast_fp16 = floor(x = lengths_13_cast_fp16)[name = tensor("lengths_cast_fp16")]; - tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([1])]; - tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = tensor("cast_2")]; - tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = tensor("transpose_315")]; - tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = x_1_cast_fp16)[name = tensor("input_1_cast_fp16")]; - tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; - tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_3_strides_0 = const()[name = tensor("input_3_strides_0"), val = tensor([2, 2])]; - tensor input_3_dilations_0 = const()[name = tensor("input_3_dilations_0"), val = tensor([1, 1])]; - tensor input_3_groups_0 = const()[name = tensor("input_3_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1856))), name = tensor("module_pre_encode_conv_0_weight_to_fp16_palettized"), shape = tensor([256, 1, 3, 3])]; - tensor module_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2048)))]; - tensor input_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; - tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_7_strides_0 = const()[name = tensor("input_7_strides_0"), val = tensor([2, 2])]; - tensor input_7_groups_0 = const()[name = tensor("input_7_groups_0"), val = tensor(256)]; - tensor input_7_dilations_0 = const()[name = tensor("input_7_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4416))), name = tensor("module_pre_encode_conv_2_weight_to_fp16_palettized"), shape = tensor([256, 1, 3, 3])]; - tensor module_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4608)))]; - tensor input_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("valid")]; - tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([1, 1])]; - tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1, 1])]; - tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_3_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54400))), name = tensor("module_pre_encode_conv_3_weight_to_fp16_palettized"), shape = tensor([256, 256, 1, 1])]; - tensor module_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54592)))]; - tensor input_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16_palettized, x = input_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; - tensor input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor("input_11_cast_fp16")]; - tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; - tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_13_strides_0 = const()[name = tensor("input_13_strides_0"), val = tensor([2, 2])]; - tensor input_13_groups_0 = const()[name = tensor("input_13_groups_0"), val = tensor(256)]; - tensor input_13_dilations_0 = const()[name = tensor("input_13_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_5_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56960))), name = tensor("module_pre_encode_conv_5_weight_to_fp16_palettized"), shape = tensor([256, 1, 3, 3])]; - tensor module_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57152)))]; - tensor input_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = tensor("input_13_cast_fp16")]; - tensor input_15_pad_type_0 = const()[name = tensor("input_15_pad_type_0"), val = tensor("valid")]; - tensor input_15_strides_0 = const()[name = tensor("input_15_strides_0"), val = tensor([1, 1])]; - tensor input_15_pad_0 = const()[name = tensor("input_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_15_dilations_0 = const()[name = tensor("input_15_dilations_0"), val = tensor([1, 1])]; - tensor input_15_groups_0 = const()[name = tensor("input_15_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_6_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106944))), name = tensor("module_pre_encode_conv_6_weight_to_fp16_palettized"), shape = tensor([256, 256, 1, 1])]; - tensor module_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107136)))]; - tensor input_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16_palettized, x = input_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; - tensor x_3_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor("x_3_cast_fp16")]; - tensor var_157_perm_0 = const()[name = tensor("op_157_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_158 = const()[name = tensor("op_158"), val = tensor([1, 188, -1])]; - tensor var_157_cast_fp16 = transpose(perm = var_157_perm_0, x = x_3_cast_fp16)[name = tensor("transpose_314")]; - tensor input_17_cast_fp16 = reshape(shape = var_158, x = var_157_cast_fp16)[name = tensor("input_17_cast_fp16")]; - tensor module_pre_encode_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3253504))), name = tensor("module_pre_encode_out_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor module_pre_encode_out_bias_to_fp16 = const()[name = tensor("module_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3253696)))]; - tensor linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16_palettized, x = input_17_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor padding_length_dtype_0 = const()[name = tensor("padding_length_dtype_0"), val = tensor("int32")]; - tensor expand_dims_0 = const()[name = tensor("expand_dims_0"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187]])]; - tensor var_196_axes_0 = const()[name = tensor("op_196_axes_0"), val = tensor([-1])]; - tensor encoder_length = cast(dtype = padding_length_dtype_0, x = lengths_cast_fp16)[name = tensor("cast_1")]; - tensor var_196 = expand_dims(axes = var_196_axes_0, x = encoder_length)[name = tensor("op_196")]; - tensor pad_mask_1 = less(x = expand_dims_0, y = var_196)[name = tensor("pad_mask_1")]; - tensor var_198_axes_0 = const()[name = tensor("op_198_axes_0"), val = tensor([1])]; - tensor var_198 = expand_dims(axes = var_198_axes_0, x = pad_mask_1)[name = tensor("op_198")]; - tensor var_199 = const()[name = tensor("op_199"), val = tensor([1, 188, 1])]; - tensor pad_mask_for_att_mask_1 = tile(reps = var_199, x = var_198)[name = tensor("pad_mask_for_att_mask_1")]; - tensor var_201_perm_0 = const()[name = tensor("op_201_perm_0"), val = tensor([0, 2, 1])]; - tensor var_201 = transpose(perm = var_201_perm_0, x = pad_mask_for_att_mask_1)[name = tensor("transpose_313")]; - tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_201)[name = tensor("pad_mask_for_att_mask")]; - tensor const_7 = const()[name = tensor("const_7"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; - tensor att_mask = logical_and(x = pad_mask_for_att_mask, y = const_7)[name = tensor("att_mask")]; - tensor mask_1 = logical_not(x = att_mask)[name = tensor("mask_1")]; - tensor pad_mask = logical_not(x = pad_mask_1)[name = tensor("pad_mask")]; - tensor input_21_axes_0 = const()[name = tensor("input_21_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3255808)))]; - tensor module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3257920)))]; - tensor var_9_to_fp16 = const()[name = tensor("op_9_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = tensor("input_21_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3260032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6405824))), name = tensor("module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_1_bias_0_to_fp16 = const()[name = tensor("linear_1_bias_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6406016)))]; - tensor linear_1_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor("input_25_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6414272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9560064))), name = tensor("module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_2_bias_0_to_fp16 = const()[name = tensor("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9560256)))]; - tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized, x = input_25_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor var_232_to_fp16 = const()[name = tensor("op_232_to_fp16"), val = tensor(0x1p-1)]; - tensor var_233_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_232_to_fp16)[name = tensor("op_233_cast_fp16")]; - tensor input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_233_cast_fp16)[name = tensor("input_31_cast_fp16")]; - tensor query_1_axes_0 = const()[name = tensor("query_1_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9562368)))]; - tensor module_layers_0_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9564480)))]; - tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("query_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9566592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10353088))), name = tensor("module_layers_0_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_3_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = tensor("linear_3_cast_fp16")]; - tensor var_249 = const()[name = tensor("op_249"), val = tensor([1, -1, 8, 128])]; - tensor q_1_cast_fp16 = reshape(shape = var_249, x = linear_3_cast_fp16)[name = tensor("q_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10353280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11139776))), name = tensor("module_layers_0_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_4_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = tensor("linear_4_cast_fp16")]; - tensor var_253 = const()[name = tensor("op_253"), val = tensor([1, -1, 8, 128])]; - tensor k_1_cast_fp16 = reshape(shape = var_253, x = linear_4_cast_fp16)[name = tensor("k_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11139968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11926464))), name = tensor("module_layers_0_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_5_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = tensor("linear_5_cast_fp16")]; - tensor var_257 = const()[name = tensor("op_257"), val = tensor([1, -1, 8, 128])]; - tensor v_1_cast_fp16 = reshape(shape = var_257, x = linear_5_cast_fp16)[name = tensor("v_1_cast_fp16")]; - tensor value_3_perm_0 = const()[name = tensor("value_3_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11926656)))]; - tensor var_269_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = tensor("op_269_cast_fp16")]; - tensor module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11928768)))]; - tensor var_271_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = tensor("op_271_cast_fp16")]; - tensor q_with_bias_v_1_perm_0 = const()[name = tensor("q_with_bias_v_1_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_7_transpose_x_0 = const()[name = tensor("x_7_transpose_x_0"), val = tensor(false)]; - tensor x_7_transpose_y_0 = const()[name = tensor("x_7_transpose_y_0"), val = tensor(false)]; - tensor op_273_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11930880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12218944))), name = tensor("op_273_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_271_cast_fp16)[name = tensor("transpose_312")]; - tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_273_to_fp16_palettized)[name = tensor("x_7_cast_fp16")]; - tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_9_mode_0 = const()[name = tensor("x_9_mode_0"), val = tensor("constant")]; - tensor const_14_to_fp16 = const()[name = tensor("const_14_to_fp16"), val = tensor(0x0p+0)]; - tensor x_9_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = tensor("x_9_cast_fp16")]; - tensor var_281 = const()[name = tensor("op_281"), val = tensor([1, 8, -1, 188])]; - tensor x_11_cast_fp16 = reshape(shape = var_281, x = x_9_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor var_285_begin_0 = const()[name = tensor("op_285_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_285_end_0 = const()[name = tensor("op_285_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_285_end_mask_0 = const()[name = tensor("op_285_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_285_cast_fp16 = slice_by_index(begin = var_285_begin_0, end = var_285_end_0, end_mask = var_285_end_mask_0, x = x_11_cast_fp16)[name = tensor("op_285_cast_fp16")]; - tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_1_cast_fp16 = reshape(shape = var_286, x = var_285_cast_fp16)[name = tensor("matrix_bd_1_cast_fp16")]; - tensor matrix_ac_1_transpose_x_0 = const()[name = tensor("matrix_ac_1_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_1_transpose_y_0 = const()[name = tensor("matrix_ac_1_transpose_y_0"), val = tensor(false)]; - tensor transpose_96_perm_0 = const()[name = tensor("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_97_perm_0 = const()[name = tensor("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = tensor("transpose_310")]; - tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_269_cast_fp16)[name = tensor("transpose_311")]; - tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = tensor("matrix_ac_1_cast_fp16")]; - tensor matrix_bd_3_begin_0 = const()[name = tensor("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_3_end_0 = const()[name = tensor("matrix_bd_3_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_3_end_mask_0 = const()[name = tensor("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = tensor("matrix_bd_3_cast_fp16")]; - tensor var_295_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = tensor("op_295_cast_fp16")]; - tensor _inversed_scores_1_y_0_to_fp16 = const()[name = tensor("_inversed_scores_1_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_1_cast_fp16 = mul(x = var_295_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = tensor("_inversed_scores_1_cast_fp16")]; - tensor mask_3_axes_0 = const()[name = tensor("mask_3_axes_0"), val = tensor([1])]; - tensor mask_3 = expand_dims(axes = mask_3_axes_0, x = mask_1)[name = tensor("mask_3")]; - tensor var_12_to_fp16 = const()[name = tensor("op_12_to_fp16"), val = tensor(-0x1.388p+13)]; - tensor scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_3)[name = tensor("scores_3_cast_fp16")]; - tensor var_301_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = tensor("op_301_cast_fp16")]; - tensor var_11_to_fp16 = const()[name = tensor("op_11_to_fp16"), val = tensor(0x0p+0)]; - tensor input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_301_cast_fp16, cond = mask_3)[name = tensor("input_33_cast_fp16")]; - tensor x_13_transpose_x_0 = const()[name = tensor("x_13_transpose_x_0"), val = tensor(false)]; - tensor x_13_transpose_y_0 = const()[name = tensor("x_13_transpose_y_0"), val = tensor(false)]; - tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = tensor("transpose_309")]; - tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_3_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_305_perm_0 = const()[name = tensor("op_305_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_306 = const()[name = tensor("op_306"), val = tensor([1, -1, 1024])]; - tensor var_305_cast_fp16 = transpose(perm = var_305_perm_0, x = x_13_cast_fp16)[name = tensor("transpose_308")]; - tensor input_35_cast_fp16 = reshape(shape = var_306, x = var_305_cast_fp16)[name = tensor("input_35_cast_fp16")]; - tensor module_layers_0_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12219136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13005632))), name = tensor("module_layers_0_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_7_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = tensor("linear_7_cast_fp16")]; - tensor input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = tensor("input_39_cast_fp16")]; - tensor x_17_axes_0 = const()[name = tensor("x_17_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13005824)))]; - tensor module_layers_0_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13007936)))]; - tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("x_17_cast_fp16")]; - tensor input_41_perm_0 = const()[name = tensor("input_41_perm_0"), val = tensor([0, 2, 1])]; - tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; - tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([1])]; - tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0])]; - tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1])]; - tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13010048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14582976))), name = tensor("module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = tensor("transpose_307")]; - tensor input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_41_cast_fp16)[name = tensor("input_43_cast_fp16")]; - tensor x_19_split_num_splits_0 = const()[name = tensor("x_19_split_num_splits_0"), val = tensor(2)]; - tensor x_19_split_axis_0 = const()[name = tensor("x_19_split_axis_0"), val = tensor(1)]; - tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = tensor("x_19_split_cast_fp16")]; - tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = tensor("x_19_split_1_sigmoid_cast_fp16")]; - tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = tensor("x_19_cast_fp16")]; - tensor var_328_axes_0 = const()[name = tensor("op_328_axes_0"), val = tensor([1])]; - tensor var_328 = expand_dims(axes = var_328_axes_0, x = pad_mask)[name = tensor("op_328")]; - tensor input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_328)[name = tensor("input_45_cast_fp16")]; - tensor input_47_pad_0 = const()[name = tensor("input_47_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_47_mode_0 = const()[name = tensor("input_47_mode_0"), val = tensor("constant")]; - tensor const_17_to_fp16 = const()[name = tensor("const_17_to_fp16"), val = tensor(0x0p+0)]; - tensor input_47_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = tensor("input_47_cast_fp16")]; - tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("valid")]; - tensor input_49_groups_0 = const()[name = tensor("input_49_groups_0"), val = tensor(1024)]; - tensor input_49_strides_0 = const()[name = tensor("input_49_strides_0"), val = tensor([1])]; - tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([0, 0])]; - tensor input_49_dilations_0 = const()[name = tensor("input_49_dilations_0"), val = tensor([1])]; - tensor const_248_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14583168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14590144))), name = tensor("const_248_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_249_to_fp16 = const()[name = tensor("const_249_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14590336)))]; - tensor input_51_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_248_to_fp16_palettized, x = input_47_cast_fp16)[name = tensor("input_51_cast_fp16")]; - tensor input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = tensor("input_53_cast_fp16")]; - tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("valid")]; - tensor x_21_strides_0 = const()[name = tensor("x_21_strides_0"), val = tensor([1])]; - tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0])]; - tensor x_21_dilations_0 = const()[name = tensor("x_21_dilations_0"), val = tensor([1])]; - tensor x_21_groups_0 = const()[name = tensor("x_21_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14592448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15378944))), name = tensor("module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_53_cast_fp16)[name = tensor("x_21_cast_fp16")]; - tensor input_55_perm_0 = const()[name = tensor("input_55_perm_0"), val = tensor([0, 2, 1])]; - tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = tensor("transpose_306")]; - tensor input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; - tensor input_59_axes_0 = const()[name = tensor("input_59_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15379136)))]; - tensor module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15381248)))]; - tensor input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("input_59_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15383360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18529152))), name = tensor("module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_8_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = tensor("linear_8_cast_fp16")]; - tensor input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = tensor("input_63_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18529344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21675136))), name = tensor("module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_9_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized, x = input_63_cast_fp16)[name = tensor("linear_9_cast_fp16")]; - tensor var_366_to_fp16 = const()[name = tensor("op_366_to_fp16"), val = tensor(0x1p-1)]; - tensor var_367_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_366_to_fp16)[name = tensor("op_367_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_367_cast_fp16)[name = tensor("input_69_cast_fp16")]; - tensor input_71_axes_0 = const()[name = tensor("input_71_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21675328)))]; - tensor module_layers_0_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21677440)))]; - tensor input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = tensor("input_71_cast_fp16")]; - tensor input_73_axes_0 = const()[name = tensor("input_73_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21679552)))]; - tensor module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21681664)))]; - tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = tensor("input_73_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21683776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24829568))), name = tensor("module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_10_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized, x = input_73_cast_fp16)[name = tensor("linear_10_cast_fp16")]; - tensor input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor("input_77_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24829760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27975552))), name = tensor("module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_11_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = tensor("linear_11_cast_fp16")]; - tensor var_395_to_fp16 = const()[name = tensor("op_395_to_fp16"), val = tensor(0x1p-1)]; - tensor var_396_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_395_to_fp16)[name = tensor("op_396_cast_fp16")]; - tensor input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_396_cast_fp16)[name = tensor("input_83_cast_fp16")]; - tensor query_3_axes_0 = const()[name = tensor("query_3_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27975744)))]; - tensor module_layers_1_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27977856)))]; - tensor query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = tensor("query_3_cast_fp16")]; - tensor module_layers_1_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27979968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28766464))), name = tensor("module_layers_1_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_12_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = tensor("linear_12_cast_fp16")]; - tensor var_412 = const()[name = tensor("op_412"), val = tensor([1, -1, 8, 128])]; - tensor q_7_cast_fp16 = reshape(shape = var_412, x = linear_12_cast_fp16)[name = tensor("q_7_cast_fp16")]; - tensor module_layers_1_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28766656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29553152))), name = tensor("module_layers_1_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_13_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = tensor("linear_13_cast_fp16")]; - tensor var_416 = const()[name = tensor("op_416"), val = tensor([1, -1, 8, 128])]; - tensor k_5_cast_fp16 = reshape(shape = var_416, x = linear_13_cast_fp16)[name = tensor("k_5_cast_fp16")]; - tensor module_layers_1_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29553344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30339840))), name = tensor("module_layers_1_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_14_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = tensor("linear_14_cast_fp16")]; - tensor var_420 = const()[name = tensor("op_420"), val = tensor([1, -1, 8, 128])]; - tensor v_3_cast_fp16 = reshape(shape = var_420, x = linear_14_cast_fp16)[name = tensor("v_3_cast_fp16")]; - tensor value_5_perm_0 = const()[name = tensor("value_5_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30340032)))]; - tensor var_432_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = tensor("op_432_cast_fp16")]; - tensor module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30342144)))]; - tensor var_434_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = tensor("op_434_cast_fp16")]; - tensor q_with_bias_v_3_perm_0 = const()[name = tensor("q_with_bias_v_3_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_29_transpose_x_0 = const()[name = tensor("x_29_transpose_x_0"), val = tensor(false)]; - tensor x_29_transpose_y_0 = const()[name = tensor("x_29_transpose_y_0"), val = tensor(false)]; - tensor op_436_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30344256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30632320))), name = tensor("op_436_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_434_cast_fp16)[name = tensor("transpose_305")]; - tensor x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_436_to_fp16_palettized)[name = tensor("x_29_cast_fp16")]; - tensor x_31_pad_0 = const()[name = tensor("x_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_31_mode_0 = const()[name = tensor("x_31_mode_0"), val = tensor("constant")]; - tensor const_24_to_fp16 = const()[name = tensor("const_24_to_fp16"), val = tensor(0x0p+0)]; - tensor x_31_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = tensor("x_31_cast_fp16")]; - tensor var_444 = const()[name = tensor("op_444"), val = tensor([1, 8, -1, 188])]; - tensor x_33_cast_fp16 = reshape(shape = var_444, x = x_31_cast_fp16)[name = tensor("x_33_cast_fp16")]; - tensor var_448_begin_0 = const()[name = tensor("op_448_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_448_end_0 = const()[name = tensor("op_448_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_448_end_mask_0 = const()[name = tensor("op_448_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_448_cast_fp16 = slice_by_index(begin = var_448_begin_0, end = var_448_end_0, end_mask = var_448_end_mask_0, x = x_33_cast_fp16)[name = tensor("op_448_cast_fp16")]; - tensor var_449 = const()[name = tensor("op_449"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_5_cast_fp16 = reshape(shape = var_449, x = var_448_cast_fp16)[name = tensor("matrix_bd_5_cast_fp16")]; - tensor matrix_ac_3_transpose_x_0 = const()[name = tensor("matrix_ac_3_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_3_transpose_y_0 = const()[name = tensor("matrix_ac_3_transpose_y_0"), val = tensor(false)]; - tensor transpose_98_perm_0 = const()[name = tensor("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_99_perm_0 = const()[name = tensor("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = tensor("transpose_303")]; - tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = var_432_cast_fp16)[name = tensor("transpose_304")]; - tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = tensor("matrix_ac_3_cast_fp16")]; - tensor matrix_bd_7_begin_0 = const()[name = tensor("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_7_end_0 = const()[name = tensor("matrix_bd_7_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_7_end_mask_0 = const()[name = tensor("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = tensor("matrix_bd_7_cast_fp16")]; - tensor var_458_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = tensor("op_458_cast_fp16")]; - tensor _inversed_scores_5_y_0_to_fp16 = const()[name = tensor("_inversed_scores_5_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_5_cast_fp16 = mul(x = var_458_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = tensor("_inversed_scores_5_cast_fp16")]; - tensor scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_3)[name = tensor("scores_7_cast_fp16")]; - tensor var_464_cast_fp16 = softmax(axis = var_30, x = scores_7_cast_fp16)[name = tensor("op_464_cast_fp16")]; - tensor input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_464_cast_fp16, cond = mask_3)[name = tensor("input_85_cast_fp16")]; - tensor x_35_transpose_x_0 = const()[name = tensor("x_35_transpose_x_0"), val = tensor(false)]; - tensor x_35_transpose_y_0 = const()[name = tensor("x_35_transpose_y_0"), val = tensor(false)]; - tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = tensor("transpose_302")]; - tensor x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_5_cast_fp16)[name = tensor("x_35_cast_fp16")]; - tensor var_468_perm_0 = const()[name = tensor("op_468_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_469 = const()[name = tensor("op_469"), val = tensor([1, -1, 1024])]; - tensor var_468_cast_fp16 = transpose(perm = var_468_perm_0, x = x_35_cast_fp16)[name = tensor("transpose_301")]; - tensor input_87_cast_fp16 = reshape(shape = var_469, x = var_468_cast_fp16)[name = tensor("input_87_cast_fp16")]; - tensor module_layers_1_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30632512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31419008))), name = tensor("module_layers_1_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_16_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16_palettized, x = input_87_cast_fp16)[name = tensor("linear_16_cast_fp16")]; - tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = tensor("input_91_cast_fp16")]; - tensor x_39_axes_0 = const()[name = tensor("x_39_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31419200)))]; - tensor module_layers_1_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31421312)))]; - tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = tensor("x_39_cast_fp16")]; - tensor input_93_perm_0 = const()[name = tensor("input_93_perm_0"), val = tensor([0, 2, 1])]; - tensor input_95_pad_type_0 = const()[name = tensor("input_95_pad_type_0"), val = tensor("valid")]; - tensor input_95_strides_0 = const()[name = tensor("input_95_strides_0"), val = tensor([1])]; - tensor input_95_pad_0 = const()[name = tensor("input_95_pad_0"), val = tensor([0, 0])]; - tensor input_95_dilations_0 = const()[name = tensor("input_95_dilations_0"), val = tensor([1])]; - tensor input_95_groups_0 = const()[name = tensor("input_95_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31423424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32996352))), name = tensor("module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = tensor("transpose_300")]; - tensor input_95_cast_fp16 = conv(dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = tensor("input_95_cast_fp16")]; - tensor x_41_split_num_splits_0 = const()[name = tensor("x_41_split_num_splits_0"), val = tensor(2)]; - tensor x_41_split_axis_0 = const()[name = tensor("x_41_split_axis_0"), val = tensor(1)]; - tensor x_41_split_cast_fp16_0, tensor x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = tensor("x_41_split_cast_fp16")]; - tensor x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = tensor("x_41_split_1_sigmoid_cast_fp16")]; - tensor x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = tensor("x_41_cast_fp16")]; - tensor input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_328)[name = tensor("input_97_cast_fp16")]; - tensor input_99_pad_0 = const()[name = tensor("input_99_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_99_mode_0 = const()[name = tensor("input_99_mode_0"), val = tensor("constant")]; - tensor const_27_to_fp16 = const()[name = tensor("const_27_to_fp16"), val = tensor(0x0p+0)]; - tensor input_99_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = tensor("input_99_cast_fp16")]; - tensor input_101_pad_type_0 = const()[name = tensor("input_101_pad_type_0"), val = tensor("valid")]; - tensor input_101_groups_0 = const()[name = tensor("input_101_groups_0"), val = tensor(1024)]; - tensor input_101_strides_0 = const()[name = tensor("input_101_strides_0"), val = tensor([1])]; - tensor input_101_pad_0 = const()[name = tensor("input_101_pad_0"), val = tensor([0, 0])]; - tensor input_101_dilations_0 = const()[name = tensor("input_101_dilations_0"), val = tensor([1])]; - tensor const_250_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32996544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33003520))), name = tensor("const_250_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_251_to_fp16 = const()[name = tensor("const_251_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33003712)))]; - tensor input_103_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_250_to_fp16_palettized, x = input_99_cast_fp16)[name = tensor("input_103_cast_fp16")]; - tensor input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = tensor("input_105_cast_fp16")]; - tensor x_43_pad_type_0 = const()[name = tensor("x_43_pad_type_0"), val = tensor("valid")]; - tensor x_43_strides_0 = const()[name = tensor("x_43_strides_0"), val = tensor([1])]; - tensor x_43_pad_0 = const()[name = tensor("x_43_pad_0"), val = tensor([0, 0])]; - tensor x_43_dilations_0 = const()[name = tensor("x_43_dilations_0"), val = tensor([1])]; - tensor x_43_groups_0 = const()[name = tensor("x_43_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33005824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33792320))), name = tensor("module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_43_cast_fp16 = conv(dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_105_cast_fp16)[name = tensor("x_43_cast_fp16")]; - tensor input_107_perm_0 = const()[name = tensor("input_107_perm_0"), val = tensor([0, 2, 1])]; - tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = tensor("transpose_299")]; - tensor input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = tensor("input_109_cast_fp16")]; - tensor input_111_axes_0 = const()[name = tensor("input_111_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33792512)))]; - tensor module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33794624)))]; - tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("input_111_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33796736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36942528))), name = tensor("module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_17_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = tensor("linear_17_cast_fp16")]; - tensor input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = tensor("input_115_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36942720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40088512))), name = tensor("module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_18_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized, x = input_115_cast_fp16)[name = tensor("linear_18_cast_fp16")]; - tensor var_529_to_fp16 = const()[name = tensor("op_529_to_fp16"), val = tensor(0x1p-1)]; - tensor var_530_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_529_to_fp16)[name = tensor("op_530_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_530_cast_fp16)[name = tensor("input_121_cast_fp16")]; - tensor input_123_axes_0 = const()[name = tensor("input_123_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40088704)))]; - tensor module_layers_1_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40090816)))]; - tensor input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("input_123_cast_fp16")]; - tensor input_125_axes_0 = const()[name = tensor("input_125_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40092928)))]; - tensor module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40095040)))]; - tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor("input_125_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40097152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43242944))), name = tensor("module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_19_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized, x = input_125_cast_fp16)[name = tensor("linear_19_cast_fp16")]; - tensor input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor("input_129_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43243136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46388928))), name = tensor("module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_20_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized, x = input_129_cast_fp16)[name = tensor("linear_20_cast_fp16")]; - tensor var_558_to_fp16 = const()[name = tensor("op_558_to_fp16"), val = tensor(0x1p-1)]; - tensor var_559_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_558_to_fp16)[name = tensor("op_559_cast_fp16")]; - tensor input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_559_cast_fp16)[name = tensor("input_135_cast_fp16")]; - tensor query_5_axes_0 = const()[name = tensor("query_5_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46389120)))]; - tensor module_layers_2_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46391232)))]; - tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = tensor("query_5_cast_fp16")]; - tensor module_layers_2_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46393344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47179840))), name = tensor("module_layers_2_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_21_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = tensor("linear_21_cast_fp16")]; - tensor var_575 = const()[name = tensor("op_575"), val = tensor([1, -1, 8, 128])]; - tensor q_13_cast_fp16 = reshape(shape = var_575, x = linear_21_cast_fp16)[name = tensor("q_13_cast_fp16")]; - tensor module_layers_2_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47180032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47966528))), name = tensor("module_layers_2_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_22_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = tensor("linear_22_cast_fp16")]; - tensor var_579 = const()[name = tensor("op_579"), val = tensor([1, -1, 8, 128])]; - tensor k_9_cast_fp16 = reshape(shape = var_579, x = linear_22_cast_fp16)[name = tensor("k_9_cast_fp16")]; - tensor module_layers_2_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47966720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48753216))), name = tensor("module_layers_2_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_23_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = tensor("linear_23_cast_fp16")]; - tensor var_583 = const()[name = tensor("op_583"), val = tensor([1, -1, 8, 128])]; - tensor v_5_cast_fp16 = reshape(shape = var_583, x = linear_23_cast_fp16)[name = tensor("v_5_cast_fp16")]; - tensor value_7_perm_0 = const()[name = tensor("value_7_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48753408)))]; - tensor var_595_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = tensor("op_595_cast_fp16")]; - tensor module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48755520)))]; - tensor var_597_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = tensor("op_597_cast_fp16")]; - tensor q_with_bias_v_5_perm_0 = const()[name = tensor("q_with_bias_v_5_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_51_transpose_x_0 = const()[name = tensor("x_51_transpose_x_0"), val = tensor(false)]; - tensor x_51_transpose_y_0 = const()[name = tensor("x_51_transpose_y_0"), val = tensor(false)]; - tensor op_599_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48757632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49045696))), name = tensor("op_599_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_597_cast_fp16)[name = tensor("transpose_298")]; - tensor x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_599_to_fp16_palettized)[name = tensor("x_51_cast_fp16")]; - tensor x_53_pad_0 = const()[name = tensor("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_53_mode_0 = const()[name = tensor("x_53_mode_0"), val = tensor("constant")]; - tensor const_34_to_fp16 = const()[name = tensor("const_34_to_fp16"), val = tensor(0x0p+0)]; - tensor x_53_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = tensor("x_53_cast_fp16")]; - tensor var_607 = const()[name = tensor("op_607"), val = tensor([1, 8, -1, 188])]; - tensor x_55_cast_fp16 = reshape(shape = var_607, x = x_53_cast_fp16)[name = tensor("x_55_cast_fp16")]; - tensor var_611_begin_0 = const()[name = tensor("op_611_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_611_end_0 = const()[name = tensor("op_611_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_611_end_mask_0 = const()[name = tensor("op_611_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_611_cast_fp16 = slice_by_index(begin = var_611_begin_0, end = var_611_end_0, end_mask = var_611_end_mask_0, x = x_55_cast_fp16)[name = tensor("op_611_cast_fp16")]; - tensor var_612 = const()[name = tensor("op_612"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_9_cast_fp16 = reshape(shape = var_612, x = var_611_cast_fp16)[name = tensor("matrix_bd_9_cast_fp16")]; - tensor matrix_ac_5_transpose_x_0 = const()[name = tensor("matrix_ac_5_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_5_transpose_y_0 = const()[name = tensor("matrix_ac_5_transpose_y_0"), val = tensor(false)]; - tensor transpose_100_perm_0 = const()[name = tensor("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_101_perm_0 = const()[name = tensor("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = tensor("transpose_296")]; - tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_595_cast_fp16)[name = tensor("transpose_297")]; - tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = tensor("matrix_ac_5_cast_fp16")]; - tensor matrix_bd_11_begin_0 = const()[name = tensor("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_11_end_0 = const()[name = tensor("matrix_bd_11_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_11_end_mask_0 = const()[name = tensor("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = tensor("matrix_bd_11_cast_fp16")]; - tensor var_621_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = tensor("op_621_cast_fp16")]; - tensor _inversed_scores_9_y_0_to_fp16 = const()[name = tensor("_inversed_scores_9_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_9_cast_fp16 = mul(x = var_621_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = tensor("_inversed_scores_9_cast_fp16")]; - tensor scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_3)[name = tensor("scores_11_cast_fp16")]; - tensor var_627_cast_fp16 = softmax(axis = var_30, x = scores_11_cast_fp16)[name = tensor("op_627_cast_fp16")]; - tensor input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_627_cast_fp16, cond = mask_3)[name = tensor("input_137_cast_fp16")]; - tensor x_57_transpose_x_0 = const()[name = tensor("x_57_transpose_x_0"), val = tensor(false)]; - tensor x_57_transpose_y_0 = const()[name = tensor("x_57_transpose_y_0"), val = tensor(false)]; - tensor value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = tensor("transpose_295")]; - tensor x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_7_cast_fp16)[name = tensor("x_57_cast_fp16")]; - tensor var_631_perm_0 = const()[name = tensor("op_631_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_632 = const()[name = tensor("op_632"), val = tensor([1, -1, 1024])]; - tensor var_631_cast_fp16 = transpose(perm = var_631_perm_0, x = x_57_cast_fp16)[name = tensor("transpose_294")]; - tensor input_139_cast_fp16 = reshape(shape = var_632, x = var_631_cast_fp16)[name = tensor("input_139_cast_fp16")]; - tensor module_layers_2_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49045888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49832384))), name = tensor("module_layers_2_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_25_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16_palettized, x = input_139_cast_fp16)[name = tensor("linear_25_cast_fp16")]; - tensor input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = tensor("input_143_cast_fp16")]; - tensor x_61_axes_0 = const()[name = tensor("x_61_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49832576)))]; - tensor module_layers_2_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49834688)))]; - tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = tensor("x_61_cast_fp16")]; - tensor input_145_perm_0 = const()[name = tensor("input_145_perm_0"), val = tensor([0, 2, 1])]; - tensor input_147_pad_type_0 = const()[name = tensor("input_147_pad_type_0"), val = tensor("valid")]; - tensor input_147_strides_0 = const()[name = tensor("input_147_strides_0"), val = tensor([1])]; - tensor input_147_pad_0 = const()[name = tensor("input_147_pad_0"), val = tensor([0, 0])]; - tensor input_147_dilations_0 = const()[name = tensor("input_147_dilations_0"), val = tensor([1])]; - tensor input_147_groups_0 = const()[name = tensor("input_147_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49836800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51409728))), name = tensor("module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = tensor("transpose_293")]; - tensor input_147_cast_fp16 = conv(dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_145_cast_fp16)[name = tensor("input_147_cast_fp16")]; - tensor x_63_split_num_splits_0 = const()[name = tensor("x_63_split_num_splits_0"), val = tensor(2)]; - tensor x_63_split_axis_0 = const()[name = tensor("x_63_split_axis_0"), val = tensor(1)]; - tensor x_63_split_cast_fp16_0, tensor x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = tensor("x_63_split_cast_fp16")]; - tensor x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = tensor("x_63_split_1_sigmoid_cast_fp16")]; - tensor x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = tensor("x_63_cast_fp16")]; - tensor input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_328)[name = tensor("input_149_cast_fp16")]; - tensor input_151_pad_0 = const()[name = tensor("input_151_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_151_mode_0 = const()[name = tensor("input_151_mode_0"), val = tensor("constant")]; - tensor const_37_to_fp16 = const()[name = tensor("const_37_to_fp16"), val = tensor(0x0p+0)]; - tensor input_151_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = tensor("input_151_cast_fp16")]; - tensor input_153_pad_type_0 = const()[name = tensor("input_153_pad_type_0"), val = tensor("valid")]; - tensor input_153_groups_0 = const()[name = tensor("input_153_groups_0"), val = tensor(1024)]; - tensor input_153_strides_0 = const()[name = tensor("input_153_strides_0"), val = tensor([1])]; - tensor input_153_pad_0 = const()[name = tensor("input_153_pad_0"), val = tensor([0, 0])]; - tensor input_153_dilations_0 = const()[name = tensor("input_153_dilations_0"), val = tensor([1])]; - tensor const_252_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51409920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51416896))), name = tensor("const_252_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_253_to_fp16 = const()[name = tensor("const_253_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51417088)))]; - tensor input_155_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_252_to_fp16_palettized, x = input_151_cast_fp16)[name = tensor("input_155_cast_fp16")]; - tensor input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = tensor("input_157_cast_fp16")]; - tensor x_65_pad_type_0 = const()[name = tensor("x_65_pad_type_0"), val = tensor("valid")]; - tensor x_65_strides_0 = const()[name = tensor("x_65_strides_0"), val = tensor([1])]; - tensor x_65_pad_0 = const()[name = tensor("x_65_pad_0"), val = tensor([0, 0])]; - tensor x_65_dilations_0 = const()[name = tensor("x_65_dilations_0"), val = tensor([1])]; - tensor x_65_groups_0 = const()[name = tensor("x_65_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51419200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52205696))), name = tensor("module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_65_cast_fp16 = conv(dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = tensor("x_65_cast_fp16")]; - tensor input_159_perm_0 = const()[name = tensor("input_159_perm_0"), val = tensor([0, 2, 1])]; - tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = tensor("transpose_292")]; - tensor input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = tensor("input_161_cast_fp16")]; - tensor input_163_axes_0 = const()[name = tensor("input_163_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52205888)))]; - tensor module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52208000)))]; - tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("input_163_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52210112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55355904))), name = tensor("module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_26_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized, x = input_163_cast_fp16)[name = tensor("linear_26_cast_fp16")]; - tensor input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = tensor("input_167_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55356096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58501888))), name = tensor("module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_27_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("linear_27_cast_fp16")]; - tensor var_692_to_fp16 = const()[name = tensor("op_692_to_fp16"), val = tensor(0x1p-1)]; - tensor var_693_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_692_to_fp16)[name = tensor("op_693_cast_fp16")]; - tensor input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_693_cast_fp16)[name = tensor("input_173_cast_fp16")]; - tensor input_175_axes_0 = const()[name = tensor("input_175_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58502080)))]; - tensor module_layers_2_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58504192)))]; - tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = tensor("input_175_cast_fp16")]; - tensor input_177_axes_0 = const()[name = tensor("input_177_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58506304)))]; - tensor module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58508416)))]; - tensor input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor("input_177_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58510528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61656320))), name = tensor("module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_28_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized, x = input_177_cast_fp16)[name = tensor("linear_28_cast_fp16")]; - tensor input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = tensor("input_181_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61656512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64802304))), name = tensor("module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_29_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = tensor("linear_29_cast_fp16")]; - tensor var_721_to_fp16 = const()[name = tensor("op_721_to_fp16"), val = tensor(0x1p-1)]; - tensor var_722_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_721_to_fp16)[name = tensor("op_722_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_722_cast_fp16)[name = tensor("input_187_cast_fp16")]; - tensor query_7_axes_0 = const()[name = tensor("query_7_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64802496)))]; - tensor module_layers_3_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64804608)))]; - tensor query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = tensor("query_7_cast_fp16")]; - tensor module_layers_3_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64806720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65593216))), name = tensor("module_layers_3_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_30_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = tensor("linear_30_cast_fp16")]; - tensor var_738 = const()[name = tensor("op_738"), val = tensor([1, -1, 8, 128])]; - tensor q_19_cast_fp16 = reshape(shape = var_738, x = linear_30_cast_fp16)[name = tensor("q_19_cast_fp16")]; - tensor module_layers_3_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65593408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66379904))), name = tensor("module_layers_3_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_31_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = tensor("linear_31_cast_fp16")]; - tensor var_742 = const()[name = tensor("op_742"), val = tensor([1, -1, 8, 128])]; - tensor k_13_cast_fp16 = reshape(shape = var_742, x = linear_31_cast_fp16)[name = tensor("k_13_cast_fp16")]; - tensor module_layers_3_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66380096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67166592))), name = tensor("module_layers_3_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_32_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = tensor("linear_32_cast_fp16")]; - tensor var_746 = const()[name = tensor("op_746"), val = tensor([1, -1, 8, 128])]; - tensor v_7_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = tensor("v_7_cast_fp16")]; - tensor value_9_perm_0 = const()[name = tensor("value_9_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67166784)))]; - tensor var_758_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = tensor("op_758_cast_fp16")]; - tensor module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67168896)))]; - tensor var_760_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = tensor("op_760_cast_fp16")]; - tensor q_with_bias_v_7_perm_0 = const()[name = tensor("q_with_bias_v_7_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_73_transpose_x_0 = const()[name = tensor("x_73_transpose_x_0"), val = tensor(false)]; - tensor x_73_transpose_y_0 = const()[name = tensor("x_73_transpose_y_0"), val = tensor(false)]; - tensor op_762_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67171008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67459072))), name = tensor("op_762_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_760_cast_fp16)[name = tensor("transpose_291")]; - tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_762_to_fp16_palettized)[name = tensor("x_73_cast_fp16")]; - tensor x_75_pad_0 = const()[name = tensor("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_75_mode_0 = const()[name = tensor("x_75_mode_0"), val = tensor("constant")]; - tensor const_44_to_fp16 = const()[name = tensor("const_44_to_fp16"), val = tensor(0x0p+0)]; - tensor x_75_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = tensor("x_75_cast_fp16")]; - tensor var_770 = const()[name = tensor("op_770"), val = tensor([1, 8, -1, 188])]; - tensor x_77_cast_fp16 = reshape(shape = var_770, x = x_75_cast_fp16)[name = tensor("x_77_cast_fp16")]; - tensor var_774_begin_0 = const()[name = tensor("op_774_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_774_end_0 = const()[name = tensor("op_774_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_774_end_mask_0 = const()[name = tensor("op_774_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_774_cast_fp16 = slice_by_index(begin = var_774_begin_0, end = var_774_end_0, end_mask = var_774_end_mask_0, x = x_77_cast_fp16)[name = tensor("op_774_cast_fp16")]; - tensor var_775 = const()[name = tensor("op_775"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_13_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = tensor("matrix_bd_13_cast_fp16")]; - tensor matrix_ac_7_transpose_x_0 = const()[name = tensor("matrix_ac_7_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_7_transpose_y_0 = const()[name = tensor("matrix_ac_7_transpose_y_0"), val = tensor(false)]; - tensor transpose_102_perm_0 = const()[name = tensor("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_103_perm_0 = const()[name = tensor("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = tensor("transpose_289")]; - tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_758_cast_fp16)[name = tensor("transpose_290")]; - tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = tensor("matrix_ac_7_cast_fp16")]; - tensor matrix_bd_15_begin_0 = const()[name = tensor("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_15_end_0 = const()[name = tensor("matrix_bd_15_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_15_end_mask_0 = const()[name = tensor("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = tensor("matrix_bd_15_cast_fp16")]; - tensor var_784_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = tensor("op_784_cast_fp16")]; - tensor _inversed_scores_13_y_0_to_fp16 = const()[name = tensor("_inversed_scores_13_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_13_cast_fp16 = mul(x = var_784_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = tensor("_inversed_scores_13_cast_fp16")]; - tensor scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_3)[name = tensor("scores_15_cast_fp16")]; - tensor var_790_cast_fp16 = softmax(axis = var_30, x = scores_15_cast_fp16)[name = tensor("op_790_cast_fp16")]; - tensor input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_790_cast_fp16, cond = mask_3)[name = tensor("input_189_cast_fp16")]; - tensor x_79_transpose_x_0 = const()[name = tensor("x_79_transpose_x_0"), val = tensor(false)]; - tensor x_79_transpose_y_0 = const()[name = tensor("x_79_transpose_y_0"), val = tensor(false)]; - tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = tensor("transpose_288")]; - tensor x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_9_cast_fp16)[name = tensor("x_79_cast_fp16")]; - tensor var_794_perm_0 = const()[name = tensor("op_794_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_795 = const()[name = tensor("op_795"), val = tensor([1, -1, 1024])]; - tensor var_794_cast_fp16 = transpose(perm = var_794_perm_0, x = x_79_cast_fp16)[name = tensor("transpose_287")]; - tensor input_191_cast_fp16 = reshape(shape = var_795, x = var_794_cast_fp16)[name = tensor("input_191_cast_fp16")]; - tensor module_layers_3_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67459264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68245760))), name = tensor("module_layers_3_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_34_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16_palettized, x = input_191_cast_fp16)[name = tensor("linear_34_cast_fp16")]; - tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = tensor("input_195_cast_fp16")]; - tensor x_83_axes_0 = const()[name = tensor("x_83_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68245952)))]; - tensor module_layers_3_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68248064)))]; - tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = tensor("x_83_cast_fp16")]; - tensor input_197_perm_0 = const()[name = tensor("input_197_perm_0"), val = tensor([0, 2, 1])]; - tensor input_199_pad_type_0 = const()[name = tensor("input_199_pad_type_0"), val = tensor("valid")]; - tensor input_199_strides_0 = const()[name = tensor("input_199_strides_0"), val = tensor([1])]; - tensor input_199_pad_0 = const()[name = tensor("input_199_pad_0"), val = tensor([0, 0])]; - tensor input_199_dilations_0 = const()[name = tensor("input_199_dilations_0"), val = tensor([1])]; - tensor input_199_groups_0 = const()[name = tensor("input_199_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68250176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69823104))), name = tensor("module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = tensor("transpose_286")]; - tensor input_199_cast_fp16 = conv(dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_197_cast_fp16)[name = tensor("input_199_cast_fp16")]; - tensor x_85_split_num_splits_0 = const()[name = tensor("x_85_split_num_splits_0"), val = tensor(2)]; - tensor x_85_split_axis_0 = const()[name = tensor("x_85_split_axis_0"), val = tensor(1)]; - tensor x_85_split_cast_fp16_0, tensor x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = tensor("x_85_split_cast_fp16")]; - tensor x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = tensor("x_85_split_1_sigmoid_cast_fp16")]; - tensor x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = tensor("x_85_cast_fp16")]; - tensor input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_328)[name = tensor("input_201_cast_fp16")]; - tensor input_203_pad_0 = const()[name = tensor("input_203_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_203_mode_0 = const()[name = tensor("input_203_mode_0"), val = tensor("constant")]; - tensor const_47_to_fp16 = const()[name = tensor("const_47_to_fp16"), val = tensor(0x0p+0)]; - tensor input_203_cast_fp16 = pad(constant_val = const_47_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = tensor("input_203_cast_fp16")]; - tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("valid")]; - tensor input_205_groups_0 = const()[name = tensor("input_205_groups_0"), val = tensor(1024)]; - tensor input_205_strides_0 = const()[name = tensor("input_205_strides_0"), val = tensor([1])]; - tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([0, 0])]; - tensor input_205_dilations_0 = const()[name = tensor("input_205_dilations_0"), val = tensor([1])]; - tensor const_254_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69823296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69830272))), name = tensor("const_254_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_255_to_fp16 = const()[name = tensor("const_255_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69830464)))]; - tensor input_207_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_254_to_fp16_palettized, x = input_203_cast_fp16)[name = tensor("input_207_cast_fp16")]; - tensor input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = tensor("input_209_cast_fp16")]; - tensor x_87_pad_type_0 = const()[name = tensor("x_87_pad_type_0"), val = tensor("valid")]; - tensor x_87_strides_0 = const()[name = tensor("x_87_strides_0"), val = tensor([1])]; - tensor x_87_pad_0 = const()[name = tensor("x_87_pad_0"), val = tensor([0, 0])]; - tensor x_87_dilations_0 = const()[name = tensor("x_87_dilations_0"), val = tensor([1])]; - tensor x_87_groups_0 = const()[name = tensor("x_87_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69832576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70619072))), name = tensor("module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_87_cast_fp16 = conv(dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_209_cast_fp16)[name = tensor("x_87_cast_fp16")]; - tensor input_211_perm_0 = const()[name = tensor("input_211_perm_0"), val = tensor([0, 2, 1])]; - tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = tensor("transpose_285")]; - tensor input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = tensor("input_213_cast_fp16")]; - tensor input_215_axes_0 = const()[name = tensor("input_215_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70619264)))]; - tensor module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70621376)))]; - tensor input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = tensor("input_215_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70623488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73769280))), name = tensor("module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_35_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized, x = input_215_cast_fp16)[name = tensor("linear_35_cast_fp16")]; - tensor input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = tensor("input_219_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73769472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76915264))), name = tensor("module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_36_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized, x = input_219_cast_fp16)[name = tensor("linear_36_cast_fp16")]; - tensor var_855_to_fp16 = const()[name = tensor("op_855_to_fp16"), val = tensor(0x1p-1)]; - tensor var_856_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_855_to_fp16)[name = tensor("op_856_cast_fp16")]; - tensor input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_856_cast_fp16)[name = tensor("input_225_cast_fp16")]; - tensor input_227_axes_0 = const()[name = tensor("input_227_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76915456)))]; - tensor module_layers_3_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76917568)))]; - tensor input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = tensor("input_227_cast_fp16")]; - tensor input_229_axes_0 = const()[name = tensor("input_229_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76919680)))]; - tensor module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76921792)))]; - tensor input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = tensor("input_229_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76923904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80069696))), name = tensor("module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_37_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = tensor("linear_37_cast_fp16")]; - tensor input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = tensor("input_233_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80069888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83215680))), name = tensor("module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_38_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized, x = input_233_cast_fp16)[name = tensor("linear_38_cast_fp16")]; - tensor var_884_to_fp16 = const()[name = tensor("op_884_to_fp16"), val = tensor(0x1p-1)]; - tensor var_885_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_884_to_fp16)[name = tensor("op_885_cast_fp16")]; - tensor input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_885_cast_fp16)[name = tensor("input_239_cast_fp16")]; - tensor query_9_axes_0 = const()[name = tensor("query_9_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83215872)))]; - tensor module_layers_4_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83217984)))]; - tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = tensor("query_9_cast_fp16")]; - tensor module_layers_4_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83220096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84006592))), name = tensor("module_layers_4_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_39_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = tensor("linear_39_cast_fp16")]; - tensor var_901 = const()[name = tensor("op_901"), val = tensor([1, -1, 8, 128])]; - tensor q_25_cast_fp16 = reshape(shape = var_901, x = linear_39_cast_fp16)[name = tensor("q_25_cast_fp16")]; - tensor module_layers_4_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84006784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84793280))), name = tensor("module_layers_4_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_40_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = tensor("linear_40_cast_fp16")]; - tensor var_905 = const()[name = tensor("op_905"), val = tensor([1, -1, 8, 128])]; - tensor k_17_cast_fp16 = reshape(shape = var_905, x = linear_40_cast_fp16)[name = tensor("k_17_cast_fp16")]; - tensor module_layers_4_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84793472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85579968))), name = tensor("module_layers_4_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_41_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = tensor("linear_41_cast_fp16")]; - tensor var_909 = const()[name = tensor("op_909"), val = tensor([1, -1, 8, 128])]; - tensor v_9_cast_fp16 = reshape(shape = var_909, x = linear_41_cast_fp16)[name = tensor("v_9_cast_fp16")]; - tensor value_11_perm_0 = const()[name = tensor("value_11_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85580160)))]; - tensor var_921_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = tensor("op_921_cast_fp16")]; - tensor module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85582272)))]; - tensor var_923_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = tensor("op_923_cast_fp16")]; - tensor q_with_bias_v_9_perm_0 = const()[name = tensor("q_with_bias_v_9_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_95_transpose_x_0 = const()[name = tensor("x_95_transpose_x_0"), val = tensor(false)]; - tensor x_95_transpose_y_0 = const()[name = tensor("x_95_transpose_y_0"), val = tensor(false)]; - tensor op_925_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85584384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85872448))), name = tensor("op_925_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_923_cast_fp16)[name = tensor("transpose_284")]; - tensor x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_925_to_fp16_palettized)[name = tensor("x_95_cast_fp16")]; - tensor x_97_pad_0 = const()[name = tensor("x_97_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_97_mode_0 = const()[name = tensor("x_97_mode_0"), val = tensor("constant")]; - tensor const_54_to_fp16 = const()[name = tensor("const_54_to_fp16"), val = tensor(0x0p+0)]; - tensor x_97_cast_fp16 = pad(constant_val = const_54_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = tensor("x_97_cast_fp16")]; - tensor var_933 = const()[name = tensor("op_933"), val = tensor([1, 8, -1, 188])]; - tensor x_99_cast_fp16 = reshape(shape = var_933, x = x_97_cast_fp16)[name = tensor("x_99_cast_fp16")]; - tensor var_937_begin_0 = const()[name = tensor("op_937_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_937_end_0 = const()[name = tensor("op_937_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_937_end_mask_0 = const()[name = tensor("op_937_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_937_cast_fp16 = slice_by_index(begin = var_937_begin_0, end = var_937_end_0, end_mask = var_937_end_mask_0, x = x_99_cast_fp16)[name = tensor("op_937_cast_fp16")]; - tensor var_938 = const()[name = tensor("op_938"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_17_cast_fp16 = reshape(shape = var_938, x = var_937_cast_fp16)[name = tensor("matrix_bd_17_cast_fp16")]; - tensor matrix_ac_9_transpose_x_0 = const()[name = tensor("matrix_ac_9_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_9_transpose_y_0 = const()[name = tensor("matrix_ac_9_transpose_y_0"), val = tensor(false)]; - tensor transpose_104_perm_0 = const()[name = tensor("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_105_perm_0 = const()[name = tensor("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = tensor("transpose_282")]; - tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = var_921_cast_fp16)[name = tensor("transpose_283")]; - tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = tensor("matrix_ac_9_cast_fp16")]; - tensor matrix_bd_19_begin_0 = const()[name = tensor("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_19_end_0 = const()[name = tensor("matrix_bd_19_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_19_end_mask_0 = const()[name = tensor("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = tensor("matrix_bd_19_cast_fp16")]; - tensor var_947_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = tensor("op_947_cast_fp16")]; - tensor _inversed_scores_17_y_0_to_fp16 = const()[name = tensor("_inversed_scores_17_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_17_cast_fp16 = mul(x = var_947_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = tensor("_inversed_scores_17_cast_fp16")]; - tensor scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_3)[name = tensor("scores_19_cast_fp16")]; - tensor var_953_cast_fp16 = softmax(axis = var_30, x = scores_19_cast_fp16)[name = tensor("op_953_cast_fp16")]; - tensor input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_953_cast_fp16, cond = mask_3)[name = tensor("input_241_cast_fp16")]; - tensor x_101_transpose_x_0 = const()[name = tensor("x_101_transpose_x_0"), val = tensor(false)]; - tensor x_101_transpose_y_0 = const()[name = tensor("x_101_transpose_y_0"), val = tensor(false)]; - tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = tensor("transpose_281")]; - tensor x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_11_cast_fp16)[name = tensor("x_101_cast_fp16")]; - tensor var_957_perm_0 = const()[name = tensor("op_957_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_958 = const()[name = tensor("op_958"), val = tensor([1, -1, 1024])]; - tensor var_957_cast_fp16 = transpose(perm = var_957_perm_0, x = x_101_cast_fp16)[name = tensor("transpose_280")]; - tensor input_243_cast_fp16 = reshape(shape = var_958, x = var_957_cast_fp16)[name = tensor("input_243_cast_fp16")]; - tensor module_layers_4_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85872640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86659136))), name = tensor("module_layers_4_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_43_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16_palettized, x = input_243_cast_fp16)[name = tensor("linear_43_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = tensor("input_247_cast_fp16")]; - tensor x_105_axes_0 = const()[name = tensor("x_105_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86659328)))]; - tensor module_layers_4_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86661440)))]; - tensor x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = tensor("x_105_cast_fp16")]; - tensor input_249_perm_0 = const()[name = tensor("input_249_perm_0"), val = tensor([0, 2, 1])]; - tensor input_251_pad_type_0 = const()[name = tensor("input_251_pad_type_0"), val = tensor("valid")]; - tensor input_251_strides_0 = const()[name = tensor("input_251_strides_0"), val = tensor([1])]; - tensor input_251_pad_0 = const()[name = tensor("input_251_pad_0"), val = tensor([0, 0])]; - tensor input_251_dilations_0 = const()[name = tensor("input_251_dilations_0"), val = tensor([1])]; - tensor input_251_groups_0 = const()[name = tensor("input_251_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86663552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88236480))), name = tensor("module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = tensor("transpose_279")]; - tensor input_251_cast_fp16 = conv(dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_249_cast_fp16)[name = tensor("input_251_cast_fp16")]; - tensor x_107_split_num_splits_0 = const()[name = tensor("x_107_split_num_splits_0"), val = tensor(2)]; - tensor x_107_split_axis_0 = const()[name = tensor("x_107_split_axis_0"), val = tensor(1)]; - tensor x_107_split_cast_fp16_0, tensor x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = tensor("x_107_split_cast_fp16")]; - tensor x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = tensor("x_107_split_1_sigmoid_cast_fp16")]; - tensor x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = tensor("x_107_cast_fp16")]; - tensor input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_328)[name = tensor("input_253_cast_fp16")]; - tensor input_255_pad_0 = const()[name = tensor("input_255_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_255_mode_0 = const()[name = tensor("input_255_mode_0"), val = tensor("constant")]; - tensor const_57_to_fp16 = const()[name = tensor("const_57_to_fp16"), val = tensor(0x0p+0)]; - tensor input_255_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = tensor("input_255_cast_fp16")]; - tensor input_257_pad_type_0 = const()[name = tensor("input_257_pad_type_0"), val = tensor("valid")]; - tensor input_257_groups_0 = const()[name = tensor("input_257_groups_0"), val = tensor(1024)]; - tensor input_257_strides_0 = const()[name = tensor("input_257_strides_0"), val = tensor([1])]; - tensor input_257_pad_0 = const()[name = tensor("input_257_pad_0"), val = tensor([0, 0])]; - tensor input_257_dilations_0 = const()[name = tensor("input_257_dilations_0"), val = tensor([1])]; - tensor const_256_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88236672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88243648))), name = tensor("const_256_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_257_to_fp16 = const()[name = tensor("const_257_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88243840)))]; - tensor input_259_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_256_to_fp16_palettized, x = input_255_cast_fp16)[name = tensor("input_259_cast_fp16")]; - tensor input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = tensor("input_261_cast_fp16")]; - tensor x_109_pad_type_0 = const()[name = tensor("x_109_pad_type_0"), val = tensor("valid")]; - tensor x_109_strides_0 = const()[name = tensor("x_109_strides_0"), val = tensor([1])]; - tensor x_109_pad_0 = const()[name = tensor("x_109_pad_0"), val = tensor([0, 0])]; - tensor x_109_dilations_0 = const()[name = tensor("x_109_dilations_0"), val = tensor([1])]; - tensor x_109_groups_0 = const()[name = tensor("x_109_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88245952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89032448))), name = tensor("module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_109_cast_fp16 = conv(dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_261_cast_fp16)[name = tensor("x_109_cast_fp16")]; - tensor input_263_perm_0 = const()[name = tensor("input_263_perm_0"), val = tensor([0, 2, 1])]; - tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = tensor("transpose_278")]; - tensor input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = tensor("input_265_cast_fp16")]; - tensor input_267_axes_0 = const()[name = tensor("input_267_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89032640)))]; - tensor module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89034752)))]; - tensor input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor("input_267_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89036864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92182656))), name = tensor("module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_44_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = tensor("linear_44_cast_fp16")]; - tensor input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = tensor("input_271_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92182848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95328640))), name = tensor("module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_45_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized, x = input_271_cast_fp16)[name = tensor("linear_45_cast_fp16")]; - tensor var_1018_to_fp16 = const()[name = tensor("op_1018_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1019_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1018_to_fp16)[name = tensor("op_1019_cast_fp16")]; - tensor input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1019_cast_fp16)[name = tensor("input_277_cast_fp16")]; - tensor input_279_axes_0 = const()[name = tensor("input_279_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95328832)))]; - tensor module_layers_4_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95330944)))]; - tensor input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = tensor("input_279_cast_fp16")]; - tensor input_281_axes_0 = const()[name = tensor("input_281_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95333056)))]; - tensor module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95335168)))]; - tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = tensor("input_281_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95337280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98483072))), name = tensor("module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_46_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized, x = input_281_cast_fp16)[name = tensor("linear_46_cast_fp16")]; - tensor input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = tensor("input_285_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98483264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101629056))), name = tensor("module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_47_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized, x = input_285_cast_fp16)[name = tensor("linear_47_cast_fp16")]; - tensor var_1047_to_fp16 = const()[name = tensor("op_1047_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1048_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1047_to_fp16)[name = tensor("op_1048_cast_fp16")]; - tensor input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1048_cast_fp16)[name = tensor("input_291_cast_fp16")]; - tensor query_11_axes_0 = const()[name = tensor("query_11_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101629248)))]; - tensor module_layers_5_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101631360)))]; - tensor query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = tensor("query_11_cast_fp16")]; - tensor module_layers_5_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101633472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102419968))), name = tensor("module_layers_5_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_48_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = tensor("linear_48_cast_fp16")]; - tensor var_1064 = const()[name = tensor("op_1064"), val = tensor([1, -1, 8, 128])]; - tensor q_31_cast_fp16 = reshape(shape = var_1064, x = linear_48_cast_fp16)[name = tensor("q_31_cast_fp16")]; - tensor module_layers_5_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102420160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103206656))), name = tensor("module_layers_5_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_49_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = tensor("linear_49_cast_fp16")]; - tensor var_1068 = const()[name = tensor("op_1068"), val = tensor([1, -1, 8, 128])]; - tensor k_21_cast_fp16 = reshape(shape = var_1068, x = linear_49_cast_fp16)[name = tensor("k_21_cast_fp16")]; - tensor module_layers_5_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103206848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103993344))), name = tensor("module_layers_5_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_50_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = tensor("linear_50_cast_fp16")]; - tensor var_1072 = const()[name = tensor("op_1072"), val = tensor([1, -1, 8, 128])]; - tensor v_11_cast_fp16 = reshape(shape = var_1072, x = linear_50_cast_fp16)[name = tensor("v_11_cast_fp16")]; - tensor value_13_perm_0 = const()[name = tensor("value_13_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103993536)))]; - tensor var_1084_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1084_cast_fp16")]; - tensor module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103995648)))]; - tensor var_1086_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1086_cast_fp16")]; - tensor q_with_bias_v_11_perm_0 = const()[name = tensor("q_with_bias_v_11_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_117_transpose_x_0 = const()[name = tensor("x_117_transpose_x_0"), val = tensor(false)]; - tensor x_117_transpose_y_0 = const()[name = tensor("x_117_transpose_y_0"), val = tensor(false)]; - tensor op_1088_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103997760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104285824))), name = tensor("op_1088_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1086_cast_fp16)[name = tensor("transpose_277")]; - tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = op_1088_to_fp16_palettized)[name = tensor("x_117_cast_fp16")]; - tensor x_119_pad_0 = const()[name = tensor("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_119_mode_0 = const()[name = tensor("x_119_mode_0"), val = tensor("constant")]; - tensor const_64_to_fp16 = const()[name = tensor("const_64_to_fp16"), val = tensor(0x0p+0)]; - tensor x_119_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = tensor("x_119_cast_fp16")]; - tensor var_1096 = const()[name = tensor("op_1096"), val = tensor([1, 8, -1, 188])]; - tensor x_121_cast_fp16 = reshape(shape = var_1096, x = x_119_cast_fp16)[name = tensor("x_121_cast_fp16")]; - tensor var_1100_begin_0 = const()[name = tensor("op_1100_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1100_end_0 = const()[name = tensor("op_1100_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1100_end_mask_0 = const()[name = tensor("op_1100_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1100_cast_fp16 = slice_by_index(begin = var_1100_begin_0, end = var_1100_end_0, end_mask = var_1100_end_mask_0, x = x_121_cast_fp16)[name = tensor("op_1100_cast_fp16")]; - tensor var_1101 = const()[name = tensor("op_1101"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1101, x = var_1100_cast_fp16)[name = tensor("matrix_bd_21_cast_fp16")]; - tensor matrix_ac_11_transpose_x_0 = const()[name = tensor("matrix_ac_11_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_11_transpose_y_0 = const()[name = tensor("matrix_ac_11_transpose_y_0"), val = tensor(false)]; - tensor transpose_106_perm_0 = const()[name = tensor("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_107_perm_0 = const()[name = tensor("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = tensor("transpose_275")]; - tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1084_cast_fp16)[name = tensor("transpose_276")]; - tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = tensor("matrix_ac_11_cast_fp16")]; - tensor matrix_bd_23_begin_0 = const()[name = tensor("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_23_end_0 = const()[name = tensor("matrix_bd_23_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_23_end_mask_0 = const()[name = tensor("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = tensor("matrix_bd_23_cast_fp16")]; - tensor var_1110_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = tensor("op_1110_cast_fp16")]; - tensor _inversed_scores_21_y_0_to_fp16 = const()[name = tensor("_inversed_scores_21_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_21_cast_fp16 = mul(x = var_1110_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = tensor("_inversed_scores_21_cast_fp16")]; - tensor scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_3)[name = tensor("scores_23_cast_fp16")]; - tensor var_1116_cast_fp16 = softmax(axis = var_30, x = scores_23_cast_fp16)[name = tensor("op_1116_cast_fp16")]; - tensor input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1116_cast_fp16, cond = mask_3)[name = tensor("input_293_cast_fp16")]; - tensor x_123_transpose_x_0 = const()[name = tensor("x_123_transpose_x_0"), val = tensor(false)]; - tensor x_123_transpose_y_0 = const()[name = tensor("x_123_transpose_y_0"), val = tensor(false)]; - tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = tensor("transpose_274")]; - tensor x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_13_cast_fp16)[name = tensor("x_123_cast_fp16")]; - tensor var_1120_perm_0 = const()[name = tensor("op_1120_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1121 = const()[name = tensor("op_1121"), val = tensor([1, -1, 1024])]; - tensor var_1120_cast_fp16 = transpose(perm = var_1120_perm_0, x = x_123_cast_fp16)[name = tensor("transpose_273")]; - tensor input_295_cast_fp16 = reshape(shape = var_1121, x = var_1120_cast_fp16)[name = tensor("input_295_cast_fp16")]; - tensor module_layers_5_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104286016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105072512))), name = tensor("module_layers_5_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_52_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = tensor("linear_52_cast_fp16")]; - tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = tensor("input_299_cast_fp16")]; - tensor x_127_axes_0 = const()[name = tensor("x_127_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105072704)))]; - tensor module_layers_5_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105074816)))]; - tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = tensor("x_127_cast_fp16")]; - tensor input_301_perm_0 = const()[name = tensor("input_301_perm_0"), val = tensor([0, 2, 1])]; - tensor input_303_pad_type_0 = const()[name = tensor("input_303_pad_type_0"), val = tensor("valid")]; - tensor input_303_strides_0 = const()[name = tensor("input_303_strides_0"), val = tensor([1])]; - tensor input_303_pad_0 = const()[name = tensor("input_303_pad_0"), val = tensor([0, 0])]; - tensor input_303_dilations_0 = const()[name = tensor("input_303_dilations_0"), val = tensor([1])]; - tensor input_303_groups_0 = const()[name = tensor("input_303_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105076928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106649856))), name = tensor("module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = tensor("transpose_272")]; - tensor input_303_cast_fp16 = conv(dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_301_cast_fp16)[name = tensor("input_303_cast_fp16")]; - tensor x_129_split_num_splits_0 = const()[name = tensor("x_129_split_num_splits_0"), val = tensor(2)]; - tensor x_129_split_axis_0 = const()[name = tensor("x_129_split_axis_0"), val = tensor(1)]; - tensor x_129_split_cast_fp16_0, tensor x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = tensor("x_129_split_cast_fp16")]; - tensor x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = tensor("x_129_split_1_sigmoid_cast_fp16")]; - tensor x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = tensor("x_129_cast_fp16")]; - tensor input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_328)[name = tensor("input_305_cast_fp16")]; - tensor input_307_pad_0 = const()[name = tensor("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_307_mode_0 = const()[name = tensor("input_307_mode_0"), val = tensor("constant")]; - tensor const_67_to_fp16 = const()[name = tensor("const_67_to_fp16"), val = tensor(0x0p+0)]; - tensor input_307_cast_fp16 = pad(constant_val = const_67_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = tensor("input_307_cast_fp16")]; - tensor input_309_pad_type_0 = const()[name = tensor("input_309_pad_type_0"), val = tensor("valid")]; - tensor input_309_groups_0 = const()[name = tensor("input_309_groups_0"), val = tensor(1024)]; - tensor input_309_strides_0 = const()[name = tensor("input_309_strides_0"), val = tensor([1])]; - tensor input_309_pad_0 = const()[name = tensor("input_309_pad_0"), val = tensor([0, 0])]; - tensor input_309_dilations_0 = const()[name = tensor("input_309_dilations_0"), val = tensor([1])]; - tensor const_258_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106650048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106657024))), name = tensor("const_258_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_259_to_fp16 = const()[name = tensor("const_259_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106657216)))]; - tensor input_311_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_258_to_fp16_palettized, x = input_307_cast_fp16)[name = tensor("input_311_cast_fp16")]; - tensor input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = tensor("input_313_cast_fp16")]; - tensor x_131_pad_type_0 = const()[name = tensor("x_131_pad_type_0"), val = tensor("valid")]; - tensor x_131_strides_0 = const()[name = tensor("x_131_strides_0"), val = tensor([1])]; - tensor x_131_pad_0 = const()[name = tensor("x_131_pad_0"), val = tensor([0, 0])]; - tensor x_131_dilations_0 = const()[name = tensor("x_131_dilations_0"), val = tensor([1])]; - tensor x_131_groups_0 = const()[name = tensor("x_131_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106659328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107445824))), name = tensor("module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_131_cast_fp16 = conv(dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_313_cast_fp16)[name = tensor("x_131_cast_fp16")]; - tensor input_315_perm_0 = const()[name = tensor("input_315_perm_0"), val = tensor([0, 2, 1])]; - tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = tensor("transpose_271")]; - tensor input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = tensor("input_317_cast_fp16")]; - tensor input_319_axes_0 = const()[name = tensor("input_319_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107446016)))]; - tensor module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107448128)))]; - tensor input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor("input_319_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107450240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110596032))), name = tensor("module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_53_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized, x = input_319_cast_fp16)[name = tensor("linear_53_cast_fp16")]; - tensor input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = tensor("input_323_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110596224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113742016))), name = tensor("module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_54_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = tensor("linear_54_cast_fp16")]; - tensor var_1181_to_fp16 = const()[name = tensor("op_1181_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1182_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1181_to_fp16)[name = tensor("op_1182_cast_fp16")]; - tensor input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1182_cast_fp16)[name = tensor("input_329_cast_fp16")]; - tensor input_331_axes_0 = const()[name = tensor("input_331_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113742208)))]; - tensor module_layers_5_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113744320)))]; - tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("input_331_cast_fp16")]; - tensor input_333_axes_0 = const()[name = tensor("input_333_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113746432)))]; - tensor module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113748544)))]; - tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("input_333_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113750656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(116896448))), name = tensor("module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_55_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized, x = input_333_cast_fp16)[name = tensor("linear_55_cast_fp16")]; - tensor input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = tensor("input_337_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(116896640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120042432))), name = tensor("module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_56_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized, x = input_337_cast_fp16)[name = tensor("linear_56_cast_fp16")]; - tensor var_1210_to_fp16 = const()[name = tensor("op_1210_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1211_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1210_to_fp16)[name = tensor("op_1211_cast_fp16")]; - tensor input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1211_cast_fp16)[name = tensor("input_343_cast_fp16")]; - tensor query_13_axes_0 = const()[name = tensor("query_13_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120042624)))]; - tensor module_layers_6_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120044736)))]; - tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = tensor("query_13_cast_fp16")]; - tensor module_layers_6_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120046848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120833344))), name = tensor("module_layers_6_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_57_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = tensor("linear_57_cast_fp16")]; - tensor var_1227 = const()[name = tensor("op_1227"), val = tensor([1, -1, 8, 128])]; - tensor q_37_cast_fp16 = reshape(shape = var_1227, x = linear_57_cast_fp16)[name = tensor("q_37_cast_fp16")]; - tensor module_layers_6_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120833536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121620032))), name = tensor("module_layers_6_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_58_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = tensor("linear_58_cast_fp16")]; - tensor var_1231 = const()[name = tensor("op_1231"), val = tensor([1, -1, 8, 128])]; - tensor k_25_cast_fp16 = reshape(shape = var_1231, x = linear_58_cast_fp16)[name = tensor("k_25_cast_fp16")]; - tensor module_layers_6_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121620224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122406720))), name = tensor("module_layers_6_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_59_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = tensor("linear_59_cast_fp16")]; - tensor var_1235 = const()[name = tensor("op_1235"), val = tensor([1, -1, 8, 128])]; - tensor v_13_cast_fp16 = reshape(shape = var_1235, x = linear_59_cast_fp16)[name = tensor("v_13_cast_fp16")]; - tensor value_15_perm_0 = const()[name = tensor("value_15_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122406912)))]; - tensor var_1247_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1247_cast_fp16")]; - tensor module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122409024)))]; - tensor var_1249_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1249_cast_fp16")]; - tensor q_with_bias_v_13_perm_0 = const()[name = tensor("q_with_bias_v_13_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_139_transpose_x_0 = const()[name = tensor("x_139_transpose_x_0"), val = tensor(false)]; - tensor x_139_transpose_y_0 = const()[name = tensor("x_139_transpose_y_0"), val = tensor(false)]; - tensor op_1251_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122411136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122699200))), name = tensor("op_1251_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1249_cast_fp16)[name = tensor("transpose_270")]; - tensor x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1251_to_fp16_palettized)[name = tensor("x_139_cast_fp16")]; - tensor x_141_pad_0 = const()[name = tensor("x_141_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_141_mode_0 = const()[name = tensor("x_141_mode_0"), val = tensor("constant")]; - tensor const_74_to_fp16 = const()[name = tensor("const_74_to_fp16"), val = tensor(0x0p+0)]; - tensor x_141_cast_fp16 = pad(constant_val = const_74_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = tensor("x_141_cast_fp16")]; - tensor var_1259 = const()[name = tensor("op_1259"), val = tensor([1, 8, -1, 188])]; - tensor x_143_cast_fp16 = reshape(shape = var_1259, x = x_141_cast_fp16)[name = tensor("x_143_cast_fp16")]; - tensor var_1263_begin_0 = const()[name = tensor("op_1263_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1263_end_0 = const()[name = tensor("op_1263_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1263_end_mask_0 = const()[name = tensor("op_1263_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1263_cast_fp16 = slice_by_index(begin = var_1263_begin_0, end = var_1263_end_0, end_mask = var_1263_end_mask_0, x = x_143_cast_fp16)[name = tensor("op_1263_cast_fp16")]; - tensor var_1264 = const()[name = tensor("op_1264"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1264, x = var_1263_cast_fp16)[name = tensor("matrix_bd_25_cast_fp16")]; - tensor matrix_ac_13_transpose_x_0 = const()[name = tensor("matrix_ac_13_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_13_transpose_y_0 = const()[name = tensor("matrix_ac_13_transpose_y_0"), val = tensor(false)]; - tensor transpose_108_perm_0 = const()[name = tensor("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_109_perm_0 = const()[name = tensor("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = tensor("transpose_268")]; - tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1247_cast_fp16)[name = tensor("transpose_269")]; - tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = tensor("matrix_ac_13_cast_fp16")]; - tensor matrix_bd_27_begin_0 = const()[name = tensor("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_27_end_0 = const()[name = tensor("matrix_bd_27_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_27_end_mask_0 = const()[name = tensor("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = tensor("matrix_bd_27_cast_fp16")]; - tensor var_1273_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = tensor("op_1273_cast_fp16")]; - tensor _inversed_scores_25_y_0_to_fp16 = const()[name = tensor("_inversed_scores_25_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_25_cast_fp16 = mul(x = var_1273_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = tensor("_inversed_scores_25_cast_fp16")]; - tensor scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_3)[name = tensor("scores_27_cast_fp16")]; - tensor var_1279_cast_fp16 = softmax(axis = var_30, x = scores_27_cast_fp16)[name = tensor("op_1279_cast_fp16")]; - tensor input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1279_cast_fp16, cond = mask_3)[name = tensor("input_345_cast_fp16")]; - tensor x_145_transpose_x_0 = const()[name = tensor("x_145_transpose_x_0"), val = tensor(false)]; - tensor x_145_transpose_y_0 = const()[name = tensor("x_145_transpose_y_0"), val = tensor(false)]; - tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = tensor("transpose_267")]; - tensor x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_15_cast_fp16)[name = tensor("x_145_cast_fp16")]; - tensor var_1283_perm_0 = const()[name = tensor("op_1283_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([1, -1, 1024])]; - tensor var_1283_cast_fp16 = transpose(perm = var_1283_perm_0, x = x_145_cast_fp16)[name = tensor("transpose_266")]; - tensor input_347_cast_fp16 = reshape(shape = var_1284, x = var_1283_cast_fp16)[name = tensor("input_347_cast_fp16")]; - tensor module_layers_6_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122699392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123485888))), name = tensor("module_layers_6_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_61_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16_palettized, x = input_347_cast_fp16)[name = tensor("linear_61_cast_fp16")]; - tensor input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = tensor("input_351_cast_fp16")]; - tensor x_149_axes_0 = const()[name = tensor("x_149_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123486080)))]; - tensor module_layers_6_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123488192)))]; - tensor x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = tensor("x_149_cast_fp16")]; - tensor input_353_perm_0 = const()[name = tensor("input_353_perm_0"), val = tensor([0, 2, 1])]; - tensor input_355_pad_type_0 = const()[name = tensor("input_355_pad_type_0"), val = tensor("valid")]; - tensor input_355_strides_0 = const()[name = tensor("input_355_strides_0"), val = tensor([1])]; - tensor input_355_pad_0 = const()[name = tensor("input_355_pad_0"), val = tensor([0, 0])]; - tensor input_355_dilations_0 = const()[name = tensor("input_355_dilations_0"), val = tensor([1])]; - tensor input_355_groups_0 = const()[name = tensor("input_355_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123490304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125063232))), name = tensor("module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = tensor("transpose_265")]; - tensor input_355_cast_fp16 = conv(dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = tensor("input_355_cast_fp16")]; - tensor x_151_split_num_splits_0 = const()[name = tensor("x_151_split_num_splits_0"), val = tensor(2)]; - tensor x_151_split_axis_0 = const()[name = tensor("x_151_split_axis_0"), val = tensor(1)]; - tensor x_151_split_cast_fp16_0, tensor x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = tensor("x_151_split_cast_fp16")]; - tensor x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = tensor("x_151_split_1_sigmoid_cast_fp16")]; - tensor x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = tensor("x_151_cast_fp16")]; - tensor input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_328)[name = tensor("input_357_cast_fp16")]; - tensor input_359_pad_0 = const()[name = tensor("input_359_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_359_mode_0 = const()[name = tensor("input_359_mode_0"), val = tensor("constant")]; - tensor const_77_to_fp16 = const()[name = tensor("const_77_to_fp16"), val = tensor(0x0p+0)]; - tensor input_359_cast_fp16 = pad(constant_val = const_77_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = tensor("input_359_cast_fp16")]; - tensor input_361_pad_type_0 = const()[name = tensor("input_361_pad_type_0"), val = tensor("valid")]; - tensor input_361_groups_0 = const()[name = tensor("input_361_groups_0"), val = tensor(1024)]; - tensor input_361_strides_0 = const()[name = tensor("input_361_strides_0"), val = tensor([1])]; - tensor input_361_pad_0 = const()[name = tensor("input_361_pad_0"), val = tensor([0, 0])]; - tensor input_361_dilations_0 = const()[name = tensor("input_361_dilations_0"), val = tensor([1])]; - tensor const_260_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125063424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125070400))), name = tensor("const_260_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_261_to_fp16 = const()[name = tensor("const_261_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125070592)))]; - tensor input_363_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_260_to_fp16_palettized, x = input_359_cast_fp16)[name = tensor("input_363_cast_fp16")]; - tensor input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; - tensor x_153_pad_type_0 = const()[name = tensor("x_153_pad_type_0"), val = tensor("valid")]; - tensor x_153_strides_0 = const()[name = tensor("x_153_strides_0"), val = tensor([1])]; - tensor x_153_pad_0 = const()[name = tensor("x_153_pad_0"), val = tensor([0, 0])]; - tensor x_153_dilations_0 = const()[name = tensor("x_153_dilations_0"), val = tensor([1])]; - tensor x_153_groups_0 = const()[name = tensor("x_153_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125072704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125859200))), name = tensor("module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_153_cast_fp16 = conv(dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_365_cast_fp16)[name = tensor("x_153_cast_fp16")]; - tensor input_367_perm_0 = const()[name = tensor("input_367_perm_0"), val = tensor([0, 2, 1])]; - tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = tensor("transpose_264")]; - tensor input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = tensor("input_369_cast_fp16")]; - tensor input_371_axes_0 = const()[name = tensor("input_371_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125859392)))]; - tensor module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125861504)))]; - tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor("input_371_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125863616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129009408))), name = tensor("module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_62_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized, x = input_371_cast_fp16)[name = tensor("linear_62_cast_fp16")]; - tensor input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = tensor("input_375_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129009600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132155392))), name = tensor("module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_63_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized, x = input_375_cast_fp16)[name = tensor("linear_63_cast_fp16")]; - tensor var_1344_to_fp16 = const()[name = tensor("op_1344_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1345_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1344_to_fp16)[name = tensor("op_1345_cast_fp16")]; - tensor input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1345_cast_fp16)[name = tensor("input_381_cast_fp16")]; - tensor input_383_axes_0 = const()[name = tensor("input_383_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132155584)))]; - tensor module_layers_6_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132157696)))]; - tensor input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = tensor("input_383_cast_fp16")]; - tensor input_385_axes_0 = const()[name = tensor("input_385_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132159808)))]; - tensor module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132161920)))]; - tensor input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = tensor("input_385_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132164032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135309824))), name = tensor("module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_64_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized, x = input_385_cast_fp16)[name = tensor("linear_64_cast_fp16")]; - tensor input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = tensor("input_389_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135310016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138455808))), name = tensor("module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_65_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = tensor("linear_65_cast_fp16")]; - tensor var_1373_to_fp16 = const()[name = tensor("op_1373_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1374_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1373_to_fp16)[name = tensor("op_1374_cast_fp16")]; - tensor input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1374_cast_fp16)[name = tensor("input_395_cast_fp16")]; - tensor query_15_axes_0 = const()[name = tensor("query_15_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138456000)))]; - tensor module_layers_7_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138458112)))]; - tensor query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = tensor("query_15_cast_fp16")]; - tensor module_layers_7_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138460224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139246720))), name = tensor("module_layers_7_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_66_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = tensor("linear_66_cast_fp16")]; - tensor var_1390 = const()[name = tensor("op_1390"), val = tensor([1, -1, 8, 128])]; - tensor q_43_cast_fp16 = reshape(shape = var_1390, x = linear_66_cast_fp16)[name = tensor("q_43_cast_fp16")]; - tensor module_layers_7_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139246912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140033408))), name = tensor("module_layers_7_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_67_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = tensor("linear_67_cast_fp16")]; - tensor var_1394 = const()[name = tensor("op_1394"), val = tensor([1, -1, 8, 128])]; - tensor k_29_cast_fp16 = reshape(shape = var_1394, x = linear_67_cast_fp16)[name = tensor("k_29_cast_fp16")]; - tensor module_layers_7_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140033600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140820096))), name = tensor("module_layers_7_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_68_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = tensor("linear_68_cast_fp16")]; - tensor var_1398 = const()[name = tensor("op_1398"), val = tensor([1, -1, 8, 128])]; - tensor v_15_cast_fp16 = reshape(shape = var_1398, x = linear_68_cast_fp16)[name = tensor("v_15_cast_fp16")]; - tensor value_17_perm_0 = const()[name = tensor("value_17_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140820288)))]; - tensor var_1410_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1410_cast_fp16")]; - tensor module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140822400)))]; - tensor var_1412_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1412_cast_fp16")]; - tensor q_with_bias_v_15_perm_0 = const()[name = tensor("q_with_bias_v_15_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_161_transpose_x_0 = const()[name = tensor("x_161_transpose_x_0"), val = tensor(false)]; - tensor x_161_transpose_y_0 = const()[name = tensor("x_161_transpose_y_0"), val = tensor(false)]; - tensor op_1414_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140824512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141112576))), name = tensor("op_1414_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1412_cast_fp16)[name = tensor("transpose_263")]; - tensor x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_1414_to_fp16_palettized)[name = tensor("x_161_cast_fp16")]; - tensor x_163_pad_0 = const()[name = tensor("x_163_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_163_mode_0 = const()[name = tensor("x_163_mode_0"), val = tensor("constant")]; - tensor const_84_to_fp16 = const()[name = tensor("const_84_to_fp16"), val = tensor(0x0p+0)]; - tensor x_163_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = tensor("x_163_cast_fp16")]; - tensor var_1422 = const()[name = tensor("op_1422"), val = tensor([1, 8, -1, 188])]; - tensor x_165_cast_fp16 = reshape(shape = var_1422, x = x_163_cast_fp16)[name = tensor("x_165_cast_fp16")]; - tensor var_1426_begin_0 = const()[name = tensor("op_1426_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1426_end_0 = const()[name = tensor("op_1426_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1426_end_mask_0 = const()[name = tensor("op_1426_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1426_cast_fp16 = slice_by_index(begin = var_1426_begin_0, end = var_1426_end_0, end_mask = var_1426_end_mask_0, x = x_165_cast_fp16)[name = tensor("op_1426_cast_fp16")]; - tensor var_1427 = const()[name = tensor("op_1427"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1427, x = var_1426_cast_fp16)[name = tensor("matrix_bd_29_cast_fp16")]; - tensor matrix_ac_15_transpose_x_0 = const()[name = tensor("matrix_ac_15_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_15_transpose_y_0 = const()[name = tensor("matrix_ac_15_transpose_y_0"), val = tensor(false)]; - tensor transpose_110_perm_0 = const()[name = tensor("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_111_perm_0 = const()[name = tensor("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = tensor("transpose_261")]; - tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1410_cast_fp16)[name = tensor("transpose_262")]; - tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = tensor("matrix_ac_15_cast_fp16")]; - tensor matrix_bd_31_begin_0 = const()[name = tensor("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_31_end_0 = const()[name = tensor("matrix_bd_31_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_31_end_mask_0 = const()[name = tensor("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = tensor("matrix_bd_31_cast_fp16")]; - tensor var_1436_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = tensor("op_1436_cast_fp16")]; - tensor _inversed_scores_29_y_0_to_fp16 = const()[name = tensor("_inversed_scores_29_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_29_cast_fp16 = mul(x = var_1436_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = tensor("_inversed_scores_29_cast_fp16")]; - tensor scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_3)[name = tensor("scores_31_cast_fp16")]; - tensor var_1442_cast_fp16 = softmax(axis = var_30, x = scores_31_cast_fp16)[name = tensor("op_1442_cast_fp16")]; - tensor input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1442_cast_fp16, cond = mask_3)[name = tensor("input_397_cast_fp16")]; - tensor x_167_transpose_x_0 = const()[name = tensor("x_167_transpose_x_0"), val = tensor(false)]; - tensor x_167_transpose_y_0 = const()[name = tensor("x_167_transpose_y_0"), val = tensor(false)]; - tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = tensor("transpose_260")]; - tensor x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_17_cast_fp16)[name = tensor("x_167_cast_fp16")]; - tensor var_1446_perm_0 = const()[name = tensor("op_1446_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1447 = const()[name = tensor("op_1447"), val = tensor([1, -1, 1024])]; - tensor var_1446_cast_fp16 = transpose(perm = var_1446_perm_0, x = x_167_cast_fp16)[name = tensor("transpose_259")]; - tensor input_399_cast_fp16 = reshape(shape = var_1447, x = var_1446_cast_fp16)[name = tensor("input_399_cast_fp16")]; - tensor module_layers_7_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141112768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141899264))), name = tensor("module_layers_7_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_70_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16_palettized, x = input_399_cast_fp16)[name = tensor("linear_70_cast_fp16")]; - tensor input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = tensor("input_403_cast_fp16")]; - tensor x_171_axes_0 = const()[name = tensor("x_171_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141899456)))]; - tensor module_layers_7_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141901568)))]; - tensor x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = tensor("x_171_cast_fp16")]; - tensor input_405_perm_0 = const()[name = tensor("input_405_perm_0"), val = tensor([0, 2, 1])]; - tensor input_407_pad_type_0 = const()[name = tensor("input_407_pad_type_0"), val = tensor("valid")]; - tensor input_407_strides_0 = const()[name = tensor("input_407_strides_0"), val = tensor([1])]; - tensor input_407_pad_0 = const()[name = tensor("input_407_pad_0"), val = tensor([0, 0])]; - tensor input_407_dilations_0 = const()[name = tensor("input_407_dilations_0"), val = tensor([1])]; - tensor input_407_groups_0 = const()[name = tensor("input_407_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141903680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143476608))), name = tensor("module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = tensor("transpose_258")]; - tensor input_407_cast_fp16 = conv(dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = tensor("input_407_cast_fp16")]; - tensor x_173_split_num_splits_0 = const()[name = tensor("x_173_split_num_splits_0"), val = tensor(2)]; - tensor x_173_split_axis_0 = const()[name = tensor("x_173_split_axis_0"), val = tensor(1)]; - tensor x_173_split_cast_fp16_0, tensor x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = tensor("x_173_split_cast_fp16")]; - tensor x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = tensor("x_173_split_1_sigmoid_cast_fp16")]; - tensor x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = tensor("x_173_cast_fp16")]; - tensor input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_328)[name = tensor("input_409_cast_fp16")]; - tensor input_411_pad_0 = const()[name = tensor("input_411_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_411_mode_0 = const()[name = tensor("input_411_mode_0"), val = tensor("constant")]; - tensor const_87_to_fp16 = const()[name = tensor("const_87_to_fp16"), val = tensor(0x0p+0)]; - tensor input_411_cast_fp16 = pad(constant_val = const_87_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = tensor("input_411_cast_fp16")]; - tensor input_413_pad_type_0 = const()[name = tensor("input_413_pad_type_0"), val = tensor("valid")]; - tensor input_413_groups_0 = const()[name = tensor("input_413_groups_0"), val = tensor(1024)]; - tensor input_413_strides_0 = const()[name = tensor("input_413_strides_0"), val = tensor([1])]; - tensor input_413_pad_0 = const()[name = tensor("input_413_pad_0"), val = tensor([0, 0])]; - tensor input_413_dilations_0 = const()[name = tensor("input_413_dilations_0"), val = tensor([1])]; - tensor const_262_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143476800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143483776))), name = tensor("const_262_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_263_to_fp16 = const()[name = tensor("const_263_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143483968)))]; - tensor input_415_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_262_to_fp16_palettized, x = input_411_cast_fp16)[name = tensor("input_415_cast_fp16")]; - tensor input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = tensor("input_417_cast_fp16")]; - tensor x_175_pad_type_0 = const()[name = tensor("x_175_pad_type_0"), val = tensor("valid")]; - tensor x_175_strides_0 = const()[name = tensor("x_175_strides_0"), val = tensor([1])]; - tensor x_175_pad_0 = const()[name = tensor("x_175_pad_0"), val = tensor([0, 0])]; - tensor x_175_dilations_0 = const()[name = tensor("x_175_dilations_0"), val = tensor([1])]; - tensor x_175_groups_0 = const()[name = tensor("x_175_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143486080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144272576))), name = tensor("module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_175_cast_fp16 = conv(dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = tensor("x_175_cast_fp16")]; - tensor input_419_perm_0 = const()[name = tensor("input_419_perm_0"), val = tensor([0, 2, 1])]; - tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = tensor("transpose_257")]; - tensor input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = tensor("input_421_cast_fp16")]; - tensor input_423_axes_0 = const()[name = tensor("input_423_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144272768)))]; - tensor module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144274880)))]; - tensor input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = tensor("input_423_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144276992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147422784))), name = tensor("module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_71_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized, x = input_423_cast_fp16)[name = tensor("linear_71_cast_fp16")]; - tensor input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = tensor("input_427_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147422976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150568768))), name = tensor("module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_72_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized, x = input_427_cast_fp16)[name = tensor("linear_72_cast_fp16")]; - tensor var_1507_to_fp16 = const()[name = tensor("op_1507_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1508_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1507_to_fp16)[name = tensor("op_1508_cast_fp16")]; - tensor input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1508_cast_fp16)[name = tensor("input_433_cast_fp16")]; - tensor input_435_axes_0 = const()[name = tensor("input_435_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150568960)))]; - tensor module_layers_7_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150571072)))]; - tensor input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = tensor("input_435_cast_fp16")]; - tensor input_437_axes_0 = const()[name = tensor("input_437_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150573184)))]; - tensor module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150575296)))]; - tensor input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = tensor("input_437_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150577408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153723200))), name = tensor("module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_73_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized, x = input_437_cast_fp16)[name = tensor("linear_73_cast_fp16")]; - tensor input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = tensor("input_441_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153723392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156869184))), name = tensor("module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_74_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized, x = input_441_cast_fp16)[name = tensor("linear_74_cast_fp16")]; - tensor var_1536_to_fp16 = const()[name = tensor("op_1536_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1537_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1536_to_fp16)[name = tensor("op_1537_cast_fp16")]; - tensor input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1537_cast_fp16)[name = tensor("input_447_cast_fp16")]; - tensor query_17_axes_0 = const()[name = tensor("query_17_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156869376)))]; - tensor module_layers_8_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156871488)))]; - tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = tensor("query_17_cast_fp16")]; - tensor module_layers_8_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156873600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157660096))), name = tensor("module_layers_8_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_75_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = tensor("linear_75_cast_fp16")]; - tensor var_1553 = const()[name = tensor("op_1553"), val = tensor([1, -1, 8, 128])]; - tensor q_49_cast_fp16 = reshape(shape = var_1553, x = linear_75_cast_fp16)[name = tensor("q_49_cast_fp16")]; - tensor module_layers_8_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157660288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158446784))), name = tensor("module_layers_8_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_76_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = tensor("linear_76_cast_fp16")]; - tensor var_1557 = const()[name = tensor("op_1557"), val = tensor([1, -1, 8, 128])]; - tensor k_33_cast_fp16 = reshape(shape = var_1557, x = linear_76_cast_fp16)[name = tensor("k_33_cast_fp16")]; - tensor module_layers_8_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158446976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159233472))), name = tensor("module_layers_8_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_77_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = tensor("linear_77_cast_fp16")]; - tensor var_1561 = const()[name = tensor("op_1561"), val = tensor([1, -1, 8, 128])]; - tensor v_17_cast_fp16 = reshape(shape = var_1561, x = linear_77_cast_fp16)[name = tensor("v_17_cast_fp16")]; - tensor value_19_perm_0 = const()[name = tensor("value_19_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159233664)))]; - tensor var_1573_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1573_cast_fp16")]; - tensor module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159235776)))]; - tensor var_1575_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1575_cast_fp16")]; - tensor q_with_bias_v_17_perm_0 = const()[name = tensor("q_with_bias_v_17_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_183_transpose_x_0 = const()[name = tensor("x_183_transpose_x_0"), val = tensor(false)]; - tensor x_183_transpose_y_0 = const()[name = tensor("x_183_transpose_y_0"), val = tensor(false)]; - tensor op_1577_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159237888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159525952))), name = tensor("op_1577_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1575_cast_fp16)[name = tensor("transpose_256")]; - tensor x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_1577_to_fp16_palettized)[name = tensor("x_183_cast_fp16")]; - tensor x_185_pad_0 = const()[name = tensor("x_185_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_185_mode_0 = const()[name = tensor("x_185_mode_0"), val = tensor("constant")]; - tensor const_94_to_fp16 = const()[name = tensor("const_94_to_fp16"), val = tensor(0x0p+0)]; - tensor x_185_cast_fp16 = pad(constant_val = const_94_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = tensor("x_185_cast_fp16")]; - tensor var_1585 = const()[name = tensor("op_1585"), val = tensor([1, 8, -1, 188])]; - tensor x_187_cast_fp16 = reshape(shape = var_1585, x = x_185_cast_fp16)[name = tensor("x_187_cast_fp16")]; - tensor var_1589_begin_0 = const()[name = tensor("op_1589_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1589_end_0 = const()[name = tensor("op_1589_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1589_end_mask_0 = const()[name = tensor("op_1589_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1589_cast_fp16 = slice_by_index(begin = var_1589_begin_0, end = var_1589_end_0, end_mask = var_1589_end_mask_0, x = x_187_cast_fp16)[name = tensor("op_1589_cast_fp16")]; - tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_33_cast_fp16 = reshape(shape = var_1590, x = var_1589_cast_fp16)[name = tensor("matrix_bd_33_cast_fp16")]; - tensor matrix_ac_17_transpose_x_0 = const()[name = tensor("matrix_ac_17_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_17_transpose_y_0 = const()[name = tensor("matrix_ac_17_transpose_y_0"), val = tensor(false)]; - tensor transpose_112_perm_0 = const()[name = tensor("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_113_perm_0 = const()[name = tensor("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = tensor("transpose_254")]; - tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1573_cast_fp16)[name = tensor("transpose_255")]; - tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = tensor("matrix_ac_17_cast_fp16")]; - tensor matrix_bd_35_begin_0 = const()[name = tensor("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_35_end_0 = const()[name = tensor("matrix_bd_35_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_35_end_mask_0 = const()[name = tensor("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = tensor("matrix_bd_35_cast_fp16")]; - tensor var_1599_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = tensor("op_1599_cast_fp16")]; - tensor _inversed_scores_33_y_0_to_fp16 = const()[name = tensor("_inversed_scores_33_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_33_cast_fp16 = mul(x = var_1599_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = tensor("_inversed_scores_33_cast_fp16")]; - tensor scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_3)[name = tensor("scores_35_cast_fp16")]; - tensor var_1605_cast_fp16 = softmax(axis = var_30, x = scores_35_cast_fp16)[name = tensor("op_1605_cast_fp16")]; - tensor input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1605_cast_fp16, cond = mask_3)[name = tensor("input_449_cast_fp16")]; - tensor x_189_transpose_x_0 = const()[name = tensor("x_189_transpose_x_0"), val = tensor(false)]; - tensor x_189_transpose_y_0 = const()[name = tensor("x_189_transpose_y_0"), val = tensor(false)]; - tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = tensor("transpose_253")]; - tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_19_cast_fp16)[name = tensor("x_189_cast_fp16")]; - tensor var_1609_perm_0 = const()[name = tensor("op_1609_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1610 = const()[name = tensor("op_1610"), val = tensor([1, -1, 1024])]; - tensor var_1609_cast_fp16 = transpose(perm = var_1609_perm_0, x = x_189_cast_fp16)[name = tensor("transpose_252")]; - tensor input_451_cast_fp16 = reshape(shape = var_1610, x = var_1609_cast_fp16)[name = tensor("input_451_cast_fp16")]; - tensor module_layers_8_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159526144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160312640))), name = tensor("module_layers_8_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_79_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16_palettized, x = input_451_cast_fp16)[name = tensor("linear_79_cast_fp16")]; - tensor input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = tensor("input_455_cast_fp16")]; - tensor x_193_axes_0 = const()[name = tensor("x_193_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160312832)))]; - tensor module_layers_8_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160314944)))]; - tensor x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = tensor("x_193_cast_fp16")]; - tensor input_457_perm_0 = const()[name = tensor("input_457_perm_0"), val = tensor([0, 2, 1])]; - tensor input_459_pad_type_0 = const()[name = tensor("input_459_pad_type_0"), val = tensor("valid")]; - tensor input_459_strides_0 = const()[name = tensor("input_459_strides_0"), val = tensor([1])]; - tensor input_459_pad_0 = const()[name = tensor("input_459_pad_0"), val = tensor([0, 0])]; - tensor input_459_dilations_0 = const()[name = tensor("input_459_dilations_0"), val = tensor([1])]; - tensor input_459_groups_0 = const()[name = tensor("input_459_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160317056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161889984))), name = tensor("module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = tensor("transpose_251")]; - tensor input_459_cast_fp16 = conv(dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_457_cast_fp16)[name = tensor("input_459_cast_fp16")]; - tensor x_195_split_num_splits_0 = const()[name = tensor("x_195_split_num_splits_0"), val = tensor(2)]; - tensor x_195_split_axis_0 = const()[name = tensor("x_195_split_axis_0"), val = tensor(1)]; - tensor x_195_split_cast_fp16_0, tensor x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = tensor("x_195_split_cast_fp16")]; - tensor x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = tensor("x_195_split_1_sigmoid_cast_fp16")]; - tensor x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = tensor("x_195_cast_fp16")]; - tensor input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_328)[name = tensor("input_461_cast_fp16")]; - tensor input_463_pad_0 = const()[name = tensor("input_463_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_463_mode_0 = const()[name = tensor("input_463_mode_0"), val = tensor("constant")]; - tensor const_97_to_fp16 = const()[name = tensor("const_97_to_fp16"), val = tensor(0x0p+0)]; - tensor input_463_cast_fp16 = pad(constant_val = const_97_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = tensor("input_463_cast_fp16")]; - tensor input_465_pad_type_0 = const()[name = tensor("input_465_pad_type_0"), val = tensor("valid")]; - tensor input_465_groups_0 = const()[name = tensor("input_465_groups_0"), val = tensor(1024)]; - tensor input_465_strides_0 = const()[name = tensor("input_465_strides_0"), val = tensor([1])]; - tensor input_465_pad_0 = const()[name = tensor("input_465_pad_0"), val = tensor([0, 0])]; - tensor input_465_dilations_0 = const()[name = tensor("input_465_dilations_0"), val = tensor([1])]; - tensor const_264_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161890176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161897152))), name = tensor("const_264_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_265_to_fp16 = const()[name = tensor("const_265_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161897344)))]; - tensor input_467_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_264_to_fp16_palettized, x = input_463_cast_fp16)[name = tensor("input_467_cast_fp16")]; - tensor input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = tensor("input_469_cast_fp16")]; - tensor x_197_pad_type_0 = const()[name = tensor("x_197_pad_type_0"), val = tensor("valid")]; - tensor x_197_strides_0 = const()[name = tensor("x_197_strides_0"), val = tensor([1])]; - tensor x_197_pad_0 = const()[name = tensor("x_197_pad_0"), val = tensor([0, 0])]; - tensor x_197_dilations_0 = const()[name = tensor("x_197_dilations_0"), val = tensor([1])]; - tensor x_197_groups_0 = const()[name = tensor("x_197_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161899456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162685952))), name = tensor("module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_197_cast_fp16 = conv(dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_469_cast_fp16)[name = tensor("x_197_cast_fp16")]; - tensor input_471_perm_0 = const()[name = tensor("input_471_perm_0"), val = tensor([0, 2, 1])]; - tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = tensor("transpose_250")]; - tensor input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = tensor("input_473_cast_fp16")]; - tensor input_475_axes_0 = const()[name = tensor("input_475_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162686144)))]; - tensor module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162688256)))]; - tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = tensor("input_475_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162690368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165836160))), name = tensor("module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_80_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized, x = input_475_cast_fp16)[name = tensor("linear_80_cast_fp16")]; - tensor input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = tensor("input_479_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165836352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168982144))), name = tensor("module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_81_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized, x = input_479_cast_fp16)[name = tensor("linear_81_cast_fp16")]; - tensor var_1670_to_fp16 = const()[name = tensor("op_1670_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1671_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1670_to_fp16)[name = tensor("op_1671_cast_fp16")]; - tensor input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1671_cast_fp16)[name = tensor("input_485_cast_fp16")]; - tensor input_487_axes_0 = const()[name = tensor("input_487_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168982336)))]; - tensor module_layers_8_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168984448)))]; - tensor input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = tensor("input_487_cast_fp16")]; - tensor input_489_axes_0 = const()[name = tensor("input_489_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168986560)))]; - tensor module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168988672)))]; - tensor input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = tensor("input_489_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168990784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172136576))), name = tensor("module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_82_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized, x = input_489_cast_fp16)[name = tensor("linear_82_cast_fp16")]; - tensor input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = tensor("input_493_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172136768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175282560))), name = tensor("module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_83_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized, x = input_493_cast_fp16)[name = tensor("linear_83_cast_fp16")]; - tensor var_1699_to_fp16 = const()[name = tensor("op_1699_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1700_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1699_to_fp16)[name = tensor("op_1700_cast_fp16")]; - tensor input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1700_cast_fp16)[name = tensor("input_499_cast_fp16")]; - tensor query_19_axes_0 = const()[name = tensor("query_19_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175282752)))]; - tensor module_layers_9_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175284864)))]; - tensor query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = tensor("query_19_cast_fp16")]; - tensor module_layers_9_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175286976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176073472))), name = tensor("module_layers_9_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_84_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = tensor("linear_84_cast_fp16")]; - tensor var_1716 = const()[name = tensor("op_1716"), val = tensor([1, -1, 8, 128])]; - tensor q_55_cast_fp16 = reshape(shape = var_1716, x = linear_84_cast_fp16)[name = tensor("q_55_cast_fp16")]; - tensor module_layers_9_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176073664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176860160))), name = tensor("module_layers_9_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_85_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = tensor("linear_85_cast_fp16")]; - tensor var_1720 = const()[name = tensor("op_1720"), val = tensor([1, -1, 8, 128])]; - tensor k_37_cast_fp16 = reshape(shape = var_1720, x = linear_85_cast_fp16)[name = tensor("k_37_cast_fp16")]; - tensor module_layers_9_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176860352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177646848))), name = tensor("module_layers_9_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_86_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = tensor("linear_86_cast_fp16")]; - tensor var_1724 = const()[name = tensor("op_1724"), val = tensor([1, -1, 8, 128])]; - tensor v_19_cast_fp16 = reshape(shape = var_1724, x = linear_86_cast_fp16)[name = tensor("v_19_cast_fp16")]; - tensor value_21_perm_0 = const()[name = tensor("value_21_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177647040)))]; - tensor var_1736_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1736_cast_fp16")]; - tensor module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177649152)))]; - tensor var_1738_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1738_cast_fp16")]; - tensor q_with_bias_v_19_perm_0 = const()[name = tensor("q_with_bias_v_19_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_205_transpose_x_0 = const()[name = tensor("x_205_transpose_x_0"), val = tensor(false)]; - tensor x_205_transpose_y_0 = const()[name = tensor("x_205_transpose_y_0"), val = tensor(false)]; - tensor op_1740_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177651264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177939328))), name = tensor("op_1740_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1738_cast_fp16)[name = tensor("transpose_249")]; - tensor x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_1740_to_fp16_palettized)[name = tensor("x_205_cast_fp16")]; - tensor x_207_pad_0 = const()[name = tensor("x_207_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_207_mode_0 = const()[name = tensor("x_207_mode_0"), val = tensor("constant")]; - tensor const_104_to_fp16 = const()[name = tensor("const_104_to_fp16"), val = tensor(0x0p+0)]; - tensor x_207_cast_fp16 = pad(constant_val = const_104_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = tensor("x_207_cast_fp16")]; - tensor var_1748 = const()[name = tensor("op_1748"), val = tensor([1, 8, -1, 188])]; - tensor x_209_cast_fp16 = reshape(shape = var_1748, x = x_207_cast_fp16)[name = tensor("x_209_cast_fp16")]; - tensor var_1752_begin_0 = const()[name = tensor("op_1752_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1752_end_0 = const()[name = tensor("op_1752_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1752_end_mask_0 = const()[name = tensor("op_1752_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1752_cast_fp16 = slice_by_index(begin = var_1752_begin_0, end = var_1752_end_0, end_mask = var_1752_end_mask_0, x = x_209_cast_fp16)[name = tensor("op_1752_cast_fp16")]; - tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1753, x = var_1752_cast_fp16)[name = tensor("matrix_bd_37_cast_fp16")]; - tensor matrix_ac_19_transpose_x_0 = const()[name = tensor("matrix_ac_19_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_19_transpose_y_0 = const()[name = tensor("matrix_ac_19_transpose_y_0"), val = tensor(false)]; - tensor transpose_114_perm_0 = const()[name = tensor("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_115_perm_0 = const()[name = tensor("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = tensor("transpose_247")]; - tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = var_1736_cast_fp16)[name = tensor("transpose_248")]; - tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = tensor("matrix_ac_19_cast_fp16")]; - tensor matrix_bd_39_begin_0 = const()[name = tensor("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_39_end_0 = const()[name = tensor("matrix_bd_39_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_39_end_mask_0 = const()[name = tensor("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = tensor("matrix_bd_39_cast_fp16")]; - tensor var_1762_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = tensor("op_1762_cast_fp16")]; - tensor _inversed_scores_37_y_0_to_fp16 = const()[name = tensor("_inversed_scores_37_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_37_cast_fp16 = mul(x = var_1762_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = tensor("_inversed_scores_37_cast_fp16")]; - tensor scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_3)[name = tensor("scores_39_cast_fp16")]; - tensor var_1768_cast_fp16 = softmax(axis = var_30, x = scores_39_cast_fp16)[name = tensor("op_1768_cast_fp16")]; - tensor input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1768_cast_fp16, cond = mask_3)[name = tensor("input_501_cast_fp16")]; - tensor x_211_transpose_x_0 = const()[name = tensor("x_211_transpose_x_0"), val = tensor(false)]; - tensor x_211_transpose_y_0 = const()[name = tensor("x_211_transpose_y_0"), val = tensor(false)]; - tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = tensor("transpose_246")]; - tensor x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_21_cast_fp16)[name = tensor("x_211_cast_fp16")]; - tensor var_1772_perm_0 = const()[name = tensor("op_1772_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1773 = const()[name = tensor("op_1773"), val = tensor([1, -1, 1024])]; - tensor var_1772_cast_fp16 = transpose(perm = var_1772_perm_0, x = x_211_cast_fp16)[name = tensor("transpose_245")]; - tensor input_503_cast_fp16 = reshape(shape = var_1773, x = var_1772_cast_fp16)[name = tensor("input_503_cast_fp16")]; - tensor module_layers_9_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177939520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178726016))), name = tensor("module_layers_9_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_88_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16_palettized, x = input_503_cast_fp16)[name = tensor("linear_88_cast_fp16")]; - tensor input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = tensor("input_507_cast_fp16")]; - tensor x_215_axes_0 = const()[name = tensor("x_215_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178726208)))]; - tensor module_layers_9_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178728320)))]; - tensor x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = tensor("x_215_cast_fp16")]; - tensor input_509_perm_0 = const()[name = tensor("input_509_perm_0"), val = tensor([0, 2, 1])]; - tensor input_511_pad_type_0 = const()[name = tensor("input_511_pad_type_0"), val = tensor("valid")]; - tensor input_511_strides_0 = const()[name = tensor("input_511_strides_0"), val = tensor([1])]; - tensor input_511_pad_0 = const()[name = tensor("input_511_pad_0"), val = tensor([0, 0])]; - tensor input_511_dilations_0 = const()[name = tensor("input_511_dilations_0"), val = tensor([1])]; - tensor input_511_groups_0 = const()[name = tensor("input_511_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178730432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180303360))), name = tensor("module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = tensor("transpose_244")]; - tensor input_511_cast_fp16 = conv(dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_509_cast_fp16)[name = tensor("input_511_cast_fp16")]; - tensor x_217_split_num_splits_0 = const()[name = tensor("x_217_split_num_splits_0"), val = tensor(2)]; - tensor x_217_split_axis_0 = const()[name = tensor("x_217_split_axis_0"), val = tensor(1)]; - tensor x_217_split_cast_fp16_0, tensor x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = tensor("x_217_split_cast_fp16")]; - tensor x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = tensor("x_217_split_1_sigmoid_cast_fp16")]; - tensor x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = tensor("x_217_cast_fp16")]; - tensor input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_328)[name = tensor("input_513_cast_fp16")]; - tensor input_515_pad_0 = const()[name = tensor("input_515_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_515_mode_0 = const()[name = tensor("input_515_mode_0"), val = tensor("constant")]; - tensor const_107_to_fp16 = const()[name = tensor("const_107_to_fp16"), val = tensor(0x0p+0)]; - tensor input_515_cast_fp16 = pad(constant_val = const_107_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = tensor("input_515_cast_fp16")]; - tensor input_517_pad_type_0 = const()[name = tensor("input_517_pad_type_0"), val = tensor("valid")]; - tensor input_517_groups_0 = const()[name = tensor("input_517_groups_0"), val = tensor(1024)]; - tensor input_517_strides_0 = const()[name = tensor("input_517_strides_0"), val = tensor([1])]; - tensor input_517_pad_0 = const()[name = tensor("input_517_pad_0"), val = tensor([0, 0])]; - tensor input_517_dilations_0 = const()[name = tensor("input_517_dilations_0"), val = tensor([1])]; - tensor const_266_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180303552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180310528))), name = tensor("const_266_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_267_to_fp16 = const()[name = tensor("const_267_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180310720)))]; - tensor input_519_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_266_to_fp16_palettized, x = input_515_cast_fp16)[name = tensor("input_519_cast_fp16")]; - tensor input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = tensor("input_521_cast_fp16")]; - tensor x_219_pad_type_0 = const()[name = tensor("x_219_pad_type_0"), val = tensor("valid")]; - tensor x_219_strides_0 = const()[name = tensor("x_219_strides_0"), val = tensor([1])]; - tensor x_219_pad_0 = const()[name = tensor("x_219_pad_0"), val = tensor([0, 0])]; - tensor x_219_dilations_0 = const()[name = tensor("x_219_dilations_0"), val = tensor([1])]; - tensor x_219_groups_0 = const()[name = tensor("x_219_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180312832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181099328))), name = tensor("module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_219_cast_fp16 = conv(dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_521_cast_fp16)[name = tensor("x_219_cast_fp16")]; - tensor input_523_perm_0 = const()[name = tensor("input_523_perm_0"), val = tensor([0, 2, 1])]; - tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = tensor("transpose_243")]; - tensor input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = tensor("input_525_cast_fp16")]; - tensor input_527_axes_0 = const()[name = tensor("input_527_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181099520)))]; - tensor module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181101632)))]; - tensor input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = tensor("input_527_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181103744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184249536))), name = tensor("module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_89_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized, x = input_527_cast_fp16)[name = tensor("linear_89_cast_fp16")]; - tensor input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = tensor("input_531_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184249728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187395520))), name = tensor("module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_90_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized, x = input_531_cast_fp16)[name = tensor("linear_90_cast_fp16")]; - tensor var_1833_to_fp16 = const()[name = tensor("op_1833_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1834_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_1833_to_fp16)[name = tensor("op_1834_cast_fp16")]; - tensor input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_1834_cast_fp16)[name = tensor("input_537_cast_fp16")]; - tensor input_539_axes_0 = const()[name = tensor("input_539_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187395712)))]; - tensor module_layers_9_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187397824)))]; - tensor input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = tensor("input_539_cast_fp16")]; - tensor input_541_axes_0 = const()[name = tensor("input_541_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187399936)))]; - tensor module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187402048)))]; - tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = tensor("input_541_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187404160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190549952))), name = tensor("module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_91_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized, x = input_541_cast_fp16)[name = tensor("linear_91_cast_fp16")]; - tensor input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = tensor("input_545_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190550144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193695936))), name = tensor("module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_92_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized, x = input_545_cast_fp16)[name = tensor("linear_92_cast_fp16")]; - tensor var_1862_to_fp16 = const()[name = tensor("op_1862_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1863_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_1862_to_fp16)[name = tensor("op_1863_cast_fp16")]; - tensor input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_1863_cast_fp16)[name = tensor("input_551_cast_fp16")]; - tensor query_21_axes_0 = const()[name = tensor("query_21_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193696128)))]; - tensor module_layers_10_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193698240)))]; - tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = tensor("query_21_cast_fp16")]; - tensor module_layers_10_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193700352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194486848))), name = tensor("module_layers_10_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_93_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = tensor("linear_93_cast_fp16")]; - tensor var_1879 = const()[name = tensor("op_1879"), val = tensor([1, -1, 8, 128])]; - tensor q_61_cast_fp16 = reshape(shape = var_1879, x = linear_93_cast_fp16)[name = tensor("q_61_cast_fp16")]; - tensor module_layers_10_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194487040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195273536))), name = tensor("module_layers_10_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_94_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = tensor("linear_94_cast_fp16")]; - tensor var_1883 = const()[name = tensor("op_1883"), val = tensor([1, -1, 8, 128])]; - tensor k_41_cast_fp16 = reshape(shape = var_1883, x = linear_94_cast_fp16)[name = tensor("k_41_cast_fp16")]; - tensor module_layers_10_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195273728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196060224))), name = tensor("module_layers_10_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_95_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = tensor("linear_95_cast_fp16")]; - tensor var_1887 = const()[name = tensor("op_1887"), val = tensor([1, -1, 8, 128])]; - tensor v_21_cast_fp16 = reshape(shape = var_1887, x = linear_95_cast_fp16)[name = tensor("v_21_cast_fp16")]; - tensor value_23_perm_0 = const()[name = tensor("value_23_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196060416)))]; - tensor var_1899_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1899_cast_fp16")]; - tensor module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196062528)))]; - tensor var_1901_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1901_cast_fp16")]; - tensor q_with_bias_v_21_perm_0 = const()[name = tensor("q_with_bias_v_21_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_227_transpose_x_0 = const()[name = tensor("x_227_transpose_x_0"), val = tensor(false)]; - tensor x_227_transpose_y_0 = const()[name = tensor("x_227_transpose_y_0"), val = tensor(false)]; - tensor op_1903_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196064640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196352704))), name = tensor("op_1903_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_1901_cast_fp16)[name = tensor("transpose_242")]; - tensor x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_1903_to_fp16_palettized)[name = tensor("x_227_cast_fp16")]; - tensor x_229_pad_0 = const()[name = tensor("x_229_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_229_mode_0 = const()[name = tensor("x_229_mode_0"), val = tensor("constant")]; - tensor const_114_to_fp16 = const()[name = tensor("const_114_to_fp16"), val = tensor(0x0p+0)]; - tensor x_229_cast_fp16 = pad(constant_val = const_114_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = tensor("x_229_cast_fp16")]; - tensor var_1911 = const()[name = tensor("op_1911"), val = tensor([1, 8, -1, 188])]; - tensor x_231_cast_fp16 = reshape(shape = var_1911, x = x_229_cast_fp16)[name = tensor("x_231_cast_fp16")]; - tensor var_1915_begin_0 = const()[name = tensor("op_1915_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1915_end_0 = const()[name = tensor("op_1915_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1915_end_mask_0 = const()[name = tensor("op_1915_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1915_cast_fp16 = slice_by_index(begin = var_1915_begin_0, end = var_1915_end_0, end_mask = var_1915_end_mask_0, x = x_231_cast_fp16)[name = tensor("op_1915_cast_fp16")]; - tensor var_1916 = const()[name = tensor("op_1916"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_41_cast_fp16 = reshape(shape = var_1916, x = var_1915_cast_fp16)[name = tensor("matrix_bd_41_cast_fp16")]; - tensor matrix_ac_21_transpose_x_0 = const()[name = tensor("matrix_ac_21_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_21_transpose_y_0 = const()[name = tensor("matrix_ac_21_transpose_y_0"), val = tensor(false)]; - tensor transpose_116_perm_0 = const()[name = tensor("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_117_perm_0 = const()[name = tensor("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = tensor("transpose_240")]; - tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = var_1899_cast_fp16)[name = tensor("transpose_241")]; - tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = tensor("matrix_ac_21_cast_fp16")]; - tensor matrix_bd_43_begin_0 = const()[name = tensor("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_43_end_0 = const()[name = tensor("matrix_bd_43_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_43_end_mask_0 = const()[name = tensor("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = tensor("matrix_bd_43_cast_fp16")]; - tensor var_1925_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = tensor("op_1925_cast_fp16")]; - tensor _inversed_scores_41_y_0_to_fp16 = const()[name = tensor("_inversed_scores_41_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_41_cast_fp16 = mul(x = var_1925_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = tensor("_inversed_scores_41_cast_fp16")]; - tensor scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_3)[name = tensor("scores_43_cast_fp16")]; - tensor var_1931_cast_fp16 = softmax(axis = var_30, x = scores_43_cast_fp16)[name = tensor("op_1931_cast_fp16")]; - tensor input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_1931_cast_fp16, cond = mask_3)[name = tensor("input_553_cast_fp16")]; - tensor x_233_transpose_x_0 = const()[name = tensor("x_233_transpose_x_0"), val = tensor(false)]; - tensor x_233_transpose_y_0 = const()[name = tensor("x_233_transpose_y_0"), val = tensor(false)]; - tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = tensor("transpose_239")]; - tensor x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_23_cast_fp16)[name = tensor("x_233_cast_fp16")]; - tensor var_1935_perm_0 = const()[name = tensor("op_1935_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1936 = const()[name = tensor("op_1936"), val = tensor([1, -1, 1024])]; - tensor var_1935_cast_fp16 = transpose(perm = var_1935_perm_0, x = x_233_cast_fp16)[name = tensor("transpose_238")]; - tensor input_555_cast_fp16 = reshape(shape = var_1936, x = var_1935_cast_fp16)[name = tensor("input_555_cast_fp16")]; - tensor module_layers_10_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196352896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197139392))), name = tensor("module_layers_10_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_97_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16_palettized, x = input_555_cast_fp16)[name = tensor("linear_97_cast_fp16")]; - tensor input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = tensor("input_559_cast_fp16")]; - tensor x_237_axes_0 = const()[name = tensor("x_237_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197139584)))]; - tensor module_layers_10_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197141696)))]; - tensor x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = tensor("x_237_cast_fp16")]; - tensor input_561_perm_0 = const()[name = tensor("input_561_perm_0"), val = tensor([0, 2, 1])]; - tensor input_563_pad_type_0 = const()[name = tensor("input_563_pad_type_0"), val = tensor("valid")]; - tensor input_563_strides_0 = const()[name = tensor("input_563_strides_0"), val = tensor([1])]; - tensor input_563_pad_0 = const()[name = tensor("input_563_pad_0"), val = tensor([0, 0])]; - tensor input_563_dilations_0 = const()[name = tensor("input_563_dilations_0"), val = tensor([1])]; - tensor input_563_groups_0 = const()[name = tensor("input_563_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197143808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198716736))), name = tensor("module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = tensor("transpose_237")]; - tensor input_563_cast_fp16 = conv(dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_561_cast_fp16)[name = tensor("input_563_cast_fp16")]; - tensor x_239_split_num_splits_0 = const()[name = tensor("x_239_split_num_splits_0"), val = tensor(2)]; - tensor x_239_split_axis_0 = const()[name = tensor("x_239_split_axis_0"), val = tensor(1)]; - tensor x_239_split_cast_fp16_0, tensor x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = tensor("x_239_split_cast_fp16")]; - tensor x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = tensor("x_239_split_1_sigmoid_cast_fp16")]; - tensor x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = tensor("x_239_cast_fp16")]; - tensor input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_328)[name = tensor("input_565_cast_fp16")]; - tensor input_567_pad_0 = const()[name = tensor("input_567_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_567_mode_0 = const()[name = tensor("input_567_mode_0"), val = tensor("constant")]; - tensor const_117_to_fp16 = const()[name = tensor("const_117_to_fp16"), val = tensor(0x0p+0)]; - tensor input_567_cast_fp16 = pad(constant_val = const_117_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = tensor("input_567_cast_fp16")]; - tensor input_569_pad_type_0 = const()[name = tensor("input_569_pad_type_0"), val = tensor("valid")]; - tensor input_569_groups_0 = const()[name = tensor("input_569_groups_0"), val = tensor(1024)]; - tensor input_569_strides_0 = const()[name = tensor("input_569_strides_0"), val = tensor([1])]; - tensor input_569_pad_0 = const()[name = tensor("input_569_pad_0"), val = tensor([0, 0])]; - tensor input_569_dilations_0 = const()[name = tensor("input_569_dilations_0"), val = tensor([1])]; - tensor const_268_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198716928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198723904))), name = tensor("const_268_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_269_to_fp16 = const()[name = tensor("const_269_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198724096)))]; - tensor input_571_cast_fp16 = conv(bias = const_269_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_268_to_fp16_palettized, x = input_567_cast_fp16)[name = tensor("input_571_cast_fp16")]; - tensor input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = tensor("input_573_cast_fp16")]; - tensor x_241_pad_type_0 = const()[name = tensor("x_241_pad_type_0"), val = tensor("valid")]; - tensor x_241_strides_0 = const()[name = tensor("x_241_strides_0"), val = tensor([1])]; - tensor x_241_pad_0 = const()[name = tensor("x_241_pad_0"), val = tensor([0, 0])]; - tensor x_241_dilations_0 = const()[name = tensor("x_241_dilations_0"), val = tensor([1])]; - tensor x_241_groups_0 = const()[name = tensor("x_241_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198726208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199512704))), name = tensor("module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_241_cast_fp16 = conv(dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_573_cast_fp16)[name = tensor("x_241_cast_fp16")]; - tensor input_575_perm_0 = const()[name = tensor("input_575_perm_0"), val = tensor([0, 2, 1])]; - tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = tensor("transpose_236")]; - tensor input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = tensor("input_577_cast_fp16")]; - tensor input_579_axes_0 = const()[name = tensor("input_579_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199512896)))]; - tensor module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199515008)))]; - tensor input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = tensor("input_579_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199517120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202662912))), name = tensor("module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_98_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized, x = input_579_cast_fp16)[name = tensor("linear_98_cast_fp16")]; - tensor input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = tensor("input_583_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202663104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205808896))), name = tensor("module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_99_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized, x = input_583_cast_fp16)[name = tensor("linear_99_cast_fp16")]; - tensor var_1996_to_fp16 = const()[name = tensor("op_1996_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1997_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_1996_to_fp16)[name = tensor("op_1997_cast_fp16")]; - tensor input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_1997_cast_fp16)[name = tensor("input_589_cast_fp16")]; - tensor input_591_axes_0 = const()[name = tensor("input_591_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205809088)))]; - tensor module_layers_10_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205811200)))]; - tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = tensor("input_591_cast_fp16")]; - tensor input_593_axes_0 = const()[name = tensor("input_593_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205813312)))]; - tensor module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205815424)))]; - tensor input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = tensor("input_593_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205817536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208963328))), name = tensor("module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_100_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized, x = input_593_cast_fp16)[name = tensor("linear_100_cast_fp16")]; - tensor input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = tensor("input_597_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208963520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212109312))), name = tensor("module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_101_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized, x = input_597_cast_fp16)[name = tensor("linear_101_cast_fp16")]; - tensor var_2025_to_fp16 = const()[name = tensor("op_2025_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2026_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2025_to_fp16)[name = tensor("op_2026_cast_fp16")]; - tensor input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2026_cast_fp16)[name = tensor("input_603_cast_fp16")]; - tensor query_23_axes_0 = const()[name = tensor("query_23_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212109504)))]; - tensor module_layers_11_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212111616)))]; - tensor query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = tensor("query_23_cast_fp16")]; - tensor module_layers_11_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212113728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212900224))), name = tensor("module_layers_11_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_102_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = tensor("linear_102_cast_fp16")]; - tensor var_2042 = const()[name = tensor("op_2042"), val = tensor([1, -1, 8, 128])]; - tensor q_67_cast_fp16 = reshape(shape = var_2042, x = linear_102_cast_fp16)[name = tensor("q_67_cast_fp16")]; - tensor module_layers_11_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212900416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213686912))), name = tensor("module_layers_11_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_103_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = tensor("linear_103_cast_fp16")]; - tensor var_2046 = const()[name = tensor("op_2046"), val = tensor([1, -1, 8, 128])]; - tensor k_45_cast_fp16 = reshape(shape = var_2046, x = linear_103_cast_fp16)[name = tensor("k_45_cast_fp16")]; - tensor module_layers_11_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213687104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214473600))), name = tensor("module_layers_11_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_104_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = tensor("linear_104_cast_fp16")]; - tensor var_2050 = const()[name = tensor("op_2050"), val = tensor([1, -1, 8, 128])]; - tensor v_23_cast_fp16 = reshape(shape = var_2050, x = linear_104_cast_fp16)[name = tensor("v_23_cast_fp16")]; - tensor value_25_perm_0 = const()[name = tensor("value_25_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214473792)))]; - tensor var_2062_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2062_cast_fp16")]; - tensor module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214475904)))]; - tensor var_2064_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2064_cast_fp16")]; - tensor q_with_bias_v_23_perm_0 = const()[name = tensor("q_with_bias_v_23_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_249_transpose_x_0 = const()[name = tensor("x_249_transpose_x_0"), val = tensor(false)]; - tensor x_249_transpose_y_0 = const()[name = tensor("x_249_transpose_y_0"), val = tensor(false)]; - tensor op_2066_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214478016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214766080))), name = tensor("op_2066_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2064_cast_fp16)[name = tensor("transpose_235")]; - tensor x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2066_to_fp16_palettized)[name = tensor("x_249_cast_fp16")]; - tensor x_251_pad_0 = const()[name = tensor("x_251_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_251_mode_0 = const()[name = tensor("x_251_mode_0"), val = tensor("constant")]; - tensor const_124_to_fp16 = const()[name = tensor("const_124_to_fp16"), val = tensor(0x0p+0)]; - tensor x_251_cast_fp16 = pad(constant_val = const_124_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = tensor("x_251_cast_fp16")]; - tensor var_2074 = const()[name = tensor("op_2074"), val = tensor([1, 8, -1, 188])]; - tensor x_253_cast_fp16 = reshape(shape = var_2074, x = x_251_cast_fp16)[name = tensor("x_253_cast_fp16")]; - tensor var_2078_begin_0 = const()[name = tensor("op_2078_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2078_end_0 = const()[name = tensor("op_2078_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2078_end_mask_0 = const()[name = tensor("op_2078_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2078_cast_fp16 = slice_by_index(begin = var_2078_begin_0, end = var_2078_end_0, end_mask = var_2078_end_mask_0, x = x_253_cast_fp16)[name = tensor("op_2078_cast_fp16")]; - tensor var_2079 = const()[name = tensor("op_2079"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2079, x = var_2078_cast_fp16)[name = tensor("matrix_bd_45_cast_fp16")]; - tensor matrix_ac_23_transpose_x_0 = const()[name = tensor("matrix_ac_23_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_23_transpose_y_0 = const()[name = tensor("matrix_ac_23_transpose_y_0"), val = tensor(false)]; - tensor transpose_118_perm_0 = const()[name = tensor("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_119_perm_0 = const()[name = tensor("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = tensor("transpose_233")]; - tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2062_cast_fp16)[name = tensor("transpose_234")]; - tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = tensor("matrix_ac_23_cast_fp16")]; - tensor matrix_bd_47_begin_0 = const()[name = tensor("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_47_end_0 = const()[name = tensor("matrix_bd_47_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_47_end_mask_0 = const()[name = tensor("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = tensor("matrix_bd_47_cast_fp16")]; - tensor var_2088_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = tensor("op_2088_cast_fp16")]; - tensor _inversed_scores_45_y_0_to_fp16 = const()[name = tensor("_inversed_scores_45_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_45_cast_fp16 = mul(x = var_2088_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = tensor("_inversed_scores_45_cast_fp16")]; - tensor scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_3)[name = tensor("scores_47_cast_fp16")]; - tensor var_2094_cast_fp16 = softmax(axis = var_30, x = scores_47_cast_fp16)[name = tensor("op_2094_cast_fp16")]; - tensor input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2094_cast_fp16, cond = mask_3)[name = tensor("input_605_cast_fp16")]; - tensor x_255_transpose_x_0 = const()[name = tensor("x_255_transpose_x_0"), val = tensor(false)]; - tensor x_255_transpose_y_0 = const()[name = tensor("x_255_transpose_y_0"), val = tensor(false)]; - tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = tensor("transpose_232")]; - tensor x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_25_cast_fp16)[name = tensor("x_255_cast_fp16")]; - tensor var_2098_perm_0 = const()[name = tensor("op_2098_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2099 = const()[name = tensor("op_2099"), val = tensor([1, -1, 1024])]; - tensor var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = x_255_cast_fp16)[name = tensor("transpose_231")]; - tensor input_607_cast_fp16 = reshape(shape = var_2099, x = var_2098_cast_fp16)[name = tensor("input_607_cast_fp16")]; - tensor module_layers_11_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214766272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215552768))), name = tensor("module_layers_11_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_106_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16_palettized, x = input_607_cast_fp16)[name = tensor("linear_106_cast_fp16")]; - tensor input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = tensor("input_611_cast_fp16")]; - tensor x_259_axes_0 = const()[name = tensor("x_259_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215552960)))]; - tensor module_layers_11_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215555072)))]; - tensor x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = tensor("x_259_cast_fp16")]; - tensor input_613_perm_0 = const()[name = tensor("input_613_perm_0"), val = tensor([0, 2, 1])]; - tensor input_615_pad_type_0 = const()[name = tensor("input_615_pad_type_0"), val = tensor("valid")]; - tensor input_615_strides_0 = const()[name = tensor("input_615_strides_0"), val = tensor([1])]; - tensor input_615_pad_0 = const()[name = tensor("input_615_pad_0"), val = tensor([0, 0])]; - tensor input_615_dilations_0 = const()[name = tensor("input_615_dilations_0"), val = tensor([1])]; - tensor input_615_groups_0 = const()[name = tensor("input_615_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215557184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217130112))), name = tensor("module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = tensor("transpose_230")]; - tensor input_615_cast_fp16 = conv(dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_613_cast_fp16)[name = tensor("input_615_cast_fp16")]; - tensor x_261_split_num_splits_0 = const()[name = tensor("x_261_split_num_splits_0"), val = tensor(2)]; - tensor x_261_split_axis_0 = const()[name = tensor("x_261_split_axis_0"), val = tensor(1)]; - tensor x_261_split_cast_fp16_0, tensor x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = tensor("x_261_split_cast_fp16")]; - tensor x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = tensor("x_261_split_1_sigmoid_cast_fp16")]; - tensor x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = tensor("x_261_cast_fp16")]; - tensor input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_328)[name = tensor("input_617_cast_fp16")]; - tensor input_619_pad_0 = const()[name = tensor("input_619_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_619_mode_0 = const()[name = tensor("input_619_mode_0"), val = tensor("constant")]; - tensor const_127_to_fp16 = const()[name = tensor("const_127_to_fp16"), val = tensor(0x0p+0)]; - tensor input_619_cast_fp16 = pad(constant_val = const_127_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = tensor("input_619_cast_fp16")]; - tensor input_621_pad_type_0 = const()[name = tensor("input_621_pad_type_0"), val = tensor("valid")]; - tensor input_621_groups_0 = const()[name = tensor("input_621_groups_0"), val = tensor(1024)]; - tensor input_621_strides_0 = const()[name = tensor("input_621_strides_0"), val = tensor([1])]; - tensor input_621_pad_0 = const()[name = tensor("input_621_pad_0"), val = tensor([0, 0])]; - tensor input_621_dilations_0 = const()[name = tensor("input_621_dilations_0"), val = tensor([1])]; - tensor const_270_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217130304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217137280))), name = tensor("const_270_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_271_to_fp16 = const()[name = tensor("const_271_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217137472)))]; - tensor input_623_cast_fp16 = conv(bias = const_271_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_270_to_fp16_palettized, x = input_619_cast_fp16)[name = tensor("input_623_cast_fp16")]; - tensor input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = tensor("input_625_cast_fp16")]; - tensor x_263_pad_type_0 = const()[name = tensor("x_263_pad_type_0"), val = tensor("valid")]; - tensor x_263_strides_0 = const()[name = tensor("x_263_strides_0"), val = tensor([1])]; - tensor x_263_pad_0 = const()[name = tensor("x_263_pad_0"), val = tensor([0, 0])]; - tensor x_263_dilations_0 = const()[name = tensor("x_263_dilations_0"), val = tensor([1])]; - tensor x_263_groups_0 = const()[name = tensor("x_263_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217139584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217926080))), name = tensor("module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_263_cast_fp16 = conv(dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_625_cast_fp16)[name = tensor("x_263_cast_fp16")]; - tensor input_627_perm_0 = const()[name = tensor("input_627_perm_0"), val = tensor([0, 2, 1])]; - tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = tensor("transpose_229")]; - tensor input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = tensor("input_629_cast_fp16")]; - tensor input_631_axes_0 = const()[name = tensor("input_631_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217926272)))]; - tensor module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217928384)))]; - tensor input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = tensor("input_631_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217930496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(221076288))), name = tensor("module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_107_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized, x = input_631_cast_fp16)[name = tensor("linear_107_cast_fp16")]; - tensor input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = tensor("input_635_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(221076480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224222272))), name = tensor("module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_108_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized, x = input_635_cast_fp16)[name = tensor("linear_108_cast_fp16")]; - tensor var_2159_to_fp16 = const()[name = tensor("op_2159_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2160_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2159_to_fp16)[name = tensor("op_2160_cast_fp16")]; - tensor input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2160_cast_fp16)[name = tensor("input_641_cast_fp16")]; - tensor input_643_axes_0 = const()[name = tensor("input_643_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224222464)))]; - tensor module_layers_11_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224224576)))]; - tensor input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = tensor("input_643_cast_fp16")]; - tensor input_645_axes_0 = const()[name = tensor("input_645_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224226688)))]; - tensor module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224228800)))]; - tensor input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = tensor("input_645_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224230912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227376704))), name = tensor("module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_109_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized, x = input_645_cast_fp16)[name = tensor("linear_109_cast_fp16")]; - tensor input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = tensor("input_649_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227376896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230522688))), name = tensor("module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_110_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized, x = input_649_cast_fp16)[name = tensor("linear_110_cast_fp16")]; - tensor var_2188_to_fp16 = const()[name = tensor("op_2188_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2189_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2188_to_fp16)[name = tensor("op_2189_cast_fp16")]; - tensor input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2189_cast_fp16)[name = tensor("input_655_cast_fp16")]; - tensor query_25_axes_0 = const()[name = tensor("query_25_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230522880)))]; - tensor module_layers_12_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230524992)))]; - tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = tensor("query_25_cast_fp16")]; - tensor module_layers_12_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230527104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231313600))), name = tensor("module_layers_12_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_111_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = tensor("linear_111_cast_fp16")]; - tensor var_2205 = const()[name = tensor("op_2205"), val = tensor([1, -1, 8, 128])]; - tensor q_73_cast_fp16 = reshape(shape = var_2205, x = linear_111_cast_fp16)[name = tensor("q_73_cast_fp16")]; - tensor module_layers_12_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231313792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232100288))), name = tensor("module_layers_12_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_112_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = tensor("linear_112_cast_fp16")]; - tensor var_2209 = const()[name = tensor("op_2209"), val = tensor([1, -1, 8, 128])]; - tensor k_49_cast_fp16 = reshape(shape = var_2209, x = linear_112_cast_fp16)[name = tensor("k_49_cast_fp16")]; - tensor module_layers_12_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232100480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232886976))), name = tensor("module_layers_12_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_113_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = tensor("linear_113_cast_fp16")]; - tensor var_2213 = const()[name = tensor("op_2213"), val = tensor([1, -1, 8, 128])]; - tensor v_25_cast_fp16 = reshape(shape = var_2213, x = linear_113_cast_fp16)[name = tensor("v_25_cast_fp16")]; - tensor value_27_perm_0 = const()[name = tensor("value_27_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232887168)))]; - tensor var_2225_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2225_cast_fp16")]; - tensor module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232889280)))]; - tensor var_2227_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2227_cast_fp16")]; - tensor q_with_bias_v_25_perm_0 = const()[name = tensor("q_with_bias_v_25_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_271_transpose_x_0 = const()[name = tensor("x_271_transpose_x_0"), val = tensor(false)]; - tensor x_271_transpose_y_0 = const()[name = tensor("x_271_transpose_y_0"), val = tensor(false)]; - tensor op_2229_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232891392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233179456))), name = tensor("op_2229_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2227_cast_fp16)[name = tensor("transpose_228")]; - tensor x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_2229_to_fp16_palettized)[name = tensor("x_271_cast_fp16")]; - tensor x_273_pad_0 = const()[name = tensor("x_273_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_273_mode_0 = const()[name = tensor("x_273_mode_0"), val = tensor("constant")]; - tensor const_134_to_fp16 = const()[name = tensor("const_134_to_fp16"), val = tensor(0x0p+0)]; - tensor x_273_cast_fp16 = pad(constant_val = const_134_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = tensor("x_273_cast_fp16")]; - tensor var_2237 = const()[name = tensor("op_2237"), val = tensor([1, 8, -1, 188])]; - tensor x_275_cast_fp16 = reshape(shape = var_2237, x = x_273_cast_fp16)[name = tensor("x_275_cast_fp16")]; - tensor var_2241_begin_0 = const()[name = tensor("op_2241_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2241_end_0 = const()[name = tensor("op_2241_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2241_end_mask_0 = const()[name = tensor("op_2241_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2241_cast_fp16 = slice_by_index(begin = var_2241_begin_0, end = var_2241_end_0, end_mask = var_2241_end_mask_0, x = x_275_cast_fp16)[name = tensor("op_2241_cast_fp16")]; - tensor var_2242 = const()[name = tensor("op_2242"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_49_cast_fp16 = reshape(shape = var_2242, x = var_2241_cast_fp16)[name = tensor("matrix_bd_49_cast_fp16")]; - tensor matrix_ac_25_transpose_x_0 = const()[name = tensor("matrix_ac_25_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_25_transpose_y_0 = const()[name = tensor("matrix_ac_25_transpose_y_0"), val = tensor(false)]; - tensor transpose_120_perm_0 = const()[name = tensor("transpose_120_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_121_perm_0 = const()[name = tensor("transpose_121_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = tensor("transpose_226")]; - tensor transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2225_cast_fp16)[name = tensor("transpose_227")]; - tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = tensor("matrix_ac_25_cast_fp16")]; - tensor matrix_bd_51_begin_0 = const()[name = tensor("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_51_end_0 = const()[name = tensor("matrix_bd_51_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_51_end_mask_0 = const()[name = tensor("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = tensor("matrix_bd_51_cast_fp16")]; - tensor var_2251_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = tensor("op_2251_cast_fp16")]; - tensor _inversed_scores_49_y_0_to_fp16 = const()[name = tensor("_inversed_scores_49_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_49_cast_fp16 = mul(x = var_2251_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = tensor("_inversed_scores_49_cast_fp16")]; - tensor scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_3)[name = tensor("scores_51_cast_fp16")]; - tensor var_2257_cast_fp16 = softmax(axis = var_30, x = scores_51_cast_fp16)[name = tensor("op_2257_cast_fp16")]; - tensor input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2257_cast_fp16, cond = mask_3)[name = tensor("input_657_cast_fp16")]; - tensor x_277_transpose_x_0 = const()[name = tensor("x_277_transpose_x_0"), val = tensor(false)]; - tensor x_277_transpose_y_0 = const()[name = tensor("x_277_transpose_y_0"), val = tensor(false)]; - tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = tensor("transpose_225")]; - tensor x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_27_cast_fp16)[name = tensor("x_277_cast_fp16")]; - tensor var_2261_perm_0 = const()[name = tensor("op_2261_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2262 = const()[name = tensor("op_2262"), val = tensor([1, -1, 1024])]; - tensor var_2261_cast_fp16 = transpose(perm = var_2261_perm_0, x = x_277_cast_fp16)[name = tensor("transpose_224")]; - tensor input_659_cast_fp16 = reshape(shape = var_2262, x = var_2261_cast_fp16)[name = tensor("input_659_cast_fp16")]; - tensor module_layers_12_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233179648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233966144))), name = tensor("module_layers_12_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_115_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16_palettized, x = input_659_cast_fp16)[name = tensor("linear_115_cast_fp16")]; - tensor input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = tensor("input_663_cast_fp16")]; - tensor x_281_axes_0 = const()[name = tensor("x_281_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233966336)))]; - tensor module_layers_12_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233968448)))]; - tensor x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = tensor("x_281_cast_fp16")]; - tensor input_665_perm_0 = const()[name = tensor("input_665_perm_0"), val = tensor([0, 2, 1])]; - tensor input_667_pad_type_0 = const()[name = tensor("input_667_pad_type_0"), val = tensor("valid")]; - tensor input_667_strides_0 = const()[name = tensor("input_667_strides_0"), val = tensor([1])]; - tensor input_667_pad_0 = const()[name = tensor("input_667_pad_0"), val = tensor([0, 0])]; - tensor input_667_dilations_0 = const()[name = tensor("input_667_dilations_0"), val = tensor([1])]; - tensor input_667_groups_0 = const()[name = tensor("input_667_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233970560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235543488))), name = tensor("module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = tensor("transpose_223")]; - tensor input_667_cast_fp16 = conv(dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_665_cast_fp16)[name = tensor("input_667_cast_fp16")]; - tensor x_283_split_num_splits_0 = const()[name = tensor("x_283_split_num_splits_0"), val = tensor(2)]; - tensor x_283_split_axis_0 = const()[name = tensor("x_283_split_axis_0"), val = tensor(1)]; - tensor x_283_split_cast_fp16_0, tensor x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = tensor("x_283_split_cast_fp16")]; - tensor x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = tensor("x_283_split_1_sigmoid_cast_fp16")]; - tensor x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = tensor("x_283_cast_fp16")]; - tensor input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_328)[name = tensor("input_669_cast_fp16")]; - tensor input_671_pad_0 = const()[name = tensor("input_671_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_671_mode_0 = const()[name = tensor("input_671_mode_0"), val = tensor("constant")]; - tensor const_137_to_fp16 = const()[name = tensor("const_137_to_fp16"), val = tensor(0x0p+0)]; - tensor input_671_cast_fp16 = pad(constant_val = const_137_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = tensor("input_671_cast_fp16")]; - tensor input_673_pad_type_0 = const()[name = tensor("input_673_pad_type_0"), val = tensor("valid")]; - tensor input_673_groups_0 = const()[name = tensor("input_673_groups_0"), val = tensor(1024)]; - tensor input_673_strides_0 = const()[name = tensor("input_673_strides_0"), val = tensor([1])]; - tensor input_673_pad_0 = const()[name = tensor("input_673_pad_0"), val = tensor([0, 0])]; - tensor input_673_dilations_0 = const()[name = tensor("input_673_dilations_0"), val = tensor([1])]; - tensor const_272_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235543680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235550656))), name = tensor("const_272_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_273_to_fp16 = const()[name = tensor("const_273_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235550848)))]; - tensor input_675_cast_fp16 = conv(bias = const_273_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_272_to_fp16_palettized, x = input_671_cast_fp16)[name = tensor("input_675_cast_fp16")]; - tensor input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = tensor("input_677_cast_fp16")]; - tensor x_285_pad_type_0 = const()[name = tensor("x_285_pad_type_0"), val = tensor("valid")]; - tensor x_285_strides_0 = const()[name = tensor("x_285_strides_0"), val = tensor([1])]; - tensor x_285_pad_0 = const()[name = tensor("x_285_pad_0"), val = tensor([0, 0])]; - tensor x_285_dilations_0 = const()[name = tensor("x_285_dilations_0"), val = tensor([1])]; - tensor x_285_groups_0 = const()[name = tensor("x_285_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235552960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236339456))), name = tensor("module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_677_cast_fp16)[name = tensor("x_285_cast_fp16")]; - tensor input_679_perm_0 = const()[name = tensor("input_679_perm_0"), val = tensor([0, 2, 1])]; - tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = tensor("transpose_222")]; - tensor input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = tensor("input_681_cast_fp16")]; - tensor input_683_axes_0 = const()[name = tensor("input_683_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236339648)))]; - tensor module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236341760)))]; - tensor input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = tensor("input_683_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236343872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239489664))), name = tensor("module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_116_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized, x = input_683_cast_fp16)[name = tensor("linear_116_cast_fp16")]; - tensor input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = tensor("input_687_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239489856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242635648))), name = tensor("module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_117_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized, x = input_687_cast_fp16)[name = tensor("linear_117_cast_fp16")]; - tensor var_2322_to_fp16 = const()[name = tensor("op_2322_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2323_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2322_to_fp16)[name = tensor("op_2323_cast_fp16")]; - tensor input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2323_cast_fp16)[name = tensor("input_693_cast_fp16")]; - tensor input_695_axes_0 = const()[name = tensor("input_695_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242635840)))]; - tensor module_layers_12_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242637952)))]; - tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = tensor("input_695_cast_fp16")]; - tensor input_697_axes_0 = const()[name = tensor("input_697_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242640064)))]; - tensor module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242642176)))]; - tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = tensor("input_697_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242644288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(245790080))), name = tensor("module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_118_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized, x = input_697_cast_fp16)[name = tensor("linear_118_cast_fp16")]; - tensor input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = tensor("input_701_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(245790272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248936064))), name = tensor("module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_119_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized, x = input_701_cast_fp16)[name = tensor("linear_119_cast_fp16")]; - tensor var_2351_to_fp16 = const()[name = tensor("op_2351_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2352_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2351_to_fp16)[name = tensor("op_2352_cast_fp16")]; - tensor input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2352_cast_fp16)[name = tensor("input_707_cast_fp16")]; - tensor query_27_axes_0 = const()[name = tensor("query_27_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248936256)))]; - tensor module_layers_13_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248938368)))]; - tensor query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = tensor("query_27_cast_fp16")]; - tensor module_layers_13_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248940480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249726976))), name = tensor("module_layers_13_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_120_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = tensor("linear_120_cast_fp16")]; - tensor var_2368 = const()[name = tensor("op_2368"), val = tensor([1, -1, 8, 128])]; - tensor q_79_cast_fp16 = reshape(shape = var_2368, x = linear_120_cast_fp16)[name = tensor("q_79_cast_fp16")]; - tensor module_layers_13_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249727168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250513664))), name = tensor("module_layers_13_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_121_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = tensor("linear_121_cast_fp16")]; - tensor var_2372 = const()[name = tensor("op_2372"), val = tensor([1, -1, 8, 128])]; - tensor k_53_cast_fp16 = reshape(shape = var_2372, x = linear_121_cast_fp16)[name = tensor("k_53_cast_fp16")]; - tensor module_layers_13_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250513856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251300352))), name = tensor("module_layers_13_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_122_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = tensor("linear_122_cast_fp16")]; - tensor var_2376 = const()[name = tensor("op_2376"), val = tensor([1, -1, 8, 128])]; - tensor v_27_cast_fp16 = reshape(shape = var_2376, x = linear_122_cast_fp16)[name = tensor("v_27_cast_fp16")]; - tensor value_29_perm_0 = const()[name = tensor("value_29_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251300544)))]; - tensor var_2388_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2388_cast_fp16")]; - tensor module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251302656)))]; - tensor var_2390_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2390_cast_fp16")]; - tensor q_with_bias_v_27_perm_0 = const()[name = tensor("q_with_bias_v_27_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_293_transpose_x_0 = const()[name = tensor("x_293_transpose_x_0"), val = tensor(false)]; - tensor x_293_transpose_y_0 = const()[name = tensor("x_293_transpose_y_0"), val = tensor(false)]; - tensor op_2392_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251304768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251592832))), name = tensor("op_2392_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2390_cast_fp16)[name = tensor("transpose_221")]; - tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_2392_to_fp16_palettized)[name = tensor("x_293_cast_fp16")]; - tensor x_295_pad_0 = const()[name = tensor("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_295_mode_0 = const()[name = tensor("x_295_mode_0"), val = tensor("constant")]; - tensor const_144_to_fp16 = const()[name = tensor("const_144_to_fp16"), val = tensor(0x0p+0)]; - tensor x_295_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = tensor("x_295_cast_fp16")]; - tensor var_2400 = const()[name = tensor("op_2400"), val = tensor([1, 8, -1, 188])]; - tensor x_297_cast_fp16 = reshape(shape = var_2400, x = x_295_cast_fp16)[name = tensor("x_297_cast_fp16")]; - tensor var_2404_begin_0 = const()[name = tensor("op_2404_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2404_end_0 = const()[name = tensor("op_2404_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2404_end_mask_0 = const()[name = tensor("op_2404_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2404_cast_fp16 = slice_by_index(begin = var_2404_begin_0, end = var_2404_end_0, end_mask = var_2404_end_mask_0, x = x_297_cast_fp16)[name = tensor("op_2404_cast_fp16")]; - tensor var_2405 = const()[name = tensor("op_2405"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2405, x = var_2404_cast_fp16)[name = tensor("matrix_bd_53_cast_fp16")]; - tensor matrix_ac_27_transpose_x_0 = const()[name = tensor("matrix_ac_27_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_27_transpose_y_0 = const()[name = tensor("matrix_ac_27_transpose_y_0"), val = tensor(false)]; - tensor transpose_122_perm_0 = const()[name = tensor("transpose_122_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_123_perm_0 = const()[name = tensor("transpose_123_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = tensor("transpose_219")]; - tensor transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2388_cast_fp16)[name = tensor("transpose_220")]; - tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = tensor("matrix_ac_27_cast_fp16")]; - tensor matrix_bd_55_begin_0 = const()[name = tensor("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_55_end_0 = const()[name = tensor("matrix_bd_55_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_55_end_mask_0 = const()[name = tensor("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = tensor("matrix_bd_55_cast_fp16")]; - tensor var_2414_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = tensor("op_2414_cast_fp16")]; - tensor _inversed_scores_53_y_0_to_fp16 = const()[name = tensor("_inversed_scores_53_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_53_cast_fp16 = mul(x = var_2414_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = tensor("_inversed_scores_53_cast_fp16")]; - tensor scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_3)[name = tensor("scores_55_cast_fp16")]; - tensor var_2420_cast_fp16 = softmax(axis = var_30, x = scores_55_cast_fp16)[name = tensor("op_2420_cast_fp16")]; - tensor input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2420_cast_fp16, cond = mask_3)[name = tensor("input_709_cast_fp16")]; - tensor x_299_transpose_x_0 = const()[name = tensor("x_299_transpose_x_0"), val = tensor(false)]; - tensor x_299_transpose_y_0 = const()[name = tensor("x_299_transpose_y_0"), val = tensor(false)]; - tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = tensor("transpose_218")]; - tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_29_cast_fp16)[name = tensor("x_299_cast_fp16")]; - tensor var_2424_perm_0 = const()[name = tensor("op_2424_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2425 = const()[name = tensor("op_2425"), val = tensor([1, -1, 1024])]; - tensor var_2424_cast_fp16 = transpose(perm = var_2424_perm_0, x = x_299_cast_fp16)[name = tensor("transpose_217")]; - tensor input_711_cast_fp16 = reshape(shape = var_2425, x = var_2424_cast_fp16)[name = tensor("input_711_cast_fp16")]; - tensor module_layers_13_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251593024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252379520))), name = tensor("module_layers_13_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_124_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16_palettized, x = input_711_cast_fp16)[name = tensor("linear_124_cast_fp16")]; - tensor input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = tensor("input_715_cast_fp16")]; - tensor x_303_axes_0 = const()[name = tensor("x_303_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252379712)))]; - tensor module_layers_13_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252381824)))]; - tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = tensor("x_303_cast_fp16")]; - tensor input_717_perm_0 = const()[name = tensor("input_717_perm_0"), val = tensor([0, 2, 1])]; - tensor input_719_pad_type_0 = const()[name = tensor("input_719_pad_type_0"), val = tensor("valid")]; - tensor input_719_strides_0 = const()[name = tensor("input_719_strides_0"), val = tensor([1])]; - tensor input_719_pad_0 = const()[name = tensor("input_719_pad_0"), val = tensor([0, 0])]; - tensor input_719_dilations_0 = const()[name = tensor("input_719_dilations_0"), val = tensor([1])]; - tensor input_719_groups_0 = const()[name = tensor("input_719_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252383936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253956864))), name = tensor("module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = tensor("transpose_216")]; - tensor input_719_cast_fp16 = conv(dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_717_cast_fp16)[name = tensor("input_719_cast_fp16")]; - tensor x_305_split_num_splits_0 = const()[name = tensor("x_305_split_num_splits_0"), val = tensor(2)]; - tensor x_305_split_axis_0 = const()[name = tensor("x_305_split_axis_0"), val = tensor(1)]; - tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = tensor("x_305_split_cast_fp16")]; - tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = tensor("x_305_split_1_sigmoid_cast_fp16")]; - tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = tensor("x_305_cast_fp16")]; - tensor input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_328)[name = tensor("input_721_cast_fp16")]; - tensor input_723_pad_0 = const()[name = tensor("input_723_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_723_mode_0 = const()[name = tensor("input_723_mode_0"), val = tensor("constant")]; - tensor const_147_to_fp16 = const()[name = tensor("const_147_to_fp16"), val = tensor(0x0p+0)]; - tensor input_723_cast_fp16 = pad(constant_val = const_147_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = tensor("input_723_cast_fp16")]; - tensor input_725_pad_type_0 = const()[name = tensor("input_725_pad_type_0"), val = tensor("valid")]; - tensor input_725_groups_0 = const()[name = tensor("input_725_groups_0"), val = tensor(1024)]; - tensor input_725_strides_0 = const()[name = tensor("input_725_strides_0"), val = tensor([1])]; - tensor input_725_pad_0 = const()[name = tensor("input_725_pad_0"), val = tensor([0, 0])]; - tensor input_725_dilations_0 = const()[name = tensor("input_725_dilations_0"), val = tensor([1])]; - tensor const_274_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253957056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253964032))), name = tensor("const_274_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_275_to_fp16 = const()[name = tensor("const_275_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253964224)))]; - tensor input_727_cast_fp16 = conv(bias = const_275_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_274_to_fp16_palettized, x = input_723_cast_fp16)[name = tensor("input_727_cast_fp16")]; - tensor input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = tensor("input_729_cast_fp16")]; - tensor x_307_pad_type_0 = const()[name = tensor("x_307_pad_type_0"), val = tensor("valid")]; - tensor x_307_strides_0 = const()[name = tensor("x_307_strides_0"), val = tensor([1])]; - tensor x_307_pad_0 = const()[name = tensor("x_307_pad_0"), val = tensor([0, 0])]; - tensor x_307_dilations_0 = const()[name = tensor("x_307_dilations_0"), val = tensor([1])]; - tensor x_307_groups_0 = const()[name = tensor("x_307_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253966336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254752832))), name = tensor("module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_729_cast_fp16)[name = tensor("x_307_cast_fp16")]; - tensor input_731_perm_0 = const()[name = tensor("input_731_perm_0"), val = tensor([0, 2, 1])]; - tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = tensor("transpose_215")]; - tensor input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = tensor("input_733_cast_fp16")]; - tensor input_735_axes_0 = const()[name = tensor("input_735_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254753024)))]; - tensor module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254755136)))]; - tensor input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = tensor("input_735_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254757248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257903040))), name = tensor("module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_125_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized, x = input_735_cast_fp16)[name = tensor("linear_125_cast_fp16")]; - tensor input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = tensor("input_739_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257903232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261049024))), name = tensor("module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_126_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized, x = input_739_cast_fp16)[name = tensor("linear_126_cast_fp16")]; - tensor var_2485_to_fp16 = const()[name = tensor("op_2485_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2486_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2485_to_fp16)[name = tensor("op_2486_cast_fp16")]; - tensor input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2486_cast_fp16)[name = tensor("input_745_cast_fp16")]; - tensor input_747_axes_0 = const()[name = tensor("input_747_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261049216)))]; - tensor module_layers_13_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261051328)))]; - tensor input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = tensor("input_747_cast_fp16")]; - tensor input_749_axes_0 = const()[name = tensor("input_749_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261053440)))]; - tensor module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261055552)))]; - tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = tensor("input_749_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261057664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264203456))), name = tensor("module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_127_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized, x = input_749_cast_fp16)[name = tensor("linear_127_cast_fp16")]; - tensor input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = tensor("input_753_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264203648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267349440))), name = tensor("module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_128_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized, x = input_753_cast_fp16)[name = tensor("linear_128_cast_fp16")]; - tensor var_2514_to_fp16 = const()[name = tensor("op_2514_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2515_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2514_to_fp16)[name = tensor("op_2515_cast_fp16")]; - tensor input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2515_cast_fp16)[name = tensor("input_759_cast_fp16")]; - tensor query_29_axes_0 = const()[name = tensor("query_29_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267349632)))]; - tensor module_layers_14_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267351744)))]; - tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = tensor("query_29_cast_fp16")]; - tensor module_layers_14_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267353856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268140352))), name = tensor("module_layers_14_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_129_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = tensor("linear_129_cast_fp16")]; - tensor var_2531 = const()[name = tensor("op_2531"), val = tensor([1, -1, 8, 128])]; - tensor q_85_cast_fp16 = reshape(shape = var_2531, x = linear_129_cast_fp16)[name = tensor("q_85_cast_fp16")]; - tensor module_layers_14_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268140544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268927040))), name = tensor("module_layers_14_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_130_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = tensor("linear_130_cast_fp16")]; - tensor var_2535 = const()[name = tensor("op_2535"), val = tensor([1, -1, 8, 128])]; - tensor k_57_cast_fp16 = reshape(shape = var_2535, x = linear_130_cast_fp16)[name = tensor("k_57_cast_fp16")]; - tensor module_layers_14_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268927232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269713728))), name = tensor("module_layers_14_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_131_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = tensor("linear_131_cast_fp16")]; - tensor var_2539 = const()[name = tensor("op_2539"), val = tensor([1, -1, 8, 128])]; - tensor v_29_cast_fp16 = reshape(shape = var_2539, x = linear_131_cast_fp16)[name = tensor("v_29_cast_fp16")]; - tensor value_31_perm_0 = const()[name = tensor("value_31_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269713920)))]; - tensor var_2551_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2551_cast_fp16")]; - tensor module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269716032)))]; - tensor var_2553_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2553_cast_fp16")]; - tensor q_with_bias_v_29_perm_0 = const()[name = tensor("q_with_bias_v_29_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_315_transpose_x_0 = const()[name = tensor("x_315_transpose_x_0"), val = tensor(false)]; - tensor x_315_transpose_y_0 = const()[name = tensor("x_315_transpose_y_0"), val = tensor(false)]; - tensor op_2555_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269718144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270006208))), name = tensor("op_2555_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2553_cast_fp16)[name = tensor("transpose_214")]; - tensor x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_2555_to_fp16_palettized)[name = tensor("x_315_cast_fp16")]; - tensor x_317_pad_0 = const()[name = tensor("x_317_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_317_mode_0 = const()[name = tensor("x_317_mode_0"), val = tensor("constant")]; - tensor const_154_to_fp16 = const()[name = tensor("const_154_to_fp16"), val = tensor(0x0p+0)]; - tensor x_317_cast_fp16 = pad(constant_val = const_154_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = tensor("x_317_cast_fp16")]; - tensor var_2563 = const()[name = tensor("op_2563"), val = tensor([1, 8, -1, 188])]; - tensor x_319_cast_fp16 = reshape(shape = var_2563, x = x_317_cast_fp16)[name = tensor("x_319_cast_fp16")]; - tensor var_2567_begin_0 = const()[name = tensor("op_2567_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2567_end_0 = const()[name = tensor("op_2567_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2567_end_mask_0 = const()[name = tensor("op_2567_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2567_cast_fp16 = slice_by_index(begin = var_2567_begin_0, end = var_2567_end_0, end_mask = var_2567_end_mask_0, x = x_319_cast_fp16)[name = tensor("op_2567_cast_fp16")]; - tensor var_2568 = const()[name = tensor("op_2568"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_57_cast_fp16 = reshape(shape = var_2568, x = var_2567_cast_fp16)[name = tensor("matrix_bd_57_cast_fp16")]; - tensor matrix_ac_29_transpose_x_0 = const()[name = tensor("matrix_ac_29_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_29_transpose_y_0 = const()[name = tensor("matrix_ac_29_transpose_y_0"), val = tensor(false)]; - tensor transpose_124_perm_0 = const()[name = tensor("transpose_124_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_125_perm_0 = const()[name = tensor("transpose_125_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = tensor("transpose_212")]; - tensor transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2551_cast_fp16)[name = tensor("transpose_213")]; - tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = tensor("matrix_ac_29_cast_fp16")]; - tensor matrix_bd_59_begin_0 = const()[name = tensor("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_59_end_0 = const()[name = tensor("matrix_bd_59_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_59_end_mask_0 = const()[name = tensor("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = tensor("matrix_bd_59_cast_fp16")]; - tensor var_2577_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = tensor("op_2577_cast_fp16")]; - tensor _inversed_scores_57_y_0_to_fp16 = const()[name = tensor("_inversed_scores_57_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_57_cast_fp16 = mul(x = var_2577_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = tensor("_inversed_scores_57_cast_fp16")]; - tensor scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_3)[name = tensor("scores_59_cast_fp16")]; - tensor var_2583_cast_fp16 = softmax(axis = var_30, x = scores_59_cast_fp16)[name = tensor("op_2583_cast_fp16")]; - tensor input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2583_cast_fp16, cond = mask_3)[name = tensor("input_761_cast_fp16")]; - tensor x_321_transpose_x_0 = const()[name = tensor("x_321_transpose_x_0"), val = tensor(false)]; - tensor x_321_transpose_y_0 = const()[name = tensor("x_321_transpose_y_0"), val = tensor(false)]; - tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = tensor("transpose_211")]; - tensor x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_31_cast_fp16)[name = tensor("x_321_cast_fp16")]; - tensor var_2587_perm_0 = const()[name = tensor("op_2587_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2588 = const()[name = tensor("op_2588"), val = tensor([1, -1, 1024])]; - tensor var_2587_cast_fp16 = transpose(perm = var_2587_perm_0, x = x_321_cast_fp16)[name = tensor("transpose_210")]; - tensor input_763_cast_fp16 = reshape(shape = var_2588, x = var_2587_cast_fp16)[name = tensor("input_763_cast_fp16")]; - tensor module_layers_14_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270006400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270792896))), name = tensor("module_layers_14_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_133_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16_palettized, x = input_763_cast_fp16)[name = tensor("linear_133_cast_fp16")]; - tensor input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = tensor("input_767_cast_fp16")]; - tensor x_325_axes_0 = const()[name = tensor("x_325_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270793088)))]; - tensor module_layers_14_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270795200)))]; - tensor x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = tensor("x_325_cast_fp16")]; - tensor input_769_perm_0 = const()[name = tensor("input_769_perm_0"), val = tensor([0, 2, 1])]; - tensor input_771_pad_type_0 = const()[name = tensor("input_771_pad_type_0"), val = tensor("valid")]; - tensor input_771_strides_0 = const()[name = tensor("input_771_strides_0"), val = tensor([1])]; - tensor input_771_pad_0 = const()[name = tensor("input_771_pad_0"), val = tensor([0, 0])]; - tensor input_771_dilations_0 = const()[name = tensor("input_771_dilations_0"), val = tensor([1])]; - tensor input_771_groups_0 = const()[name = tensor("input_771_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270797312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272370240))), name = tensor("module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = tensor("transpose_209")]; - tensor input_771_cast_fp16 = conv(dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_769_cast_fp16)[name = tensor("input_771_cast_fp16")]; - tensor x_327_split_num_splits_0 = const()[name = tensor("x_327_split_num_splits_0"), val = tensor(2)]; - tensor x_327_split_axis_0 = const()[name = tensor("x_327_split_axis_0"), val = tensor(1)]; - tensor x_327_split_cast_fp16_0, tensor x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = tensor("x_327_split_cast_fp16")]; - tensor x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = tensor("x_327_split_1_sigmoid_cast_fp16")]; - tensor x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = tensor("x_327_cast_fp16")]; - tensor input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_328)[name = tensor("input_773_cast_fp16")]; - tensor input_775_pad_0 = const()[name = tensor("input_775_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_775_mode_0 = const()[name = tensor("input_775_mode_0"), val = tensor("constant")]; - tensor const_157_to_fp16 = const()[name = tensor("const_157_to_fp16"), val = tensor(0x0p+0)]; - tensor input_775_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = tensor("input_775_cast_fp16")]; - tensor input_777_pad_type_0 = const()[name = tensor("input_777_pad_type_0"), val = tensor("valid")]; - tensor input_777_groups_0 = const()[name = tensor("input_777_groups_0"), val = tensor(1024)]; - tensor input_777_strides_0 = const()[name = tensor("input_777_strides_0"), val = tensor([1])]; - tensor input_777_pad_0 = const()[name = tensor("input_777_pad_0"), val = tensor([0, 0])]; - tensor input_777_dilations_0 = const()[name = tensor("input_777_dilations_0"), val = tensor([1])]; - tensor const_276_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272370432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272377408))), name = tensor("const_276_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_277_to_fp16 = const()[name = tensor("const_277_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272377600)))]; - tensor input_779_cast_fp16 = conv(bias = const_277_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_276_to_fp16_palettized, x = input_775_cast_fp16)[name = tensor("input_779_cast_fp16")]; - tensor input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = tensor("input_781_cast_fp16")]; - tensor x_329_pad_type_0 = const()[name = tensor("x_329_pad_type_0"), val = tensor("valid")]; - tensor x_329_strides_0 = const()[name = tensor("x_329_strides_0"), val = tensor([1])]; - tensor x_329_pad_0 = const()[name = tensor("x_329_pad_0"), val = tensor([0, 0])]; - tensor x_329_dilations_0 = const()[name = tensor("x_329_dilations_0"), val = tensor([1])]; - tensor x_329_groups_0 = const()[name = tensor("x_329_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272379712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273166208))), name = tensor("module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_329_cast_fp16 = conv(dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_781_cast_fp16)[name = tensor("x_329_cast_fp16")]; - tensor input_783_perm_0 = const()[name = tensor("input_783_perm_0"), val = tensor([0, 2, 1])]; - tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = tensor("transpose_208")]; - tensor input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = tensor("input_785_cast_fp16")]; - tensor input_787_axes_0 = const()[name = tensor("input_787_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273166400)))]; - tensor module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273168512)))]; - tensor input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = tensor("input_787_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273170624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(276316416))), name = tensor("module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_134_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized, x = input_787_cast_fp16)[name = tensor("linear_134_cast_fp16")]; - tensor input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = tensor("input_791_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(276316608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279462400))), name = tensor("module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_135_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized, x = input_791_cast_fp16)[name = tensor("linear_135_cast_fp16")]; - tensor var_2648_to_fp16 = const()[name = tensor("op_2648_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2649_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2648_to_fp16)[name = tensor("op_2649_cast_fp16")]; - tensor input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2649_cast_fp16)[name = tensor("input_797_cast_fp16")]; - tensor input_799_axes_0 = const()[name = tensor("input_799_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279462592)))]; - tensor module_layers_14_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279464704)))]; - tensor input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = tensor("input_799_cast_fp16")]; - tensor input_801_axes_0 = const()[name = tensor("input_801_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279466816)))]; - tensor module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279468928)))]; - tensor input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = tensor("input_801_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279471040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282616832))), name = tensor("module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_136_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized, x = input_801_cast_fp16)[name = tensor("linear_136_cast_fp16")]; - tensor input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = tensor("input_805_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282617024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285762816))), name = tensor("module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_137_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized, x = input_805_cast_fp16)[name = tensor("linear_137_cast_fp16")]; - tensor var_2677_to_fp16 = const()[name = tensor("op_2677_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2678_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2677_to_fp16)[name = tensor("op_2678_cast_fp16")]; - tensor input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2678_cast_fp16)[name = tensor("input_811_cast_fp16")]; - tensor query_31_axes_0 = const()[name = tensor("query_31_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285763008)))]; - tensor module_layers_15_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285765120)))]; - tensor query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = tensor("query_31_cast_fp16")]; - tensor module_layers_15_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285767232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286553728))), name = tensor("module_layers_15_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_138_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = tensor("linear_138_cast_fp16")]; - tensor var_2694 = const()[name = tensor("op_2694"), val = tensor([1, -1, 8, 128])]; - tensor q_91_cast_fp16 = reshape(shape = var_2694, x = linear_138_cast_fp16)[name = tensor("q_91_cast_fp16")]; - tensor module_layers_15_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286553920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287340416))), name = tensor("module_layers_15_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_139_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = tensor("linear_139_cast_fp16")]; - tensor var_2698 = const()[name = tensor("op_2698"), val = tensor([1, -1, 8, 128])]; - tensor k_61_cast_fp16 = reshape(shape = var_2698, x = linear_139_cast_fp16)[name = tensor("k_61_cast_fp16")]; - tensor module_layers_15_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287340608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288127104))), name = tensor("module_layers_15_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_140_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = tensor("linear_140_cast_fp16")]; - tensor var_2702 = const()[name = tensor("op_2702"), val = tensor([1, -1, 8, 128])]; - tensor v_31_cast_fp16 = reshape(shape = var_2702, x = linear_140_cast_fp16)[name = tensor("v_31_cast_fp16")]; - tensor value_33_perm_0 = const()[name = tensor("value_33_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288127296)))]; - tensor var_2714_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2714_cast_fp16")]; - tensor module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288129408)))]; - tensor var_2716_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2716_cast_fp16")]; - tensor q_with_bias_v_31_perm_0 = const()[name = tensor("q_with_bias_v_31_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_337_transpose_x_0 = const()[name = tensor("x_337_transpose_x_0"), val = tensor(false)]; - tensor x_337_transpose_y_0 = const()[name = tensor("x_337_transpose_y_0"), val = tensor(false)]; - tensor op_2718_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288131520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288419584))), name = tensor("op_2718_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2716_cast_fp16)[name = tensor("transpose_207")]; - tensor x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_2718_to_fp16_palettized)[name = tensor("x_337_cast_fp16")]; - tensor x_339_pad_0 = const()[name = tensor("x_339_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_339_mode_0 = const()[name = tensor("x_339_mode_0"), val = tensor("constant")]; - tensor const_164_to_fp16 = const()[name = tensor("const_164_to_fp16"), val = tensor(0x0p+0)]; - tensor x_339_cast_fp16 = pad(constant_val = const_164_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = tensor("x_339_cast_fp16")]; - tensor var_2726 = const()[name = tensor("op_2726"), val = tensor([1, 8, -1, 188])]; - tensor x_341_cast_fp16 = reshape(shape = var_2726, x = x_339_cast_fp16)[name = tensor("x_341_cast_fp16")]; - tensor var_2730_begin_0 = const()[name = tensor("op_2730_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2730_end_0 = const()[name = tensor("op_2730_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2730_end_mask_0 = const()[name = tensor("op_2730_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2730_cast_fp16 = slice_by_index(begin = var_2730_begin_0, end = var_2730_end_0, end_mask = var_2730_end_mask_0, x = x_341_cast_fp16)[name = tensor("op_2730_cast_fp16")]; - tensor var_2731 = const()[name = tensor("op_2731"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_61_cast_fp16 = reshape(shape = var_2731, x = var_2730_cast_fp16)[name = tensor("matrix_bd_61_cast_fp16")]; - tensor matrix_ac_31_transpose_x_0 = const()[name = tensor("matrix_ac_31_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_31_transpose_y_0 = const()[name = tensor("matrix_ac_31_transpose_y_0"), val = tensor(false)]; - tensor transpose_126_perm_0 = const()[name = tensor("transpose_126_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_127_perm_0 = const()[name = tensor("transpose_127_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = tensor("transpose_205")]; - tensor transpose_126 = transpose(perm = transpose_126_perm_0, x = var_2714_cast_fp16)[name = tensor("transpose_206")]; - tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = tensor("matrix_ac_31_cast_fp16")]; - tensor matrix_bd_63_begin_0 = const()[name = tensor("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_63_end_0 = const()[name = tensor("matrix_bd_63_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_63_end_mask_0 = const()[name = tensor("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = tensor("matrix_bd_63_cast_fp16")]; - tensor var_2740_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = tensor("op_2740_cast_fp16")]; - tensor _inversed_scores_61_y_0_to_fp16 = const()[name = tensor("_inversed_scores_61_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_61_cast_fp16 = mul(x = var_2740_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = tensor("_inversed_scores_61_cast_fp16")]; - tensor scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_3)[name = tensor("scores_63_cast_fp16")]; - tensor var_2746_cast_fp16 = softmax(axis = var_30, x = scores_63_cast_fp16)[name = tensor("op_2746_cast_fp16")]; - tensor input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_2746_cast_fp16, cond = mask_3)[name = tensor("input_813_cast_fp16")]; - tensor x_343_transpose_x_0 = const()[name = tensor("x_343_transpose_x_0"), val = tensor(false)]; - tensor x_343_transpose_y_0 = const()[name = tensor("x_343_transpose_y_0"), val = tensor(false)]; - tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = tensor("transpose_204")]; - tensor x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_33_cast_fp16)[name = tensor("x_343_cast_fp16")]; - tensor var_2750_perm_0 = const()[name = tensor("op_2750_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2751 = const()[name = tensor("op_2751"), val = tensor([1, -1, 1024])]; - tensor var_2750_cast_fp16 = transpose(perm = var_2750_perm_0, x = x_343_cast_fp16)[name = tensor("transpose_203")]; - tensor input_815_cast_fp16 = reshape(shape = var_2751, x = var_2750_cast_fp16)[name = tensor("input_815_cast_fp16")]; - tensor module_layers_15_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288419776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289206272))), name = tensor("module_layers_15_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_142_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16_palettized, x = input_815_cast_fp16)[name = tensor("linear_142_cast_fp16")]; - tensor input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = tensor("input_819_cast_fp16")]; - tensor x_347_axes_0 = const()[name = tensor("x_347_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289206464)))]; - tensor module_layers_15_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289208576)))]; - tensor x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = tensor("x_347_cast_fp16")]; - tensor input_821_perm_0 = const()[name = tensor("input_821_perm_0"), val = tensor([0, 2, 1])]; - tensor input_823_pad_type_0 = const()[name = tensor("input_823_pad_type_0"), val = tensor("valid")]; - tensor input_823_strides_0 = const()[name = tensor("input_823_strides_0"), val = tensor([1])]; - tensor input_823_pad_0 = const()[name = tensor("input_823_pad_0"), val = tensor([0, 0])]; - tensor input_823_dilations_0 = const()[name = tensor("input_823_dilations_0"), val = tensor([1])]; - tensor input_823_groups_0 = const()[name = tensor("input_823_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289210688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290783616))), name = tensor("module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = tensor("transpose_202")]; - tensor input_823_cast_fp16 = conv(dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_821_cast_fp16)[name = tensor("input_823_cast_fp16")]; - tensor x_349_split_num_splits_0 = const()[name = tensor("x_349_split_num_splits_0"), val = tensor(2)]; - tensor x_349_split_axis_0 = const()[name = tensor("x_349_split_axis_0"), val = tensor(1)]; - tensor x_349_split_cast_fp16_0, tensor x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = tensor("x_349_split_cast_fp16")]; - tensor x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = tensor("x_349_split_1_sigmoid_cast_fp16")]; - tensor x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = tensor("x_349_cast_fp16")]; - tensor input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_328)[name = tensor("input_825_cast_fp16")]; - tensor input_827_pad_0 = const()[name = tensor("input_827_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_827_mode_0 = const()[name = tensor("input_827_mode_0"), val = tensor("constant")]; - tensor const_167_to_fp16 = const()[name = tensor("const_167_to_fp16"), val = tensor(0x0p+0)]; - tensor input_827_cast_fp16 = pad(constant_val = const_167_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = tensor("input_827_cast_fp16")]; - tensor input_829_pad_type_0 = const()[name = tensor("input_829_pad_type_0"), val = tensor("valid")]; - tensor input_829_groups_0 = const()[name = tensor("input_829_groups_0"), val = tensor(1024)]; - tensor input_829_strides_0 = const()[name = tensor("input_829_strides_0"), val = tensor([1])]; - tensor input_829_pad_0 = const()[name = tensor("input_829_pad_0"), val = tensor([0, 0])]; - tensor input_829_dilations_0 = const()[name = tensor("input_829_dilations_0"), val = tensor([1])]; - tensor const_278_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290783808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290790784))), name = tensor("const_278_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_279_to_fp16 = const()[name = tensor("const_279_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290790976)))]; - tensor input_831_cast_fp16 = conv(bias = const_279_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_278_to_fp16_palettized, x = input_827_cast_fp16)[name = tensor("input_831_cast_fp16")]; - tensor input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = tensor("input_833_cast_fp16")]; - tensor x_351_pad_type_0 = const()[name = tensor("x_351_pad_type_0"), val = tensor("valid")]; - tensor x_351_strides_0 = const()[name = tensor("x_351_strides_0"), val = tensor([1])]; - tensor x_351_pad_0 = const()[name = tensor("x_351_pad_0"), val = tensor([0, 0])]; - tensor x_351_dilations_0 = const()[name = tensor("x_351_dilations_0"), val = tensor([1])]; - tensor x_351_groups_0 = const()[name = tensor("x_351_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290793088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291579584))), name = tensor("module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_351_cast_fp16 = conv(dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_833_cast_fp16)[name = tensor("x_351_cast_fp16")]; - tensor input_835_perm_0 = const()[name = tensor("input_835_perm_0"), val = tensor([0, 2, 1])]; - tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = tensor("transpose_201")]; - tensor input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = tensor("input_837_cast_fp16")]; - tensor input_839_axes_0 = const()[name = tensor("input_839_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291579776)))]; - tensor module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291581888)))]; - tensor input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = tensor("input_839_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291584000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294729792))), name = tensor("module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_143_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized, x = input_839_cast_fp16)[name = tensor("linear_143_cast_fp16")]; - tensor input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = tensor("input_843_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294729984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297875776))), name = tensor("module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_144_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized, x = input_843_cast_fp16)[name = tensor("linear_144_cast_fp16")]; - tensor var_2811_to_fp16 = const()[name = tensor("op_2811_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2812_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_2811_to_fp16)[name = tensor("op_2812_cast_fp16")]; - tensor input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_2812_cast_fp16)[name = tensor("input_849_cast_fp16")]; - tensor input_851_axes_0 = const()[name = tensor("input_851_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297875968)))]; - tensor module_layers_15_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297878080)))]; - tensor input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = tensor("input_851_cast_fp16")]; - tensor input_853_axes_0 = const()[name = tensor("input_853_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297880192)))]; - tensor module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297882304)))]; - tensor input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = tensor("input_853_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297884416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301030208))), name = tensor("module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_145_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized, x = input_853_cast_fp16)[name = tensor("linear_145_cast_fp16")]; - tensor input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = tensor("input_857_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301030400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304176192))), name = tensor("module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_146_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized, x = input_857_cast_fp16)[name = tensor("linear_146_cast_fp16")]; - tensor var_2840_to_fp16 = const()[name = tensor("op_2840_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2841_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_2840_to_fp16)[name = tensor("op_2841_cast_fp16")]; - tensor input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_2841_cast_fp16)[name = tensor("input_863_cast_fp16")]; - tensor query_33_axes_0 = const()[name = tensor("query_33_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304176384)))]; - tensor module_layers_16_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304178496)))]; - tensor query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = tensor("query_33_cast_fp16")]; - tensor module_layers_16_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304180608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304967104))), name = tensor("module_layers_16_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_147_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = tensor("linear_147_cast_fp16")]; - tensor var_2857 = const()[name = tensor("op_2857"), val = tensor([1, -1, 8, 128])]; - tensor q_97_cast_fp16 = reshape(shape = var_2857, x = linear_147_cast_fp16)[name = tensor("q_97_cast_fp16")]; - tensor module_layers_16_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304967296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(305753792))), name = tensor("module_layers_16_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_148_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = tensor("linear_148_cast_fp16")]; - tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, -1, 8, 128])]; - tensor k_65_cast_fp16 = reshape(shape = var_2861, x = linear_148_cast_fp16)[name = tensor("k_65_cast_fp16")]; - tensor module_layers_16_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(305753984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306540480))), name = tensor("module_layers_16_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_149_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = tensor("linear_149_cast_fp16")]; - tensor var_2865 = const()[name = tensor("op_2865"), val = tensor([1, -1, 8, 128])]; - tensor v_33_cast_fp16 = reshape(shape = var_2865, x = linear_149_cast_fp16)[name = tensor("v_33_cast_fp16")]; - tensor value_35_perm_0 = const()[name = tensor("value_35_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306540672)))]; - tensor var_2877_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2877_cast_fp16")]; - tensor module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306542784)))]; - tensor var_2879_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2879_cast_fp16")]; - tensor q_with_bias_v_33_perm_0 = const()[name = tensor("q_with_bias_v_33_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_359_transpose_x_0 = const()[name = tensor("x_359_transpose_x_0"), val = tensor(false)]; - tensor x_359_transpose_y_0 = const()[name = tensor("x_359_transpose_y_0"), val = tensor(false)]; - tensor op_2881_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306544896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306832960))), name = tensor("op_2881_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_2879_cast_fp16)[name = tensor("transpose_200")]; - tensor x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_2881_to_fp16_palettized)[name = tensor("x_359_cast_fp16")]; - tensor x_361_pad_0 = const()[name = tensor("x_361_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_361_mode_0 = const()[name = tensor("x_361_mode_0"), val = tensor("constant")]; - tensor const_174_to_fp16 = const()[name = tensor("const_174_to_fp16"), val = tensor(0x0p+0)]; - tensor x_361_cast_fp16 = pad(constant_val = const_174_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = tensor("x_361_cast_fp16")]; - tensor var_2889 = const()[name = tensor("op_2889"), val = tensor([1, 8, -1, 188])]; - tensor x_363_cast_fp16 = reshape(shape = var_2889, x = x_361_cast_fp16)[name = tensor("x_363_cast_fp16")]; - tensor var_2893_begin_0 = const()[name = tensor("op_2893_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2893_end_0 = const()[name = tensor("op_2893_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2893_end_mask_0 = const()[name = tensor("op_2893_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2893_cast_fp16 = slice_by_index(begin = var_2893_begin_0, end = var_2893_end_0, end_mask = var_2893_end_mask_0, x = x_363_cast_fp16)[name = tensor("op_2893_cast_fp16")]; - tensor var_2894 = const()[name = tensor("op_2894"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_65_cast_fp16 = reshape(shape = var_2894, x = var_2893_cast_fp16)[name = tensor("matrix_bd_65_cast_fp16")]; - tensor matrix_ac_33_transpose_x_0 = const()[name = tensor("matrix_ac_33_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_33_transpose_y_0 = const()[name = tensor("matrix_ac_33_transpose_y_0"), val = tensor(false)]; - tensor transpose_128_perm_0 = const()[name = tensor("transpose_128_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_129_perm_0 = const()[name = tensor("transpose_129_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = tensor("transpose_198")]; - tensor transpose_128 = transpose(perm = transpose_128_perm_0, x = var_2877_cast_fp16)[name = tensor("transpose_199")]; - tensor matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = tensor("matrix_ac_33_cast_fp16")]; - tensor matrix_bd_67_begin_0 = const()[name = tensor("matrix_bd_67_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_67_end_0 = const()[name = tensor("matrix_bd_67_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_67_end_mask_0 = const()[name = tensor("matrix_bd_67_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = tensor("matrix_bd_67_cast_fp16")]; - tensor var_2903_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = tensor("op_2903_cast_fp16")]; - tensor _inversed_scores_65_y_0_to_fp16 = const()[name = tensor("_inversed_scores_65_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_65_cast_fp16 = mul(x = var_2903_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = tensor("_inversed_scores_65_cast_fp16")]; - tensor scores_67_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_3)[name = tensor("scores_67_cast_fp16")]; - tensor var_2909_cast_fp16 = softmax(axis = var_30, x = scores_67_cast_fp16)[name = tensor("op_2909_cast_fp16")]; - tensor input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_2909_cast_fp16, cond = mask_3)[name = tensor("input_865_cast_fp16")]; - tensor x_365_transpose_x_0 = const()[name = tensor("x_365_transpose_x_0"), val = tensor(false)]; - tensor x_365_transpose_y_0 = const()[name = tensor("x_365_transpose_y_0"), val = tensor(false)]; - tensor value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_33_cast_fp16)[name = tensor("transpose_197")]; - tensor x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_35_cast_fp16)[name = tensor("x_365_cast_fp16")]; - tensor var_2913_perm_0 = const()[name = tensor("op_2913_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2914 = const()[name = tensor("op_2914"), val = tensor([1, -1, 1024])]; - tensor var_2913_cast_fp16 = transpose(perm = var_2913_perm_0, x = x_365_cast_fp16)[name = tensor("transpose_196")]; - tensor input_867_cast_fp16 = reshape(shape = var_2914, x = var_2913_cast_fp16)[name = tensor("input_867_cast_fp16")]; - tensor module_layers_16_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306833152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307619648))), name = tensor("module_layers_16_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_151_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16_palettized, x = input_867_cast_fp16)[name = tensor("linear_151_cast_fp16")]; - tensor input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = tensor("input_871_cast_fp16")]; - tensor x_369_axes_0 = const()[name = tensor("x_369_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307619840)))]; - tensor module_layers_16_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307621952)))]; - tensor x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = tensor("x_369_cast_fp16")]; - tensor input_873_perm_0 = const()[name = tensor("input_873_perm_0"), val = tensor([0, 2, 1])]; - tensor input_875_pad_type_0 = const()[name = tensor("input_875_pad_type_0"), val = tensor("valid")]; - tensor input_875_strides_0 = const()[name = tensor("input_875_strides_0"), val = tensor([1])]; - tensor input_875_pad_0 = const()[name = tensor("input_875_pad_0"), val = tensor([0, 0])]; - tensor input_875_dilations_0 = const()[name = tensor("input_875_dilations_0"), val = tensor([1])]; - tensor input_875_groups_0 = const()[name = tensor("input_875_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307624064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309196992))), name = tensor("module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = tensor("transpose_195")]; - tensor input_875_cast_fp16 = conv(dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_873_cast_fp16)[name = tensor("input_875_cast_fp16")]; - tensor x_371_split_num_splits_0 = const()[name = tensor("x_371_split_num_splits_0"), val = tensor(2)]; - tensor x_371_split_axis_0 = const()[name = tensor("x_371_split_axis_0"), val = tensor(1)]; - tensor x_371_split_cast_fp16_0, tensor x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = tensor("x_371_split_cast_fp16")]; - tensor x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = tensor("x_371_split_1_sigmoid_cast_fp16")]; - tensor x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = tensor("x_371_cast_fp16")]; - tensor input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_328)[name = tensor("input_877_cast_fp16")]; - tensor input_879_pad_0 = const()[name = tensor("input_879_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_879_mode_0 = const()[name = tensor("input_879_mode_0"), val = tensor("constant")]; - tensor const_177_to_fp16 = const()[name = tensor("const_177_to_fp16"), val = tensor(0x0p+0)]; - tensor input_879_cast_fp16 = pad(constant_val = const_177_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = tensor("input_879_cast_fp16")]; - tensor input_881_pad_type_0 = const()[name = tensor("input_881_pad_type_0"), val = tensor("valid")]; - tensor input_881_groups_0 = const()[name = tensor("input_881_groups_0"), val = tensor(1024)]; - tensor input_881_strides_0 = const()[name = tensor("input_881_strides_0"), val = tensor([1])]; - tensor input_881_pad_0 = const()[name = tensor("input_881_pad_0"), val = tensor([0, 0])]; - tensor input_881_dilations_0 = const()[name = tensor("input_881_dilations_0"), val = tensor([1])]; - tensor const_280_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309197184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309204160))), name = tensor("const_280_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_281_to_fp16 = const()[name = tensor("const_281_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309204352)))]; - tensor input_883_cast_fp16 = conv(bias = const_281_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_280_to_fp16_palettized, x = input_879_cast_fp16)[name = tensor("input_883_cast_fp16")]; - tensor input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = tensor("input_885_cast_fp16")]; - tensor x_373_pad_type_0 = const()[name = tensor("x_373_pad_type_0"), val = tensor("valid")]; - tensor x_373_strides_0 = const()[name = tensor("x_373_strides_0"), val = tensor([1])]; - tensor x_373_pad_0 = const()[name = tensor("x_373_pad_0"), val = tensor([0, 0])]; - tensor x_373_dilations_0 = const()[name = tensor("x_373_dilations_0"), val = tensor([1])]; - tensor x_373_groups_0 = const()[name = tensor("x_373_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309206464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309992960))), name = tensor("module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_373_cast_fp16 = conv(dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_885_cast_fp16)[name = tensor("x_373_cast_fp16")]; - tensor input_887_perm_0 = const()[name = tensor("input_887_perm_0"), val = tensor([0, 2, 1])]; - tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = tensor("transpose_194")]; - tensor input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = tensor("input_889_cast_fp16")]; - tensor input_891_axes_0 = const()[name = tensor("input_891_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309993152)))]; - tensor module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309995264)))]; - tensor input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = tensor("input_891_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309997376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313143168))), name = tensor("module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_152_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized, x = input_891_cast_fp16)[name = tensor("linear_152_cast_fp16")]; - tensor input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = tensor("input_895_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313143360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316289152))), name = tensor("module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_153_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized, x = input_895_cast_fp16)[name = tensor("linear_153_cast_fp16")]; - tensor var_2974_to_fp16 = const()[name = tensor("op_2974_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2975_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_2974_to_fp16)[name = tensor("op_2975_cast_fp16")]; - tensor input_901_cast_fp16 = add(x = input_889_cast_fp16, y = var_2975_cast_fp16)[name = tensor("input_901_cast_fp16")]; - tensor input_903_axes_0 = const()[name = tensor("input_903_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316289344)))]; - tensor module_layers_16_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316291456)))]; - tensor input_903_cast_fp16 = layer_norm(axes = input_903_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_901_cast_fp16)[name = tensor("input_903_cast_fp16")]; - tensor input_905_axes_0 = const()[name = tensor("input_905_axes_0"), val = tensor([-1])]; - tensor module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316293568)))]; - tensor module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316295680)))]; - tensor input_905_cast_fp16 = layer_norm(axes = input_905_axes_0, beta = module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_903_cast_fp16)[name = tensor("input_905_cast_fp16")]; - tensor module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316297792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319443584))), name = tensor("module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_154_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized, x = input_905_cast_fp16)[name = tensor("linear_154_cast_fp16")]; - tensor input_909_cast_fp16 = silu(x = linear_154_cast_fp16)[name = tensor("input_909_cast_fp16")]; - tensor module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319443776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322589568))), name = tensor("module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_155_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized, x = input_909_cast_fp16)[name = tensor("linear_155_cast_fp16")]; - tensor var_3003_to_fp16 = const()[name = tensor("op_3003_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3004_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3003_to_fp16)[name = tensor("op_3004_cast_fp16")]; - tensor input_915_cast_fp16 = add(x = input_903_cast_fp16, y = var_3004_cast_fp16)[name = tensor("input_915_cast_fp16")]; - tensor query_35_axes_0 = const()[name = tensor("query_35_axes_0"), val = tensor([-1])]; - tensor module_layers_17_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_17_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322589760)))]; - tensor module_layers_17_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_17_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322591872)))]; - tensor query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_self_att_weight_to_fp16, x = input_915_cast_fp16)[name = tensor("query_35_cast_fp16")]; - tensor module_layers_17_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322593984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323380480))), name = tensor("module_layers_17_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_156_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_q_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = tensor("linear_156_cast_fp16")]; - tensor var_3020 = const()[name = tensor("op_3020"), val = tensor([1, -1, 8, 128])]; - tensor q_103_cast_fp16 = reshape(shape = var_3020, x = linear_156_cast_fp16)[name = tensor("q_103_cast_fp16")]; - tensor module_layers_17_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323380672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324167168))), name = tensor("module_layers_17_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_157_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_k_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = tensor("linear_157_cast_fp16")]; - tensor var_3024 = const()[name = tensor("op_3024"), val = tensor([1, -1, 8, 128])]; - tensor k_69_cast_fp16 = reshape(shape = var_3024, x = linear_157_cast_fp16)[name = tensor("k_69_cast_fp16")]; - tensor module_layers_17_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324167360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324953856))), name = tensor("module_layers_17_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_158_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_v_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = tensor("linear_158_cast_fp16")]; - tensor var_3028 = const()[name = tensor("op_3028"), val = tensor([1, -1, 8, 128])]; - tensor v_35_cast_fp16 = reshape(shape = var_3028, x = linear_158_cast_fp16)[name = tensor("v_35_cast_fp16")]; - tensor value_37_perm_0 = const()[name = tensor("value_37_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324954048)))]; - tensor var_3040_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3040_cast_fp16")]; - tensor module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324956160)))]; - tensor var_3042_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3042_cast_fp16")]; - tensor q_with_bias_v_35_perm_0 = const()[name = tensor("q_with_bias_v_35_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_381_transpose_x_0 = const()[name = tensor("x_381_transpose_x_0"), val = tensor(false)]; - tensor x_381_transpose_y_0 = const()[name = tensor("x_381_transpose_y_0"), val = tensor(false)]; - tensor op_3044_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324958272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325246336))), name = tensor("op_3044_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3042_cast_fp16)[name = tensor("transpose_193")]; - tensor x_381_cast_fp16 = matmul(transpose_x = x_381_transpose_x_0, transpose_y = x_381_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_3044_to_fp16_palettized)[name = tensor("x_381_cast_fp16")]; - tensor x_383_pad_0 = const()[name = tensor("x_383_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_383_mode_0 = const()[name = tensor("x_383_mode_0"), val = tensor("constant")]; - tensor const_184_to_fp16 = const()[name = tensor("const_184_to_fp16"), val = tensor(0x0p+0)]; - tensor x_383_cast_fp16 = pad(constant_val = const_184_to_fp16, mode = x_383_mode_0, pad = x_383_pad_0, x = x_381_cast_fp16)[name = tensor("x_383_cast_fp16")]; - tensor var_3052 = const()[name = tensor("op_3052"), val = tensor([1, 8, -1, 188])]; - tensor x_385_cast_fp16 = reshape(shape = var_3052, x = x_383_cast_fp16)[name = tensor("x_385_cast_fp16")]; - tensor var_3056_begin_0 = const()[name = tensor("op_3056_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3056_end_0 = const()[name = tensor("op_3056_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3056_end_mask_0 = const()[name = tensor("op_3056_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3056_cast_fp16 = slice_by_index(begin = var_3056_begin_0, end = var_3056_end_0, end_mask = var_3056_end_mask_0, x = x_385_cast_fp16)[name = tensor("op_3056_cast_fp16")]; - tensor var_3057 = const()[name = tensor("op_3057"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_69_cast_fp16 = reshape(shape = var_3057, x = var_3056_cast_fp16)[name = tensor("matrix_bd_69_cast_fp16")]; - tensor matrix_ac_35_transpose_x_0 = const()[name = tensor("matrix_ac_35_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_35_transpose_y_0 = const()[name = tensor("matrix_ac_35_transpose_y_0"), val = tensor(false)]; - tensor transpose_130_perm_0 = const()[name = tensor("transpose_130_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_131_perm_0 = const()[name = tensor("transpose_131_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = tensor("transpose_191")]; - tensor transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3040_cast_fp16)[name = tensor("transpose_192")]; - tensor matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = tensor("matrix_ac_35_cast_fp16")]; - tensor matrix_bd_71_begin_0 = const()[name = tensor("matrix_bd_71_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_71_end_0 = const()[name = tensor("matrix_bd_71_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_71_end_mask_0 = const()[name = tensor("matrix_bd_71_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = tensor("matrix_bd_71_cast_fp16")]; - tensor var_3066_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = tensor("op_3066_cast_fp16")]; - tensor _inversed_scores_69_y_0_to_fp16 = const()[name = tensor("_inversed_scores_69_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_69_cast_fp16 = mul(x = var_3066_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = tensor("_inversed_scores_69_cast_fp16")]; - tensor scores_71_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_3)[name = tensor("scores_71_cast_fp16")]; - tensor var_3072_cast_fp16 = softmax(axis = var_30, x = scores_71_cast_fp16)[name = tensor("op_3072_cast_fp16")]; - tensor input_917_cast_fp16 = select(a = var_11_to_fp16, b = var_3072_cast_fp16, cond = mask_3)[name = tensor("input_917_cast_fp16")]; - tensor x_387_transpose_x_0 = const()[name = tensor("x_387_transpose_x_0"), val = tensor(false)]; - tensor x_387_transpose_y_0 = const()[name = tensor("x_387_transpose_y_0"), val = tensor(false)]; - tensor value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_35_cast_fp16)[name = tensor("transpose_190")]; - tensor x_387_cast_fp16 = matmul(transpose_x = x_387_transpose_x_0, transpose_y = x_387_transpose_y_0, x = input_917_cast_fp16, y = value_37_cast_fp16)[name = tensor("x_387_cast_fp16")]; - tensor var_3076_perm_0 = const()[name = tensor("op_3076_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3077 = const()[name = tensor("op_3077"), val = tensor([1, -1, 1024])]; - tensor var_3076_cast_fp16 = transpose(perm = var_3076_perm_0, x = x_387_cast_fp16)[name = tensor("transpose_189")]; - tensor input_919_cast_fp16 = reshape(shape = var_3077, x = var_3076_cast_fp16)[name = tensor("input_919_cast_fp16")]; - tensor module_layers_17_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325246528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(326033024))), name = tensor("module_layers_17_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_160_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_out_weight_to_fp16_palettized, x = input_919_cast_fp16)[name = tensor("linear_160_cast_fp16")]; - tensor input_923_cast_fp16 = add(x = input_915_cast_fp16, y = linear_160_cast_fp16)[name = tensor("input_923_cast_fp16")]; - tensor x_391_axes_0 = const()[name = tensor("x_391_axes_0"), val = tensor([-1])]; - tensor module_layers_17_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_17_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(326033216)))]; - tensor module_layers_17_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_17_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(326035328)))]; - tensor x_391_cast_fp16 = layer_norm(axes = x_391_axes_0, beta = module_layers_17_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_conv_weight_to_fp16, x = input_923_cast_fp16)[name = tensor("x_391_cast_fp16")]; - tensor input_925_perm_0 = const()[name = tensor("input_925_perm_0"), val = tensor([0, 2, 1])]; - tensor input_927_pad_type_0 = const()[name = tensor("input_927_pad_type_0"), val = tensor("valid")]; - tensor input_927_strides_0 = const()[name = tensor("input_927_strides_0"), val = tensor([1])]; - tensor input_927_pad_0 = const()[name = tensor("input_927_pad_0"), val = tensor([0, 0])]; - tensor input_927_dilations_0 = const()[name = tensor("input_927_dilations_0"), val = tensor([1])]; - tensor input_927_groups_0 = const()[name = tensor("input_927_groups_0"), val = tensor(1)]; - tensor module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(326037440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(327610368))), name = tensor("module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_925_cast_fp16 = transpose(perm = input_925_perm_0, x = x_391_cast_fp16)[name = tensor("transpose_188")]; - tensor input_927_cast_fp16 = conv(dilations = input_927_dilations_0, groups = input_927_groups_0, pad = input_927_pad_0, pad_type = input_927_pad_type_0, strides = input_927_strides_0, weight = module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_925_cast_fp16)[name = tensor("input_927_cast_fp16")]; - tensor x_393_split_num_splits_0 = const()[name = tensor("x_393_split_num_splits_0"), val = tensor(2)]; - tensor x_393_split_axis_0 = const()[name = tensor("x_393_split_axis_0"), val = tensor(1)]; - tensor x_393_split_cast_fp16_0, tensor x_393_split_cast_fp16_1 = split(axis = x_393_split_axis_0, num_splits = x_393_split_num_splits_0, x = input_927_cast_fp16)[name = tensor("x_393_split_cast_fp16")]; - tensor x_393_split_1_sigmoid_cast_fp16 = sigmoid(x = x_393_split_cast_fp16_1)[name = tensor("x_393_split_1_sigmoid_cast_fp16")]; - tensor x_393_cast_fp16 = mul(x = x_393_split_cast_fp16_0, y = x_393_split_1_sigmoid_cast_fp16)[name = tensor("x_393_cast_fp16")]; - tensor input_929_cast_fp16 = select(a = var_11_to_fp16, b = x_393_cast_fp16, cond = var_328)[name = tensor("input_929_cast_fp16")]; - tensor input_931_pad_0 = const()[name = tensor("input_931_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_931_mode_0 = const()[name = tensor("input_931_mode_0"), val = tensor("constant")]; - tensor const_187_to_fp16 = const()[name = tensor("const_187_to_fp16"), val = tensor(0x0p+0)]; - tensor input_931_cast_fp16 = pad(constant_val = const_187_to_fp16, mode = input_931_mode_0, pad = input_931_pad_0, x = input_929_cast_fp16)[name = tensor("input_931_cast_fp16")]; - tensor input_933_pad_type_0 = const()[name = tensor("input_933_pad_type_0"), val = tensor("valid")]; - tensor input_933_groups_0 = const()[name = tensor("input_933_groups_0"), val = tensor(1024)]; - tensor input_933_strides_0 = const()[name = tensor("input_933_strides_0"), val = tensor([1])]; - tensor input_933_pad_0 = const()[name = tensor("input_933_pad_0"), val = tensor([0, 0])]; - tensor input_933_dilations_0 = const()[name = tensor("input_933_dilations_0"), val = tensor([1])]; - tensor const_282_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(327610560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(327617536))), name = tensor("const_282_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_283_to_fp16 = const()[name = tensor("const_283_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(327617728)))]; - tensor input_935_cast_fp16 = conv(bias = const_283_to_fp16, dilations = input_933_dilations_0, groups = input_933_groups_0, pad = input_933_pad_0, pad_type = input_933_pad_type_0, strides = input_933_strides_0, weight = const_282_to_fp16_palettized, x = input_931_cast_fp16)[name = tensor("input_935_cast_fp16")]; - tensor input_937_cast_fp16 = silu(x = input_935_cast_fp16)[name = tensor("input_937_cast_fp16")]; - tensor x_395_pad_type_0 = const()[name = tensor("x_395_pad_type_0"), val = tensor("valid")]; - tensor x_395_strides_0 = const()[name = tensor("x_395_strides_0"), val = tensor([1])]; - tensor x_395_pad_0 = const()[name = tensor("x_395_pad_0"), val = tensor([0, 0])]; - tensor x_395_dilations_0 = const()[name = tensor("x_395_dilations_0"), val = tensor([1])]; - tensor x_395_groups_0 = const()[name = tensor("x_395_groups_0"), val = tensor(1)]; - tensor module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(327619840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328406336))), name = tensor("module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_395_cast_fp16 = conv(dilations = x_395_dilations_0, groups = x_395_groups_0, pad = x_395_pad_0, pad_type = x_395_pad_type_0, strides = x_395_strides_0, weight = module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_937_cast_fp16)[name = tensor("x_395_cast_fp16")]; - tensor input_939_perm_0 = const()[name = tensor("input_939_perm_0"), val = tensor([0, 2, 1])]; - tensor input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_395_cast_fp16)[name = tensor("transpose_187")]; - tensor input_941_cast_fp16 = add(x = input_923_cast_fp16, y = input_939_cast_fp16)[name = tensor("input_941_cast_fp16")]; - tensor input_943_axes_0 = const()[name = tensor("input_943_axes_0"), val = tensor([-1])]; - tensor module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328406528)))]; - tensor module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328408640)))]; - tensor input_943_cast_fp16 = layer_norm(axes = input_943_axes_0, beta = module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_941_cast_fp16)[name = tensor("input_943_cast_fp16")]; - tensor module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328410752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(331556544))), name = tensor("module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_161_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized, x = input_943_cast_fp16)[name = tensor("linear_161_cast_fp16")]; - tensor input_947_cast_fp16 = silu(x = linear_161_cast_fp16)[name = tensor("input_947_cast_fp16")]; - tensor module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(331556736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334702528))), name = tensor("module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_162_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized, x = input_947_cast_fp16)[name = tensor("linear_162_cast_fp16")]; - tensor var_3137_to_fp16 = const()[name = tensor("op_3137_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3138_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3137_to_fp16)[name = tensor("op_3138_cast_fp16")]; - tensor input_953_cast_fp16 = add(x = input_941_cast_fp16, y = var_3138_cast_fp16)[name = tensor("input_953_cast_fp16")]; - tensor input_955_axes_0 = const()[name = tensor("input_955_axes_0"), val = tensor([-1])]; - tensor module_layers_17_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_17_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334702720)))]; - tensor module_layers_17_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_17_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334704832)))]; - tensor input_955_cast_fp16 = layer_norm(axes = input_955_axes_0, beta = module_layers_17_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_out_weight_to_fp16, x = input_953_cast_fp16)[name = tensor("input_955_cast_fp16")]; - tensor input_957_axes_0 = const()[name = tensor("input_957_axes_0"), val = tensor([-1])]; - tensor module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334706944)))]; - tensor module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334709056)))]; - tensor input_957_cast_fp16 = layer_norm(axes = input_957_axes_0, beta = module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_955_cast_fp16)[name = tensor("input_957_cast_fp16")]; - tensor module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334711168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337856960))), name = tensor("module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_163_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized, x = input_957_cast_fp16)[name = tensor("linear_163_cast_fp16")]; - tensor input_961_cast_fp16 = silu(x = linear_163_cast_fp16)[name = tensor("input_961_cast_fp16")]; - tensor module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337857152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341002944))), name = tensor("module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_164_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized, x = input_961_cast_fp16)[name = tensor("linear_164_cast_fp16")]; - tensor var_3166_to_fp16 = const()[name = tensor("op_3166_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3167_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3166_to_fp16)[name = tensor("op_3167_cast_fp16")]; - tensor input_967_cast_fp16 = add(x = input_955_cast_fp16, y = var_3167_cast_fp16)[name = tensor("input_967_cast_fp16")]; - tensor query_37_axes_0 = const()[name = tensor("query_37_axes_0"), val = tensor([-1])]; - tensor module_layers_18_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_18_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341003136)))]; - tensor module_layers_18_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_18_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341005248)))]; - tensor query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_self_att_weight_to_fp16, x = input_967_cast_fp16)[name = tensor("query_37_cast_fp16")]; - tensor module_layers_18_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341007360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341793856))), name = tensor("module_layers_18_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_165_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_q_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = tensor("linear_165_cast_fp16")]; - tensor var_3183 = const()[name = tensor("op_3183"), val = tensor([1, -1, 8, 128])]; - tensor q_109_cast_fp16 = reshape(shape = var_3183, x = linear_165_cast_fp16)[name = tensor("q_109_cast_fp16")]; - tensor module_layers_18_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341794048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(342580544))), name = tensor("module_layers_18_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_166_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_k_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = tensor("linear_166_cast_fp16")]; - tensor var_3187 = const()[name = tensor("op_3187"), val = tensor([1, -1, 8, 128])]; - tensor k_73_cast_fp16 = reshape(shape = var_3187, x = linear_166_cast_fp16)[name = tensor("k_73_cast_fp16")]; - tensor module_layers_18_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(342580736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343367232))), name = tensor("module_layers_18_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_167_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_v_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = tensor("linear_167_cast_fp16")]; - tensor var_3191 = const()[name = tensor("op_3191"), val = tensor([1, -1, 8, 128])]; - tensor v_37_cast_fp16 = reshape(shape = var_3191, x = linear_167_cast_fp16)[name = tensor("v_37_cast_fp16")]; - tensor value_39_perm_0 = const()[name = tensor("value_39_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343367424)))]; - tensor var_3203_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3203_cast_fp16")]; - tensor module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343369536)))]; - tensor var_3205_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3205_cast_fp16")]; - tensor q_with_bias_v_37_perm_0 = const()[name = tensor("q_with_bias_v_37_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_403_transpose_x_0 = const()[name = tensor("x_403_transpose_x_0"), val = tensor(false)]; - tensor x_403_transpose_y_0 = const()[name = tensor("x_403_transpose_y_0"), val = tensor(false)]; - tensor op_3207_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343371648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343659712))), name = tensor("op_3207_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3205_cast_fp16)[name = tensor("transpose_186")]; - tensor x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_3207_to_fp16_palettized)[name = tensor("x_403_cast_fp16")]; - tensor x_405_pad_0 = const()[name = tensor("x_405_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_405_mode_0 = const()[name = tensor("x_405_mode_0"), val = tensor("constant")]; - tensor const_194_to_fp16 = const()[name = tensor("const_194_to_fp16"), val = tensor(0x0p+0)]; - tensor x_405_cast_fp16 = pad(constant_val = const_194_to_fp16, mode = x_405_mode_0, pad = x_405_pad_0, x = x_403_cast_fp16)[name = tensor("x_405_cast_fp16")]; - tensor var_3215 = const()[name = tensor("op_3215"), val = tensor([1, 8, -1, 188])]; - tensor x_407_cast_fp16 = reshape(shape = var_3215, x = x_405_cast_fp16)[name = tensor("x_407_cast_fp16")]; - tensor var_3219_begin_0 = const()[name = tensor("op_3219_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3219_end_0 = const()[name = tensor("op_3219_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3219_end_mask_0 = const()[name = tensor("op_3219_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3219_cast_fp16 = slice_by_index(begin = var_3219_begin_0, end = var_3219_end_0, end_mask = var_3219_end_mask_0, x = x_407_cast_fp16)[name = tensor("op_3219_cast_fp16")]; - tensor var_3220 = const()[name = tensor("op_3220"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_73_cast_fp16 = reshape(shape = var_3220, x = var_3219_cast_fp16)[name = tensor("matrix_bd_73_cast_fp16")]; - tensor matrix_ac_37_transpose_x_0 = const()[name = tensor("matrix_ac_37_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_37_transpose_y_0 = const()[name = tensor("matrix_ac_37_transpose_y_0"), val = tensor(false)]; - tensor transpose_132_perm_0 = const()[name = tensor("transpose_132_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_133_perm_0 = const()[name = tensor("transpose_133_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = tensor("transpose_184")]; - tensor transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3203_cast_fp16)[name = tensor("transpose_185")]; - tensor matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = tensor("matrix_ac_37_cast_fp16")]; - tensor matrix_bd_75_begin_0 = const()[name = tensor("matrix_bd_75_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_75_end_0 = const()[name = tensor("matrix_bd_75_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_75_end_mask_0 = const()[name = tensor("matrix_bd_75_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = tensor("matrix_bd_75_cast_fp16")]; - tensor var_3229_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = tensor("op_3229_cast_fp16")]; - tensor _inversed_scores_73_y_0_to_fp16 = const()[name = tensor("_inversed_scores_73_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_73_cast_fp16 = mul(x = var_3229_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = tensor("_inversed_scores_73_cast_fp16")]; - tensor scores_75_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_3)[name = tensor("scores_75_cast_fp16")]; - tensor var_3235_cast_fp16 = softmax(axis = var_30, x = scores_75_cast_fp16)[name = tensor("op_3235_cast_fp16")]; - tensor input_969_cast_fp16 = select(a = var_11_to_fp16, b = var_3235_cast_fp16, cond = mask_3)[name = tensor("input_969_cast_fp16")]; - tensor x_409_transpose_x_0 = const()[name = tensor("x_409_transpose_x_0"), val = tensor(false)]; - tensor x_409_transpose_y_0 = const()[name = tensor("x_409_transpose_y_0"), val = tensor(false)]; - tensor value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_37_cast_fp16)[name = tensor("transpose_183")]; - tensor x_409_cast_fp16 = matmul(transpose_x = x_409_transpose_x_0, transpose_y = x_409_transpose_y_0, x = input_969_cast_fp16, y = value_39_cast_fp16)[name = tensor("x_409_cast_fp16")]; - tensor var_3239_perm_0 = const()[name = tensor("op_3239_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3240 = const()[name = tensor("op_3240"), val = tensor([1, -1, 1024])]; - tensor var_3239_cast_fp16 = transpose(perm = var_3239_perm_0, x = x_409_cast_fp16)[name = tensor("transpose_182")]; - tensor input_971_cast_fp16 = reshape(shape = var_3240, x = var_3239_cast_fp16)[name = tensor("input_971_cast_fp16")]; - tensor module_layers_18_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343659904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344446400))), name = tensor("module_layers_18_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_169_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_out_weight_to_fp16_palettized, x = input_971_cast_fp16)[name = tensor("linear_169_cast_fp16")]; - tensor input_975_cast_fp16 = add(x = input_967_cast_fp16, y = linear_169_cast_fp16)[name = tensor("input_975_cast_fp16")]; - tensor x_413_axes_0 = const()[name = tensor("x_413_axes_0"), val = tensor([-1])]; - tensor module_layers_18_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_18_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344446592)))]; - tensor module_layers_18_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_18_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344448704)))]; - tensor x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = module_layers_18_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_conv_weight_to_fp16, x = input_975_cast_fp16)[name = tensor("x_413_cast_fp16")]; - tensor input_977_perm_0 = const()[name = tensor("input_977_perm_0"), val = tensor([0, 2, 1])]; - tensor input_979_pad_type_0 = const()[name = tensor("input_979_pad_type_0"), val = tensor("valid")]; - tensor input_979_strides_0 = const()[name = tensor("input_979_strides_0"), val = tensor([1])]; - tensor input_979_pad_0 = const()[name = tensor("input_979_pad_0"), val = tensor([0, 0])]; - tensor input_979_dilations_0 = const()[name = tensor("input_979_dilations_0"), val = tensor([1])]; - tensor input_979_groups_0 = const()[name = tensor("input_979_groups_0"), val = tensor(1)]; - tensor module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344450816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346023744))), name = tensor("module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_977_cast_fp16 = transpose(perm = input_977_perm_0, x = x_413_cast_fp16)[name = tensor("transpose_181")]; - tensor input_979_cast_fp16 = conv(dilations = input_979_dilations_0, groups = input_979_groups_0, pad = input_979_pad_0, pad_type = input_979_pad_type_0, strides = input_979_strides_0, weight = module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_977_cast_fp16)[name = tensor("input_979_cast_fp16")]; - tensor x_415_split_num_splits_0 = const()[name = tensor("x_415_split_num_splits_0"), val = tensor(2)]; - tensor x_415_split_axis_0 = const()[name = tensor("x_415_split_axis_0"), val = tensor(1)]; - tensor x_415_split_cast_fp16_0, tensor x_415_split_cast_fp16_1 = split(axis = x_415_split_axis_0, num_splits = x_415_split_num_splits_0, x = input_979_cast_fp16)[name = tensor("x_415_split_cast_fp16")]; - tensor x_415_split_1_sigmoid_cast_fp16 = sigmoid(x = x_415_split_cast_fp16_1)[name = tensor("x_415_split_1_sigmoid_cast_fp16")]; - tensor x_415_cast_fp16 = mul(x = x_415_split_cast_fp16_0, y = x_415_split_1_sigmoid_cast_fp16)[name = tensor("x_415_cast_fp16")]; - tensor input_981_cast_fp16 = select(a = var_11_to_fp16, b = x_415_cast_fp16, cond = var_328)[name = tensor("input_981_cast_fp16")]; - tensor input_983_pad_0 = const()[name = tensor("input_983_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_983_mode_0 = const()[name = tensor("input_983_mode_0"), val = tensor("constant")]; - tensor const_197_to_fp16 = const()[name = tensor("const_197_to_fp16"), val = tensor(0x0p+0)]; - tensor input_983_cast_fp16 = pad(constant_val = const_197_to_fp16, mode = input_983_mode_0, pad = input_983_pad_0, x = input_981_cast_fp16)[name = tensor("input_983_cast_fp16")]; - tensor input_985_pad_type_0 = const()[name = tensor("input_985_pad_type_0"), val = tensor("valid")]; - tensor input_985_groups_0 = const()[name = tensor("input_985_groups_0"), val = tensor(1024)]; - tensor input_985_strides_0 = const()[name = tensor("input_985_strides_0"), val = tensor([1])]; - tensor input_985_pad_0 = const()[name = tensor("input_985_pad_0"), val = tensor([0, 0])]; - tensor input_985_dilations_0 = const()[name = tensor("input_985_dilations_0"), val = tensor([1])]; - tensor const_284_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346023936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346030912))), name = tensor("const_284_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_285_to_fp16 = const()[name = tensor("const_285_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346031104)))]; - tensor input_987_cast_fp16 = conv(bias = const_285_to_fp16, dilations = input_985_dilations_0, groups = input_985_groups_0, pad = input_985_pad_0, pad_type = input_985_pad_type_0, strides = input_985_strides_0, weight = const_284_to_fp16_palettized, x = input_983_cast_fp16)[name = tensor("input_987_cast_fp16")]; - tensor input_989_cast_fp16 = silu(x = input_987_cast_fp16)[name = tensor("input_989_cast_fp16")]; - tensor x_417_pad_type_0 = const()[name = tensor("x_417_pad_type_0"), val = tensor("valid")]; - tensor x_417_strides_0 = const()[name = tensor("x_417_strides_0"), val = tensor([1])]; - tensor x_417_pad_0 = const()[name = tensor("x_417_pad_0"), val = tensor([0, 0])]; - tensor x_417_dilations_0 = const()[name = tensor("x_417_dilations_0"), val = tensor([1])]; - tensor x_417_groups_0 = const()[name = tensor("x_417_groups_0"), val = tensor(1)]; - tensor module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346033216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346819712))), name = tensor("module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_417_cast_fp16 = conv(dilations = x_417_dilations_0, groups = x_417_groups_0, pad = x_417_pad_0, pad_type = x_417_pad_type_0, strides = x_417_strides_0, weight = module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_989_cast_fp16)[name = tensor("x_417_cast_fp16")]; - tensor input_991_perm_0 = const()[name = tensor("input_991_perm_0"), val = tensor([0, 2, 1])]; - tensor input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_417_cast_fp16)[name = tensor("transpose_180")]; - tensor input_993_cast_fp16 = add(x = input_975_cast_fp16, y = input_991_cast_fp16)[name = tensor("input_993_cast_fp16")]; - tensor input_995_axes_0 = const()[name = tensor("input_995_axes_0"), val = tensor([-1])]; - tensor module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346819904)))]; - tensor module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346822016)))]; - tensor input_995_cast_fp16 = layer_norm(axes = input_995_axes_0, beta = module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_993_cast_fp16)[name = tensor("input_995_cast_fp16")]; - tensor module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346824128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(349969920))), name = tensor("module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_170_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized, x = input_995_cast_fp16)[name = tensor("linear_170_cast_fp16")]; - tensor input_999_cast_fp16 = silu(x = linear_170_cast_fp16)[name = tensor("input_999_cast_fp16")]; - tensor module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(349970112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353115904))), name = tensor("module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_171_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized, x = input_999_cast_fp16)[name = tensor("linear_171_cast_fp16")]; - tensor var_3300_to_fp16 = const()[name = tensor("op_3300_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3301_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3300_to_fp16)[name = tensor("op_3301_cast_fp16")]; - tensor input_1005_cast_fp16 = add(x = input_993_cast_fp16, y = var_3301_cast_fp16)[name = tensor("input_1005_cast_fp16")]; - tensor input_1007_axes_0 = const()[name = tensor("input_1007_axes_0"), val = tensor([-1])]; - tensor module_layers_18_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_18_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353116096)))]; - tensor module_layers_18_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_18_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353118208)))]; - tensor input_1007_cast_fp16 = layer_norm(axes = input_1007_axes_0, beta = module_layers_18_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_out_weight_to_fp16, x = input_1005_cast_fp16)[name = tensor("input_1007_cast_fp16")]; - tensor input_1009_axes_0 = const()[name = tensor("input_1009_axes_0"), val = tensor([-1])]; - tensor module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353120320)))]; - tensor module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353122432)))]; - tensor input_1009_cast_fp16 = layer_norm(axes = input_1009_axes_0, beta = module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1007_cast_fp16)[name = tensor("input_1009_cast_fp16")]; - tensor module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353124544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(356270336))), name = tensor("module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_172_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1009_cast_fp16)[name = tensor("linear_172_cast_fp16")]; - tensor input_1013_cast_fp16 = silu(x = linear_172_cast_fp16)[name = tensor("input_1013_cast_fp16")]; - tensor module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(356270528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359416320))), name = tensor("module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_173_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1013_cast_fp16)[name = tensor("linear_173_cast_fp16")]; - tensor var_3329_to_fp16 = const()[name = tensor("op_3329_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3330_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3329_to_fp16)[name = tensor("op_3330_cast_fp16")]; - tensor input_1019_cast_fp16 = add(x = input_1007_cast_fp16, y = var_3330_cast_fp16)[name = tensor("input_1019_cast_fp16")]; - tensor query_39_axes_0 = const()[name = tensor("query_39_axes_0"), val = tensor([-1])]; - tensor module_layers_19_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_19_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359416512)))]; - tensor module_layers_19_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_19_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359418624)))]; - tensor query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_self_att_weight_to_fp16, x = input_1019_cast_fp16)[name = tensor("query_39_cast_fp16")]; - tensor module_layers_19_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359420736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(360207232))), name = tensor("module_layers_19_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_174_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_q_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = tensor("linear_174_cast_fp16")]; - tensor var_3346 = const()[name = tensor("op_3346"), val = tensor([1, -1, 8, 128])]; - tensor q_115_cast_fp16 = reshape(shape = var_3346, x = linear_174_cast_fp16)[name = tensor("q_115_cast_fp16")]; - tensor module_layers_19_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(360207424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(360993920))), name = tensor("module_layers_19_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_175_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_k_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = tensor("linear_175_cast_fp16")]; - tensor var_3350 = const()[name = tensor("op_3350"), val = tensor([1, -1, 8, 128])]; - tensor k_77_cast_fp16 = reshape(shape = var_3350, x = linear_175_cast_fp16)[name = tensor("k_77_cast_fp16")]; - tensor module_layers_19_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(360994112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361780608))), name = tensor("module_layers_19_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_176_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_v_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = tensor("linear_176_cast_fp16")]; - tensor var_3354 = const()[name = tensor("op_3354"), val = tensor([1, -1, 8, 128])]; - tensor v_39_cast_fp16 = reshape(shape = var_3354, x = linear_176_cast_fp16)[name = tensor("v_39_cast_fp16")]; - tensor value_41_perm_0 = const()[name = tensor("value_41_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361780800)))]; - tensor var_3366_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3366_cast_fp16")]; - tensor module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361782912)))]; - tensor var_3368_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3368_cast_fp16")]; - tensor q_with_bias_v_39_perm_0 = const()[name = tensor("q_with_bias_v_39_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_425_transpose_x_0 = const()[name = tensor("x_425_transpose_x_0"), val = tensor(false)]; - tensor x_425_transpose_y_0 = const()[name = tensor("x_425_transpose_y_0"), val = tensor(false)]; - tensor op_3370_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361785024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362073088))), name = tensor("op_3370_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3368_cast_fp16)[name = tensor("transpose_179")]; - tensor x_425_cast_fp16 = matmul(transpose_x = x_425_transpose_x_0, transpose_y = x_425_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_3370_to_fp16_palettized)[name = tensor("x_425_cast_fp16")]; - tensor x_427_pad_0 = const()[name = tensor("x_427_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_427_mode_0 = const()[name = tensor("x_427_mode_0"), val = tensor("constant")]; - tensor const_204_to_fp16 = const()[name = tensor("const_204_to_fp16"), val = tensor(0x0p+0)]; - tensor x_427_cast_fp16 = pad(constant_val = const_204_to_fp16, mode = x_427_mode_0, pad = x_427_pad_0, x = x_425_cast_fp16)[name = tensor("x_427_cast_fp16")]; - tensor var_3378 = const()[name = tensor("op_3378"), val = tensor([1, 8, -1, 188])]; - tensor x_429_cast_fp16 = reshape(shape = var_3378, x = x_427_cast_fp16)[name = tensor("x_429_cast_fp16")]; - tensor var_3382_begin_0 = const()[name = tensor("op_3382_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3382_end_0 = const()[name = tensor("op_3382_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3382_end_mask_0 = const()[name = tensor("op_3382_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3382_cast_fp16 = slice_by_index(begin = var_3382_begin_0, end = var_3382_end_0, end_mask = var_3382_end_mask_0, x = x_429_cast_fp16)[name = tensor("op_3382_cast_fp16")]; - tensor var_3383 = const()[name = tensor("op_3383"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_77_cast_fp16 = reshape(shape = var_3383, x = var_3382_cast_fp16)[name = tensor("matrix_bd_77_cast_fp16")]; - tensor matrix_ac_39_transpose_x_0 = const()[name = tensor("matrix_ac_39_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_39_transpose_y_0 = const()[name = tensor("matrix_ac_39_transpose_y_0"), val = tensor(false)]; - tensor transpose_134_perm_0 = const()[name = tensor("transpose_134_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_135_perm_0 = const()[name = tensor("transpose_135_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = tensor("transpose_177")]; - tensor transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3366_cast_fp16)[name = tensor("transpose_178")]; - tensor matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = tensor("matrix_ac_39_cast_fp16")]; - tensor matrix_bd_79_begin_0 = const()[name = tensor("matrix_bd_79_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_79_end_0 = const()[name = tensor("matrix_bd_79_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_79_end_mask_0 = const()[name = tensor("matrix_bd_79_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = tensor("matrix_bd_79_cast_fp16")]; - tensor var_3392_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = tensor("op_3392_cast_fp16")]; - tensor _inversed_scores_77_y_0_to_fp16 = const()[name = tensor("_inversed_scores_77_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_77_cast_fp16 = mul(x = var_3392_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = tensor("_inversed_scores_77_cast_fp16")]; - tensor scores_79_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_3)[name = tensor("scores_79_cast_fp16")]; - tensor var_3398_cast_fp16 = softmax(axis = var_30, x = scores_79_cast_fp16)[name = tensor("op_3398_cast_fp16")]; - tensor input_1021_cast_fp16 = select(a = var_11_to_fp16, b = var_3398_cast_fp16, cond = mask_3)[name = tensor("input_1021_cast_fp16")]; - tensor x_431_transpose_x_0 = const()[name = tensor("x_431_transpose_x_0"), val = tensor(false)]; - tensor x_431_transpose_y_0 = const()[name = tensor("x_431_transpose_y_0"), val = tensor(false)]; - tensor value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_39_cast_fp16)[name = tensor("transpose_176")]; - tensor x_431_cast_fp16 = matmul(transpose_x = x_431_transpose_x_0, transpose_y = x_431_transpose_y_0, x = input_1021_cast_fp16, y = value_41_cast_fp16)[name = tensor("x_431_cast_fp16")]; - tensor var_3402_perm_0 = const()[name = tensor("op_3402_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3403 = const()[name = tensor("op_3403"), val = tensor([1, -1, 1024])]; - tensor var_3402_cast_fp16 = transpose(perm = var_3402_perm_0, x = x_431_cast_fp16)[name = tensor("transpose_175")]; - tensor input_1023_cast_fp16 = reshape(shape = var_3403, x = var_3402_cast_fp16)[name = tensor("input_1023_cast_fp16")]; - tensor module_layers_19_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362073280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362859776))), name = tensor("module_layers_19_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_178_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_out_weight_to_fp16_palettized, x = input_1023_cast_fp16)[name = tensor("linear_178_cast_fp16")]; - tensor input_1027_cast_fp16 = add(x = input_1019_cast_fp16, y = linear_178_cast_fp16)[name = tensor("input_1027_cast_fp16")]; - tensor x_435_axes_0 = const()[name = tensor("x_435_axes_0"), val = tensor([-1])]; - tensor module_layers_19_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_19_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362859968)))]; - tensor module_layers_19_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_19_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362862080)))]; - tensor x_435_cast_fp16 = layer_norm(axes = x_435_axes_0, beta = module_layers_19_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_conv_weight_to_fp16, x = input_1027_cast_fp16)[name = tensor("x_435_cast_fp16")]; - tensor input_1029_perm_0 = const()[name = tensor("input_1029_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1031_pad_type_0 = const()[name = tensor("input_1031_pad_type_0"), val = tensor("valid")]; - tensor input_1031_strides_0 = const()[name = tensor("input_1031_strides_0"), val = tensor([1])]; - tensor input_1031_pad_0 = const()[name = tensor("input_1031_pad_0"), val = tensor([0, 0])]; - tensor input_1031_dilations_0 = const()[name = tensor("input_1031_dilations_0"), val = tensor([1])]; - tensor input_1031_groups_0 = const()[name = tensor("input_1031_groups_0"), val = tensor(1)]; - tensor module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362864192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364437120))), name = tensor("module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_1029_cast_fp16 = transpose(perm = input_1029_perm_0, x = x_435_cast_fp16)[name = tensor("transpose_174")]; - tensor input_1031_cast_fp16 = conv(dilations = input_1031_dilations_0, groups = input_1031_groups_0, pad = input_1031_pad_0, pad_type = input_1031_pad_type_0, strides = input_1031_strides_0, weight = module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1029_cast_fp16)[name = tensor("input_1031_cast_fp16")]; - tensor x_437_split_num_splits_0 = const()[name = tensor("x_437_split_num_splits_0"), val = tensor(2)]; - tensor x_437_split_axis_0 = const()[name = tensor("x_437_split_axis_0"), val = tensor(1)]; - tensor x_437_split_cast_fp16_0, tensor x_437_split_cast_fp16_1 = split(axis = x_437_split_axis_0, num_splits = x_437_split_num_splits_0, x = input_1031_cast_fp16)[name = tensor("x_437_split_cast_fp16")]; - tensor x_437_split_1_sigmoid_cast_fp16 = sigmoid(x = x_437_split_cast_fp16_1)[name = tensor("x_437_split_1_sigmoid_cast_fp16")]; - tensor x_437_cast_fp16 = mul(x = x_437_split_cast_fp16_0, y = x_437_split_1_sigmoid_cast_fp16)[name = tensor("x_437_cast_fp16")]; - tensor input_1033_cast_fp16 = select(a = var_11_to_fp16, b = x_437_cast_fp16, cond = var_328)[name = tensor("input_1033_cast_fp16")]; - tensor input_1035_pad_0 = const()[name = tensor("input_1035_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_1035_mode_0 = const()[name = tensor("input_1035_mode_0"), val = tensor("constant")]; - tensor const_207_to_fp16 = const()[name = tensor("const_207_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1035_cast_fp16 = pad(constant_val = const_207_to_fp16, mode = input_1035_mode_0, pad = input_1035_pad_0, x = input_1033_cast_fp16)[name = tensor("input_1035_cast_fp16")]; - tensor input_1037_pad_type_0 = const()[name = tensor("input_1037_pad_type_0"), val = tensor("valid")]; - tensor input_1037_groups_0 = const()[name = tensor("input_1037_groups_0"), val = tensor(1024)]; - tensor input_1037_strides_0 = const()[name = tensor("input_1037_strides_0"), val = tensor([1])]; - tensor input_1037_pad_0 = const()[name = tensor("input_1037_pad_0"), val = tensor([0, 0])]; - tensor input_1037_dilations_0 = const()[name = tensor("input_1037_dilations_0"), val = tensor([1])]; - tensor const_286_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364437312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364444288))), name = tensor("const_286_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_287_to_fp16 = const()[name = tensor("const_287_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364444480)))]; - tensor input_1039_cast_fp16 = conv(bias = const_287_to_fp16, dilations = input_1037_dilations_0, groups = input_1037_groups_0, pad = input_1037_pad_0, pad_type = input_1037_pad_type_0, strides = input_1037_strides_0, weight = const_286_to_fp16_palettized, x = input_1035_cast_fp16)[name = tensor("input_1039_cast_fp16")]; - tensor input_1041_cast_fp16 = silu(x = input_1039_cast_fp16)[name = tensor("input_1041_cast_fp16")]; - tensor x_439_pad_type_0 = const()[name = tensor("x_439_pad_type_0"), val = tensor("valid")]; - tensor x_439_strides_0 = const()[name = tensor("x_439_strides_0"), val = tensor([1])]; - tensor x_439_pad_0 = const()[name = tensor("x_439_pad_0"), val = tensor([0, 0])]; - tensor x_439_dilations_0 = const()[name = tensor("x_439_dilations_0"), val = tensor([1])]; - tensor x_439_groups_0 = const()[name = tensor("x_439_groups_0"), val = tensor(1)]; - tensor module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364446592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365233088))), name = tensor("module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_439_cast_fp16 = conv(dilations = x_439_dilations_0, groups = x_439_groups_0, pad = x_439_pad_0, pad_type = x_439_pad_type_0, strides = x_439_strides_0, weight = module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1041_cast_fp16)[name = tensor("x_439_cast_fp16")]; - tensor input_1043_perm_0 = const()[name = tensor("input_1043_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_439_cast_fp16)[name = tensor("transpose_173")]; - tensor input_1045_cast_fp16 = add(x = input_1027_cast_fp16, y = input_1043_cast_fp16)[name = tensor("input_1045_cast_fp16")]; - tensor input_1047_axes_0 = const()[name = tensor("input_1047_axes_0"), val = tensor([-1])]; - tensor module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365233280)))]; - tensor module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365235392)))]; - tensor input_1047_cast_fp16 = layer_norm(axes = input_1047_axes_0, beta = module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1045_cast_fp16)[name = tensor("input_1047_cast_fp16")]; - tensor module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365237504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(368383296))), name = tensor("module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_179_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1047_cast_fp16)[name = tensor("linear_179_cast_fp16")]; - tensor input_1051_cast_fp16 = silu(x = linear_179_cast_fp16)[name = tensor("input_1051_cast_fp16")]; - tensor module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(368383488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371529280))), name = tensor("module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_180_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1051_cast_fp16)[name = tensor("linear_180_cast_fp16")]; - tensor var_3463_to_fp16 = const()[name = tensor("op_3463_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3464_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3463_to_fp16)[name = tensor("op_3464_cast_fp16")]; - tensor input_1057_cast_fp16 = add(x = input_1045_cast_fp16, y = var_3464_cast_fp16)[name = tensor("input_1057_cast_fp16")]; - tensor input_1059_axes_0 = const()[name = tensor("input_1059_axes_0"), val = tensor([-1])]; - tensor module_layers_19_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_19_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371529472)))]; - tensor module_layers_19_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_19_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371531584)))]; - tensor input_1059_cast_fp16 = layer_norm(axes = input_1059_axes_0, beta = module_layers_19_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_out_weight_to_fp16, x = input_1057_cast_fp16)[name = tensor("input_1059_cast_fp16")]; - tensor input_1061_axes_0 = const()[name = tensor("input_1061_axes_0"), val = tensor([-1])]; - tensor module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371533696)))]; - tensor module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371535808)))]; - tensor input_1061_cast_fp16 = layer_norm(axes = input_1061_axes_0, beta = module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1059_cast_fp16)[name = tensor("input_1061_cast_fp16")]; - tensor module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371537920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(374683712))), name = tensor("module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_181_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1061_cast_fp16)[name = tensor("linear_181_cast_fp16")]; - tensor input_1065_cast_fp16 = silu(x = linear_181_cast_fp16)[name = tensor("input_1065_cast_fp16")]; - tensor module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(374683904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(377829696))), name = tensor("module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_182_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1065_cast_fp16)[name = tensor("linear_182_cast_fp16")]; - tensor var_3492_to_fp16 = const()[name = tensor("op_3492_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3493_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3492_to_fp16)[name = tensor("op_3493_cast_fp16")]; - tensor input_1071_cast_fp16 = add(x = input_1059_cast_fp16, y = var_3493_cast_fp16)[name = tensor("input_1071_cast_fp16")]; - tensor query_41_axes_0 = const()[name = tensor("query_41_axes_0"), val = tensor([-1])]; - tensor module_layers_20_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_20_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(377829888)))]; - tensor module_layers_20_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_20_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(377832000)))]; - tensor query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_self_att_weight_to_fp16, x = input_1071_cast_fp16)[name = tensor("query_41_cast_fp16")]; - tensor module_layers_20_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(377834112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378620608))), name = tensor("module_layers_20_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_183_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_q_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = tensor("linear_183_cast_fp16")]; - tensor var_3509 = const()[name = tensor("op_3509"), val = tensor([1, -1, 8, 128])]; - tensor q_121_cast_fp16 = reshape(shape = var_3509, x = linear_183_cast_fp16)[name = tensor("q_121_cast_fp16")]; - tensor module_layers_20_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378620800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(379407296))), name = tensor("module_layers_20_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_184_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_k_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = tensor("linear_184_cast_fp16")]; - tensor var_3513 = const()[name = tensor("op_3513"), val = tensor([1, -1, 8, 128])]; - tensor k_81_cast_fp16 = reshape(shape = var_3513, x = linear_184_cast_fp16)[name = tensor("k_81_cast_fp16")]; - tensor module_layers_20_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(379407488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380193984))), name = tensor("module_layers_20_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_185_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_v_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = tensor("linear_185_cast_fp16")]; - tensor var_3517 = const()[name = tensor("op_3517"), val = tensor([1, -1, 8, 128])]; - tensor v_41_cast_fp16 = reshape(shape = var_3517, x = linear_185_cast_fp16)[name = tensor("v_41_cast_fp16")]; - tensor value_43_perm_0 = const()[name = tensor("value_43_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380194176)))]; - tensor var_3529_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3529_cast_fp16")]; - tensor module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380196288)))]; - tensor var_3531_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3531_cast_fp16")]; - tensor q_with_bias_v_41_perm_0 = const()[name = tensor("q_with_bias_v_41_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_447_transpose_x_0 = const()[name = tensor("x_447_transpose_x_0"), val = tensor(false)]; - tensor x_447_transpose_y_0 = const()[name = tensor("x_447_transpose_y_0"), val = tensor(false)]; - tensor op_3533_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380198400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380486464))), name = tensor("op_3533_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3531_cast_fp16)[name = tensor("transpose_172")]; - tensor x_447_cast_fp16 = matmul(transpose_x = x_447_transpose_x_0, transpose_y = x_447_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_3533_to_fp16_palettized)[name = tensor("x_447_cast_fp16")]; - tensor x_449_pad_0 = const()[name = tensor("x_449_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_449_mode_0 = const()[name = tensor("x_449_mode_0"), val = tensor("constant")]; - tensor const_214_to_fp16 = const()[name = tensor("const_214_to_fp16"), val = tensor(0x0p+0)]; - tensor x_449_cast_fp16 = pad(constant_val = const_214_to_fp16, mode = x_449_mode_0, pad = x_449_pad_0, x = x_447_cast_fp16)[name = tensor("x_449_cast_fp16")]; - tensor var_3541 = const()[name = tensor("op_3541"), val = tensor([1, 8, -1, 188])]; - tensor x_451_cast_fp16 = reshape(shape = var_3541, x = x_449_cast_fp16)[name = tensor("x_451_cast_fp16")]; - tensor var_3545_begin_0 = const()[name = tensor("op_3545_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3545_end_0 = const()[name = tensor("op_3545_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3545_end_mask_0 = const()[name = tensor("op_3545_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3545_cast_fp16 = slice_by_index(begin = var_3545_begin_0, end = var_3545_end_0, end_mask = var_3545_end_mask_0, x = x_451_cast_fp16)[name = tensor("op_3545_cast_fp16")]; - tensor var_3546 = const()[name = tensor("op_3546"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_81_cast_fp16 = reshape(shape = var_3546, x = var_3545_cast_fp16)[name = tensor("matrix_bd_81_cast_fp16")]; - tensor matrix_ac_41_transpose_x_0 = const()[name = tensor("matrix_ac_41_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_41_transpose_y_0 = const()[name = tensor("matrix_ac_41_transpose_y_0"), val = tensor(false)]; - tensor transpose_136_perm_0 = const()[name = tensor("transpose_136_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_137_perm_0 = const()[name = tensor("transpose_137_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = tensor("transpose_170")]; - tensor transpose_136 = transpose(perm = transpose_136_perm_0, x = var_3529_cast_fp16)[name = tensor("transpose_171")]; - tensor matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = tensor("matrix_ac_41_cast_fp16")]; - tensor matrix_bd_83_begin_0 = const()[name = tensor("matrix_bd_83_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_83_end_0 = const()[name = tensor("matrix_bd_83_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_83_end_mask_0 = const()[name = tensor("matrix_bd_83_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = tensor("matrix_bd_83_cast_fp16")]; - tensor var_3555_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = tensor("op_3555_cast_fp16")]; - tensor _inversed_scores_81_y_0_to_fp16 = const()[name = tensor("_inversed_scores_81_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_81_cast_fp16 = mul(x = var_3555_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = tensor("_inversed_scores_81_cast_fp16")]; - tensor scores_83_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_3)[name = tensor("scores_83_cast_fp16")]; - tensor var_3561_cast_fp16 = softmax(axis = var_30, x = scores_83_cast_fp16)[name = tensor("op_3561_cast_fp16")]; - tensor input_1073_cast_fp16 = select(a = var_11_to_fp16, b = var_3561_cast_fp16, cond = mask_3)[name = tensor("input_1073_cast_fp16")]; - tensor x_453_transpose_x_0 = const()[name = tensor("x_453_transpose_x_0"), val = tensor(false)]; - tensor x_453_transpose_y_0 = const()[name = tensor("x_453_transpose_y_0"), val = tensor(false)]; - tensor value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_41_cast_fp16)[name = tensor("transpose_169")]; - tensor x_453_cast_fp16 = matmul(transpose_x = x_453_transpose_x_0, transpose_y = x_453_transpose_y_0, x = input_1073_cast_fp16, y = value_43_cast_fp16)[name = tensor("x_453_cast_fp16")]; - tensor var_3565_perm_0 = const()[name = tensor("op_3565_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3566 = const()[name = tensor("op_3566"), val = tensor([1, -1, 1024])]; - tensor var_3565_cast_fp16 = transpose(perm = var_3565_perm_0, x = x_453_cast_fp16)[name = tensor("transpose_168")]; - tensor input_1075_cast_fp16 = reshape(shape = var_3566, x = var_3565_cast_fp16)[name = tensor("input_1075_cast_fp16")]; - tensor module_layers_20_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380486656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(381273152))), name = tensor("module_layers_20_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_187_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_out_weight_to_fp16_palettized, x = input_1075_cast_fp16)[name = tensor("linear_187_cast_fp16")]; - tensor input_1079_cast_fp16 = add(x = input_1071_cast_fp16, y = linear_187_cast_fp16)[name = tensor("input_1079_cast_fp16")]; - tensor x_457_axes_0 = const()[name = tensor("x_457_axes_0"), val = tensor([-1])]; - tensor module_layers_20_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_20_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(381273344)))]; - tensor module_layers_20_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_20_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(381275456)))]; - tensor x_457_cast_fp16 = layer_norm(axes = x_457_axes_0, beta = module_layers_20_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_conv_weight_to_fp16, x = input_1079_cast_fp16)[name = tensor("x_457_cast_fp16")]; - tensor input_1081_perm_0 = const()[name = tensor("input_1081_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1083_pad_type_0 = const()[name = tensor("input_1083_pad_type_0"), val = tensor("valid")]; - tensor input_1083_strides_0 = const()[name = tensor("input_1083_strides_0"), val = tensor([1])]; - tensor input_1083_pad_0 = const()[name = tensor("input_1083_pad_0"), val = tensor([0, 0])]; - tensor input_1083_dilations_0 = const()[name = tensor("input_1083_dilations_0"), val = tensor([1])]; - tensor input_1083_groups_0 = const()[name = tensor("input_1083_groups_0"), val = tensor(1)]; - tensor module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(381277568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382850496))), name = tensor("module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_1081_cast_fp16 = transpose(perm = input_1081_perm_0, x = x_457_cast_fp16)[name = tensor("transpose_167")]; - tensor input_1083_cast_fp16 = conv(dilations = input_1083_dilations_0, groups = input_1083_groups_0, pad = input_1083_pad_0, pad_type = input_1083_pad_type_0, strides = input_1083_strides_0, weight = module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1081_cast_fp16)[name = tensor("input_1083_cast_fp16")]; - tensor x_459_split_num_splits_0 = const()[name = tensor("x_459_split_num_splits_0"), val = tensor(2)]; - tensor x_459_split_axis_0 = const()[name = tensor("x_459_split_axis_0"), val = tensor(1)]; - tensor x_459_split_cast_fp16_0, tensor x_459_split_cast_fp16_1 = split(axis = x_459_split_axis_0, num_splits = x_459_split_num_splits_0, x = input_1083_cast_fp16)[name = tensor("x_459_split_cast_fp16")]; - tensor x_459_split_1_sigmoid_cast_fp16 = sigmoid(x = x_459_split_cast_fp16_1)[name = tensor("x_459_split_1_sigmoid_cast_fp16")]; - tensor x_459_cast_fp16 = mul(x = x_459_split_cast_fp16_0, y = x_459_split_1_sigmoid_cast_fp16)[name = tensor("x_459_cast_fp16")]; - tensor input_1085_cast_fp16 = select(a = var_11_to_fp16, b = x_459_cast_fp16, cond = var_328)[name = tensor("input_1085_cast_fp16")]; - tensor input_1087_pad_0 = const()[name = tensor("input_1087_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_1087_mode_0 = const()[name = tensor("input_1087_mode_0"), val = tensor("constant")]; - tensor const_217_to_fp16 = const()[name = tensor("const_217_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1087_cast_fp16 = pad(constant_val = const_217_to_fp16, mode = input_1087_mode_0, pad = input_1087_pad_0, x = input_1085_cast_fp16)[name = tensor("input_1087_cast_fp16")]; - tensor input_1089_pad_type_0 = const()[name = tensor("input_1089_pad_type_0"), val = tensor("valid")]; - tensor input_1089_groups_0 = const()[name = tensor("input_1089_groups_0"), val = tensor(1024)]; - tensor input_1089_strides_0 = const()[name = tensor("input_1089_strides_0"), val = tensor([1])]; - tensor input_1089_pad_0 = const()[name = tensor("input_1089_pad_0"), val = tensor([0, 0])]; - tensor input_1089_dilations_0 = const()[name = tensor("input_1089_dilations_0"), val = tensor([1])]; - tensor const_288_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382850688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382857664))), name = tensor("const_288_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_289_to_fp16 = const()[name = tensor("const_289_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382857856)))]; - tensor input_1091_cast_fp16 = conv(bias = const_289_to_fp16, dilations = input_1089_dilations_0, groups = input_1089_groups_0, pad = input_1089_pad_0, pad_type = input_1089_pad_type_0, strides = input_1089_strides_0, weight = const_288_to_fp16_palettized, x = input_1087_cast_fp16)[name = tensor("input_1091_cast_fp16")]; - tensor input_1093_cast_fp16 = silu(x = input_1091_cast_fp16)[name = tensor("input_1093_cast_fp16")]; - tensor x_461_pad_type_0 = const()[name = tensor("x_461_pad_type_0"), val = tensor("valid")]; - tensor x_461_strides_0 = const()[name = tensor("x_461_strides_0"), val = tensor([1])]; - tensor x_461_pad_0 = const()[name = tensor("x_461_pad_0"), val = tensor([0, 0])]; - tensor x_461_dilations_0 = const()[name = tensor("x_461_dilations_0"), val = tensor([1])]; - tensor x_461_groups_0 = const()[name = tensor("x_461_groups_0"), val = tensor(1)]; - tensor module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382859968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383646464))), name = tensor("module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_461_cast_fp16 = conv(dilations = x_461_dilations_0, groups = x_461_groups_0, pad = x_461_pad_0, pad_type = x_461_pad_type_0, strides = x_461_strides_0, weight = module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1093_cast_fp16)[name = tensor("x_461_cast_fp16")]; - tensor input_1095_perm_0 = const()[name = tensor("input_1095_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_461_cast_fp16)[name = tensor("transpose_166")]; - tensor input_1097_cast_fp16 = add(x = input_1079_cast_fp16, y = input_1095_cast_fp16)[name = tensor("input_1097_cast_fp16")]; - tensor input_1099_axes_0 = const()[name = tensor("input_1099_axes_0"), val = tensor([-1])]; - tensor module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383646656)))]; - tensor module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383648768)))]; - tensor input_1099_cast_fp16 = layer_norm(axes = input_1099_axes_0, beta = module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1097_cast_fp16)[name = tensor("input_1099_cast_fp16")]; - tensor module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383650880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(386796672))), name = tensor("module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_188_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1099_cast_fp16)[name = tensor("linear_188_cast_fp16")]; - tensor input_1103_cast_fp16 = silu(x = linear_188_cast_fp16)[name = tensor("input_1103_cast_fp16")]; - tensor module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(386796864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389942656))), name = tensor("module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_189_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1103_cast_fp16)[name = tensor("linear_189_cast_fp16")]; - tensor var_3626_to_fp16 = const()[name = tensor("op_3626_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3627_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_3626_to_fp16)[name = tensor("op_3627_cast_fp16")]; - tensor input_1109_cast_fp16 = add(x = input_1097_cast_fp16, y = var_3627_cast_fp16)[name = tensor("input_1109_cast_fp16")]; - tensor input_1111_axes_0 = const()[name = tensor("input_1111_axes_0"), val = tensor([-1])]; - tensor module_layers_20_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_20_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389942848)))]; - tensor module_layers_20_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_20_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389944960)))]; - tensor input_1111_cast_fp16 = layer_norm(axes = input_1111_axes_0, beta = module_layers_20_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_out_weight_to_fp16, x = input_1109_cast_fp16)[name = tensor("input_1111_cast_fp16")]; - tensor input_1113_axes_0 = const()[name = tensor("input_1113_axes_0"), val = tensor([-1])]; - tensor module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389947072)))]; - tensor module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389949184)))]; - tensor input_1113_cast_fp16 = layer_norm(axes = input_1113_axes_0, beta = module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1111_cast_fp16)[name = tensor("input_1113_cast_fp16")]; - tensor module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389951296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393097088))), name = tensor("module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_190_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1113_cast_fp16)[name = tensor("linear_190_cast_fp16")]; - tensor input_1117_cast_fp16 = silu(x = linear_190_cast_fp16)[name = tensor("input_1117_cast_fp16")]; - tensor module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393097280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(396243072))), name = tensor("module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_191_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1117_cast_fp16)[name = tensor("linear_191_cast_fp16")]; - tensor var_3655_to_fp16 = const()[name = tensor("op_3655_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3656_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_3655_to_fp16)[name = tensor("op_3656_cast_fp16")]; - tensor input_1123_cast_fp16 = add(x = input_1111_cast_fp16, y = var_3656_cast_fp16)[name = tensor("input_1123_cast_fp16")]; - tensor query_43_axes_0 = const()[name = tensor("query_43_axes_0"), val = tensor([-1])]; - tensor module_layers_21_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_21_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(396243264)))]; - tensor module_layers_21_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_21_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(396245376)))]; - tensor query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_self_att_weight_to_fp16, x = input_1123_cast_fp16)[name = tensor("query_43_cast_fp16")]; - tensor module_layers_21_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(396247488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397033984))), name = tensor("module_layers_21_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_192_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_q_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = tensor("linear_192_cast_fp16")]; - tensor var_3672 = const()[name = tensor("op_3672"), val = tensor([1, -1, 8, 128])]; - tensor q_127_cast_fp16 = reshape(shape = var_3672, x = linear_192_cast_fp16)[name = tensor("q_127_cast_fp16")]; - tensor module_layers_21_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397034176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397820672))), name = tensor("module_layers_21_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_193_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_k_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = tensor("linear_193_cast_fp16")]; - tensor var_3676 = const()[name = tensor("op_3676"), val = tensor([1, -1, 8, 128])]; - tensor k_85_cast_fp16 = reshape(shape = var_3676, x = linear_193_cast_fp16)[name = tensor("k_85_cast_fp16")]; - tensor module_layers_21_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397820864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398607360))), name = tensor("module_layers_21_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_194_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_v_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = tensor("linear_194_cast_fp16")]; - tensor var_3680 = const()[name = tensor("op_3680"), val = tensor([1, -1, 8, 128])]; - tensor v_43_cast_fp16 = reshape(shape = var_3680, x = linear_194_cast_fp16)[name = tensor("v_43_cast_fp16")]; - tensor value_45_perm_0 = const()[name = tensor("value_45_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398607552)))]; - tensor var_3692_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3692_cast_fp16")]; - tensor module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398609664)))]; - tensor var_3694_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3694_cast_fp16")]; - tensor q_with_bias_v_43_perm_0 = const()[name = tensor("q_with_bias_v_43_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_469_transpose_x_0 = const()[name = tensor("x_469_transpose_x_0"), val = tensor(false)]; - tensor x_469_transpose_y_0 = const()[name = tensor("x_469_transpose_y_0"), val = tensor(false)]; - tensor op_3696_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398611776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398899840))), name = tensor("op_3696_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3694_cast_fp16)[name = tensor("transpose_165")]; - tensor x_469_cast_fp16 = matmul(transpose_x = x_469_transpose_x_0, transpose_y = x_469_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_3696_to_fp16_palettized)[name = tensor("x_469_cast_fp16")]; - tensor x_471_pad_0 = const()[name = tensor("x_471_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_471_mode_0 = const()[name = tensor("x_471_mode_0"), val = tensor("constant")]; - tensor const_224_to_fp16 = const()[name = tensor("const_224_to_fp16"), val = tensor(0x0p+0)]; - tensor x_471_cast_fp16 = pad(constant_val = const_224_to_fp16, mode = x_471_mode_0, pad = x_471_pad_0, x = x_469_cast_fp16)[name = tensor("x_471_cast_fp16")]; - tensor var_3704 = const()[name = tensor("op_3704"), val = tensor([1, 8, -1, 188])]; - tensor x_473_cast_fp16 = reshape(shape = var_3704, x = x_471_cast_fp16)[name = tensor("x_473_cast_fp16")]; - tensor var_3708_begin_0 = const()[name = tensor("op_3708_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3708_end_0 = const()[name = tensor("op_3708_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3708_end_mask_0 = const()[name = tensor("op_3708_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3708_cast_fp16 = slice_by_index(begin = var_3708_begin_0, end = var_3708_end_0, end_mask = var_3708_end_mask_0, x = x_473_cast_fp16)[name = tensor("op_3708_cast_fp16")]; - tensor var_3709 = const()[name = tensor("op_3709"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_85_cast_fp16 = reshape(shape = var_3709, x = var_3708_cast_fp16)[name = tensor("matrix_bd_85_cast_fp16")]; - tensor matrix_ac_43_transpose_x_0 = const()[name = tensor("matrix_ac_43_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_43_transpose_y_0 = const()[name = tensor("matrix_ac_43_transpose_y_0"), val = tensor(false)]; - tensor transpose_138_perm_0 = const()[name = tensor("transpose_138_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_139_perm_0 = const()[name = tensor("transpose_139_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = tensor("transpose_163")]; - tensor transpose_138 = transpose(perm = transpose_138_perm_0, x = var_3692_cast_fp16)[name = tensor("transpose_164")]; - tensor matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = tensor("matrix_ac_43_cast_fp16")]; - tensor matrix_bd_87_begin_0 = const()[name = tensor("matrix_bd_87_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_87_end_0 = const()[name = tensor("matrix_bd_87_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_87_end_mask_0 = const()[name = tensor("matrix_bd_87_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = tensor("matrix_bd_87_cast_fp16")]; - tensor var_3718_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = tensor("op_3718_cast_fp16")]; - tensor _inversed_scores_85_y_0_to_fp16 = const()[name = tensor("_inversed_scores_85_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_85_cast_fp16 = mul(x = var_3718_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = tensor("_inversed_scores_85_cast_fp16")]; - tensor scores_87_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_3)[name = tensor("scores_87_cast_fp16")]; - tensor var_3724_cast_fp16 = softmax(axis = var_30, x = scores_87_cast_fp16)[name = tensor("op_3724_cast_fp16")]; - tensor input_1125_cast_fp16 = select(a = var_11_to_fp16, b = var_3724_cast_fp16, cond = mask_3)[name = tensor("input_1125_cast_fp16")]; - tensor x_475_transpose_x_0 = const()[name = tensor("x_475_transpose_x_0"), val = tensor(false)]; - tensor x_475_transpose_y_0 = const()[name = tensor("x_475_transpose_y_0"), val = tensor(false)]; - tensor value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_43_cast_fp16)[name = tensor("transpose_162")]; - tensor x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = input_1125_cast_fp16, y = value_45_cast_fp16)[name = tensor("x_475_cast_fp16")]; - tensor var_3728_perm_0 = const()[name = tensor("op_3728_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3729 = const()[name = tensor("op_3729"), val = tensor([1, -1, 1024])]; - tensor var_3728_cast_fp16 = transpose(perm = var_3728_perm_0, x = x_475_cast_fp16)[name = tensor("transpose_161")]; - tensor input_1127_cast_fp16 = reshape(shape = var_3729, x = var_3728_cast_fp16)[name = tensor("input_1127_cast_fp16")]; - tensor module_layers_21_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398900032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(399686528))), name = tensor("module_layers_21_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_196_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_out_weight_to_fp16_palettized, x = input_1127_cast_fp16)[name = tensor("linear_196_cast_fp16")]; - tensor input_1131_cast_fp16 = add(x = input_1123_cast_fp16, y = linear_196_cast_fp16)[name = tensor("input_1131_cast_fp16")]; - tensor x_479_axes_0 = const()[name = tensor("x_479_axes_0"), val = tensor([-1])]; - tensor module_layers_21_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_21_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(399686720)))]; - tensor module_layers_21_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_21_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(399688832)))]; - tensor x_479_cast_fp16 = layer_norm(axes = x_479_axes_0, beta = module_layers_21_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_conv_weight_to_fp16, x = input_1131_cast_fp16)[name = tensor("x_479_cast_fp16")]; - tensor input_1133_perm_0 = const()[name = tensor("input_1133_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1135_pad_type_0 = const()[name = tensor("input_1135_pad_type_0"), val = tensor("valid")]; - tensor input_1135_strides_0 = const()[name = tensor("input_1135_strides_0"), val = tensor([1])]; - tensor input_1135_pad_0 = const()[name = tensor("input_1135_pad_0"), val = tensor([0, 0])]; - tensor input_1135_dilations_0 = const()[name = tensor("input_1135_dilations_0"), val = tensor([1])]; - tensor input_1135_groups_0 = const()[name = tensor("input_1135_groups_0"), val = tensor(1)]; - tensor module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(399690944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401263872))), name = tensor("module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_1133_cast_fp16 = transpose(perm = input_1133_perm_0, x = x_479_cast_fp16)[name = tensor("transpose_160")]; - tensor input_1135_cast_fp16 = conv(dilations = input_1135_dilations_0, groups = input_1135_groups_0, pad = input_1135_pad_0, pad_type = input_1135_pad_type_0, strides = input_1135_strides_0, weight = module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1133_cast_fp16)[name = tensor("input_1135_cast_fp16")]; - tensor x_481_split_num_splits_0 = const()[name = tensor("x_481_split_num_splits_0"), val = tensor(2)]; - tensor x_481_split_axis_0 = const()[name = tensor("x_481_split_axis_0"), val = tensor(1)]; - tensor x_481_split_cast_fp16_0, tensor x_481_split_cast_fp16_1 = split(axis = x_481_split_axis_0, num_splits = x_481_split_num_splits_0, x = input_1135_cast_fp16)[name = tensor("x_481_split_cast_fp16")]; - tensor x_481_split_1_sigmoid_cast_fp16 = sigmoid(x = x_481_split_cast_fp16_1)[name = tensor("x_481_split_1_sigmoid_cast_fp16")]; - tensor x_481_cast_fp16 = mul(x = x_481_split_cast_fp16_0, y = x_481_split_1_sigmoid_cast_fp16)[name = tensor("x_481_cast_fp16")]; - tensor input_1137_cast_fp16 = select(a = var_11_to_fp16, b = x_481_cast_fp16, cond = var_328)[name = tensor("input_1137_cast_fp16")]; - tensor input_1139_pad_0 = const()[name = tensor("input_1139_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_1139_mode_0 = const()[name = tensor("input_1139_mode_0"), val = tensor("constant")]; - tensor const_227_to_fp16 = const()[name = tensor("const_227_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1139_cast_fp16 = pad(constant_val = const_227_to_fp16, mode = input_1139_mode_0, pad = input_1139_pad_0, x = input_1137_cast_fp16)[name = tensor("input_1139_cast_fp16")]; - tensor input_1141_pad_type_0 = const()[name = tensor("input_1141_pad_type_0"), val = tensor("valid")]; - tensor input_1141_groups_0 = const()[name = tensor("input_1141_groups_0"), val = tensor(1024)]; - tensor input_1141_strides_0 = const()[name = tensor("input_1141_strides_0"), val = tensor([1])]; - tensor input_1141_pad_0 = const()[name = tensor("input_1141_pad_0"), val = tensor([0, 0])]; - tensor input_1141_dilations_0 = const()[name = tensor("input_1141_dilations_0"), val = tensor([1])]; - tensor const_290_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401264064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401271040))), name = tensor("const_290_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_291_to_fp16 = const()[name = tensor("const_291_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401271232)))]; - tensor input_1143_cast_fp16 = conv(bias = const_291_to_fp16, dilations = input_1141_dilations_0, groups = input_1141_groups_0, pad = input_1141_pad_0, pad_type = input_1141_pad_type_0, strides = input_1141_strides_0, weight = const_290_to_fp16_palettized, x = input_1139_cast_fp16)[name = tensor("input_1143_cast_fp16")]; - tensor input_1145_cast_fp16 = silu(x = input_1143_cast_fp16)[name = tensor("input_1145_cast_fp16")]; - tensor x_483_pad_type_0 = const()[name = tensor("x_483_pad_type_0"), val = tensor("valid")]; - tensor x_483_strides_0 = const()[name = tensor("x_483_strides_0"), val = tensor([1])]; - tensor x_483_pad_0 = const()[name = tensor("x_483_pad_0"), val = tensor([0, 0])]; - tensor x_483_dilations_0 = const()[name = tensor("x_483_dilations_0"), val = tensor([1])]; - tensor x_483_groups_0 = const()[name = tensor("x_483_groups_0"), val = tensor(1)]; - tensor module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401273344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402059840))), name = tensor("module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_483_cast_fp16 = conv(dilations = x_483_dilations_0, groups = x_483_groups_0, pad = x_483_pad_0, pad_type = x_483_pad_type_0, strides = x_483_strides_0, weight = module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1145_cast_fp16)[name = tensor("x_483_cast_fp16")]; - tensor input_1147_perm_0 = const()[name = tensor("input_1147_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_483_cast_fp16)[name = tensor("transpose_159")]; - tensor input_1149_cast_fp16 = add(x = input_1131_cast_fp16, y = input_1147_cast_fp16)[name = tensor("input_1149_cast_fp16")]; - tensor input_1151_axes_0 = const()[name = tensor("input_1151_axes_0"), val = tensor([-1])]; - tensor module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402060032)))]; - tensor module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402062144)))]; - tensor input_1151_cast_fp16 = layer_norm(axes = input_1151_axes_0, beta = module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1149_cast_fp16)[name = tensor("input_1151_cast_fp16")]; - tensor module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402064256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405210048))), name = tensor("module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_197_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1151_cast_fp16)[name = tensor("linear_197_cast_fp16")]; - tensor input_1155_cast_fp16 = silu(x = linear_197_cast_fp16)[name = tensor("input_1155_cast_fp16")]; - tensor module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405210240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408356032))), name = tensor("module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_198_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1155_cast_fp16)[name = tensor("linear_198_cast_fp16")]; - tensor var_3789_to_fp16 = const()[name = tensor("op_3789_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3790_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_3789_to_fp16)[name = tensor("op_3790_cast_fp16")]; - tensor input_1161_cast_fp16 = add(x = input_1149_cast_fp16, y = var_3790_cast_fp16)[name = tensor("input_1161_cast_fp16")]; - tensor input_1163_axes_0 = const()[name = tensor("input_1163_axes_0"), val = tensor([-1])]; - tensor module_layers_21_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_21_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408356224)))]; - tensor module_layers_21_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_21_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408358336)))]; - tensor input_1163_cast_fp16 = layer_norm(axes = input_1163_axes_0, beta = module_layers_21_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_out_weight_to_fp16, x = input_1161_cast_fp16)[name = tensor("input_1163_cast_fp16")]; - tensor input_1165_axes_0 = const()[name = tensor("input_1165_axes_0"), val = tensor([-1])]; - tensor module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408360448)))]; - tensor module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408362560)))]; - tensor input_1165_cast_fp16 = layer_norm(axes = input_1165_axes_0, beta = module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1163_cast_fp16)[name = tensor("input_1165_cast_fp16")]; - tensor module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408364672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(411510464))), name = tensor("module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_199_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1165_cast_fp16)[name = tensor("linear_199_cast_fp16")]; - tensor input_1169_cast_fp16 = silu(x = linear_199_cast_fp16)[name = tensor("input_1169_cast_fp16")]; - tensor module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(411510656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414656448))), name = tensor("module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_200_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1169_cast_fp16)[name = tensor("linear_200_cast_fp16")]; - tensor var_3818_to_fp16 = const()[name = tensor("op_3818_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3819_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_3818_to_fp16)[name = tensor("op_3819_cast_fp16")]; - tensor input_1175_cast_fp16 = add(x = input_1163_cast_fp16, y = var_3819_cast_fp16)[name = tensor("input_1175_cast_fp16")]; - tensor query_45_axes_0 = const()[name = tensor("query_45_axes_0"), val = tensor([-1])]; - tensor module_layers_22_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_22_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414656640)))]; - tensor module_layers_22_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_22_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414658752)))]; - tensor query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_self_att_weight_to_fp16, x = input_1175_cast_fp16)[name = tensor("query_45_cast_fp16")]; - tensor module_layers_22_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414660864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415447360))), name = tensor("module_layers_22_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_201_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_q_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = tensor("linear_201_cast_fp16")]; - tensor var_3835 = const()[name = tensor("op_3835"), val = tensor([1, -1, 8, 128])]; - tensor q_133_cast_fp16 = reshape(shape = var_3835, x = linear_201_cast_fp16)[name = tensor("q_133_cast_fp16")]; - tensor module_layers_22_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415447552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416234048))), name = tensor("module_layers_22_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_202_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_k_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = tensor("linear_202_cast_fp16")]; - tensor var_3839 = const()[name = tensor("op_3839"), val = tensor([1, -1, 8, 128])]; - tensor k_89_cast_fp16 = reshape(shape = var_3839, x = linear_202_cast_fp16)[name = tensor("k_89_cast_fp16")]; - tensor module_layers_22_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416234240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417020736))), name = tensor("module_layers_22_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_203_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_v_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = tensor("linear_203_cast_fp16")]; - tensor var_3843 = const()[name = tensor("op_3843"), val = tensor([1, -1, 8, 128])]; - tensor v_45_cast_fp16 = reshape(shape = var_3843, x = linear_203_cast_fp16)[name = tensor("v_45_cast_fp16")]; - tensor value_47_perm_0 = const()[name = tensor("value_47_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417020928)))]; - tensor var_3855_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3855_cast_fp16")]; - tensor module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417023040)))]; - tensor var_3857_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3857_cast_fp16")]; - tensor q_with_bias_v_45_perm_0 = const()[name = tensor("q_with_bias_v_45_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_491_transpose_x_0 = const()[name = tensor("x_491_transpose_x_0"), val = tensor(false)]; - tensor x_491_transpose_y_0 = const()[name = tensor("x_491_transpose_y_0"), val = tensor(false)]; - tensor op_3859_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417025152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417313216))), name = tensor("op_3859_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_3857_cast_fp16)[name = tensor("transpose_158")]; - tensor x_491_cast_fp16 = matmul(transpose_x = x_491_transpose_x_0, transpose_y = x_491_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_3859_to_fp16_palettized)[name = tensor("x_491_cast_fp16")]; - tensor x_493_pad_0 = const()[name = tensor("x_493_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_493_mode_0 = const()[name = tensor("x_493_mode_0"), val = tensor("constant")]; - tensor const_234_to_fp16 = const()[name = tensor("const_234_to_fp16"), val = tensor(0x0p+0)]; - tensor x_493_cast_fp16 = pad(constant_val = const_234_to_fp16, mode = x_493_mode_0, pad = x_493_pad_0, x = x_491_cast_fp16)[name = tensor("x_493_cast_fp16")]; - tensor var_3867 = const()[name = tensor("op_3867"), val = tensor([1, 8, -1, 188])]; - tensor x_495_cast_fp16 = reshape(shape = var_3867, x = x_493_cast_fp16)[name = tensor("x_495_cast_fp16")]; - tensor var_3871_begin_0 = const()[name = tensor("op_3871_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3871_end_0 = const()[name = tensor("op_3871_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3871_end_mask_0 = const()[name = tensor("op_3871_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3871_cast_fp16 = slice_by_index(begin = var_3871_begin_0, end = var_3871_end_0, end_mask = var_3871_end_mask_0, x = x_495_cast_fp16)[name = tensor("op_3871_cast_fp16")]; - tensor var_3872 = const()[name = tensor("op_3872"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_89_cast_fp16 = reshape(shape = var_3872, x = var_3871_cast_fp16)[name = tensor("matrix_bd_89_cast_fp16")]; - tensor matrix_ac_45_transpose_x_0 = const()[name = tensor("matrix_ac_45_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_45_transpose_y_0 = const()[name = tensor("matrix_ac_45_transpose_y_0"), val = tensor(false)]; - tensor transpose_140_perm_0 = const()[name = tensor("transpose_140_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_141_perm_0 = const()[name = tensor("transpose_141_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = tensor("transpose_156")]; - tensor transpose_140 = transpose(perm = transpose_140_perm_0, x = var_3855_cast_fp16)[name = tensor("transpose_157")]; - tensor matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = tensor("matrix_ac_45_cast_fp16")]; - tensor matrix_bd_91_begin_0 = const()[name = tensor("matrix_bd_91_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_91_end_0 = const()[name = tensor("matrix_bd_91_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_91_end_mask_0 = const()[name = tensor("matrix_bd_91_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = tensor("matrix_bd_91_cast_fp16")]; - tensor var_3881_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = tensor("op_3881_cast_fp16")]; - tensor _inversed_scores_89_y_0_to_fp16 = const()[name = tensor("_inversed_scores_89_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_89_cast_fp16 = mul(x = var_3881_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = tensor("_inversed_scores_89_cast_fp16")]; - tensor scores_91_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_3)[name = tensor("scores_91_cast_fp16")]; - tensor var_3887_cast_fp16 = softmax(axis = var_30, x = scores_91_cast_fp16)[name = tensor("op_3887_cast_fp16")]; - tensor input_1177_cast_fp16 = select(a = var_11_to_fp16, b = var_3887_cast_fp16, cond = mask_3)[name = tensor("input_1177_cast_fp16")]; - tensor x_497_transpose_x_0 = const()[name = tensor("x_497_transpose_x_0"), val = tensor(false)]; - tensor x_497_transpose_y_0 = const()[name = tensor("x_497_transpose_y_0"), val = tensor(false)]; - tensor value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_45_cast_fp16)[name = tensor("transpose_155")]; - tensor x_497_cast_fp16 = matmul(transpose_x = x_497_transpose_x_0, transpose_y = x_497_transpose_y_0, x = input_1177_cast_fp16, y = value_47_cast_fp16)[name = tensor("x_497_cast_fp16")]; - tensor var_3891_perm_0 = const()[name = tensor("op_3891_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3892 = const()[name = tensor("op_3892"), val = tensor([1, -1, 1024])]; - tensor var_3891_cast_fp16 = transpose(perm = var_3891_perm_0, x = x_497_cast_fp16)[name = tensor("transpose_154")]; - tensor input_1179_cast_fp16 = reshape(shape = var_3892, x = var_3891_cast_fp16)[name = tensor("input_1179_cast_fp16")]; - tensor module_layers_22_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417313408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418099904))), name = tensor("module_layers_22_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_205_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_out_weight_to_fp16_palettized, x = input_1179_cast_fp16)[name = tensor("linear_205_cast_fp16")]; - tensor input_1183_cast_fp16 = add(x = input_1175_cast_fp16, y = linear_205_cast_fp16)[name = tensor("input_1183_cast_fp16")]; - tensor x_501_axes_0 = const()[name = tensor("x_501_axes_0"), val = tensor([-1])]; - tensor module_layers_22_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_22_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418100096)))]; - tensor module_layers_22_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_22_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418102208)))]; - tensor x_501_cast_fp16 = layer_norm(axes = x_501_axes_0, beta = module_layers_22_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_conv_weight_to_fp16, x = input_1183_cast_fp16)[name = tensor("x_501_cast_fp16")]; - tensor input_1185_perm_0 = const()[name = tensor("input_1185_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1187_pad_type_0 = const()[name = tensor("input_1187_pad_type_0"), val = tensor("valid")]; - tensor input_1187_strides_0 = const()[name = tensor("input_1187_strides_0"), val = tensor([1])]; - tensor input_1187_pad_0 = const()[name = tensor("input_1187_pad_0"), val = tensor([0, 0])]; - tensor input_1187_dilations_0 = const()[name = tensor("input_1187_dilations_0"), val = tensor([1])]; - tensor input_1187_groups_0 = const()[name = tensor("input_1187_groups_0"), val = tensor(1)]; - tensor module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418104320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419677248))), name = tensor("module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_1185_cast_fp16 = transpose(perm = input_1185_perm_0, x = x_501_cast_fp16)[name = tensor("transpose_153")]; - tensor input_1187_cast_fp16 = conv(dilations = input_1187_dilations_0, groups = input_1187_groups_0, pad = input_1187_pad_0, pad_type = input_1187_pad_type_0, strides = input_1187_strides_0, weight = module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1185_cast_fp16)[name = tensor("input_1187_cast_fp16")]; - tensor x_503_split_num_splits_0 = const()[name = tensor("x_503_split_num_splits_0"), val = tensor(2)]; - tensor x_503_split_axis_0 = const()[name = tensor("x_503_split_axis_0"), val = tensor(1)]; - tensor x_503_split_cast_fp16_0, tensor x_503_split_cast_fp16_1 = split(axis = x_503_split_axis_0, num_splits = x_503_split_num_splits_0, x = input_1187_cast_fp16)[name = tensor("x_503_split_cast_fp16")]; - tensor x_503_split_1_sigmoid_cast_fp16 = sigmoid(x = x_503_split_cast_fp16_1)[name = tensor("x_503_split_1_sigmoid_cast_fp16")]; - tensor x_503_cast_fp16 = mul(x = x_503_split_cast_fp16_0, y = x_503_split_1_sigmoid_cast_fp16)[name = tensor("x_503_cast_fp16")]; - tensor input_1189_cast_fp16 = select(a = var_11_to_fp16, b = x_503_cast_fp16, cond = var_328)[name = tensor("input_1189_cast_fp16")]; - tensor input_1191_pad_0 = const()[name = tensor("input_1191_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_1191_mode_0 = const()[name = tensor("input_1191_mode_0"), val = tensor("constant")]; - tensor const_237_to_fp16 = const()[name = tensor("const_237_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1191_cast_fp16 = pad(constant_val = const_237_to_fp16, mode = input_1191_mode_0, pad = input_1191_pad_0, x = input_1189_cast_fp16)[name = tensor("input_1191_cast_fp16")]; - tensor input_1193_pad_type_0 = const()[name = tensor("input_1193_pad_type_0"), val = tensor("valid")]; - tensor input_1193_groups_0 = const()[name = tensor("input_1193_groups_0"), val = tensor(1024)]; - tensor input_1193_strides_0 = const()[name = tensor("input_1193_strides_0"), val = tensor([1])]; - tensor input_1193_pad_0 = const()[name = tensor("input_1193_pad_0"), val = tensor([0, 0])]; - tensor input_1193_dilations_0 = const()[name = tensor("input_1193_dilations_0"), val = tensor([1])]; - tensor const_292_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419677440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419684416))), name = tensor("const_292_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_293_to_fp16 = const()[name = tensor("const_293_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419684608)))]; - tensor input_1195_cast_fp16 = conv(bias = const_293_to_fp16, dilations = input_1193_dilations_0, groups = input_1193_groups_0, pad = input_1193_pad_0, pad_type = input_1193_pad_type_0, strides = input_1193_strides_0, weight = const_292_to_fp16_palettized, x = input_1191_cast_fp16)[name = tensor("input_1195_cast_fp16")]; - tensor input_1197_cast_fp16 = silu(x = input_1195_cast_fp16)[name = tensor("input_1197_cast_fp16")]; - tensor x_505_pad_type_0 = const()[name = tensor("x_505_pad_type_0"), val = tensor("valid")]; - tensor x_505_strides_0 = const()[name = tensor("x_505_strides_0"), val = tensor([1])]; - tensor x_505_pad_0 = const()[name = tensor("x_505_pad_0"), val = tensor([0, 0])]; - tensor x_505_dilations_0 = const()[name = tensor("x_505_dilations_0"), val = tensor([1])]; - tensor x_505_groups_0 = const()[name = tensor("x_505_groups_0"), val = tensor(1)]; - tensor module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419686720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420473216))), name = tensor("module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_505_cast_fp16 = conv(dilations = x_505_dilations_0, groups = x_505_groups_0, pad = x_505_pad_0, pad_type = x_505_pad_type_0, strides = x_505_strides_0, weight = module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1197_cast_fp16)[name = tensor("x_505_cast_fp16")]; - tensor input_1199_perm_0 = const()[name = tensor("input_1199_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_505_cast_fp16)[name = tensor("transpose_152")]; - tensor input_1201_cast_fp16 = add(x = input_1183_cast_fp16, y = input_1199_cast_fp16)[name = tensor("input_1201_cast_fp16")]; - tensor input_1203_axes_0 = const()[name = tensor("input_1203_axes_0"), val = tensor([-1])]; - tensor module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420473408)))]; - tensor module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420475520)))]; - tensor input_1203_cast_fp16 = layer_norm(axes = input_1203_axes_0, beta = module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1201_cast_fp16)[name = tensor("input_1203_cast_fp16")]; - tensor module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420477632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(423623424))), name = tensor("module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_206_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1203_cast_fp16)[name = tensor("linear_206_cast_fp16")]; - tensor input_1207_cast_fp16 = silu(x = linear_206_cast_fp16)[name = tensor("input_1207_cast_fp16")]; - tensor module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(423623616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426769408))), name = tensor("module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_207_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1207_cast_fp16)[name = tensor("linear_207_cast_fp16")]; - tensor var_3952_to_fp16 = const()[name = tensor("op_3952_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3953_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_3952_to_fp16)[name = tensor("op_3953_cast_fp16")]; - tensor input_1213_cast_fp16 = add(x = input_1201_cast_fp16, y = var_3953_cast_fp16)[name = tensor("input_1213_cast_fp16")]; - tensor input_1215_axes_0 = const()[name = tensor("input_1215_axes_0"), val = tensor([-1])]; - tensor module_layers_22_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_22_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426769600)))]; - tensor module_layers_22_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_22_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426771712)))]; - tensor input_1215_cast_fp16 = layer_norm(axes = input_1215_axes_0, beta = module_layers_22_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_out_weight_to_fp16, x = input_1213_cast_fp16)[name = tensor("input_1215_cast_fp16")]; - tensor input_1217_axes_0 = const()[name = tensor("input_1217_axes_0"), val = tensor([-1])]; - tensor module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426773824)))]; - tensor module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426775936)))]; - tensor input_1217_cast_fp16 = layer_norm(axes = input_1217_axes_0, beta = module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1215_cast_fp16)[name = tensor("input_1217_cast_fp16")]; - tensor module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426778048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429923840))), name = tensor("module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_208_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1217_cast_fp16)[name = tensor("linear_208_cast_fp16")]; - tensor input_1221_cast_fp16 = silu(x = linear_208_cast_fp16)[name = tensor("input_1221_cast_fp16")]; - tensor module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429924032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433069824))), name = tensor("module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_209_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1221_cast_fp16)[name = tensor("linear_209_cast_fp16")]; - tensor var_3981_to_fp16 = const()[name = tensor("op_3981_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3982_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_3981_to_fp16)[name = tensor("op_3982_cast_fp16")]; - tensor input_1227_cast_fp16 = add(x = input_1215_cast_fp16, y = var_3982_cast_fp16)[name = tensor("input_1227_cast_fp16")]; - tensor query_axes_0 = const()[name = tensor("query_axes_0"), val = tensor([-1])]; - tensor module_layers_23_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_23_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433070016)))]; - tensor module_layers_23_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_23_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433072128)))]; - tensor query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_self_att_weight_to_fp16, x = input_1227_cast_fp16)[name = tensor("query_cast_fp16")]; - tensor module_layers_23_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433074240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433860736))), name = tensor("module_layers_23_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_210_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_q_weight_to_fp16_palettized, x = query_cast_fp16)[name = tensor("linear_210_cast_fp16")]; - tensor var_3998 = const()[name = tensor("op_3998"), val = tensor([1, -1, 8, 128])]; - tensor q_139_cast_fp16 = reshape(shape = var_3998, x = linear_210_cast_fp16)[name = tensor("q_139_cast_fp16")]; - tensor module_layers_23_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433860928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434647424))), name = tensor("module_layers_23_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_211_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_k_weight_to_fp16_palettized, x = query_cast_fp16)[name = tensor("linear_211_cast_fp16")]; - tensor var_4002 = const()[name = tensor("op_4002"), val = tensor([1, -1, 8, 128])]; - tensor k_93_cast_fp16 = reshape(shape = var_4002, x = linear_211_cast_fp16)[name = tensor("k_93_cast_fp16")]; - tensor module_layers_23_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434647616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435434112))), name = tensor("module_layers_23_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_212_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_v_weight_to_fp16_palettized, x = query_cast_fp16)[name = tensor("linear_212_cast_fp16")]; - tensor var_4006 = const()[name = tensor("op_4006"), val = tensor([1, -1, 8, 128])]; - tensor v_cast_fp16 = reshape(shape = var_4006, x = linear_212_cast_fp16)[name = tensor("v_cast_fp16")]; - tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435434304)))]; - tensor var_4018_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_u_to_fp16)[name = tensor("op_4018_cast_fp16")]; - tensor module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435436416)))]; - tensor var_4020_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_v_to_fp16)[name = tensor("op_4020_cast_fp16")]; - tensor q_with_bias_v_perm_0 = const()[name = tensor("q_with_bias_v_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_513_transpose_x_0 = const()[name = tensor("x_513_transpose_x_0"), val = tensor(false)]; - tensor x_513_transpose_y_0 = const()[name = tensor("x_513_transpose_y_0"), val = tensor(false)]; - tensor op_4022_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435438528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435726592))), name = tensor("op_4022_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4020_cast_fp16)[name = tensor("transpose_151")]; - tensor x_513_cast_fp16 = matmul(transpose_x = x_513_transpose_x_0, transpose_y = x_513_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_4022_to_fp16_palettized)[name = tensor("x_513_cast_fp16")]; - tensor x_515_pad_0 = const()[name = tensor("x_515_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_515_mode_0 = const()[name = tensor("x_515_mode_0"), val = tensor("constant")]; - tensor const_244_to_fp16 = const()[name = tensor("const_244_to_fp16"), val = tensor(0x0p+0)]; - tensor x_515_cast_fp16 = pad(constant_val = const_244_to_fp16, mode = x_515_mode_0, pad = x_515_pad_0, x = x_513_cast_fp16)[name = tensor("x_515_cast_fp16")]; - tensor var_4030 = const()[name = tensor("op_4030"), val = tensor([1, 8, -1, 188])]; - tensor x_517_cast_fp16 = reshape(shape = var_4030, x = x_515_cast_fp16)[name = tensor("x_517_cast_fp16")]; - tensor var_4034_begin_0 = const()[name = tensor("op_4034_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_4034_end_0 = const()[name = tensor("op_4034_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_4034_end_mask_0 = const()[name = tensor("op_4034_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_4034_cast_fp16 = slice_by_index(begin = var_4034_begin_0, end = var_4034_end_0, end_mask = var_4034_end_mask_0, x = x_517_cast_fp16)[name = tensor("op_4034_cast_fp16")]; - tensor var_4035 = const()[name = tensor("op_4035"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_93_cast_fp16 = reshape(shape = var_4035, x = var_4034_cast_fp16)[name = tensor("matrix_bd_93_cast_fp16")]; - tensor matrix_ac_transpose_x_0 = const()[name = tensor("matrix_ac_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_transpose_y_0 = const()[name = tensor("matrix_ac_transpose_y_0"), val = tensor(false)]; - tensor transpose_142_perm_0 = const()[name = tensor("transpose_142_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_143_perm_0 = const()[name = tensor("transpose_143_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = tensor("transpose_149")]; - tensor transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4018_cast_fp16)[name = tensor("transpose_150")]; - tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = tensor("matrix_ac_cast_fp16")]; - tensor matrix_bd_begin_0 = const()[name = tensor("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_end_0 = const()[name = tensor("matrix_bd_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_end_mask_0 = const()[name = tensor("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = tensor("matrix_bd_cast_fp16")]; - tensor var_4044_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = tensor("op_4044_cast_fp16")]; - tensor _inversed_scores_93_y_0_to_fp16 = const()[name = tensor("_inversed_scores_93_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_93_cast_fp16 = mul(x = var_4044_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = tensor("_inversed_scores_93_cast_fp16")]; - tensor scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_3)[name = tensor("scores_cast_fp16")]; - tensor var_4050_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = tensor("op_4050_cast_fp16")]; - tensor input_1229_cast_fp16 = select(a = var_11_to_fp16, b = var_4050_cast_fp16, cond = mask_3)[name = tensor("input_1229_cast_fp16")]; - tensor x_519_transpose_x_0 = const()[name = tensor("x_519_transpose_x_0"), val = tensor(false)]; - tensor x_519_transpose_y_0 = const()[name = tensor("x_519_transpose_y_0"), val = tensor(false)]; - tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = tensor("transpose_148")]; - tensor x_519_cast_fp16 = matmul(transpose_x = x_519_transpose_x_0, transpose_y = x_519_transpose_y_0, x = input_1229_cast_fp16, y = value_cast_fp16)[name = tensor("x_519_cast_fp16")]; - tensor var_4054_perm_0 = const()[name = tensor("op_4054_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_4055 = const()[name = tensor("op_4055"), val = tensor([1, -1, 1024])]; - tensor var_4054_cast_fp16 = transpose(perm = var_4054_perm_0, x = x_519_cast_fp16)[name = tensor("transpose_147")]; - tensor input_1231_cast_fp16 = reshape(shape = var_4055, x = var_4054_cast_fp16)[name = tensor("input_1231_cast_fp16")]; - tensor module_layers_23_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435726784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436513280))), name = tensor("module_layers_23_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_214_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_out_weight_to_fp16_palettized, x = input_1231_cast_fp16)[name = tensor("linear_214_cast_fp16")]; - tensor input_1235_cast_fp16 = add(x = input_1227_cast_fp16, y = linear_214_cast_fp16)[name = tensor("input_1235_cast_fp16")]; - tensor x_523_axes_0 = const()[name = tensor("x_523_axes_0"), val = tensor([-1])]; - tensor module_layers_23_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_23_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436513472)))]; - tensor module_layers_23_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_23_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436515584)))]; - tensor x_523_cast_fp16 = layer_norm(axes = x_523_axes_0, beta = module_layers_23_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_conv_weight_to_fp16, x = input_1235_cast_fp16)[name = tensor("x_523_cast_fp16")]; - tensor input_1237_perm_0 = const()[name = tensor("input_1237_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1239_pad_type_0 = const()[name = tensor("input_1239_pad_type_0"), val = tensor("valid")]; - tensor input_1239_strides_0 = const()[name = tensor("input_1239_strides_0"), val = tensor([1])]; - tensor input_1239_pad_0 = const()[name = tensor("input_1239_pad_0"), val = tensor([0, 0])]; - tensor input_1239_dilations_0 = const()[name = tensor("input_1239_dilations_0"), val = tensor([1])]; - tensor input_1239_groups_0 = const()[name = tensor("input_1239_groups_0"), val = tensor(1)]; - tensor module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436517696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438090624))), name = tensor("module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_1237_cast_fp16 = transpose(perm = input_1237_perm_0, x = x_523_cast_fp16)[name = tensor("transpose_146")]; - tensor input_1239_cast_fp16 = conv(dilations = input_1239_dilations_0, groups = input_1239_groups_0, pad = input_1239_pad_0, pad_type = input_1239_pad_type_0, strides = input_1239_strides_0, weight = module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1237_cast_fp16)[name = tensor("input_1239_cast_fp16")]; - tensor x_525_split_num_splits_0 = const()[name = tensor("x_525_split_num_splits_0"), val = tensor(2)]; - tensor x_525_split_axis_0 = const()[name = tensor("x_525_split_axis_0"), val = tensor(1)]; - tensor x_525_split_cast_fp16_0, tensor x_525_split_cast_fp16_1 = split(axis = x_525_split_axis_0, num_splits = x_525_split_num_splits_0, x = input_1239_cast_fp16)[name = tensor("x_525_split_cast_fp16")]; - tensor x_525_split_1_sigmoid_cast_fp16 = sigmoid(x = x_525_split_cast_fp16_1)[name = tensor("x_525_split_1_sigmoid_cast_fp16")]; - tensor x_525_cast_fp16 = mul(x = x_525_split_cast_fp16_0, y = x_525_split_1_sigmoid_cast_fp16)[name = tensor("x_525_cast_fp16")]; - tensor input_1241_cast_fp16 = select(a = var_11_to_fp16, b = x_525_cast_fp16, cond = var_328)[name = tensor("input_1241_cast_fp16")]; - tensor input_1243_pad_0 = const()[name = tensor("input_1243_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_1243_mode_0 = const()[name = tensor("input_1243_mode_0"), val = tensor("constant")]; - tensor const_247_to_fp16 = const()[name = tensor("const_247_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1243_cast_fp16 = pad(constant_val = const_247_to_fp16, mode = input_1243_mode_0, pad = input_1243_pad_0, x = input_1241_cast_fp16)[name = tensor("input_1243_cast_fp16")]; - tensor input_1245_pad_type_0 = const()[name = tensor("input_1245_pad_type_0"), val = tensor("valid")]; - tensor input_1245_groups_0 = const()[name = tensor("input_1245_groups_0"), val = tensor(1024)]; - tensor input_1245_strides_0 = const()[name = tensor("input_1245_strides_0"), val = tensor([1])]; - tensor input_1245_pad_0 = const()[name = tensor("input_1245_pad_0"), val = tensor([0, 0])]; - tensor input_1245_dilations_0 = const()[name = tensor("input_1245_dilations_0"), val = tensor([1])]; - tensor const_294_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438090816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438097792))), name = tensor("const_294_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_295_to_fp16 = const()[name = tensor("const_295_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438097984)))]; - tensor input_1247_cast_fp16 = conv(bias = const_295_to_fp16, dilations = input_1245_dilations_0, groups = input_1245_groups_0, pad = input_1245_pad_0, pad_type = input_1245_pad_type_0, strides = input_1245_strides_0, weight = const_294_to_fp16_palettized, x = input_1243_cast_fp16)[name = tensor("input_1247_cast_fp16")]; - tensor input_1249_cast_fp16 = silu(x = input_1247_cast_fp16)[name = tensor("input_1249_cast_fp16")]; - tensor x_527_pad_type_0 = const()[name = tensor("x_527_pad_type_0"), val = tensor("valid")]; - tensor x_527_strides_0 = const()[name = tensor("x_527_strides_0"), val = tensor([1])]; - tensor x_527_pad_0 = const()[name = tensor("x_527_pad_0"), val = tensor([0, 0])]; - tensor x_527_dilations_0 = const()[name = tensor("x_527_dilations_0"), val = tensor([1])]; - tensor x_527_groups_0 = const()[name = tensor("x_527_groups_0"), val = tensor(1)]; - tensor module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438100096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438886592))), name = tensor("module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_527_cast_fp16 = conv(dilations = x_527_dilations_0, groups = x_527_groups_0, pad = x_527_pad_0, pad_type = x_527_pad_type_0, strides = x_527_strides_0, weight = module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1249_cast_fp16)[name = tensor("x_527_cast_fp16")]; - tensor input_1251_perm_0 = const()[name = tensor("input_1251_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_527_cast_fp16)[name = tensor("transpose_145")]; - tensor input_1253_cast_fp16 = add(x = input_1235_cast_fp16, y = input_1251_cast_fp16)[name = tensor("input_1253_cast_fp16")]; - tensor input_1255_axes_0 = const()[name = tensor("input_1255_axes_0"), val = tensor([-1])]; - tensor module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438886784)))]; - tensor module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438888896)))]; - tensor input_1255_cast_fp16 = layer_norm(axes = input_1255_axes_0, beta = module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1253_cast_fp16)[name = tensor("input_1255_cast_fp16")]; - tensor module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438891008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(442036800))), name = tensor("module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_215_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1255_cast_fp16)[name = tensor("linear_215_cast_fp16")]; - tensor input_1259_cast_fp16 = silu(x = linear_215_cast_fp16)[name = tensor("input_1259_cast_fp16")]; - tensor module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(442036992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445182784))), name = tensor("module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_216_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1259_cast_fp16)[name = tensor("linear_216_cast_fp16")]; - tensor var_4115_to_fp16 = const()[name = tensor("op_4115_to_fp16"), val = tensor(0x1p-1)]; - tensor var_4116_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4115_to_fp16)[name = tensor("op_4116_cast_fp16")]; - tensor input_cast_fp16 = add(x = input_1253_cast_fp16, y = var_4116_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor audio_signal_axes_0 = const()[name = tensor("audio_signal_axes_0"), val = tensor([-1])]; - tensor module_layers_23_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_23_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445182976)))]; - tensor module_layers_23_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_23_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445185088)))]; - tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_23_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("audio_signal_cast_fp16")]; - tensor obj_1_perm_0 = const()[name = tensor("obj_1_perm_0"), val = tensor([0, 2, 1])]; - tensor obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_1_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = tensor("transpose_144")]; - tensor encoder = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = tensor("cast_0")]; - } -> (encoder, encoder_length); -} \ No newline at end of file diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/weights/weight.bin b/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/weights/weight.bin deleted file mode 100644 index e6313f81d9d94af46347f28087f51a60eb28164c..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4adc7ad44f9d05e1bffeb2b06d3bb02861a5c7602dff63a6b494aed3bf8a6c3e -size 445187200 diff --git a/models/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/analytics/coremldata.bin 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a/models/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/metadata.json b/models/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/metadata.json deleted file mode 100644 index db3b2bc33cfda6f7cd1073d21182446180e53f94..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/metadata.json +++ /dev/null @@ -1,103 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet single-step joint decision (current frame)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_id", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_prob", - "type" : "MultiArray" - }, - { - 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"inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1024 × 1)", - "shortDescription" : "", - "shape" : "[1, 1024, 1]", - "name" : "encoder_step", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder_step", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2025-09-25", - "com.github.apple.coremltools.source" : "torch==2.7.0", - "com.github.apple.coremltools.version" : "9.0b1", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "parakeet_joint_decision_single_step", - "method" : "predict" - } -] \ No newline at end of file diff --git a/models/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/model.mil b/models/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/model.mil deleted file mode 100644 index 339c35b303b074de7fe047bb099311a4cc08cf53..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/model.mil +++ /dev/null @@ -1,58 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] -{ - func main(tensor decoder_step, tensor encoder_step) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_step_to_fp16_dtype_0 = const()[name = tensor("encoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_step_to_fp16_dtype_0 = const()[name = tensor("decoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1310848)))]; - tensor encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor("cast_3")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312192)))]; - tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2131456)))]; - tensor decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor("cast_2")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; - tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; - tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; - tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2132800)))]; - tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3451264)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 1, 1, 1025])]; - tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; - tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 1, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; - tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; - tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; - tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; - tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; - tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; - tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; - tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; - tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_1")]; - tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; - tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; - tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; - tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; - tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; - tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; - tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_0")]; - } -> (token_id, token_prob, duration); -} \ No newline at end of file diff --git a/models/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/weights/weight.bin b/models/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/weights/weight.bin deleted file mode 100644 index 8764a151a786b38079c36a409fe5144ddf8b95a9..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ca22a65903a05e64137677da608077578a8606090a598abf4875fa6199aaa19d -size 3453388 diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin b/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 84e7c818c15d2650b6c55b20e618fba3e6289763..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:03ab3c1327a054c54c07a40325db967ec574f2c91dcc8192bfa44aa561bcf2d8 -size 243 diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/coremldata.bin b/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/coremldata.bin deleted file mode 100644 index b53fff2bc93fbe4b468811db15aa17767f3cb33c..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d88ea1fc349459c9e100d6a96688c5b29a1f0d865f544be103001724b986b6d6 -size 494 diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/metadata.json b/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/metadata.json deleted file mode 100644 index 887f7d4adc6d7f933686f7b984e23a47b45f57e3..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/metadata.json +++ /dev/null @@ -1,110 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "int8-linear quantized - preprocessor", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32)", - "shortDescription" : "", - "shape" : "[]", - "name" : "mel", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Int8", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Range1d" : 2, - "Ios17.reshape" : 2, - "Ios17.matmul" : 1, - "Ios17.expandDims" : 10, - "Select" : 3, - "Ios17.add" : 4, - "Tile" : 2, - "Ios17.sliceByIndex" : 3, - "Ios16.reduceSum" : 4, - "Shape" : 3, - "Ios17.gather" : 3, - "Pad" : 1, - "Ios17.log" : 1, - "Ios16.constexprAffineDequantize" : 3, - "Ios17.conv" : 2, - "Ios17.sub" : 4, - "Ios17.pow" : 2, - "Ios17.cast" : 10, - "Ios17.realDiv" : 4, - "Stack" : 1, - "Ios17.concat" : 3, - "Ios17.floorDiv" : 1, - "Ios17.less" : 1, - "Ios17.sqrt" : 1, - "Ios17.greaterEqual" : 1, - "Ios17.mul" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32, UInt16)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "dataType" : "Float32", - "hasShapeFlexibility" : "1", - "isOptional" : "0", - "shapeFlexibility" : "1 × 1...240000", - "shapeRange" : "[[1, 1], [1, 240000]]", - "formattedType" : "MultiArray (Float32 1 × 1)", - "type" : "MultiArray", - "shape" : "[1, 1]", - "name" : "audio_signal", - "shortDescription" : "" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "audio_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2025-09-25", - "com.github.apple.coremltools.source" : "torch==2.7.0", - "com.github.apple.coremltools.version" : "9.0b1", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "parakeet_preprocessor", - "method" : "predict" - } -] \ No newline at end of file diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/model.mil b/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/model.mil deleted file mode 100644 index e94cce4bc7fbf55e55788449bf067ffe09a5ca91..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/model.mil +++ /dev/null @@ -1,169 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})] -{ - func main(tensor audio_length, tensor audio_signal) [FlexibleShapeInformation = tuple, dict, tensor>>, tuple, dict, list, ?>>>>((("DefaultShapes", {{"audio_signal", [1, 1]}}), ("RangeDims", {{"audio_signal", [[1, 1], [1, 240000]]}})))] { - tensor var_9 = const()[name = tensor("op_9"), val = tensor(1)]; - tensor var_10 = const()[name = tensor("op_10"), val = tensor(160)]; - tensor var_34 = const()[name = tensor("op_34"), val = tensor(512)]; - tensor var_35 = add(x = audio_length, y = var_34)[name = tensor("op_35")]; - tensor var_36 = const()[name = tensor("op_36"), val = tensor(512)]; - tensor var_37 = sub(x = var_35, y = var_36)[name = tensor("op_37")]; - tensor floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor("floor_div_0")]; - tensor var_38_to_fp16_dtype_0 = const()[name = tensor("op_38_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_39_promoted_to_fp16 = const()[name = tensor("op_39_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor floor_div_0_to_fp16 = cast(dtype = var_38_to_fp16_dtype_0, x = floor_div_0)[name = tensor("cast_9")]; - tensor seq_len_1_cast_fp16 = add(x = floor_div_0_to_fp16, y = var_39_promoted_to_fp16)[name = tensor("seq_len_1_cast_fp16")]; - tensor seq_len_dtype_0 = const()[name = tensor("seq_len_dtype_0"), val = tensor("int32")]; - tensor var_43_begin_0 = const()[name = tensor("op_43_begin_0"), val = tensor([0, 0])]; - tensor var_43_end_0 = const()[name = tensor("op_43_end_0"), val = tensor([1, 1])]; - tensor var_43_end_mask_0 = const()[name = tensor("op_43_end_mask_0"), val = tensor([true, false])]; - tensor var_43_squeeze_mask_0 = const()[name = tensor("op_43_squeeze_mask_0"), val = tensor([false, true])]; - tensor audio_signal_to_fp16_dtype_0 = const()[name = tensor("audio_signal_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor("cast_8")]; - tensor var_43_cast_fp16 = slice_by_index(begin = var_43_begin_0, end = var_43_end_0, end_mask = var_43_end_mask_0, squeeze_mask = var_43_squeeze_mask_0, x = audio_signal_to_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_44_axes_0 = const()[name = tensor("op_44_axes_0"), val = tensor([1])]; - tensor var_44_cast_fp16 = expand_dims(axes = var_44_axes_0, x = var_43_cast_fp16)[name = tensor("op_44_cast_fp16")]; - tensor var_46_begin_0 = const()[name = tensor("op_46_begin_0"), val = tensor([0, 1])]; - tensor var_46_end_0 = const()[name = tensor("op_46_end_0"), val = tensor([1, 0])]; - tensor var_46_end_mask_0 = const()[name = tensor("op_46_end_mask_0"), val = tensor([true, true])]; - tensor var_46_cast_fp16 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_46_cast_fp16")]; - tensor var_48_begin_0 = const()[name = tensor("op_48_begin_0"), val = tensor([0, 0])]; - tensor var_48_end_0 = const()[name = tensor("op_48_end_0"), val = tensor([1, -1])]; - tensor var_48_end_mask_0 = const()[name = tensor("op_48_end_mask_0"), val = tensor([true, false])]; - tensor var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_48_cast_fp16")]; - tensor var_49_to_fp16 = const()[name = tensor("op_49_to_fp16"), val = tensor(0x1.f0cp-1)]; - tensor var_50_cast_fp16 = mul(x = var_48_cast_fp16, y = var_49_to_fp16)[name = tensor("op_50_cast_fp16")]; - tensor var_51_cast_fp16 = sub(x = var_46_cast_fp16, y = var_50_cast_fp16)[name = tensor("op_51_cast_fp16")]; - tensor input_1_interleave_0 = const()[name = tensor("input_1_interleave_0"), val = tensor(false)]; - tensor input_1_cast_fp16 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44_cast_fp16, var_51_cast_fp16))[name = tensor("input_1_cast_fp16")]; - tensor concat_0x = const()[name = tensor("concat_0x"), val = tensor([1, 1, -1])]; - tensor input_3_cast_fp16 = reshape(shape = concat_0x, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; - tensor input_5_mode_0 = const()[name = tensor("input_5_mode_0"), val = tensor("reflect")]; - tensor const_1_to_fp16 = const()[name = tensor("const_1_to_fp16"), val = tensor(0x0p+0)]; - tensor input_5_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor concat_1x = const()[name = tensor("concat_1x"), val = tensor([1, -1])]; - tensor input_cast_fp16 = reshape(shape = concat_1x, x = input_5_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([160])]; - tensor expand_dims_4_axes_0 = const()[name = tensor("expand_dims_4_axes_0"), val = tensor([1])]; - tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = tensor("expand_dims_4_cast_fp16")]; - tensor conv_0_pad_type_0 = const()[name = tensor("conv_0_pad_type_0"), val = tensor("valid")]; - tensor conv_0_pad_0 = const()[name = tensor("conv_0_pad_0"), val = tensor([0, 0])]; - tensor conv_0_dilations_0 = const()[name = tensor("conv_0_dilations_0"), val = tensor([1])]; - tensor conv_0_groups_0 = const()[name = tensor("conv_0_groups_0"), val = tensor(1)]; - tensor expand_dims_1_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("expand_dims_1_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132096))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131712)))]; - tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16_quantized, x = expand_dims_4_cast_fp16)[name = tensor("conv_0_cast_fp16")]; - tensor conv_1_pad_type_0 = const()[name = tensor("conv_1_pad_type_0"), val = tensor("valid")]; - tensor conv_1_pad_0 = const()[name = tensor("conv_1_pad_0"), val = tensor([0, 0])]; - tensor conv_1_dilations_0 = const()[name = tensor("conv_1_dilations_0"), val = tensor([1])]; - tensor conv_1_groups_0 = const()[name = tensor("conv_1_groups_0"), val = tensor(1)]; - tensor expand_dims_2_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("expand_dims_2_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132736))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264768))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264384)))]; - tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16_quantized, x = expand_dims_4_cast_fp16)[name = tensor("conv_1_cast_fp16")]; - tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(-1)]; - tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor("stack_0_cast_fp16")]; - tensor var_17_promoted_to_fp16 = const()[name = tensor("op_17_promoted_to_fp16"), val = tensor(0x1p+1)]; - tensor var_67_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_17_promoted_to_fp16)[name = tensor("op_67_cast_fp16")]; - tensor var_69_axes_0 = const()[name = tensor("op_69_axes_0"), val = tensor([-1])]; - tensor var_69_keep_dims_0 = const()[name = tensor("op_69_keep_dims_0"), val = tensor(false)]; - tensor var_69_cast_fp16 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67_cast_fp16)[name = tensor("op_69_cast_fp16")]; - tensor x_11_transpose_x_0 = const()[name = tensor("x_11_transpose_x_0"), val = tensor(false)]; - tensor x_11_transpose_y_0 = const()[name = tensor("x_11_transpose_y_0"), val = tensor(false)]; - tensor const_2_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(1), name = tensor("const_2_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265408))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298560))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298368)))]; - tensor x_11_cast_fp16 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = const_2_to_fp16_quantized, y = var_69_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor var_76_to_fp16 = const()[name = tensor("op_76_to_fp16"), val = tensor(0x1p-24)]; - tensor var_77_cast_fp16 = add(x = x_11_cast_fp16, y = var_76_to_fp16)[name = tensor("op_77_cast_fp16")]; - tensor x_13_epsilon_0 = const()[name = tensor("x_13_epsilon_0"), val = tensor(0x1p-149)]; - tensor x_13_cast_fp16 = log(epsilon = x_13_epsilon_0, x = var_77_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_79_shape_cast_fp16 = shape(x = x_13_cast_fp16)[name = tensor("op_79_shape_cast_fp16")]; - tensor gather_4 = const()[name = tensor("gather_4"), val = tensor(1)]; - tensor gather_5_axis_0 = const()[name = tensor("gather_5_axis_0"), val = tensor(0)]; - tensor gather_5_batch_dims_0 = const()[name = tensor("gather_5_batch_dims_0"), val = tensor(0)]; - tensor gather_5_validate_indices_0 = const()[name = tensor("gather_5_validate_indices_0"), val = tensor(false)]; - tensor var_79_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_79_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor gather_5_indices_0_to_uint16 = const()[name = tensor("gather_5_indices_0_to_uint16"), val = tensor(2)]; - tensor var_79_shape_cast_fp16_to_uint16 = cast(dtype = var_79_shape_cast_fp16_to_uint16_dtype_0, x = var_79_shape_cast_fp16)[name = tensor("cast_7")]; - tensor gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = gather_5_indices_0_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_79_shape_cast_fp16_to_uint16)[name = tensor("gather_5_cast_uint16")]; - tensor gather_5_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_5_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_3 = const()[name = tensor("const_3"), val = tensor(0)]; - tensor const_4 = const()[name = tensor("const_4"), val = tensor(1)]; - tensor gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = tensor("cast_6")]; - tensor var_81 = range_1d(end = gather_5_cast_uint16_to_int32, start = const_3, step = const_4)[name = tensor("op_81")]; - tensor var_82_axes_0 = const()[name = tensor("op_82_axes_0"), val = tensor([0])]; - tensor var_82 = expand_dims(axes = var_82_axes_0, x = var_81)[name = tensor("op_82")]; - tensor concat_2_axis_0 = const()[name = tensor("concat_2_axis_0"), val = tensor(0)]; - tensor concat_2_interleave_0 = const()[name = tensor("concat_2_interleave_0"), val = tensor(false)]; - tensor concat_2 = concat(axis = concat_2_axis_0, interleave = concat_2_interleave_0, values = (gather_4, gather_5_cast_uint16_to_int32))[name = tensor("concat_2")]; - tensor shape_0 = shape(x = var_82)[name = tensor("shape_0")]; - tensor real_div_0 = real_div(x = concat_2, y = shape_0)[name = tensor("real_div_0")]; - tensor time_steps = tile(reps = real_div_0, x = var_82)[name = tensor("time_steps")]; - tensor var_85_axes_0 = const()[name = tensor("op_85_axes_0"), val = tensor([1])]; - tensor mel_length = cast(dtype = seq_len_dtype_0, x = seq_len_1_cast_fp16)[name = tensor("cast_5")]; - tensor var_85 = expand_dims(axes = var_85_axes_0, x = mel_length)[name = tensor("op_85")]; - tensor valid_mask = less(x = time_steps, y = var_85)[name = tensor("valid_mask")]; - tensor var_87_axes_0 = const()[name = tensor("op_87_axes_0"), val = tensor([1])]; - tensor var_87 = expand_dims(axes = var_87_axes_0, x = valid_mask)[name = tensor("op_87")]; - tensor var_24_to_fp16 = const()[name = tensor("op_24_to_fp16"), val = tensor(0x0p+0)]; - tensor var_88_cast_fp16 = select(a = x_13_cast_fp16, b = var_24_to_fp16, cond = var_87)[name = tensor("op_88_cast_fp16")]; - tensor x_mean_numerator_axes_0 = const()[name = tensor("x_mean_numerator_axes_0"), val = tensor([2])]; - tensor x_mean_numerator_keep_dims_0 = const()[name = tensor("x_mean_numerator_keep_dims_0"), val = tensor(false)]; - tensor x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_88_cast_fp16)[name = tensor("x_mean_numerator_cast_fp16")]; - tensor x_mean_denominator_axes_0 = const()[name = tensor("x_mean_denominator_axes_0"), val = tensor([1])]; - tensor x_mean_denominator_keep_dims_0 = const()[name = tensor("x_mean_denominator_keep_dims_0"), val = tensor(false)]; - tensor cast_3_to_fp16_dtype_0 = const()[name = tensor("cast_3_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor valid_mask_to_fp16 = cast(dtype = cast_3_to_fp16_dtype_0, x = valid_mask)[name = tensor("cast_4")]; - tensor x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor("x_mean_denominator_cast_fp16")]; - tensor var_93_axes_0 = const()[name = tensor("op_93_axes_0"), val = tensor([1])]; - tensor var_93_cast_fp16 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor("op_93_cast_fp16")]; - tensor x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_93_cast_fp16)[name = tensor("x_mean_cast_fp16")]; - tensor var_96_axes_0 = const()[name = tensor("op_96_axes_0"), val = tensor([2])]; - tensor var_96_cast_fp16 = expand_dims(axes = var_96_axes_0, x = x_mean_cast_fp16)[name = tensor("op_96_cast_fp16")]; - tensor var_97_cast_fp16 = sub(x = x_13_cast_fp16, y = var_96_cast_fp16)[name = tensor("op_97_cast_fp16")]; - tensor var_98_cast_fp16 = select(a = var_97_cast_fp16, b = var_24_to_fp16, cond = var_87)[name = tensor("op_98_cast_fp16")]; - tensor var_17_promoted_1_to_fp16 = const()[name = tensor("op_17_promoted_1_to_fp16"), val = tensor(0x1p+1)]; - tensor var_99_cast_fp16 = pow(x = var_98_cast_fp16, y = var_17_promoted_1_to_fp16)[name = tensor("op_99_cast_fp16")]; - tensor var_101_axes_0 = const()[name = tensor("op_101_axes_0"), val = tensor([2])]; - tensor var_101_keep_dims_0 = const()[name = tensor("op_101_keep_dims_0"), val = tensor(false)]; - tensor var_101_cast_fp16 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99_cast_fp16)[name = tensor("op_101_cast_fp16")]; - tensor var_103_to_fp16 = const()[name = tensor("op_103_to_fp16"), val = tensor(0x1p+0)]; - tensor var_104_cast_fp16 = sub(x = var_93_cast_fp16, y = var_103_to_fp16)[name = tensor("op_104_cast_fp16")]; - tensor var_105_cast_fp16 = real_div(x = var_101_cast_fp16, y = var_104_cast_fp16)[name = tensor("op_105_cast_fp16")]; - tensor x_std_1_cast_fp16 = sqrt(x = var_105_cast_fp16)[name = tensor("x_std_1_cast_fp16")]; - tensor var_25_to_fp16 = const()[name = tensor("op_25_to_fp16"), val = tensor(0x1.5p-17)]; - tensor x_std_cast_fp16 = add(x = x_std_1_cast_fp16, y = var_25_to_fp16)[name = tensor("x_std_cast_fp16")]; - tensor var_110_axes_0 = const()[name = tensor("op_110_axes_0"), val = tensor([2])]; - tensor var_110_cast_fp16 = expand_dims(axes = var_110_axes_0, x = x_std_cast_fp16)[name = tensor("op_110_cast_fp16")]; - tensor x_cast_fp16 = real_div(x = var_97_cast_fp16, y = var_110_cast_fp16)[name = tensor("x_cast_fp16")]; - tensor var_112_shape_cast_fp16 = shape(x = x_cast_fp16)[name = tensor("op_112_shape_cast_fp16")]; - tensor gather_6_batch_dims_0 = const()[name = tensor("gather_6_batch_dims_0"), val = tensor(0)]; - tensor gather_6_validate_indices_0 = const()[name = tensor("gather_6_validate_indices_0"), val = tensor(false)]; - tensor var_112_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_112_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor gather_6_cast_uint16_axis_0 = const()[name = tensor("gather_6_cast_uint16_axis_0"), val = tensor(0)]; - tensor select_0_to_uint16 = const()[name = tensor("select_0_to_uint16"), val = tensor(2)]; - tensor var_112_shape_cast_fp16_to_uint16 = cast(dtype = var_112_shape_cast_fp16_to_uint16_dtype_0, x = var_112_shape_cast_fp16)[name = tensor("cast_3")]; - tensor gather_6_cast_uint16_cast_uint16 = gather(axis = gather_6_cast_uint16_axis_0, batch_dims = gather_6_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_6_validate_indices_0, x = var_112_shape_cast_fp16_to_uint16)[name = tensor("gather_6_cast_uint16_cast_uint16")]; - tensor gather_6_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_6_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_5 = const()[name = tensor("const_5"), val = tensor(0)]; - tensor const_6 = const()[name = tensor("const_6"), val = tensor(1)]; - tensor gather_6_cast_uint16_to_int32 = cast(dtype = gather_6_cast_uint16_to_int32_dtype_0, x = gather_6_cast_uint16_cast_uint16)[name = tensor("cast_2")]; - tensor mask_1 = range_1d(end = gather_6_cast_uint16_to_int32, start = const_5, step = const_6)[name = tensor("mask_1")]; - tensor gather_7_axis_0 = const()[name = tensor("gather_7_axis_0"), val = tensor(0)]; - tensor gather_7_batch_dims_0 = const()[name = tensor("gather_7_batch_dims_0"), val = tensor(0)]; - tensor gather_7_validate_indices_0 = const()[name = tensor("gather_7_validate_indices_0"), val = tensor(false)]; - tensor gather_7_indices_0_to_uint16 = const()[name = tensor("gather_7_indices_0_to_uint16"), val = tensor(0)]; - tensor gather_7_cast_uint16 = gather(axis = gather_7_axis_0, batch_dims = gather_7_batch_dims_0, indices = gather_7_indices_0_to_uint16, validate_indices = gather_7_validate_indices_0, x = var_112_shape_cast_fp16_to_uint16)[name = tensor("gather_7_cast_uint16")]; - tensor gather_7_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_7_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor concat_3_axis_0 = const()[name = tensor("concat_3_axis_0"), val = tensor(0)]; - tensor concat_3_interleave_0 = const()[name = tensor("concat_3_interleave_0"), val = tensor(false)]; - tensor gather_7_cast_uint16_to_int32 = cast(dtype = gather_7_cast_uint16_to_int32_dtype_0, x = gather_7_cast_uint16)[name = tensor("cast_1")]; - tensor concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (gather_7_cast_uint16_to_int32, var_9))[name = tensor("concat_3")]; - tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0])]; - tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = tensor("expand_dims_0")]; - tensor var_116 = tile(reps = concat_3, x = expand_dims_0)[name = tensor("op_116")]; - tensor mask = greater_equal(x = var_116, y = var_85)[name = tensor("mask")]; - tensor var_119_axes_0 = const()[name = tensor("op_119_axes_0"), val = tensor([1])]; - tensor var_119 = expand_dims(axes = var_119_axes_0, x = mask)[name = tensor("op_119")]; - tensor processed_signal_cast_fp16 = select(a = var_24_to_fp16, b = x_cast_fp16, cond = var_119)[name = tensor("processed_signal_cast_fp16")]; - tensor processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("processed_signal_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor mel = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = tensor("cast_0")]; - } -> (mel, mel_length); -} \ No newline at end of file diff --git a/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/weights/weight.bin b/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/weights/weight.bin deleted file mode 100644 index a9af1ac1e407ef336eebc2fd4da9de57e6d172ad..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a5f7df6c7f47147ae9486fe18cc7792f9a44d093ec3c6a11e91ef2dc363c48dc -size 298880 diff --git a/models/parakeet-tdt-0.6b-v2-coreml/config.json b/models/parakeet-tdt-0.6b-v2-coreml/config.json deleted file mode 100644 index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/config.json +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/models/parakeet-tdt-0.6b-v2-coreml/parakeet_vocab.json b/models/parakeet-tdt-0.6b-v2-coreml/parakeet_vocab.json deleted file mode 100644 index a111d3bd29d44781c4588447c3eac1ea549cb1f1..0000000000000000000000000000000000000000 --- a/models/parakeet-tdt-0.6b-v2-coreml/parakeet_vocab.json +++ /dev/null @@ -1,1033 +0,0 @@ -{ - "479" : "▁happ", - "493" : "ial", - "386" : "▁diff", - "398" : "▁little", - "168" : "▁ch", - "872" : "2", - "227" : "ie", - "251" : "▁R", - "488" : "▁day", - "38" : "ed", - "610" : "▁might", - "127" : "▁can", - "70" : "ct", - "903" : "Ă ", - "329" : "▁been", - "694" : "ren", - "1005" : "Ćœ", - "661" : "▁reg", - "597" : "▁around", - "442" : "ys", - "764" : "▁number", - "705" : "▁trans", - "275" : "▁had", - "271" : "ak", - "1024" : "▁warming", - "564" : "▁play", - "32" : "▁y", - "463" : "▁under", - "684" : "ute", - "529" : "▁quest", - "719" : "ock", - "235" : "her", - "861" : "q", - "987" : "Č", - "593" : "urn", - "555" : "ath", - "216" : "▁think", - "211" : "el", - "500" : "▁bit", - "353" : "ry", - "490" : "▁Yeah", - "750" : "▁sim", - "283" : "▁these", - "320" : "▁sp", - "945" : "ÎČ", - "179" : "ca", - "934" : "т", - "258" : "ear", - "870" : "R", - "974" : "Ί", - "157" : "▁H", - "280" : "ine", - "381" : "▁well", - "21" : "▁m", - "668" : "▁la", - "429" : "▁bo", - "812" : "▁ty", - "537" : "ual", - "743" : "ss", - "119" : "▁kn", - "863" : "P", - "855" : "0", - "333" : "ple", - "850" : "x", - "959" : "я", - "467" : "▁bu", - "262" : "▁would", - "835" : "g", - "254" : "▁who", - "233" : "▁here", - "993" : "ĆŒ", - "889" : "Z", - "231" : "ack", - "788" : "ution", - "466" : "ning", - "332" : "▁did", - "388" : "cc", - "260" : "▁E", - "839" : ",", - "582" : "▁three", - "1000" : "Ă”", - "73" : "ver", - "486" : "ign", - "541" : "▁ser", - "800" : "▁exper", - "192" : "um", - "199" : "▁from", - "223" : "▁because", - "68" : "ent", - "608" : "▁tra", - "383" : "▁pre", - "774" : "▁place", - "194" : "▁your", - "148" : "ra", - "955" : "Ü", - "158" : "▁fr", - "670" : "▁sub", - "906" : "π", - "829" : "l", - "202" : "pe", - "948" : "ÎŽ", - "480" : "ater", - "342" : "ree", - "958" : "ƛ", - "967" : "Á", - "171" : "ate", - "91" : "am", - "114" : "ation", - "390" : "int", - "1013" : "À", - "794" : "▁read", - "1011" : "ф", - "355" : "ble", - "921" : "Ć«", - "234" : "▁pe", - "573" : "▁id", - "785" : "gan", - "335" : "▁other", - "963" : "Ö", - "570" : "get", - "2" : "▁th", - "273" : "ide", - "574" : "▁Oh", - "807" : "▁open", - "155" : "▁The", - "506" : "vers", - "729" : "▁sort", - "318" : "▁got", - "550" : "ank", - "896" : "Ăł", - "563" : "ict", - "1010" : "Đ·", - "520" : "▁gonna", - "1020" : "Κ", - "548" : "ved", - "848" : "S", - "578" : "▁rel", - "828" : "r", - "361" : "▁cont", - "379" : "ish", - "259" : "▁tim", - "441" : "▁imp", - "44" : "as", - "620" : "ways", - "34" : "▁I", - "87" : "st", - "13" : "nd", - "350" : "▁every", - "866" : "D", - "545" : "▁V", - "182" : "if", - "615" : "cial", - "69" : "ke", - "276" : "so", - "732" : "ics", - "749" : "als", - "605" : "aking", - "857" : "-", - "446" : "▁down", - "41" : "ar", - "203" : "un", - "816" : "▁met", - "543" : "▁ind", - "926" : "ĂŠ", - "64" : "▁on", - "246" : "▁them", - "172" : "qu", - "229" : "▁tr", - "775" : "▁gener", - "592" : "ower", - "617" : "▁give", - "399" : "ical", - "319" : "ag", - "669" : "▁last", - "400" : "▁gr", - "389" : "ittle", - "533" : "▁fu", - "460" : "uct", - "825" : "i", - "992" : "Ç", - "432" : "▁much", - "638" : "ction", - "700" : "▁love", - "988" : "έ", - "658" : "▁inv", - "549" : "▁still", - "322" : "act", - "143" : "il", - "966" : "ĐČ", - "245" : "▁It", - "445" : "▁yeah", - "524" : "self", - "742" : "ata", - "380" : "kay", - "176" : "▁su", - "624" : "▁gen", - "766" : "▁saying", - "42" : "▁that", - "136" : "▁ab", - "224" : "▁by", - "811" : "▁getting", - "76" : "id", - "430" : "be", - "124" : "▁know", - "88" : "ch", - "420" : "▁she", - "822" : "o", - "991" : "Å", - "623" : "ily", - "230" : "▁then", - "65" : "▁T", - "86" : "▁st", - "873" : "J", - "464" : "▁br", - "165" : "pp", - "325" : "iz", - "237" : "▁F", - "939" : "č", - "250" : "▁say", - "423" : "ord", - "516" : "▁fl", - "1030" : "▁urge", - "977" : "Îč", - "358" : "▁kind", - "471" : "co", - "7" : "▁w", - "252" : "▁people", - "12" : "er", - "932" : "ψ", - "632" : "▁cour", - "440" : "▁comm", - "664" : "▁cr", - "637" : "▁num", - "588" : "▁To", - "286" : "▁sa", - "292" : "pt", - "772" : "▁better", - "396" : "▁him", - "1007" : "Γ", - "26" : "▁d", - "450" : "ens", - "780" : "cept", - "936" : "ω", - "123" : "▁And", - "349" : "▁need", - "536" : "we", - "882" : "8", - "546" : "he", - "854" : "?", - "875" : ":", - "712" : "▁exam", - "437" : "ary", - "394" : "ip", - "344" : "own", - "709" : "▁fin", - "457" : "▁K", - "996" : "χ", - "990" : "ы", - "89" : "▁li", - "878" : "3", - "503" : "fe", - "61" : "et", - "782" : "▁understand", - "126" : "op", - "135" : "▁at", - "831" : "u", - "456" : "ody", - "594" : "▁okay", - "695" : "erest", - "324" : "▁also", - "51" : "▁it", - "542" : "▁rem", - "174" : "▁ex", - "1006" : "ț", - "128" : "▁or", - "357" : "ue", - "641" : "▁um", - "802" : "▁ele", - "213" : "▁some", - "606" : "▁pos", - "929" : "Îœ", - "885" : "!", - "784" : "▁thought", - "716" : "▁stud", - "232" : "▁pl", - "522" : "ces", - "184" : "▁if", - "37" : "is", - "867" : "N", - "653" : "uc", - "413" : "ult", - "167" : "ess", - "22" : "en", - "439" : "ving", - "104" : "▁r", - "755" : "oc", - "63" : "▁re", - "622" : "ward", - "1003" : "Ɓ", - "468" : "▁use", - "879" : "K", - "838" : "p", - "11" : "ou", - "54" : "le", - "118" : "▁not", - "558" : "ft", - "858" : "M", - "579" : "▁before", - "652" : "les", - "98" : "▁do", - "1026" : "▁issue", - "360" : "▁back", - "790" : "ason", - "731" : "▁today", - "706" : "▁count", - "650" : "▁didn", - "348" : "▁where", - "371" : "ep", - "898" : "ĂŒ", - "384" : "▁two", - "121" : "▁B", - "720" : "▁used", - "120" : "ight", - "1022" : "Ο", - "345" : "▁tw", - "307" : "▁work", - "655" : "ating", - "745" : "ween", - "596" : "▁bel", - "852" : "B", - "986" : "ĂŹ", - "197" : "▁get", - "79" : "▁he", - "504" : "▁doing", - "644" : "▁own", - "791" : "▁problem", - "931" : "Îł", - "33" : "▁l", - "187" : "ab", - "771" : "▁between", - "783" : "▁fun", - "823" : "a", - "723" : "▁No", - "303" : "▁mo", - "482" : "ition", - "164" : "▁M", - "346" : "▁part", - "103" : "ad", - "902" : "ç", - "781" : "ull", - "411" : "▁actually", - "598" : "ful", - "1002" : "ħ", - "351" : "pl", - "343" : "▁into", - "908" : "Ăș", - "337" : "ite", - "894" : "ĂĄ", - "912" : "Ä«", - "864" : "z", - "925" : "τ", - "153" : "▁con", - "144" : "▁but", - "433" : "▁per", - "711" : "▁pol", - "631" : "▁sm", - "431" : "ount", - "999" : "Í", - "946" : "Ăž", - "141" : "use", - "77" : "▁for", - "733" : "▁vide", - "730" : "▁For", - "418" : "ations", - "693" : "▁always", - "642" : "ood", - "1015" : "Ā", - "154" : "▁all", - "659" : "ably", - "1027" : "▁stay", - "777" : "▁ins", - "740" : "▁keep", - "96" : "▁so", - "806" : "▁partic", - "313" : "▁im", - "507" : "av", - "880" : "4", - "792" : "▁doesn", - "426" : "▁am", - "1001" : "ě", - "526" : "▁If", - "465" : "▁take", - "436" : "vel", - "116" : "ere", - "576" : "ever", - "568" : "oth", - "166" : "▁com", - "151" : "ul", - "798" : "ah", - "779" : "cond", - "556" : "▁end", - "161" : "ea", - "39" : "▁g", - "765" : "ention", - "100" : "th", - "461" : "▁only", - "590" : "▁hel", - "505" : "▁St", - "957" : "Ƅ", - "876" : "5", - "580" : "▁feel", - "321" : "ans", - "972" : "ь", - "718" : "▁car", - "851" : "W", - "960" : "đ", - "469" : "▁Ch", - "291" : "one", - "83" : "ly", - "295" : "▁has", - "84" : "▁go", - "981" : "Ƒ", - "923" : "λ", - "52" : "▁be", - "821" : "t", - "323" : "▁te", - "50" : "al", - "760" : "ense", - "149" : "ore", - "306" : "▁le", - "403" : "▁thr", - "628" : "ob", - "299" : "▁look", - "406" : "▁This", - "0" : "", - "75" : "all", - "475" : "▁call", - "341" : "reat", - "519" : "ents", - "66" : "▁A", - "186" : "nt", - "928" : "ĐŸ", - "244" : "ople", - "393" : "ence", - "834" : "m", - "833" : "y", - "414" : "able", - "297" : "▁very", - "364" : "▁pr", - "865" : "L", - "35" : "it", - "339" : "omet", - "27" : "es", - "150" : "▁there", - "715" : "ell", - "677" : "▁import", - "681" : "▁ear", - "820" : "e", - "139" : "▁So", - "449" : "na", - "302" : "▁time", - "532" : "▁What", - "133" : "ck", - "970" : "Îż", - "773" : "cus", - "228" : "▁us", - "552" : "▁wr", - "680" : "▁made", - "871" : "E", - "874" : "U", - "895" : "ÂŁ", - "846" : "T", - "55" : "ion", - "492" : "ile", - "787" : "cy", - "105" : "ir", - "662" : "lic", - "629" : "▁tell", - "367" : "▁good", - "514" : "form", - "656" : "olog", - "334" : "ually", - "294" : "ong", - "485" : "ade", - "682" : "▁ac", - "106" : "▁was", - "1028" : "▁together", - "910" : "ĂŁ", - "142" : "ter", - "922" : "Δ", - "175" : "very", - "314" : "▁ag", - "327" : "▁That", - "826" : "s", - "183" : "ive", - "979" : "Đł", - "751" : "vern", - "997" : "э", - "278" : "eah", - "517" : "fter", - "311" : "per", - "535" : "▁show", - "918" : "^", - "954" : "ĆĄ", - "722" : "stand", - "6" : "re", - "93" : "ce", - "435" : "▁differe", - "746" : "▁stuff", - "915" : "ρ", - "602" : "▁supp", - "209" : "▁L", - "767" : "▁commun", - "769" : "akes", - "375" : "▁lot", - "859" : "H", - "397" : "▁make", - "340" : "ber", - "886" : "%", - "736" : "▁Al", - "600" : "ise", - "938" : "ć", - "478" : "ting", - "962" : "ĐŒ", - "138" : "ol", - "125" : "ome", - "309" : "are", - "673" : "▁inst", - "97" : "▁have", - "562" : "ject", - "678" : "ific", - "257" : "ect", - "17" : "on", - "953" : "с", - "933" : "ē", - "844" : "'", - "949" : "η", - "190" : "▁v", - "980" : "Đș", - "452" : "▁fo", - "247" : "ame", - "612" : "▁help", - "501" : "▁spe", - "604" : "ange", - "654" : "ib", - "842" : "k", - "815" : "ave", - "687" : "▁form", - "222" : "res", - "421" : "sel", - "477" : "other", - "308" : "▁their", - "212" : "▁N", - "737" : "▁important", - "90" : "▁u", - "630" : "▁Now", - "425" : "ia", - "14" : "▁i", - "315" : "▁J", - "331" : "▁fe", - "304" : "▁ar", - "29" : "ll", - "499" : "▁sc", - "919" : "€", - "301" : "itt", - "201" : "ri", - "137" : "ould", - "289" : "▁man", - "81" : "▁this", - "1008" : "П", - "312" : "ions", - "725" : "ks", - "837" : "f", - "458" : "▁through", - "714" : "▁maybe", - "487" : "thing", - "424" : "▁may", - "31" : "▁and", - "78" : "ro", - "961" : "Đ»", - "956" : "Ă„", - "198" : "cause", - "95" : "im", - "899" : "ñ", - "111" : "ally", - "523" : "▁There", - "538" : "ons", - "797" : "▁el", - "726" : "▁interest", - "53" : "▁wh", - "296" : "▁any", - "489" : "fore", - "911" : "φ", - "248" : "▁We", - "639" : "▁add", - "108" : "▁W", - "521" : "▁point", - "416" : "▁dis", - "739" : "▁run", - "747" : "ract", - "3" : "▁a", - "530" : "▁most", - "734" : "▁bec", - "338" : "age", - "544" : "▁pers", - "113" : "▁se", - "691" : "▁able", - "847" : "A", - "651" : "stem", - "115" : "od", - "527" : "▁same", - "575" : "ves", - "696" : "▁As", - "277" : "▁qu", - "728" : "ited", - "640" : "▁set", - "502" : "ub", - "497" : "▁try", - "881" : "V", - "219" : "▁G", - "561" : "ph", - "759" : "▁All", - "177" : "ain", - "515" : "ors", - "71" : "▁S", - "509" : "ian", - "803" : "▁cou", - "25" : "an", - "583" : "iss", - "417" : "▁first", - "840" : "b", - "768" : "▁An", - "419" : "▁something", - "569" : "▁acc", - "607" : "atch", - "534" : "ug", - "195" : "▁my", - "832" : "c", - "634" : "cess", - "809" : "▁everything", - "1" : "▁t", - "557" : "▁bas", - "481" : "▁inc", - "57" : "ot", - "518" : "ail", - "924" : "α", - "173" : "▁lo", - "905" : "ÎŒ", - "762" : "▁probably", - "626" : "▁dec", - "647" : "▁its", - "415" : "orm", - "917" : "ĂŽ", - "560" : "body", - "474" : "▁put", - "572" : 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(Float16, Float32, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.5.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 1 × 1501 × 80)", - "shortDescription" : "", - "shape" : "[1, 1, 1501, 80]", - "name" : "melspectrogram_features", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1, 1]", - "name" : "input_1", - "type" : "MultiArray" - } - ], - "generatedClassName" : "AudioEncoder_mixedBitPalettized_6_bit", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/model.mil b/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/model.mil deleted file mode 100644 index f394aeed7e0f3e8b25ae16e713f2a80558435158..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/model.mil +++ /dev/null @@ -1,4887 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}})] -{ - func main(tensor input_1, tensor melspectrogram_features) { - tensor pos_emb_to_fp16 = const()[name = tensor("pos_emb_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor var_42_cast_fp16 = sub(x = pos_emb_to_fp16, y = input_1)[name = tensor("op_42_cast_fp16")]; - tensor obj_3_cast_fp16 = mul(x = var_42_cast_fp16, y = input_1)[name = tensor("obj_3_cast_fp16")]; - tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("custom")]; - tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_1_strides_0 = const()[name = tensor("input_1_strides_0"), val = tensor([2, 2])]; - tensor input_1_dilations_0 = const()[name = tensor("input_1_dilations_0"), val = tensor([1, 1])]; - tensor input_1_groups_0 = const()[name = tensor("input_1_groups_0"), val = tensor(1)]; - tensor pre_encode_conv_0_weight_to_fp16 = const()[name = tensor("pre_encode_conv_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(384128)))]; - tensor pre_encode_conv_0_bias_to_fp16 = const()[name = tensor("pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(388800)))]; - tensor input_1_cast_fp16 = conv(bias = pre_encode_conv_0_bias_to_fp16, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = pre_encode_conv_0_weight_to_fp16, x = melspectrogram_features)[name = tensor("input_1_cast_fp16")]; - tensor input_3_cast_fp16 = relu(x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor input_5_pad_type_0 = const()[name = tensor("input_5_pad_type_0"), val = tensor("custom")]; - tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_5_strides_0 = const()[name = tensor("input_5_strides_0"), val = tensor([2, 2])]; - tensor input_5_groups_0 = const()[name = tensor("input_5_groups_0"), val = tensor(256)]; - tensor input_5_dilations_0 = const()[name = tensor("input_5_dilations_0"), val = tensor([1, 1])]; - tensor pre_encode_conv_2_weight_to_fp16 = const()[name = tensor("pre_encode_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389376)))]; - tensor pre_encode_conv_2_bias_to_fp16 = const()[name = tensor("pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(394048)))]; - tensor input_5_cast_fp16 = conv(bias = pre_encode_conv_2_bias_to_fp16, dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = pre_encode_conv_2_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("valid")]; - tensor input_7_strides_0 = const()[name = tensor("input_7_strides_0"), val = tensor([1, 1])]; - tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_7_dilations_0 = const()[name = tensor("input_7_dilations_0"), val = tensor([1, 1])]; - tensor input_7_groups_0 = const()[name = tensor("input_7_groups_0"), val = tensor(1)]; - tensor pre_encode_conv_3_weight_to_fp16 = const()[name = tensor("pre_encode_conv_3_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(394624)))]; - tensor pre_encode_conv_3_bias_to_fp16 = const()[name = tensor("pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(525760)))]; - tensor input_7_cast_fp16 = conv(bias = pre_encode_conv_3_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = pre_encode_conv_3_weight_to_fp16, x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor input_9_cast_fp16 = relu(x = input_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; - tensor input_11_pad_type_0 = const()[name = tensor("input_11_pad_type_0"), val = tensor("custom")]; - tensor input_11_pad_0 = const()[name = tensor("input_11_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_11_strides_0 = const()[name = tensor("input_11_strides_0"), val = tensor([2, 2])]; - tensor input_11_groups_0 = const()[name = tensor("input_11_groups_0"), val = tensor(256)]; - tensor input_11_dilations_0 = const()[name = tensor("input_11_dilations_0"), val = tensor([1, 1])]; - tensor pre_encode_conv_5_weight_to_fp16 = const()[name = tensor("pre_encode_conv_5_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526336)))]; - tensor pre_encode_conv_5_bias_to_fp16 = const()[name = tensor("pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(531008)))]; - tensor input_11_cast_fp16 = conv(bias = pre_encode_conv_5_bias_to_fp16, dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = pre_encode_conv_5_weight_to_fp16, x = input_9_cast_fp16)[name = tensor("input_11_cast_fp16")]; - tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("valid")]; - tensor input_13_strides_0 = const()[name = tensor("input_13_strides_0"), val = tensor([1, 1])]; - tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_13_dilations_0 = const()[name = tensor("input_13_dilations_0"), val = tensor([1, 1])]; - tensor input_13_groups_0 = const()[name = tensor("input_13_groups_0"), val = tensor(1)]; - tensor pre_encode_conv_6_weight_to_fp16 = const()[name = tensor("pre_encode_conv_6_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(531584)))]; - tensor pre_encode_conv_6_bias_to_fp16 = const()[name = tensor("pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(662720)))]; - tensor input_13_cast_fp16 = conv(bias = pre_encode_conv_6_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = pre_encode_conv_6_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("input_13_cast_fp16")]; - tensor x_1_cast_fp16 = relu(x = input_13_cast_fp16)[name = tensor("x_1_cast_fp16")]; - tensor var_103_perm_0 = const()[name = tensor("op_103_perm_0"), val = tensor([0, 1, 3, 2])]; - tensor var_106 = const()[name = tensor("op_106"), val = tensor([1, 2560, 1, 188])]; - tensor var_103_cast_fp16 = transpose(perm = var_103_perm_0, x = x_1_cast_fp16)[name = tensor("transpose_0")]; - tensor input_15_cast_fp16 = reshape(shape = var_106, x = var_103_cast_fp16)[name = tensor("input_15_cast_fp16")]; - tensor var_116_pad_type_0 = const()[name = tensor("op_116_pad_type_0"), val = tensor("valid")]; - tensor var_116_strides_0 = const()[name = tensor("op_116_strides_0"), val = tensor([1, 1])]; - tensor var_116_pad_0 = const()[name = tensor("op_116_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_116_dilations_0 = const()[name = tensor("op_116_dilations_0"), val = tensor([1, 1])]; - tensor var_116_groups_0 = const()[name = tensor("op_116_groups_0"), val = tensor(1)]; - tensor pre_encode_out_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(663296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1646400))), name = tensor("pre_encode_out_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2560, 1, 1])]; - tensor pre_encode_out_inlier_module_bias_to_fp16 = const()[name = tensor("pre_encode_out_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1646592)))]; - tensor var_116_cast_fp16 = conv(bias = pre_encode_out_inlier_module_bias_to_fp16, dilations = var_116_dilations_0, groups = var_116_groups_0, pad = var_116_pad_0, pad_type = var_116_pad_type_0, strides = var_116_strides_0, weight = pre_encode_out_inlier_module_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("op_116_cast_fp16")]; - tensor var_122_pad_type_0 = const()[name = tensor("op_122_pad_type_0"), val = tensor("valid")]; - tensor var_122_strides_0 = const()[name = tensor("op_122_strides_0"), val = tensor([1, 1])]; - tensor var_122_pad_0 = const()[name = tensor("op_122_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_122_dilations_0 = const()[name = tensor("op_122_dilations_0"), val = tensor([1, 1])]; - tensor var_122_groups_0 = const()[name = tensor("op_122_groups_0"), val = tensor(1)]; - tensor pre_encode_out_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696256))), name = tensor("pre_encode_out_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1647680))), shape = tensor([512, 2560, 1, 1])]; - tensor var_122_cast_fp16 = conv(dilations = var_122_dilations_0, groups = var_122_groups_0, pad = var_122_pad_0, pad_type = var_122_pad_type_0, strides = var_122_strides_0, weight = pre_encode_out_outlier_module_weight_to_fp16_sparsified, x = input_15_cast_fp16)[name = tensor("op_122_cast_fp16")]; - tensor inputs_1_cast_fp16 = add(x = var_116_cast_fp16, y = var_122_cast_fp16)[name = tensor("inputs_1_cast_fp16")]; - tensor var_128 = const()[name = tensor("op_128"), val = tensor(3)]; - tensor out_1_axes_0 = const()[name = tensor("out_1_axes_0"), val = tensor([1])]; - tensor var_159_to_fp16 = const()[name = tensor("op_159_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_159_to_fp16, x = inputs_1_cast_fp16)[name = tensor("out_1_cast_fp16")]; - tensor input_17_mean_0_to_fp16 = const()[name = tensor("input_17_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860160)))]; - tensor input_17_variance_0_to_fp16 = const()[name = tensor("input_17_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1861248)))]; - tensor input_17_gamma_0_to_fp16 = const()[name = tensor("input_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1862336)))]; - tensor input_17_beta_0_to_fp16 = const()[name = tensor("input_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863424)))]; - tensor input_17_epsilon_0_to_fp16 = const()[name = tensor("input_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_17_cast_fp16 = batch_norm(beta = input_17_beta_0_to_fp16, epsilon = input_17_epsilon_0_to_fp16, gamma = input_17_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_1_cast_fp16)[name = tensor("input_17_cast_fp16")]; - tensor var_179_pad_type_0 = const()[name = tensor("op_179_pad_type_0"), val = tensor("valid")]; - tensor var_179_strides_0 = const()[name = tensor("op_179_strides_0"), val = tensor([1, 1])]; - tensor var_179_pad_0 = const()[name = tensor("op_179_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_179_dilations_0 = const()[name = tensor("op_179_dilations_0"), val = tensor([1, 1])]; - tensor var_179_groups_0 = const()[name = tensor("op_179_groups_0"), val = tensor(1)]; - tensor layers_0_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1864512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2651008))), name = tensor("layers_0_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_0_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_0_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2651200)))]; - tensor var_179_cast_fp16 = conv(bias = layers_0_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_179_dilations_0, groups = var_179_groups_0, pad = var_179_pad_0, pad_type = var_179_pad_type_0, strides = var_179_strides_0, weight = layers_0_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_17_cast_fp16)[name = tensor("op_179_cast_fp16")]; - tensor var_185_pad_type_0 = const()[name = tensor("op_185_pad_type_0"), val = tensor("valid")]; - tensor var_185_strides_0 = const()[name = tensor("op_185_strides_0"), val = tensor([1, 1])]; - tensor var_185_pad_0 = const()[name = tensor("op_185_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_185_dilations_0 = const()[name = tensor("op_185_dilations_0"), val = tensor([1, 1])]; - tensor var_185_groups_0 = const()[name = tensor("op_185_groups_0"), val = tensor(1)]; - tensor layers_0_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2688192))), name = tensor("layers_0_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2655360))), shape = tensor([2048, 512, 1, 1])]; - tensor var_185_cast_fp16 = conv(dilations = var_185_dilations_0, groups = var_185_groups_0, pad = var_185_pad_0, pad_type = var_185_pad_type_0, strides = var_185_strides_0, weight = layers_0_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_17_cast_fp16)[name = tensor("op_185_cast_fp16")]; - tensor input_19_cast_fp16 = add(x = var_179_cast_fp16, y = var_185_cast_fp16)[name = tensor("input_19_cast_fp16")]; - tensor input_21_cast_fp16 = silu(x = input_19_cast_fp16)[name = tensor("input_21_cast_fp16")]; - tensor var_196_pad_type_0 = const()[name = tensor("op_196_pad_type_0"), val = tensor("valid")]; - tensor var_196_strides_0 = const()[name = tensor("op_196_strides_0"), val = tensor([1, 1])]; - tensor var_196_pad_0 = const()[name = tensor("op_196_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_196_dilations_0 = const()[name = tensor("op_196_dilations_0"), val = tensor([1, 1])]; - tensor var_196_groups_0 = const()[name = tensor("op_196_groups_0"), val = tensor(1)]; - tensor layers_0_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2819328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3605824))), name = tensor("layers_0_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_0_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_0_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3606016)))]; - tensor var_196_cast_fp16 = conv(bias = layers_0_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_196_dilations_0, groups = var_196_groups_0, pad = var_196_pad_0, pad_type = var_196_pad_type_0, strides = var_196_strides_0, weight = layers_0_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("op_196_cast_fp16")]; - tensor var_202_pad_type_0 = const()[name = tensor("op_202_pad_type_0"), val = tensor("valid")]; - tensor var_202_strides_0 = const()[name = tensor("op_202_strides_0"), val = tensor([1, 1])]; - tensor var_202_pad_0 = const()[name = tensor("op_202_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_202_dilations_0 = const()[name = tensor("op_202_dilations_0"), val = tensor([1, 1])]; - tensor var_202_groups_0 = const()[name = tensor("op_202_groups_0"), val = tensor(1)]; - tensor layers_0_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3635200))), name = tensor("layers_0_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3607104))), shape = tensor([512, 2048, 1, 1])]; - tensor var_202_cast_fp16 = conv(dilations = var_202_dilations_0, groups = var_202_groups_0, pad = var_202_pad_0, pad_type = var_202_pad_type_0, strides = var_202_strides_0, weight = layers_0_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_21_cast_fp16)[name = tensor("op_202_cast_fp16")]; - tensor x_3_cast_fp16 = add(x = var_196_cast_fp16, y = var_202_cast_fp16)[name = tensor("x_3_cast_fp16")]; - tensor var_204_to_fp16 = const()[name = tensor("op_204_to_fp16"), val = tensor(0x1p-1)]; - tensor var_205_cast_fp16 = mul(x = x_3_cast_fp16, y = var_204_to_fp16)[name = tensor("op_205_cast_fp16")]; - tensor inputs_3_cast_fp16 = add(x = inputs_1_cast_fp16, y = var_205_cast_fp16)[name = tensor("inputs_3_cast_fp16")]; - tensor out_3_axes_0 = const()[name = tensor("out_3_axes_0"), val = tensor([1])]; - tensor var_215_to_fp16 = const()[name = tensor("op_215_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_3_cast_fp16 = layer_norm(axes = out_3_axes_0, epsilon = var_215_to_fp16, x = inputs_3_cast_fp16)[name = tensor("out_3_cast_fp16")]; - tensor obj_1_gamma_0_to_fp16 = const()[name = tensor("obj_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3766336)))]; - tensor obj_1_beta_0_to_fp16 = const()[name = tensor("obj_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3767424)))]; - tensor obj_1_epsilon_0_to_fp16 = const()[name = tensor("obj_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_1_cast_fp16 = batch_norm(beta = obj_1_beta_0_to_fp16, epsilon = obj_1_epsilon_0_to_fp16, gamma = obj_1_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_3_cast_fp16)[name = tensor("obj_1_cast_fp16")]; - tensor var_240_pad_type_0 = const()[name = tensor("op_240_pad_type_0"), val = tensor("valid")]; - tensor var_240_strides_0 = const()[name = tensor("op_240_strides_0"), val = tensor([1, 1])]; - tensor var_240_pad_0 = const()[name = tensor("op_240_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_240_dilations_0 = const()[name = tensor("op_240_dilations_0"), val = tensor([1, 1])]; - tensor var_240_groups_0 = const()[name = tensor("op_240_groups_0"), val = tensor(1)]; - tensor layers_0_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3768512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3965184))), name = tensor("layers_0_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_0_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_0_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3965376)))]; - tensor var_240_cast_fp16 = conv(bias = layers_0_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_240_dilations_0, groups = var_240_groups_0, pad = var_240_pad_0, pad_type = var_240_pad_type_0, strides = var_240_strides_0, weight = layers_0_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = tensor("op_240_cast_fp16")]; - tensor var_246_pad_type_0 = const()[name = tensor("op_246_pad_type_0"), val = tensor("valid")]; - tensor var_246_strides_0 = const()[name = tensor("op_246_strides_0"), val = tensor([1, 1])]; - tensor var_246_pad_0 = const()[name = tensor("op_246_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_246_dilations_0 = const()[name = tensor("op_246_dilations_0"), val = tensor([1, 1])]; - tensor var_246_groups_0 = const()[name = tensor("op_246_groups_0"), val = tensor(1)]; - tensor layers_0_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3977984))), name = tensor("layers_0_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3966464))), shape = tensor([512, 512, 1, 1])]; - tensor var_246_cast_fp16 = conv(dilations = var_246_dilations_0, groups = var_246_groups_0, pad = var_246_pad_0, pad_type = var_246_pad_type_0, strides = var_246_strides_0, weight = layers_0_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_1_cast_fp16)[name = tensor("op_246_cast_fp16")]; - tensor query_1_cast_fp16 = add(x = var_240_cast_fp16, y = var_246_cast_fp16)[name = tensor("query_1_cast_fp16")]; - tensor var_255_pad_type_0 = const()[name = tensor("op_255_pad_type_0"), val = tensor("valid")]; - tensor var_255_strides_0 = const()[name = tensor("op_255_strides_0"), val = tensor([1, 1])]; - tensor var_255_pad_0 = const()[name = tensor("op_255_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_255_dilations_0 = const()[name = tensor("op_255_dilations_0"), val = tensor([1, 1])]; - tensor var_255_groups_0 = const()[name = tensor("op_255_groups_0"), val = tensor(1)]; - tensor layers_0_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4010816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4207488))), name = tensor("layers_0_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_255_cast_fp16 = conv(dilations = var_255_dilations_0, groups = var_255_groups_0, pad = var_255_pad_0, pad_type = var_255_pad_type_0, strides = var_255_strides_0, weight = layers_0_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = tensor("op_255_cast_fp16")]; - tensor var_261_pad_type_0 = const()[name = tensor("op_261_pad_type_0"), val = tensor("valid")]; - tensor var_261_strides_0 = const()[name = tensor("op_261_strides_0"), val = tensor([1, 1])]; - tensor var_261_pad_0 = const()[name = tensor("op_261_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_261_dilations_0 = const()[name = tensor("op_261_dilations_0"), val = tensor([1, 1])]; - tensor var_261_groups_0 = const()[name = tensor("op_261_groups_0"), val = tensor(1)]; - tensor layers_0_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4220160))), name = tensor("layers_0_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4207680))), shape = tensor([512, 512, 1, 1])]; - tensor var_261_cast_fp16 = conv(dilations = var_261_dilations_0, groups = var_261_groups_0, pad = var_261_pad_0, pad_type = var_261_pad_type_0, strides = var_261_strides_0, weight = layers_0_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_1_cast_fp16)[name = tensor("op_261_cast_fp16")]; - tensor key_1_cast_fp16 = add(x = var_255_cast_fp16, y = var_261_cast_fp16)[name = tensor("key_1_cast_fp16")]; - tensor var_271_pad_type_0 = const()[name = tensor("op_271_pad_type_0"), val = tensor("valid")]; - tensor var_271_strides_0 = const()[name = tensor("op_271_strides_0"), val = tensor([1, 1])]; - tensor var_271_pad_0 = const()[name = tensor("op_271_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_271_dilations_0 = const()[name = tensor("op_271_dilations_0"), val = tensor([1, 1])]; - tensor var_271_groups_0 = const()[name = tensor("op_271_groups_0"), val = tensor(1)]; - tensor layers_0_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4252992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4449664))), name = tensor("layers_0_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_0_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_0_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4449856)))]; - tensor var_271_cast_fp16 = conv(bias = layers_0_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_271_dilations_0, groups = var_271_groups_0, pad = var_271_pad_0, pad_type = var_271_pad_type_0, strides = var_271_strides_0, weight = layers_0_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = tensor("op_271_cast_fp16")]; - tensor var_277_pad_type_0 = const()[name = tensor("op_277_pad_type_0"), val = tensor("valid")]; - tensor var_277_strides_0 = const()[name = tensor("op_277_strides_0"), val = tensor([1, 1])]; - tensor var_277_pad_0 = const()[name = tensor("op_277_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_277_dilations_0 = const()[name = tensor("op_277_dilations_0"), val = tensor([1, 1])]; - tensor var_277_groups_0 = const()[name = tensor("op_277_groups_0"), val = tensor(1)]; - tensor layers_0_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4461312))), name = tensor("layers_0_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4450944))), shape = tensor([512, 512, 1, 1])]; - tensor var_277_cast_fp16 = conv(dilations = var_277_dilations_0, groups = var_277_groups_0, pad = var_277_pad_0, pad_type = var_277_pad_type_0, strides = var_277_strides_0, weight = layers_0_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_1_cast_fp16)[name = tensor("op_277_cast_fp16")]; - tensor value_1_cast_fp16 = add(x = var_271_cast_fp16, y = var_277_cast_fp16)[name = tensor("value_1_cast_fp16")]; - tensor var_280_to_fp16 = const()[name = tensor("op_280_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4494144)))]; - tensor query_3_cast_fp16 = add(x = query_1_cast_fp16, y = var_280_to_fp16)[name = tensor("query_3_cast_fp16")]; - tensor var_283_to_fp16 = const()[name = tensor("op_283_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4495232)))]; - tensor q_with_bias_v_1_cast_fp16 = add(x = query_1_cast_fp16, y = var_283_to_fp16)[name = tensor("q_with_bias_v_1_cast_fp16")]; - tensor var_293_pad_type_0 = const()[name = tensor("op_293_pad_type_0"), val = tensor("valid")]; - tensor var_293_strides_0 = const()[name = tensor("op_293_strides_0"), val = tensor([1, 1])]; - tensor var_293_pad_0 = const()[name = tensor("op_293_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_293_dilations_0 = const()[name = tensor("op_293_dilations_0"), val = tensor([1, 1])]; - tensor var_293_groups_0 = const()[name = tensor("op_293_groups_0"), val = tensor(1)]; - tensor layers_0_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4496320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4692992))), name = tensor("layers_0_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_293_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_293_dilations_0, groups = var_293_groups_0, pad = var_293_pad_0, pad_type = var_293_pad_type_0, strides = var_293_strides_0, weight = layers_0_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_293_cast_fp16")]; - tensor var_299_pad_type_0 = const()[name = tensor("op_299_pad_type_0"), val = tensor("valid")]; - tensor var_299_strides_0 = const()[name = tensor("op_299_strides_0"), val = tensor([1, 1])]; - tensor var_299_pad_0 = const()[name = tensor("op_299_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_299_dilations_0 = const()[name = tensor("op_299_dilations_0"), val = tensor([1, 1])]; - tensor var_299_groups_0 = const()[name = tensor("op_299_groups_0"), val = tensor(1)]; - tensor layers_0_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4712704))), name = tensor("layers_0_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4693184))), shape = tensor([512, 512, 1, 1])]; - tensor var_299_cast_fp16 = conv(dilations = var_299_dilations_0, groups = var_299_groups_0, pad = var_299_pad_0, pad_type = var_299_pad_type_0, strides = var_299_strides_0, weight = layers_0_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_299_cast_fp16")]; - tensor p_1_cast_fp16 = add(x = var_293_cast_fp16, y = var_299_cast_fp16)[name = tensor("p_1_cast_fp16")]; - tensor var_303 = const()[name = tensor("op_303"), val = tensor([1, 8, 64, 188])]; - tensor var_304_cast_fp16 = reshape(shape = var_303, x = q_with_bias_v_1_cast_fp16)[name = tensor("op_304_cast_fp16")]; - tensor var_305 = const()[name = tensor("op_305"), val = tensor([1, 8, 64, -1])]; - tensor var_306_cast_fp16 = reshape(shape = var_305, x = p_1_cast_fp16)[name = tensor("op_306_cast_fp16")]; - tensor matrix_bd_1_transpose_x_0 = const()[name = tensor("matrix_bd_1_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_1_transpose_y_0 = const()[name = tensor("matrix_bd_1_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_1_cast_fp16 = matmul(transpose_x = matrix_bd_1_transpose_x_0, transpose_y = matrix_bd_1_transpose_y_0, x = var_304_cast_fp16, y = var_306_cast_fp16)[name = tensor("matrix_bd_1_cast_fp16")]; - tensor matrix_bd_3_pad_0 = const()[name = tensor("matrix_bd_3_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_3_mode_0 = const()[name = tensor("matrix_bd_3_mode_0"), val = tensor("constant")]; - tensor const_10_to_fp16 = const()[name = tensor("const_10_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_3_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = matrix_bd_3_mode_0, pad = matrix_bd_3_pad_0, x = matrix_bd_1_cast_fp16)[name = tensor("matrix_bd_3_cast_fp16")]; - tensor var_315 = const()[name = tensor("op_315"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_5_cast_fp16 = reshape(shape = var_315, x = matrix_bd_3_cast_fp16)[name = tensor("matrix_bd_5_cast_fp16")]; - tensor var_319_begin_0 = const()[name = tensor("op_319_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_319_end_0 = const()[name = tensor("op_319_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_319_end_mask_0 = const()[name = tensor("op_319_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_319_cast_fp16 = slice_by_index(begin = var_319_begin_0, end = var_319_end_0, end_mask = var_319_end_mask_0, x = matrix_bd_5_cast_fp16)[name = tensor("op_319_cast_fp16")]; - tensor var_320 = const()[name = tensor("op_320"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_7_cast_fp16 = reshape(shape = var_320, x = var_319_cast_fp16)[name = tensor("matrix_bd_7_cast_fp16")]; - tensor var_325_begin_0 = const()[name = tensor("op_325_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_325_end_0 = const()[name = tensor("op_325_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_325_end_mask_0 = const()[name = tensor("op_325_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_325_cast_fp16 = slice_by_index(begin = var_325_begin_0, end = var_325_end_0, end_mask = var_325_end_mask_0, x = matrix_bd_7_cast_fp16)[name = tensor("op_325_cast_fp16")]; - tensor var_326_to_fp16 = const()[name = tensor("op_326_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_1_cast_fp16 = mul(x = var_325_cast_fp16, y = var_326_to_fp16)[name = tensor("qk_mask_1_cast_fp16")]; - tensor var_330 = const()[name = tensor("op_330"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_1_cast_fp16 = reshape(shape = var_330, x = query_3_cast_fp16)[name = tensor("mh_q_1_cast_fp16")]; - tensor var_332_to_fp16 = const()[name = tensor("op_332_to_fp16"), val = tensor(0x1p-3)]; - tensor var_333_cast_fp16 = mul(x = mh_q_1_cast_fp16, y = var_332_to_fp16)[name = tensor("op_333_cast_fp16")]; - tensor var_336 = const()[name = tensor("op_336"), val = tensor([1, 8, 64, 188])]; - tensor var_337_cast_fp16 = reshape(shape = var_336, x = key_1_cast_fp16)[name = tensor("op_337_cast_fp16")]; - tensor mh_w_1_transpose_x_0 = const()[name = tensor("mh_w_1_transpose_x_0"), val = tensor(true)]; - tensor mh_w_1_transpose_y_0 = const()[name = tensor("mh_w_1_transpose_y_0"), val = tensor(false)]; - tensor mh_w_1_cast_fp16 = matmul(transpose_x = mh_w_1_transpose_x_0, transpose_y = mh_w_1_transpose_y_0, x = var_333_cast_fp16, y = var_337_cast_fp16)[name = tensor("mh_w_1_cast_fp16")]; - tensor mh_w_3_cast_fp16 = add(x = mh_w_1_cast_fp16, y = qk_mask_1_cast_fp16)[name = tensor("mh_w_3_cast_fp16")]; - tensor var_341_cast_fp16 = softmax(axis = var_128, x = mh_w_3_cast_fp16)[name = tensor("op_341_cast_fp16")]; - tensor var_342 = const()[name = tensor("op_342"), val = tensor([1, 8, 64, 188])]; - tensor var_343_cast_fp16 = reshape(shape = var_342, x = value_1_cast_fp16)[name = tensor("op_343_cast_fp16")]; - tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; - tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; - tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_343_cast_fp16, y = var_341_cast_fp16)[name = tensor("attn_1_cast_fp16")]; - tensor var_346 = const()[name = tensor("op_346"), val = tensor([1, 512, 1, 188])]; - tensor input_23_cast_fp16 = reshape(shape = var_346, x = attn_1_cast_fp16)[name = tensor("input_23_cast_fp16")]; - tensor var_356_pad_type_0 = const()[name = tensor("op_356_pad_type_0"), val = tensor("valid")]; - tensor var_356_strides_0 = const()[name = tensor("op_356_strides_0"), val = tensor([1, 1])]; - tensor var_356_pad_0 = const()[name = tensor("op_356_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_356_dilations_0 = const()[name = tensor("op_356_dilations_0"), val = tensor([1, 1])]; - tensor var_356_groups_0 = const()[name = tensor("op_356_groups_0"), val = tensor(1)]; - tensor layers_0_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4745536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4942208))), name = tensor("layers_0_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_0_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_0_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4942400)))]; - tensor var_356_cast_fp16 = conv(bias = layers_0_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_356_dilations_0, groups = var_356_groups_0, pad = var_356_pad_0, pad_type = var_356_pad_type_0, strides = var_356_strides_0, weight = layers_0_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("op_356_cast_fp16")]; - tensor var_362_pad_type_0 = const()[name = tensor("op_362_pad_type_0"), val = tensor("valid")]; - tensor var_362_strides_0 = const()[name = tensor("op_362_strides_0"), val = tensor([1, 1])]; - tensor var_362_pad_0 = const()[name = tensor("op_362_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_362_dilations_0 = const()[name = tensor("op_362_dilations_0"), val = tensor([1, 1])]; - tensor var_362_groups_0 = const()[name = tensor("op_362_groups_0"), val = tensor(1)]; - tensor layers_0_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4952448))), name = tensor("layers_0_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4943488))), shape = tensor([512, 512, 1, 1])]; - tensor var_362_cast_fp16 = conv(dilations = var_362_dilations_0, groups = var_362_groups_0, pad = var_362_pad_0, pad_type = var_362_pad_type_0, strides = var_362_strides_0, weight = layers_0_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_23_cast_fp16)[name = tensor("op_362_cast_fp16")]; - tensor obj_5_cast_fp16 = add(x = var_356_cast_fp16, y = var_362_cast_fp16)[name = tensor("obj_5_cast_fp16")]; - tensor inputs_5_cast_fp16 = add(x = inputs_3_cast_fp16, y = obj_5_cast_fp16)[name = tensor("inputs_5_cast_fp16")]; - tensor out_5_axes_0 = const()[name = tensor("out_5_axes_0"), val = tensor([1])]; - tensor var_373_to_fp16 = const()[name = tensor("op_373_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_5_cast_fp16 = layer_norm(axes = out_5_axes_0, epsilon = var_373_to_fp16, x = inputs_5_cast_fp16)[name = tensor("out_5_cast_fp16")]; - tensor input_25_gamma_0_to_fp16 = const()[name = tensor("input_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4985280)))]; - tensor input_25_beta_0_to_fp16 = const()[name = tensor("input_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4986368)))]; - tensor input_25_epsilon_0_to_fp16 = const()[name = tensor("input_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_25_cast_fp16 = batch_norm(beta = input_25_beta_0_to_fp16, epsilon = input_25_epsilon_0_to_fp16, gamma = input_25_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_5_cast_fp16)[name = tensor("input_25_cast_fp16")]; - tensor var_395_pad_type_0 = const()[name = tensor("op_395_pad_type_0"), val = tensor("valid")]; - tensor var_395_strides_0 = const()[name = tensor("op_395_strides_0"), val = tensor([1, 1])]; - tensor var_395_pad_0 = const()[name = tensor("op_395_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_395_dilations_0 = const()[name = tensor("op_395_dilations_0"), val = tensor([1, 1])]; - tensor var_395_groups_0 = const()[name = tensor("op_395_groups_0"), val = tensor(1)]; - tensor layers_0_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4987456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5380736))), name = tensor("layers_0_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_0_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_0_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5380928)))]; - tensor var_395_cast_fp16 = conv(bias = layers_0_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_395_dilations_0, groups = var_395_groups_0, pad = var_395_pad_0, pad_type = var_395_pad_type_0, strides = var_395_strides_0, weight = layers_0_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_25_cast_fp16)[name = tensor("op_395_cast_fp16")]; - tensor var_401_pad_type_0 = const()[name = tensor("op_401_pad_type_0"), val = tensor("valid")]; - tensor var_401_strides_0 = const()[name = tensor("op_401_strides_0"), val = tensor([1, 1])]; - tensor var_401_pad_0 = const()[name = tensor("op_401_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_401_dilations_0 = const()[name = tensor("op_401_dilations_0"), val = tensor([1, 1])]; - tensor var_401_groups_0 = const()[name = tensor("op_401_groups_0"), val = tensor(1)]; - tensor layers_0_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5401344))), name = tensor("layers_0_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5383040))), shape = tensor([1024, 512, 1, 1])]; - tensor var_401_cast_fp16 = conv(dilations = var_401_dilations_0, groups = var_401_groups_0, pad = var_401_pad_0, pad_type = var_401_pad_type_0, strides = var_401_strides_0, weight = layers_0_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_25_cast_fp16)[name = tensor("op_401_cast_fp16")]; - tensor input_27_cast_fp16 = add(x = var_395_cast_fp16, y = var_401_cast_fp16)[name = tensor("input_27_cast_fp16")]; - tensor input_29_split_num_splits_0 = const()[name = tensor("input_29_split_num_splits_0"), val = tensor(2)]; - tensor input_29_split_axis_0 = const()[name = tensor("input_29_split_axis_0"), val = tensor(1)]; - tensor input_29_split_cast_fp16_0, tensor input_29_split_cast_fp16_1 = split(axis = input_29_split_axis_0, num_splits = input_29_split_num_splits_0, x = input_27_cast_fp16)[name = tensor("input_29_split_cast_fp16")]; - tensor input_29_split_1_sigmoid_cast_fp16 = sigmoid(x = input_29_split_cast_fp16_1)[name = tensor("input_29_split_1_sigmoid_cast_fp16")]; - tensor input_29_cast_fp16 = mul(x = input_29_split_cast_fp16_0, y = input_29_split_1_sigmoid_cast_fp16)[name = tensor("input_29_cast_fp16")]; - tensor input_31_pad_type_0 = const()[name = tensor("input_31_pad_type_0"), val = tensor("custom")]; - tensor input_31_pad_0 = const()[name = tensor("input_31_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_31_groups_0 = const()[name = tensor("input_31_groups_0"), val = tensor(512)]; - tensor input_31_strides_0 = const()[name = tensor("input_31_strides_0"), val = tensor([1, 1])]; - tensor input_31_dilations_0 = const()[name = tensor("input_31_dilations_0"), val = tensor([1, 1])]; - tensor const_191_to_fp16 = const()[name = tensor("const_191_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5466944)))]; - tensor const_192_to_fp16 = const()[name = tensor("const_192_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5476224)))]; - tensor input_33_cast_fp16 = conv(bias = const_192_to_fp16, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = const_191_to_fp16, x = input_29_cast_fp16)[name = tensor("input_33_cast_fp16")]; - tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = tensor("input_35_cast_fp16")]; - tensor var_425_pad_type_0 = const()[name = tensor("op_425_pad_type_0"), val = tensor("valid")]; - tensor var_425_strides_0 = const()[name = tensor("op_425_strides_0"), val = tensor([1, 1])]; - tensor var_425_pad_0 = const()[name = tensor("op_425_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_425_dilations_0 = const()[name = tensor("op_425_dilations_0"), val = tensor([1, 1])]; - tensor var_425_groups_0 = const()[name = tensor("op_425_groups_0"), val = tensor(1)]; - tensor layers_0_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5477312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5673984))), name = tensor("layers_0_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_0_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_0_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5674176)))]; - tensor var_425_cast_fp16 = conv(bias = layers_0_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_425_dilations_0, groups = var_425_groups_0, pad = var_425_pad_0, pad_type = var_425_pad_type_0, strides = var_425_strides_0, weight = layers_0_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = tensor("op_425_cast_fp16")]; - tensor var_431_pad_type_0 = const()[name = tensor("op_431_pad_type_0"), val = tensor("valid")]; - tensor var_431_strides_0 = const()[name = tensor("op_431_strides_0"), val = tensor([1, 1])]; - tensor var_431_pad_0 = const()[name = tensor("op_431_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_431_dilations_0 = const()[name = tensor("op_431_dilations_0"), val = tensor([1, 1])]; - tensor var_431_groups_0 = const()[name = tensor("op_431_groups_0"), val = tensor(1)]; - tensor layers_0_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5683840))), name = tensor("layers_0_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5675264))), shape = tensor([512, 512, 1, 1])]; - tensor var_431_cast_fp16 = conv(dilations = var_431_dilations_0, groups = var_431_groups_0, pad = var_431_pad_0, pad_type = var_431_pad_type_0, strides = var_431_strides_0, weight = layers_0_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_35_cast_fp16)[name = tensor("op_431_cast_fp16")]; - tensor x_5_cast_fp16 = add(x = var_425_cast_fp16, y = var_431_cast_fp16)[name = tensor("x_5_cast_fp16")]; - tensor inputs_7_cast_fp16 = add(x = inputs_5_cast_fp16, y = x_5_cast_fp16)[name = tensor("inputs_7_cast_fp16")]; - tensor out_7_axes_0 = const()[name = tensor("out_7_axes_0"), val = tensor([1])]; - tensor var_442_to_fp16 = const()[name = tensor("op_442_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_442_to_fp16, x = inputs_7_cast_fp16)[name = tensor("out_7_cast_fp16")]; - tensor input_37_gamma_0_to_fp16 = const()[name = tensor("input_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5716672)))]; - tensor input_37_beta_0_to_fp16 = const()[name = tensor("input_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5717760)))]; - tensor input_37_epsilon_0_to_fp16 = const()[name = tensor("input_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_37_cast_fp16 = batch_norm(beta = input_37_beta_0_to_fp16, epsilon = input_37_epsilon_0_to_fp16, gamma = input_37_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_7_cast_fp16)[name = tensor("input_37_cast_fp16")]; - tensor var_462_pad_type_0 = const()[name = tensor("op_462_pad_type_0"), val = tensor("valid")]; - tensor var_462_strides_0 = const()[name = tensor("op_462_strides_0"), val = tensor([1, 1])]; - tensor var_462_pad_0 = const()[name = tensor("op_462_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_462_dilations_0 = const()[name = tensor("op_462_dilations_0"), val = tensor([1, 1])]; - tensor var_462_groups_0 = const()[name = tensor("op_462_groups_0"), val = tensor(1)]; - tensor layers_0_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5718848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6505344))), name = tensor("layers_0_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_0_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_0_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6505536)))]; - tensor var_462_cast_fp16 = conv(bias = layers_0_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_462_dilations_0, groups = var_462_groups_0, pad = var_462_pad_0, pad_type = var_462_pad_type_0, strides = var_462_strides_0, weight = layers_0_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_37_cast_fp16)[name = tensor("op_462_cast_fp16")]; - tensor var_468_pad_type_0 = const()[name = tensor("op_468_pad_type_0"), val = tensor("valid")]; - tensor var_468_strides_0 = const()[name = tensor("op_468_strides_0"), val = tensor([1, 1])]; - tensor var_468_pad_0 = const()[name = tensor("op_468_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_468_dilations_0 = const()[name = tensor("op_468_dilations_0"), val = tensor([1, 1])]; - tensor var_468_groups_0 = const()[name = tensor("op_468_groups_0"), val = tensor(1)]; - tensor layers_0_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6541376))), name = tensor("layers_0_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6509696))), shape = tensor([2048, 512, 1, 1])]; - tensor var_468_cast_fp16 = conv(dilations = var_468_dilations_0, groups = var_468_groups_0, pad = var_468_pad_0, pad_type = var_468_pad_type_0, strides = var_468_strides_0, weight = layers_0_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_37_cast_fp16)[name = tensor("op_468_cast_fp16")]; - tensor input_39_cast_fp16 = add(x = var_462_cast_fp16, y = var_468_cast_fp16)[name = tensor("input_39_cast_fp16")]; - tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = tensor("input_41_cast_fp16")]; - tensor var_479_pad_type_0 = const()[name = tensor("op_479_pad_type_0"), val = tensor("valid")]; - tensor var_479_strides_0 = const()[name = tensor("op_479_strides_0"), val = tensor([1, 1])]; - tensor var_479_pad_0 = const()[name = tensor("op_479_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_479_dilations_0 = const()[name = tensor("op_479_dilations_0"), val = tensor([1, 1])]; - tensor var_479_groups_0 = const()[name = tensor("op_479_groups_0"), val = tensor(1)]; - tensor layers_0_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6672512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7459008))), name = tensor("layers_0_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_0_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_0_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7459200)))]; - tensor var_479_cast_fp16 = conv(bias = layers_0_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_479_dilations_0, groups = var_479_groups_0, pad = var_479_pad_0, pad_type = var_479_pad_type_0, strides = var_479_strides_0, weight = layers_0_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_41_cast_fp16)[name = tensor("op_479_cast_fp16")]; - tensor var_485_pad_type_0 = const()[name = tensor("op_485_pad_type_0"), val = tensor("valid")]; - tensor var_485_strides_0 = const()[name = tensor("op_485_strides_0"), val = tensor([1, 1])]; - tensor var_485_pad_0 = const()[name = tensor("op_485_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_485_dilations_0 = const()[name = tensor("op_485_dilations_0"), val = tensor([1, 1])]; - tensor var_485_groups_0 = const()[name = tensor("op_485_groups_0"), val = tensor(1)]; - tensor layers_0_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7490688))), name = tensor("layers_0_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7460288))), shape = tensor([512, 2048, 1, 1])]; - tensor var_485_cast_fp16 = conv(dilations = var_485_dilations_0, groups = var_485_groups_0, pad = var_485_pad_0, pad_type = var_485_pad_type_0, strides = var_485_strides_0, weight = layers_0_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_41_cast_fp16)[name = tensor("op_485_cast_fp16")]; - tensor x_7_cast_fp16 = add(x = var_479_cast_fp16, y = var_485_cast_fp16)[name = tensor("x_7_cast_fp16")]; - tensor var_487_to_fp16 = const()[name = tensor("op_487_to_fp16"), val = tensor(0x1p-1)]; - tensor var_488_cast_fp16 = mul(x = x_7_cast_fp16, y = var_487_to_fp16)[name = tensor("op_488_cast_fp16")]; - tensor inputs_9_cast_fp16 = add(x = inputs_7_cast_fp16, y = var_488_cast_fp16)[name = tensor("inputs_9_cast_fp16")]; - tensor out_9_axes_0 = const()[name = tensor("out_9_axes_0"), val = tensor([1])]; - tensor var_498_to_fp16 = const()[name = tensor("op_498_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_498_to_fp16, x = inputs_9_cast_fp16)[name = tensor("out_9_cast_fp16")]; - tensor inputs_11_gamma_0_to_fp16 = const()[name = tensor("inputs_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7621824)))]; - tensor inputs_11_beta_0_to_fp16 = const()[name = tensor("inputs_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7622912)))]; - tensor inputs_11_epsilon_0_to_fp16 = const()[name = tensor("inputs_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_11_cast_fp16 = batch_norm(beta = inputs_11_beta_0_to_fp16, epsilon = inputs_11_epsilon_0_to_fp16, gamma = inputs_11_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_9_cast_fp16)[name = tensor("inputs_11_cast_fp16")]; - tensor var_512 = const()[name = tensor("op_512"), val = tensor(3)]; - tensor out_11_axes_0 = const()[name = tensor("out_11_axes_0"), val = tensor([1])]; - tensor var_543_to_fp16 = const()[name = tensor("op_543_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_11_cast_fp16 = layer_norm(axes = out_11_axes_0, epsilon = var_543_to_fp16, x = inputs_11_cast_fp16)[name = tensor("out_11_cast_fp16")]; - tensor input_43_gamma_0_to_fp16 = const()[name = tensor("input_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7624000)))]; - tensor input_43_beta_0_to_fp16 = const()[name = tensor("input_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7625088)))]; - tensor input_43_epsilon_0_to_fp16 = const()[name = tensor("input_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_43_cast_fp16 = batch_norm(beta = input_43_beta_0_to_fp16, epsilon = input_43_epsilon_0_to_fp16, gamma = input_43_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_11_cast_fp16)[name = tensor("input_43_cast_fp16")]; - tensor var_563_pad_type_0 = const()[name = tensor("op_563_pad_type_0"), val = tensor("valid")]; - tensor var_563_strides_0 = const()[name = tensor("op_563_strides_0"), val = tensor([1, 1])]; - tensor var_563_pad_0 = const()[name = tensor("op_563_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_563_dilations_0 = const()[name = tensor("op_563_dilations_0"), val = tensor([1, 1])]; - tensor var_563_groups_0 = const()[name = tensor("op_563_groups_0"), val = tensor(1)]; - tensor layers_1_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7626176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8412672))), name = tensor("layers_1_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_1_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_1_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8412864)))]; - tensor var_563_cast_fp16 = conv(bias = layers_1_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_563_dilations_0, groups = var_563_groups_0, pad = var_563_pad_0, pad_type = var_563_pad_type_0, strides = var_563_strides_0, weight = layers_1_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = tensor("op_563_cast_fp16")]; - tensor var_569_pad_type_0 = const()[name = tensor("op_569_pad_type_0"), val = tensor("valid")]; - tensor var_569_strides_0 = const()[name = tensor("op_569_strides_0"), val = tensor([1, 1])]; - tensor var_569_pad_0 = const()[name = tensor("op_569_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_569_dilations_0 = const()[name = tensor("op_569_dilations_0"), val = tensor([1, 1])]; - tensor var_569_groups_0 = const()[name = tensor("op_569_groups_0"), val = tensor(1)]; - tensor layers_1_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8451136))), name = tensor("layers_1_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8417024))), shape = tensor([2048, 512, 1, 1])]; - tensor var_569_cast_fp16 = conv(dilations = var_569_dilations_0, groups = var_569_groups_0, pad = var_569_pad_0, pad_type = var_569_pad_type_0, strides = var_569_strides_0, weight = layers_1_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_43_cast_fp16)[name = tensor("op_569_cast_fp16")]; - tensor input_45_cast_fp16 = add(x = var_563_cast_fp16, y = var_569_cast_fp16)[name = tensor("input_45_cast_fp16")]; - tensor input_47_cast_fp16 = silu(x = input_45_cast_fp16)[name = tensor("input_47_cast_fp16")]; - tensor var_580_pad_type_0 = const()[name = tensor("op_580_pad_type_0"), val = tensor("valid")]; - tensor var_580_strides_0 = const()[name = tensor("op_580_strides_0"), val = tensor([1, 1])]; - tensor var_580_pad_0 = const()[name = tensor("op_580_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_580_dilations_0 = const()[name = tensor("op_580_dilations_0"), val = tensor([1, 1])]; - tensor var_580_groups_0 = const()[name = tensor("op_580_groups_0"), val = tensor(1)]; - tensor layers_1_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8582272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9368768))), name = tensor("layers_1_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_1_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_1_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9368960)))]; - tensor var_580_cast_fp16 = conv(bias = layers_1_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_580_dilations_0, groups = var_580_groups_0, pad = var_580_pad_0, pad_type = var_580_pad_type_0, strides = var_580_strides_0, weight = layers_1_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = tensor("op_580_cast_fp16")]; - tensor var_586_pad_type_0 = const()[name = tensor("op_586_pad_type_0"), val = tensor("valid")]; - tensor var_586_strides_0 = const()[name = tensor("op_586_strides_0"), val = tensor([1, 1])]; - tensor var_586_pad_0 = const()[name = tensor("op_586_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_586_dilations_0 = const()[name = tensor("op_586_dilations_0"), val = tensor([1, 1])]; - tensor var_586_groups_0 = const()[name = tensor("op_586_groups_0"), val = tensor(1)]; - tensor layers_1_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9406336))), name = tensor("layers_1_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9370048))), shape = tensor([512, 2048, 1, 1])]; - tensor var_586_cast_fp16 = conv(dilations = var_586_dilations_0, groups = var_586_groups_0, pad = var_586_pad_0, pad_type = var_586_pad_type_0, strides = var_586_strides_0, weight = layers_1_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_47_cast_fp16)[name = tensor("op_586_cast_fp16")]; - tensor x_9_cast_fp16 = add(x = var_580_cast_fp16, y = var_586_cast_fp16)[name = tensor("x_9_cast_fp16")]; - tensor var_588_to_fp16 = const()[name = tensor("op_588_to_fp16"), val = tensor(0x1p-1)]; - tensor var_589_cast_fp16 = mul(x = x_9_cast_fp16, y = var_588_to_fp16)[name = tensor("op_589_cast_fp16")]; - tensor inputs_13_cast_fp16 = add(x = inputs_11_cast_fp16, y = var_589_cast_fp16)[name = tensor("inputs_13_cast_fp16")]; - tensor out_13_axes_0 = const()[name = tensor("out_13_axes_0"), val = tensor([1])]; - tensor var_599_to_fp16 = const()[name = tensor("op_599_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_599_to_fp16, x = inputs_13_cast_fp16)[name = tensor("out_13_cast_fp16")]; - tensor obj_7_gamma_0_to_fp16 = const()[name = tensor("obj_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9537472)))]; - tensor obj_7_beta_0_to_fp16 = const()[name = tensor("obj_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9538560)))]; - tensor obj_7_epsilon_0_to_fp16 = const()[name = tensor("obj_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_7_cast_fp16 = batch_norm(beta = obj_7_beta_0_to_fp16, epsilon = obj_7_epsilon_0_to_fp16, gamma = obj_7_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_13_cast_fp16)[name = tensor("obj_7_cast_fp16")]; - tensor var_624_pad_type_0 = const()[name = tensor("op_624_pad_type_0"), val = tensor("valid")]; - tensor var_624_strides_0 = const()[name = tensor("op_624_strides_0"), val = tensor([1, 1])]; - tensor var_624_pad_0 = const()[name = tensor("op_624_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_624_dilations_0 = const()[name = tensor("op_624_dilations_0"), val = tensor([1, 1])]; - tensor var_624_groups_0 = const()[name = tensor("op_624_groups_0"), val = tensor(1)]; - tensor layers_1_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9539648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9736320))), name = tensor("layers_1_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_1_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_1_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9736512)))]; - tensor var_624_cast_fp16 = conv(bias = layers_1_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_624_dilations_0, groups = var_624_groups_0, pad = var_624_pad_0, pad_type = var_624_pad_type_0, strides = var_624_strides_0, weight = layers_1_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_7_cast_fp16)[name = tensor("op_624_cast_fp16")]; - tensor var_630_pad_type_0 = const()[name = tensor("op_630_pad_type_0"), val = tensor("valid")]; - tensor var_630_strides_0 = const()[name = tensor("op_630_strides_0"), val = tensor([1, 1])]; - tensor var_630_pad_0 = const()[name = tensor("op_630_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_630_dilations_0 = const()[name = tensor("op_630_dilations_0"), val = tensor([1, 1])]; - tensor var_630_groups_0 = const()[name = tensor("op_630_groups_0"), val = tensor(1)]; - tensor layers_1_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9746432))), name = tensor("layers_1_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9737600))), shape = tensor([512, 512, 1, 1])]; - tensor var_630_cast_fp16 = conv(dilations = var_630_dilations_0, groups = var_630_groups_0, pad = var_630_pad_0, pad_type = var_630_pad_type_0, strides = var_630_strides_0, weight = layers_1_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_7_cast_fp16)[name = tensor("op_630_cast_fp16")]; - tensor query_5_cast_fp16 = add(x = var_624_cast_fp16, y = var_630_cast_fp16)[name = tensor("query_5_cast_fp16")]; - tensor var_639_pad_type_0 = const()[name = tensor("op_639_pad_type_0"), val = tensor("valid")]; - tensor var_639_strides_0 = const()[name = tensor("op_639_strides_0"), val = tensor([1, 1])]; - tensor var_639_pad_0 = const()[name = tensor("op_639_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_639_dilations_0 = const()[name = tensor("op_639_dilations_0"), val = tensor([1, 1])]; - tensor var_639_groups_0 = const()[name = tensor("op_639_groups_0"), val = tensor(1)]; - tensor layers_1_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9779264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9975936))), name = tensor("layers_1_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_639_cast_fp16 = conv(dilations = var_639_dilations_0, groups = var_639_groups_0, pad = var_639_pad_0, pad_type = var_639_pad_type_0, strides = var_639_strides_0, weight = layers_1_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_7_cast_fp16)[name = tensor("op_639_cast_fp16")]; - tensor var_645_pad_type_0 = const()[name = tensor("op_645_pad_type_0"), val = tensor("valid")]; - tensor var_645_strides_0 = const()[name = tensor("op_645_strides_0"), val = tensor([1, 1])]; - tensor var_645_pad_0 = const()[name = tensor("op_645_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_645_dilations_0 = const()[name = tensor("op_645_dilations_0"), val = tensor([1, 1])]; - tensor var_645_groups_0 = const()[name = tensor("op_645_groups_0"), val = tensor(1)]; - tensor layers_1_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9985344))), name = tensor("layers_1_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9976128))), shape = tensor([512, 512, 1, 1])]; - tensor var_645_cast_fp16 = conv(dilations = var_645_dilations_0, groups = var_645_groups_0, pad = var_645_pad_0, pad_type = var_645_pad_type_0, strides = var_645_strides_0, weight = layers_1_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_7_cast_fp16)[name = tensor("op_645_cast_fp16")]; - tensor key_3_cast_fp16 = add(x = var_639_cast_fp16, y = var_645_cast_fp16)[name = tensor("key_3_cast_fp16")]; - tensor var_655_pad_type_0 = const()[name = tensor("op_655_pad_type_0"), val = tensor("valid")]; - tensor var_655_strides_0 = const()[name = tensor("op_655_strides_0"), val = tensor([1, 1])]; - tensor var_655_pad_0 = const()[name = tensor("op_655_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_655_dilations_0 = const()[name = tensor("op_655_dilations_0"), val = tensor([1, 1])]; - tensor var_655_groups_0 = const()[name = tensor("op_655_groups_0"), val = tensor(1)]; - tensor layers_1_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10018176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10214848))), name = tensor("layers_1_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_1_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_1_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10215040)))]; - tensor var_655_cast_fp16 = conv(bias = layers_1_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_655_dilations_0, groups = var_655_groups_0, pad = var_655_pad_0, pad_type = var_655_pad_type_0, strides = var_655_strides_0, weight = layers_1_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_7_cast_fp16)[name = tensor("op_655_cast_fp16")]; - tensor var_661_pad_type_0 = const()[name = tensor("op_661_pad_type_0"), val = tensor("valid")]; - tensor var_661_strides_0 = const()[name = tensor("op_661_strides_0"), val = tensor([1, 1])]; - tensor var_661_pad_0 = const()[name = tensor("op_661_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_661_dilations_0 = const()[name = tensor("op_661_dilations_0"), val = tensor([1, 1])]; - tensor var_661_groups_0 = const()[name = tensor("op_661_groups_0"), val = tensor(1)]; - tensor layers_1_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10223872))), name = tensor("layers_1_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10216128))), shape = tensor([512, 512, 1, 1])]; - tensor var_661_cast_fp16 = conv(dilations = var_661_dilations_0, groups = var_661_groups_0, pad = var_661_pad_0, pad_type = var_661_pad_type_0, strides = var_661_strides_0, weight = layers_1_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_7_cast_fp16)[name = tensor("op_661_cast_fp16")]; - tensor value_3_cast_fp16 = add(x = var_655_cast_fp16, y = var_661_cast_fp16)[name = tensor("value_3_cast_fp16")]; - tensor var_664_to_fp16 = const()[name = tensor("op_664_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10256704)))]; - tensor query_7_cast_fp16 = add(x = query_5_cast_fp16, y = var_664_to_fp16)[name = tensor("query_7_cast_fp16")]; - tensor var_667_to_fp16 = const()[name = tensor("op_667_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10257792)))]; - tensor q_with_bias_v_3_cast_fp16 = add(x = query_5_cast_fp16, y = var_667_to_fp16)[name = tensor("q_with_bias_v_3_cast_fp16")]; - tensor var_677_pad_type_0 = const()[name = tensor("op_677_pad_type_0"), val = tensor("valid")]; - tensor var_677_strides_0 = const()[name = tensor("op_677_strides_0"), val = tensor([1, 1])]; - tensor var_677_pad_0 = const()[name = tensor("op_677_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_677_dilations_0 = const()[name = tensor("op_677_dilations_0"), val = tensor([1, 1])]; - tensor var_677_groups_0 = const()[name = tensor("op_677_groups_0"), val = tensor(1)]; - tensor layers_1_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10258880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10455552))), name = tensor("layers_1_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_677_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_677_dilations_0, groups = var_677_groups_0, pad = var_677_pad_0, pad_type = var_677_pad_type_0, strides = var_677_strides_0, weight = layers_1_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_677_cast_fp16")]; - tensor var_683_pad_type_0 = const()[name = tensor("op_683_pad_type_0"), val = tensor("valid")]; - tensor var_683_strides_0 = const()[name = tensor("op_683_strides_0"), val = tensor([1, 1])]; - tensor var_683_pad_0 = const()[name = tensor("op_683_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_683_dilations_0 = const()[name = tensor("op_683_dilations_0"), val = tensor([1, 1])]; - tensor var_683_groups_0 = const()[name = tensor("op_683_groups_0"), val = tensor(1)]; - tensor layers_1_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10477632))), name = tensor("layers_1_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10455744))), shape = tensor([512, 512, 1, 1])]; - tensor var_683_cast_fp16 = conv(dilations = var_683_dilations_0, groups = var_683_groups_0, pad = var_683_pad_0, pad_type = var_683_pad_type_0, strides = var_683_strides_0, weight = layers_1_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_683_cast_fp16")]; - tensor p_3_cast_fp16 = add(x = var_677_cast_fp16, y = var_683_cast_fp16)[name = tensor("p_3_cast_fp16")]; - tensor var_687 = const()[name = tensor("op_687"), val = tensor([1, 8, 64, 188])]; - tensor var_688_cast_fp16 = reshape(shape = var_687, x = q_with_bias_v_3_cast_fp16)[name = tensor("op_688_cast_fp16")]; - tensor var_689 = const()[name = tensor("op_689"), val = tensor([1, 8, 64, -1])]; - tensor var_690_cast_fp16 = reshape(shape = var_689, x = p_3_cast_fp16)[name = tensor("op_690_cast_fp16")]; - tensor matrix_bd_9_transpose_x_0 = const()[name = tensor("matrix_bd_9_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_9_transpose_y_0 = const()[name = tensor("matrix_bd_9_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_9_cast_fp16 = matmul(transpose_x = matrix_bd_9_transpose_x_0, transpose_y = matrix_bd_9_transpose_y_0, x = var_688_cast_fp16, y = var_690_cast_fp16)[name = tensor("matrix_bd_9_cast_fp16")]; - tensor matrix_bd_11_pad_0 = const()[name = tensor("matrix_bd_11_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_11_mode_0 = const()[name = tensor("matrix_bd_11_mode_0"), val = tensor("constant")]; - tensor const_21_to_fp16 = const()[name = tensor("const_21_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_11_cast_fp16 = pad(constant_val = const_21_to_fp16, mode = matrix_bd_11_mode_0, pad = matrix_bd_11_pad_0, x = matrix_bd_9_cast_fp16)[name = tensor("matrix_bd_11_cast_fp16")]; - tensor var_699 = const()[name = tensor("op_699"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_13_cast_fp16 = reshape(shape = var_699, x = matrix_bd_11_cast_fp16)[name = tensor("matrix_bd_13_cast_fp16")]; - tensor var_703_begin_0 = const()[name = tensor("op_703_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_703_end_0 = const()[name = tensor("op_703_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_703_end_mask_0 = const()[name = tensor("op_703_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_703_cast_fp16 = slice_by_index(begin = var_703_begin_0, end = var_703_end_0, end_mask = var_703_end_mask_0, x = matrix_bd_13_cast_fp16)[name = tensor("op_703_cast_fp16")]; - tensor var_704 = const()[name = tensor("op_704"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_15_cast_fp16 = reshape(shape = var_704, x = var_703_cast_fp16)[name = tensor("matrix_bd_15_cast_fp16")]; - tensor var_709_begin_0 = const()[name = tensor("op_709_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_709_end_0 = const()[name = tensor("op_709_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_709_end_mask_0 = const()[name = tensor("op_709_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_709_cast_fp16 = slice_by_index(begin = var_709_begin_0, end = var_709_end_0, end_mask = var_709_end_mask_0, x = matrix_bd_15_cast_fp16)[name = tensor("op_709_cast_fp16")]; - tensor var_710_to_fp16 = const()[name = tensor("op_710_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_3_cast_fp16 = mul(x = var_709_cast_fp16, y = var_710_to_fp16)[name = tensor("qk_mask_3_cast_fp16")]; - tensor var_714 = const()[name = tensor("op_714"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_3_cast_fp16 = reshape(shape = var_714, x = query_7_cast_fp16)[name = tensor("mh_q_3_cast_fp16")]; - tensor var_716_to_fp16 = const()[name = tensor("op_716_to_fp16"), val = tensor(0x1p-3)]; - tensor var_717_cast_fp16 = mul(x = mh_q_3_cast_fp16, y = var_716_to_fp16)[name = tensor("op_717_cast_fp16")]; - tensor var_720 = const()[name = tensor("op_720"), val = tensor([1, 8, 64, 188])]; - tensor var_721_cast_fp16 = reshape(shape = var_720, x = key_3_cast_fp16)[name = tensor("op_721_cast_fp16")]; - tensor mh_w_5_transpose_x_0 = const()[name = tensor("mh_w_5_transpose_x_0"), val = tensor(true)]; - tensor mh_w_5_transpose_y_0 = const()[name = tensor("mh_w_5_transpose_y_0"), val = tensor(false)]; - tensor mh_w_5_cast_fp16 = matmul(transpose_x = mh_w_5_transpose_x_0, transpose_y = mh_w_5_transpose_y_0, x = var_717_cast_fp16, y = var_721_cast_fp16)[name = tensor("mh_w_5_cast_fp16")]; - tensor mh_w_7_cast_fp16 = add(x = mh_w_5_cast_fp16, y = qk_mask_3_cast_fp16)[name = tensor("mh_w_7_cast_fp16")]; - tensor var_725_cast_fp16 = softmax(axis = var_512, x = mh_w_7_cast_fp16)[name = tensor("op_725_cast_fp16")]; - tensor var_726 = const()[name = tensor("op_726"), val = tensor([1, 8, 64, 188])]; - tensor var_727_cast_fp16 = reshape(shape = var_726, x = value_3_cast_fp16)[name = tensor("op_727_cast_fp16")]; - tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; - tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; - tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_727_cast_fp16, y = var_725_cast_fp16)[name = tensor("attn_3_cast_fp16")]; - tensor var_730 = const()[name = tensor("op_730"), val = tensor([1, 512, 1, 188])]; - tensor input_49_cast_fp16 = reshape(shape = var_730, x = attn_3_cast_fp16)[name = tensor("input_49_cast_fp16")]; - tensor var_740_pad_type_0 = const()[name = tensor("op_740_pad_type_0"), val = tensor("valid")]; - tensor var_740_strides_0 = const()[name = tensor("op_740_strides_0"), val = tensor([1, 1])]; - tensor var_740_pad_0 = const()[name = tensor("op_740_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_740_dilations_0 = const()[name = tensor("op_740_dilations_0"), val = tensor([1, 1])]; - tensor var_740_groups_0 = const()[name = tensor("op_740_groups_0"), val = tensor(1)]; - tensor layers_1_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10510464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10707136))), name = tensor("layers_1_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_1_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_1_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10707328)))]; - tensor var_740_cast_fp16 = conv(bias = layers_1_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_740_dilations_0, groups = var_740_groups_0, pad = var_740_pad_0, pad_type = var_740_pad_type_0, strides = var_740_strides_0, weight = layers_1_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_49_cast_fp16)[name = tensor("op_740_cast_fp16")]; - tensor var_746_pad_type_0 = const()[name = tensor("op_746_pad_type_0"), val = tensor("valid")]; - tensor var_746_strides_0 = const()[name = tensor("op_746_strides_0"), val = tensor([1, 1])]; - tensor var_746_pad_0 = const()[name = tensor("op_746_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_746_dilations_0 = const()[name = tensor("op_746_dilations_0"), val = tensor([1, 1])]; - tensor var_746_groups_0 = const()[name = tensor("op_746_groups_0"), val = tensor(1)]; - tensor layers_1_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10716288))), name = tensor("layers_1_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10708416))), shape = tensor([512, 512, 1, 1])]; - tensor var_746_cast_fp16 = conv(dilations = var_746_dilations_0, groups = var_746_groups_0, pad = var_746_pad_0, pad_type = var_746_pad_type_0, strides = var_746_strides_0, weight = layers_1_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_49_cast_fp16)[name = tensor("op_746_cast_fp16")]; - tensor obj_9_cast_fp16 = add(x = var_740_cast_fp16, y = var_746_cast_fp16)[name = tensor("obj_9_cast_fp16")]; - tensor inputs_15_cast_fp16 = add(x = inputs_13_cast_fp16, y = obj_9_cast_fp16)[name = tensor("inputs_15_cast_fp16")]; - tensor out_15_axes_0 = const()[name = tensor("out_15_axes_0"), val = tensor([1])]; - tensor var_757_to_fp16 = const()[name = tensor("op_757_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_15_cast_fp16 = layer_norm(axes = out_15_axes_0, epsilon = var_757_to_fp16, x = inputs_15_cast_fp16)[name = tensor("out_15_cast_fp16")]; - tensor input_51_gamma_0_to_fp16 = const()[name = tensor("input_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10749120)))]; - tensor input_51_beta_0_to_fp16 = const()[name = tensor("input_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10750208)))]; - tensor input_51_epsilon_0_to_fp16 = const()[name = tensor("input_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_51_cast_fp16 = batch_norm(beta = input_51_beta_0_to_fp16, epsilon = input_51_epsilon_0_to_fp16, gamma = input_51_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_15_cast_fp16)[name = tensor("input_51_cast_fp16")]; - tensor var_779_pad_type_0 = const()[name = tensor("op_779_pad_type_0"), val = tensor("valid")]; - tensor var_779_strides_0 = const()[name = tensor("op_779_strides_0"), val = tensor([1, 1])]; - tensor var_779_pad_0 = const()[name = tensor("op_779_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_779_dilations_0 = const()[name = tensor("op_779_dilations_0"), val = tensor([1, 1])]; - tensor var_779_groups_0 = const()[name = tensor("op_779_groups_0"), val = tensor(1)]; - tensor layers_1_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10751296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11144576))), name = tensor("layers_1_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_1_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_1_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11144768)))]; - tensor var_779_cast_fp16 = conv(bias = layers_1_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_779_dilations_0, groups = var_779_groups_0, pad = var_779_pad_0, pad_type = var_779_pad_type_0, strides = var_779_strides_0, weight = layers_1_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = tensor("op_779_cast_fp16")]; - tensor var_785_pad_type_0 = const()[name = tensor("op_785_pad_type_0"), val = tensor("valid")]; - tensor var_785_strides_0 = const()[name = tensor("op_785_strides_0"), val = tensor([1, 1])]; - tensor var_785_pad_0 = const()[name = tensor("op_785_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_785_dilations_0 = const()[name = tensor("op_785_dilations_0"), val = tensor([1, 1])]; - tensor var_785_groups_0 = const()[name = tensor("op_785_groups_0"), val = tensor(1)]; - tensor layers_1_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11165248))), name = tensor("layers_1_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11146880))), shape = tensor([1024, 512, 1, 1])]; - tensor var_785_cast_fp16 = conv(dilations = var_785_dilations_0, groups = var_785_groups_0, pad = var_785_pad_0, pad_type = var_785_pad_type_0, strides = var_785_strides_0, weight = layers_1_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_51_cast_fp16)[name = tensor("op_785_cast_fp16")]; - tensor input_53_cast_fp16 = add(x = var_779_cast_fp16, y = var_785_cast_fp16)[name = tensor("input_53_cast_fp16")]; - tensor input_55_split_num_splits_0 = const()[name = tensor("input_55_split_num_splits_0"), val = tensor(2)]; - tensor input_55_split_axis_0 = const()[name = tensor("input_55_split_axis_0"), val = tensor(1)]; - tensor input_55_split_cast_fp16_0, tensor input_55_split_cast_fp16_1 = split(axis = input_55_split_axis_0, num_splits = input_55_split_num_splits_0, x = input_53_cast_fp16)[name = tensor("input_55_split_cast_fp16")]; - tensor input_55_split_1_sigmoid_cast_fp16 = sigmoid(x = input_55_split_cast_fp16_1)[name = tensor("input_55_split_1_sigmoid_cast_fp16")]; - tensor input_55_cast_fp16 = mul(x = input_55_split_cast_fp16_0, y = input_55_split_1_sigmoid_cast_fp16)[name = tensor("input_55_cast_fp16")]; - tensor input_57_pad_type_0 = const()[name = tensor("input_57_pad_type_0"), val = tensor("custom")]; - tensor input_57_pad_0 = const()[name = tensor("input_57_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_57_groups_0 = const()[name = tensor("input_57_groups_0"), val = tensor(512)]; - tensor input_57_strides_0 = const()[name = tensor("input_57_strides_0"), val = tensor([1, 1])]; - tensor input_57_dilations_0 = const()[name = tensor("input_57_dilations_0"), val = tensor([1, 1])]; - tensor const_193_to_fp16 = const()[name = tensor("const_193_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11230848)))]; - tensor const_194_to_fp16 = const()[name = tensor("const_194_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11240128)))]; - tensor input_59_cast_fp16 = conv(bias = const_194_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_193_to_fp16, x = input_55_cast_fp16)[name = tensor("input_59_cast_fp16")]; - tensor input_61_cast_fp16 = silu(x = input_59_cast_fp16)[name = tensor("input_61_cast_fp16")]; - tensor var_809_pad_type_0 = const()[name = tensor("op_809_pad_type_0"), val = tensor("valid")]; - tensor var_809_strides_0 = const()[name = tensor("op_809_strides_0"), val = tensor([1, 1])]; - tensor var_809_pad_0 = const()[name = tensor("op_809_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_809_dilations_0 = const()[name = tensor("op_809_dilations_0"), val = tensor([1, 1])]; - tensor var_809_groups_0 = const()[name = tensor("op_809_groups_0"), val = tensor(1)]; - tensor layers_1_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11241216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11437888))), name = tensor("layers_1_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_1_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_1_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11438080)))]; - tensor var_809_cast_fp16 = conv(bias = layers_1_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_809_dilations_0, groups = var_809_groups_0, pad = var_809_pad_0, pad_type = var_809_pad_type_0, strides = var_809_strides_0, weight = layers_1_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = tensor("op_809_cast_fp16")]; - tensor var_815_pad_type_0 = const()[name = tensor("op_815_pad_type_0"), val = tensor("valid")]; - tensor var_815_strides_0 = const()[name = tensor("op_815_strides_0"), val = tensor([1, 1])]; - tensor var_815_pad_0 = const()[name = tensor("op_815_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_815_dilations_0 = const()[name = tensor("op_815_dilations_0"), val = tensor([1, 1])]; - tensor var_815_groups_0 = const()[name = tensor("op_815_groups_0"), val = tensor(1)]; - tensor layers_1_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11447872))), name = tensor("layers_1_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11439168))), shape = tensor([512, 512, 1, 1])]; - tensor var_815_cast_fp16 = conv(dilations = var_815_dilations_0, groups = var_815_groups_0, pad = var_815_pad_0, pad_type = var_815_pad_type_0, strides = var_815_strides_0, weight = layers_1_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_61_cast_fp16)[name = tensor("op_815_cast_fp16")]; - tensor x_11_cast_fp16 = add(x = var_809_cast_fp16, y = var_815_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor inputs_17_cast_fp16 = add(x = inputs_15_cast_fp16, y = x_11_cast_fp16)[name = tensor("inputs_17_cast_fp16")]; - tensor out_17_axes_0 = const()[name = tensor("out_17_axes_0"), val = tensor([1])]; - tensor var_826_to_fp16 = const()[name = tensor("op_826_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_826_to_fp16, x = inputs_17_cast_fp16)[name = tensor("out_17_cast_fp16")]; - tensor input_63_gamma_0_to_fp16 = const()[name = tensor("input_63_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11480704)))]; - tensor input_63_beta_0_to_fp16 = const()[name = tensor("input_63_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11481792)))]; - tensor input_63_epsilon_0_to_fp16 = const()[name = tensor("input_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_63_cast_fp16 = batch_norm(beta = input_63_beta_0_to_fp16, epsilon = input_63_epsilon_0_to_fp16, gamma = input_63_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_17_cast_fp16)[name = tensor("input_63_cast_fp16")]; - tensor var_846_pad_type_0 = const()[name = tensor("op_846_pad_type_0"), val = tensor("valid")]; - tensor var_846_strides_0 = const()[name = tensor("op_846_strides_0"), val = tensor([1, 1])]; - tensor var_846_pad_0 = const()[name = tensor("op_846_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_846_dilations_0 = const()[name = tensor("op_846_dilations_0"), val = tensor([1, 1])]; - tensor var_846_groups_0 = const()[name = tensor("op_846_groups_0"), val = tensor(1)]; - tensor layers_1_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11482880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12269376))), name = tensor("layers_1_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_1_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_1_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12269568)))]; - tensor var_846_cast_fp16 = conv(bias = layers_1_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_846_dilations_0, groups = var_846_groups_0, pad = var_846_pad_0, pad_type = var_846_pad_type_0, strides = var_846_strides_0, weight = layers_1_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_63_cast_fp16)[name = tensor("op_846_cast_fp16")]; - tensor var_852_pad_type_0 = const()[name = tensor("op_852_pad_type_0"), val = tensor("valid")]; - tensor var_852_strides_0 = const()[name = tensor("op_852_strides_0"), val = tensor([1, 1])]; - tensor var_852_pad_0 = const()[name = tensor("op_852_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_852_dilations_0 = const()[name = tensor("op_852_dilations_0"), val = tensor([1, 1])]; - tensor var_852_groups_0 = const()[name = tensor("op_852_groups_0"), val = tensor(1)]; - tensor layers_1_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12306112))), name = tensor("layers_1_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12273728))), shape = tensor([2048, 512, 1, 1])]; - tensor var_852_cast_fp16 = conv(dilations = var_852_dilations_0, groups = var_852_groups_0, pad = var_852_pad_0, pad_type = var_852_pad_type_0, strides = var_852_strides_0, weight = layers_1_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_63_cast_fp16)[name = tensor("op_852_cast_fp16")]; - tensor input_65_cast_fp16 = add(x = var_846_cast_fp16, y = var_852_cast_fp16)[name = tensor("input_65_cast_fp16")]; - tensor input_67_cast_fp16 = silu(x = input_65_cast_fp16)[name = tensor("input_67_cast_fp16")]; - tensor var_863_pad_type_0 = const()[name = tensor("op_863_pad_type_0"), val = tensor("valid")]; - tensor var_863_strides_0 = const()[name = tensor("op_863_strides_0"), val = tensor([1, 1])]; - tensor var_863_pad_0 = const()[name = tensor("op_863_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_863_dilations_0 = const()[name = tensor("op_863_dilations_0"), val = tensor([1, 1])]; - tensor var_863_groups_0 = const()[name = tensor("op_863_groups_0"), val = tensor(1)]; - tensor layers_1_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12437248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13223744))), name = tensor("layers_1_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_1_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_1_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13223936)))]; - tensor var_863_cast_fp16 = conv(bias = layers_1_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_863_dilations_0, groups = var_863_groups_0, pad = var_863_pad_0, pad_type = var_863_pad_type_0, strides = var_863_strides_0, weight = layers_1_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = tensor("op_863_cast_fp16")]; - tensor var_869_pad_type_0 = const()[name = tensor("op_869_pad_type_0"), val = tensor("valid")]; - tensor var_869_strides_0 = const()[name = tensor("op_869_strides_0"), val = tensor([1, 1])]; - tensor var_869_pad_0 = const()[name = tensor("op_869_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_869_dilations_0 = const()[name = tensor("op_869_dilations_0"), val = tensor([1, 1])]; - tensor var_869_groups_0 = const()[name = tensor("op_869_groups_0"), val = tensor(1)]; - tensor layers_1_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13260736))), name = tensor("layers_1_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13225024))), shape = tensor([512, 2048, 1, 1])]; - tensor var_869_cast_fp16 = conv(dilations = var_869_dilations_0, groups = var_869_groups_0, pad = var_869_pad_0, pad_type = var_869_pad_type_0, strides = var_869_strides_0, weight = layers_1_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_67_cast_fp16)[name = tensor("op_869_cast_fp16")]; - tensor x_13_cast_fp16 = add(x = var_863_cast_fp16, y = var_869_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_871_to_fp16 = const()[name = tensor("op_871_to_fp16"), val = tensor(0x1p-1)]; - tensor var_872_cast_fp16 = mul(x = x_13_cast_fp16, y = var_871_to_fp16)[name = tensor("op_872_cast_fp16")]; - tensor inputs_19_cast_fp16 = add(x = inputs_17_cast_fp16, y = var_872_cast_fp16)[name = tensor("inputs_19_cast_fp16")]; - tensor out_19_axes_0 = const()[name = tensor("out_19_axes_0"), val = tensor([1])]; - tensor var_882_to_fp16 = const()[name = tensor("op_882_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_882_to_fp16, x = inputs_19_cast_fp16)[name = tensor("out_19_cast_fp16")]; - tensor inputs_21_gamma_0_to_fp16 = const()[name = tensor("inputs_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13391872)))]; - tensor inputs_21_beta_0_to_fp16 = const()[name = tensor("inputs_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13392960)))]; - tensor inputs_21_epsilon_0_to_fp16 = const()[name = tensor("inputs_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_21_cast_fp16 = batch_norm(beta = inputs_21_beta_0_to_fp16, epsilon = inputs_21_epsilon_0_to_fp16, gamma = inputs_21_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_19_cast_fp16)[name = tensor("inputs_21_cast_fp16")]; - tensor var_896 = const()[name = tensor("op_896"), val = tensor(3)]; - tensor out_21_axes_0 = const()[name = tensor("out_21_axes_0"), val = tensor([1])]; - tensor var_927_to_fp16 = const()[name = tensor("op_927_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_21_cast_fp16 = layer_norm(axes = out_21_axes_0, epsilon = var_927_to_fp16, x = inputs_21_cast_fp16)[name = tensor("out_21_cast_fp16")]; - tensor input_69_gamma_0_to_fp16 = const()[name = tensor("input_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13394048)))]; - tensor input_69_beta_0_to_fp16 = const()[name = tensor("input_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13395136)))]; - tensor input_69_epsilon_0_to_fp16 = const()[name = tensor("input_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_69_cast_fp16 = batch_norm(beta = input_69_beta_0_to_fp16, epsilon = input_69_epsilon_0_to_fp16, gamma = input_69_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_21_cast_fp16)[name = tensor("input_69_cast_fp16")]; - tensor var_947_pad_type_0 = const()[name = tensor("op_947_pad_type_0"), val = tensor("valid")]; - tensor var_947_strides_0 = const()[name = tensor("op_947_strides_0"), val = tensor([1, 1])]; - tensor var_947_pad_0 = const()[name = tensor("op_947_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_947_dilations_0 = const()[name = tensor("op_947_dilations_0"), val = tensor([1, 1])]; - tensor var_947_groups_0 = const()[name = tensor("op_947_groups_0"), val = tensor(1)]; - tensor layers_2_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13396224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14182720))), name = tensor("layers_2_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_2_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_2_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14182912)))]; - tensor var_947_cast_fp16 = conv(bias = layers_2_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_947_dilations_0, groups = var_947_groups_0, pad = var_947_pad_0, pad_type = var_947_pad_type_0, strides = var_947_strides_0, weight = layers_2_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = tensor("op_947_cast_fp16")]; - tensor var_953_pad_type_0 = const()[name = tensor("op_953_pad_type_0"), val = tensor("valid")]; - tensor var_953_strides_0 = const()[name = tensor("op_953_strides_0"), val = tensor([1, 1])]; - tensor var_953_pad_0 = const()[name = tensor("op_953_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_953_dilations_0 = const()[name = tensor("op_953_dilations_0"), val = tensor([1, 1])]; - tensor var_953_groups_0 = const()[name = tensor("op_953_groups_0"), val = tensor(1)]; - tensor layers_2_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14219904))), name = tensor("layers_2_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14187072))), shape = tensor([2048, 512, 1, 1])]; - tensor var_953_cast_fp16 = conv(dilations = var_953_dilations_0, groups = var_953_groups_0, pad = var_953_pad_0, pad_type = var_953_pad_type_0, strides = var_953_strides_0, weight = layers_2_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_69_cast_fp16)[name = tensor("op_953_cast_fp16")]; - tensor input_71_cast_fp16 = add(x = var_947_cast_fp16, y = var_953_cast_fp16)[name = tensor("input_71_cast_fp16")]; - tensor input_73_cast_fp16 = silu(x = input_71_cast_fp16)[name = tensor("input_73_cast_fp16")]; - tensor var_964_pad_type_0 = const()[name = tensor("op_964_pad_type_0"), val = tensor("valid")]; - tensor var_964_strides_0 = const()[name = tensor("op_964_strides_0"), val = tensor([1, 1])]; - tensor var_964_pad_0 = const()[name = tensor("op_964_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_964_dilations_0 = const()[name = tensor("op_964_dilations_0"), val = tensor([1, 1])]; - tensor var_964_groups_0 = const()[name = tensor("op_964_groups_0"), val = tensor(1)]; - tensor layers_2_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14351040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15137536))), name = tensor("layers_2_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_2_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_2_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15137728)))]; - tensor var_964_cast_fp16 = conv(bias = layers_2_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_964_dilations_0, groups = var_964_groups_0, pad = var_964_pad_0, pad_type = var_964_pad_type_0, strides = var_964_strides_0, weight = layers_2_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_73_cast_fp16)[name = tensor("op_964_cast_fp16")]; - tensor var_970_pad_type_0 = const()[name = tensor("op_970_pad_type_0"), val = tensor("valid")]; - tensor var_970_strides_0 = const()[name = tensor("op_970_strides_0"), val = tensor([1, 1])]; - tensor var_970_pad_0 = const()[name = tensor("op_970_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_970_dilations_0 = const()[name = tensor("op_970_dilations_0"), val = tensor([1, 1])]; - tensor var_970_groups_0 = const()[name = tensor("op_970_groups_0"), val = tensor(1)]; - tensor layers_2_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15173824))), name = tensor("layers_2_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15138816))), shape = tensor([512, 2048, 1, 1])]; - tensor var_970_cast_fp16 = conv(dilations = var_970_dilations_0, groups = var_970_groups_0, pad = var_970_pad_0, pad_type = var_970_pad_type_0, strides = var_970_strides_0, weight = layers_2_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_73_cast_fp16)[name = tensor("op_970_cast_fp16")]; - tensor x_15_cast_fp16 = add(x = var_964_cast_fp16, y = var_970_cast_fp16)[name = tensor("x_15_cast_fp16")]; - tensor var_972_to_fp16 = const()[name = tensor("op_972_to_fp16"), val = tensor(0x1p-1)]; - tensor var_973_cast_fp16 = mul(x = x_15_cast_fp16, y = var_972_to_fp16)[name = tensor("op_973_cast_fp16")]; - tensor inputs_23_cast_fp16 = add(x = inputs_21_cast_fp16, y = var_973_cast_fp16)[name = tensor("inputs_23_cast_fp16")]; - tensor out_23_axes_0 = const()[name = tensor("out_23_axes_0"), val = tensor([1])]; - tensor var_983_to_fp16 = const()[name = tensor("op_983_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_23_cast_fp16 = layer_norm(axes = out_23_axes_0, epsilon = var_983_to_fp16, x = inputs_23_cast_fp16)[name = tensor("out_23_cast_fp16")]; - tensor obj_11_gamma_0_to_fp16 = const()[name = tensor("obj_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15304960)))]; - tensor obj_11_beta_0_to_fp16 = const()[name = tensor("obj_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15306048)))]; - tensor obj_11_epsilon_0_to_fp16 = const()[name = tensor("obj_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_11_cast_fp16 = batch_norm(beta = obj_11_beta_0_to_fp16, epsilon = obj_11_epsilon_0_to_fp16, gamma = obj_11_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_23_cast_fp16)[name = tensor("obj_11_cast_fp16")]; - tensor var_1008_pad_type_0 = const()[name = tensor("op_1008_pad_type_0"), val = tensor("valid")]; - tensor var_1008_strides_0 = const()[name = tensor("op_1008_strides_0"), val = tensor([1, 1])]; - tensor var_1008_pad_0 = const()[name = tensor("op_1008_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1008_dilations_0 = const()[name = tensor("op_1008_dilations_0"), val = tensor([1, 1])]; - tensor var_1008_groups_0 = const()[name = tensor("op_1008_groups_0"), val = tensor(1)]; - tensor layers_2_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15307136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15503808))), name = tensor("layers_2_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_2_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_2_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15504000)))]; - tensor var_1008_cast_fp16 = conv(bias = layers_2_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_1008_dilations_0, groups = var_1008_groups_0, pad = var_1008_pad_0, pad_type = var_1008_pad_type_0, strides = var_1008_strides_0, weight = layers_2_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_11_cast_fp16)[name = tensor("op_1008_cast_fp16")]; - tensor var_1014_pad_type_0 = const()[name = tensor("op_1014_pad_type_0"), val = tensor("valid")]; - tensor var_1014_strides_0 = const()[name = tensor("op_1014_strides_0"), val = tensor([1, 1])]; - tensor var_1014_pad_0 = const()[name = tensor("op_1014_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1014_dilations_0 = const()[name = tensor("op_1014_dilations_0"), val = tensor([1, 1])]; - tensor var_1014_groups_0 = const()[name = tensor("op_1014_groups_0"), val = tensor(1)]; - tensor layers_2_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15513472))), name = tensor("layers_2_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15505088))), shape = tensor([512, 512, 1, 1])]; - tensor var_1014_cast_fp16 = conv(dilations = var_1014_dilations_0, groups = var_1014_groups_0, pad = var_1014_pad_0, pad_type = var_1014_pad_type_0, strides = var_1014_strides_0, weight = layers_2_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_11_cast_fp16)[name = tensor("op_1014_cast_fp16")]; - tensor query_9_cast_fp16 = add(x = var_1008_cast_fp16, y = var_1014_cast_fp16)[name = tensor("query_9_cast_fp16")]; - tensor var_1023_pad_type_0 = const()[name = tensor("op_1023_pad_type_0"), val = tensor("valid")]; - tensor var_1023_strides_0 = const()[name = tensor("op_1023_strides_0"), val = tensor([1, 1])]; - tensor var_1023_pad_0 = const()[name = tensor("op_1023_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1023_dilations_0 = const()[name = tensor("op_1023_dilations_0"), val = tensor([1, 1])]; - tensor var_1023_groups_0 = const()[name = tensor("op_1023_groups_0"), val = tensor(1)]; - tensor layers_2_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15546304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15742976))), name = tensor("layers_2_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_1023_cast_fp16 = conv(dilations = var_1023_dilations_0, groups = var_1023_groups_0, pad = var_1023_pad_0, pad_type = var_1023_pad_type_0, strides = var_1023_strides_0, weight = layers_2_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_11_cast_fp16)[name = tensor("op_1023_cast_fp16")]; - tensor var_1029_pad_type_0 = const()[name = tensor("op_1029_pad_type_0"), val = tensor("valid")]; - tensor var_1029_strides_0 = const()[name = tensor("op_1029_strides_0"), val = tensor([1, 1])]; - tensor var_1029_pad_0 = const()[name = tensor("op_1029_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1029_dilations_0 = const()[name = tensor("op_1029_dilations_0"), val = tensor([1, 1])]; - tensor var_1029_groups_0 = const()[name = tensor("op_1029_groups_0"), val = tensor(1)]; - tensor layers_2_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15751936))), name = tensor("layers_2_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15743168))), shape = tensor([512, 512, 1, 1])]; - tensor var_1029_cast_fp16 = conv(dilations = var_1029_dilations_0, groups = var_1029_groups_0, pad = var_1029_pad_0, pad_type = var_1029_pad_type_0, strides = var_1029_strides_0, weight = layers_2_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_11_cast_fp16)[name = tensor("op_1029_cast_fp16")]; - tensor key_5_cast_fp16 = add(x = var_1023_cast_fp16, y = var_1029_cast_fp16)[name = tensor("key_5_cast_fp16")]; - tensor var_1039_pad_type_0 = const()[name = tensor("op_1039_pad_type_0"), val = tensor("valid")]; - tensor var_1039_strides_0 = const()[name = tensor("op_1039_strides_0"), val = tensor([1, 1])]; - tensor var_1039_pad_0 = const()[name = tensor("op_1039_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1039_dilations_0 = const()[name = tensor("op_1039_dilations_0"), val = tensor([1, 1])]; - tensor var_1039_groups_0 = const()[name = tensor("op_1039_groups_0"), val = tensor(1)]; - tensor layers_2_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15784768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15981440))), name = tensor("layers_2_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_2_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_2_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15981632)))]; - tensor var_1039_cast_fp16 = conv(bias = layers_2_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_1039_dilations_0, groups = var_1039_groups_0, pad = var_1039_pad_0, pad_type = var_1039_pad_type_0, strides = var_1039_strides_0, weight = layers_2_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_11_cast_fp16)[name = tensor("op_1039_cast_fp16")]; - tensor var_1045_pad_type_0 = const()[name = tensor("op_1045_pad_type_0"), val = tensor("valid")]; - tensor var_1045_strides_0 = const()[name = tensor("op_1045_strides_0"), val = tensor([1, 1])]; - tensor var_1045_pad_0 = const()[name = tensor("op_1045_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1045_dilations_0 = const()[name = tensor("op_1045_dilations_0"), val = tensor([1, 1])]; - tensor var_1045_groups_0 = const()[name = tensor("op_1045_groups_0"), val = tensor(1)]; - tensor layers_2_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15990208))), name = tensor("layers_2_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15982720))), shape = tensor([512, 512, 1, 1])]; - tensor var_1045_cast_fp16 = conv(dilations = var_1045_dilations_0, groups = var_1045_groups_0, pad = var_1045_pad_0, pad_type = var_1045_pad_type_0, strides = var_1045_strides_0, weight = layers_2_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_11_cast_fp16)[name = tensor("op_1045_cast_fp16")]; - tensor value_5_cast_fp16 = add(x = var_1039_cast_fp16, y = var_1045_cast_fp16)[name = tensor("value_5_cast_fp16")]; - tensor var_1048_to_fp16 = const()[name = tensor("op_1048_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16023040)))]; - tensor query_11_cast_fp16 = add(x = query_9_cast_fp16, y = var_1048_to_fp16)[name = tensor("query_11_cast_fp16")]; - tensor var_1051_to_fp16 = const()[name = tensor("op_1051_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16024128)))]; - tensor q_with_bias_v_5_cast_fp16 = add(x = query_9_cast_fp16, y = var_1051_to_fp16)[name = tensor("q_with_bias_v_5_cast_fp16")]; - tensor var_1061_pad_type_0 = const()[name = tensor("op_1061_pad_type_0"), val = tensor("valid")]; - tensor var_1061_strides_0 = const()[name = tensor("op_1061_strides_0"), val = tensor([1, 1])]; - tensor var_1061_pad_0 = const()[name = tensor("op_1061_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1061_dilations_0 = const()[name = tensor("op_1061_dilations_0"), val = tensor([1, 1])]; - tensor var_1061_groups_0 = const()[name = tensor("op_1061_groups_0"), val = tensor(1)]; - tensor layers_2_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16025216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16221888))), name = tensor("layers_2_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_1061_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_1061_dilations_0, groups = var_1061_groups_0, pad = var_1061_pad_0, pad_type = var_1061_pad_type_0, strides = var_1061_strides_0, weight = layers_2_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_1061_cast_fp16")]; - tensor var_1067_pad_type_0 = const()[name = tensor("op_1067_pad_type_0"), val = tensor("valid")]; - tensor var_1067_strides_0 = const()[name = tensor("op_1067_strides_0"), val = tensor([1, 1])]; - tensor var_1067_pad_0 = const()[name = tensor("op_1067_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1067_dilations_0 = const()[name = tensor("op_1067_dilations_0"), val = tensor([1, 1])]; - tensor var_1067_groups_0 = const()[name = tensor("op_1067_groups_0"), val = tensor(1)]; - tensor layers_2_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16243968))), name = tensor("layers_2_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16222080))), shape = tensor([512, 512, 1, 1])]; - tensor var_1067_cast_fp16 = conv(dilations = var_1067_dilations_0, groups = var_1067_groups_0, pad = var_1067_pad_0, pad_type = var_1067_pad_type_0, strides = var_1067_strides_0, weight = layers_2_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_1067_cast_fp16")]; - tensor p_5_cast_fp16 = add(x = var_1061_cast_fp16, y = var_1067_cast_fp16)[name = tensor("p_5_cast_fp16")]; - tensor var_1071 = const()[name = tensor("op_1071"), val = tensor([1, 8, 64, 188])]; - tensor var_1072_cast_fp16 = reshape(shape = var_1071, x = q_with_bias_v_5_cast_fp16)[name = tensor("op_1072_cast_fp16")]; - tensor var_1073 = const()[name = tensor("op_1073"), val = tensor([1, 8, 64, -1])]; - tensor var_1074_cast_fp16 = reshape(shape = var_1073, x = p_5_cast_fp16)[name = tensor("op_1074_cast_fp16")]; - tensor matrix_bd_17_transpose_x_0 = const()[name = tensor("matrix_bd_17_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_17_transpose_y_0 = const()[name = tensor("matrix_bd_17_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_17_cast_fp16 = matmul(transpose_x = matrix_bd_17_transpose_x_0, transpose_y = matrix_bd_17_transpose_y_0, x = var_1072_cast_fp16, y = var_1074_cast_fp16)[name = tensor("matrix_bd_17_cast_fp16")]; - tensor matrix_bd_19_pad_0 = const()[name = tensor("matrix_bd_19_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_19_mode_0 = const()[name = tensor("matrix_bd_19_mode_0"), val = tensor("constant")]; - tensor const_32_to_fp16 = const()[name = tensor("const_32_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_19_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = matrix_bd_19_mode_0, pad = matrix_bd_19_pad_0, x = matrix_bd_17_cast_fp16)[name = tensor("matrix_bd_19_cast_fp16")]; - tensor var_1083 = const()[name = tensor("op_1083"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1083, x = matrix_bd_19_cast_fp16)[name = tensor("matrix_bd_21_cast_fp16")]; - tensor var_1087_begin_0 = const()[name = tensor("op_1087_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1087_end_0 = const()[name = tensor("op_1087_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1087_end_mask_0 = const()[name = tensor("op_1087_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1087_cast_fp16 = slice_by_index(begin = var_1087_begin_0, end = var_1087_end_0, end_mask = var_1087_end_mask_0, x = matrix_bd_21_cast_fp16)[name = tensor("op_1087_cast_fp16")]; - tensor var_1088 = const()[name = tensor("op_1088"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_23_cast_fp16 = reshape(shape = var_1088, x = var_1087_cast_fp16)[name = tensor("matrix_bd_23_cast_fp16")]; - tensor var_1093_begin_0 = const()[name = tensor("op_1093_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1093_end_0 = const()[name = tensor("op_1093_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_1093_end_mask_0 = const()[name = tensor("op_1093_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_1093_cast_fp16 = slice_by_index(begin = var_1093_begin_0, end = var_1093_end_0, end_mask = var_1093_end_mask_0, x = matrix_bd_23_cast_fp16)[name = tensor("op_1093_cast_fp16")]; - tensor var_1094_to_fp16 = const()[name = tensor("op_1094_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_5_cast_fp16 = mul(x = var_1093_cast_fp16, y = var_1094_to_fp16)[name = tensor("qk_mask_5_cast_fp16")]; - tensor var_1098 = const()[name = tensor("op_1098"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_5_cast_fp16 = reshape(shape = var_1098, x = query_11_cast_fp16)[name = tensor("mh_q_5_cast_fp16")]; - tensor var_1100_to_fp16 = const()[name = tensor("op_1100_to_fp16"), val = tensor(0x1p-3)]; - tensor var_1101_cast_fp16 = mul(x = mh_q_5_cast_fp16, y = var_1100_to_fp16)[name = tensor("op_1101_cast_fp16")]; - tensor var_1104 = const()[name = tensor("op_1104"), val = tensor([1, 8, 64, 188])]; - tensor var_1105_cast_fp16 = reshape(shape = var_1104, x = key_5_cast_fp16)[name = tensor("op_1105_cast_fp16")]; - tensor mh_w_9_transpose_x_0 = const()[name = tensor("mh_w_9_transpose_x_0"), val = tensor(true)]; - tensor mh_w_9_transpose_y_0 = const()[name = tensor("mh_w_9_transpose_y_0"), val = tensor(false)]; - tensor mh_w_9_cast_fp16 = matmul(transpose_x = mh_w_9_transpose_x_0, transpose_y = mh_w_9_transpose_y_0, x = var_1101_cast_fp16, y = var_1105_cast_fp16)[name = tensor("mh_w_9_cast_fp16")]; - tensor mh_w_11_cast_fp16 = add(x = mh_w_9_cast_fp16, y = qk_mask_5_cast_fp16)[name = tensor("mh_w_11_cast_fp16")]; - tensor var_1109_cast_fp16 = softmax(axis = var_896, x = mh_w_11_cast_fp16)[name = tensor("op_1109_cast_fp16")]; - tensor var_1110 = const()[name = tensor("op_1110"), val = tensor([1, 8, 64, 188])]; - tensor var_1111_cast_fp16 = reshape(shape = var_1110, x = value_5_cast_fp16)[name = tensor("op_1111_cast_fp16")]; - tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; - tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; - tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_1111_cast_fp16, y = var_1109_cast_fp16)[name = tensor("attn_5_cast_fp16")]; - tensor var_1114 = const()[name = tensor("op_1114"), val = tensor([1, 512, 1, 188])]; - tensor input_75_cast_fp16 = reshape(shape = var_1114, x = attn_5_cast_fp16)[name = tensor("input_75_cast_fp16")]; - tensor var_1124_pad_type_0 = const()[name = tensor("op_1124_pad_type_0"), val = tensor("valid")]; - tensor var_1124_strides_0 = const()[name = tensor("op_1124_strides_0"), val = tensor([1, 1])]; - tensor var_1124_pad_0 = const()[name = tensor("op_1124_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1124_dilations_0 = const()[name = tensor("op_1124_dilations_0"), val = tensor([1, 1])]; - tensor var_1124_groups_0 = const()[name = tensor("op_1124_groups_0"), val = tensor(1)]; - tensor layers_2_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16276800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16473472))), name = tensor("layers_2_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_2_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_2_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16473664)))]; - tensor var_1124_cast_fp16 = conv(bias = layers_2_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_1124_dilations_0, groups = var_1124_groups_0, pad = var_1124_pad_0, pad_type = var_1124_pad_type_0, strides = var_1124_strides_0, weight = layers_2_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = tensor("op_1124_cast_fp16")]; - tensor var_1130_pad_type_0 = const()[name = tensor("op_1130_pad_type_0"), val = tensor("valid")]; - tensor var_1130_strides_0 = const()[name = tensor("op_1130_strides_0"), val = tensor([1, 1])]; - tensor var_1130_pad_0 = const()[name = tensor("op_1130_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1130_dilations_0 = const()[name = tensor("op_1130_dilations_0"), val = tensor([1, 1])]; - tensor var_1130_groups_0 = const()[name = tensor("op_1130_groups_0"), val = tensor(1)]; - tensor layers_2_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16482560))), name = tensor("layers_2_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16474752))), shape = tensor([512, 512, 1, 1])]; - tensor var_1130_cast_fp16 = conv(dilations = var_1130_dilations_0, groups = var_1130_groups_0, pad = var_1130_pad_0, pad_type = var_1130_pad_type_0, strides = var_1130_strides_0, weight = layers_2_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_75_cast_fp16)[name = tensor("op_1130_cast_fp16")]; - tensor obj_13_cast_fp16 = add(x = var_1124_cast_fp16, y = var_1130_cast_fp16)[name = tensor("obj_13_cast_fp16")]; - tensor inputs_25_cast_fp16 = add(x = inputs_23_cast_fp16, y = obj_13_cast_fp16)[name = tensor("inputs_25_cast_fp16")]; - tensor out_25_axes_0 = const()[name = tensor("out_25_axes_0"), val = tensor([1])]; - tensor var_1141_to_fp16 = const()[name = tensor("op_1141_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_1141_to_fp16, x = inputs_25_cast_fp16)[name = tensor("out_25_cast_fp16")]; - tensor input_77_gamma_0_to_fp16 = const()[name = tensor("input_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16515392)))]; - tensor input_77_beta_0_to_fp16 = const()[name = tensor("input_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16516480)))]; - tensor input_77_epsilon_0_to_fp16 = const()[name = tensor("input_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_77_cast_fp16 = batch_norm(beta = input_77_beta_0_to_fp16, epsilon = input_77_epsilon_0_to_fp16, gamma = input_77_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_25_cast_fp16)[name = tensor("input_77_cast_fp16")]; - tensor var_1163_pad_type_0 = const()[name = tensor("op_1163_pad_type_0"), val = tensor("valid")]; - tensor var_1163_strides_0 = const()[name = tensor("op_1163_strides_0"), val = tensor([1, 1])]; - tensor var_1163_pad_0 = const()[name = tensor("op_1163_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1163_dilations_0 = const()[name = tensor("op_1163_dilations_0"), val = tensor([1, 1])]; - tensor var_1163_groups_0 = const()[name = tensor("op_1163_groups_0"), val = tensor(1)]; - tensor layers_2_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16517568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16910848))), name = tensor("layers_2_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_2_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_2_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16911040)))]; - tensor var_1163_cast_fp16 = conv(bias = layers_2_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_1163_dilations_0, groups = var_1163_groups_0, pad = var_1163_pad_0, pad_type = var_1163_pad_type_0, strides = var_1163_strides_0, weight = layers_2_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = tensor("op_1163_cast_fp16")]; - tensor var_1169_pad_type_0 = const()[name = tensor("op_1169_pad_type_0"), val = tensor("valid")]; - tensor var_1169_strides_0 = const()[name = tensor("op_1169_strides_0"), val = tensor([1, 1])]; - tensor var_1169_pad_0 = const()[name = tensor("op_1169_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1169_dilations_0 = const()[name = tensor("op_1169_dilations_0"), val = tensor([1, 1])]; - tensor var_1169_groups_0 = const()[name = tensor("op_1169_groups_0"), val = tensor(1)]; - tensor layers_2_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16930752))), name = tensor("layers_2_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16913152))), shape = tensor([1024, 512, 1, 1])]; - tensor var_1169_cast_fp16 = conv(dilations = var_1169_dilations_0, groups = var_1169_groups_0, pad = var_1169_pad_0, pad_type = var_1169_pad_type_0, strides = var_1169_strides_0, weight = layers_2_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_77_cast_fp16)[name = tensor("op_1169_cast_fp16")]; - tensor input_79_cast_fp16 = add(x = var_1163_cast_fp16, y = var_1169_cast_fp16)[name = tensor("input_79_cast_fp16")]; - tensor input_81_split_num_splits_0 = const()[name = tensor("input_81_split_num_splits_0"), val = tensor(2)]; - tensor input_81_split_axis_0 = const()[name = tensor("input_81_split_axis_0"), val = tensor(1)]; - tensor input_81_split_cast_fp16_0, tensor input_81_split_cast_fp16_1 = split(axis = input_81_split_axis_0, num_splits = input_81_split_num_splits_0, x = input_79_cast_fp16)[name = tensor("input_81_split_cast_fp16")]; - tensor input_81_split_1_sigmoid_cast_fp16 = sigmoid(x = input_81_split_cast_fp16_1)[name = tensor("input_81_split_1_sigmoid_cast_fp16")]; - tensor input_81_cast_fp16 = mul(x = input_81_split_cast_fp16_0, y = input_81_split_1_sigmoid_cast_fp16)[name = tensor("input_81_cast_fp16")]; - tensor input_83_pad_type_0 = const()[name = tensor("input_83_pad_type_0"), val = tensor("custom")]; - tensor input_83_pad_0 = const()[name = tensor("input_83_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_83_groups_0 = const()[name = tensor("input_83_groups_0"), val = tensor(512)]; - tensor input_83_strides_0 = const()[name = tensor("input_83_strides_0"), val = tensor([1, 1])]; - tensor input_83_dilations_0 = const()[name = tensor("input_83_dilations_0"), val = tensor([1, 1])]; - tensor const_195_to_fp16 = const()[name = tensor("const_195_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16996352)))]; - tensor const_196_to_fp16 = const()[name = tensor("const_196_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17005632)))]; - tensor input_85_cast_fp16 = conv(bias = const_196_to_fp16, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_195_to_fp16, x = input_81_cast_fp16)[name = tensor("input_85_cast_fp16")]; - tensor input_87_cast_fp16 = silu(x = input_85_cast_fp16)[name = tensor("input_87_cast_fp16")]; - tensor var_1193_pad_type_0 = const()[name = tensor("op_1193_pad_type_0"), val = tensor("valid")]; - tensor var_1193_strides_0 = const()[name = tensor("op_1193_strides_0"), val = tensor([1, 1])]; - tensor var_1193_pad_0 = const()[name = tensor("op_1193_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1193_dilations_0 = const()[name = tensor("op_1193_dilations_0"), val = tensor([1, 1])]; - tensor var_1193_groups_0 = const()[name = tensor("op_1193_groups_0"), val = tensor(1)]; - tensor layers_2_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17006720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17203392))), name = tensor("layers_2_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_2_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_2_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17203584)))]; - tensor var_1193_cast_fp16 = conv(bias = layers_2_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_1193_dilations_0, groups = var_1193_groups_0, pad = var_1193_pad_0, pad_type = var_1193_pad_type_0, strides = var_1193_strides_0, weight = layers_2_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_87_cast_fp16)[name = tensor("op_1193_cast_fp16")]; - tensor var_1199_pad_type_0 = const()[name = tensor("op_1199_pad_type_0"), val = tensor("valid")]; - tensor var_1199_strides_0 = const()[name = tensor("op_1199_strides_0"), val = tensor([1, 1])]; - tensor var_1199_pad_0 = const()[name = tensor("op_1199_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1199_dilations_0 = const()[name = tensor("op_1199_dilations_0"), val = tensor([1, 1])]; - tensor var_1199_groups_0 = const()[name = tensor("op_1199_groups_0"), val = tensor(1)]; - tensor layers_2_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17213248))), name = tensor("layers_2_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17204672))), shape = tensor([512, 512, 1, 1])]; - tensor var_1199_cast_fp16 = conv(dilations = var_1199_dilations_0, groups = var_1199_groups_0, pad = var_1199_pad_0, pad_type = var_1199_pad_type_0, strides = var_1199_strides_0, weight = layers_2_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_87_cast_fp16)[name = tensor("op_1199_cast_fp16")]; - tensor x_17_cast_fp16 = add(x = var_1193_cast_fp16, y = var_1199_cast_fp16)[name = tensor("x_17_cast_fp16")]; - tensor inputs_27_cast_fp16 = add(x = inputs_25_cast_fp16, y = x_17_cast_fp16)[name = tensor("inputs_27_cast_fp16")]; - tensor out_27_axes_0 = const()[name = tensor("out_27_axes_0"), val = tensor([1])]; - tensor var_1210_to_fp16 = const()[name = tensor("op_1210_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_27_cast_fp16 = layer_norm(axes = out_27_axes_0, epsilon = var_1210_to_fp16, x = inputs_27_cast_fp16)[name = tensor("out_27_cast_fp16")]; - tensor input_89_gamma_0_to_fp16 = const()[name = tensor("input_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17246080)))]; - tensor input_89_beta_0_to_fp16 = const()[name = tensor("input_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17247168)))]; - tensor input_89_epsilon_0_to_fp16 = const()[name = tensor("input_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_89_cast_fp16 = batch_norm(beta = input_89_beta_0_to_fp16, epsilon = input_89_epsilon_0_to_fp16, gamma = input_89_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_27_cast_fp16)[name = tensor("input_89_cast_fp16")]; - tensor var_1230_pad_type_0 = const()[name = tensor("op_1230_pad_type_0"), val = tensor("valid")]; - tensor var_1230_strides_0 = const()[name = tensor("op_1230_strides_0"), val = tensor([1, 1])]; - tensor var_1230_pad_0 = const()[name = tensor("op_1230_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1230_dilations_0 = const()[name = tensor("op_1230_dilations_0"), val = tensor([1, 1])]; - tensor var_1230_groups_0 = const()[name = tensor("op_1230_groups_0"), val = tensor(1)]; - tensor layers_2_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17248256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18034752))), name = tensor("layers_2_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_2_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_2_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18034944)))]; - tensor var_1230_cast_fp16 = conv(bias = layers_2_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_1230_dilations_0, groups = var_1230_groups_0, pad = var_1230_pad_0, pad_type = var_1230_pad_type_0, strides = var_1230_strides_0, weight = layers_2_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_89_cast_fp16)[name = tensor("op_1230_cast_fp16")]; - tensor var_1236_pad_type_0 = const()[name = tensor("op_1236_pad_type_0"), val = tensor("valid")]; - tensor var_1236_strides_0 = const()[name = tensor("op_1236_strides_0"), val = tensor([1, 1])]; - tensor var_1236_pad_0 = const()[name = tensor("op_1236_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1236_dilations_0 = const()[name = tensor("op_1236_dilations_0"), val = tensor([1, 1])]; - tensor var_1236_groups_0 = const()[name = tensor("op_1236_groups_0"), val = tensor(1)]; - tensor layers_2_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18070784))), name = tensor("layers_2_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18039104))), shape = tensor([2048, 512, 1, 1])]; - tensor var_1236_cast_fp16 = conv(dilations = var_1236_dilations_0, groups = var_1236_groups_0, pad = var_1236_pad_0, pad_type = var_1236_pad_type_0, strides = var_1236_strides_0, weight = layers_2_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_89_cast_fp16)[name = tensor("op_1236_cast_fp16")]; - tensor input_91_cast_fp16 = add(x = var_1230_cast_fp16, y = var_1236_cast_fp16)[name = tensor("input_91_cast_fp16")]; - tensor input_93_cast_fp16 = silu(x = input_91_cast_fp16)[name = tensor("input_93_cast_fp16")]; - tensor var_1247_pad_type_0 = const()[name = tensor("op_1247_pad_type_0"), val = tensor("valid")]; - tensor var_1247_strides_0 = const()[name = tensor("op_1247_strides_0"), val = tensor([1, 1])]; - tensor var_1247_pad_0 = const()[name = tensor("op_1247_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1247_dilations_0 = const()[name = tensor("op_1247_dilations_0"), val = tensor([1, 1])]; - tensor var_1247_groups_0 = const()[name = tensor("op_1247_groups_0"), val = tensor(1)]; - tensor layers_2_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18201920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18988416))), name = tensor("layers_2_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_2_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_2_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18988608)))]; - tensor var_1247_cast_fp16 = conv(bias = layers_2_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_1247_dilations_0, groups = var_1247_groups_0, pad = var_1247_pad_0, pad_type = var_1247_pad_type_0, strides = var_1247_strides_0, weight = layers_2_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = tensor("op_1247_cast_fp16")]; - tensor var_1253_pad_type_0 = const()[name = tensor("op_1253_pad_type_0"), val = tensor("valid")]; - tensor var_1253_strides_0 = const()[name = tensor("op_1253_strides_0"), val = tensor([1, 1])]; - tensor var_1253_pad_0 = const()[name = tensor("op_1253_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1253_dilations_0 = const()[name = tensor("op_1253_dilations_0"), val = tensor([1, 1])]; - tensor var_1253_groups_0 = const()[name = tensor("op_1253_groups_0"), val = tensor(1)]; - tensor layers_2_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19025600))), name = tensor("layers_2_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18989696))), shape = tensor([512, 2048, 1, 1])]; - tensor var_1253_cast_fp16 = conv(dilations = var_1253_dilations_0, groups = var_1253_groups_0, pad = var_1253_pad_0, pad_type = var_1253_pad_type_0, strides = var_1253_strides_0, weight = layers_2_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_93_cast_fp16)[name = tensor("op_1253_cast_fp16")]; - tensor x_19_cast_fp16 = add(x = var_1247_cast_fp16, y = var_1253_cast_fp16)[name = tensor("x_19_cast_fp16")]; - tensor var_1255_to_fp16 = const()[name = tensor("op_1255_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1256_cast_fp16 = mul(x = x_19_cast_fp16, y = var_1255_to_fp16)[name = tensor("op_1256_cast_fp16")]; - tensor inputs_29_cast_fp16 = add(x = inputs_27_cast_fp16, y = var_1256_cast_fp16)[name = tensor("inputs_29_cast_fp16")]; - tensor out_29_axes_0 = const()[name = tensor("out_29_axes_0"), val = tensor([1])]; - tensor var_1266_to_fp16 = const()[name = tensor("op_1266_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_29_cast_fp16 = layer_norm(axes = out_29_axes_0, epsilon = var_1266_to_fp16, x = inputs_29_cast_fp16)[name = tensor("out_29_cast_fp16")]; - tensor inputs_31_gamma_0_to_fp16 = const()[name = tensor("inputs_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19156736)))]; - tensor inputs_31_beta_0_to_fp16 = const()[name = tensor("inputs_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19157824)))]; - tensor inputs_31_epsilon_0_to_fp16 = const()[name = tensor("inputs_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_31_cast_fp16 = batch_norm(beta = inputs_31_beta_0_to_fp16, epsilon = inputs_31_epsilon_0_to_fp16, gamma = inputs_31_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_29_cast_fp16)[name = tensor("inputs_31_cast_fp16")]; - tensor var_1280 = const()[name = tensor("op_1280"), val = tensor(3)]; - tensor out_31_axes_0 = const()[name = tensor("out_31_axes_0"), val = tensor([1])]; - tensor var_1311_to_fp16 = const()[name = tensor("op_1311_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_31_cast_fp16 = layer_norm(axes = out_31_axes_0, epsilon = var_1311_to_fp16, x = inputs_31_cast_fp16)[name = tensor("out_31_cast_fp16")]; - tensor input_95_gamma_0_to_fp16 = const()[name = tensor("input_95_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19158912)))]; - tensor input_95_beta_0_to_fp16 = const()[name = tensor("input_95_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19160000)))]; - tensor input_95_epsilon_0_to_fp16 = const()[name = tensor("input_95_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_95_cast_fp16 = batch_norm(beta = input_95_beta_0_to_fp16, epsilon = input_95_epsilon_0_to_fp16, gamma = input_95_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_31_cast_fp16)[name = tensor("input_95_cast_fp16")]; - tensor var_1331_pad_type_0 = const()[name = tensor("op_1331_pad_type_0"), val = tensor("valid")]; - tensor var_1331_strides_0 = const()[name = tensor("op_1331_strides_0"), val = tensor([1, 1])]; - tensor var_1331_pad_0 = const()[name = tensor("op_1331_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1331_dilations_0 = const()[name = tensor("op_1331_dilations_0"), val = tensor([1, 1])]; - tensor var_1331_groups_0 = const()[name = tensor("op_1331_groups_0"), val = tensor(1)]; - tensor layers_3_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19161088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19947584))), name = tensor("layers_3_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_3_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_3_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19947776)))]; - tensor var_1331_cast_fp16 = conv(bias = layers_3_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_1331_dilations_0, groups = var_1331_groups_0, pad = var_1331_pad_0, pad_type = var_1331_pad_type_0, strides = var_1331_strides_0, weight = layers_3_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = tensor("op_1331_cast_fp16")]; - tensor var_1337_pad_type_0 = const()[name = tensor("op_1337_pad_type_0"), val = tensor("valid")]; - tensor var_1337_strides_0 = const()[name = tensor("op_1337_strides_0"), val = tensor([1, 1])]; - tensor var_1337_pad_0 = const()[name = tensor("op_1337_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1337_dilations_0 = const()[name = tensor("op_1337_dilations_0"), val = tensor([1, 1])]; - tensor var_1337_groups_0 = const()[name = tensor("op_1337_groups_0"), val = tensor(1)]; - tensor layers_3_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19984128))), name = tensor("layers_3_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19951936))), shape = tensor([2048, 512, 1, 1])]; - tensor var_1337_cast_fp16 = conv(dilations = var_1337_dilations_0, groups = var_1337_groups_0, pad = var_1337_pad_0, pad_type = var_1337_pad_type_0, strides = var_1337_strides_0, weight = layers_3_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_95_cast_fp16)[name = tensor("op_1337_cast_fp16")]; - tensor input_97_cast_fp16 = add(x = var_1331_cast_fp16, y = var_1337_cast_fp16)[name = tensor("input_97_cast_fp16")]; - tensor input_99_cast_fp16 = silu(x = input_97_cast_fp16)[name = tensor("input_99_cast_fp16")]; - tensor var_1348_pad_type_0 = const()[name = tensor("op_1348_pad_type_0"), val = tensor("valid")]; - tensor var_1348_strides_0 = const()[name = tensor("op_1348_strides_0"), val = tensor([1, 1])]; - tensor var_1348_pad_0 = const()[name = tensor("op_1348_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1348_dilations_0 = const()[name = tensor("op_1348_dilations_0"), val = tensor([1, 1])]; - tensor var_1348_groups_0 = const()[name = tensor("op_1348_groups_0"), val = tensor(1)]; - tensor layers_3_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20115264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20901760))), name = tensor("layers_3_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_3_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_3_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20901952)))]; - tensor var_1348_cast_fp16 = conv(bias = layers_3_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_1348_dilations_0, groups = var_1348_groups_0, pad = var_1348_pad_0, pad_type = var_1348_pad_type_0, strides = var_1348_strides_0, weight = layers_3_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = tensor("op_1348_cast_fp16")]; - tensor var_1354_pad_type_0 = const()[name = tensor("op_1354_pad_type_0"), val = tensor("valid")]; - tensor var_1354_strides_0 = const()[name = tensor("op_1354_strides_0"), val = tensor([1, 1])]; - tensor var_1354_pad_0 = const()[name = tensor("op_1354_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1354_dilations_0 = const()[name = tensor("op_1354_dilations_0"), val = tensor([1, 1])]; - tensor var_1354_groups_0 = const()[name = tensor("op_1354_groups_0"), val = tensor(1)]; - tensor layers_3_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20938176))), name = tensor("layers_3_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20903040))), shape = tensor([512, 2048, 1, 1])]; - tensor var_1354_cast_fp16 = conv(dilations = var_1354_dilations_0, groups = var_1354_groups_0, pad = var_1354_pad_0, pad_type = var_1354_pad_type_0, strides = var_1354_strides_0, weight = layers_3_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_99_cast_fp16)[name = tensor("op_1354_cast_fp16")]; - tensor x_21_cast_fp16 = add(x = var_1348_cast_fp16, y = var_1354_cast_fp16)[name = tensor("x_21_cast_fp16")]; - tensor var_1356_to_fp16 = const()[name = tensor("op_1356_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1357_cast_fp16 = mul(x = x_21_cast_fp16, y = var_1356_to_fp16)[name = tensor("op_1357_cast_fp16")]; - tensor inputs_33_cast_fp16 = add(x = inputs_31_cast_fp16, y = var_1357_cast_fp16)[name = tensor("inputs_33_cast_fp16")]; - tensor out_33_axes_0 = const()[name = tensor("out_33_axes_0"), val = tensor([1])]; - tensor var_1367_to_fp16 = const()[name = tensor("op_1367_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_1367_to_fp16, x = inputs_33_cast_fp16)[name = tensor("out_33_cast_fp16")]; - tensor obj_15_gamma_0_to_fp16 = const()[name = tensor("obj_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21069312)))]; - tensor obj_15_beta_0_to_fp16 = const()[name = tensor("obj_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21070400)))]; - tensor obj_15_epsilon_0_to_fp16 = const()[name = tensor("obj_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_15_cast_fp16 = batch_norm(beta = obj_15_beta_0_to_fp16, epsilon = obj_15_epsilon_0_to_fp16, gamma = obj_15_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_33_cast_fp16)[name = tensor("obj_15_cast_fp16")]; - tensor var_1392_pad_type_0 = const()[name = tensor("op_1392_pad_type_0"), val = tensor("valid")]; - tensor var_1392_strides_0 = const()[name = tensor("op_1392_strides_0"), val = tensor([1, 1])]; - tensor var_1392_pad_0 = const()[name = tensor("op_1392_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1392_dilations_0 = const()[name = tensor("op_1392_dilations_0"), val = tensor([1, 1])]; - tensor var_1392_groups_0 = const()[name = tensor("op_1392_groups_0"), val = tensor(1)]; - tensor layers_3_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21071488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21268160))), name = tensor("layers_3_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_3_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_3_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21268352)))]; - tensor var_1392_cast_fp16 = conv(bias = layers_3_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_1392_dilations_0, groups = var_1392_groups_0, pad = var_1392_pad_0, pad_type = var_1392_pad_type_0, strides = var_1392_strides_0, weight = layers_3_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_15_cast_fp16)[name = tensor("op_1392_cast_fp16")]; - tensor var_1398_pad_type_0 = const()[name = tensor("op_1398_pad_type_0"), val = tensor("valid")]; - tensor var_1398_strides_0 = const()[name = tensor("op_1398_strides_0"), val = tensor([1, 1])]; - tensor var_1398_pad_0 = const()[name = tensor("op_1398_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1398_dilations_0 = const()[name = tensor("op_1398_dilations_0"), val = tensor([1, 1])]; - tensor var_1398_groups_0 = const()[name = tensor("op_1398_groups_0"), val = tensor(1)]; - tensor layers_3_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21278208))), name = tensor("layers_3_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21269440))), shape = tensor([512, 512, 1, 1])]; - tensor var_1398_cast_fp16 = conv(dilations = var_1398_dilations_0, groups = var_1398_groups_0, pad = var_1398_pad_0, pad_type = var_1398_pad_type_0, strides = var_1398_strides_0, weight = layers_3_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_15_cast_fp16)[name = tensor("op_1398_cast_fp16")]; - tensor query_13_cast_fp16 = add(x = var_1392_cast_fp16, y = var_1398_cast_fp16)[name = tensor("query_13_cast_fp16")]; - tensor var_1407_pad_type_0 = const()[name = tensor("op_1407_pad_type_0"), val = tensor("valid")]; - tensor var_1407_strides_0 = const()[name = tensor("op_1407_strides_0"), val = tensor([1, 1])]; - tensor var_1407_pad_0 = const()[name = tensor("op_1407_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1407_dilations_0 = const()[name = tensor("op_1407_dilations_0"), val = tensor([1, 1])]; - tensor var_1407_groups_0 = const()[name = tensor("op_1407_groups_0"), val = tensor(1)]; - tensor layers_3_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21311040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21507712))), name = tensor("layers_3_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_1407_cast_fp16 = conv(dilations = var_1407_dilations_0, groups = var_1407_groups_0, pad = var_1407_pad_0, pad_type = var_1407_pad_type_0, strides = var_1407_strides_0, weight = layers_3_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_15_cast_fp16)[name = tensor("op_1407_cast_fp16")]; - tensor var_1413_pad_type_0 = const()[name = tensor("op_1413_pad_type_0"), val = tensor("valid")]; - tensor var_1413_strides_0 = const()[name = tensor("op_1413_strides_0"), val = tensor([1, 1])]; - tensor var_1413_pad_0 = const()[name = tensor("op_1413_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1413_dilations_0 = const()[name = tensor("op_1413_dilations_0"), val = tensor([1, 1])]; - tensor var_1413_groups_0 = const()[name = tensor("op_1413_groups_0"), val = tensor(1)]; - tensor layers_3_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21517824))), name = tensor("layers_3_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21507904))), shape = tensor([512, 512, 1, 1])]; - tensor var_1413_cast_fp16 = conv(dilations = var_1413_dilations_0, groups = var_1413_groups_0, pad = var_1413_pad_0, pad_type = var_1413_pad_type_0, strides = var_1413_strides_0, weight = layers_3_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_15_cast_fp16)[name = tensor("op_1413_cast_fp16")]; - tensor key_7_cast_fp16 = add(x = var_1407_cast_fp16, y = var_1413_cast_fp16)[name = tensor("key_7_cast_fp16")]; - tensor var_1423_pad_type_0 = const()[name = tensor("op_1423_pad_type_0"), val = tensor("valid")]; - tensor var_1423_strides_0 = const()[name = tensor("op_1423_strides_0"), val = tensor([1, 1])]; - tensor var_1423_pad_0 = const()[name = tensor("op_1423_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1423_dilations_0 = const()[name = tensor("op_1423_dilations_0"), val = tensor([1, 1])]; - tensor var_1423_groups_0 = const()[name = tensor("op_1423_groups_0"), val = tensor(1)]; - tensor layers_3_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21550656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21747328))), name = tensor("layers_3_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_3_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_3_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21747520)))]; - tensor var_1423_cast_fp16 = conv(bias = layers_3_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_1423_dilations_0, groups = var_1423_groups_0, pad = var_1423_pad_0, pad_type = var_1423_pad_type_0, strides = var_1423_strides_0, weight = layers_3_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_15_cast_fp16)[name = tensor("op_1423_cast_fp16")]; - tensor var_1429_pad_type_0 = const()[name = tensor("op_1429_pad_type_0"), val = tensor("valid")]; - tensor var_1429_strides_0 = const()[name = tensor("op_1429_strides_0"), val = tensor([1, 1])]; - tensor var_1429_pad_0 = const()[name = tensor("op_1429_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1429_dilations_0 = const()[name = tensor("op_1429_dilations_0"), val = tensor([1, 1])]; - tensor var_1429_groups_0 = const()[name = tensor("op_1429_groups_0"), val = tensor(1)]; - tensor layers_3_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21756352))), name = tensor("layers_3_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21748608))), shape = tensor([512, 512, 1, 1])]; - tensor var_1429_cast_fp16 = conv(dilations = var_1429_dilations_0, groups = var_1429_groups_0, pad = var_1429_pad_0, pad_type = var_1429_pad_type_0, strides = var_1429_strides_0, weight = layers_3_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_15_cast_fp16)[name = tensor("op_1429_cast_fp16")]; - tensor value_7_cast_fp16 = add(x = var_1423_cast_fp16, y = var_1429_cast_fp16)[name = tensor("value_7_cast_fp16")]; - tensor var_1432_to_fp16 = const()[name = tensor("op_1432_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21789184)))]; - tensor query_15_cast_fp16 = add(x = query_13_cast_fp16, y = var_1432_to_fp16)[name = tensor("query_15_cast_fp16")]; - tensor var_1435_to_fp16 = const()[name = tensor("op_1435_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21790272)))]; - tensor q_with_bias_v_7_cast_fp16 = add(x = query_13_cast_fp16, y = var_1435_to_fp16)[name = tensor("q_with_bias_v_7_cast_fp16")]; - tensor var_1445_pad_type_0 = const()[name = tensor("op_1445_pad_type_0"), val = tensor("valid")]; - tensor var_1445_strides_0 = const()[name = tensor("op_1445_strides_0"), val = tensor([1, 1])]; - tensor var_1445_pad_0 = const()[name = tensor("op_1445_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1445_dilations_0 = const()[name = tensor("op_1445_dilations_0"), val = tensor([1, 1])]; - tensor var_1445_groups_0 = const()[name = tensor("op_1445_groups_0"), val = tensor(1)]; - tensor layers_3_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21791360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21988032))), name = tensor("layers_3_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_1445_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_1445_dilations_0, groups = var_1445_groups_0, pad = var_1445_pad_0, pad_type = var_1445_pad_type_0, strides = var_1445_strides_0, weight = layers_3_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_1445_cast_fp16")]; - tensor var_1451_pad_type_0 = const()[name = tensor("op_1451_pad_type_0"), val = tensor("valid")]; - tensor var_1451_strides_0 = const()[name = tensor("op_1451_strides_0"), val = tensor([1, 1])]; - tensor var_1451_pad_0 = const()[name = tensor("op_1451_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1451_dilations_0 = const()[name = tensor("op_1451_dilations_0"), val = tensor([1, 1])]; - tensor var_1451_groups_0 = const()[name = tensor("op_1451_groups_0"), val = tensor(1)]; - tensor layers_3_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22007936))), name = tensor("layers_3_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21988224))), shape = tensor([512, 512, 1, 1])]; - tensor var_1451_cast_fp16 = conv(dilations = var_1451_dilations_0, groups = var_1451_groups_0, pad = var_1451_pad_0, pad_type = var_1451_pad_type_0, strides = var_1451_strides_0, weight = layers_3_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_1451_cast_fp16")]; - tensor p_7_cast_fp16 = add(x = var_1445_cast_fp16, y = var_1451_cast_fp16)[name = tensor("p_7_cast_fp16")]; - tensor var_1455 = const()[name = tensor("op_1455"), val = tensor([1, 8, 64, 188])]; - tensor var_1456_cast_fp16 = reshape(shape = var_1455, x = q_with_bias_v_7_cast_fp16)[name = tensor("op_1456_cast_fp16")]; - tensor var_1457 = const()[name = tensor("op_1457"), val = tensor([1, 8, 64, -1])]; - tensor var_1458_cast_fp16 = reshape(shape = var_1457, x = p_7_cast_fp16)[name = tensor("op_1458_cast_fp16")]; - tensor matrix_bd_25_transpose_x_0 = const()[name = tensor("matrix_bd_25_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_25_transpose_y_0 = const()[name = tensor("matrix_bd_25_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_25_cast_fp16 = matmul(transpose_x = matrix_bd_25_transpose_x_0, transpose_y = matrix_bd_25_transpose_y_0, x = var_1456_cast_fp16, y = var_1458_cast_fp16)[name = tensor("matrix_bd_25_cast_fp16")]; - tensor matrix_bd_27_pad_0 = const()[name = tensor("matrix_bd_27_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_27_mode_0 = const()[name = tensor("matrix_bd_27_mode_0"), val = tensor("constant")]; - tensor const_43_to_fp16 = const()[name = tensor("const_43_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_27_cast_fp16 = pad(constant_val = const_43_to_fp16, mode = matrix_bd_27_mode_0, pad = matrix_bd_27_pad_0, x = matrix_bd_25_cast_fp16)[name = tensor("matrix_bd_27_cast_fp16")]; - tensor var_1467 = const()[name = tensor("op_1467"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1467, x = matrix_bd_27_cast_fp16)[name = tensor("matrix_bd_29_cast_fp16")]; - tensor var_1471_begin_0 = const()[name = tensor("op_1471_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1471_end_0 = const()[name = tensor("op_1471_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1471_end_mask_0 = const()[name = tensor("op_1471_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1471_cast_fp16 = slice_by_index(begin = var_1471_begin_0, end = var_1471_end_0, end_mask = var_1471_end_mask_0, x = matrix_bd_29_cast_fp16)[name = tensor("op_1471_cast_fp16")]; - tensor var_1472 = const()[name = tensor("op_1472"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_31_cast_fp16 = reshape(shape = var_1472, x = var_1471_cast_fp16)[name = tensor("matrix_bd_31_cast_fp16")]; - tensor var_1477_begin_0 = const()[name = tensor("op_1477_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1477_end_0 = const()[name = tensor("op_1477_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_1477_end_mask_0 = const()[name = tensor("op_1477_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_1477_cast_fp16 = slice_by_index(begin = var_1477_begin_0, end = var_1477_end_0, end_mask = var_1477_end_mask_0, x = matrix_bd_31_cast_fp16)[name = tensor("op_1477_cast_fp16")]; - tensor var_1478_to_fp16 = const()[name = tensor("op_1478_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_7_cast_fp16 = mul(x = var_1477_cast_fp16, y = var_1478_to_fp16)[name = tensor("qk_mask_7_cast_fp16")]; - tensor var_1482 = const()[name = tensor("op_1482"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_7_cast_fp16 = reshape(shape = var_1482, x = query_15_cast_fp16)[name = tensor("mh_q_7_cast_fp16")]; - tensor var_1484_to_fp16 = const()[name = tensor("op_1484_to_fp16"), val = tensor(0x1p-3)]; - tensor var_1485_cast_fp16 = mul(x = mh_q_7_cast_fp16, y = var_1484_to_fp16)[name = tensor("op_1485_cast_fp16")]; - tensor var_1488 = const()[name = tensor("op_1488"), val = tensor([1, 8, 64, 188])]; - tensor var_1489_cast_fp16 = reshape(shape = var_1488, x = key_7_cast_fp16)[name = tensor("op_1489_cast_fp16")]; - tensor mh_w_13_transpose_x_0 = const()[name = tensor("mh_w_13_transpose_x_0"), val = tensor(true)]; - tensor mh_w_13_transpose_y_0 = const()[name = tensor("mh_w_13_transpose_y_0"), val = tensor(false)]; - tensor mh_w_13_cast_fp16 = matmul(transpose_x = mh_w_13_transpose_x_0, transpose_y = mh_w_13_transpose_y_0, x = var_1485_cast_fp16, y = var_1489_cast_fp16)[name = tensor("mh_w_13_cast_fp16")]; - tensor mh_w_15_cast_fp16 = add(x = mh_w_13_cast_fp16, y = qk_mask_7_cast_fp16)[name = tensor("mh_w_15_cast_fp16")]; - tensor var_1493_cast_fp16 = softmax(axis = var_1280, x = mh_w_15_cast_fp16)[name = tensor("op_1493_cast_fp16")]; - tensor var_1494 = const()[name = tensor("op_1494"), val = tensor([1, 8, 64, 188])]; - tensor var_1495_cast_fp16 = reshape(shape = var_1494, x = value_7_cast_fp16)[name = tensor("op_1495_cast_fp16")]; - tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; - tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; - tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_1495_cast_fp16, y = var_1493_cast_fp16)[name = tensor("attn_7_cast_fp16")]; - tensor var_1498 = const()[name = tensor("op_1498"), val = tensor([1, 512, 1, 188])]; - tensor input_101_cast_fp16 = reshape(shape = var_1498, x = attn_7_cast_fp16)[name = tensor("input_101_cast_fp16")]; - tensor var_1508_pad_type_0 = const()[name = tensor("op_1508_pad_type_0"), val = tensor("valid")]; - tensor var_1508_strides_0 = const()[name = tensor("op_1508_strides_0"), val = tensor([1, 1])]; - tensor var_1508_pad_0 = const()[name = tensor("op_1508_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1508_dilations_0 = const()[name = tensor("op_1508_dilations_0"), val = tensor([1, 1])]; - tensor var_1508_groups_0 = const()[name = tensor("op_1508_groups_0"), val = tensor(1)]; - tensor layers_3_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22040768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22237440))), name = tensor("layers_3_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_3_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_3_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22237632)))]; - tensor var_1508_cast_fp16 = conv(bias = layers_3_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_1508_dilations_0, groups = var_1508_groups_0, pad = var_1508_pad_0, pad_type = var_1508_pad_type_0, strides = var_1508_strides_0, weight = layers_3_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = tensor("op_1508_cast_fp16")]; - tensor var_1514_pad_type_0 = const()[name = tensor("op_1514_pad_type_0"), val = tensor("valid")]; - tensor var_1514_strides_0 = const()[name = tensor("op_1514_strides_0"), val = tensor([1, 1])]; - tensor var_1514_pad_0 = const()[name = tensor("op_1514_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1514_dilations_0 = const()[name = tensor("op_1514_dilations_0"), val = tensor([1, 1])]; - tensor var_1514_groups_0 = const()[name = tensor("op_1514_groups_0"), val = tensor(1)]; - tensor layers_3_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22247104))), name = tensor("layers_3_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22238720))), shape = tensor([512, 512, 1, 1])]; - tensor var_1514_cast_fp16 = conv(dilations = var_1514_dilations_0, groups = var_1514_groups_0, pad = var_1514_pad_0, pad_type = var_1514_pad_type_0, strides = var_1514_strides_0, weight = layers_3_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_101_cast_fp16)[name = tensor("op_1514_cast_fp16")]; - tensor obj_17_cast_fp16 = add(x = var_1508_cast_fp16, y = var_1514_cast_fp16)[name = tensor("obj_17_cast_fp16")]; - tensor inputs_35_cast_fp16 = add(x = inputs_33_cast_fp16, y = obj_17_cast_fp16)[name = tensor("inputs_35_cast_fp16")]; - tensor out_35_axes_0 = const()[name = tensor("out_35_axes_0"), val = tensor([1])]; - tensor var_1525_to_fp16 = const()[name = tensor("op_1525_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_35_cast_fp16 = layer_norm(axes = out_35_axes_0, epsilon = var_1525_to_fp16, x = inputs_35_cast_fp16)[name = tensor("out_35_cast_fp16")]; - tensor input_103_gamma_0_to_fp16 = const()[name = tensor("input_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22279936)))]; - tensor input_103_beta_0_to_fp16 = const()[name = tensor("input_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22281024)))]; - tensor input_103_epsilon_0_to_fp16 = const()[name = tensor("input_103_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_103_cast_fp16 = batch_norm(beta = input_103_beta_0_to_fp16, epsilon = input_103_epsilon_0_to_fp16, gamma = input_103_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_35_cast_fp16)[name = tensor("input_103_cast_fp16")]; - tensor var_1547_pad_type_0 = const()[name = tensor("op_1547_pad_type_0"), val = tensor("valid")]; - tensor var_1547_strides_0 = const()[name = tensor("op_1547_strides_0"), val = tensor([1, 1])]; - tensor var_1547_pad_0 = const()[name = tensor("op_1547_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1547_dilations_0 = const()[name = tensor("op_1547_dilations_0"), val = tensor([1, 1])]; - tensor var_1547_groups_0 = const()[name = tensor("op_1547_groups_0"), val = tensor(1)]; - tensor layers_3_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22282112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22675392))), name = tensor("layers_3_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_3_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_3_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22675584)))]; - tensor var_1547_cast_fp16 = conv(bias = layers_3_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_1547_dilations_0, groups = var_1547_groups_0, pad = var_1547_pad_0, pad_type = var_1547_pad_type_0, strides = var_1547_strides_0, weight = layers_3_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_103_cast_fp16)[name = tensor("op_1547_cast_fp16")]; - tensor var_1553_pad_type_0 = const()[name = tensor("op_1553_pad_type_0"), val = tensor("valid")]; - tensor var_1553_strides_0 = const()[name = tensor("op_1553_strides_0"), val = tensor([1, 1])]; - tensor var_1553_pad_0 = const()[name = tensor("op_1553_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1553_dilations_0 = const()[name = tensor("op_1553_dilations_0"), val = tensor([1, 1])]; - tensor var_1553_groups_0 = const()[name = tensor("op_1553_groups_0"), val = tensor(1)]; - tensor layers_3_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22695360))), name = tensor("layers_3_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22677696))), shape = tensor([1024, 512, 1, 1])]; - tensor var_1553_cast_fp16 = conv(dilations = var_1553_dilations_0, groups = var_1553_groups_0, pad = var_1553_pad_0, pad_type = var_1553_pad_type_0, strides = var_1553_strides_0, weight = layers_3_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_103_cast_fp16)[name = tensor("op_1553_cast_fp16")]; - tensor input_105_cast_fp16 = add(x = var_1547_cast_fp16, y = var_1553_cast_fp16)[name = tensor("input_105_cast_fp16")]; - tensor input_107_split_num_splits_0 = const()[name = tensor("input_107_split_num_splits_0"), val = tensor(2)]; - tensor input_107_split_axis_0 = const()[name = tensor("input_107_split_axis_0"), val = tensor(1)]; - tensor input_107_split_cast_fp16_0, tensor input_107_split_cast_fp16_1 = split(axis = input_107_split_axis_0, num_splits = input_107_split_num_splits_0, x = input_105_cast_fp16)[name = tensor("input_107_split_cast_fp16")]; - tensor input_107_split_1_sigmoid_cast_fp16 = sigmoid(x = input_107_split_cast_fp16_1)[name = tensor("input_107_split_1_sigmoid_cast_fp16")]; - tensor input_107_cast_fp16 = mul(x = input_107_split_cast_fp16_0, y = input_107_split_1_sigmoid_cast_fp16)[name = tensor("input_107_cast_fp16")]; - tensor input_109_pad_type_0 = const()[name = tensor("input_109_pad_type_0"), val = tensor("custom")]; - tensor input_109_pad_0 = const()[name = tensor("input_109_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_109_groups_0 = const()[name = tensor("input_109_groups_0"), val = tensor(512)]; - tensor input_109_strides_0 = const()[name = tensor("input_109_strides_0"), val = tensor([1, 1])]; - tensor input_109_dilations_0 = const()[name = tensor("input_109_dilations_0"), val = tensor([1, 1])]; - tensor const_197_to_fp16 = const()[name = tensor("const_197_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22760960)))]; - tensor const_198_to_fp16 = const()[name = tensor("const_198_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22770240)))]; - tensor input_111_cast_fp16 = conv(bias = const_198_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_197_to_fp16, x = input_107_cast_fp16)[name = tensor("input_111_cast_fp16")]; - tensor input_113_cast_fp16 = silu(x = input_111_cast_fp16)[name = tensor("input_113_cast_fp16")]; - tensor var_1577_pad_type_0 = const()[name = tensor("op_1577_pad_type_0"), val = tensor("valid")]; - tensor var_1577_strides_0 = const()[name = tensor("op_1577_strides_0"), val = tensor([1, 1])]; - tensor var_1577_pad_0 = const()[name = tensor("op_1577_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1577_dilations_0 = const()[name = tensor("op_1577_dilations_0"), val = tensor([1, 1])]; - tensor var_1577_groups_0 = const()[name = tensor("op_1577_groups_0"), val = tensor(1)]; - tensor layers_3_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22771328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22968000))), name = tensor("layers_3_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_3_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_3_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22968192)))]; - tensor var_1577_cast_fp16 = conv(bias = layers_3_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_1577_dilations_0, groups = var_1577_groups_0, pad = var_1577_pad_0, pad_type = var_1577_pad_type_0, strides = var_1577_strides_0, weight = layers_3_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = tensor("op_1577_cast_fp16")]; - tensor var_1583_pad_type_0 = const()[name = tensor("op_1583_pad_type_0"), val = tensor("valid")]; - tensor var_1583_strides_0 = const()[name = tensor("op_1583_strides_0"), val = tensor([1, 1])]; - tensor var_1583_pad_0 = const()[name = tensor("op_1583_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1583_dilations_0 = const()[name = tensor("op_1583_dilations_0"), val = tensor([1, 1])]; - tensor var_1583_groups_0 = const()[name = tensor("op_1583_groups_0"), val = tensor(1)]; - tensor layers_3_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22978048))), name = tensor("layers_3_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22969280))), shape = tensor([512, 512, 1, 1])]; - tensor var_1583_cast_fp16 = conv(dilations = var_1583_dilations_0, groups = var_1583_groups_0, pad = var_1583_pad_0, pad_type = var_1583_pad_type_0, strides = var_1583_strides_0, weight = layers_3_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_113_cast_fp16)[name = tensor("op_1583_cast_fp16")]; - tensor x_23_cast_fp16 = add(x = var_1577_cast_fp16, y = var_1583_cast_fp16)[name = tensor("x_23_cast_fp16")]; - tensor inputs_37_cast_fp16 = add(x = inputs_35_cast_fp16, y = x_23_cast_fp16)[name = tensor("inputs_37_cast_fp16")]; - tensor out_37_axes_0 = const()[name = tensor("out_37_axes_0"), val = tensor([1])]; - tensor var_1594_to_fp16 = const()[name = tensor("op_1594_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_37_cast_fp16 = layer_norm(axes = out_37_axes_0, epsilon = var_1594_to_fp16, x = inputs_37_cast_fp16)[name = tensor("out_37_cast_fp16")]; - tensor input_115_gamma_0_to_fp16 = const()[name = tensor("input_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23010880)))]; - tensor input_115_beta_0_to_fp16 = const()[name = tensor("input_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23011968)))]; - tensor input_115_epsilon_0_to_fp16 = const()[name = tensor("input_115_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_115_cast_fp16 = batch_norm(beta = input_115_beta_0_to_fp16, epsilon = input_115_epsilon_0_to_fp16, gamma = input_115_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_37_cast_fp16)[name = tensor("input_115_cast_fp16")]; - tensor var_1614_pad_type_0 = const()[name = tensor("op_1614_pad_type_0"), val = tensor("valid")]; - tensor var_1614_strides_0 = const()[name = tensor("op_1614_strides_0"), val = tensor([1, 1])]; - tensor var_1614_pad_0 = const()[name = tensor("op_1614_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1614_dilations_0 = const()[name = tensor("op_1614_dilations_0"), val = tensor([1, 1])]; - tensor var_1614_groups_0 = const()[name = tensor("op_1614_groups_0"), val = tensor(1)]; - tensor layers_3_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23013056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23799552))), name = tensor("layers_3_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_3_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_3_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23799744)))]; - tensor var_1614_cast_fp16 = conv(bias = layers_3_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_1614_dilations_0, groups = var_1614_groups_0, pad = var_1614_pad_0, pad_type = var_1614_pad_type_0, strides = var_1614_strides_0, weight = layers_3_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_115_cast_fp16)[name = tensor("op_1614_cast_fp16")]; - tensor var_1620_pad_type_0 = const()[name = tensor("op_1620_pad_type_0"), val = tensor("valid")]; - tensor var_1620_strides_0 = const()[name = tensor("op_1620_strides_0"), val = tensor([1, 1])]; - tensor var_1620_pad_0 = const()[name = tensor("op_1620_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1620_dilations_0 = const()[name = tensor("op_1620_dilations_0"), val = tensor([1, 1])]; - tensor var_1620_groups_0 = const()[name = tensor("op_1620_groups_0"), val = tensor(1)]; - tensor layers_3_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23835840))), name = tensor("layers_3_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23803904))), shape = tensor([2048, 512, 1, 1])]; - tensor var_1620_cast_fp16 = conv(dilations = var_1620_dilations_0, groups = var_1620_groups_0, pad = var_1620_pad_0, pad_type = var_1620_pad_type_0, strides = var_1620_strides_0, weight = layers_3_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_115_cast_fp16)[name = tensor("op_1620_cast_fp16")]; - tensor input_117_cast_fp16 = add(x = var_1614_cast_fp16, y = var_1620_cast_fp16)[name = tensor("input_117_cast_fp16")]; - tensor input_119_cast_fp16 = silu(x = input_117_cast_fp16)[name = tensor("input_119_cast_fp16")]; - tensor var_1631_pad_type_0 = const()[name = tensor("op_1631_pad_type_0"), val = tensor("valid")]; - tensor var_1631_strides_0 = const()[name = tensor("op_1631_strides_0"), val = tensor([1, 1])]; - tensor var_1631_pad_0 = const()[name = tensor("op_1631_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1631_dilations_0 = const()[name = tensor("op_1631_dilations_0"), val = tensor([1, 1])]; - tensor var_1631_groups_0 = const()[name = tensor("op_1631_groups_0"), val = tensor(1)]; - tensor layers_3_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23966976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24753472))), name = tensor("layers_3_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_3_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_3_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24753664)))]; - tensor var_1631_cast_fp16 = conv(bias = layers_3_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_1631_dilations_0, groups = var_1631_groups_0, pad = var_1631_pad_0, pad_type = var_1631_pad_type_0, strides = var_1631_strides_0, weight = layers_3_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = tensor("op_1631_cast_fp16")]; - tensor var_1637_pad_type_0 = const()[name = tensor("op_1637_pad_type_0"), val = tensor("valid")]; - tensor var_1637_strides_0 = const()[name = tensor("op_1637_strides_0"), val = tensor([1, 1])]; - tensor var_1637_pad_0 = const()[name = tensor("op_1637_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1637_dilations_0 = const()[name = tensor("op_1637_dilations_0"), val = tensor([1, 1])]; - tensor var_1637_groups_0 = const()[name = tensor("op_1637_groups_0"), val = tensor(1)]; - tensor layers_3_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24790848))), name = tensor("layers_3_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24754752))), shape = tensor([512, 2048, 1, 1])]; - tensor var_1637_cast_fp16 = conv(dilations = var_1637_dilations_0, groups = var_1637_groups_0, pad = var_1637_pad_0, pad_type = var_1637_pad_type_0, strides = var_1637_strides_0, weight = layers_3_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_119_cast_fp16)[name = tensor("op_1637_cast_fp16")]; - tensor x_25_cast_fp16 = add(x = var_1631_cast_fp16, y = var_1637_cast_fp16)[name = tensor("x_25_cast_fp16")]; - tensor var_1639_to_fp16 = const()[name = tensor("op_1639_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1640_cast_fp16 = mul(x = x_25_cast_fp16, y = var_1639_to_fp16)[name = tensor("op_1640_cast_fp16")]; - tensor inputs_39_cast_fp16 = add(x = inputs_37_cast_fp16, y = var_1640_cast_fp16)[name = tensor("inputs_39_cast_fp16")]; - tensor out_39_axes_0 = const()[name = tensor("out_39_axes_0"), val = tensor([1])]; - tensor var_1650_to_fp16 = const()[name = tensor("op_1650_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_39_cast_fp16 = layer_norm(axes = out_39_axes_0, epsilon = var_1650_to_fp16, x = inputs_39_cast_fp16)[name = tensor("out_39_cast_fp16")]; - tensor inputs_41_gamma_0_to_fp16 = const()[name = tensor("inputs_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24921984)))]; - tensor inputs_41_beta_0_to_fp16 = const()[name = tensor("inputs_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24923072)))]; - tensor inputs_41_epsilon_0_to_fp16 = const()[name = tensor("inputs_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_41_cast_fp16 = batch_norm(beta = inputs_41_beta_0_to_fp16, epsilon = inputs_41_epsilon_0_to_fp16, gamma = inputs_41_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_39_cast_fp16)[name = tensor("inputs_41_cast_fp16")]; - tensor var_1664 = const()[name = tensor("op_1664"), val = tensor(3)]; - tensor out_41_axes_0 = const()[name = tensor("out_41_axes_0"), val = tensor([1])]; - tensor var_1695_to_fp16 = const()[name = tensor("op_1695_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_1695_to_fp16, x = inputs_41_cast_fp16)[name = tensor("out_41_cast_fp16")]; - tensor input_121_gamma_0_to_fp16 = const()[name = tensor("input_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24924160)))]; - tensor input_121_beta_0_to_fp16 = const()[name = tensor("input_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24925248)))]; - tensor input_121_epsilon_0_to_fp16 = const()[name = tensor("input_121_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_121_cast_fp16 = batch_norm(beta = input_121_beta_0_to_fp16, epsilon = input_121_epsilon_0_to_fp16, gamma = input_121_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_41_cast_fp16)[name = tensor("input_121_cast_fp16")]; - tensor var_1715_pad_type_0 = const()[name = tensor("op_1715_pad_type_0"), val = tensor("valid")]; - tensor var_1715_strides_0 = const()[name = tensor("op_1715_strides_0"), val = tensor([1, 1])]; - tensor var_1715_pad_0 = const()[name = tensor("op_1715_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1715_dilations_0 = const()[name = tensor("op_1715_dilations_0"), val = tensor([1, 1])]; - tensor var_1715_groups_0 = const()[name = tensor("op_1715_groups_0"), val = tensor(1)]; - tensor layers_4_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24926336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25712832))), name = tensor("layers_4_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_4_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_4_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25713024)))]; - tensor var_1715_cast_fp16 = conv(bias = layers_4_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_1715_dilations_0, groups = var_1715_groups_0, pad = var_1715_pad_0, pad_type = var_1715_pad_type_0, strides = var_1715_strides_0, weight = layers_4_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_121_cast_fp16)[name = tensor("op_1715_cast_fp16")]; - tensor var_1721_pad_type_0 = const()[name = tensor("op_1721_pad_type_0"), val = tensor("valid")]; - tensor var_1721_strides_0 = const()[name = tensor("op_1721_strides_0"), val = tensor([1, 1])]; - tensor var_1721_pad_0 = const()[name = tensor("op_1721_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1721_dilations_0 = const()[name = tensor("op_1721_dilations_0"), val = tensor([1, 1])]; - tensor var_1721_groups_0 = const()[name = tensor("op_1721_groups_0"), val = tensor(1)]; - tensor layers_4_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25749248))), name = tensor("layers_4_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25717184))), shape = tensor([2048, 512, 1, 1])]; - tensor var_1721_cast_fp16 = conv(dilations = var_1721_dilations_0, groups = var_1721_groups_0, pad = var_1721_pad_0, pad_type = var_1721_pad_type_0, strides = var_1721_strides_0, weight = layers_4_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_121_cast_fp16)[name = tensor("op_1721_cast_fp16")]; - tensor input_123_cast_fp16 = add(x = var_1715_cast_fp16, y = var_1721_cast_fp16)[name = tensor("input_123_cast_fp16")]; - tensor input_125_cast_fp16 = silu(x = input_123_cast_fp16)[name = tensor("input_125_cast_fp16")]; - tensor var_1732_pad_type_0 = const()[name = tensor("op_1732_pad_type_0"), val = tensor("valid")]; - tensor var_1732_strides_0 = const()[name = tensor("op_1732_strides_0"), val = tensor([1, 1])]; - tensor var_1732_pad_0 = const()[name = tensor("op_1732_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1732_dilations_0 = const()[name = tensor("op_1732_dilations_0"), val = tensor([1, 1])]; - tensor var_1732_groups_0 = const()[name = tensor("op_1732_groups_0"), val = tensor(1)]; - tensor layers_4_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25880384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26666880))), name = tensor("layers_4_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_4_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_4_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26667072)))]; - tensor var_1732_cast_fp16 = conv(bias = layers_4_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_1732_dilations_0, groups = var_1732_groups_0, pad = var_1732_pad_0, pad_type = var_1732_pad_type_0, strides = var_1732_strides_0, weight = layers_4_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_125_cast_fp16)[name = tensor("op_1732_cast_fp16")]; - tensor var_1738_pad_type_0 = const()[name = tensor("op_1738_pad_type_0"), val = tensor("valid")]; - tensor var_1738_strides_0 = const()[name = tensor("op_1738_strides_0"), val = tensor([1, 1])]; - tensor var_1738_pad_0 = const()[name = tensor("op_1738_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1738_dilations_0 = const()[name = tensor("op_1738_dilations_0"), val = tensor([1, 1])]; - tensor var_1738_groups_0 = const()[name = tensor("op_1738_groups_0"), val = tensor(1)]; - tensor layers_4_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26704896))), name = tensor("layers_4_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26668160))), shape = tensor([512, 2048, 1, 1])]; - tensor var_1738_cast_fp16 = conv(dilations = var_1738_dilations_0, groups = var_1738_groups_0, pad = var_1738_pad_0, pad_type = var_1738_pad_type_0, strides = var_1738_strides_0, weight = layers_4_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_125_cast_fp16)[name = tensor("op_1738_cast_fp16")]; - tensor x_27_cast_fp16 = add(x = var_1732_cast_fp16, y = var_1738_cast_fp16)[name = tensor("x_27_cast_fp16")]; - tensor var_1740_to_fp16 = const()[name = tensor("op_1740_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1741_cast_fp16 = mul(x = x_27_cast_fp16, y = var_1740_to_fp16)[name = tensor("op_1741_cast_fp16")]; - tensor inputs_43_cast_fp16 = add(x = inputs_41_cast_fp16, y = var_1741_cast_fp16)[name = tensor("inputs_43_cast_fp16")]; - tensor out_43_axes_0 = const()[name = tensor("out_43_axes_0"), val = tensor([1])]; - tensor var_1751_to_fp16 = const()[name = tensor("op_1751_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_43_cast_fp16 = layer_norm(axes = out_43_axes_0, epsilon = var_1751_to_fp16, x = inputs_43_cast_fp16)[name = tensor("out_43_cast_fp16")]; - tensor obj_19_gamma_0_to_fp16 = const()[name = tensor("obj_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26836032)))]; - tensor obj_19_beta_0_to_fp16 = const()[name = tensor("obj_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26837120)))]; - tensor obj_19_epsilon_0_to_fp16 = const()[name = tensor("obj_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_19_cast_fp16 = batch_norm(beta = obj_19_beta_0_to_fp16, epsilon = obj_19_epsilon_0_to_fp16, gamma = obj_19_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_43_cast_fp16)[name = tensor("obj_19_cast_fp16")]; - tensor var_1776_pad_type_0 = const()[name = tensor("op_1776_pad_type_0"), val = tensor("valid")]; - tensor var_1776_strides_0 = const()[name = tensor("op_1776_strides_0"), val = tensor([1, 1])]; - tensor var_1776_pad_0 = const()[name = tensor("op_1776_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1776_dilations_0 = const()[name = tensor("op_1776_dilations_0"), val = tensor([1, 1])]; - tensor var_1776_groups_0 = const()[name = tensor("op_1776_groups_0"), val = tensor(1)]; - tensor layers_4_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26838208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27034880))), name = tensor("layers_4_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_4_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_4_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27035072)))]; - tensor var_1776_cast_fp16 = conv(bias = layers_4_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_1776_dilations_0, groups = var_1776_groups_0, pad = var_1776_pad_0, pad_type = var_1776_pad_type_0, strides = var_1776_strides_0, weight = layers_4_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_19_cast_fp16)[name = tensor("op_1776_cast_fp16")]; - tensor var_1782_pad_type_0 = const()[name = tensor("op_1782_pad_type_0"), val = tensor("valid")]; - tensor var_1782_strides_0 = const()[name = tensor("op_1782_strides_0"), val = tensor([1, 1])]; - tensor var_1782_pad_0 = const()[name = tensor("op_1782_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1782_dilations_0 = const()[name = tensor("op_1782_dilations_0"), val = tensor([1, 1])]; - tensor var_1782_groups_0 = const()[name = tensor("op_1782_groups_0"), val = tensor(1)]; - tensor layers_4_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27044608))), name = tensor("layers_4_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27036160))), shape = tensor([512, 512, 1, 1])]; - tensor var_1782_cast_fp16 = conv(dilations = var_1782_dilations_0, groups = var_1782_groups_0, pad = var_1782_pad_0, pad_type = var_1782_pad_type_0, strides = var_1782_strides_0, weight = layers_4_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_19_cast_fp16)[name = tensor("op_1782_cast_fp16")]; - tensor query_17_cast_fp16 = add(x = var_1776_cast_fp16, y = var_1782_cast_fp16)[name = tensor("query_17_cast_fp16")]; - tensor var_1791_pad_type_0 = const()[name = tensor("op_1791_pad_type_0"), val = tensor("valid")]; - tensor var_1791_strides_0 = const()[name = tensor("op_1791_strides_0"), val = tensor([1, 1])]; - tensor var_1791_pad_0 = const()[name = tensor("op_1791_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1791_dilations_0 = const()[name = tensor("op_1791_dilations_0"), val = tensor([1, 1])]; - tensor var_1791_groups_0 = const()[name = tensor("op_1791_groups_0"), val = tensor(1)]; - tensor layers_4_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27077440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27274112))), name = tensor("layers_4_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_1791_cast_fp16 = conv(dilations = var_1791_dilations_0, groups = var_1791_groups_0, pad = var_1791_pad_0, pad_type = var_1791_pad_type_0, strides = var_1791_strides_0, weight = layers_4_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_19_cast_fp16)[name = tensor("op_1791_cast_fp16")]; - tensor var_1797_pad_type_0 = const()[name = tensor("op_1797_pad_type_0"), val = tensor("valid")]; - tensor var_1797_strides_0 = const()[name = tensor("op_1797_strides_0"), val = tensor([1, 1])]; - tensor var_1797_pad_0 = const()[name = tensor("op_1797_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1797_dilations_0 = const()[name = tensor("op_1797_dilations_0"), val = tensor([1, 1])]; - tensor var_1797_groups_0 = const()[name = tensor("op_1797_groups_0"), val = tensor(1)]; - tensor layers_4_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27283968))), name = tensor("layers_4_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27274304))), shape = tensor([512, 512, 1, 1])]; - tensor var_1797_cast_fp16 = conv(dilations = var_1797_dilations_0, groups = var_1797_groups_0, pad = var_1797_pad_0, pad_type = var_1797_pad_type_0, strides = var_1797_strides_0, weight = layers_4_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_19_cast_fp16)[name = tensor("op_1797_cast_fp16")]; - tensor key_9_cast_fp16 = add(x = var_1791_cast_fp16, y = var_1797_cast_fp16)[name = tensor("key_9_cast_fp16")]; - tensor var_1807_pad_type_0 = const()[name = tensor("op_1807_pad_type_0"), val = tensor("valid")]; - tensor var_1807_strides_0 = const()[name = tensor("op_1807_strides_0"), val = tensor([1, 1])]; - tensor var_1807_pad_0 = const()[name = tensor("op_1807_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1807_dilations_0 = const()[name = tensor("op_1807_dilations_0"), val = tensor([1, 1])]; - tensor var_1807_groups_0 = const()[name = tensor("op_1807_groups_0"), val = tensor(1)]; - tensor layers_4_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27316800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27513472))), name = tensor("layers_4_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_4_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_4_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27513664)))]; - tensor var_1807_cast_fp16 = conv(bias = layers_4_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_1807_dilations_0, groups = var_1807_groups_0, pad = var_1807_pad_0, pad_type = var_1807_pad_type_0, strides = var_1807_strides_0, weight = layers_4_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_19_cast_fp16)[name = tensor("op_1807_cast_fp16")]; - tensor var_1813_pad_type_0 = const()[name = tensor("op_1813_pad_type_0"), val = tensor("valid")]; - tensor var_1813_strides_0 = const()[name = tensor("op_1813_strides_0"), val = tensor([1, 1])]; - tensor var_1813_pad_0 = const()[name = tensor("op_1813_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1813_dilations_0 = const()[name = tensor("op_1813_dilations_0"), val = tensor([1, 1])]; - tensor var_1813_groups_0 = const()[name = tensor("op_1813_groups_0"), val = tensor(1)]; - tensor layers_4_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27522816))), name = tensor("layers_4_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27514752))), shape = tensor([512, 512, 1, 1])]; - tensor var_1813_cast_fp16 = conv(dilations = var_1813_dilations_0, groups = var_1813_groups_0, pad = var_1813_pad_0, pad_type = var_1813_pad_type_0, strides = var_1813_strides_0, weight = layers_4_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_19_cast_fp16)[name = tensor("op_1813_cast_fp16")]; - tensor value_9_cast_fp16 = add(x = var_1807_cast_fp16, y = var_1813_cast_fp16)[name = tensor("value_9_cast_fp16")]; - tensor var_1816_to_fp16 = const()[name = tensor("op_1816_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27555648)))]; - tensor query_19_cast_fp16 = add(x = query_17_cast_fp16, y = var_1816_to_fp16)[name = tensor("query_19_cast_fp16")]; - tensor var_1819_to_fp16 = const()[name = tensor("op_1819_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27556736)))]; - tensor q_with_bias_v_9_cast_fp16 = add(x = query_17_cast_fp16, y = var_1819_to_fp16)[name = tensor("q_with_bias_v_9_cast_fp16")]; - tensor var_1829_pad_type_0 = const()[name = tensor("op_1829_pad_type_0"), val = tensor("valid")]; - tensor var_1829_strides_0 = const()[name = tensor("op_1829_strides_0"), val = tensor([1, 1])]; - tensor var_1829_pad_0 = const()[name = tensor("op_1829_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1829_dilations_0 = const()[name = tensor("op_1829_dilations_0"), val = tensor([1, 1])]; - tensor var_1829_groups_0 = const()[name = tensor("op_1829_groups_0"), val = tensor(1)]; - tensor layers_4_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27557824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27754496))), name = tensor("layers_4_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_1829_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_1829_dilations_0, groups = var_1829_groups_0, pad = var_1829_pad_0, pad_type = var_1829_pad_type_0, strides = var_1829_strides_0, weight = layers_4_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_1829_cast_fp16")]; - tensor var_1835_pad_type_0 = const()[name = tensor("op_1835_pad_type_0"), val = tensor("valid")]; - tensor var_1835_strides_0 = const()[name = tensor("op_1835_strides_0"), val = tensor([1, 1])]; - tensor var_1835_pad_0 = const()[name = tensor("op_1835_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1835_dilations_0 = const()[name = tensor("op_1835_dilations_0"), val = tensor([1, 1])]; - tensor var_1835_groups_0 = const()[name = tensor("op_1835_groups_0"), val = tensor(1)]; - tensor layers_4_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27776128))), name = tensor("layers_4_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27754688))), shape = tensor([512, 512, 1, 1])]; - tensor var_1835_cast_fp16 = conv(dilations = var_1835_dilations_0, groups = var_1835_groups_0, pad = var_1835_pad_0, pad_type = var_1835_pad_type_0, strides = var_1835_strides_0, weight = layers_4_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_1835_cast_fp16")]; - tensor p_9_cast_fp16 = add(x = var_1829_cast_fp16, y = var_1835_cast_fp16)[name = tensor("p_9_cast_fp16")]; - tensor var_1839 = const()[name = tensor("op_1839"), val = tensor([1, 8, 64, 188])]; - tensor var_1840_cast_fp16 = reshape(shape = var_1839, x = q_with_bias_v_9_cast_fp16)[name = tensor("op_1840_cast_fp16")]; - tensor var_1841 = const()[name = tensor("op_1841"), val = tensor([1, 8, 64, -1])]; - tensor var_1842_cast_fp16 = reshape(shape = var_1841, x = p_9_cast_fp16)[name = tensor("op_1842_cast_fp16")]; - tensor matrix_bd_33_transpose_x_0 = const()[name = tensor("matrix_bd_33_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_33_transpose_y_0 = const()[name = tensor("matrix_bd_33_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_33_cast_fp16 = matmul(transpose_x = matrix_bd_33_transpose_x_0, transpose_y = matrix_bd_33_transpose_y_0, x = var_1840_cast_fp16, y = var_1842_cast_fp16)[name = tensor("matrix_bd_33_cast_fp16")]; - tensor matrix_bd_35_pad_0 = const()[name = tensor("matrix_bd_35_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_35_mode_0 = const()[name = tensor("matrix_bd_35_mode_0"), val = tensor("constant")]; - tensor const_54_to_fp16 = const()[name = tensor("const_54_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_35_cast_fp16 = pad(constant_val = const_54_to_fp16, mode = matrix_bd_35_mode_0, pad = matrix_bd_35_pad_0, x = matrix_bd_33_cast_fp16)[name = tensor("matrix_bd_35_cast_fp16")]; - tensor var_1851 = const()[name = tensor("op_1851"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1851, x = matrix_bd_35_cast_fp16)[name = tensor("matrix_bd_37_cast_fp16")]; - tensor var_1855_begin_0 = const()[name = tensor("op_1855_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1855_end_0 = const()[name = tensor("op_1855_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1855_end_mask_0 = const()[name = tensor("op_1855_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1855_cast_fp16 = slice_by_index(begin = var_1855_begin_0, end = var_1855_end_0, end_mask = var_1855_end_mask_0, x = matrix_bd_37_cast_fp16)[name = tensor("op_1855_cast_fp16")]; - tensor var_1856 = const()[name = tensor("op_1856"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_39_cast_fp16 = reshape(shape = var_1856, x = var_1855_cast_fp16)[name = tensor("matrix_bd_39_cast_fp16")]; - tensor var_1861_begin_0 = const()[name = tensor("op_1861_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1861_end_0 = const()[name = tensor("op_1861_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_1861_end_mask_0 = const()[name = tensor("op_1861_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_1861_cast_fp16 = slice_by_index(begin = var_1861_begin_0, end = var_1861_end_0, end_mask = var_1861_end_mask_0, x = matrix_bd_39_cast_fp16)[name = tensor("op_1861_cast_fp16")]; - tensor var_1862_to_fp16 = const()[name = tensor("op_1862_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_9_cast_fp16 = mul(x = var_1861_cast_fp16, y = var_1862_to_fp16)[name = tensor("qk_mask_9_cast_fp16")]; - tensor var_1866 = const()[name = tensor("op_1866"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_9_cast_fp16 = reshape(shape = var_1866, x = query_19_cast_fp16)[name = tensor("mh_q_9_cast_fp16")]; - tensor var_1868_to_fp16 = const()[name = tensor("op_1868_to_fp16"), val = tensor(0x1p-3)]; - tensor var_1869_cast_fp16 = mul(x = mh_q_9_cast_fp16, y = var_1868_to_fp16)[name = tensor("op_1869_cast_fp16")]; - tensor var_1872 = const()[name = tensor("op_1872"), val = tensor([1, 8, 64, 188])]; - tensor var_1873_cast_fp16 = reshape(shape = var_1872, x = key_9_cast_fp16)[name = tensor("op_1873_cast_fp16")]; - tensor mh_w_17_transpose_x_0 = const()[name = tensor("mh_w_17_transpose_x_0"), val = tensor(true)]; - tensor mh_w_17_transpose_y_0 = const()[name = tensor("mh_w_17_transpose_y_0"), val = tensor(false)]; - tensor mh_w_17_cast_fp16 = matmul(transpose_x = mh_w_17_transpose_x_0, transpose_y = mh_w_17_transpose_y_0, x = var_1869_cast_fp16, y = var_1873_cast_fp16)[name = tensor("mh_w_17_cast_fp16")]; - tensor mh_w_19_cast_fp16 = add(x = mh_w_17_cast_fp16, y = qk_mask_9_cast_fp16)[name = tensor("mh_w_19_cast_fp16")]; - tensor var_1877_cast_fp16 = softmax(axis = var_1664, x = mh_w_19_cast_fp16)[name = tensor("op_1877_cast_fp16")]; - tensor var_1878 = const()[name = tensor("op_1878"), val = tensor([1, 8, 64, 188])]; - tensor var_1879_cast_fp16 = reshape(shape = var_1878, x = value_9_cast_fp16)[name = tensor("op_1879_cast_fp16")]; - tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; - tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; - tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_1879_cast_fp16, y = var_1877_cast_fp16)[name = tensor("attn_9_cast_fp16")]; - tensor var_1882 = const()[name = tensor("op_1882"), val = tensor([1, 512, 1, 188])]; - tensor input_127_cast_fp16 = reshape(shape = var_1882, x = attn_9_cast_fp16)[name = tensor("input_127_cast_fp16")]; - tensor var_1892_pad_type_0 = const()[name = tensor("op_1892_pad_type_0"), val = tensor("valid")]; - tensor var_1892_strides_0 = const()[name = tensor("op_1892_strides_0"), val = tensor([1, 1])]; - tensor var_1892_pad_0 = const()[name = tensor("op_1892_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1892_dilations_0 = const()[name = tensor("op_1892_dilations_0"), val = tensor([1, 1])]; - tensor var_1892_groups_0 = const()[name = tensor("op_1892_groups_0"), val = tensor(1)]; - tensor layers_4_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27808960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28005632))), name = tensor("layers_4_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_4_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_4_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28005824)))]; - tensor var_1892_cast_fp16 = conv(bias = layers_4_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_1892_dilations_0, groups = var_1892_groups_0, pad = var_1892_pad_0, pad_type = var_1892_pad_type_0, strides = var_1892_strides_0, weight = layers_4_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_127_cast_fp16)[name = tensor("op_1892_cast_fp16")]; - tensor var_1898_pad_type_0 = const()[name = tensor("op_1898_pad_type_0"), val = tensor("valid")]; - tensor var_1898_strides_0 = const()[name = tensor("op_1898_strides_0"), val = tensor([1, 1])]; - tensor var_1898_pad_0 = const()[name = tensor("op_1898_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1898_dilations_0 = const()[name = tensor("op_1898_dilations_0"), val = tensor([1, 1])]; - tensor var_1898_groups_0 = const()[name = tensor("op_1898_groups_0"), val = tensor(1)]; - tensor layers_4_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28014848))), name = tensor("layers_4_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28006912))), shape = tensor([512, 512, 1, 1])]; - tensor var_1898_cast_fp16 = conv(dilations = var_1898_dilations_0, groups = var_1898_groups_0, pad = var_1898_pad_0, pad_type = var_1898_pad_type_0, strides = var_1898_strides_0, weight = layers_4_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_127_cast_fp16)[name = tensor("op_1898_cast_fp16")]; - tensor obj_21_cast_fp16 = add(x = var_1892_cast_fp16, y = var_1898_cast_fp16)[name = tensor("obj_21_cast_fp16")]; - tensor inputs_45_cast_fp16 = add(x = inputs_43_cast_fp16, y = obj_21_cast_fp16)[name = tensor("inputs_45_cast_fp16")]; - tensor out_45_axes_0 = const()[name = tensor("out_45_axes_0"), val = tensor([1])]; - tensor var_1909_to_fp16 = const()[name = tensor("op_1909_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_45_cast_fp16 = layer_norm(axes = out_45_axes_0, epsilon = var_1909_to_fp16, x = inputs_45_cast_fp16)[name = tensor("out_45_cast_fp16")]; - tensor input_129_gamma_0_to_fp16 = const()[name = tensor("input_129_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28047680)))]; - tensor input_129_beta_0_to_fp16 = const()[name = tensor("input_129_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28048768)))]; - tensor input_129_epsilon_0_to_fp16 = const()[name = tensor("input_129_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_129_cast_fp16 = batch_norm(beta = input_129_beta_0_to_fp16, epsilon = input_129_epsilon_0_to_fp16, gamma = input_129_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_45_cast_fp16)[name = tensor("input_129_cast_fp16")]; - tensor var_1931_pad_type_0 = const()[name = tensor("op_1931_pad_type_0"), val = tensor("valid")]; - tensor var_1931_strides_0 = const()[name = tensor("op_1931_strides_0"), val = tensor([1, 1])]; - tensor var_1931_pad_0 = const()[name = tensor("op_1931_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1931_dilations_0 = const()[name = tensor("op_1931_dilations_0"), val = tensor([1, 1])]; - tensor var_1931_groups_0 = const()[name = tensor("op_1931_groups_0"), val = tensor(1)]; - tensor layers_4_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28049856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28443136))), name = tensor("layers_4_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_4_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_4_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28443328)))]; - tensor var_1931_cast_fp16 = conv(bias = layers_4_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_1931_dilations_0, groups = var_1931_groups_0, pad = var_1931_pad_0, pad_type = var_1931_pad_type_0, strides = var_1931_strides_0, weight = layers_4_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_129_cast_fp16)[name = tensor("op_1931_cast_fp16")]; - tensor var_1937_pad_type_0 = const()[name = tensor("op_1937_pad_type_0"), val = tensor("valid")]; - tensor var_1937_strides_0 = const()[name = tensor("op_1937_strides_0"), val = tensor([1, 1])]; - tensor var_1937_pad_0 = const()[name = tensor("op_1937_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1937_dilations_0 = const()[name = tensor("op_1937_dilations_0"), val = tensor([1, 1])]; - tensor var_1937_groups_0 = const()[name = tensor("op_1937_groups_0"), val = tensor(1)]; - tensor layers_4_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28463360))), name = tensor("layers_4_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28445440))), shape = tensor([1024, 512, 1, 1])]; - tensor var_1937_cast_fp16 = conv(dilations = var_1937_dilations_0, groups = var_1937_groups_0, pad = var_1937_pad_0, pad_type = var_1937_pad_type_0, strides = var_1937_strides_0, weight = layers_4_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_129_cast_fp16)[name = tensor("op_1937_cast_fp16")]; - tensor input_131_cast_fp16 = add(x = var_1931_cast_fp16, y = var_1937_cast_fp16)[name = tensor("input_131_cast_fp16")]; - tensor input_133_split_num_splits_0 = const()[name = tensor("input_133_split_num_splits_0"), val = tensor(2)]; - tensor input_133_split_axis_0 = const()[name = tensor("input_133_split_axis_0"), val = tensor(1)]; - tensor input_133_split_cast_fp16_0, tensor input_133_split_cast_fp16_1 = split(axis = input_133_split_axis_0, num_splits = input_133_split_num_splits_0, x = input_131_cast_fp16)[name = tensor("input_133_split_cast_fp16")]; - tensor input_133_split_1_sigmoid_cast_fp16 = sigmoid(x = input_133_split_cast_fp16_1)[name = tensor("input_133_split_1_sigmoid_cast_fp16")]; - tensor input_133_cast_fp16 = mul(x = input_133_split_cast_fp16_0, y = input_133_split_1_sigmoid_cast_fp16)[name = tensor("input_133_cast_fp16")]; - tensor input_135_pad_type_0 = const()[name = tensor("input_135_pad_type_0"), val = tensor("custom")]; - tensor input_135_pad_0 = const()[name = tensor("input_135_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_135_groups_0 = const()[name = tensor("input_135_groups_0"), val = tensor(512)]; - tensor input_135_strides_0 = const()[name = tensor("input_135_strides_0"), val = tensor([1, 1])]; - tensor input_135_dilations_0 = const()[name = tensor("input_135_dilations_0"), val = tensor([1, 1])]; - tensor const_199_to_fp16 = const()[name = tensor("const_199_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28528960)))]; - tensor const_200_to_fp16 = const()[name = tensor("const_200_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28538240)))]; - tensor input_137_cast_fp16 = conv(bias = const_200_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = const_199_to_fp16, x = input_133_cast_fp16)[name = tensor("input_137_cast_fp16")]; - tensor input_139_cast_fp16 = silu(x = input_137_cast_fp16)[name = tensor("input_139_cast_fp16")]; - tensor var_1961_pad_type_0 = const()[name = tensor("op_1961_pad_type_0"), val = tensor("valid")]; - tensor var_1961_strides_0 = const()[name = tensor("op_1961_strides_0"), val = tensor([1, 1])]; - tensor var_1961_pad_0 = const()[name = tensor("op_1961_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1961_dilations_0 = const()[name = tensor("op_1961_dilations_0"), val = tensor([1, 1])]; - tensor var_1961_groups_0 = const()[name = tensor("op_1961_groups_0"), val = tensor(1)]; - tensor layers_4_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28539328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28736000))), name = tensor("layers_4_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_4_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_4_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28736192)))]; - tensor var_1961_cast_fp16 = conv(bias = layers_4_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_1961_dilations_0, groups = var_1961_groups_0, pad = var_1961_pad_0, pad_type = var_1961_pad_type_0, strides = var_1961_strides_0, weight = layers_4_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_139_cast_fp16)[name = tensor("op_1961_cast_fp16")]; - tensor var_1967_pad_type_0 = const()[name = tensor("op_1967_pad_type_0"), val = tensor("valid")]; - tensor var_1967_strides_0 = const()[name = tensor("op_1967_strides_0"), val = tensor([1, 1])]; - tensor var_1967_pad_0 = const()[name = tensor("op_1967_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1967_dilations_0 = const()[name = tensor("op_1967_dilations_0"), val = tensor([1, 1])]; - tensor var_1967_groups_0 = const()[name = tensor("op_1967_groups_0"), val = tensor(1)]; - tensor layers_4_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28745856))), name = tensor("layers_4_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28737280))), shape = tensor([512, 512, 1, 1])]; - tensor var_1967_cast_fp16 = conv(dilations = var_1967_dilations_0, groups = var_1967_groups_0, pad = var_1967_pad_0, pad_type = var_1967_pad_type_0, strides = var_1967_strides_0, weight = layers_4_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_139_cast_fp16)[name = tensor("op_1967_cast_fp16")]; - tensor x_29_cast_fp16 = add(x = var_1961_cast_fp16, y = var_1967_cast_fp16)[name = tensor("x_29_cast_fp16")]; - tensor inputs_47_cast_fp16 = add(x = inputs_45_cast_fp16, y = x_29_cast_fp16)[name = tensor("inputs_47_cast_fp16")]; - tensor out_47_axes_0 = const()[name = tensor("out_47_axes_0"), val = tensor([1])]; - tensor var_1978_to_fp16 = const()[name = tensor("op_1978_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_47_cast_fp16 = layer_norm(axes = out_47_axes_0, epsilon = var_1978_to_fp16, x = inputs_47_cast_fp16)[name = tensor("out_47_cast_fp16")]; - tensor input_141_gamma_0_to_fp16 = const()[name = tensor("input_141_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28778688)))]; - tensor input_141_beta_0_to_fp16 = const()[name = tensor("input_141_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28779776)))]; - tensor input_141_epsilon_0_to_fp16 = const()[name = tensor("input_141_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_141_cast_fp16 = batch_norm(beta = input_141_beta_0_to_fp16, epsilon = input_141_epsilon_0_to_fp16, gamma = input_141_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_47_cast_fp16)[name = tensor("input_141_cast_fp16")]; - tensor var_1998_pad_type_0 = const()[name = tensor("op_1998_pad_type_0"), val = tensor("valid")]; - tensor var_1998_strides_0 = const()[name = tensor("op_1998_strides_0"), val = tensor([1, 1])]; - tensor var_1998_pad_0 = const()[name = tensor("op_1998_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1998_dilations_0 = const()[name = tensor("op_1998_dilations_0"), val = tensor([1, 1])]; - tensor var_1998_groups_0 = const()[name = tensor("op_1998_groups_0"), val = tensor(1)]; - tensor layers_4_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28780864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29567360))), name = tensor("layers_4_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_4_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_4_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29567552)))]; - tensor var_1998_cast_fp16 = conv(bias = layers_4_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_1998_dilations_0, groups = var_1998_groups_0, pad = var_1998_pad_0, pad_type = var_1998_pad_type_0, strides = var_1998_strides_0, weight = layers_4_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_141_cast_fp16)[name = tensor("op_1998_cast_fp16")]; - tensor var_2004_pad_type_0 = const()[name = tensor("op_2004_pad_type_0"), val = tensor("valid")]; - tensor var_2004_strides_0 = const()[name = tensor("op_2004_strides_0"), val = tensor([1, 1])]; - tensor var_2004_pad_0 = const()[name = tensor("op_2004_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2004_dilations_0 = const()[name = tensor("op_2004_dilations_0"), val = tensor([1, 1])]; - tensor var_2004_groups_0 = const()[name = tensor("op_2004_groups_0"), val = tensor(1)]; - tensor layers_4_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29603136))), name = tensor("layers_4_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29571712))), shape = tensor([2048, 512, 1, 1])]; - tensor var_2004_cast_fp16 = conv(dilations = var_2004_dilations_0, groups = var_2004_groups_0, pad = var_2004_pad_0, pad_type = var_2004_pad_type_0, strides = var_2004_strides_0, weight = layers_4_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_141_cast_fp16)[name = tensor("op_2004_cast_fp16")]; - tensor input_143_cast_fp16 = add(x = var_1998_cast_fp16, y = var_2004_cast_fp16)[name = tensor("input_143_cast_fp16")]; - tensor input_145_cast_fp16 = silu(x = input_143_cast_fp16)[name = tensor("input_145_cast_fp16")]; - tensor var_2015_pad_type_0 = const()[name = tensor("op_2015_pad_type_0"), val = tensor("valid")]; - tensor var_2015_strides_0 = const()[name = tensor("op_2015_strides_0"), val = tensor([1, 1])]; - tensor var_2015_pad_0 = const()[name = tensor("op_2015_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2015_dilations_0 = const()[name = tensor("op_2015_dilations_0"), val = tensor([1, 1])]; - tensor var_2015_groups_0 = const()[name = tensor("op_2015_groups_0"), val = tensor(1)]; - tensor layers_4_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29734272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30520768))), name = tensor("layers_4_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_4_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_4_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30520960)))]; - tensor var_2015_cast_fp16 = conv(bias = layers_4_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_2015_dilations_0, groups = var_2015_groups_0, pad = var_2015_pad_0, pad_type = var_2015_pad_type_0, strides = var_2015_strides_0, weight = layers_4_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_145_cast_fp16)[name = tensor("op_2015_cast_fp16")]; - tensor var_2021_pad_type_0 = const()[name = tensor("op_2021_pad_type_0"), val = tensor("valid")]; - tensor var_2021_strides_0 = const()[name = tensor("op_2021_strides_0"), val = tensor([1, 1])]; - tensor var_2021_pad_0 = const()[name = tensor("op_2021_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2021_dilations_0 = const()[name = tensor("op_2021_dilations_0"), val = tensor([1, 1])]; - tensor var_2021_groups_0 = const()[name = tensor("op_2021_groups_0"), val = tensor(1)]; - tensor layers_4_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30556864))), name = tensor("layers_4_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30522048))), shape = tensor([512, 2048, 1, 1])]; - tensor var_2021_cast_fp16 = conv(dilations = var_2021_dilations_0, groups = var_2021_groups_0, pad = var_2021_pad_0, pad_type = var_2021_pad_type_0, strides = var_2021_strides_0, weight = layers_4_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_145_cast_fp16)[name = tensor("op_2021_cast_fp16")]; - tensor x_31_cast_fp16 = add(x = var_2015_cast_fp16, y = var_2021_cast_fp16)[name = tensor("x_31_cast_fp16")]; - tensor var_2023_to_fp16 = const()[name = tensor("op_2023_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2024_cast_fp16 = mul(x = x_31_cast_fp16, y = var_2023_to_fp16)[name = tensor("op_2024_cast_fp16")]; - tensor inputs_49_cast_fp16 = add(x = inputs_47_cast_fp16, y = var_2024_cast_fp16)[name = tensor("inputs_49_cast_fp16")]; - tensor out_49_axes_0 = const()[name = tensor("out_49_axes_0"), val = tensor([1])]; - tensor var_2034_to_fp16 = const()[name = tensor("op_2034_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_2034_to_fp16, x = inputs_49_cast_fp16)[name = tensor("out_49_cast_fp16")]; - tensor inputs_51_gamma_0_to_fp16 = const()[name = tensor("inputs_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30688000)))]; - tensor inputs_51_beta_0_to_fp16 = const()[name = tensor("inputs_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30689088)))]; - tensor inputs_51_epsilon_0_to_fp16 = const()[name = tensor("inputs_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_51_cast_fp16 = batch_norm(beta = inputs_51_beta_0_to_fp16, epsilon = inputs_51_epsilon_0_to_fp16, gamma = inputs_51_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_49_cast_fp16)[name = tensor("inputs_51_cast_fp16")]; - tensor var_2048 = const()[name = tensor("op_2048"), val = tensor(3)]; - tensor out_51_axes_0 = const()[name = tensor("out_51_axes_0"), val = tensor([1])]; - tensor var_2079_to_fp16 = const()[name = tensor("op_2079_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_51_cast_fp16 = layer_norm(axes = out_51_axes_0, epsilon = var_2079_to_fp16, x = inputs_51_cast_fp16)[name = tensor("out_51_cast_fp16")]; - tensor input_147_gamma_0_to_fp16 = const()[name = tensor("input_147_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30690176)))]; - tensor input_147_beta_0_to_fp16 = const()[name = tensor("input_147_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30691264)))]; - tensor input_147_epsilon_0_to_fp16 = const()[name = tensor("input_147_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_147_cast_fp16 = batch_norm(beta = input_147_beta_0_to_fp16, epsilon = input_147_epsilon_0_to_fp16, gamma = input_147_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_51_cast_fp16)[name = tensor("input_147_cast_fp16")]; - tensor var_2099_pad_type_0 = const()[name = tensor("op_2099_pad_type_0"), val = tensor("valid")]; - tensor var_2099_strides_0 = const()[name = tensor("op_2099_strides_0"), val = tensor([1, 1])]; - tensor var_2099_pad_0 = const()[name = tensor("op_2099_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2099_dilations_0 = const()[name = tensor("op_2099_dilations_0"), val = tensor([1, 1])]; - tensor var_2099_groups_0 = const()[name = tensor("op_2099_groups_0"), val = tensor(1)]; - tensor layers_5_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30692352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31478848))), name = tensor("layers_5_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_5_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_5_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31479040)))]; - tensor var_2099_cast_fp16 = conv(bias = layers_5_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_2099_dilations_0, groups = var_2099_groups_0, pad = var_2099_pad_0, pad_type = var_2099_pad_type_0, strides = var_2099_strides_0, weight = layers_5_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = tensor("op_2099_cast_fp16")]; - tensor var_2105_pad_type_0 = const()[name = tensor("op_2105_pad_type_0"), val = tensor("valid")]; - tensor var_2105_strides_0 = const()[name = tensor("op_2105_strides_0"), val = tensor([1, 1])]; - tensor var_2105_pad_0 = const()[name = tensor("op_2105_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2105_dilations_0 = const()[name = tensor("op_2105_dilations_0"), val = tensor([1, 1])]; - tensor var_2105_groups_0 = const()[name = tensor("op_2105_groups_0"), val = tensor(1)]; - tensor layers_5_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31515392))), name = tensor("layers_5_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31483200))), shape = tensor([2048, 512, 1, 1])]; - tensor var_2105_cast_fp16 = conv(dilations = var_2105_dilations_0, groups = var_2105_groups_0, pad = var_2105_pad_0, pad_type = var_2105_pad_type_0, strides = var_2105_strides_0, weight = layers_5_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_147_cast_fp16)[name = tensor("op_2105_cast_fp16")]; - tensor input_149_cast_fp16 = add(x = var_2099_cast_fp16, y = var_2105_cast_fp16)[name = tensor("input_149_cast_fp16")]; - tensor input_151_cast_fp16 = silu(x = input_149_cast_fp16)[name = tensor("input_151_cast_fp16")]; - tensor var_2116_pad_type_0 = const()[name = tensor("op_2116_pad_type_0"), val = tensor("valid")]; - tensor var_2116_strides_0 = const()[name = tensor("op_2116_strides_0"), val = tensor([1, 1])]; - tensor var_2116_pad_0 = const()[name = tensor("op_2116_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2116_dilations_0 = const()[name = tensor("op_2116_dilations_0"), val = tensor([1, 1])]; - tensor var_2116_groups_0 = const()[name = tensor("op_2116_groups_0"), val = tensor(1)]; - tensor layers_5_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31646528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32433024))), name = tensor("layers_5_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_5_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_5_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32433216)))]; - tensor var_2116_cast_fp16 = conv(bias = layers_5_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_2116_dilations_0, groups = var_2116_groups_0, pad = var_2116_pad_0, pad_type = var_2116_pad_type_0, strides = var_2116_strides_0, weight = layers_5_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_151_cast_fp16)[name = tensor("op_2116_cast_fp16")]; - tensor var_2122_pad_type_0 = const()[name = tensor("op_2122_pad_type_0"), val = tensor("valid")]; - tensor var_2122_strides_0 = const()[name = tensor("op_2122_strides_0"), val = tensor([1, 1])]; - tensor var_2122_pad_0 = const()[name = tensor("op_2122_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2122_dilations_0 = const()[name = tensor("op_2122_dilations_0"), val = tensor([1, 1])]; - tensor var_2122_groups_0 = const()[name = tensor("op_2122_groups_0"), val = tensor(1)]; - tensor layers_5_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32470784))), name = tensor("layers_5_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32434304))), shape = tensor([512, 2048, 1, 1])]; - tensor var_2122_cast_fp16 = conv(dilations = var_2122_dilations_0, groups = var_2122_groups_0, pad = var_2122_pad_0, pad_type = var_2122_pad_type_0, strides = var_2122_strides_0, weight = layers_5_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_151_cast_fp16)[name = tensor("op_2122_cast_fp16")]; - tensor x_33_cast_fp16 = add(x = var_2116_cast_fp16, y = var_2122_cast_fp16)[name = tensor("x_33_cast_fp16")]; - tensor var_2124_to_fp16 = const()[name = tensor("op_2124_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2125_cast_fp16 = mul(x = x_33_cast_fp16, y = var_2124_to_fp16)[name = tensor("op_2125_cast_fp16")]; - tensor inputs_53_cast_fp16 = add(x = inputs_51_cast_fp16, y = var_2125_cast_fp16)[name = tensor("inputs_53_cast_fp16")]; - tensor out_53_axes_0 = const()[name = tensor("out_53_axes_0"), val = tensor([1])]; - tensor var_2135_to_fp16 = const()[name = tensor("op_2135_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_53_cast_fp16 = layer_norm(axes = out_53_axes_0, epsilon = var_2135_to_fp16, x = inputs_53_cast_fp16)[name = tensor("out_53_cast_fp16")]; - tensor obj_23_gamma_0_to_fp16 = const()[name = tensor("obj_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32601920)))]; - tensor obj_23_beta_0_to_fp16 = const()[name = tensor("obj_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32603008)))]; - tensor obj_23_epsilon_0_to_fp16 = const()[name = tensor("obj_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_23_cast_fp16 = batch_norm(beta = obj_23_beta_0_to_fp16, epsilon = obj_23_epsilon_0_to_fp16, gamma = obj_23_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_53_cast_fp16)[name = tensor("obj_23_cast_fp16")]; - tensor var_2160_pad_type_0 = const()[name = tensor("op_2160_pad_type_0"), val = tensor("valid")]; - tensor var_2160_strides_0 = const()[name = tensor("op_2160_strides_0"), val = tensor([1, 1])]; - tensor var_2160_pad_0 = const()[name = tensor("op_2160_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2160_dilations_0 = const()[name = tensor("op_2160_dilations_0"), val = tensor([1, 1])]; - tensor var_2160_groups_0 = const()[name = tensor("op_2160_groups_0"), val = tensor(1)]; - tensor layers_5_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32604096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32800768))), name = tensor("layers_5_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_5_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_5_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32800960)))]; - tensor var_2160_cast_fp16 = conv(bias = layers_5_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_2160_dilations_0, groups = var_2160_groups_0, pad = var_2160_pad_0, pad_type = var_2160_pad_type_0, strides = var_2160_strides_0, weight = layers_5_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_23_cast_fp16)[name = tensor("op_2160_cast_fp16")]; - tensor var_2166_pad_type_0 = const()[name = tensor("op_2166_pad_type_0"), val = tensor("valid")]; - tensor var_2166_strides_0 = const()[name = tensor("op_2166_strides_0"), val = tensor([1, 1])]; - tensor var_2166_pad_0 = const()[name = tensor("op_2166_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2166_dilations_0 = const()[name = tensor("op_2166_dilations_0"), val = tensor([1, 1])]; - tensor var_2166_groups_0 = const()[name = tensor("op_2166_groups_0"), val = tensor(1)]; - tensor layers_5_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32810752))), name = tensor("layers_5_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32802048))), shape = tensor([512, 512, 1, 1])]; - tensor var_2166_cast_fp16 = conv(dilations = var_2166_dilations_0, groups = var_2166_groups_0, pad = var_2166_pad_0, pad_type = var_2166_pad_type_0, strides = var_2166_strides_0, weight = layers_5_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_23_cast_fp16)[name = tensor("op_2166_cast_fp16")]; - tensor query_21_cast_fp16 = add(x = var_2160_cast_fp16, y = var_2166_cast_fp16)[name = tensor("query_21_cast_fp16")]; - tensor var_2175_pad_type_0 = const()[name = tensor("op_2175_pad_type_0"), val = tensor("valid")]; - tensor var_2175_strides_0 = const()[name = tensor("op_2175_strides_0"), val = tensor([1, 1])]; - tensor var_2175_pad_0 = const()[name = tensor("op_2175_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2175_dilations_0 = const()[name = tensor("op_2175_dilations_0"), val = tensor([1, 1])]; - tensor var_2175_groups_0 = const()[name = tensor("op_2175_groups_0"), val = tensor(1)]; - tensor layers_5_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32843584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33040256))), name = tensor("layers_5_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_2175_cast_fp16 = conv(dilations = var_2175_dilations_0, groups = var_2175_groups_0, pad = var_2175_pad_0, pad_type = var_2175_pad_type_0, strides = var_2175_strides_0, weight = layers_5_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_23_cast_fp16)[name = tensor("op_2175_cast_fp16")]; - tensor var_2181_pad_type_0 = const()[name = tensor("op_2181_pad_type_0"), val = tensor("valid")]; - tensor var_2181_strides_0 = const()[name = tensor("op_2181_strides_0"), val = tensor([1, 1])]; - tensor var_2181_pad_0 = const()[name = tensor("op_2181_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2181_dilations_0 = const()[name = tensor("op_2181_dilations_0"), val = tensor([1, 1])]; - tensor var_2181_groups_0 = const()[name = tensor("op_2181_groups_0"), val = tensor(1)]; - tensor layers_5_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33051712))), name = tensor("layers_5_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33040448))), shape = tensor([512, 512, 1, 1])]; - tensor var_2181_cast_fp16 = conv(dilations = var_2181_dilations_0, groups = var_2181_groups_0, pad = var_2181_pad_0, pad_type = var_2181_pad_type_0, strides = var_2181_strides_0, weight = layers_5_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_23_cast_fp16)[name = tensor("op_2181_cast_fp16")]; - tensor key_11_cast_fp16 = add(x = var_2175_cast_fp16, y = var_2181_cast_fp16)[name = tensor("key_11_cast_fp16")]; - tensor var_2191_pad_type_0 = const()[name = tensor("op_2191_pad_type_0"), val = tensor("valid")]; - tensor var_2191_strides_0 = const()[name = tensor("op_2191_strides_0"), val = tensor([1, 1])]; - tensor var_2191_pad_0 = const()[name = tensor("op_2191_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2191_dilations_0 = const()[name = tensor("op_2191_dilations_0"), val = tensor([1, 1])]; - tensor var_2191_groups_0 = const()[name = tensor("op_2191_groups_0"), val = tensor(1)]; - tensor layers_5_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33084544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33281216))), name = tensor("layers_5_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_5_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_5_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33281408)))]; - tensor var_2191_cast_fp16 = conv(bias = layers_5_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_2191_dilations_0, groups = var_2191_groups_0, pad = var_2191_pad_0, pad_type = var_2191_pad_type_0, strides = var_2191_strides_0, weight = layers_5_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_23_cast_fp16)[name = tensor("op_2191_cast_fp16")]; - tensor var_2197_pad_type_0 = const()[name = tensor("op_2197_pad_type_0"), val = tensor("valid")]; - tensor var_2197_strides_0 = const()[name = tensor("op_2197_strides_0"), val = tensor([1, 1])]; - tensor var_2197_pad_0 = const()[name = tensor("op_2197_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2197_dilations_0 = const()[name = tensor("op_2197_dilations_0"), val = tensor([1, 1])]; - tensor var_2197_groups_0 = const()[name = tensor("op_2197_groups_0"), val = tensor(1)]; - tensor layers_5_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33290816))), name = tensor("layers_5_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33282496))), shape = tensor([512, 512, 1, 1])]; - tensor var_2197_cast_fp16 = conv(dilations = var_2197_dilations_0, groups = var_2197_groups_0, pad = var_2197_pad_0, pad_type = var_2197_pad_type_0, strides = var_2197_strides_0, weight = layers_5_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_23_cast_fp16)[name = tensor("op_2197_cast_fp16")]; - tensor value_11_cast_fp16 = add(x = var_2191_cast_fp16, y = var_2197_cast_fp16)[name = tensor("value_11_cast_fp16")]; - tensor var_2200_to_fp16 = const()[name = tensor("op_2200_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33323648)))]; - tensor query_23_cast_fp16 = add(x = query_21_cast_fp16, y = var_2200_to_fp16)[name = tensor("query_23_cast_fp16")]; - tensor var_2203_to_fp16 = const()[name = tensor("op_2203_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33324736)))]; - tensor q_with_bias_v_11_cast_fp16 = add(x = query_21_cast_fp16, y = var_2203_to_fp16)[name = tensor("q_with_bias_v_11_cast_fp16")]; - tensor var_2213_pad_type_0 = const()[name = tensor("op_2213_pad_type_0"), val = tensor("valid")]; - tensor var_2213_strides_0 = const()[name = tensor("op_2213_strides_0"), val = tensor([1, 1])]; - tensor var_2213_pad_0 = const()[name = tensor("op_2213_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2213_dilations_0 = const()[name = tensor("op_2213_dilations_0"), val = tensor([1, 1])]; - tensor var_2213_groups_0 = const()[name = tensor("op_2213_groups_0"), val = tensor(1)]; - tensor layers_5_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33325824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33522496))), name = tensor("layers_5_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_2213_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_2213_dilations_0, groups = var_2213_groups_0, pad = var_2213_pad_0, pad_type = var_2213_pad_type_0, strides = var_2213_strides_0, weight = layers_5_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_2213_cast_fp16")]; - tensor var_2219_pad_type_0 = const()[name = tensor("op_2219_pad_type_0"), val = tensor("valid")]; - tensor var_2219_strides_0 = const()[name = tensor("op_2219_strides_0"), val = tensor([1, 1])]; - tensor var_2219_pad_0 = const()[name = tensor("op_2219_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2219_dilations_0 = const()[name = tensor("op_2219_dilations_0"), val = tensor([1, 1])]; - tensor var_2219_groups_0 = const()[name = tensor("op_2219_groups_0"), val = tensor(1)]; - tensor layers_5_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33541568))), name = tensor("layers_5_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33522688))), shape = tensor([512, 512, 1, 1])]; - tensor var_2219_cast_fp16 = conv(dilations = var_2219_dilations_0, groups = var_2219_groups_0, pad = var_2219_pad_0, pad_type = var_2219_pad_type_0, strides = var_2219_strides_0, weight = layers_5_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_2219_cast_fp16")]; - tensor p_11_cast_fp16 = add(x = var_2213_cast_fp16, y = var_2219_cast_fp16)[name = tensor("p_11_cast_fp16")]; - tensor var_2223 = const()[name = tensor("op_2223"), val = tensor([1, 8, 64, 188])]; - tensor var_2224_cast_fp16 = reshape(shape = var_2223, x = q_with_bias_v_11_cast_fp16)[name = tensor("op_2224_cast_fp16")]; - tensor var_2225 = const()[name = tensor("op_2225"), val = tensor([1, 8, 64, -1])]; - tensor var_2226_cast_fp16 = reshape(shape = var_2225, x = p_11_cast_fp16)[name = tensor("op_2226_cast_fp16")]; - tensor matrix_bd_41_transpose_x_0 = const()[name = tensor("matrix_bd_41_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_41_transpose_y_0 = const()[name = tensor("matrix_bd_41_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_41_cast_fp16 = matmul(transpose_x = matrix_bd_41_transpose_x_0, transpose_y = matrix_bd_41_transpose_y_0, x = var_2224_cast_fp16, y = var_2226_cast_fp16)[name = tensor("matrix_bd_41_cast_fp16")]; - tensor matrix_bd_43_pad_0 = const()[name = tensor("matrix_bd_43_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_43_mode_0 = const()[name = tensor("matrix_bd_43_mode_0"), val = tensor("constant")]; - tensor const_65_to_fp16 = const()[name = tensor("const_65_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_43_cast_fp16 = pad(constant_val = const_65_to_fp16, mode = matrix_bd_43_mode_0, pad = matrix_bd_43_pad_0, x = matrix_bd_41_cast_fp16)[name = tensor("matrix_bd_43_cast_fp16")]; - tensor var_2235 = const()[name = tensor("op_2235"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2235, x = matrix_bd_43_cast_fp16)[name = tensor("matrix_bd_45_cast_fp16")]; - tensor var_2239_begin_0 = const()[name = tensor("op_2239_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2239_end_0 = const()[name = tensor("op_2239_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2239_end_mask_0 = const()[name = tensor("op_2239_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2239_cast_fp16 = slice_by_index(begin = var_2239_begin_0, end = var_2239_end_0, end_mask = var_2239_end_mask_0, x = matrix_bd_45_cast_fp16)[name = tensor("op_2239_cast_fp16")]; - tensor var_2240 = const()[name = tensor("op_2240"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_47_cast_fp16 = reshape(shape = var_2240, x = var_2239_cast_fp16)[name = tensor("matrix_bd_47_cast_fp16")]; - tensor var_2245_begin_0 = const()[name = tensor("op_2245_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2245_end_0 = const()[name = tensor("op_2245_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_2245_end_mask_0 = const()[name = tensor("op_2245_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_2245_cast_fp16 = slice_by_index(begin = var_2245_begin_0, end = var_2245_end_0, end_mask = var_2245_end_mask_0, x = matrix_bd_47_cast_fp16)[name = tensor("op_2245_cast_fp16")]; - tensor var_2246_to_fp16 = const()[name = tensor("op_2246_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_11_cast_fp16 = mul(x = var_2245_cast_fp16, y = var_2246_to_fp16)[name = tensor("qk_mask_11_cast_fp16")]; - tensor var_2250 = const()[name = tensor("op_2250"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_11_cast_fp16 = reshape(shape = var_2250, x = query_23_cast_fp16)[name = tensor("mh_q_11_cast_fp16")]; - tensor var_2252_to_fp16 = const()[name = tensor("op_2252_to_fp16"), val = tensor(0x1p-3)]; - tensor var_2253_cast_fp16 = mul(x = mh_q_11_cast_fp16, y = var_2252_to_fp16)[name = tensor("op_2253_cast_fp16")]; - tensor var_2256 = const()[name = tensor("op_2256"), val = tensor([1, 8, 64, 188])]; - tensor var_2257_cast_fp16 = reshape(shape = var_2256, x = key_11_cast_fp16)[name = tensor("op_2257_cast_fp16")]; - tensor mh_w_21_transpose_x_0 = const()[name = tensor("mh_w_21_transpose_x_0"), val = tensor(true)]; - tensor mh_w_21_transpose_y_0 = const()[name = tensor("mh_w_21_transpose_y_0"), val = tensor(false)]; - tensor mh_w_21_cast_fp16 = matmul(transpose_x = mh_w_21_transpose_x_0, transpose_y = mh_w_21_transpose_y_0, x = var_2253_cast_fp16, y = var_2257_cast_fp16)[name = tensor("mh_w_21_cast_fp16")]; - tensor mh_w_23_cast_fp16 = add(x = mh_w_21_cast_fp16, y = qk_mask_11_cast_fp16)[name = tensor("mh_w_23_cast_fp16")]; - tensor var_2261_cast_fp16 = softmax(axis = var_2048, x = mh_w_23_cast_fp16)[name = tensor("op_2261_cast_fp16")]; - tensor var_2262 = const()[name = tensor("op_2262"), val = tensor([1, 8, 64, 188])]; - tensor var_2263_cast_fp16 = reshape(shape = var_2262, x = value_11_cast_fp16)[name = tensor("op_2263_cast_fp16")]; - tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; - tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; - tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_2263_cast_fp16, y = var_2261_cast_fp16)[name = tensor("attn_11_cast_fp16")]; - tensor var_2266 = const()[name = tensor("op_2266"), val = tensor([1, 512, 1, 188])]; - tensor input_153_cast_fp16 = reshape(shape = var_2266, x = attn_11_cast_fp16)[name = tensor("input_153_cast_fp16")]; - tensor var_2276_pad_type_0 = const()[name = tensor("op_2276_pad_type_0"), val = tensor("valid")]; - tensor var_2276_strides_0 = const()[name = tensor("op_2276_strides_0"), val = tensor([1, 1])]; - tensor var_2276_pad_0 = const()[name = tensor("op_2276_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2276_dilations_0 = const()[name = tensor("op_2276_dilations_0"), val = tensor([1, 1])]; - tensor var_2276_groups_0 = const()[name = tensor("op_2276_groups_0"), val = tensor(1)]; - tensor layers_5_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33574400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33771072))), name = tensor("layers_5_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_5_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_5_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33771264)))]; - tensor var_2276_cast_fp16 = conv(bias = layers_5_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_2276_dilations_0, groups = var_2276_groups_0, pad = var_2276_pad_0, pad_type = var_2276_pad_type_0, strides = var_2276_strides_0, weight = layers_5_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_153_cast_fp16)[name = tensor("op_2276_cast_fp16")]; - tensor var_2282_pad_type_0 = const()[name = tensor("op_2282_pad_type_0"), val = tensor("valid")]; - tensor var_2282_strides_0 = const()[name = tensor("op_2282_strides_0"), val = tensor([1, 1])]; - tensor var_2282_pad_0 = const()[name = tensor("op_2282_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2282_dilations_0 = const()[name = tensor("op_2282_dilations_0"), val = tensor([1, 1])]; - tensor var_2282_groups_0 = const()[name = tensor("op_2282_groups_0"), val = tensor(1)]; - tensor layers_5_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33780736))), name = tensor("layers_5_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33772352))), shape = tensor([512, 512, 1, 1])]; - tensor var_2282_cast_fp16 = conv(dilations = var_2282_dilations_0, groups = var_2282_groups_0, pad = var_2282_pad_0, pad_type = var_2282_pad_type_0, strides = var_2282_strides_0, weight = layers_5_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_153_cast_fp16)[name = tensor("op_2282_cast_fp16")]; - tensor obj_25_cast_fp16 = add(x = var_2276_cast_fp16, y = var_2282_cast_fp16)[name = tensor("obj_25_cast_fp16")]; - tensor inputs_55_cast_fp16 = add(x = inputs_53_cast_fp16, y = obj_25_cast_fp16)[name = tensor("inputs_55_cast_fp16")]; - tensor out_55_axes_0 = const()[name = tensor("out_55_axes_0"), val = tensor([1])]; - tensor var_2293_to_fp16 = const()[name = tensor("op_2293_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_55_cast_fp16 = layer_norm(axes = out_55_axes_0, epsilon = var_2293_to_fp16, x = inputs_55_cast_fp16)[name = tensor("out_55_cast_fp16")]; - tensor input_155_gamma_0_to_fp16 = const()[name = tensor("input_155_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33813568)))]; - tensor input_155_beta_0_to_fp16 = const()[name = tensor("input_155_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33814656)))]; - tensor input_155_epsilon_0_to_fp16 = const()[name = tensor("input_155_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_155_cast_fp16 = batch_norm(beta = input_155_beta_0_to_fp16, epsilon = input_155_epsilon_0_to_fp16, gamma = input_155_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_55_cast_fp16)[name = tensor("input_155_cast_fp16")]; - tensor var_2315_pad_type_0 = const()[name = tensor("op_2315_pad_type_0"), val = tensor("valid")]; - tensor var_2315_strides_0 = const()[name = tensor("op_2315_strides_0"), val = tensor([1, 1])]; - tensor var_2315_pad_0 = const()[name = tensor("op_2315_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2315_dilations_0 = const()[name = tensor("op_2315_dilations_0"), val = tensor([1, 1])]; - tensor var_2315_groups_0 = const()[name = tensor("op_2315_groups_0"), val = tensor(1)]; - tensor layers_5_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33815744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34209024))), name = tensor("layers_5_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_5_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_5_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34209216)))]; - tensor var_2315_cast_fp16 = conv(bias = layers_5_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_2315_dilations_0, groups = var_2315_groups_0, pad = var_2315_pad_0, pad_type = var_2315_pad_type_0, strides = var_2315_strides_0, weight = layers_5_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_155_cast_fp16)[name = tensor("op_2315_cast_fp16")]; - tensor var_2321_pad_type_0 = const()[name = tensor("op_2321_pad_type_0"), val = tensor("valid")]; - tensor var_2321_strides_0 = const()[name = tensor("op_2321_strides_0"), val = tensor([1, 1])]; - tensor var_2321_pad_0 = const()[name = tensor("op_2321_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2321_dilations_0 = const()[name = tensor("op_2321_dilations_0"), val = tensor([1, 1])]; - tensor var_2321_groups_0 = const()[name = tensor("op_2321_groups_0"), val = tensor(1)]; - tensor layers_5_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34228480))), name = tensor("layers_5_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34211328))), shape = tensor([1024, 512, 1, 1])]; - tensor var_2321_cast_fp16 = conv(dilations = var_2321_dilations_0, groups = var_2321_groups_0, pad = var_2321_pad_0, pad_type = var_2321_pad_type_0, strides = var_2321_strides_0, weight = layers_5_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_155_cast_fp16)[name = tensor("op_2321_cast_fp16")]; - tensor input_157_cast_fp16 = add(x = var_2315_cast_fp16, y = var_2321_cast_fp16)[name = tensor("input_157_cast_fp16")]; - tensor input_159_split_num_splits_0 = const()[name = tensor("input_159_split_num_splits_0"), val = tensor(2)]; - tensor input_159_split_axis_0 = const()[name = tensor("input_159_split_axis_0"), val = tensor(1)]; - tensor input_159_split_cast_fp16_0, tensor input_159_split_cast_fp16_1 = split(axis = input_159_split_axis_0, num_splits = input_159_split_num_splits_0, x = input_157_cast_fp16)[name = tensor("input_159_split_cast_fp16")]; - tensor input_159_split_1_sigmoid_cast_fp16 = sigmoid(x = input_159_split_cast_fp16_1)[name = tensor("input_159_split_1_sigmoid_cast_fp16")]; - tensor input_159_cast_fp16 = mul(x = input_159_split_cast_fp16_0, y = input_159_split_1_sigmoid_cast_fp16)[name = tensor("input_159_cast_fp16")]; - tensor input_161_pad_type_0 = const()[name = tensor("input_161_pad_type_0"), val = tensor("custom")]; - tensor input_161_pad_0 = const()[name = tensor("input_161_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_161_groups_0 = const()[name = tensor("input_161_groups_0"), val = tensor(512)]; - tensor input_161_strides_0 = const()[name = tensor("input_161_strides_0"), val = tensor([1, 1])]; - tensor input_161_dilations_0 = const()[name = tensor("input_161_dilations_0"), val = tensor([1, 1])]; - tensor const_201_to_fp16 = const()[name = tensor("const_201_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34294080)))]; - tensor const_202_to_fp16 = const()[name = tensor("const_202_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34303360)))]; - tensor input_163_cast_fp16 = conv(bias = const_202_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_201_to_fp16, x = input_159_cast_fp16)[name = tensor("input_163_cast_fp16")]; - tensor input_165_cast_fp16 = silu(x = input_163_cast_fp16)[name = tensor("input_165_cast_fp16")]; - tensor var_2345_pad_type_0 = const()[name = tensor("op_2345_pad_type_0"), val = tensor("valid")]; - tensor var_2345_strides_0 = const()[name = tensor("op_2345_strides_0"), val = tensor([1, 1])]; - tensor var_2345_pad_0 = const()[name = tensor("op_2345_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2345_dilations_0 = const()[name = tensor("op_2345_dilations_0"), val = tensor([1, 1])]; - tensor var_2345_groups_0 = const()[name = tensor("op_2345_groups_0"), val = tensor(1)]; - tensor layers_5_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34304448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34501120))), name = tensor("layers_5_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_5_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_5_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34501312)))]; - tensor var_2345_cast_fp16 = conv(bias = layers_5_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_2345_dilations_0, groups = var_2345_groups_0, pad = var_2345_pad_0, pad_type = var_2345_pad_type_0, strides = var_2345_strides_0, weight = layers_5_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = tensor("op_2345_cast_fp16")]; - tensor var_2351_pad_type_0 = const()[name = tensor("op_2351_pad_type_0"), val = tensor("valid")]; - tensor var_2351_strides_0 = const()[name = tensor("op_2351_strides_0"), val = tensor([1, 1])]; - tensor var_2351_pad_0 = const()[name = tensor("op_2351_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2351_dilations_0 = const()[name = tensor("op_2351_dilations_0"), val = tensor([1, 1])]; - tensor var_2351_groups_0 = const()[name = tensor("op_2351_groups_0"), val = tensor(1)]; - tensor layers_5_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34510656))), name = tensor("layers_5_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34502400))), shape = tensor([512, 512, 1, 1])]; - tensor var_2351_cast_fp16 = conv(dilations = var_2351_dilations_0, groups = var_2351_groups_0, pad = var_2351_pad_0, pad_type = var_2351_pad_type_0, strides = var_2351_strides_0, weight = layers_5_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_165_cast_fp16)[name = tensor("op_2351_cast_fp16")]; - tensor x_35_cast_fp16 = add(x = var_2345_cast_fp16, y = var_2351_cast_fp16)[name = tensor("x_35_cast_fp16")]; - tensor inputs_57_cast_fp16 = add(x = inputs_55_cast_fp16, y = x_35_cast_fp16)[name = tensor("inputs_57_cast_fp16")]; - tensor out_57_axes_0 = const()[name = tensor("out_57_axes_0"), val = tensor([1])]; - tensor var_2362_to_fp16 = const()[name = tensor("op_2362_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_2362_to_fp16, x = inputs_57_cast_fp16)[name = tensor("out_57_cast_fp16")]; - tensor input_167_gamma_0_to_fp16 = const()[name = tensor("input_167_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34543488)))]; - tensor input_167_beta_0_to_fp16 = const()[name = tensor("input_167_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34544576)))]; - tensor input_167_epsilon_0_to_fp16 = const()[name = tensor("input_167_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_167_cast_fp16 = batch_norm(beta = input_167_beta_0_to_fp16, epsilon = input_167_epsilon_0_to_fp16, gamma = input_167_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_57_cast_fp16)[name = tensor("input_167_cast_fp16")]; - tensor var_2382_pad_type_0 = const()[name = tensor("op_2382_pad_type_0"), val = tensor("valid")]; - tensor var_2382_strides_0 = const()[name = tensor("op_2382_strides_0"), val = tensor([1, 1])]; - tensor var_2382_pad_0 = const()[name = tensor("op_2382_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2382_dilations_0 = const()[name = tensor("op_2382_dilations_0"), val = tensor([1, 1])]; - tensor var_2382_groups_0 = const()[name = tensor("op_2382_groups_0"), val = tensor(1)]; - tensor layers_5_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34545664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35332160))), name = tensor("layers_5_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_5_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_5_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35332352)))]; - tensor var_2382_cast_fp16 = conv(bias = layers_5_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_2382_dilations_0, groups = var_2382_groups_0, pad = var_2382_pad_0, pad_type = var_2382_pad_type_0, strides = var_2382_strides_0, weight = layers_5_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("op_2382_cast_fp16")]; - tensor var_2388_pad_type_0 = const()[name = tensor("op_2388_pad_type_0"), val = tensor("valid")]; - tensor var_2388_strides_0 = const()[name = tensor("op_2388_strides_0"), val = tensor([1, 1])]; - tensor var_2388_pad_0 = const()[name = tensor("op_2388_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2388_dilations_0 = const()[name = tensor("op_2388_dilations_0"), val = tensor([1, 1])]; - tensor var_2388_groups_0 = const()[name = tensor("op_2388_groups_0"), val = tensor(1)]; - tensor layers_5_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35369792))), name = tensor("layers_5_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35336512))), shape = tensor([2048, 512, 1, 1])]; - tensor var_2388_cast_fp16 = conv(dilations = var_2388_dilations_0, groups = var_2388_groups_0, pad = var_2388_pad_0, pad_type = var_2388_pad_type_0, strides = var_2388_strides_0, weight = layers_5_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_167_cast_fp16)[name = tensor("op_2388_cast_fp16")]; - tensor input_169_cast_fp16 = add(x = var_2382_cast_fp16, y = var_2388_cast_fp16)[name = tensor("input_169_cast_fp16")]; - tensor input_171_cast_fp16 = silu(x = input_169_cast_fp16)[name = tensor("input_171_cast_fp16")]; - tensor var_2399_pad_type_0 = const()[name = tensor("op_2399_pad_type_0"), val = tensor("valid")]; - tensor var_2399_strides_0 = const()[name = tensor("op_2399_strides_0"), val = tensor([1, 1])]; - tensor var_2399_pad_0 = const()[name = tensor("op_2399_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2399_dilations_0 = const()[name = tensor("op_2399_dilations_0"), val = tensor([1, 1])]; - tensor var_2399_groups_0 = const()[name = tensor("op_2399_groups_0"), val = tensor(1)]; - tensor layers_5_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35500928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36287424))), name = tensor("layers_5_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_5_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_5_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36287616)))]; - tensor var_2399_cast_fp16 = conv(bias = layers_5_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_2399_dilations_0, groups = var_2399_groups_0, pad = var_2399_pad_0, pad_type = var_2399_pad_type_0, strides = var_2399_strides_0, weight = layers_5_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_171_cast_fp16)[name = tensor("op_2399_cast_fp16")]; - tensor var_2405_pad_type_0 = const()[name = tensor("op_2405_pad_type_0"), val = tensor("valid")]; - tensor var_2405_strides_0 = const()[name = tensor("op_2405_strides_0"), val = tensor([1, 1])]; - tensor var_2405_pad_0 = const()[name = tensor("op_2405_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2405_dilations_0 = const()[name = tensor("op_2405_dilations_0"), val = tensor([1, 1])]; - tensor var_2405_groups_0 = const()[name = tensor("op_2405_groups_0"), val = tensor(1)]; - tensor layers_5_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36328256))), name = tensor("layers_5_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36288704))), shape = tensor([512, 2048, 1, 1])]; - tensor var_2405_cast_fp16 = conv(dilations = var_2405_dilations_0, groups = var_2405_groups_0, pad = var_2405_pad_0, pad_type = var_2405_pad_type_0, strides = var_2405_strides_0, weight = layers_5_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_171_cast_fp16)[name = tensor("op_2405_cast_fp16")]; - tensor x_37_cast_fp16 = add(x = var_2399_cast_fp16, y = var_2405_cast_fp16)[name = tensor("x_37_cast_fp16")]; - tensor var_2407_to_fp16 = const()[name = tensor("op_2407_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2408_cast_fp16 = mul(x = x_37_cast_fp16, y = var_2407_to_fp16)[name = tensor("op_2408_cast_fp16")]; - tensor inputs_59_cast_fp16 = add(x = inputs_57_cast_fp16, y = var_2408_cast_fp16)[name = tensor("inputs_59_cast_fp16")]; - tensor out_59_axes_0 = const()[name = tensor("out_59_axes_0"), val = tensor([1])]; - tensor var_2418_to_fp16 = const()[name = tensor("op_2418_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_59_cast_fp16 = layer_norm(axes = out_59_axes_0, epsilon = var_2418_to_fp16, x = inputs_59_cast_fp16)[name = tensor("out_59_cast_fp16")]; - tensor inputs_61_gamma_0_to_fp16 = const()[name = tensor("inputs_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36459392)))]; - tensor inputs_61_beta_0_to_fp16 = const()[name = tensor("inputs_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36460480)))]; - tensor inputs_61_epsilon_0_to_fp16 = const()[name = tensor("inputs_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_61_cast_fp16 = batch_norm(beta = inputs_61_beta_0_to_fp16, epsilon = inputs_61_epsilon_0_to_fp16, gamma = inputs_61_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_59_cast_fp16)[name = tensor("inputs_61_cast_fp16")]; - tensor var_2432 = const()[name = tensor("op_2432"), val = tensor(3)]; - tensor out_61_axes_0 = const()[name = tensor("out_61_axes_0"), val = tensor([1])]; - tensor var_2463_to_fp16 = const()[name = tensor("op_2463_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_61_cast_fp16 = layer_norm(axes = out_61_axes_0, epsilon = var_2463_to_fp16, x = inputs_61_cast_fp16)[name = tensor("out_61_cast_fp16")]; - tensor input_173_gamma_0_to_fp16 = const()[name = tensor("input_173_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36461568)))]; - tensor input_173_beta_0_to_fp16 = const()[name = tensor("input_173_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36462656)))]; - tensor input_173_epsilon_0_to_fp16 = const()[name = tensor("input_173_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_173_cast_fp16 = batch_norm(beta = input_173_beta_0_to_fp16, epsilon = input_173_epsilon_0_to_fp16, gamma = input_173_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_61_cast_fp16)[name = tensor("input_173_cast_fp16")]; - tensor var_2483_pad_type_0 = const()[name = tensor("op_2483_pad_type_0"), val = tensor("valid")]; - tensor var_2483_strides_0 = const()[name = tensor("op_2483_strides_0"), val = tensor([1, 1])]; - tensor var_2483_pad_0 = const()[name = tensor("op_2483_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2483_dilations_0 = const()[name = tensor("op_2483_dilations_0"), val = tensor([1, 1])]; - tensor var_2483_groups_0 = const()[name = tensor("op_2483_groups_0"), val = tensor(1)]; - tensor layers_6_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36463744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37250240))), name = tensor("layers_6_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_6_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_6_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37250432)))]; - tensor var_2483_cast_fp16 = conv(bias = layers_6_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_2483_dilations_0, groups = var_2483_groups_0, pad = var_2483_pad_0, pad_type = var_2483_pad_type_0, strides = var_2483_strides_0, weight = layers_6_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = tensor("op_2483_cast_fp16")]; - tensor var_2489_pad_type_0 = const()[name = tensor("op_2489_pad_type_0"), val = tensor("valid")]; - tensor var_2489_strides_0 = const()[name = tensor("op_2489_strides_0"), val = tensor([1, 1])]; - tensor var_2489_pad_0 = const()[name = tensor("op_2489_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2489_dilations_0 = const()[name = tensor("op_2489_dilations_0"), val = tensor([1, 1])]; - tensor var_2489_groups_0 = const()[name = tensor("op_2489_groups_0"), val = tensor(1)]; - tensor layers_6_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37288576))), name = tensor("layers_6_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37254592))), shape = tensor([2048, 512, 1, 1])]; - tensor var_2489_cast_fp16 = conv(dilations = var_2489_dilations_0, groups = var_2489_groups_0, pad = var_2489_pad_0, pad_type = var_2489_pad_type_0, strides = var_2489_strides_0, weight = layers_6_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_173_cast_fp16)[name = tensor("op_2489_cast_fp16")]; - tensor input_175_cast_fp16 = add(x = var_2483_cast_fp16, y = var_2489_cast_fp16)[name = tensor("input_175_cast_fp16")]; - tensor input_177_cast_fp16 = silu(x = input_175_cast_fp16)[name = tensor("input_177_cast_fp16")]; - tensor var_2500_pad_type_0 = const()[name = tensor("op_2500_pad_type_0"), val = tensor("valid")]; - tensor var_2500_strides_0 = const()[name = tensor("op_2500_strides_0"), val = tensor([1, 1])]; - tensor var_2500_pad_0 = const()[name = tensor("op_2500_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2500_dilations_0 = const()[name = tensor("op_2500_dilations_0"), val = tensor([1, 1])]; - tensor var_2500_groups_0 = const()[name = tensor("op_2500_groups_0"), val = tensor(1)]; - tensor layers_6_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37419712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38206208))), name = tensor("layers_6_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_6_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_6_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38206400)))]; - tensor var_2500_cast_fp16 = conv(bias = layers_6_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_2500_dilations_0, groups = var_2500_groups_0, pad = var_2500_pad_0, pad_type = var_2500_pad_type_0, strides = var_2500_strides_0, weight = layers_6_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_177_cast_fp16)[name = tensor("op_2500_cast_fp16")]; - tensor var_2506_pad_type_0 = const()[name = tensor("op_2506_pad_type_0"), val = tensor("valid")]; - tensor var_2506_strides_0 = const()[name = tensor("op_2506_strides_0"), val = tensor([1, 1])]; - tensor var_2506_pad_0 = const()[name = tensor("op_2506_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2506_dilations_0 = const()[name = tensor("op_2506_dilations_0"), val = tensor([1, 1])]; - tensor var_2506_groups_0 = const()[name = tensor("op_2506_groups_0"), val = tensor(1)]; - tensor layers_6_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38248448))), name = tensor("layers_6_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38207488))), shape = tensor([512, 2048, 1, 1])]; - tensor var_2506_cast_fp16 = conv(dilations = var_2506_dilations_0, groups = var_2506_groups_0, pad = var_2506_pad_0, pad_type = var_2506_pad_type_0, strides = var_2506_strides_0, weight = layers_6_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_177_cast_fp16)[name = tensor("op_2506_cast_fp16")]; - tensor x_39_cast_fp16 = add(x = var_2500_cast_fp16, y = var_2506_cast_fp16)[name = tensor("x_39_cast_fp16")]; - tensor var_2508_to_fp16 = const()[name = tensor("op_2508_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2509_cast_fp16 = mul(x = x_39_cast_fp16, y = var_2508_to_fp16)[name = tensor("op_2509_cast_fp16")]; - tensor inputs_63_cast_fp16 = add(x = inputs_61_cast_fp16, y = var_2509_cast_fp16)[name = tensor("inputs_63_cast_fp16")]; - tensor out_63_axes_0 = const()[name = tensor("out_63_axes_0"), val = tensor([1])]; - tensor var_2519_to_fp16 = const()[name = tensor("op_2519_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_63_cast_fp16 = layer_norm(axes = out_63_axes_0, epsilon = var_2519_to_fp16, x = inputs_63_cast_fp16)[name = tensor("out_63_cast_fp16")]; - tensor obj_27_gamma_0_to_fp16 = const()[name = tensor("obj_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38379584)))]; - tensor obj_27_beta_0_to_fp16 = const()[name = tensor("obj_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38380672)))]; - tensor obj_27_epsilon_0_to_fp16 = const()[name = tensor("obj_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_27_cast_fp16 = batch_norm(beta = obj_27_beta_0_to_fp16, epsilon = obj_27_epsilon_0_to_fp16, gamma = obj_27_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_63_cast_fp16)[name = tensor("obj_27_cast_fp16")]; - tensor var_2544_pad_type_0 = const()[name = tensor("op_2544_pad_type_0"), val = tensor("valid")]; - tensor var_2544_strides_0 = const()[name = tensor("op_2544_strides_0"), val = tensor([1, 1])]; - tensor var_2544_pad_0 = const()[name = tensor("op_2544_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2544_dilations_0 = const()[name = tensor("op_2544_dilations_0"), val = tensor([1, 1])]; - tensor var_2544_groups_0 = const()[name = tensor("op_2544_groups_0"), val = tensor(1)]; - tensor layers_6_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38381760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38578432))), name = tensor("layers_6_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_6_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_6_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38578624)))]; - tensor var_2544_cast_fp16 = conv(bias = layers_6_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_2544_dilations_0, groups = var_2544_groups_0, pad = var_2544_pad_0, pad_type = var_2544_pad_type_0, strides = var_2544_strides_0, weight = layers_6_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_27_cast_fp16)[name = tensor("op_2544_cast_fp16")]; - tensor var_2550_pad_type_0 = const()[name = tensor("op_2550_pad_type_0"), val = tensor("valid")]; - tensor var_2550_strides_0 = const()[name = tensor("op_2550_strides_0"), val = tensor([1, 1])]; - tensor var_2550_pad_0 = const()[name = tensor("op_2550_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2550_dilations_0 = const()[name = tensor("op_2550_dilations_0"), val = tensor([1, 1])]; - tensor var_2550_groups_0 = const()[name = tensor("op_2550_groups_0"), val = tensor(1)]; - tensor layers_6_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38588992))), name = tensor("layers_6_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38579712))), shape = tensor([512, 512, 1, 1])]; - tensor var_2550_cast_fp16 = conv(dilations = var_2550_dilations_0, groups = var_2550_groups_0, pad = var_2550_pad_0, pad_type = var_2550_pad_type_0, strides = var_2550_strides_0, weight = layers_6_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_27_cast_fp16)[name = tensor("op_2550_cast_fp16")]; - tensor query_25_cast_fp16 = add(x = var_2544_cast_fp16, y = var_2550_cast_fp16)[name = tensor("query_25_cast_fp16")]; - tensor var_2559_pad_type_0 = const()[name = tensor("op_2559_pad_type_0"), val = tensor("valid")]; - tensor var_2559_strides_0 = const()[name = tensor("op_2559_strides_0"), val = tensor([1, 1])]; - tensor var_2559_pad_0 = const()[name = tensor("op_2559_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2559_dilations_0 = const()[name = tensor("op_2559_dilations_0"), val = tensor([1, 1])]; - tensor var_2559_groups_0 = const()[name = tensor("op_2559_groups_0"), val = tensor(1)]; - tensor layers_6_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38621824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38818496))), name = tensor("layers_6_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_2559_cast_fp16 = conv(dilations = var_2559_dilations_0, groups = var_2559_groups_0, pad = var_2559_pad_0, pad_type = var_2559_pad_type_0, strides = var_2559_strides_0, weight = layers_6_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_27_cast_fp16)[name = tensor("op_2559_cast_fp16")]; - tensor var_2565_pad_type_0 = const()[name = tensor("op_2565_pad_type_0"), val = tensor("valid")]; - tensor var_2565_strides_0 = const()[name = tensor("op_2565_strides_0"), val = tensor([1, 1])]; - tensor var_2565_pad_0 = const()[name = tensor("op_2565_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2565_dilations_0 = const()[name = tensor("op_2565_dilations_0"), val = tensor([1, 1])]; - tensor var_2565_groups_0 = const()[name = tensor("op_2565_groups_0"), val = tensor(1)]; - tensor layers_6_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38829632))), name = tensor("layers_6_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38818688))), shape = tensor([512, 512, 1, 1])]; - tensor var_2565_cast_fp16 = conv(dilations = var_2565_dilations_0, groups = var_2565_groups_0, pad = var_2565_pad_0, pad_type = var_2565_pad_type_0, strides = var_2565_strides_0, weight = layers_6_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_27_cast_fp16)[name = tensor("op_2565_cast_fp16")]; - tensor key_13_cast_fp16 = add(x = var_2559_cast_fp16, y = var_2565_cast_fp16)[name = tensor("key_13_cast_fp16")]; - tensor var_2575_pad_type_0 = const()[name = tensor("op_2575_pad_type_0"), val = tensor("valid")]; - tensor var_2575_strides_0 = const()[name = tensor("op_2575_strides_0"), val = tensor([1, 1])]; - tensor var_2575_pad_0 = const()[name = tensor("op_2575_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2575_dilations_0 = const()[name = tensor("op_2575_dilations_0"), val = tensor([1, 1])]; - tensor var_2575_groups_0 = const()[name = tensor("op_2575_groups_0"), val = tensor(1)]; - tensor layers_6_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38862464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39059136))), name = tensor("layers_6_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_6_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_6_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39059328)))]; - tensor var_2575_cast_fp16 = conv(bias = layers_6_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_2575_dilations_0, groups = var_2575_groups_0, pad = var_2575_pad_0, pad_type = var_2575_pad_type_0, strides = var_2575_strides_0, weight = layers_6_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_27_cast_fp16)[name = tensor("op_2575_cast_fp16")]; - tensor var_2581_pad_type_0 = const()[name = tensor("op_2581_pad_type_0"), val = tensor("valid")]; - tensor var_2581_strides_0 = const()[name = tensor("op_2581_strides_0"), val = tensor([1, 1])]; - tensor var_2581_pad_0 = const()[name = tensor("op_2581_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2581_dilations_0 = const()[name = tensor("op_2581_dilations_0"), val = tensor([1, 1])]; - tensor var_2581_groups_0 = const()[name = tensor("op_2581_groups_0"), val = tensor(1)]; - tensor layers_6_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39068864))), name = tensor("layers_6_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39060416))), shape = tensor([512, 512, 1, 1])]; - tensor var_2581_cast_fp16 = conv(dilations = var_2581_dilations_0, groups = var_2581_groups_0, pad = var_2581_pad_0, pad_type = var_2581_pad_type_0, strides = var_2581_strides_0, weight = layers_6_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_27_cast_fp16)[name = tensor("op_2581_cast_fp16")]; - tensor value_13_cast_fp16 = add(x = var_2575_cast_fp16, y = var_2581_cast_fp16)[name = tensor("value_13_cast_fp16")]; - tensor var_2584_to_fp16 = const()[name = tensor("op_2584_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39101696)))]; - tensor query_27_cast_fp16 = add(x = query_25_cast_fp16, y = var_2584_to_fp16)[name = tensor("query_27_cast_fp16")]; - tensor var_2587_to_fp16 = const()[name = tensor("op_2587_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39102784)))]; - tensor q_with_bias_v_13_cast_fp16 = add(x = query_25_cast_fp16, y = var_2587_to_fp16)[name = tensor("q_with_bias_v_13_cast_fp16")]; - tensor var_2597_pad_type_0 = const()[name = tensor("op_2597_pad_type_0"), val = tensor("valid")]; - tensor var_2597_strides_0 = const()[name = tensor("op_2597_strides_0"), val = tensor([1, 1])]; - tensor var_2597_pad_0 = const()[name = tensor("op_2597_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2597_dilations_0 = const()[name = tensor("op_2597_dilations_0"), val = tensor([1, 1])]; - tensor var_2597_groups_0 = const()[name = tensor("op_2597_groups_0"), val = tensor(1)]; - tensor layers_6_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39103872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39300544))), name = tensor("layers_6_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_2597_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_2597_dilations_0, groups = var_2597_groups_0, pad = var_2597_pad_0, pad_type = var_2597_pad_type_0, strides = var_2597_strides_0, weight = layers_6_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_2597_cast_fp16")]; - tensor var_2603_pad_type_0 = const()[name = tensor("op_2603_pad_type_0"), val = tensor("valid")]; - tensor var_2603_strides_0 = const()[name = tensor("op_2603_strides_0"), val = tensor([1, 1])]; - tensor var_2603_pad_0 = const()[name = tensor("op_2603_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2603_dilations_0 = const()[name = tensor("op_2603_dilations_0"), val = tensor([1, 1])]; - tensor var_2603_groups_0 = const()[name = tensor("op_2603_groups_0"), val = tensor(1)]; - tensor layers_6_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39321024))), name = tensor("layers_6_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39300736))), shape = tensor([512, 512, 1, 1])]; - tensor var_2603_cast_fp16 = conv(dilations = var_2603_dilations_0, groups = var_2603_groups_0, pad = var_2603_pad_0, pad_type = var_2603_pad_type_0, strides = var_2603_strides_0, weight = layers_6_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_2603_cast_fp16")]; - tensor p_13_cast_fp16 = add(x = var_2597_cast_fp16, y = var_2603_cast_fp16)[name = tensor("p_13_cast_fp16")]; - tensor var_2607 = const()[name = tensor("op_2607"), val = tensor([1, 8, 64, 188])]; - tensor var_2608_cast_fp16 = reshape(shape = var_2607, x = q_with_bias_v_13_cast_fp16)[name = tensor("op_2608_cast_fp16")]; - tensor var_2609 = const()[name = tensor("op_2609"), val = tensor([1, 8, 64, -1])]; - tensor var_2610_cast_fp16 = reshape(shape = var_2609, x = p_13_cast_fp16)[name = tensor("op_2610_cast_fp16")]; - tensor matrix_bd_49_transpose_x_0 = const()[name = tensor("matrix_bd_49_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_49_transpose_y_0 = const()[name = tensor("matrix_bd_49_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_49_cast_fp16 = matmul(transpose_x = matrix_bd_49_transpose_x_0, transpose_y = matrix_bd_49_transpose_y_0, x = var_2608_cast_fp16, y = var_2610_cast_fp16)[name = tensor("matrix_bd_49_cast_fp16")]; - tensor matrix_bd_51_pad_0 = const()[name = tensor("matrix_bd_51_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_51_mode_0 = const()[name = tensor("matrix_bd_51_mode_0"), val = tensor("constant")]; - tensor const_76_to_fp16 = const()[name = tensor("const_76_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_51_cast_fp16 = pad(constant_val = const_76_to_fp16, mode = matrix_bd_51_mode_0, pad = matrix_bd_51_pad_0, x = matrix_bd_49_cast_fp16)[name = tensor("matrix_bd_51_cast_fp16")]; - tensor var_2619 = const()[name = tensor("op_2619"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2619, x = matrix_bd_51_cast_fp16)[name = tensor("matrix_bd_53_cast_fp16")]; - tensor var_2623_begin_0 = const()[name = tensor("op_2623_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2623_end_0 = const()[name = tensor("op_2623_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2623_end_mask_0 = const()[name = tensor("op_2623_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2623_cast_fp16 = slice_by_index(begin = var_2623_begin_0, end = var_2623_end_0, end_mask = var_2623_end_mask_0, x = matrix_bd_53_cast_fp16)[name = tensor("op_2623_cast_fp16")]; - tensor var_2624 = const()[name = tensor("op_2624"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_55_cast_fp16 = reshape(shape = var_2624, x = var_2623_cast_fp16)[name = tensor("matrix_bd_55_cast_fp16")]; - tensor var_2629_begin_0 = const()[name = tensor("op_2629_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2629_end_0 = const()[name = tensor("op_2629_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_2629_end_mask_0 = const()[name = tensor("op_2629_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_2629_cast_fp16 = slice_by_index(begin = var_2629_begin_0, end = var_2629_end_0, end_mask = var_2629_end_mask_0, x = matrix_bd_55_cast_fp16)[name = tensor("op_2629_cast_fp16")]; - tensor var_2630_to_fp16 = const()[name = tensor("op_2630_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_13_cast_fp16 = mul(x = var_2629_cast_fp16, y = var_2630_to_fp16)[name = tensor("qk_mask_13_cast_fp16")]; - tensor var_2634 = const()[name = tensor("op_2634"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_13_cast_fp16 = reshape(shape = var_2634, x = query_27_cast_fp16)[name = tensor("mh_q_13_cast_fp16")]; - tensor var_2636_to_fp16 = const()[name = tensor("op_2636_to_fp16"), val = tensor(0x1p-3)]; - tensor var_2637_cast_fp16 = mul(x = mh_q_13_cast_fp16, y = var_2636_to_fp16)[name = tensor("op_2637_cast_fp16")]; - tensor var_2640 = const()[name = tensor("op_2640"), val = tensor([1, 8, 64, 188])]; - tensor var_2641_cast_fp16 = reshape(shape = var_2640, x = key_13_cast_fp16)[name = tensor("op_2641_cast_fp16")]; - tensor mh_w_25_transpose_x_0 = const()[name = tensor("mh_w_25_transpose_x_0"), val = tensor(true)]; - tensor mh_w_25_transpose_y_0 = const()[name = tensor("mh_w_25_transpose_y_0"), val = tensor(false)]; - tensor mh_w_25_cast_fp16 = matmul(transpose_x = mh_w_25_transpose_x_0, transpose_y = mh_w_25_transpose_y_0, x = var_2637_cast_fp16, y = var_2641_cast_fp16)[name = tensor("mh_w_25_cast_fp16")]; - tensor mh_w_27_cast_fp16 = add(x = mh_w_25_cast_fp16, y = qk_mask_13_cast_fp16)[name = tensor("mh_w_27_cast_fp16")]; - tensor var_2645_cast_fp16 = softmax(axis = var_2432, x = mh_w_27_cast_fp16)[name = tensor("op_2645_cast_fp16")]; - tensor var_2646 = const()[name = tensor("op_2646"), val = tensor([1, 8, 64, 188])]; - tensor var_2647_cast_fp16 = reshape(shape = var_2646, x = value_13_cast_fp16)[name = tensor("op_2647_cast_fp16")]; - tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; - tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; - tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_2647_cast_fp16, y = var_2645_cast_fp16)[name = tensor("attn_13_cast_fp16")]; - tensor var_2650 = const()[name = tensor("op_2650"), val = tensor([1, 512, 1, 188])]; - tensor input_179_cast_fp16 = reshape(shape = var_2650, x = attn_13_cast_fp16)[name = tensor("input_179_cast_fp16")]; - tensor var_2660_pad_type_0 = const()[name = tensor("op_2660_pad_type_0"), val = tensor("valid")]; - tensor var_2660_strides_0 = const()[name = tensor("op_2660_strides_0"), val = tensor([1, 1])]; - tensor var_2660_pad_0 = const()[name = tensor("op_2660_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2660_dilations_0 = const()[name = tensor("op_2660_dilations_0"), val = tensor([1, 1])]; - tensor var_2660_groups_0 = const()[name = tensor("op_2660_groups_0"), val = tensor(1)]; - tensor layers_6_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39353856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39550528))), name = tensor("layers_6_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_6_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_6_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39550720)))]; - tensor var_2660_cast_fp16 = conv(bias = layers_6_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_2660_dilations_0, groups = var_2660_groups_0, pad = var_2660_pad_0, pad_type = var_2660_pad_type_0, strides = var_2660_strides_0, weight = layers_6_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_179_cast_fp16)[name = tensor("op_2660_cast_fp16")]; - tensor var_2666_pad_type_0 = const()[name = tensor("op_2666_pad_type_0"), val = tensor("valid")]; - tensor var_2666_strides_0 = const()[name = tensor("op_2666_strides_0"), val = tensor([1, 1])]; - tensor var_2666_pad_0 = const()[name = tensor("op_2666_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2666_dilations_0 = const()[name = tensor("op_2666_dilations_0"), val = tensor([1, 1])]; - tensor var_2666_groups_0 = const()[name = tensor("op_2666_groups_0"), val = tensor(1)]; - tensor layers_6_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39559936))), name = tensor("layers_6_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39551808))), shape = tensor([512, 512, 1, 1])]; - tensor var_2666_cast_fp16 = conv(dilations = var_2666_dilations_0, groups = var_2666_groups_0, pad = var_2666_pad_0, pad_type = var_2666_pad_type_0, strides = var_2666_strides_0, weight = layers_6_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_179_cast_fp16)[name = tensor("op_2666_cast_fp16")]; - tensor obj_29_cast_fp16 = add(x = var_2660_cast_fp16, y = var_2666_cast_fp16)[name = tensor("obj_29_cast_fp16")]; - tensor inputs_65_cast_fp16 = add(x = inputs_63_cast_fp16, y = obj_29_cast_fp16)[name = tensor("inputs_65_cast_fp16")]; - tensor out_65_axes_0 = const()[name = tensor("out_65_axes_0"), val = tensor([1])]; - tensor var_2677_to_fp16 = const()[name = tensor("op_2677_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_2677_to_fp16, x = inputs_65_cast_fp16)[name = tensor("out_65_cast_fp16")]; - tensor input_181_gamma_0_to_fp16 = const()[name = tensor("input_181_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39592768)))]; - tensor input_181_beta_0_to_fp16 = const()[name = tensor("input_181_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39593856)))]; - tensor input_181_epsilon_0_to_fp16 = const()[name = tensor("input_181_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_181_cast_fp16 = batch_norm(beta = input_181_beta_0_to_fp16, epsilon = input_181_epsilon_0_to_fp16, gamma = input_181_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_65_cast_fp16)[name = tensor("input_181_cast_fp16")]; - tensor var_2699_pad_type_0 = const()[name = tensor("op_2699_pad_type_0"), val = tensor("valid")]; - tensor var_2699_strides_0 = const()[name = tensor("op_2699_strides_0"), val = tensor([1, 1])]; - tensor var_2699_pad_0 = const()[name = tensor("op_2699_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2699_dilations_0 = const()[name = tensor("op_2699_dilations_0"), val = tensor([1, 1])]; - tensor var_2699_groups_0 = const()[name = tensor("op_2699_groups_0"), val = tensor(1)]; - tensor layers_6_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39594944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39988224))), name = tensor("layers_6_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_6_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_6_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39988416)))]; - tensor var_2699_cast_fp16 = conv(bias = layers_6_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_2699_dilations_0, groups = var_2699_groups_0, pad = var_2699_pad_0, pad_type = var_2699_pad_type_0, strides = var_2699_strides_0, weight = layers_6_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = tensor("op_2699_cast_fp16")]; - tensor var_2705_pad_type_0 = const()[name = tensor("op_2705_pad_type_0"), val = tensor("valid")]; - tensor var_2705_strides_0 = const()[name = tensor("op_2705_strides_0"), val = tensor([1, 1])]; - tensor var_2705_pad_0 = const()[name = tensor("op_2705_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2705_dilations_0 = const()[name = tensor("op_2705_dilations_0"), val = tensor([1, 1])]; - tensor var_2705_groups_0 = const()[name = tensor("op_2705_groups_0"), val = tensor(1)]; - tensor layers_6_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40007936))), name = tensor("layers_6_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39990528))), shape = tensor([1024, 512, 1, 1])]; - tensor var_2705_cast_fp16 = conv(dilations = var_2705_dilations_0, groups = var_2705_groups_0, pad = var_2705_pad_0, pad_type = var_2705_pad_type_0, strides = var_2705_strides_0, weight = layers_6_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_181_cast_fp16)[name = tensor("op_2705_cast_fp16")]; - tensor input_183_cast_fp16 = add(x = var_2699_cast_fp16, y = var_2705_cast_fp16)[name = tensor("input_183_cast_fp16")]; - tensor input_185_split_num_splits_0 = const()[name = tensor("input_185_split_num_splits_0"), val = tensor(2)]; - tensor input_185_split_axis_0 = const()[name = tensor("input_185_split_axis_0"), val = tensor(1)]; - tensor input_185_split_cast_fp16_0, tensor input_185_split_cast_fp16_1 = split(axis = input_185_split_axis_0, num_splits = input_185_split_num_splits_0, x = input_183_cast_fp16)[name = tensor("input_185_split_cast_fp16")]; - tensor input_185_split_1_sigmoid_cast_fp16 = sigmoid(x = input_185_split_cast_fp16_1)[name = tensor("input_185_split_1_sigmoid_cast_fp16")]; - tensor input_185_cast_fp16 = mul(x = input_185_split_cast_fp16_0, y = input_185_split_1_sigmoid_cast_fp16)[name = tensor("input_185_cast_fp16")]; - tensor input_187_pad_type_0 = const()[name = tensor("input_187_pad_type_0"), val = tensor("custom")]; - tensor input_187_pad_0 = const()[name = tensor("input_187_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_187_groups_0 = const()[name = tensor("input_187_groups_0"), val = tensor(512)]; - tensor input_187_strides_0 = const()[name = tensor("input_187_strides_0"), val = tensor([1, 1])]; - tensor input_187_dilations_0 = const()[name = tensor("input_187_dilations_0"), val = tensor([1, 1])]; - tensor const_203_to_fp16 = const()[name = tensor("const_203_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40073536)))]; - tensor const_204_to_fp16 = const()[name = tensor("const_204_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40082816)))]; - tensor input_189_cast_fp16 = conv(bias = const_204_to_fp16, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_203_to_fp16, x = input_185_cast_fp16)[name = tensor("input_189_cast_fp16")]; - tensor input_191_cast_fp16 = silu(x = input_189_cast_fp16)[name = tensor("input_191_cast_fp16")]; - tensor var_2729_pad_type_0 = const()[name = tensor("op_2729_pad_type_0"), val = tensor("valid")]; - tensor var_2729_strides_0 = const()[name = tensor("op_2729_strides_0"), val = tensor([1, 1])]; - tensor var_2729_pad_0 = const()[name = tensor("op_2729_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2729_dilations_0 = const()[name = tensor("op_2729_dilations_0"), val = tensor([1, 1])]; - tensor var_2729_groups_0 = const()[name = tensor("op_2729_groups_0"), val = tensor(1)]; - tensor layers_6_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40083904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40280576))), name = tensor("layers_6_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_6_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_6_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40280768)))]; - tensor var_2729_cast_fp16 = conv(bias = layers_6_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_2729_dilations_0, groups = var_2729_groups_0, pad = var_2729_pad_0, pad_type = var_2729_pad_type_0, strides = var_2729_strides_0, weight = layers_6_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_191_cast_fp16)[name = tensor("op_2729_cast_fp16")]; - tensor var_2735_pad_type_0 = const()[name = tensor("op_2735_pad_type_0"), val = tensor("valid")]; - tensor var_2735_strides_0 = const()[name = tensor("op_2735_strides_0"), val = tensor([1, 1])]; - tensor var_2735_pad_0 = const()[name = tensor("op_2735_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2735_dilations_0 = const()[name = tensor("op_2735_dilations_0"), val = tensor([1, 1])]; - tensor var_2735_groups_0 = const()[name = tensor("op_2735_groups_0"), val = tensor(1)]; - tensor layers_6_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40290496))), name = tensor("layers_6_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40281856))), shape = tensor([512, 512, 1, 1])]; - tensor var_2735_cast_fp16 = conv(dilations = var_2735_dilations_0, groups = var_2735_groups_0, pad = var_2735_pad_0, pad_type = var_2735_pad_type_0, strides = var_2735_strides_0, weight = layers_6_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_191_cast_fp16)[name = tensor("op_2735_cast_fp16")]; - tensor x_41_cast_fp16 = add(x = var_2729_cast_fp16, y = var_2735_cast_fp16)[name = tensor("x_41_cast_fp16")]; - tensor inputs_67_cast_fp16 = add(x = inputs_65_cast_fp16, y = x_41_cast_fp16)[name = tensor("inputs_67_cast_fp16")]; - tensor out_67_axes_0 = const()[name = tensor("out_67_axes_0"), val = tensor([1])]; - tensor var_2746_to_fp16 = const()[name = tensor("op_2746_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_67_cast_fp16 = layer_norm(axes = out_67_axes_0, epsilon = var_2746_to_fp16, x = inputs_67_cast_fp16)[name = tensor("out_67_cast_fp16")]; - tensor input_193_gamma_0_to_fp16 = const()[name = tensor("input_193_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40323328)))]; - tensor input_193_beta_0_to_fp16 = const()[name = tensor("input_193_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40324416)))]; - tensor input_193_epsilon_0_to_fp16 = const()[name = tensor("input_193_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_193_cast_fp16 = batch_norm(beta = input_193_beta_0_to_fp16, epsilon = input_193_epsilon_0_to_fp16, gamma = input_193_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_67_cast_fp16)[name = tensor("input_193_cast_fp16")]; - tensor var_2766_pad_type_0 = const()[name = tensor("op_2766_pad_type_0"), val = tensor("valid")]; - tensor var_2766_strides_0 = const()[name = tensor("op_2766_strides_0"), val = tensor([1, 1])]; - tensor var_2766_pad_0 = const()[name = tensor("op_2766_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2766_dilations_0 = const()[name = tensor("op_2766_dilations_0"), val = tensor([1, 1])]; - tensor var_2766_groups_0 = const()[name = tensor("op_2766_groups_0"), val = tensor(1)]; - tensor layers_6_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40325504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41112000))), name = tensor("layers_6_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_6_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_6_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41112192)))]; - tensor var_2766_cast_fp16 = conv(bias = layers_6_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_2766_dilations_0, groups = var_2766_groups_0, pad = var_2766_pad_0, pad_type = var_2766_pad_type_0, strides = var_2766_strides_0, weight = layers_6_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = tensor("op_2766_cast_fp16")]; - tensor var_2772_pad_type_0 = const()[name = tensor("op_2772_pad_type_0"), val = tensor("valid")]; - tensor var_2772_strides_0 = const()[name = tensor("op_2772_strides_0"), val = tensor([1, 1])]; - tensor var_2772_pad_0 = const()[name = tensor("op_2772_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2772_dilations_0 = const()[name = tensor("op_2772_dilations_0"), val = tensor([1, 1])]; - tensor var_2772_groups_0 = const()[name = tensor("op_2772_groups_0"), val = tensor(1)]; - tensor layers_6_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41149888))), name = tensor("layers_6_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41116352))), shape = tensor([2048, 512, 1, 1])]; - tensor var_2772_cast_fp16 = conv(dilations = var_2772_dilations_0, groups = var_2772_groups_0, pad = var_2772_pad_0, pad_type = var_2772_pad_type_0, strides = var_2772_strides_0, weight = layers_6_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_193_cast_fp16)[name = tensor("op_2772_cast_fp16")]; - tensor input_195_cast_fp16 = add(x = var_2766_cast_fp16, y = var_2772_cast_fp16)[name = tensor("input_195_cast_fp16")]; - tensor input_197_cast_fp16 = silu(x = input_195_cast_fp16)[name = tensor("input_197_cast_fp16")]; - tensor var_2783_pad_type_0 = const()[name = tensor("op_2783_pad_type_0"), val = tensor("valid")]; - tensor var_2783_strides_0 = const()[name = tensor("op_2783_strides_0"), val = tensor([1, 1])]; - tensor var_2783_pad_0 = const()[name = tensor("op_2783_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2783_dilations_0 = const()[name = tensor("op_2783_dilations_0"), val = tensor([1, 1])]; - tensor var_2783_groups_0 = const()[name = tensor("op_2783_groups_0"), val = tensor(1)]; - tensor layers_6_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41281024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42067520))), name = tensor("layers_6_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_6_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_6_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42067712)))]; - tensor var_2783_cast_fp16 = conv(bias = layers_6_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_2783_dilations_0, groups = var_2783_groups_0, pad = var_2783_pad_0, pad_type = var_2783_pad_type_0, strides = var_2783_strides_0, weight = layers_6_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_197_cast_fp16)[name = tensor("op_2783_cast_fp16")]; - tensor var_2789_pad_type_0 = const()[name = tensor("op_2789_pad_type_0"), val = tensor("valid")]; - tensor var_2789_strides_0 = const()[name = tensor("op_2789_strides_0"), val = tensor([1, 1])]; - tensor var_2789_pad_0 = const()[name = tensor("op_2789_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2789_dilations_0 = const()[name = tensor("op_2789_dilations_0"), val = tensor([1, 1])]; - tensor var_2789_groups_0 = const()[name = tensor("op_2789_groups_0"), val = tensor(1)]; - tensor layers_6_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42107840))), name = tensor("layers_6_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42068800))), shape = tensor([512, 2048, 1, 1])]; - tensor var_2789_cast_fp16 = conv(dilations = var_2789_dilations_0, groups = var_2789_groups_0, pad = var_2789_pad_0, pad_type = var_2789_pad_type_0, strides = var_2789_strides_0, weight = layers_6_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_197_cast_fp16)[name = tensor("op_2789_cast_fp16")]; - tensor x_43_cast_fp16 = add(x = var_2783_cast_fp16, y = var_2789_cast_fp16)[name = tensor("x_43_cast_fp16")]; - tensor var_2791_to_fp16 = const()[name = tensor("op_2791_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2792_cast_fp16 = mul(x = x_43_cast_fp16, y = var_2791_to_fp16)[name = tensor("op_2792_cast_fp16")]; - tensor inputs_69_cast_fp16 = add(x = inputs_67_cast_fp16, y = var_2792_cast_fp16)[name = tensor("inputs_69_cast_fp16")]; - tensor out_69_axes_0 = const()[name = tensor("out_69_axes_0"), val = tensor([1])]; - tensor var_2802_to_fp16 = const()[name = tensor("op_2802_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_69_cast_fp16 = layer_norm(axes = out_69_axes_0, epsilon = var_2802_to_fp16, x = inputs_69_cast_fp16)[name = tensor("out_69_cast_fp16")]; - tensor inputs_71_gamma_0_to_fp16 = const()[name = tensor("inputs_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42238976)))]; - tensor inputs_71_beta_0_to_fp16 = const()[name = tensor("inputs_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42240064)))]; - tensor inputs_71_epsilon_0_to_fp16 = const()[name = tensor("inputs_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_71_cast_fp16 = batch_norm(beta = inputs_71_beta_0_to_fp16, epsilon = inputs_71_epsilon_0_to_fp16, gamma = inputs_71_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_69_cast_fp16)[name = tensor("inputs_71_cast_fp16")]; - tensor var_2816 = const()[name = tensor("op_2816"), val = tensor(3)]; - tensor out_71_axes_0 = const()[name = tensor("out_71_axes_0"), val = tensor([1])]; - tensor var_2847_to_fp16 = const()[name = tensor("op_2847_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_71_cast_fp16 = layer_norm(axes = out_71_axes_0, epsilon = var_2847_to_fp16, x = inputs_71_cast_fp16)[name = tensor("out_71_cast_fp16")]; - tensor input_199_gamma_0_to_fp16 = const()[name = tensor("input_199_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42241152)))]; - tensor input_199_beta_0_to_fp16 = const()[name = tensor("input_199_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42242240)))]; - tensor input_199_epsilon_0_to_fp16 = const()[name = tensor("input_199_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_199_cast_fp16 = batch_norm(beta = input_199_beta_0_to_fp16, epsilon = input_199_epsilon_0_to_fp16, gamma = input_199_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_71_cast_fp16)[name = tensor("input_199_cast_fp16")]; - tensor var_2867_pad_type_0 = const()[name = tensor("op_2867_pad_type_0"), val = tensor("valid")]; - tensor var_2867_strides_0 = const()[name = tensor("op_2867_strides_0"), val = tensor([1, 1])]; - tensor var_2867_pad_0 = const()[name = tensor("op_2867_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2867_dilations_0 = const()[name = tensor("op_2867_dilations_0"), val = tensor([1, 1])]; - tensor var_2867_groups_0 = const()[name = tensor("op_2867_groups_0"), val = tensor(1)]; - tensor layers_7_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42243328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43029824))), name = tensor("layers_7_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_7_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_7_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43030016)))]; - tensor var_2867_cast_fp16 = conv(bias = layers_7_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_2867_dilations_0, groups = var_2867_groups_0, pad = var_2867_pad_0, pad_type = var_2867_pad_type_0, strides = var_2867_strides_0, weight = layers_7_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_199_cast_fp16)[name = tensor("op_2867_cast_fp16")]; - tensor var_2873_pad_type_0 = const()[name = tensor("op_2873_pad_type_0"), val = tensor("valid")]; - tensor var_2873_strides_0 = const()[name = tensor("op_2873_strides_0"), val = tensor([1, 1])]; - tensor var_2873_pad_0 = const()[name = tensor("op_2873_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2873_dilations_0 = const()[name = tensor("op_2873_dilations_0"), val = tensor([1, 1])]; - tensor var_2873_groups_0 = const()[name = tensor("op_2873_groups_0"), val = tensor(1)]; - tensor layers_7_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43068288))), name = tensor("layers_7_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43034176))), shape = tensor([2048, 512, 1, 1])]; - tensor var_2873_cast_fp16 = conv(dilations = var_2873_dilations_0, groups = var_2873_groups_0, pad = var_2873_pad_0, pad_type = var_2873_pad_type_0, strides = var_2873_strides_0, weight = layers_7_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_199_cast_fp16)[name = tensor("op_2873_cast_fp16")]; - tensor input_201_cast_fp16 = add(x = var_2867_cast_fp16, y = var_2873_cast_fp16)[name = tensor("input_201_cast_fp16")]; - tensor input_203_cast_fp16 = silu(x = input_201_cast_fp16)[name = tensor("input_203_cast_fp16")]; - tensor var_2884_pad_type_0 = const()[name = tensor("op_2884_pad_type_0"), val = tensor("valid")]; - tensor var_2884_strides_0 = const()[name = tensor("op_2884_strides_0"), val = tensor([1, 1])]; - tensor var_2884_pad_0 = const()[name = tensor("op_2884_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2884_dilations_0 = const()[name = tensor("op_2884_dilations_0"), val = tensor([1, 1])]; - tensor var_2884_groups_0 = const()[name = tensor("op_2884_groups_0"), val = tensor(1)]; - tensor layers_7_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43199424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43985920))), name = tensor("layers_7_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_7_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_7_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43986112)))]; - tensor var_2884_cast_fp16 = conv(bias = layers_7_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_2884_dilations_0, groups = var_2884_groups_0, pad = var_2884_pad_0, pad_type = var_2884_pad_type_0, strides = var_2884_strides_0, weight = layers_7_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_203_cast_fp16)[name = tensor("op_2884_cast_fp16")]; - tensor var_2890_pad_type_0 = const()[name = tensor("op_2890_pad_type_0"), val = tensor("valid")]; - tensor var_2890_strides_0 = const()[name = tensor("op_2890_strides_0"), val = tensor([1, 1])]; - tensor var_2890_pad_0 = const()[name = tensor("op_2890_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2890_dilations_0 = const()[name = tensor("op_2890_dilations_0"), val = tensor([1, 1])]; - tensor var_2890_groups_0 = const()[name = tensor("op_2890_groups_0"), val = tensor(1)]; - tensor layers_7_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44027328))), name = tensor("layers_7_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43987200))), shape = tensor([512, 2048, 1, 1])]; - tensor var_2890_cast_fp16 = conv(dilations = var_2890_dilations_0, groups = var_2890_groups_0, pad = var_2890_pad_0, pad_type = var_2890_pad_type_0, strides = var_2890_strides_0, weight = layers_7_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_203_cast_fp16)[name = tensor("op_2890_cast_fp16")]; - tensor x_45_cast_fp16 = add(x = var_2884_cast_fp16, y = var_2890_cast_fp16)[name = tensor("x_45_cast_fp16")]; - tensor var_2892_to_fp16 = const()[name = tensor("op_2892_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2893_cast_fp16 = mul(x = x_45_cast_fp16, y = var_2892_to_fp16)[name = tensor("op_2893_cast_fp16")]; - tensor inputs_73_cast_fp16 = add(x = inputs_71_cast_fp16, y = var_2893_cast_fp16)[name = tensor("inputs_73_cast_fp16")]; - tensor out_73_axes_0 = const()[name = tensor("out_73_axes_0"), val = tensor([1])]; - tensor var_2903_to_fp16 = const()[name = tensor("op_2903_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_2903_to_fp16, x = inputs_73_cast_fp16)[name = tensor("out_73_cast_fp16")]; - tensor obj_31_gamma_0_to_fp16 = const()[name = tensor("obj_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44158464)))]; - tensor obj_31_beta_0_to_fp16 = const()[name = tensor("obj_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44159552)))]; - tensor obj_31_epsilon_0_to_fp16 = const()[name = tensor("obj_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_31_cast_fp16 = batch_norm(beta = obj_31_beta_0_to_fp16, epsilon = obj_31_epsilon_0_to_fp16, gamma = obj_31_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_73_cast_fp16)[name = tensor("obj_31_cast_fp16")]; - tensor var_2928_pad_type_0 = const()[name = tensor("op_2928_pad_type_0"), val = tensor("valid")]; - tensor var_2928_strides_0 = const()[name = tensor("op_2928_strides_0"), val = tensor([1, 1])]; - tensor var_2928_pad_0 = const()[name = tensor("op_2928_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2928_dilations_0 = const()[name = tensor("op_2928_dilations_0"), val = tensor([1, 1])]; - tensor var_2928_groups_0 = const()[name = tensor("op_2928_groups_0"), val = tensor(1)]; - tensor layers_7_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44160640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44357312))), name = tensor("layers_7_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_7_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_7_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44357504)))]; - tensor var_2928_cast_fp16 = conv(bias = layers_7_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_2928_dilations_0, groups = var_2928_groups_0, pad = var_2928_pad_0, pad_type = var_2928_pad_type_0, strides = var_2928_strides_0, weight = layers_7_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_31_cast_fp16)[name = tensor("op_2928_cast_fp16")]; - tensor var_2934_pad_type_0 = const()[name = tensor("op_2934_pad_type_0"), val = tensor("valid")]; - tensor var_2934_strides_0 = const()[name = tensor("op_2934_strides_0"), val = tensor([1, 1])]; - tensor var_2934_pad_0 = const()[name = tensor("op_2934_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2934_dilations_0 = const()[name = tensor("op_2934_dilations_0"), val = tensor([1, 1])]; - tensor var_2934_groups_0 = const()[name = tensor("op_2934_groups_0"), val = tensor(1)]; - tensor layers_7_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44368832))), name = tensor("layers_7_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44358592))), shape = tensor([512, 512, 1, 1])]; - tensor var_2934_cast_fp16 = conv(dilations = var_2934_dilations_0, groups = var_2934_groups_0, pad = var_2934_pad_0, pad_type = var_2934_pad_type_0, strides = var_2934_strides_0, weight = layers_7_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_31_cast_fp16)[name = tensor("op_2934_cast_fp16")]; - tensor query_29_cast_fp16 = add(x = var_2928_cast_fp16, y = var_2934_cast_fp16)[name = tensor("query_29_cast_fp16")]; - tensor var_2943_pad_type_0 = const()[name = tensor("op_2943_pad_type_0"), val = tensor("valid")]; - tensor var_2943_strides_0 = const()[name = tensor("op_2943_strides_0"), val = tensor([1, 1])]; - tensor var_2943_pad_0 = const()[name = tensor("op_2943_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2943_dilations_0 = const()[name = tensor("op_2943_dilations_0"), val = tensor([1, 1])]; - tensor var_2943_groups_0 = const()[name = tensor("op_2943_groups_0"), val = tensor(1)]; - tensor layers_7_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44401664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44598336))), name = tensor("layers_7_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_2943_cast_fp16 = conv(dilations = var_2943_dilations_0, groups = var_2943_groups_0, pad = var_2943_pad_0, pad_type = var_2943_pad_type_0, strides = var_2943_strides_0, weight = layers_7_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_31_cast_fp16)[name = tensor("op_2943_cast_fp16")]; - tensor var_2949_pad_type_0 = const()[name = tensor("op_2949_pad_type_0"), val = tensor("valid")]; - tensor var_2949_strides_0 = const()[name = tensor("op_2949_strides_0"), val = tensor([1, 1])]; - tensor var_2949_pad_0 = const()[name = tensor("op_2949_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2949_dilations_0 = const()[name = tensor("op_2949_dilations_0"), val = tensor([1, 1])]; - tensor var_2949_groups_0 = const()[name = tensor("op_2949_groups_0"), val = tensor(1)]; - tensor layers_7_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44611072))), name = tensor("layers_7_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44598528))), shape = tensor([512, 512, 1, 1])]; - tensor var_2949_cast_fp16 = conv(dilations = var_2949_dilations_0, groups = var_2949_groups_0, pad = var_2949_pad_0, pad_type = var_2949_pad_type_0, strides = var_2949_strides_0, weight = layers_7_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_31_cast_fp16)[name = tensor("op_2949_cast_fp16")]; - tensor key_15_cast_fp16 = add(x = var_2943_cast_fp16, y = var_2949_cast_fp16)[name = tensor("key_15_cast_fp16")]; - tensor var_2959_pad_type_0 = const()[name = tensor("op_2959_pad_type_0"), val = tensor("valid")]; - tensor var_2959_strides_0 = const()[name = tensor("op_2959_strides_0"), val = tensor([1, 1])]; - tensor var_2959_pad_0 = const()[name = tensor("op_2959_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2959_dilations_0 = const()[name = tensor("op_2959_dilations_0"), val = tensor([1, 1])]; - tensor var_2959_groups_0 = const()[name = tensor("op_2959_groups_0"), val = tensor(1)]; - tensor layers_7_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44643904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44840576))), name = tensor("layers_7_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_7_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_7_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44840768)))]; - tensor var_2959_cast_fp16 = conv(bias = layers_7_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_2959_dilations_0, groups = var_2959_groups_0, pad = var_2959_pad_0, pad_type = var_2959_pad_type_0, strides = var_2959_strides_0, weight = layers_7_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_31_cast_fp16)[name = tensor("op_2959_cast_fp16")]; - tensor var_2965_pad_type_0 = const()[name = tensor("op_2965_pad_type_0"), val = tensor("valid")]; - tensor var_2965_strides_0 = const()[name = tensor("op_2965_strides_0"), val = tensor([1, 1])]; - tensor var_2965_pad_0 = const()[name = tensor("op_2965_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2965_dilations_0 = const()[name = tensor("op_2965_dilations_0"), val = tensor([1, 1])]; - tensor var_2965_groups_0 = const()[name = tensor("op_2965_groups_0"), val = tensor(1)]; - tensor layers_7_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44851456))), name = tensor("layers_7_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44841856))), shape = tensor([512, 512, 1, 1])]; - tensor var_2965_cast_fp16 = conv(dilations = var_2965_dilations_0, groups = var_2965_groups_0, pad = var_2965_pad_0, pad_type = var_2965_pad_type_0, strides = var_2965_strides_0, weight = layers_7_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_31_cast_fp16)[name = tensor("op_2965_cast_fp16")]; - tensor value_15_cast_fp16 = add(x = var_2959_cast_fp16, y = var_2965_cast_fp16)[name = tensor("value_15_cast_fp16")]; - tensor var_2968_to_fp16 = const()[name = tensor("op_2968_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44884288)))]; - tensor query_31_cast_fp16 = add(x = query_29_cast_fp16, y = var_2968_to_fp16)[name = tensor("query_31_cast_fp16")]; - tensor var_2971_to_fp16 = const()[name = tensor("op_2971_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44885376)))]; - tensor q_with_bias_v_15_cast_fp16 = add(x = query_29_cast_fp16, y = var_2971_to_fp16)[name = tensor("q_with_bias_v_15_cast_fp16")]; - tensor var_2981_pad_type_0 = const()[name = tensor("op_2981_pad_type_0"), val = tensor("valid")]; - tensor var_2981_strides_0 = const()[name = tensor("op_2981_strides_0"), val = tensor([1, 1])]; - tensor var_2981_pad_0 = const()[name = tensor("op_2981_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2981_dilations_0 = const()[name = tensor("op_2981_dilations_0"), val = tensor([1, 1])]; - tensor var_2981_groups_0 = const()[name = tensor("op_2981_groups_0"), val = tensor(1)]; - tensor layers_7_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44886464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45083136))), name = tensor("layers_7_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_2981_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_2981_dilations_0, groups = var_2981_groups_0, pad = var_2981_pad_0, pad_type = var_2981_pad_type_0, strides = var_2981_strides_0, weight = layers_7_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_2981_cast_fp16")]; - tensor var_2987_pad_type_0 = const()[name = tensor("op_2987_pad_type_0"), val = tensor("valid")]; - tensor var_2987_strides_0 = const()[name = tensor("op_2987_strides_0"), val = tensor([1, 1])]; - tensor var_2987_pad_0 = const()[name = tensor("op_2987_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2987_dilations_0 = const()[name = tensor("op_2987_dilations_0"), val = tensor([1, 1])]; - tensor var_2987_groups_0 = const()[name = tensor("op_2987_groups_0"), val = tensor(1)]; - tensor layers_7_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45102592))), name = tensor("layers_7_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45083328))), shape = tensor([512, 512, 1, 1])]; - tensor var_2987_cast_fp16 = conv(dilations = var_2987_dilations_0, groups = var_2987_groups_0, pad = var_2987_pad_0, pad_type = var_2987_pad_type_0, strides = var_2987_strides_0, weight = layers_7_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_2987_cast_fp16")]; - tensor p_15_cast_fp16 = add(x = var_2981_cast_fp16, y = var_2987_cast_fp16)[name = tensor("p_15_cast_fp16")]; - tensor var_2991 = const()[name = tensor("op_2991"), val = tensor([1, 8, 64, 188])]; - tensor var_2992_cast_fp16 = reshape(shape = var_2991, x = q_with_bias_v_15_cast_fp16)[name = tensor("op_2992_cast_fp16")]; - tensor var_2993 = const()[name = tensor("op_2993"), val = tensor([1, 8, 64, -1])]; - tensor var_2994_cast_fp16 = reshape(shape = var_2993, x = p_15_cast_fp16)[name = tensor("op_2994_cast_fp16")]; - tensor matrix_bd_57_transpose_x_0 = const()[name = tensor("matrix_bd_57_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_57_transpose_y_0 = const()[name = tensor("matrix_bd_57_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_57_cast_fp16 = matmul(transpose_x = matrix_bd_57_transpose_x_0, transpose_y = matrix_bd_57_transpose_y_0, x = var_2992_cast_fp16, y = var_2994_cast_fp16)[name = tensor("matrix_bd_57_cast_fp16")]; - tensor matrix_bd_59_pad_0 = const()[name = tensor("matrix_bd_59_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_59_mode_0 = const()[name = tensor("matrix_bd_59_mode_0"), val = tensor("constant")]; - tensor const_87_to_fp16 = const()[name = tensor("const_87_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_59_cast_fp16 = pad(constant_val = const_87_to_fp16, mode = matrix_bd_59_mode_0, pad = matrix_bd_59_pad_0, x = matrix_bd_57_cast_fp16)[name = tensor("matrix_bd_59_cast_fp16")]; - tensor var_3003 = const()[name = tensor("op_3003"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_61_cast_fp16 = reshape(shape = var_3003, x = matrix_bd_59_cast_fp16)[name = tensor("matrix_bd_61_cast_fp16")]; - tensor var_3007_begin_0 = const()[name = tensor("op_3007_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3007_end_0 = const()[name = tensor("op_3007_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3007_end_mask_0 = const()[name = tensor("op_3007_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3007_cast_fp16 = slice_by_index(begin = var_3007_begin_0, end = var_3007_end_0, end_mask = var_3007_end_mask_0, x = matrix_bd_61_cast_fp16)[name = tensor("op_3007_cast_fp16")]; - tensor var_3008 = const()[name = tensor("op_3008"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_63_cast_fp16 = reshape(shape = var_3008, x = var_3007_cast_fp16)[name = tensor("matrix_bd_63_cast_fp16")]; - tensor var_3013_begin_0 = const()[name = tensor("op_3013_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3013_end_0 = const()[name = tensor("op_3013_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_3013_end_mask_0 = const()[name = tensor("op_3013_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_3013_cast_fp16 = slice_by_index(begin = var_3013_begin_0, end = var_3013_end_0, end_mask = var_3013_end_mask_0, x = matrix_bd_63_cast_fp16)[name = tensor("op_3013_cast_fp16")]; - tensor var_3014_to_fp16 = const()[name = tensor("op_3014_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_15_cast_fp16 = mul(x = var_3013_cast_fp16, y = var_3014_to_fp16)[name = tensor("qk_mask_15_cast_fp16")]; - tensor var_3018 = const()[name = tensor("op_3018"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_15_cast_fp16 = reshape(shape = var_3018, x = query_31_cast_fp16)[name = tensor("mh_q_15_cast_fp16")]; - tensor var_3020_to_fp16 = const()[name = tensor("op_3020_to_fp16"), val = tensor(0x1p-3)]; - tensor var_3021_cast_fp16 = mul(x = mh_q_15_cast_fp16, y = var_3020_to_fp16)[name = tensor("op_3021_cast_fp16")]; - tensor var_3024 = const()[name = tensor("op_3024"), val = tensor([1, 8, 64, 188])]; - tensor var_3025_cast_fp16 = reshape(shape = var_3024, x = key_15_cast_fp16)[name = tensor("op_3025_cast_fp16")]; - tensor mh_w_29_transpose_x_0 = const()[name = tensor("mh_w_29_transpose_x_0"), val = tensor(true)]; - tensor mh_w_29_transpose_y_0 = const()[name = tensor("mh_w_29_transpose_y_0"), val = tensor(false)]; - tensor mh_w_29_cast_fp16 = matmul(transpose_x = mh_w_29_transpose_x_0, transpose_y = mh_w_29_transpose_y_0, x = var_3021_cast_fp16, y = var_3025_cast_fp16)[name = tensor("mh_w_29_cast_fp16")]; - tensor mh_w_31_cast_fp16 = add(x = mh_w_29_cast_fp16, y = qk_mask_15_cast_fp16)[name = tensor("mh_w_31_cast_fp16")]; - tensor var_3029_cast_fp16 = softmax(axis = var_2816, x = mh_w_31_cast_fp16)[name = tensor("op_3029_cast_fp16")]; - tensor var_3030 = const()[name = tensor("op_3030"), val = tensor([1, 8, 64, 188])]; - tensor var_3031_cast_fp16 = reshape(shape = var_3030, x = value_15_cast_fp16)[name = tensor("op_3031_cast_fp16")]; - tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; - tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; - tensor attn_15_cast_fp16 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_3031_cast_fp16, y = var_3029_cast_fp16)[name = tensor("attn_15_cast_fp16")]; - tensor var_3034 = const()[name = tensor("op_3034"), val = tensor([1, 512, 1, 188])]; - tensor input_205_cast_fp16 = reshape(shape = var_3034, x = attn_15_cast_fp16)[name = tensor("input_205_cast_fp16")]; - tensor var_3044_pad_type_0 = const()[name = tensor("op_3044_pad_type_0"), val = tensor("valid")]; - tensor var_3044_strides_0 = const()[name = tensor("op_3044_strides_0"), val = tensor([1, 1])]; - tensor var_3044_pad_0 = const()[name = tensor("op_3044_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3044_dilations_0 = const()[name = tensor("op_3044_dilations_0"), val = tensor([1, 1])]; - tensor var_3044_groups_0 = const()[name = tensor("op_3044_groups_0"), val = tensor(1)]; - tensor layers_7_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45135424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45332096))), name = tensor("layers_7_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_7_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_7_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45332288)))]; - tensor var_3044_cast_fp16 = conv(bias = layers_7_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_3044_dilations_0, groups = var_3044_groups_0, pad = var_3044_pad_0, pad_type = var_3044_pad_type_0, strides = var_3044_strides_0, weight = layers_7_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = tensor("op_3044_cast_fp16")]; - tensor var_3050_pad_type_0 = const()[name = tensor("op_3050_pad_type_0"), val = tensor("valid")]; - tensor var_3050_strides_0 = const()[name = tensor("op_3050_strides_0"), val = tensor([1, 1])]; - tensor var_3050_pad_0 = const()[name = tensor("op_3050_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3050_dilations_0 = const()[name = tensor("op_3050_dilations_0"), val = tensor([1, 1])]; - tensor var_3050_groups_0 = const()[name = tensor("op_3050_groups_0"), val = tensor(1)]; - tensor layers_7_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45342656))), name = tensor("layers_7_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45333376))), shape = tensor([512, 512, 1, 1])]; - tensor var_3050_cast_fp16 = conv(dilations = var_3050_dilations_0, groups = var_3050_groups_0, pad = var_3050_pad_0, pad_type = var_3050_pad_type_0, strides = var_3050_strides_0, weight = layers_7_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_205_cast_fp16)[name = tensor("op_3050_cast_fp16")]; - tensor obj_33_cast_fp16 = add(x = var_3044_cast_fp16, y = var_3050_cast_fp16)[name = tensor("obj_33_cast_fp16")]; - tensor inputs_75_cast_fp16 = add(x = inputs_73_cast_fp16, y = obj_33_cast_fp16)[name = tensor("inputs_75_cast_fp16")]; - tensor out_75_axes_0 = const()[name = tensor("out_75_axes_0"), val = tensor([1])]; - tensor var_3061_to_fp16 = const()[name = tensor("op_3061_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_75_cast_fp16 = layer_norm(axes = out_75_axes_0, epsilon = var_3061_to_fp16, x = inputs_75_cast_fp16)[name = tensor("out_75_cast_fp16")]; - tensor input_207_gamma_0_to_fp16 = const()[name = tensor("input_207_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45375488)))]; - tensor input_207_beta_0_to_fp16 = const()[name = tensor("input_207_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45376576)))]; - tensor input_207_epsilon_0_to_fp16 = const()[name = tensor("input_207_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_207_cast_fp16 = batch_norm(beta = input_207_beta_0_to_fp16, epsilon = input_207_epsilon_0_to_fp16, gamma = input_207_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_75_cast_fp16)[name = tensor("input_207_cast_fp16")]; - tensor var_3083_pad_type_0 = const()[name = tensor("op_3083_pad_type_0"), val = tensor("valid")]; - tensor var_3083_strides_0 = const()[name = tensor("op_3083_strides_0"), val = tensor([1, 1])]; - tensor var_3083_pad_0 = const()[name = tensor("op_3083_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3083_dilations_0 = const()[name = tensor("op_3083_dilations_0"), val = tensor([1, 1])]; - tensor var_3083_groups_0 = const()[name = tensor("op_3083_groups_0"), val = tensor(1)]; - tensor layers_7_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45377664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45770944))), name = tensor("layers_7_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_7_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_7_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45771136)))]; - tensor var_3083_cast_fp16 = conv(bias = layers_7_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_3083_dilations_0, groups = var_3083_groups_0, pad = var_3083_pad_0, pad_type = var_3083_pad_type_0, strides = var_3083_strides_0, weight = layers_7_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_207_cast_fp16)[name = tensor("op_3083_cast_fp16")]; - tensor var_3089_pad_type_0 = const()[name = tensor("op_3089_pad_type_0"), val = tensor("valid")]; - tensor var_3089_strides_0 = const()[name = tensor("op_3089_strides_0"), val = tensor([1, 1])]; - tensor var_3089_pad_0 = const()[name = tensor("op_3089_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3089_dilations_0 = const()[name = tensor("op_3089_dilations_0"), val = tensor([1, 1])]; - tensor var_3089_groups_0 = const()[name = tensor("op_3089_groups_0"), val = tensor(1)]; - tensor layers_7_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45791168))), name = tensor("layers_7_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45773248))), shape = tensor([1024, 512, 1, 1])]; - tensor var_3089_cast_fp16 = conv(dilations = var_3089_dilations_0, groups = var_3089_groups_0, pad = var_3089_pad_0, pad_type = var_3089_pad_type_0, strides = var_3089_strides_0, weight = layers_7_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_207_cast_fp16)[name = tensor("op_3089_cast_fp16")]; - tensor input_209_cast_fp16 = add(x = var_3083_cast_fp16, y = var_3089_cast_fp16)[name = tensor("input_209_cast_fp16")]; - tensor input_211_split_num_splits_0 = const()[name = tensor("input_211_split_num_splits_0"), val = tensor(2)]; - tensor input_211_split_axis_0 = const()[name = tensor("input_211_split_axis_0"), val = tensor(1)]; - tensor input_211_split_cast_fp16_0, tensor input_211_split_cast_fp16_1 = split(axis = input_211_split_axis_0, num_splits = input_211_split_num_splits_0, x = input_209_cast_fp16)[name = tensor("input_211_split_cast_fp16")]; - tensor input_211_split_1_sigmoid_cast_fp16 = sigmoid(x = input_211_split_cast_fp16_1)[name = tensor("input_211_split_1_sigmoid_cast_fp16")]; - tensor input_211_cast_fp16 = mul(x = input_211_split_cast_fp16_0, y = input_211_split_1_sigmoid_cast_fp16)[name = tensor("input_211_cast_fp16")]; - tensor input_213_pad_type_0 = const()[name = tensor("input_213_pad_type_0"), val = tensor("custom")]; - tensor input_213_pad_0 = const()[name = tensor("input_213_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_213_groups_0 = const()[name = tensor("input_213_groups_0"), val = tensor(512)]; - tensor input_213_strides_0 = const()[name = tensor("input_213_strides_0"), val = tensor([1, 1])]; - tensor input_213_dilations_0 = const()[name = tensor("input_213_dilations_0"), val = tensor([1, 1])]; - tensor const_205_to_fp16 = const()[name = tensor("const_205_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45856768)))]; - tensor const_206_to_fp16 = const()[name = tensor("const_206_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45866048)))]; - tensor input_215_cast_fp16 = conv(bias = const_206_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = const_205_to_fp16, x = input_211_cast_fp16)[name = tensor("input_215_cast_fp16")]; - tensor input_217_cast_fp16 = silu(x = input_215_cast_fp16)[name = tensor("input_217_cast_fp16")]; - tensor var_3113_pad_type_0 = const()[name = tensor("op_3113_pad_type_0"), val = tensor("valid")]; - tensor var_3113_strides_0 = const()[name = tensor("op_3113_strides_0"), val = tensor([1, 1])]; - tensor var_3113_pad_0 = const()[name = tensor("op_3113_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3113_dilations_0 = const()[name = tensor("op_3113_dilations_0"), val = tensor([1, 1])]; - tensor var_3113_groups_0 = const()[name = tensor("op_3113_groups_0"), val = tensor(1)]; - tensor layers_7_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45867136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46063808))), name = tensor("layers_7_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_7_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_7_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46064000)))]; - tensor var_3113_cast_fp16 = conv(bias = layers_7_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_3113_dilations_0, groups = var_3113_groups_0, pad = var_3113_pad_0, pad_type = var_3113_pad_type_0, strides = var_3113_strides_0, weight = layers_7_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = tensor("op_3113_cast_fp16")]; - tensor var_3119_pad_type_0 = const()[name = tensor("op_3119_pad_type_0"), val = tensor("valid")]; - tensor var_3119_strides_0 = const()[name = tensor("op_3119_strides_0"), val = tensor([1, 1])]; - tensor var_3119_pad_0 = const()[name = tensor("op_3119_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3119_dilations_0 = const()[name = tensor("op_3119_dilations_0"), val = tensor([1, 1])]; - tensor var_3119_groups_0 = const()[name = tensor("op_3119_groups_0"), val = tensor(1)]; - tensor layers_7_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46073536))), name = tensor("layers_7_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46065088))), shape = tensor([512, 512, 1, 1])]; - tensor var_3119_cast_fp16 = conv(dilations = var_3119_dilations_0, groups = var_3119_groups_0, pad = var_3119_pad_0, pad_type = var_3119_pad_type_0, strides = var_3119_strides_0, weight = layers_7_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_217_cast_fp16)[name = tensor("op_3119_cast_fp16")]; - tensor x_47_cast_fp16 = add(x = var_3113_cast_fp16, y = var_3119_cast_fp16)[name = tensor("x_47_cast_fp16")]; - tensor inputs_77_cast_fp16 = add(x = inputs_75_cast_fp16, y = x_47_cast_fp16)[name = tensor("inputs_77_cast_fp16")]; - tensor out_77_axes_0 = const()[name = tensor("out_77_axes_0"), val = tensor([1])]; - tensor var_3130_to_fp16 = const()[name = tensor("op_3130_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_77_cast_fp16 = layer_norm(axes = out_77_axes_0, epsilon = var_3130_to_fp16, x = inputs_77_cast_fp16)[name = tensor("out_77_cast_fp16")]; - tensor input_219_gamma_0_to_fp16 = const()[name = tensor("input_219_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46106368)))]; - tensor input_219_beta_0_to_fp16 = const()[name = tensor("input_219_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46107456)))]; - tensor input_219_epsilon_0_to_fp16 = const()[name = tensor("input_219_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_219_cast_fp16 = batch_norm(beta = input_219_beta_0_to_fp16, epsilon = input_219_epsilon_0_to_fp16, gamma = input_219_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_77_cast_fp16)[name = tensor("input_219_cast_fp16")]; - tensor var_3150_pad_type_0 = const()[name = tensor("op_3150_pad_type_0"), val = tensor("valid")]; - tensor var_3150_strides_0 = const()[name = tensor("op_3150_strides_0"), val = tensor([1, 1])]; - tensor var_3150_pad_0 = const()[name = tensor("op_3150_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3150_dilations_0 = const()[name = tensor("op_3150_dilations_0"), val = tensor([1, 1])]; - tensor var_3150_groups_0 = const()[name = tensor("op_3150_groups_0"), val = tensor(1)]; - tensor layers_7_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46108544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46895040))), name = tensor("layers_7_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_7_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_7_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46895232)))]; - tensor var_3150_cast_fp16 = conv(bias = layers_7_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_3150_dilations_0, groups = var_3150_groups_0, pad = var_3150_pad_0, pad_type = var_3150_pad_type_0, strides = var_3150_strides_0, weight = layers_7_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_219_cast_fp16)[name = tensor("op_3150_cast_fp16")]; - tensor var_3156_pad_type_0 = const()[name = tensor("op_3156_pad_type_0"), val = tensor("valid")]; - tensor var_3156_strides_0 = const()[name = tensor("op_3156_strides_0"), val = tensor([1, 1])]; - tensor var_3156_pad_0 = const()[name = tensor("op_3156_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3156_dilations_0 = const()[name = tensor("op_3156_dilations_0"), val = tensor([1, 1])]; - tensor var_3156_groups_0 = const()[name = tensor("op_3156_groups_0"), val = tensor(1)]; - tensor layers_7_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46930816))), name = tensor("layers_7_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46899392))), shape = tensor([2048, 512, 1, 1])]; - tensor var_3156_cast_fp16 = conv(dilations = var_3156_dilations_0, groups = var_3156_groups_0, pad = var_3156_pad_0, pad_type = var_3156_pad_type_0, strides = var_3156_strides_0, weight = layers_7_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_219_cast_fp16)[name = tensor("op_3156_cast_fp16")]; - tensor input_221_cast_fp16 = add(x = var_3150_cast_fp16, y = var_3156_cast_fp16)[name = tensor("input_221_cast_fp16")]; - tensor input_223_cast_fp16 = silu(x = input_221_cast_fp16)[name = tensor("input_223_cast_fp16")]; - tensor var_3167_pad_type_0 = const()[name = tensor("op_3167_pad_type_0"), val = tensor("valid")]; - tensor var_3167_strides_0 = const()[name = tensor("op_3167_strides_0"), val = tensor([1, 1])]; - tensor var_3167_pad_0 = const()[name = tensor("op_3167_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3167_dilations_0 = const()[name = tensor("op_3167_dilations_0"), val = tensor([1, 1])]; - tensor var_3167_groups_0 = const()[name = tensor("op_3167_groups_0"), val = tensor(1)]; - tensor layers_7_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47061952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47848448))), name = tensor("layers_7_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_7_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_7_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47848640)))]; - tensor var_3167_cast_fp16 = conv(bias = layers_7_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_3167_dilations_0, groups = var_3167_groups_0, pad = var_3167_pad_0, pad_type = var_3167_pad_type_0, strides = var_3167_strides_0, weight = layers_7_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_223_cast_fp16)[name = tensor("op_3167_cast_fp16")]; - tensor var_3173_pad_type_0 = const()[name = tensor("op_3173_pad_type_0"), val = tensor("valid")]; - tensor var_3173_strides_0 = const()[name = tensor("op_3173_strides_0"), val = tensor([1, 1])]; - tensor var_3173_pad_0 = const()[name = tensor("op_3173_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3173_dilations_0 = const()[name = tensor("op_3173_dilations_0"), val = tensor([1, 1])]; - tensor var_3173_groups_0 = const()[name = tensor("op_3173_groups_0"), val = tensor(1)]; - tensor layers_7_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47885632))), name = tensor("layers_7_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47849728))), shape = tensor([512, 2048, 1, 1])]; - tensor var_3173_cast_fp16 = conv(dilations = var_3173_dilations_0, groups = var_3173_groups_0, pad = var_3173_pad_0, pad_type = var_3173_pad_type_0, strides = var_3173_strides_0, weight = layers_7_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_223_cast_fp16)[name = tensor("op_3173_cast_fp16")]; - tensor x_49_cast_fp16 = add(x = var_3167_cast_fp16, y = var_3173_cast_fp16)[name = tensor("x_49_cast_fp16")]; - tensor var_3175_to_fp16 = const()[name = tensor("op_3175_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3176_cast_fp16 = mul(x = x_49_cast_fp16, y = var_3175_to_fp16)[name = tensor("op_3176_cast_fp16")]; - tensor inputs_79_cast_fp16 = add(x = inputs_77_cast_fp16, y = var_3176_cast_fp16)[name = tensor("inputs_79_cast_fp16")]; - tensor out_79_axes_0 = const()[name = tensor("out_79_axes_0"), val = tensor([1])]; - tensor var_3186_to_fp16 = const()[name = tensor("op_3186_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_79_cast_fp16 = layer_norm(axes = out_79_axes_0, epsilon = var_3186_to_fp16, x = inputs_79_cast_fp16)[name = tensor("out_79_cast_fp16")]; - tensor inputs_81_gamma_0_to_fp16 = const()[name = tensor("inputs_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48016768)))]; - tensor inputs_81_beta_0_to_fp16 = const()[name = tensor("inputs_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48017856)))]; - tensor inputs_81_epsilon_0_to_fp16 = const()[name = tensor("inputs_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_81_cast_fp16 = batch_norm(beta = inputs_81_beta_0_to_fp16, epsilon = inputs_81_epsilon_0_to_fp16, gamma = inputs_81_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_79_cast_fp16)[name = tensor("inputs_81_cast_fp16")]; - tensor var_3200 = const()[name = tensor("op_3200"), val = tensor(3)]; - tensor out_81_axes_0 = const()[name = tensor("out_81_axes_0"), val = tensor([1])]; - tensor var_3231_to_fp16 = const()[name = tensor("op_3231_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_3231_to_fp16, x = inputs_81_cast_fp16)[name = tensor("out_81_cast_fp16")]; - tensor input_225_gamma_0_to_fp16 = const()[name = tensor("input_225_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48018944)))]; - tensor input_225_beta_0_to_fp16 = const()[name = tensor("input_225_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48020032)))]; - tensor input_225_epsilon_0_to_fp16 = const()[name = tensor("input_225_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_225_cast_fp16 = batch_norm(beta = input_225_beta_0_to_fp16, epsilon = input_225_epsilon_0_to_fp16, gamma = input_225_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_81_cast_fp16)[name = tensor("input_225_cast_fp16")]; - tensor var_3251_pad_type_0 = const()[name = tensor("op_3251_pad_type_0"), val = tensor("valid")]; - tensor var_3251_strides_0 = const()[name = tensor("op_3251_strides_0"), val = tensor([1, 1])]; - tensor var_3251_pad_0 = const()[name = tensor("op_3251_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3251_dilations_0 = const()[name = tensor("op_3251_dilations_0"), val = tensor([1, 1])]; - tensor var_3251_groups_0 = const()[name = tensor("op_3251_groups_0"), val = tensor(1)]; - tensor layers_8_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48021120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48807616))), name = tensor("layers_8_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_8_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_8_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48807808)))]; - tensor var_3251_cast_fp16 = conv(bias = layers_8_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_3251_dilations_0, groups = var_3251_groups_0, pad = var_3251_pad_0, pad_type = var_3251_pad_type_0, strides = var_3251_strides_0, weight = layers_8_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = tensor("op_3251_cast_fp16")]; - tensor var_3257_pad_type_0 = const()[name = tensor("op_3257_pad_type_0"), val = tensor("valid")]; - tensor var_3257_strides_0 = const()[name = tensor("op_3257_strides_0"), val = tensor([1, 1])]; - tensor var_3257_pad_0 = const()[name = tensor("op_3257_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3257_dilations_0 = const()[name = tensor("op_3257_dilations_0"), val = tensor([1, 1])]; - tensor var_3257_groups_0 = const()[name = tensor("op_3257_groups_0"), val = tensor(1)]; - tensor layers_8_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48844544))), name = tensor("layers_8_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48811968))), shape = tensor([2048, 512, 1, 1])]; - tensor var_3257_cast_fp16 = conv(dilations = var_3257_dilations_0, groups = var_3257_groups_0, pad = var_3257_pad_0, pad_type = var_3257_pad_type_0, strides = var_3257_strides_0, weight = layers_8_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_225_cast_fp16)[name = tensor("op_3257_cast_fp16")]; - tensor input_227_cast_fp16 = add(x = var_3251_cast_fp16, y = var_3257_cast_fp16)[name = tensor("input_227_cast_fp16")]; - tensor input_229_cast_fp16 = silu(x = input_227_cast_fp16)[name = tensor("input_229_cast_fp16")]; - tensor var_3268_pad_type_0 = const()[name = tensor("op_3268_pad_type_0"), val = tensor("valid")]; - tensor var_3268_strides_0 = const()[name = tensor("op_3268_strides_0"), val = tensor([1, 1])]; - tensor var_3268_pad_0 = const()[name = tensor("op_3268_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3268_dilations_0 = const()[name = tensor("op_3268_dilations_0"), val = tensor([1, 1])]; - tensor var_3268_groups_0 = const()[name = tensor("op_3268_groups_0"), val = tensor(1)]; - tensor layers_8_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48975680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49762176))), name = tensor("layers_8_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_8_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_8_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49762368)))]; - tensor var_3268_cast_fp16 = conv(bias = layers_8_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_3268_dilations_0, groups = var_3268_groups_0, pad = var_3268_pad_0, pad_type = var_3268_pad_type_0, strides = var_3268_strides_0, weight = layers_8_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = tensor("op_3268_cast_fp16")]; - tensor var_3274_pad_type_0 = const()[name = tensor("op_3274_pad_type_0"), val = tensor("valid")]; - tensor var_3274_strides_0 = const()[name = tensor("op_3274_strides_0"), val = tensor([1, 1])]; - tensor var_3274_pad_0 = const()[name = tensor("op_3274_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3274_dilations_0 = const()[name = tensor("op_3274_dilations_0"), val = tensor([1, 1])]; - tensor var_3274_groups_0 = const()[name = tensor("op_3274_groups_0"), val = tensor(1)]; - tensor layers_8_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49800128))), name = tensor("layers_8_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49763456))), shape = tensor([512, 2048, 1, 1])]; - tensor var_3274_cast_fp16 = conv(dilations = var_3274_dilations_0, groups = var_3274_groups_0, pad = var_3274_pad_0, pad_type = var_3274_pad_type_0, strides = var_3274_strides_0, weight = layers_8_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_229_cast_fp16)[name = tensor("op_3274_cast_fp16")]; - tensor x_51_cast_fp16 = add(x = var_3268_cast_fp16, y = var_3274_cast_fp16)[name = tensor("x_51_cast_fp16")]; - tensor var_3276_to_fp16 = const()[name = tensor("op_3276_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3277_cast_fp16 = mul(x = x_51_cast_fp16, y = var_3276_to_fp16)[name = tensor("op_3277_cast_fp16")]; - tensor inputs_83_cast_fp16 = add(x = inputs_81_cast_fp16, y = var_3277_cast_fp16)[name = tensor("inputs_83_cast_fp16")]; - tensor out_83_axes_0 = const()[name = tensor("out_83_axes_0"), val = tensor([1])]; - tensor var_3287_to_fp16 = const()[name = tensor("op_3287_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_83_cast_fp16 = layer_norm(axes = out_83_axes_0, epsilon = var_3287_to_fp16, x = inputs_83_cast_fp16)[name = tensor("out_83_cast_fp16")]; - tensor obj_35_gamma_0_to_fp16 = const()[name = tensor("obj_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49931264)))]; - tensor obj_35_beta_0_to_fp16 = const()[name = tensor("obj_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49932352)))]; - tensor obj_35_epsilon_0_to_fp16 = const()[name = tensor("obj_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_35_cast_fp16 = batch_norm(beta = obj_35_beta_0_to_fp16, epsilon = obj_35_epsilon_0_to_fp16, gamma = obj_35_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_83_cast_fp16)[name = tensor("obj_35_cast_fp16")]; - tensor var_3312_pad_type_0 = const()[name = tensor("op_3312_pad_type_0"), val = tensor("valid")]; - tensor var_3312_strides_0 = const()[name = tensor("op_3312_strides_0"), val = tensor([1, 1])]; - tensor var_3312_pad_0 = const()[name = tensor("op_3312_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3312_dilations_0 = const()[name = tensor("op_3312_dilations_0"), val = tensor([1, 1])]; - tensor var_3312_groups_0 = const()[name = tensor("op_3312_groups_0"), val = tensor(1)]; - tensor layers_8_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49933440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50130112))), name = tensor("layers_8_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_8_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_8_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50130304)))]; - tensor var_3312_cast_fp16 = conv(bias = layers_8_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_3312_dilations_0, groups = var_3312_groups_0, pad = var_3312_pad_0, pad_type = var_3312_pad_type_0, strides = var_3312_strides_0, weight = layers_8_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_35_cast_fp16)[name = tensor("op_3312_cast_fp16")]; - tensor var_3318_pad_type_0 = const()[name = tensor("op_3318_pad_type_0"), val = tensor("valid")]; - tensor var_3318_strides_0 = const()[name = tensor("op_3318_strides_0"), val = tensor([1, 1])]; - tensor var_3318_pad_0 = const()[name = tensor("op_3318_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3318_dilations_0 = const()[name = tensor("op_3318_dilations_0"), val = tensor([1, 1])]; - tensor var_3318_groups_0 = const()[name = tensor("op_3318_groups_0"), val = tensor(1)]; - tensor layers_8_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50142912))), name = tensor("layers_8_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50131392))), shape = tensor([512, 512, 1, 1])]; - tensor var_3318_cast_fp16 = conv(dilations = var_3318_dilations_0, groups = var_3318_groups_0, pad = var_3318_pad_0, pad_type = var_3318_pad_type_0, strides = var_3318_strides_0, weight = layers_8_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_35_cast_fp16)[name = tensor("op_3318_cast_fp16")]; - tensor query_33_cast_fp16 = add(x = var_3312_cast_fp16, y = var_3318_cast_fp16)[name = tensor("query_33_cast_fp16")]; - tensor var_3327_pad_type_0 = const()[name = tensor("op_3327_pad_type_0"), val = tensor("valid")]; - tensor var_3327_strides_0 = const()[name = tensor("op_3327_strides_0"), val = tensor([1, 1])]; - tensor var_3327_pad_0 = const()[name = tensor("op_3327_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3327_dilations_0 = const()[name = tensor("op_3327_dilations_0"), val = tensor([1, 1])]; - tensor var_3327_groups_0 = const()[name = tensor("op_3327_groups_0"), val = tensor(1)]; - tensor layers_8_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50175744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50372416))), name = tensor("layers_8_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_3327_cast_fp16 = conv(dilations = var_3327_dilations_0, groups = var_3327_groups_0, pad = var_3327_pad_0, pad_type = var_3327_pad_type_0, strides = var_3327_strides_0, weight = layers_8_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_35_cast_fp16)[name = tensor("op_3327_cast_fp16")]; - tensor var_3333_pad_type_0 = const()[name = tensor("op_3333_pad_type_0"), val = tensor("valid")]; - tensor var_3333_strides_0 = const()[name = tensor("op_3333_strides_0"), val = tensor([1, 1])]; - tensor var_3333_pad_0 = const()[name = tensor("op_3333_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3333_dilations_0 = const()[name = tensor("op_3333_dilations_0"), val = tensor([1, 1])]; - tensor var_3333_groups_0 = const()[name = tensor("op_3333_groups_0"), val = tensor(1)]; - tensor layers_8_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50381632))), name = tensor("layers_8_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50372608))), shape = tensor([512, 512, 1, 1])]; - tensor var_3333_cast_fp16 = conv(dilations = var_3333_dilations_0, groups = var_3333_groups_0, pad = var_3333_pad_0, pad_type = var_3333_pad_type_0, strides = var_3333_strides_0, weight = layers_8_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_35_cast_fp16)[name = tensor("op_3333_cast_fp16")]; - tensor key_17_cast_fp16 = add(x = var_3327_cast_fp16, y = var_3333_cast_fp16)[name = tensor("key_17_cast_fp16")]; - tensor var_3343_pad_type_0 = const()[name = tensor("op_3343_pad_type_0"), val = tensor("valid")]; - tensor var_3343_strides_0 = const()[name = tensor("op_3343_strides_0"), val = tensor([1, 1])]; - tensor var_3343_pad_0 = const()[name = tensor("op_3343_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3343_dilations_0 = const()[name = tensor("op_3343_dilations_0"), val = tensor([1, 1])]; - tensor var_3343_groups_0 = const()[name = tensor("op_3343_groups_0"), val = tensor(1)]; - tensor layers_8_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50414464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50611136))), name = tensor("layers_8_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_8_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_8_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50611328)))]; - tensor var_3343_cast_fp16 = conv(bias = layers_8_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_3343_dilations_0, groups = var_3343_groups_0, pad = var_3343_pad_0, pad_type = var_3343_pad_type_0, strides = var_3343_strides_0, weight = layers_8_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_35_cast_fp16)[name = tensor("op_3343_cast_fp16")]; - tensor var_3349_pad_type_0 = const()[name = tensor("op_3349_pad_type_0"), val = tensor("valid")]; - tensor var_3349_strides_0 = const()[name = tensor("op_3349_strides_0"), val = tensor([1, 1])]; - tensor var_3349_pad_0 = const()[name = tensor("op_3349_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3349_dilations_0 = const()[name = tensor("op_3349_dilations_0"), val = tensor([1, 1])]; - tensor var_3349_groups_0 = const()[name = tensor("op_3349_groups_0"), val = tensor(1)]; - tensor layers_8_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50621184))), name = tensor("layers_8_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50612416))), shape = tensor([512, 512, 1, 1])]; - tensor var_3349_cast_fp16 = conv(dilations = var_3349_dilations_0, groups = var_3349_groups_0, pad = var_3349_pad_0, pad_type = var_3349_pad_type_0, strides = var_3349_strides_0, weight = layers_8_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_35_cast_fp16)[name = tensor("op_3349_cast_fp16")]; - tensor value_17_cast_fp16 = add(x = var_3343_cast_fp16, y = var_3349_cast_fp16)[name = tensor("value_17_cast_fp16")]; - tensor var_3352_to_fp16 = const()[name = tensor("op_3352_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50654016)))]; - tensor query_35_cast_fp16 = add(x = query_33_cast_fp16, y = var_3352_to_fp16)[name = tensor("query_35_cast_fp16")]; - tensor var_3355_to_fp16 = const()[name = tensor("op_3355_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50655104)))]; - tensor q_with_bias_v_17_cast_fp16 = add(x = query_33_cast_fp16, y = var_3355_to_fp16)[name = tensor("q_with_bias_v_17_cast_fp16")]; - tensor var_3365_pad_type_0 = const()[name = tensor("op_3365_pad_type_0"), val = tensor("valid")]; - tensor var_3365_strides_0 = const()[name = tensor("op_3365_strides_0"), val = tensor([1, 1])]; - tensor var_3365_pad_0 = const()[name = tensor("op_3365_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3365_dilations_0 = const()[name = tensor("op_3365_dilations_0"), val = tensor([1, 1])]; - tensor var_3365_groups_0 = const()[name = tensor("op_3365_groups_0"), val = tensor(1)]; - tensor layers_8_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50656192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50852864))), name = tensor("layers_8_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_3365_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_3365_dilations_0, groups = var_3365_groups_0, pad = var_3365_pad_0, pad_type = var_3365_pad_type_0, strides = var_3365_strides_0, weight = layers_8_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_3365_cast_fp16")]; - tensor var_3371_pad_type_0 = const()[name = tensor("op_3371_pad_type_0"), val = tensor("valid")]; - tensor var_3371_strides_0 = const()[name = tensor("op_3371_strides_0"), val = tensor([1, 1])]; - tensor var_3371_pad_0 = const()[name = tensor("op_3371_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3371_dilations_0 = const()[name = tensor("op_3371_dilations_0"), val = tensor([1, 1])]; - tensor var_3371_groups_0 = const()[name = tensor("op_3371_groups_0"), val = tensor(1)]; - tensor layers_8_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50867840))), name = tensor("layers_8_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50853056))), shape = tensor([512, 512, 1, 1])]; - tensor var_3371_cast_fp16 = conv(dilations = var_3371_dilations_0, groups = var_3371_groups_0, pad = var_3371_pad_0, pad_type = var_3371_pad_type_0, strides = var_3371_strides_0, weight = layers_8_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_3371_cast_fp16")]; - tensor p_17_cast_fp16 = add(x = var_3365_cast_fp16, y = var_3371_cast_fp16)[name = tensor("p_17_cast_fp16")]; - tensor var_3375 = const()[name = tensor("op_3375"), val = tensor([1, 8, 64, 188])]; - tensor var_3376_cast_fp16 = reshape(shape = var_3375, x = q_with_bias_v_17_cast_fp16)[name = tensor("op_3376_cast_fp16")]; - tensor var_3377 = const()[name = tensor("op_3377"), val = tensor([1, 8, 64, -1])]; - tensor var_3378_cast_fp16 = reshape(shape = var_3377, x = p_17_cast_fp16)[name = tensor("op_3378_cast_fp16")]; - tensor matrix_bd_65_transpose_x_0 = const()[name = tensor("matrix_bd_65_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_65_transpose_y_0 = const()[name = tensor("matrix_bd_65_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_65_cast_fp16 = matmul(transpose_x = matrix_bd_65_transpose_x_0, transpose_y = matrix_bd_65_transpose_y_0, x = var_3376_cast_fp16, y = var_3378_cast_fp16)[name = tensor("matrix_bd_65_cast_fp16")]; - tensor matrix_bd_67_pad_0 = const()[name = tensor("matrix_bd_67_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_67_mode_0 = const()[name = tensor("matrix_bd_67_mode_0"), val = tensor("constant")]; - tensor const_98_to_fp16 = const()[name = tensor("const_98_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_67_cast_fp16 = pad(constant_val = const_98_to_fp16, mode = matrix_bd_67_mode_0, pad = matrix_bd_67_pad_0, x = matrix_bd_65_cast_fp16)[name = tensor("matrix_bd_67_cast_fp16")]; - tensor var_3387 = const()[name = tensor("op_3387"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_69_cast_fp16 = reshape(shape = var_3387, x = matrix_bd_67_cast_fp16)[name = tensor("matrix_bd_69_cast_fp16")]; - tensor var_3391_begin_0 = const()[name = tensor("op_3391_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3391_end_0 = const()[name = tensor("op_3391_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3391_end_mask_0 = const()[name = tensor("op_3391_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3391_cast_fp16 = slice_by_index(begin = var_3391_begin_0, end = var_3391_end_0, end_mask = var_3391_end_mask_0, x = matrix_bd_69_cast_fp16)[name = tensor("op_3391_cast_fp16")]; - tensor var_3392 = const()[name = tensor("op_3392"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_71_cast_fp16 = reshape(shape = var_3392, x = var_3391_cast_fp16)[name = tensor("matrix_bd_71_cast_fp16")]; - tensor var_3397_begin_0 = const()[name = tensor("op_3397_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3397_end_0 = const()[name = tensor("op_3397_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_3397_end_mask_0 = const()[name = tensor("op_3397_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_3397_cast_fp16 = slice_by_index(begin = var_3397_begin_0, end = var_3397_end_0, end_mask = var_3397_end_mask_0, x = matrix_bd_71_cast_fp16)[name = tensor("op_3397_cast_fp16")]; - tensor var_3398_to_fp16 = const()[name = tensor("op_3398_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_17_cast_fp16 = mul(x = var_3397_cast_fp16, y = var_3398_to_fp16)[name = tensor("qk_mask_17_cast_fp16")]; - tensor var_3402 = const()[name = tensor("op_3402"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_17_cast_fp16 = reshape(shape = var_3402, x = query_35_cast_fp16)[name = tensor("mh_q_17_cast_fp16")]; - tensor var_3404_to_fp16 = const()[name = tensor("op_3404_to_fp16"), val = tensor(0x1p-3)]; - tensor var_3405_cast_fp16 = mul(x = mh_q_17_cast_fp16, y = var_3404_to_fp16)[name = tensor("op_3405_cast_fp16")]; - tensor var_3408 = const()[name = tensor("op_3408"), val = tensor([1, 8, 64, 188])]; - tensor var_3409_cast_fp16 = reshape(shape = var_3408, x = key_17_cast_fp16)[name = tensor("op_3409_cast_fp16")]; - tensor mh_w_33_transpose_x_0 = const()[name = tensor("mh_w_33_transpose_x_0"), val = tensor(true)]; - tensor mh_w_33_transpose_y_0 = const()[name = tensor("mh_w_33_transpose_y_0"), val = tensor(false)]; - tensor mh_w_33_cast_fp16 = matmul(transpose_x = mh_w_33_transpose_x_0, transpose_y = mh_w_33_transpose_y_0, x = var_3405_cast_fp16, y = var_3409_cast_fp16)[name = tensor("mh_w_33_cast_fp16")]; - tensor mh_w_35_cast_fp16 = add(x = mh_w_33_cast_fp16, y = qk_mask_17_cast_fp16)[name = tensor("mh_w_35_cast_fp16")]; - tensor var_3413_cast_fp16 = softmax(axis = var_3200, x = mh_w_35_cast_fp16)[name = tensor("op_3413_cast_fp16")]; - tensor var_3414 = const()[name = tensor("op_3414"), val = tensor([1, 8, 64, 188])]; - tensor var_3415_cast_fp16 = reshape(shape = var_3414, x = value_17_cast_fp16)[name = tensor("op_3415_cast_fp16")]; - tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; - tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; - tensor attn_17_cast_fp16 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_3415_cast_fp16, y = var_3413_cast_fp16)[name = tensor("attn_17_cast_fp16")]; - tensor var_3418 = const()[name = tensor("op_3418"), val = tensor([1, 512, 1, 188])]; - tensor input_231_cast_fp16 = reshape(shape = var_3418, x = attn_17_cast_fp16)[name = tensor("input_231_cast_fp16")]; - tensor var_3428_pad_type_0 = const()[name = tensor("op_3428_pad_type_0"), val = tensor("valid")]; - tensor var_3428_strides_0 = const()[name = tensor("op_3428_strides_0"), val = tensor([1, 1])]; - tensor var_3428_pad_0 = const()[name = tensor("op_3428_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3428_dilations_0 = const()[name = tensor("op_3428_dilations_0"), val = tensor([1, 1])]; - tensor var_3428_groups_0 = const()[name = tensor("op_3428_groups_0"), val = tensor(1)]; - tensor layers_8_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50900672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51097344))), name = tensor("layers_8_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_8_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_8_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51097536)))]; - tensor var_3428_cast_fp16 = conv(bias = layers_8_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_3428_dilations_0, groups = var_3428_groups_0, pad = var_3428_pad_0, pad_type = var_3428_pad_type_0, strides = var_3428_strides_0, weight = layers_8_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_231_cast_fp16)[name = tensor("op_3428_cast_fp16")]; - tensor var_3434_pad_type_0 = const()[name = tensor("op_3434_pad_type_0"), val = tensor("valid")]; - tensor var_3434_strides_0 = const()[name = tensor("op_3434_strides_0"), val = tensor([1, 1])]; - tensor var_3434_pad_0 = const()[name = tensor("op_3434_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3434_dilations_0 = const()[name = tensor("op_3434_dilations_0"), val = tensor([1, 1])]; - tensor var_3434_groups_0 = const()[name = tensor("op_3434_groups_0"), val = tensor(1)]; - tensor layers_8_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51107392))), name = tensor("layers_8_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51098624))), shape = tensor([512, 512, 1, 1])]; - tensor var_3434_cast_fp16 = conv(dilations = var_3434_dilations_0, groups = var_3434_groups_0, pad = var_3434_pad_0, pad_type = var_3434_pad_type_0, strides = var_3434_strides_0, weight = layers_8_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_231_cast_fp16)[name = tensor("op_3434_cast_fp16")]; - tensor obj_37_cast_fp16 = add(x = var_3428_cast_fp16, y = var_3434_cast_fp16)[name = tensor("obj_37_cast_fp16")]; - tensor inputs_85_cast_fp16 = add(x = inputs_83_cast_fp16, y = obj_37_cast_fp16)[name = tensor("inputs_85_cast_fp16")]; - tensor out_85_axes_0 = const()[name = tensor("out_85_axes_0"), val = tensor([1])]; - tensor var_3445_to_fp16 = const()[name = tensor("op_3445_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_85_cast_fp16 = layer_norm(axes = out_85_axes_0, epsilon = var_3445_to_fp16, x = inputs_85_cast_fp16)[name = tensor("out_85_cast_fp16")]; - tensor input_233_gamma_0_to_fp16 = const()[name = tensor("input_233_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51140224)))]; - tensor input_233_beta_0_to_fp16 = const()[name = tensor("input_233_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51141312)))]; - tensor input_233_epsilon_0_to_fp16 = const()[name = tensor("input_233_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_233_cast_fp16 = batch_norm(beta = input_233_beta_0_to_fp16, epsilon = input_233_epsilon_0_to_fp16, gamma = input_233_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_85_cast_fp16)[name = tensor("input_233_cast_fp16")]; - tensor var_3467_pad_type_0 = const()[name = tensor("op_3467_pad_type_0"), val = tensor("valid")]; - tensor var_3467_strides_0 = const()[name = tensor("op_3467_strides_0"), val = tensor([1, 1])]; - tensor var_3467_pad_0 = const()[name = tensor("op_3467_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3467_dilations_0 = const()[name = tensor("op_3467_dilations_0"), val = tensor([1, 1])]; - tensor var_3467_groups_0 = const()[name = tensor("op_3467_groups_0"), val = tensor(1)]; - tensor layers_8_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51142400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51535680))), name = tensor("layers_8_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_8_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_8_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51535872)))]; - tensor var_3467_cast_fp16 = conv(bias = layers_8_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_3467_dilations_0, groups = var_3467_groups_0, pad = var_3467_pad_0, pad_type = var_3467_pad_type_0, strides = var_3467_strides_0, weight = layers_8_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_233_cast_fp16)[name = tensor("op_3467_cast_fp16")]; - tensor var_3473_pad_type_0 = const()[name = tensor("op_3473_pad_type_0"), val = tensor("valid")]; - tensor var_3473_strides_0 = const()[name = tensor("op_3473_strides_0"), val = tensor([1, 1])]; - tensor var_3473_pad_0 = const()[name = tensor("op_3473_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3473_dilations_0 = const()[name = tensor("op_3473_dilations_0"), val = tensor([1, 1])]; - tensor var_3473_groups_0 = const()[name = tensor("op_3473_groups_0"), val = tensor(1)]; - tensor layers_8_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51555520))), name = tensor("layers_8_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51537984))), shape = tensor([1024, 512, 1, 1])]; - tensor var_3473_cast_fp16 = conv(dilations = var_3473_dilations_0, groups = var_3473_groups_0, pad = var_3473_pad_0, pad_type = var_3473_pad_type_0, strides = var_3473_strides_0, weight = layers_8_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_233_cast_fp16)[name = tensor("op_3473_cast_fp16")]; - tensor input_235_cast_fp16 = add(x = var_3467_cast_fp16, y = var_3473_cast_fp16)[name = tensor("input_235_cast_fp16")]; - tensor input_237_split_num_splits_0 = const()[name = tensor("input_237_split_num_splits_0"), val = tensor(2)]; - tensor input_237_split_axis_0 = const()[name = tensor("input_237_split_axis_0"), val = tensor(1)]; - tensor input_237_split_cast_fp16_0, tensor input_237_split_cast_fp16_1 = split(axis = input_237_split_axis_0, num_splits = input_237_split_num_splits_0, x = input_235_cast_fp16)[name = tensor("input_237_split_cast_fp16")]; - tensor input_237_split_1_sigmoid_cast_fp16 = sigmoid(x = input_237_split_cast_fp16_1)[name = tensor("input_237_split_1_sigmoid_cast_fp16")]; - tensor input_237_cast_fp16 = mul(x = input_237_split_cast_fp16_0, y = input_237_split_1_sigmoid_cast_fp16)[name = tensor("input_237_cast_fp16")]; - tensor input_239_pad_type_0 = const()[name = tensor("input_239_pad_type_0"), val = tensor("custom")]; - tensor input_239_pad_0 = const()[name = tensor("input_239_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_239_groups_0 = const()[name = tensor("input_239_groups_0"), val = tensor(512)]; - tensor input_239_strides_0 = const()[name = tensor("input_239_strides_0"), val = tensor([1, 1])]; - tensor input_239_dilations_0 = const()[name = tensor("input_239_dilations_0"), val = tensor([1, 1])]; - tensor const_207_to_fp16 = const()[name = tensor("const_207_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51621120)))]; - tensor const_208_to_fp16 = const()[name = tensor("const_208_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51630400)))]; - tensor input_241_cast_fp16 = conv(bias = const_208_to_fp16, dilations = input_239_dilations_0, groups = input_239_groups_0, pad = input_239_pad_0, pad_type = input_239_pad_type_0, strides = input_239_strides_0, weight = const_207_to_fp16, x = input_237_cast_fp16)[name = tensor("input_241_cast_fp16")]; - tensor input_243_cast_fp16 = silu(x = input_241_cast_fp16)[name = tensor("input_243_cast_fp16")]; - tensor var_3497_pad_type_0 = const()[name = tensor("op_3497_pad_type_0"), val = tensor("valid")]; - tensor var_3497_strides_0 = const()[name = tensor("op_3497_strides_0"), val = tensor([1, 1])]; - tensor var_3497_pad_0 = const()[name = tensor("op_3497_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3497_dilations_0 = const()[name = tensor("op_3497_dilations_0"), val = tensor([1, 1])]; - tensor var_3497_groups_0 = const()[name = tensor("op_3497_groups_0"), val = tensor(1)]; - tensor layers_8_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51631488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51828160))), name = tensor("layers_8_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_8_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_8_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51828352)))]; - tensor var_3497_cast_fp16 = conv(bias = layers_8_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_3497_dilations_0, groups = var_3497_groups_0, pad = var_3497_pad_0, pad_type = var_3497_pad_type_0, strides = var_3497_strides_0, weight = layers_8_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_243_cast_fp16)[name = tensor("op_3497_cast_fp16")]; - tensor var_3503_pad_type_0 = const()[name = tensor("op_3503_pad_type_0"), val = tensor("valid")]; - tensor var_3503_strides_0 = const()[name = tensor("op_3503_strides_0"), val = tensor([1, 1])]; - tensor var_3503_pad_0 = const()[name = tensor("op_3503_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3503_dilations_0 = const()[name = tensor("op_3503_dilations_0"), val = tensor([1, 1])]; - tensor var_3503_groups_0 = const()[name = tensor("op_3503_groups_0"), val = tensor(1)]; - tensor layers_8_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51837312))), name = tensor("layers_8_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51829440))), shape = tensor([512, 512, 1, 1])]; - tensor var_3503_cast_fp16 = conv(dilations = var_3503_dilations_0, groups = var_3503_groups_0, pad = var_3503_pad_0, pad_type = var_3503_pad_type_0, strides = var_3503_strides_0, weight = layers_8_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_243_cast_fp16)[name = tensor("op_3503_cast_fp16")]; - tensor x_53_cast_fp16 = add(x = var_3497_cast_fp16, y = var_3503_cast_fp16)[name = tensor("x_53_cast_fp16")]; - tensor inputs_87_cast_fp16 = add(x = inputs_85_cast_fp16, y = x_53_cast_fp16)[name = tensor("inputs_87_cast_fp16")]; - tensor out_87_axes_0 = const()[name = tensor("out_87_axes_0"), val = tensor([1])]; - tensor var_3514_to_fp16 = const()[name = tensor("op_3514_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_87_cast_fp16 = layer_norm(axes = out_87_axes_0, epsilon = var_3514_to_fp16, x = inputs_87_cast_fp16)[name = tensor("out_87_cast_fp16")]; - tensor input_245_gamma_0_to_fp16 = const()[name = tensor("input_245_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51870144)))]; - tensor input_245_beta_0_to_fp16 = const()[name = tensor("input_245_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51871232)))]; - tensor input_245_epsilon_0_to_fp16 = const()[name = tensor("input_245_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_245_cast_fp16 = batch_norm(beta = input_245_beta_0_to_fp16, epsilon = input_245_epsilon_0_to_fp16, gamma = input_245_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_87_cast_fp16)[name = tensor("input_245_cast_fp16")]; - tensor var_3534_pad_type_0 = const()[name = tensor("op_3534_pad_type_0"), val = tensor("valid")]; - tensor var_3534_strides_0 = const()[name = tensor("op_3534_strides_0"), val = tensor([1, 1])]; - tensor var_3534_pad_0 = const()[name = tensor("op_3534_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3534_dilations_0 = const()[name = tensor("op_3534_dilations_0"), val = tensor([1, 1])]; - tensor var_3534_groups_0 = const()[name = tensor("op_3534_groups_0"), val = tensor(1)]; - tensor layers_8_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51872320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52658816))), name = tensor("layers_8_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_8_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_8_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52659008)))]; - tensor var_3534_cast_fp16 = conv(bias = layers_8_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_3534_dilations_0, groups = var_3534_groups_0, pad = var_3534_pad_0, pad_type = var_3534_pad_type_0, strides = var_3534_strides_0, weight = layers_8_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_245_cast_fp16)[name = tensor("op_3534_cast_fp16")]; - tensor var_3540_pad_type_0 = const()[name = tensor("op_3540_pad_type_0"), val = tensor("valid")]; - tensor var_3540_strides_0 = const()[name = tensor("op_3540_strides_0"), val = tensor([1, 1])]; - tensor var_3540_pad_0 = const()[name = tensor("op_3540_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3540_dilations_0 = const()[name = tensor("op_3540_dilations_0"), val = tensor([1, 1])]; - tensor var_3540_groups_0 = const()[name = tensor("op_3540_groups_0"), val = tensor(1)]; - tensor layers_8_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52694080))), name = tensor("layers_8_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52663168))), shape = tensor([2048, 512, 1, 1])]; - tensor var_3540_cast_fp16 = conv(dilations = var_3540_dilations_0, groups = var_3540_groups_0, pad = var_3540_pad_0, pad_type = var_3540_pad_type_0, strides = var_3540_strides_0, weight = layers_8_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_245_cast_fp16)[name = tensor("op_3540_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = var_3534_cast_fp16, y = var_3540_cast_fp16)[name = tensor("input_247_cast_fp16")]; - tensor input_249_cast_fp16 = silu(x = input_247_cast_fp16)[name = tensor("input_249_cast_fp16")]; - tensor var_3551_pad_type_0 = const()[name = tensor("op_3551_pad_type_0"), val = tensor("valid")]; - tensor var_3551_strides_0 = const()[name = tensor("op_3551_strides_0"), val = tensor([1, 1])]; - tensor var_3551_pad_0 = const()[name = tensor("op_3551_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3551_dilations_0 = const()[name = tensor("op_3551_dilations_0"), val = tensor([1, 1])]; - tensor var_3551_groups_0 = const()[name = tensor("op_3551_groups_0"), val = tensor(1)]; - tensor layers_8_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52825216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53611712))), name = tensor("layers_8_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_8_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_8_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53611904)))]; - tensor var_3551_cast_fp16 = conv(bias = layers_8_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_3551_dilations_0, groups = var_3551_groups_0, pad = var_3551_pad_0, pad_type = var_3551_pad_type_0, strides = var_3551_strides_0, weight = layers_8_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_249_cast_fp16)[name = tensor("op_3551_cast_fp16")]; - tensor var_3557_pad_type_0 = const()[name = tensor("op_3557_pad_type_0"), val = tensor("valid")]; - tensor var_3557_strides_0 = const()[name = tensor("op_3557_strides_0"), val = tensor([1, 1])]; - tensor var_3557_pad_0 = const()[name = tensor("op_3557_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3557_dilations_0 = const()[name = tensor("op_3557_dilations_0"), val = tensor([1, 1])]; - tensor var_3557_groups_0 = const()[name = tensor("op_3557_groups_0"), val = tensor(1)]; - tensor layers_8_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53646336))), name = tensor("layers_8_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53612992))), shape = tensor([512, 2048, 1, 1])]; - tensor var_3557_cast_fp16 = conv(dilations = var_3557_dilations_0, groups = var_3557_groups_0, pad = var_3557_pad_0, pad_type = var_3557_pad_type_0, strides = var_3557_strides_0, weight = layers_8_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_249_cast_fp16)[name = tensor("op_3557_cast_fp16")]; - tensor x_55_cast_fp16 = add(x = var_3551_cast_fp16, y = var_3557_cast_fp16)[name = tensor("x_55_cast_fp16")]; - tensor var_3559_to_fp16 = const()[name = tensor("op_3559_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3560_cast_fp16 = mul(x = x_55_cast_fp16, y = var_3559_to_fp16)[name = tensor("op_3560_cast_fp16")]; - tensor inputs_89_cast_fp16 = add(x = inputs_87_cast_fp16, y = var_3560_cast_fp16)[name = tensor("inputs_89_cast_fp16")]; - tensor out_89_axes_0 = const()[name = tensor("out_89_axes_0"), val = tensor([1])]; - tensor var_3570_to_fp16 = const()[name = tensor("op_3570_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_3570_to_fp16, x = inputs_89_cast_fp16)[name = tensor("out_89_cast_fp16")]; - tensor inputs_91_gamma_0_to_fp16 = const()[name = tensor("inputs_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53777472)))]; - tensor inputs_91_beta_0_to_fp16 = const()[name = tensor("inputs_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53778560)))]; - tensor inputs_91_epsilon_0_to_fp16 = const()[name = tensor("inputs_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_91_cast_fp16 = batch_norm(beta = inputs_91_beta_0_to_fp16, epsilon = inputs_91_epsilon_0_to_fp16, gamma = inputs_91_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_89_cast_fp16)[name = tensor("inputs_91_cast_fp16")]; - tensor var_3584 = const()[name = tensor("op_3584"), val = tensor(3)]; - tensor out_91_axes_0 = const()[name = tensor("out_91_axes_0"), val = tensor([1])]; - tensor var_3615_to_fp16 = const()[name = tensor("op_3615_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_91_cast_fp16 = layer_norm(axes = out_91_axes_0, epsilon = var_3615_to_fp16, x = inputs_91_cast_fp16)[name = tensor("out_91_cast_fp16")]; - tensor input_251_gamma_0_to_fp16 = const()[name = tensor("input_251_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53779648)))]; - tensor input_251_beta_0_to_fp16 = const()[name = tensor("input_251_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53780736)))]; - tensor input_251_epsilon_0_to_fp16 = const()[name = tensor("input_251_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_251_cast_fp16 = batch_norm(beta = input_251_beta_0_to_fp16, epsilon = input_251_epsilon_0_to_fp16, gamma = input_251_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_91_cast_fp16)[name = tensor("input_251_cast_fp16")]; - tensor var_3635_pad_type_0 = const()[name = tensor("op_3635_pad_type_0"), val = tensor("valid")]; - tensor var_3635_strides_0 = const()[name = tensor("op_3635_strides_0"), val = tensor([1, 1])]; - tensor var_3635_pad_0 = const()[name = tensor("op_3635_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3635_dilations_0 = const()[name = tensor("op_3635_dilations_0"), val = tensor([1, 1])]; - tensor var_3635_groups_0 = const()[name = tensor("op_3635_groups_0"), val = tensor(1)]; - tensor layers_9_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53781824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54568320))), name = tensor("layers_9_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_9_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_9_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54568512)))]; - tensor var_3635_cast_fp16 = conv(bias = layers_9_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_3635_dilations_0, groups = var_3635_groups_0, pad = var_3635_pad_0, pad_type = var_3635_pad_type_0, strides = var_3635_strides_0, weight = layers_9_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = tensor("op_3635_cast_fp16")]; - tensor var_3641_pad_type_0 = const()[name = tensor("op_3641_pad_type_0"), val = tensor("valid")]; - tensor var_3641_strides_0 = const()[name = tensor("op_3641_strides_0"), val = tensor([1, 1])]; - tensor var_3641_pad_0 = const()[name = tensor("op_3641_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3641_dilations_0 = const()[name = tensor("op_3641_dilations_0"), val = tensor([1, 1])]; - tensor var_3641_groups_0 = const()[name = tensor("op_3641_groups_0"), val = tensor(1)]; - tensor layers_9_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54604800))), name = tensor("layers_9_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54572672))), shape = tensor([2048, 512, 1, 1])]; - tensor var_3641_cast_fp16 = conv(dilations = var_3641_dilations_0, groups = var_3641_groups_0, pad = var_3641_pad_0, pad_type = var_3641_pad_type_0, strides = var_3641_strides_0, weight = layers_9_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_251_cast_fp16)[name = tensor("op_3641_cast_fp16")]; - tensor input_253_cast_fp16 = add(x = var_3635_cast_fp16, y = var_3641_cast_fp16)[name = tensor("input_253_cast_fp16")]; - tensor input_255_cast_fp16 = silu(x = input_253_cast_fp16)[name = tensor("input_255_cast_fp16")]; - tensor var_3652_pad_type_0 = const()[name = tensor("op_3652_pad_type_0"), val = tensor("valid")]; - tensor var_3652_strides_0 = const()[name = tensor("op_3652_strides_0"), val = tensor([1, 1])]; - tensor var_3652_pad_0 = const()[name = tensor("op_3652_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3652_dilations_0 = const()[name = tensor("op_3652_dilations_0"), val = tensor([1, 1])]; - tensor var_3652_groups_0 = const()[name = tensor("op_3652_groups_0"), val = tensor(1)]; - tensor layers_9_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54735936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55522432))), name = tensor("layers_9_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_9_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_9_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55522624)))]; - tensor var_3652_cast_fp16 = conv(bias = layers_9_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_3652_dilations_0, groups = var_3652_groups_0, pad = var_3652_pad_0, pad_type = var_3652_pad_type_0, strides = var_3652_strides_0, weight = layers_9_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = tensor("op_3652_cast_fp16")]; - tensor var_3658_pad_type_0 = const()[name = tensor("op_3658_pad_type_0"), val = tensor("valid")]; - tensor var_3658_strides_0 = const()[name = tensor("op_3658_strides_0"), val = tensor([1, 1])]; - tensor var_3658_pad_0 = const()[name = tensor("op_3658_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3658_dilations_0 = const()[name = tensor("op_3658_dilations_0"), val = tensor([1, 1])]; - tensor var_3658_groups_0 = const()[name = tensor("op_3658_groups_0"), val = tensor(1)]; - tensor layers_9_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55559424))), name = tensor("layers_9_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55523712))), shape = tensor([512, 2048, 1, 1])]; - tensor var_3658_cast_fp16 = conv(dilations = var_3658_dilations_0, groups = var_3658_groups_0, pad = var_3658_pad_0, pad_type = var_3658_pad_type_0, strides = var_3658_strides_0, weight = layers_9_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_255_cast_fp16)[name = tensor("op_3658_cast_fp16")]; - tensor x_57_cast_fp16 = add(x = var_3652_cast_fp16, y = var_3658_cast_fp16)[name = tensor("x_57_cast_fp16")]; - tensor var_3660_to_fp16 = const()[name = tensor("op_3660_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3661_cast_fp16 = mul(x = x_57_cast_fp16, y = var_3660_to_fp16)[name = tensor("op_3661_cast_fp16")]; - tensor inputs_93_cast_fp16 = add(x = inputs_91_cast_fp16, y = var_3661_cast_fp16)[name = tensor("inputs_93_cast_fp16")]; - tensor out_93_axes_0 = const()[name = tensor("out_93_axes_0"), val = tensor([1])]; - tensor var_3671_to_fp16 = const()[name = tensor("op_3671_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_93_cast_fp16 = layer_norm(axes = out_93_axes_0, epsilon = var_3671_to_fp16, x = inputs_93_cast_fp16)[name = tensor("out_93_cast_fp16")]; - tensor obj_39_gamma_0_to_fp16 = const()[name = tensor("obj_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55690560)))]; - tensor obj_39_beta_0_to_fp16 = const()[name = tensor("obj_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55691648)))]; - tensor obj_39_epsilon_0_to_fp16 = const()[name = tensor("obj_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_39_cast_fp16 = batch_norm(beta = obj_39_beta_0_to_fp16, epsilon = obj_39_epsilon_0_to_fp16, gamma = obj_39_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_93_cast_fp16)[name = tensor("obj_39_cast_fp16")]; - tensor var_3696_pad_type_0 = const()[name = tensor("op_3696_pad_type_0"), val = tensor("valid")]; - tensor var_3696_strides_0 = const()[name = tensor("op_3696_strides_0"), val = tensor([1, 1])]; - tensor var_3696_pad_0 = const()[name = tensor("op_3696_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3696_dilations_0 = const()[name = tensor("op_3696_dilations_0"), val = tensor([1, 1])]; - tensor var_3696_groups_0 = const()[name = tensor("op_3696_groups_0"), val = tensor(1)]; - tensor layers_9_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55692736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55889408))), name = tensor("layers_9_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_9_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_9_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55889600)))]; - tensor var_3696_cast_fp16 = conv(bias = layers_9_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_3696_dilations_0, groups = var_3696_groups_0, pad = var_3696_pad_0, pad_type = var_3696_pad_type_0, strides = var_3696_strides_0, weight = layers_9_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_39_cast_fp16)[name = tensor("op_3696_cast_fp16")]; - tensor var_3702_pad_type_0 = const()[name = tensor("op_3702_pad_type_0"), val = tensor("valid")]; - tensor var_3702_strides_0 = const()[name = tensor("op_3702_strides_0"), val = tensor([1, 1])]; - tensor var_3702_pad_0 = const()[name = tensor("op_3702_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3702_dilations_0 = const()[name = tensor("op_3702_dilations_0"), val = tensor([1, 1])]; - tensor var_3702_groups_0 = const()[name = tensor("op_3702_groups_0"), val = tensor(1)]; - tensor layers_9_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55900672))), name = tensor("layers_9_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55890688))), shape = tensor([512, 512, 1, 1])]; - tensor var_3702_cast_fp16 = conv(dilations = var_3702_dilations_0, groups = var_3702_groups_0, pad = var_3702_pad_0, pad_type = var_3702_pad_type_0, strides = var_3702_strides_0, weight = layers_9_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_39_cast_fp16)[name = tensor("op_3702_cast_fp16")]; - tensor query_37_cast_fp16 = add(x = var_3696_cast_fp16, y = var_3702_cast_fp16)[name = tensor("query_37_cast_fp16")]; - tensor var_3711_pad_type_0 = const()[name = tensor("op_3711_pad_type_0"), val = tensor("valid")]; - tensor var_3711_strides_0 = const()[name = tensor("op_3711_strides_0"), val = tensor([1, 1])]; - tensor var_3711_pad_0 = const()[name = tensor("op_3711_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3711_dilations_0 = const()[name = tensor("op_3711_dilations_0"), val = tensor([1, 1])]; - tensor var_3711_groups_0 = const()[name = tensor("op_3711_groups_0"), val = tensor(1)]; - tensor layers_9_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55933504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56130176))), name = tensor("layers_9_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_3711_cast_fp16 = conv(dilations = var_3711_dilations_0, groups = var_3711_groups_0, pad = var_3711_pad_0, pad_type = var_3711_pad_type_0, strides = var_3711_strides_0, weight = layers_9_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_39_cast_fp16)[name = tensor("op_3711_cast_fp16")]; - tensor var_3717_pad_type_0 = const()[name = tensor("op_3717_pad_type_0"), val = tensor("valid")]; - tensor var_3717_strides_0 = const()[name = tensor("op_3717_strides_0"), val = tensor([1, 1])]; - tensor var_3717_pad_0 = const()[name = tensor("op_3717_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3717_dilations_0 = const()[name = tensor("op_3717_dilations_0"), val = tensor([1, 1])]; - tensor var_3717_groups_0 = const()[name = tensor("op_3717_groups_0"), val = tensor(1)]; - tensor layers_9_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56138432))), name = tensor("layers_9_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56130368))), shape = tensor([512, 512, 1, 1])]; - tensor var_3717_cast_fp16 = conv(dilations = var_3717_dilations_0, groups = var_3717_groups_0, pad = var_3717_pad_0, pad_type = var_3717_pad_type_0, strides = var_3717_strides_0, weight = layers_9_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_39_cast_fp16)[name = tensor("op_3717_cast_fp16")]; - tensor key_19_cast_fp16 = add(x = var_3711_cast_fp16, y = var_3717_cast_fp16)[name = tensor("key_19_cast_fp16")]; - tensor var_3727_pad_type_0 = const()[name = tensor("op_3727_pad_type_0"), val = tensor("valid")]; - tensor var_3727_strides_0 = const()[name = tensor("op_3727_strides_0"), val = tensor([1, 1])]; - tensor var_3727_pad_0 = const()[name = tensor("op_3727_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3727_dilations_0 = const()[name = tensor("op_3727_dilations_0"), val = tensor([1, 1])]; - tensor var_3727_groups_0 = const()[name = tensor("op_3727_groups_0"), val = tensor(1)]; - tensor layers_9_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56171264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56367936))), name = tensor("layers_9_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_9_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_9_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56368128)))]; - tensor var_3727_cast_fp16 = conv(bias = layers_9_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_3727_dilations_0, groups = var_3727_groups_0, pad = var_3727_pad_0, pad_type = var_3727_pad_type_0, strides = var_3727_strides_0, weight = layers_9_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_39_cast_fp16)[name = tensor("op_3727_cast_fp16")]; - tensor var_3733_pad_type_0 = const()[name = tensor("op_3733_pad_type_0"), val = tensor("valid")]; - tensor var_3733_strides_0 = const()[name = tensor("op_3733_strides_0"), val = tensor([1, 1])]; - tensor var_3733_pad_0 = const()[name = tensor("op_3733_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3733_dilations_0 = const()[name = tensor("op_3733_dilations_0"), val = tensor([1, 1])]; - tensor var_3733_groups_0 = const()[name = tensor("op_3733_groups_0"), val = tensor(1)]; - tensor layers_9_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56376768))), name = tensor("layers_9_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56369216))), shape = tensor([512, 512, 1, 1])]; - tensor var_3733_cast_fp16 = conv(dilations = var_3733_dilations_0, groups = var_3733_groups_0, pad = var_3733_pad_0, pad_type = var_3733_pad_type_0, strides = var_3733_strides_0, weight = layers_9_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_39_cast_fp16)[name = tensor("op_3733_cast_fp16")]; - tensor value_19_cast_fp16 = add(x = var_3727_cast_fp16, y = var_3733_cast_fp16)[name = tensor("value_19_cast_fp16")]; - tensor var_3736_to_fp16 = const()[name = tensor("op_3736_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56409600)))]; - tensor query_39_cast_fp16 = add(x = query_37_cast_fp16, y = var_3736_to_fp16)[name = tensor("query_39_cast_fp16")]; - tensor var_3739_to_fp16 = const()[name = tensor("op_3739_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56410688)))]; - tensor q_with_bias_v_19_cast_fp16 = add(x = query_37_cast_fp16, y = var_3739_to_fp16)[name = tensor("q_with_bias_v_19_cast_fp16")]; - tensor var_3749_pad_type_0 = const()[name = tensor("op_3749_pad_type_0"), val = tensor("valid")]; - tensor var_3749_strides_0 = const()[name = tensor("op_3749_strides_0"), val = tensor([1, 1])]; - tensor var_3749_pad_0 = const()[name = tensor("op_3749_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3749_dilations_0 = const()[name = tensor("op_3749_dilations_0"), val = tensor([1, 1])]; - tensor var_3749_groups_0 = const()[name = tensor("op_3749_groups_0"), val = tensor(1)]; - tensor layers_9_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56411776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56608448))), name = tensor("layers_9_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_3749_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_3749_dilations_0, groups = var_3749_groups_0, pad = var_3749_pad_0, pad_type = var_3749_pad_type_0, strides = var_3749_strides_0, weight = layers_9_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_3749_cast_fp16")]; - tensor var_3755_pad_type_0 = const()[name = tensor("op_3755_pad_type_0"), val = tensor("valid")]; - tensor var_3755_strides_0 = const()[name = tensor("op_3755_strides_0"), val = tensor([1, 1])]; - tensor var_3755_pad_0 = const()[name = tensor("op_3755_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3755_dilations_0 = const()[name = tensor("op_3755_dilations_0"), val = tensor([1, 1])]; - tensor var_3755_groups_0 = const()[name = tensor("op_3755_groups_0"), val = tensor(1)]; - tensor layers_9_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56624256))), name = tensor("layers_9_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56608640))), shape = tensor([512, 512, 1, 1])]; - tensor var_3755_cast_fp16 = conv(dilations = var_3755_dilations_0, groups = var_3755_groups_0, pad = var_3755_pad_0, pad_type = var_3755_pad_type_0, strides = var_3755_strides_0, weight = layers_9_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_3755_cast_fp16")]; - tensor p_19_cast_fp16 = add(x = var_3749_cast_fp16, y = var_3755_cast_fp16)[name = tensor("p_19_cast_fp16")]; - tensor var_3759 = const()[name = tensor("op_3759"), val = tensor([1, 8, 64, 188])]; - tensor var_3760_cast_fp16 = reshape(shape = var_3759, x = q_with_bias_v_19_cast_fp16)[name = tensor("op_3760_cast_fp16")]; - tensor var_3761 = const()[name = tensor("op_3761"), val = tensor([1, 8, 64, -1])]; - tensor var_3762_cast_fp16 = reshape(shape = var_3761, x = p_19_cast_fp16)[name = tensor("op_3762_cast_fp16")]; - tensor matrix_bd_73_transpose_x_0 = const()[name = tensor("matrix_bd_73_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_73_transpose_y_0 = const()[name = tensor("matrix_bd_73_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_73_cast_fp16 = matmul(transpose_x = matrix_bd_73_transpose_x_0, transpose_y = matrix_bd_73_transpose_y_0, x = var_3760_cast_fp16, y = var_3762_cast_fp16)[name = tensor("matrix_bd_73_cast_fp16")]; - tensor matrix_bd_75_pad_0 = const()[name = tensor("matrix_bd_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_75_mode_0 = const()[name = tensor("matrix_bd_75_mode_0"), val = tensor("constant")]; - tensor const_109_to_fp16 = const()[name = tensor("const_109_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_75_cast_fp16 = pad(constant_val = const_109_to_fp16, mode = matrix_bd_75_mode_0, pad = matrix_bd_75_pad_0, x = matrix_bd_73_cast_fp16)[name = tensor("matrix_bd_75_cast_fp16")]; - tensor var_3771 = const()[name = tensor("op_3771"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_77_cast_fp16 = reshape(shape = var_3771, x = matrix_bd_75_cast_fp16)[name = tensor("matrix_bd_77_cast_fp16")]; - tensor var_3775_begin_0 = const()[name = tensor("op_3775_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3775_end_0 = const()[name = tensor("op_3775_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3775_end_mask_0 = const()[name = tensor("op_3775_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3775_cast_fp16 = slice_by_index(begin = var_3775_begin_0, end = var_3775_end_0, end_mask = var_3775_end_mask_0, x = matrix_bd_77_cast_fp16)[name = tensor("op_3775_cast_fp16")]; - tensor var_3776 = const()[name = tensor("op_3776"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_79_cast_fp16 = reshape(shape = var_3776, x = var_3775_cast_fp16)[name = tensor("matrix_bd_79_cast_fp16")]; - tensor var_3781_begin_0 = const()[name = tensor("op_3781_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3781_end_0 = const()[name = tensor("op_3781_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_3781_end_mask_0 = const()[name = tensor("op_3781_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_3781_cast_fp16 = slice_by_index(begin = var_3781_begin_0, end = var_3781_end_0, end_mask = var_3781_end_mask_0, x = matrix_bd_79_cast_fp16)[name = tensor("op_3781_cast_fp16")]; - tensor var_3782_to_fp16 = const()[name = tensor("op_3782_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_19_cast_fp16 = mul(x = var_3781_cast_fp16, y = var_3782_to_fp16)[name = tensor("qk_mask_19_cast_fp16")]; - tensor var_3786 = const()[name = tensor("op_3786"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_19_cast_fp16 = reshape(shape = var_3786, x = query_39_cast_fp16)[name = tensor("mh_q_19_cast_fp16")]; - tensor var_3788_to_fp16 = const()[name = tensor("op_3788_to_fp16"), val = tensor(0x1p-3)]; - tensor var_3789_cast_fp16 = mul(x = mh_q_19_cast_fp16, y = var_3788_to_fp16)[name = tensor("op_3789_cast_fp16")]; - tensor var_3792 = const()[name = tensor("op_3792"), val = tensor([1, 8, 64, 188])]; - tensor var_3793_cast_fp16 = reshape(shape = var_3792, x = key_19_cast_fp16)[name = tensor("op_3793_cast_fp16")]; - tensor mh_w_37_transpose_x_0 = const()[name = tensor("mh_w_37_transpose_x_0"), val = tensor(true)]; - tensor mh_w_37_transpose_y_0 = const()[name = tensor("mh_w_37_transpose_y_0"), val = tensor(false)]; - tensor mh_w_37_cast_fp16 = matmul(transpose_x = mh_w_37_transpose_x_0, transpose_y = mh_w_37_transpose_y_0, x = var_3789_cast_fp16, y = var_3793_cast_fp16)[name = tensor("mh_w_37_cast_fp16")]; - tensor mh_w_39_cast_fp16 = add(x = mh_w_37_cast_fp16, y = qk_mask_19_cast_fp16)[name = tensor("mh_w_39_cast_fp16")]; - tensor var_3797_cast_fp16 = softmax(axis = var_3584, x = mh_w_39_cast_fp16)[name = tensor("op_3797_cast_fp16")]; - tensor var_3798 = const()[name = tensor("op_3798"), val = tensor([1, 8, 64, 188])]; - tensor var_3799_cast_fp16 = reshape(shape = var_3798, x = value_19_cast_fp16)[name = tensor("op_3799_cast_fp16")]; - tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; - tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; - tensor attn_19_cast_fp16 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_3799_cast_fp16, y = var_3797_cast_fp16)[name = tensor("attn_19_cast_fp16")]; - tensor var_3802 = const()[name = tensor("op_3802"), val = tensor([1, 512, 1, 188])]; - tensor input_257_cast_fp16 = reshape(shape = var_3802, x = attn_19_cast_fp16)[name = tensor("input_257_cast_fp16")]; - tensor var_3812_pad_type_0 = const()[name = tensor("op_3812_pad_type_0"), val = tensor("valid")]; - tensor var_3812_strides_0 = const()[name = tensor("op_3812_strides_0"), val = tensor([1, 1])]; - tensor var_3812_pad_0 = const()[name = tensor("op_3812_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3812_dilations_0 = const()[name = tensor("op_3812_dilations_0"), val = tensor([1, 1])]; - tensor var_3812_groups_0 = const()[name = tensor("op_3812_groups_0"), val = tensor(1)]; - tensor layers_9_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56657088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56853760))), name = tensor("layers_9_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_9_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_9_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56853952)))]; - tensor var_3812_cast_fp16 = conv(bias = layers_9_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_3812_dilations_0, groups = var_3812_groups_0, pad = var_3812_pad_0, pad_type = var_3812_pad_type_0, strides = var_3812_strides_0, weight = layers_9_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_257_cast_fp16)[name = tensor("op_3812_cast_fp16")]; - tensor var_3818_pad_type_0 = const()[name = tensor("op_3818_pad_type_0"), val = tensor("valid")]; - tensor var_3818_strides_0 = const()[name = tensor("op_3818_strides_0"), val = tensor([1, 1])]; - tensor var_3818_pad_0 = const()[name = tensor("op_3818_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3818_dilations_0 = const()[name = tensor("op_3818_dilations_0"), val = tensor([1, 1])]; - tensor var_3818_groups_0 = const()[name = tensor("op_3818_groups_0"), val = tensor(1)]; - tensor layers_9_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56863616))), name = tensor("layers_9_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56855040))), shape = tensor([512, 512, 1, 1])]; - tensor var_3818_cast_fp16 = conv(dilations = var_3818_dilations_0, groups = var_3818_groups_0, pad = var_3818_pad_0, pad_type = var_3818_pad_type_0, strides = var_3818_strides_0, weight = layers_9_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_257_cast_fp16)[name = tensor("op_3818_cast_fp16")]; - tensor obj_41_cast_fp16 = add(x = var_3812_cast_fp16, y = var_3818_cast_fp16)[name = tensor("obj_41_cast_fp16")]; - tensor inputs_95_cast_fp16 = add(x = inputs_93_cast_fp16, y = obj_41_cast_fp16)[name = tensor("inputs_95_cast_fp16")]; - tensor out_95_axes_0 = const()[name = tensor("out_95_axes_0"), val = tensor([1])]; - tensor var_3829_to_fp16 = const()[name = tensor("op_3829_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_95_cast_fp16 = layer_norm(axes = out_95_axes_0, epsilon = var_3829_to_fp16, x = inputs_95_cast_fp16)[name = tensor("out_95_cast_fp16")]; - tensor input_259_gamma_0_to_fp16 = const()[name = tensor("input_259_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56896448)))]; - tensor input_259_beta_0_to_fp16 = const()[name = tensor("input_259_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56897536)))]; - tensor input_259_epsilon_0_to_fp16 = const()[name = tensor("input_259_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_259_cast_fp16 = batch_norm(beta = input_259_beta_0_to_fp16, epsilon = input_259_epsilon_0_to_fp16, gamma = input_259_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_95_cast_fp16)[name = tensor("input_259_cast_fp16")]; - tensor var_3851_pad_type_0 = const()[name = tensor("op_3851_pad_type_0"), val = tensor("valid")]; - tensor var_3851_strides_0 = const()[name = tensor("op_3851_strides_0"), val = tensor([1, 1])]; - tensor var_3851_pad_0 = const()[name = tensor("op_3851_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3851_dilations_0 = const()[name = tensor("op_3851_dilations_0"), val = tensor([1, 1])]; - tensor var_3851_groups_0 = const()[name = tensor("op_3851_groups_0"), val = tensor(1)]; - tensor layers_9_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56898624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57291904))), name = tensor("layers_9_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_9_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_9_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57292096)))]; - tensor var_3851_cast_fp16 = conv(bias = layers_9_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_3851_dilations_0, groups = var_3851_groups_0, pad = var_3851_pad_0, pad_type = var_3851_pad_type_0, strides = var_3851_strides_0, weight = layers_9_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = tensor("op_3851_cast_fp16")]; - tensor var_3857_pad_type_0 = const()[name = tensor("op_3857_pad_type_0"), val = tensor("valid")]; - tensor var_3857_strides_0 = const()[name = tensor("op_3857_strides_0"), val = tensor([1, 1])]; - tensor var_3857_pad_0 = const()[name = tensor("op_3857_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3857_dilations_0 = const()[name = tensor("op_3857_dilations_0"), val = tensor([1, 1])]; - tensor var_3857_groups_0 = const()[name = tensor("op_3857_groups_0"), val = tensor(1)]; - tensor layers_9_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57311296))), name = tensor("layers_9_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57294208))), shape = tensor([1024, 512, 1, 1])]; - tensor var_3857_cast_fp16 = conv(dilations = var_3857_dilations_0, groups = var_3857_groups_0, pad = var_3857_pad_0, pad_type = var_3857_pad_type_0, strides = var_3857_strides_0, weight = layers_9_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_259_cast_fp16)[name = tensor("op_3857_cast_fp16")]; - tensor input_261_cast_fp16 = add(x = var_3851_cast_fp16, y = var_3857_cast_fp16)[name = tensor("input_261_cast_fp16")]; - tensor input_263_split_num_splits_0 = const()[name = tensor("input_263_split_num_splits_0"), val = tensor(2)]; - tensor input_263_split_axis_0 = const()[name = tensor("input_263_split_axis_0"), val = tensor(1)]; - tensor input_263_split_cast_fp16_0, tensor input_263_split_cast_fp16_1 = split(axis = input_263_split_axis_0, num_splits = input_263_split_num_splits_0, x = input_261_cast_fp16)[name = tensor("input_263_split_cast_fp16")]; - tensor input_263_split_1_sigmoid_cast_fp16 = sigmoid(x = input_263_split_cast_fp16_1)[name = tensor("input_263_split_1_sigmoid_cast_fp16")]; - tensor input_263_cast_fp16 = mul(x = input_263_split_cast_fp16_0, y = input_263_split_1_sigmoid_cast_fp16)[name = tensor("input_263_cast_fp16")]; - tensor input_265_pad_type_0 = const()[name = tensor("input_265_pad_type_0"), val = tensor("custom")]; - tensor input_265_pad_0 = const()[name = tensor("input_265_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_265_groups_0 = const()[name = tensor("input_265_groups_0"), val = tensor(512)]; - tensor input_265_strides_0 = const()[name = tensor("input_265_strides_0"), val = tensor([1, 1])]; - tensor input_265_dilations_0 = const()[name = tensor("input_265_dilations_0"), val = tensor([1, 1])]; - tensor const_209_to_fp16 = const()[name = tensor("const_209_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57376896)))]; - tensor const_210_to_fp16 = const()[name = tensor("const_210_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57386176)))]; - tensor input_267_cast_fp16 = conv(bias = const_210_to_fp16, dilations = input_265_dilations_0, groups = input_265_groups_0, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = input_265_strides_0, weight = const_209_to_fp16, x = input_263_cast_fp16)[name = tensor("input_267_cast_fp16")]; - tensor input_269_cast_fp16 = silu(x = input_267_cast_fp16)[name = tensor("input_269_cast_fp16")]; - tensor var_3881_pad_type_0 = const()[name = tensor("op_3881_pad_type_0"), val = tensor("valid")]; - tensor var_3881_strides_0 = const()[name = tensor("op_3881_strides_0"), val = tensor([1, 1])]; - tensor var_3881_pad_0 = const()[name = tensor("op_3881_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3881_dilations_0 = const()[name = tensor("op_3881_dilations_0"), val = tensor([1, 1])]; - tensor var_3881_groups_0 = const()[name = tensor("op_3881_groups_0"), val = tensor(1)]; - tensor layers_9_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57387264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57583936))), name = tensor("layers_9_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_9_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_9_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57584128)))]; - tensor var_3881_cast_fp16 = conv(bias = layers_9_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_3881_dilations_0, groups = var_3881_groups_0, pad = var_3881_pad_0, pad_type = var_3881_pad_type_0, strides = var_3881_strides_0, weight = layers_9_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_269_cast_fp16)[name = tensor("op_3881_cast_fp16")]; - tensor var_3887_pad_type_0 = const()[name = tensor("op_3887_pad_type_0"), val = tensor("valid")]; - tensor var_3887_strides_0 = const()[name = tensor("op_3887_strides_0"), val = tensor([1, 1])]; - tensor var_3887_pad_0 = const()[name = tensor("op_3887_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3887_dilations_0 = const()[name = tensor("op_3887_dilations_0"), val = tensor([1, 1])]; - tensor var_3887_groups_0 = const()[name = tensor("op_3887_groups_0"), val = tensor(1)]; - tensor layers_9_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57593408))), name = tensor("layers_9_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57585216))), shape = tensor([512, 512, 1, 1])]; - tensor var_3887_cast_fp16 = conv(dilations = var_3887_dilations_0, groups = var_3887_groups_0, pad = var_3887_pad_0, pad_type = var_3887_pad_type_0, strides = var_3887_strides_0, weight = layers_9_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_269_cast_fp16)[name = tensor("op_3887_cast_fp16")]; - tensor x_59_cast_fp16 = add(x = var_3881_cast_fp16, y = var_3887_cast_fp16)[name = tensor("x_59_cast_fp16")]; - tensor inputs_97_cast_fp16 = add(x = inputs_95_cast_fp16, y = x_59_cast_fp16)[name = tensor("inputs_97_cast_fp16")]; - tensor out_97_axes_0 = const()[name = tensor("out_97_axes_0"), val = tensor([1])]; - tensor var_3898_to_fp16 = const()[name = tensor("op_3898_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_3898_to_fp16, x = inputs_97_cast_fp16)[name = tensor("out_97_cast_fp16")]; - tensor input_271_gamma_0_to_fp16 = const()[name = tensor("input_271_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57626240)))]; - tensor input_271_beta_0_to_fp16 = const()[name = tensor("input_271_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57627328)))]; - tensor input_271_epsilon_0_to_fp16 = const()[name = tensor("input_271_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_271_cast_fp16 = batch_norm(beta = input_271_beta_0_to_fp16, epsilon = input_271_epsilon_0_to_fp16, gamma = input_271_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_97_cast_fp16)[name = tensor("input_271_cast_fp16")]; - tensor var_3918_pad_type_0 = const()[name = tensor("op_3918_pad_type_0"), val = tensor("valid")]; - tensor var_3918_strides_0 = const()[name = tensor("op_3918_strides_0"), val = tensor([1, 1])]; - tensor var_3918_pad_0 = const()[name = tensor("op_3918_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3918_dilations_0 = const()[name = tensor("op_3918_dilations_0"), val = tensor([1, 1])]; - tensor var_3918_groups_0 = const()[name = tensor("op_3918_groups_0"), val = tensor(1)]; - tensor layers_9_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57628416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58414912))), name = tensor("layers_9_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_9_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_9_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58415104)))]; - tensor var_3918_cast_fp16 = conv(bias = layers_9_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_3918_dilations_0, groups = var_3918_groups_0, pad = var_3918_pad_0, pad_type = var_3918_pad_type_0, strides = var_3918_strides_0, weight = layers_9_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_271_cast_fp16)[name = tensor("op_3918_cast_fp16")]; - tensor var_3924_pad_type_0 = const()[name = tensor("op_3924_pad_type_0"), val = tensor("valid")]; - tensor var_3924_strides_0 = const()[name = tensor("op_3924_strides_0"), val = tensor([1, 1])]; - tensor var_3924_pad_0 = const()[name = tensor("op_3924_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3924_dilations_0 = const()[name = tensor("op_3924_dilations_0"), val = tensor([1, 1])]; - tensor var_3924_groups_0 = const()[name = tensor("op_3924_groups_0"), val = tensor(1)]; - tensor layers_9_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58451456))), name = tensor("layers_9_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58419264))), shape = tensor([2048, 512, 1, 1])]; - tensor var_3924_cast_fp16 = conv(dilations = var_3924_dilations_0, groups = var_3924_groups_0, pad = var_3924_pad_0, pad_type = var_3924_pad_type_0, strides = var_3924_strides_0, weight = layers_9_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_271_cast_fp16)[name = tensor("op_3924_cast_fp16")]; - tensor input_273_cast_fp16 = add(x = var_3918_cast_fp16, y = var_3924_cast_fp16)[name = tensor("input_273_cast_fp16")]; - tensor input_275_cast_fp16 = silu(x = input_273_cast_fp16)[name = tensor("input_275_cast_fp16")]; - tensor var_3935_pad_type_0 = const()[name = tensor("op_3935_pad_type_0"), val = tensor("valid")]; - tensor var_3935_strides_0 = const()[name = tensor("op_3935_strides_0"), val = tensor([1, 1])]; - tensor var_3935_pad_0 = const()[name = tensor("op_3935_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3935_dilations_0 = const()[name = tensor("op_3935_dilations_0"), val = tensor([1, 1])]; - tensor var_3935_groups_0 = const()[name = tensor("op_3935_groups_0"), val = tensor(1)]; - tensor layers_9_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58582592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59369088))), name = tensor("layers_9_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_9_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_9_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59369280)))]; - tensor var_3935_cast_fp16 = conv(bias = layers_9_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_3935_dilations_0, groups = var_3935_groups_0, pad = var_3935_pad_0, pad_type = var_3935_pad_type_0, strides = var_3935_strides_0, weight = layers_9_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = tensor("op_3935_cast_fp16")]; - tensor var_3941_pad_type_0 = const()[name = tensor("op_3941_pad_type_0"), val = tensor("valid")]; - tensor var_3941_strides_0 = const()[name = tensor("op_3941_strides_0"), val = tensor([1, 1])]; - tensor var_3941_pad_0 = const()[name = tensor("op_3941_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3941_dilations_0 = const()[name = tensor("op_3941_dilations_0"), val = tensor([1, 1])]; - tensor var_3941_groups_0 = const()[name = tensor("op_3941_groups_0"), val = tensor(1)]; - tensor layers_9_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59408640))), name = tensor("layers_9_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59370368))), shape = tensor([512, 2048, 1, 1])]; - tensor var_3941_cast_fp16 = conv(dilations = var_3941_dilations_0, groups = var_3941_groups_0, pad = var_3941_pad_0, pad_type = var_3941_pad_type_0, strides = var_3941_strides_0, weight = layers_9_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_275_cast_fp16)[name = tensor("op_3941_cast_fp16")]; - tensor x_61_cast_fp16 = add(x = var_3935_cast_fp16, y = var_3941_cast_fp16)[name = tensor("x_61_cast_fp16")]; - tensor var_3943_to_fp16 = const()[name = tensor("op_3943_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3944_cast_fp16 = mul(x = x_61_cast_fp16, y = var_3943_to_fp16)[name = tensor("op_3944_cast_fp16")]; - tensor inputs_99_cast_fp16 = add(x = inputs_97_cast_fp16, y = var_3944_cast_fp16)[name = tensor("inputs_99_cast_fp16")]; - tensor out_99_axes_0 = const()[name = tensor("out_99_axes_0"), val = tensor([1])]; - tensor var_3954_to_fp16 = const()[name = tensor("op_3954_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_99_cast_fp16 = layer_norm(axes = out_99_axes_0, epsilon = var_3954_to_fp16, x = inputs_99_cast_fp16)[name = tensor("out_99_cast_fp16")]; - tensor inputs_101_gamma_0_to_fp16 = const()[name = tensor("inputs_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59539776)))]; - tensor inputs_101_beta_0_to_fp16 = const()[name = tensor("inputs_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59540864)))]; - tensor inputs_101_epsilon_0_to_fp16 = const()[name = tensor("inputs_101_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_101_cast_fp16 = batch_norm(beta = inputs_101_beta_0_to_fp16, epsilon = inputs_101_epsilon_0_to_fp16, gamma = inputs_101_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_99_cast_fp16)[name = tensor("inputs_101_cast_fp16")]; - tensor var_3968 = const()[name = tensor("op_3968"), val = tensor(3)]; - tensor out_101_axes_0 = const()[name = tensor("out_101_axes_0"), val = tensor([1])]; - tensor var_3999_to_fp16 = const()[name = tensor("op_3999_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_101_cast_fp16 = layer_norm(axes = out_101_axes_0, epsilon = var_3999_to_fp16, x = inputs_101_cast_fp16)[name = tensor("out_101_cast_fp16")]; - tensor input_277_gamma_0_to_fp16 = const()[name = tensor("input_277_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59541952)))]; - tensor input_277_beta_0_to_fp16 = const()[name = tensor("input_277_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59543040)))]; - tensor input_277_epsilon_0_to_fp16 = const()[name = tensor("input_277_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_277_cast_fp16 = batch_norm(beta = input_277_beta_0_to_fp16, epsilon = input_277_epsilon_0_to_fp16, gamma = input_277_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_101_cast_fp16)[name = tensor("input_277_cast_fp16")]; - tensor var_4019_pad_type_0 = const()[name = tensor("op_4019_pad_type_0"), val = tensor("valid")]; - tensor var_4019_strides_0 = const()[name = tensor("op_4019_strides_0"), val = tensor([1, 1])]; - tensor var_4019_pad_0 = const()[name = tensor("op_4019_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4019_dilations_0 = const()[name = tensor("op_4019_dilations_0"), val = tensor([1, 1])]; - tensor var_4019_groups_0 = const()[name = tensor("op_4019_groups_0"), val = tensor(1)]; - tensor layers_10_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59544128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60330624))), name = tensor("layers_10_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_10_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_10_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60330816)))]; - tensor var_4019_cast_fp16 = conv(bias = layers_10_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_4019_dilations_0, groups = var_4019_groups_0, pad = var_4019_pad_0, pad_type = var_4019_pad_type_0, strides = var_4019_strides_0, weight = layers_10_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_277_cast_fp16)[name = tensor("op_4019_cast_fp16")]; - tensor var_4025_pad_type_0 = const()[name = tensor("op_4025_pad_type_0"), val = tensor("valid")]; - tensor var_4025_strides_0 = const()[name = tensor("op_4025_strides_0"), val = tensor([1, 1])]; - tensor var_4025_pad_0 = const()[name = tensor("op_4025_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4025_dilations_0 = const()[name = tensor("op_4025_dilations_0"), val = tensor([1, 1])]; - tensor var_4025_groups_0 = const()[name = tensor("op_4025_groups_0"), val = tensor(1)]; - tensor layers_10_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60369216))), name = tensor("layers_10_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60334976))), shape = tensor([2048, 512, 1, 1])]; - tensor var_4025_cast_fp16 = conv(dilations = var_4025_dilations_0, groups = var_4025_groups_0, pad = var_4025_pad_0, pad_type = var_4025_pad_type_0, strides = var_4025_strides_0, weight = layers_10_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_277_cast_fp16)[name = tensor("op_4025_cast_fp16")]; - tensor input_279_cast_fp16 = add(x = var_4019_cast_fp16, y = var_4025_cast_fp16)[name = tensor("input_279_cast_fp16")]; - tensor input_281_cast_fp16 = silu(x = input_279_cast_fp16)[name = tensor("input_281_cast_fp16")]; - tensor var_4036_pad_type_0 = const()[name = tensor("op_4036_pad_type_0"), val = tensor("valid")]; - tensor var_4036_strides_0 = const()[name = tensor("op_4036_strides_0"), val = tensor([1, 1])]; - tensor var_4036_pad_0 = const()[name = tensor("op_4036_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4036_dilations_0 = const()[name = tensor("op_4036_dilations_0"), val = tensor([1, 1])]; - tensor var_4036_groups_0 = const()[name = tensor("op_4036_groups_0"), val = tensor(1)]; - tensor layers_10_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60500352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61286848))), name = tensor("layers_10_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_10_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_10_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61287040)))]; - tensor var_4036_cast_fp16 = conv(bias = layers_10_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_4036_dilations_0, groups = var_4036_groups_0, pad = var_4036_pad_0, pad_type = var_4036_pad_type_0, strides = var_4036_strides_0, weight = layers_10_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_281_cast_fp16)[name = tensor("op_4036_cast_fp16")]; - tensor var_4042_pad_type_0 = const()[name = tensor("op_4042_pad_type_0"), val = tensor("valid")]; - tensor var_4042_strides_0 = const()[name = tensor("op_4042_strides_0"), val = tensor([1, 1])]; - tensor var_4042_pad_0 = const()[name = tensor("op_4042_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4042_dilations_0 = const()[name = tensor("op_4042_dilations_0"), val = tensor([1, 1])]; - tensor var_4042_groups_0 = const()[name = tensor("op_4042_groups_0"), val = tensor(1)]; - tensor layers_10_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61328448))), name = tensor("layers_10_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61288128))), shape = tensor([512, 2048, 1, 1])]; - tensor var_4042_cast_fp16 = conv(dilations = var_4042_dilations_0, groups = var_4042_groups_0, pad = var_4042_pad_0, pad_type = var_4042_pad_type_0, strides = var_4042_strides_0, weight = layers_10_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_281_cast_fp16)[name = tensor("op_4042_cast_fp16")]; - tensor x_63_cast_fp16 = add(x = var_4036_cast_fp16, y = var_4042_cast_fp16)[name = tensor("x_63_cast_fp16")]; - tensor var_4044_to_fp16 = const()[name = tensor("op_4044_to_fp16"), val = tensor(0x1p-1)]; - tensor var_4045_cast_fp16 = mul(x = x_63_cast_fp16, y = var_4044_to_fp16)[name = tensor("op_4045_cast_fp16")]; - tensor inputs_103_cast_fp16 = add(x = inputs_101_cast_fp16, y = var_4045_cast_fp16)[name = tensor("inputs_103_cast_fp16")]; - tensor out_103_axes_0 = const()[name = tensor("out_103_axes_0"), val = tensor([1])]; - tensor var_4055_to_fp16 = const()[name = tensor("op_4055_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_103_cast_fp16 = layer_norm(axes = out_103_axes_0, epsilon = var_4055_to_fp16, x = inputs_103_cast_fp16)[name = tensor("out_103_cast_fp16")]; - tensor obj_43_gamma_0_to_fp16 = const()[name = tensor("obj_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61459584)))]; - tensor obj_43_beta_0_to_fp16 = const()[name = tensor("obj_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61460672)))]; - tensor obj_43_epsilon_0_to_fp16 = const()[name = tensor("obj_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_43_cast_fp16 = batch_norm(beta = obj_43_beta_0_to_fp16, epsilon = obj_43_epsilon_0_to_fp16, gamma = obj_43_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_103_cast_fp16)[name = tensor("obj_43_cast_fp16")]; - tensor var_4080_pad_type_0 = const()[name = tensor("op_4080_pad_type_0"), val = tensor("valid")]; - tensor var_4080_strides_0 = const()[name = tensor("op_4080_strides_0"), val = tensor([1, 1])]; - tensor var_4080_pad_0 = const()[name = tensor("op_4080_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4080_dilations_0 = const()[name = tensor("op_4080_dilations_0"), val = tensor([1, 1])]; - tensor var_4080_groups_0 = const()[name = tensor("op_4080_groups_0"), val = tensor(1)]; - tensor layers_10_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61461760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61658432))), name = tensor("layers_10_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_10_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_10_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61658624)))]; - tensor var_4080_cast_fp16 = conv(bias = layers_10_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_4080_dilations_0, groups = var_4080_groups_0, pad = var_4080_pad_0, pad_type = var_4080_pad_type_0, strides = var_4080_strides_0, weight = layers_10_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_43_cast_fp16)[name = tensor("op_4080_cast_fp16")]; - tensor var_4086_pad_type_0 = const()[name = tensor("op_4086_pad_type_0"), val = tensor("valid")]; - tensor var_4086_strides_0 = const()[name = tensor("op_4086_strides_0"), val = tensor([1, 1])]; - tensor var_4086_pad_0 = const()[name = tensor("op_4086_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4086_dilations_0 = const()[name = tensor("op_4086_dilations_0"), val = tensor([1, 1])]; - tensor var_4086_groups_0 = const()[name = tensor("op_4086_groups_0"), val = tensor(1)]; - tensor layers_10_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61669056))), name = tensor("layers_10_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61659712))), shape = tensor([512, 512, 1, 1])]; - tensor var_4086_cast_fp16 = conv(dilations = var_4086_dilations_0, groups = var_4086_groups_0, pad = var_4086_pad_0, pad_type = var_4086_pad_type_0, strides = var_4086_strides_0, weight = layers_10_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_43_cast_fp16)[name = tensor("op_4086_cast_fp16")]; - tensor query_41_cast_fp16 = add(x = var_4080_cast_fp16, y = var_4086_cast_fp16)[name = tensor("query_41_cast_fp16")]; - tensor var_4095_pad_type_0 = const()[name = tensor("op_4095_pad_type_0"), val = tensor("valid")]; - tensor var_4095_strides_0 = const()[name = tensor("op_4095_strides_0"), val = tensor([1, 1])]; - tensor var_4095_pad_0 = const()[name = tensor("op_4095_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4095_dilations_0 = const()[name = tensor("op_4095_dilations_0"), val = tensor([1, 1])]; - tensor var_4095_groups_0 = const()[name = tensor("op_4095_groups_0"), val = tensor(1)]; - tensor layers_10_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61701888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61898560))), name = tensor("layers_10_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_4095_cast_fp16 = conv(dilations = var_4095_dilations_0, groups = var_4095_groups_0, pad = var_4095_pad_0, pad_type = var_4095_pad_type_0, strides = var_4095_strides_0, weight = layers_10_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_43_cast_fp16)[name = tensor("op_4095_cast_fp16")]; - tensor var_4101_pad_type_0 = const()[name = tensor("op_4101_pad_type_0"), val = tensor("valid")]; - tensor var_4101_strides_0 = const()[name = tensor("op_4101_strides_0"), val = tensor([1, 1])]; - tensor var_4101_pad_0 = const()[name = tensor("op_4101_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4101_dilations_0 = const()[name = tensor("op_4101_dilations_0"), val = tensor([1, 1])]; - tensor var_4101_groups_0 = const()[name = tensor("op_4101_groups_0"), val = tensor(1)]; - tensor layers_10_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61906368))), name = tensor("layers_10_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61898752))), shape = tensor([512, 512, 1, 1])]; - tensor var_4101_cast_fp16 = conv(dilations = var_4101_dilations_0, groups = var_4101_groups_0, pad = var_4101_pad_0, pad_type = var_4101_pad_type_0, strides = var_4101_strides_0, weight = layers_10_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_43_cast_fp16)[name = tensor("op_4101_cast_fp16")]; - tensor key_21_cast_fp16 = add(x = var_4095_cast_fp16, y = var_4101_cast_fp16)[name = tensor("key_21_cast_fp16")]; - tensor var_4111_pad_type_0 = const()[name = tensor("op_4111_pad_type_0"), val = tensor("valid")]; - tensor var_4111_strides_0 = const()[name = tensor("op_4111_strides_0"), val = tensor([1, 1])]; - tensor var_4111_pad_0 = const()[name = tensor("op_4111_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4111_dilations_0 = const()[name = tensor("op_4111_dilations_0"), val = tensor([1, 1])]; - tensor var_4111_groups_0 = const()[name = tensor("op_4111_groups_0"), val = tensor(1)]; - tensor layers_10_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61939200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62135872))), name = tensor("layers_10_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_10_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_10_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62136064)))]; - tensor var_4111_cast_fp16 = conv(bias = layers_10_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_4111_dilations_0, groups = var_4111_groups_0, pad = var_4111_pad_0, pad_type = var_4111_pad_type_0, strides = var_4111_strides_0, weight = layers_10_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_43_cast_fp16)[name = tensor("op_4111_cast_fp16")]; - tensor var_4117_pad_type_0 = const()[name = tensor("op_4117_pad_type_0"), val = tensor("valid")]; - tensor var_4117_strides_0 = const()[name = tensor("op_4117_strides_0"), val = tensor([1, 1])]; - tensor var_4117_pad_0 = const()[name = tensor("op_4117_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4117_dilations_0 = const()[name = tensor("op_4117_dilations_0"), val = tensor([1, 1])]; - tensor var_4117_groups_0 = const()[name = tensor("op_4117_groups_0"), val = tensor(1)]; - tensor layers_10_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62145024))), name = tensor("layers_10_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62137152))), shape = tensor([512, 512, 1, 1])]; - tensor var_4117_cast_fp16 = conv(dilations = var_4117_dilations_0, groups = var_4117_groups_0, pad = var_4117_pad_0, pad_type = var_4117_pad_type_0, strides = var_4117_strides_0, weight = layers_10_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_43_cast_fp16)[name = tensor("op_4117_cast_fp16")]; - tensor value_21_cast_fp16 = add(x = var_4111_cast_fp16, y = var_4117_cast_fp16)[name = tensor("value_21_cast_fp16")]; - tensor var_4120_to_fp16 = const()[name = tensor("op_4120_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62177856)))]; - tensor query_43_cast_fp16 = add(x = query_41_cast_fp16, y = var_4120_to_fp16)[name = tensor("query_43_cast_fp16")]; - tensor var_4123_to_fp16 = const()[name = tensor("op_4123_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62178944)))]; - tensor q_with_bias_v_21_cast_fp16 = add(x = query_41_cast_fp16, y = var_4123_to_fp16)[name = tensor("q_with_bias_v_21_cast_fp16")]; - tensor var_4133_pad_type_0 = const()[name = tensor("op_4133_pad_type_0"), val = tensor("valid")]; - tensor var_4133_strides_0 = const()[name = tensor("op_4133_strides_0"), val = tensor([1, 1])]; - tensor var_4133_pad_0 = const()[name = tensor("op_4133_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4133_dilations_0 = const()[name = tensor("op_4133_dilations_0"), val = tensor([1, 1])]; - tensor var_4133_groups_0 = const()[name = tensor("op_4133_groups_0"), val = tensor(1)]; - tensor layers_10_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62180032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62376704))), name = tensor("layers_10_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_4133_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_4133_dilations_0, groups = var_4133_groups_0, pad = var_4133_pad_0, pad_type = var_4133_pad_type_0, strides = var_4133_strides_0, weight = layers_10_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_4133_cast_fp16")]; - tensor var_4139_pad_type_0 = const()[name = tensor("op_4139_pad_type_0"), val = tensor("valid")]; - tensor var_4139_strides_0 = const()[name = tensor("op_4139_strides_0"), val = tensor([1, 1])]; - tensor var_4139_pad_0 = const()[name = tensor("op_4139_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4139_dilations_0 = const()[name = tensor("op_4139_dilations_0"), val = tensor([1, 1])]; - tensor var_4139_groups_0 = const()[name = tensor("op_4139_groups_0"), val = tensor(1)]; - tensor layers_10_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62393344))), name = tensor("layers_10_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62376896))), shape = tensor([512, 512, 1, 1])]; - tensor var_4139_cast_fp16 = conv(dilations = var_4139_dilations_0, groups = var_4139_groups_0, pad = var_4139_pad_0, pad_type = var_4139_pad_type_0, strides = var_4139_strides_0, weight = layers_10_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_4139_cast_fp16")]; - tensor p_21_cast_fp16 = add(x = var_4133_cast_fp16, y = var_4139_cast_fp16)[name = tensor("p_21_cast_fp16")]; - tensor var_4143 = const()[name = tensor("op_4143"), val = tensor([1, 8, 64, 188])]; - tensor var_4144_cast_fp16 = reshape(shape = var_4143, x = q_with_bias_v_21_cast_fp16)[name = tensor("op_4144_cast_fp16")]; - tensor var_4145 = const()[name = tensor("op_4145"), val = tensor([1, 8, 64, -1])]; - tensor var_4146_cast_fp16 = reshape(shape = var_4145, x = p_21_cast_fp16)[name = tensor("op_4146_cast_fp16")]; - tensor matrix_bd_81_transpose_x_0 = const()[name = tensor("matrix_bd_81_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_81_transpose_y_0 = const()[name = tensor("matrix_bd_81_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_81_cast_fp16 = matmul(transpose_x = matrix_bd_81_transpose_x_0, transpose_y = matrix_bd_81_transpose_y_0, x = var_4144_cast_fp16, y = var_4146_cast_fp16)[name = tensor("matrix_bd_81_cast_fp16")]; - tensor matrix_bd_83_pad_0 = const()[name = tensor("matrix_bd_83_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_83_mode_0 = const()[name = tensor("matrix_bd_83_mode_0"), val = tensor("constant")]; - tensor const_120_to_fp16 = const()[name = tensor("const_120_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_83_cast_fp16 = pad(constant_val = const_120_to_fp16, mode = matrix_bd_83_mode_0, pad = matrix_bd_83_pad_0, x = matrix_bd_81_cast_fp16)[name = tensor("matrix_bd_83_cast_fp16")]; - tensor var_4155 = const()[name = tensor("op_4155"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_85_cast_fp16 = reshape(shape = var_4155, x = matrix_bd_83_cast_fp16)[name = tensor("matrix_bd_85_cast_fp16")]; - tensor var_4159_begin_0 = const()[name = tensor("op_4159_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_4159_end_0 = const()[name = tensor("op_4159_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_4159_end_mask_0 = const()[name = tensor("op_4159_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_4159_cast_fp16 = slice_by_index(begin = var_4159_begin_0, end = var_4159_end_0, end_mask = var_4159_end_mask_0, x = matrix_bd_85_cast_fp16)[name = tensor("op_4159_cast_fp16")]; - tensor var_4160 = const()[name = tensor("op_4160"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_87_cast_fp16 = reshape(shape = var_4160, x = var_4159_cast_fp16)[name = tensor("matrix_bd_87_cast_fp16")]; - tensor var_4165_begin_0 = const()[name = tensor("op_4165_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4165_end_0 = const()[name = tensor("op_4165_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_4165_end_mask_0 = const()[name = tensor("op_4165_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_4165_cast_fp16 = slice_by_index(begin = var_4165_begin_0, end = var_4165_end_0, end_mask = var_4165_end_mask_0, x = matrix_bd_87_cast_fp16)[name = tensor("op_4165_cast_fp16")]; - tensor var_4166_to_fp16 = const()[name = tensor("op_4166_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_21_cast_fp16 = mul(x = var_4165_cast_fp16, y = var_4166_to_fp16)[name = tensor("qk_mask_21_cast_fp16")]; - tensor var_4170 = const()[name = tensor("op_4170"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_21_cast_fp16 = reshape(shape = var_4170, x = query_43_cast_fp16)[name = tensor("mh_q_21_cast_fp16")]; - tensor var_4172_to_fp16 = const()[name = tensor("op_4172_to_fp16"), val = tensor(0x1p-3)]; - tensor var_4173_cast_fp16 = mul(x = mh_q_21_cast_fp16, y = var_4172_to_fp16)[name = tensor("op_4173_cast_fp16")]; - tensor var_4176 = const()[name = tensor("op_4176"), val = tensor([1, 8, 64, 188])]; - tensor var_4177_cast_fp16 = reshape(shape = var_4176, x = key_21_cast_fp16)[name = tensor("op_4177_cast_fp16")]; - tensor mh_w_41_transpose_x_0 = const()[name = tensor("mh_w_41_transpose_x_0"), val = tensor(true)]; - tensor mh_w_41_transpose_y_0 = const()[name = tensor("mh_w_41_transpose_y_0"), val = tensor(false)]; - tensor mh_w_41_cast_fp16 = matmul(transpose_x = mh_w_41_transpose_x_0, transpose_y = mh_w_41_transpose_y_0, x = var_4173_cast_fp16, y = var_4177_cast_fp16)[name = tensor("mh_w_41_cast_fp16")]; - tensor mh_w_43_cast_fp16 = add(x = mh_w_41_cast_fp16, y = qk_mask_21_cast_fp16)[name = tensor("mh_w_43_cast_fp16")]; - tensor var_4181_cast_fp16 = softmax(axis = var_3968, x = mh_w_43_cast_fp16)[name = tensor("op_4181_cast_fp16")]; - tensor var_4182 = const()[name = tensor("op_4182"), val = tensor([1, 8, 64, 188])]; - tensor var_4183_cast_fp16 = reshape(shape = var_4182, x = value_21_cast_fp16)[name = tensor("op_4183_cast_fp16")]; - tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; - tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; - tensor attn_21_cast_fp16 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_4183_cast_fp16, y = var_4181_cast_fp16)[name = tensor("attn_21_cast_fp16")]; - tensor var_4186 = const()[name = tensor("op_4186"), val = tensor([1, 512, 1, 188])]; - tensor input_283_cast_fp16 = reshape(shape = var_4186, x = attn_21_cast_fp16)[name = tensor("input_283_cast_fp16")]; - tensor var_4196_pad_type_0 = const()[name = tensor("op_4196_pad_type_0"), val = tensor("valid")]; - tensor var_4196_strides_0 = const()[name = tensor("op_4196_strides_0"), val = tensor([1, 1])]; - tensor var_4196_pad_0 = const()[name = tensor("op_4196_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4196_dilations_0 = const()[name = tensor("op_4196_dilations_0"), val = tensor([1, 1])]; - tensor var_4196_groups_0 = const()[name = tensor("op_4196_groups_0"), val = tensor(1)]; - tensor layers_10_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62426176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62622848))), name = tensor("layers_10_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_10_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_10_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62623040)))]; - tensor var_4196_cast_fp16 = conv(bias = layers_10_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_4196_dilations_0, groups = var_4196_groups_0, pad = var_4196_pad_0, pad_type = var_4196_pad_type_0, strides = var_4196_strides_0, weight = layers_10_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = tensor("op_4196_cast_fp16")]; - tensor var_4202_pad_type_0 = const()[name = tensor("op_4202_pad_type_0"), val = tensor("valid")]; - tensor var_4202_strides_0 = const()[name = tensor("op_4202_strides_0"), val = tensor([1, 1])]; - tensor var_4202_pad_0 = const()[name = tensor("op_4202_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4202_dilations_0 = const()[name = tensor("op_4202_dilations_0"), val = tensor([1, 1])]; - tensor var_4202_groups_0 = const()[name = tensor("op_4202_groups_0"), val = tensor(1)]; - tensor layers_10_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62632832))), name = tensor("layers_10_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62624128))), shape = tensor([512, 512, 1, 1])]; - tensor var_4202_cast_fp16 = conv(dilations = var_4202_dilations_0, groups = var_4202_groups_0, pad = var_4202_pad_0, pad_type = var_4202_pad_type_0, strides = var_4202_strides_0, weight = layers_10_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_283_cast_fp16)[name = tensor("op_4202_cast_fp16")]; - tensor obj_45_cast_fp16 = add(x = var_4196_cast_fp16, y = var_4202_cast_fp16)[name = tensor("obj_45_cast_fp16")]; - tensor inputs_105_cast_fp16 = add(x = inputs_103_cast_fp16, y = obj_45_cast_fp16)[name = tensor("inputs_105_cast_fp16")]; - tensor out_105_axes_0 = const()[name = tensor("out_105_axes_0"), val = tensor([1])]; - tensor var_4213_to_fp16 = const()[name = tensor("op_4213_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_4213_to_fp16, x = inputs_105_cast_fp16)[name = tensor("out_105_cast_fp16")]; - tensor input_285_gamma_0_to_fp16 = const()[name = tensor("input_285_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62665664)))]; - tensor input_285_beta_0_to_fp16 = const()[name = tensor("input_285_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62666752)))]; - tensor input_285_epsilon_0_to_fp16 = const()[name = tensor("input_285_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_285_cast_fp16 = batch_norm(beta = input_285_beta_0_to_fp16, epsilon = input_285_epsilon_0_to_fp16, gamma = input_285_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_105_cast_fp16)[name = tensor("input_285_cast_fp16")]; - tensor var_4235_pad_type_0 = const()[name = tensor("op_4235_pad_type_0"), val = tensor("valid")]; - tensor var_4235_strides_0 = const()[name = tensor("op_4235_strides_0"), val = tensor([1, 1])]; - tensor var_4235_pad_0 = const()[name = tensor("op_4235_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4235_dilations_0 = const()[name = tensor("op_4235_dilations_0"), val = tensor([1, 1])]; - tensor var_4235_groups_0 = const()[name = tensor("op_4235_groups_0"), val = tensor(1)]; - tensor layers_10_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62667840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63061120))), name = tensor("layers_10_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_10_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_10_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63061312)))]; - tensor var_4235_cast_fp16 = conv(bias = layers_10_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_4235_dilations_0, groups = var_4235_groups_0, pad = var_4235_pad_0, pad_type = var_4235_pad_type_0, strides = var_4235_strides_0, weight = layers_10_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_285_cast_fp16)[name = tensor("op_4235_cast_fp16")]; - tensor var_4241_pad_type_0 = const()[name = tensor("op_4241_pad_type_0"), val = tensor("valid")]; - tensor var_4241_strides_0 = const()[name = tensor("op_4241_strides_0"), val = tensor([1, 1])]; - tensor var_4241_pad_0 = const()[name = tensor("op_4241_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4241_dilations_0 = const()[name = tensor("op_4241_dilations_0"), val = tensor([1, 1])]; - tensor var_4241_groups_0 = const()[name = tensor("op_4241_groups_0"), val = tensor(1)]; - tensor layers_10_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63080960))), name = tensor("layers_10_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63063424))), shape = tensor([1024, 512, 1, 1])]; - tensor var_4241_cast_fp16 = conv(dilations = var_4241_dilations_0, groups = var_4241_groups_0, pad = var_4241_pad_0, pad_type = var_4241_pad_type_0, strides = var_4241_strides_0, weight = layers_10_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_285_cast_fp16)[name = tensor("op_4241_cast_fp16")]; - tensor input_287_cast_fp16 = add(x = var_4235_cast_fp16, y = var_4241_cast_fp16)[name = tensor("input_287_cast_fp16")]; - tensor input_289_split_num_splits_0 = const()[name = tensor("input_289_split_num_splits_0"), val = tensor(2)]; - tensor input_289_split_axis_0 = const()[name = tensor("input_289_split_axis_0"), val = tensor(1)]; - tensor input_289_split_cast_fp16_0, tensor input_289_split_cast_fp16_1 = split(axis = input_289_split_axis_0, num_splits = input_289_split_num_splits_0, x = input_287_cast_fp16)[name = tensor("input_289_split_cast_fp16")]; - tensor input_289_split_1_sigmoid_cast_fp16 = sigmoid(x = input_289_split_cast_fp16_1)[name = tensor("input_289_split_1_sigmoid_cast_fp16")]; - tensor input_289_cast_fp16 = mul(x = input_289_split_cast_fp16_0, y = input_289_split_1_sigmoid_cast_fp16)[name = tensor("input_289_cast_fp16")]; - tensor input_291_pad_type_0 = const()[name = tensor("input_291_pad_type_0"), val = tensor("custom")]; - tensor input_291_pad_0 = const()[name = tensor("input_291_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_291_groups_0 = const()[name = tensor("input_291_groups_0"), val = tensor(512)]; - tensor input_291_strides_0 = const()[name = tensor("input_291_strides_0"), val = tensor([1, 1])]; - tensor input_291_dilations_0 = const()[name = tensor("input_291_dilations_0"), val = tensor([1, 1])]; - tensor const_211_to_fp16 = const()[name = tensor("const_211_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63146560)))]; - tensor const_212_to_fp16 = const()[name = tensor("const_212_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63155840)))]; - tensor input_293_cast_fp16 = conv(bias = const_212_to_fp16, dilations = input_291_dilations_0, groups = input_291_groups_0, pad = input_291_pad_0, pad_type = input_291_pad_type_0, strides = input_291_strides_0, weight = const_211_to_fp16, x = input_289_cast_fp16)[name = tensor("input_293_cast_fp16")]; - tensor input_295_cast_fp16 = silu(x = input_293_cast_fp16)[name = tensor("input_295_cast_fp16")]; - tensor var_4265_pad_type_0 = const()[name = tensor("op_4265_pad_type_0"), val = tensor("valid")]; - tensor var_4265_strides_0 = const()[name = tensor("op_4265_strides_0"), val = tensor([1, 1])]; - tensor var_4265_pad_0 = const()[name = tensor("op_4265_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4265_dilations_0 = const()[name = tensor("op_4265_dilations_0"), val = tensor([1, 1])]; - tensor var_4265_groups_0 = const()[name = tensor("op_4265_groups_0"), val = tensor(1)]; - tensor layers_10_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63156928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63353600))), name = tensor("layers_10_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_10_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_10_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63353792)))]; - tensor var_4265_cast_fp16 = conv(bias = layers_10_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_4265_dilations_0, groups = var_4265_groups_0, pad = var_4265_pad_0, pad_type = var_4265_pad_type_0, strides = var_4265_strides_0, weight = layers_10_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = tensor("op_4265_cast_fp16")]; - tensor var_4271_pad_type_0 = const()[name = tensor("op_4271_pad_type_0"), val = tensor("valid")]; - tensor var_4271_strides_0 = const()[name = tensor("op_4271_strides_0"), val = tensor([1, 1])]; - tensor var_4271_pad_0 = const()[name = tensor("op_4271_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4271_dilations_0 = const()[name = tensor("op_4271_dilations_0"), val = tensor([1, 1])]; - tensor var_4271_groups_0 = const()[name = tensor("op_4271_groups_0"), val = tensor(1)]; - tensor layers_10_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63363712))), name = tensor("layers_10_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63354880))), shape = tensor([512, 512, 1, 1])]; - tensor var_4271_cast_fp16 = conv(dilations = var_4271_dilations_0, groups = var_4271_groups_0, pad = var_4271_pad_0, pad_type = var_4271_pad_type_0, strides = var_4271_strides_0, weight = layers_10_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_295_cast_fp16)[name = tensor("op_4271_cast_fp16")]; - tensor x_65_cast_fp16 = add(x = var_4265_cast_fp16, y = var_4271_cast_fp16)[name = tensor("x_65_cast_fp16")]; - tensor inputs_107_cast_fp16 = add(x = inputs_105_cast_fp16, y = x_65_cast_fp16)[name = tensor("inputs_107_cast_fp16")]; - tensor out_107_axes_0 = const()[name = tensor("out_107_axes_0"), val = tensor([1])]; - tensor var_4282_to_fp16 = const()[name = tensor("op_4282_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_107_cast_fp16 = layer_norm(axes = out_107_axes_0, epsilon = var_4282_to_fp16, x = inputs_107_cast_fp16)[name = tensor("out_107_cast_fp16")]; - tensor input_297_gamma_0_to_fp16 = const()[name = tensor("input_297_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63396544)))]; - tensor input_297_beta_0_to_fp16 = const()[name = tensor("input_297_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63397632)))]; - tensor input_297_epsilon_0_to_fp16 = const()[name = tensor("input_297_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_297_cast_fp16 = batch_norm(beta = input_297_beta_0_to_fp16, epsilon = input_297_epsilon_0_to_fp16, gamma = input_297_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_107_cast_fp16)[name = tensor("input_297_cast_fp16")]; - tensor var_4302_pad_type_0 = const()[name = tensor("op_4302_pad_type_0"), val = tensor("valid")]; - tensor var_4302_strides_0 = const()[name = tensor("op_4302_strides_0"), val = tensor([1, 1])]; - tensor var_4302_pad_0 = const()[name = tensor("op_4302_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4302_dilations_0 = const()[name = tensor("op_4302_dilations_0"), val = tensor([1, 1])]; - tensor var_4302_groups_0 = const()[name = tensor("op_4302_groups_0"), val = tensor(1)]; - tensor layers_10_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63398720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64185216))), name = tensor("layers_10_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_10_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_10_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64185408)))]; - tensor var_4302_cast_fp16 = conv(bias = layers_10_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_4302_dilations_0, groups = var_4302_groups_0, pad = var_4302_pad_0, pad_type = var_4302_pad_type_0, strides = var_4302_strides_0, weight = layers_10_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_297_cast_fp16)[name = tensor("op_4302_cast_fp16")]; - tensor var_4308_pad_type_0 = const()[name = tensor("op_4308_pad_type_0"), val = tensor("valid")]; - tensor var_4308_strides_0 = const()[name = tensor("op_4308_strides_0"), val = tensor([1, 1])]; - tensor var_4308_pad_0 = const()[name = tensor("op_4308_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4308_dilations_0 = const()[name = tensor("op_4308_dilations_0"), val = tensor([1, 1])]; - tensor var_4308_groups_0 = const()[name = tensor("op_4308_groups_0"), val = tensor(1)]; - tensor layers_10_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64224256))), name = tensor("layers_10_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64189568))), shape = tensor([2048, 512, 1, 1])]; - tensor var_4308_cast_fp16 = conv(dilations = var_4308_dilations_0, groups = var_4308_groups_0, pad = var_4308_pad_0, pad_type = var_4308_pad_type_0, strides = var_4308_strides_0, weight = layers_10_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_297_cast_fp16)[name = tensor("op_4308_cast_fp16")]; - tensor input_299_cast_fp16 = add(x = var_4302_cast_fp16, y = var_4308_cast_fp16)[name = tensor("input_299_cast_fp16")]; - tensor input_301_cast_fp16 = silu(x = input_299_cast_fp16)[name = tensor("input_301_cast_fp16")]; - tensor var_4319_pad_type_0 = const()[name = tensor("op_4319_pad_type_0"), val = tensor("valid")]; - tensor var_4319_strides_0 = const()[name = tensor("op_4319_strides_0"), val = tensor([1, 1])]; - tensor var_4319_pad_0 = const()[name = tensor("op_4319_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4319_dilations_0 = const()[name = tensor("op_4319_dilations_0"), val = tensor([1, 1])]; - tensor var_4319_groups_0 = const()[name = tensor("op_4319_groups_0"), val = tensor(1)]; - tensor layers_10_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64355392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65141888))), name = tensor("layers_10_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_10_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_10_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65142080)))]; - tensor var_4319_cast_fp16 = conv(bias = layers_10_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_4319_dilations_0, groups = var_4319_groups_0, pad = var_4319_pad_0, pad_type = var_4319_pad_type_0, strides = var_4319_strides_0, weight = layers_10_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_301_cast_fp16)[name = tensor("op_4319_cast_fp16")]; - tensor var_4325_pad_type_0 = const()[name = tensor("op_4325_pad_type_0"), val = tensor("valid")]; - tensor var_4325_strides_0 = const()[name = tensor("op_4325_strides_0"), val = tensor([1, 1])]; - tensor var_4325_pad_0 = const()[name = tensor("op_4325_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4325_dilations_0 = const()[name = tensor("op_4325_dilations_0"), val = tensor([1, 1])]; - tensor var_4325_groups_0 = const()[name = tensor("op_4325_groups_0"), val = tensor(1)]; - tensor layers_10_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65185344))), name = tensor("layers_10_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65143168))), shape = tensor([512, 2048, 1, 1])]; - tensor var_4325_cast_fp16 = conv(dilations = var_4325_dilations_0, groups = var_4325_groups_0, pad = var_4325_pad_0, pad_type = var_4325_pad_type_0, strides = var_4325_strides_0, weight = layers_10_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_301_cast_fp16)[name = tensor("op_4325_cast_fp16")]; - tensor x_67_cast_fp16 = add(x = var_4319_cast_fp16, y = var_4325_cast_fp16)[name = tensor("x_67_cast_fp16")]; - tensor var_4327_to_fp16 = const()[name = tensor("op_4327_to_fp16"), val = tensor(0x1p-1)]; - tensor var_4328_cast_fp16 = mul(x = x_67_cast_fp16, y = var_4327_to_fp16)[name = tensor("op_4328_cast_fp16")]; - tensor inputs_109_cast_fp16 = add(x = inputs_107_cast_fp16, y = var_4328_cast_fp16)[name = tensor("inputs_109_cast_fp16")]; - tensor out_109_axes_0 = const()[name = tensor("out_109_axes_0"), val = tensor([1])]; - tensor var_4338_to_fp16 = const()[name = tensor("op_4338_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_109_cast_fp16 = layer_norm(axes = out_109_axes_0, epsilon = var_4338_to_fp16, x = inputs_109_cast_fp16)[name = tensor("out_109_cast_fp16")]; - tensor inputs_111_gamma_0_to_fp16 = const()[name = tensor("inputs_111_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65316480)))]; - tensor inputs_111_beta_0_to_fp16 = const()[name = tensor("inputs_111_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65317568)))]; - tensor inputs_111_epsilon_0_to_fp16 = const()[name = tensor("inputs_111_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_111_cast_fp16 = batch_norm(beta = inputs_111_beta_0_to_fp16, epsilon = inputs_111_epsilon_0_to_fp16, gamma = inputs_111_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_109_cast_fp16)[name = tensor("inputs_111_cast_fp16")]; - tensor var_4352 = const()[name = tensor("op_4352"), val = tensor(3)]; - tensor out_111_axes_0 = const()[name = tensor("out_111_axes_0"), val = tensor([1])]; - tensor var_4383_to_fp16 = const()[name = tensor("op_4383_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_111_cast_fp16 = layer_norm(axes = out_111_axes_0, epsilon = var_4383_to_fp16, x = inputs_111_cast_fp16)[name = tensor("out_111_cast_fp16")]; - tensor input_303_gamma_0_to_fp16 = const()[name = tensor("input_303_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65318656)))]; - tensor input_303_beta_0_to_fp16 = const()[name = tensor("input_303_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65319744)))]; - tensor input_303_epsilon_0_to_fp16 = const()[name = tensor("input_303_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_303_cast_fp16 = batch_norm(beta = input_303_beta_0_to_fp16, epsilon = input_303_epsilon_0_to_fp16, gamma = input_303_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_111_cast_fp16)[name = tensor("input_303_cast_fp16")]; - tensor var_4403_pad_type_0 = const()[name = tensor("op_4403_pad_type_0"), val = tensor("valid")]; - tensor var_4403_strides_0 = const()[name = tensor("op_4403_strides_0"), val = tensor([1, 1])]; - tensor var_4403_pad_0 = const()[name = tensor("op_4403_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4403_dilations_0 = const()[name = tensor("op_4403_dilations_0"), val = tensor([1, 1])]; - tensor var_4403_groups_0 = const()[name = tensor("op_4403_groups_0"), val = tensor(1)]; - tensor layers_11_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65320832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66107328))), name = tensor("layers_11_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_11_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_11_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66107520)))]; - tensor var_4403_cast_fp16 = conv(bias = layers_11_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_4403_dilations_0, groups = var_4403_groups_0, pad = var_4403_pad_0, pad_type = var_4403_pad_type_0, strides = var_4403_strides_0, weight = layers_11_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = tensor("op_4403_cast_fp16")]; - tensor var_4409_pad_type_0 = const()[name = tensor("op_4409_pad_type_0"), val = tensor("valid")]; - tensor var_4409_strides_0 = const()[name = tensor("op_4409_strides_0"), val = tensor([1, 1])]; - tensor var_4409_pad_0 = const()[name = tensor("op_4409_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4409_dilations_0 = const()[name = tensor("op_4409_dilations_0"), val = tensor([1, 1])]; - tensor var_4409_groups_0 = const()[name = tensor("op_4409_groups_0"), val = tensor(1)]; - tensor layers_11_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66147264))), name = tensor("layers_11_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66111680))), shape = tensor([2048, 512, 1, 1])]; - tensor var_4409_cast_fp16 = conv(dilations = var_4409_dilations_0, groups = var_4409_groups_0, pad = var_4409_pad_0, pad_type = var_4409_pad_type_0, strides = var_4409_strides_0, weight = layers_11_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_303_cast_fp16)[name = tensor("op_4409_cast_fp16")]; - tensor input_305_cast_fp16 = add(x = var_4403_cast_fp16, y = var_4409_cast_fp16)[name = tensor("input_305_cast_fp16")]; - tensor input_307_cast_fp16 = silu(x = input_305_cast_fp16)[name = tensor("input_307_cast_fp16")]; - tensor var_4420_pad_type_0 = const()[name = tensor("op_4420_pad_type_0"), val = tensor("valid")]; - tensor var_4420_strides_0 = const()[name = tensor("op_4420_strides_0"), val = tensor([1, 1])]; - tensor var_4420_pad_0 = const()[name = tensor("op_4420_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4420_dilations_0 = const()[name = tensor("op_4420_dilations_0"), val = tensor([1, 1])]; - tensor var_4420_groups_0 = const()[name = tensor("op_4420_groups_0"), val = tensor(1)]; - tensor layers_11_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66278400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67064896))), name = tensor("layers_11_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_11_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_11_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67065088)))]; - tensor var_4420_cast_fp16 = conv(bias = layers_11_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_4420_dilations_0, groups = var_4420_groups_0, pad = var_4420_pad_0, pad_type = var_4420_pad_type_0, strides = var_4420_strides_0, weight = layers_11_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = tensor("op_4420_cast_fp16")]; - tensor var_4426_pad_type_0 = const()[name = tensor("op_4426_pad_type_0"), val = tensor("valid")]; - tensor var_4426_strides_0 = const()[name = tensor("op_4426_strides_0"), val = tensor([1, 1])]; - tensor var_4426_pad_0 = const()[name = tensor("op_4426_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4426_dilations_0 = const()[name = tensor("op_4426_dilations_0"), val = tensor([1, 1])]; - tensor var_4426_groups_0 = const()[name = tensor("op_4426_groups_0"), val = tensor(1)]; - tensor layers_11_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67105920))), name = tensor("layers_11_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67066176))), shape = tensor([512, 2048, 1, 1])]; - tensor var_4426_cast_fp16 = conv(dilations = var_4426_dilations_0, groups = var_4426_groups_0, pad = var_4426_pad_0, pad_type = var_4426_pad_type_0, strides = var_4426_strides_0, weight = layers_11_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_307_cast_fp16)[name = tensor("op_4426_cast_fp16")]; - tensor x_69_cast_fp16 = add(x = var_4420_cast_fp16, y = var_4426_cast_fp16)[name = tensor("x_69_cast_fp16")]; - tensor var_4428_to_fp16 = const()[name = tensor("op_4428_to_fp16"), val = tensor(0x1p-1)]; - tensor var_4429_cast_fp16 = mul(x = x_69_cast_fp16, y = var_4428_to_fp16)[name = tensor("op_4429_cast_fp16")]; - tensor inputs_113_cast_fp16 = add(x = inputs_111_cast_fp16, y = var_4429_cast_fp16)[name = tensor("inputs_113_cast_fp16")]; - tensor out_113_axes_0 = const()[name = tensor("out_113_axes_0"), val = tensor([1])]; - tensor var_4439_to_fp16 = const()[name = tensor("op_4439_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_113_cast_fp16 = layer_norm(axes = out_113_axes_0, epsilon = var_4439_to_fp16, x = inputs_113_cast_fp16)[name = tensor("out_113_cast_fp16")]; - tensor obj_47_gamma_0_to_fp16 = const()[name = tensor("obj_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67237056)))]; - tensor obj_47_beta_0_to_fp16 = const()[name = tensor("obj_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67238144)))]; - tensor obj_47_epsilon_0_to_fp16 = const()[name = tensor("obj_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_47_cast_fp16 = batch_norm(beta = obj_47_beta_0_to_fp16, epsilon = obj_47_epsilon_0_to_fp16, gamma = obj_47_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_113_cast_fp16)[name = tensor("obj_47_cast_fp16")]; - tensor var_4464_pad_type_0 = const()[name = tensor("op_4464_pad_type_0"), val = tensor("valid")]; - tensor var_4464_strides_0 = const()[name = tensor("op_4464_strides_0"), val = tensor([1, 1])]; - tensor var_4464_pad_0 = const()[name = tensor("op_4464_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4464_dilations_0 = const()[name = tensor("op_4464_dilations_0"), val = tensor([1, 1])]; - tensor var_4464_groups_0 = const()[name = tensor("op_4464_groups_0"), val = tensor(1)]; - tensor layers_11_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67239232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67435904))), name = tensor("layers_11_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_11_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_11_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67436096)))]; - tensor var_4464_cast_fp16 = conv(bias = layers_11_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_4464_dilations_0, groups = var_4464_groups_0, pad = var_4464_pad_0, pad_type = var_4464_pad_type_0, strides = var_4464_strides_0, weight = layers_11_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_47_cast_fp16)[name = tensor("op_4464_cast_fp16")]; - tensor var_4470_pad_type_0 = const()[name = tensor("op_4470_pad_type_0"), val = tensor("valid")]; - tensor var_4470_strides_0 = const()[name = tensor("op_4470_strides_0"), val = tensor([1, 1])]; - tensor var_4470_pad_0 = const()[name = tensor("op_4470_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4470_dilations_0 = const()[name = tensor("op_4470_dilations_0"), val = tensor([1, 1])]; - tensor var_4470_groups_0 = const()[name = tensor("op_4470_groups_0"), val = tensor(1)]; - tensor layers_11_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67446528))), name = tensor("layers_11_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67437184))), shape = tensor([512, 512, 1, 1])]; - tensor var_4470_cast_fp16 = conv(dilations = var_4470_dilations_0, groups = var_4470_groups_0, pad = var_4470_pad_0, pad_type = var_4470_pad_type_0, strides = var_4470_strides_0, weight = layers_11_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_47_cast_fp16)[name = tensor("op_4470_cast_fp16")]; - tensor query_45_cast_fp16 = add(x = var_4464_cast_fp16, y = var_4470_cast_fp16)[name = tensor("query_45_cast_fp16")]; - tensor var_4479_pad_type_0 = const()[name = tensor("op_4479_pad_type_0"), val = tensor("valid")]; - tensor var_4479_strides_0 = const()[name = tensor("op_4479_strides_0"), val = tensor([1, 1])]; - tensor var_4479_pad_0 = const()[name = tensor("op_4479_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4479_dilations_0 = const()[name = tensor("op_4479_dilations_0"), val = tensor([1, 1])]; - tensor var_4479_groups_0 = const()[name = tensor("op_4479_groups_0"), val = tensor(1)]; - tensor layers_11_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67479360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67676032))), name = tensor("layers_11_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_4479_cast_fp16 = conv(dilations = var_4479_dilations_0, groups = var_4479_groups_0, pad = var_4479_pad_0, pad_type = var_4479_pad_type_0, strides = var_4479_strides_0, weight = layers_11_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_47_cast_fp16)[name = tensor("op_4479_cast_fp16")]; - tensor var_4485_pad_type_0 = const()[name = tensor("op_4485_pad_type_0"), val = tensor("valid")]; - tensor var_4485_strides_0 = const()[name = tensor("op_4485_strides_0"), val = tensor([1, 1])]; - tensor var_4485_pad_0 = const()[name = tensor("op_4485_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4485_dilations_0 = const()[name = tensor("op_4485_dilations_0"), val = tensor([1, 1])]; - tensor var_4485_groups_0 = const()[name = tensor("op_4485_groups_0"), val = tensor(1)]; - tensor layers_11_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67685952))), name = tensor("layers_11_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67676224))), shape = tensor([512, 512, 1, 1])]; - tensor var_4485_cast_fp16 = conv(dilations = var_4485_dilations_0, groups = var_4485_groups_0, pad = var_4485_pad_0, pad_type = var_4485_pad_type_0, strides = var_4485_strides_0, weight = layers_11_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_47_cast_fp16)[name = tensor("op_4485_cast_fp16")]; - tensor key_23_cast_fp16 = add(x = var_4479_cast_fp16, y = var_4485_cast_fp16)[name = tensor("key_23_cast_fp16")]; - tensor var_4495_pad_type_0 = const()[name = tensor("op_4495_pad_type_0"), val = tensor("valid")]; - tensor var_4495_strides_0 = const()[name = tensor("op_4495_strides_0"), val = tensor([1, 1])]; - tensor var_4495_pad_0 = const()[name = tensor("op_4495_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4495_dilations_0 = const()[name = tensor("op_4495_dilations_0"), val = tensor([1, 1])]; - tensor var_4495_groups_0 = const()[name = tensor("op_4495_groups_0"), val = tensor(1)]; - tensor layers_11_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67718784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67915456))), name = tensor("layers_11_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_11_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_11_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67915648)))]; - tensor var_4495_cast_fp16 = conv(bias = layers_11_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_4495_dilations_0, groups = var_4495_groups_0, pad = var_4495_pad_0, pad_type = var_4495_pad_type_0, strides = var_4495_strides_0, weight = layers_11_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_47_cast_fp16)[name = tensor("op_4495_cast_fp16")]; - tensor var_4501_pad_type_0 = const()[name = tensor("op_4501_pad_type_0"), val = tensor("valid")]; - tensor var_4501_strides_0 = const()[name = tensor("op_4501_strides_0"), val = tensor([1, 1])]; - tensor var_4501_pad_0 = const()[name = tensor("op_4501_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4501_dilations_0 = const()[name = tensor("op_4501_dilations_0"), val = tensor([1, 1])]; - tensor var_4501_groups_0 = const()[name = tensor("op_4501_groups_0"), val = tensor(1)]; - tensor layers_11_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67925056))), name = tensor("layers_11_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67916736))), shape = tensor([512, 512, 1, 1])]; - tensor var_4501_cast_fp16 = conv(dilations = var_4501_dilations_0, groups = var_4501_groups_0, pad = var_4501_pad_0, pad_type = var_4501_pad_type_0, strides = var_4501_strides_0, weight = layers_11_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_47_cast_fp16)[name = tensor("op_4501_cast_fp16")]; - tensor value_23_cast_fp16 = add(x = var_4495_cast_fp16, y = var_4501_cast_fp16)[name = tensor("value_23_cast_fp16")]; - tensor var_4504_to_fp16 = const()[name = tensor("op_4504_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67957888)))]; - tensor query_47_cast_fp16 = add(x = query_45_cast_fp16, y = var_4504_to_fp16)[name = tensor("query_47_cast_fp16")]; - tensor var_4507_to_fp16 = const()[name = tensor("op_4507_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67958976)))]; - tensor q_with_bias_v_23_cast_fp16 = add(x = query_45_cast_fp16, y = var_4507_to_fp16)[name = tensor("q_with_bias_v_23_cast_fp16")]; - tensor var_4517_pad_type_0 = const()[name = tensor("op_4517_pad_type_0"), val = tensor("valid")]; - tensor var_4517_strides_0 = const()[name = tensor("op_4517_strides_0"), val = tensor([1, 1])]; - tensor var_4517_pad_0 = const()[name = tensor("op_4517_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4517_dilations_0 = const()[name = tensor("op_4517_dilations_0"), val = tensor([1, 1])]; - tensor var_4517_groups_0 = const()[name = tensor("op_4517_groups_0"), val = tensor(1)]; - tensor layers_11_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67960064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68156736))), name = tensor("layers_11_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_4517_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_4517_dilations_0, groups = var_4517_groups_0, pad = var_4517_pad_0, pad_type = var_4517_pad_type_0, strides = var_4517_strides_0, weight = layers_11_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_4517_cast_fp16")]; - tensor var_4523_pad_type_0 = const()[name = tensor("op_4523_pad_type_0"), val = tensor("valid")]; - tensor var_4523_strides_0 = const()[name = tensor("op_4523_strides_0"), val = tensor([1, 1])]; - tensor var_4523_pad_0 = const()[name = tensor("op_4523_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4523_dilations_0 = const()[name = tensor("op_4523_dilations_0"), val = tensor([1, 1])]; - tensor var_4523_groups_0 = const()[name = tensor("op_4523_groups_0"), val = tensor(1)]; - tensor layers_11_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68178304))), name = tensor("layers_11_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68156928))), shape = tensor([512, 512, 1, 1])]; - tensor var_4523_cast_fp16 = conv(dilations = var_4523_dilations_0, groups = var_4523_groups_0, pad = var_4523_pad_0, pad_type = var_4523_pad_type_0, strides = var_4523_strides_0, weight = layers_11_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_4523_cast_fp16")]; - tensor p_23_cast_fp16 = add(x = var_4517_cast_fp16, y = var_4523_cast_fp16)[name = tensor("p_23_cast_fp16")]; - tensor var_4527 = const()[name = tensor("op_4527"), val = tensor([1, 8, 64, 188])]; - tensor var_4528_cast_fp16 = reshape(shape = var_4527, x = q_with_bias_v_23_cast_fp16)[name = tensor("op_4528_cast_fp16")]; - tensor var_4529 = const()[name = tensor("op_4529"), val = tensor([1, 8, 64, -1])]; - tensor var_4530_cast_fp16 = reshape(shape = var_4529, x = p_23_cast_fp16)[name = tensor("op_4530_cast_fp16")]; - tensor matrix_bd_89_transpose_x_0 = const()[name = tensor("matrix_bd_89_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_89_transpose_y_0 = const()[name = tensor("matrix_bd_89_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_89_cast_fp16 = matmul(transpose_x = matrix_bd_89_transpose_x_0, transpose_y = matrix_bd_89_transpose_y_0, x = var_4528_cast_fp16, y = var_4530_cast_fp16)[name = tensor("matrix_bd_89_cast_fp16")]; - tensor matrix_bd_91_pad_0 = const()[name = tensor("matrix_bd_91_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_91_mode_0 = const()[name = tensor("matrix_bd_91_mode_0"), val = tensor("constant")]; - tensor const_131_to_fp16 = const()[name = tensor("const_131_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_91_cast_fp16 = pad(constant_val = const_131_to_fp16, mode = matrix_bd_91_mode_0, pad = matrix_bd_91_pad_0, x = matrix_bd_89_cast_fp16)[name = tensor("matrix_bd_91_cast_fp16")]; - tensor var_4539 = const()[name = tensor("op_4539"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_93_cast_fp16 = reshape(shape = var_4539, x = matrix_bd_91_cast_fp16)[name = tensor("matrix_bd_93_cast_fp16")]; - tensor var_4543_begin_0 = const()[name = tensor("op_4543_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_4543_end_0 = const()[name = tensor("op_4543_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_4543_end_mask_0 = const()[name = tensor("op_4543_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_4543_cast_fp16 = slice_by_index(begin = var_4543_begin_0, end = var_4543_end_0, end_mask = var_4543_end_mask_0, x = matrix_bd_93_cast_fp16)[name = tensor("op_4543_cast_fp16")]; - tensor var_4544 = const()[name = tensor("op_4544"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_95_cast_fp16 = reshape(shape = var_4544, x = var_4543_cast_fp16)[name = tensor("matrix_bd_95_cast_fp16")]; - tensor var_4549_begin_0 = const()[name = tensor("op_4549_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4549_end_0 = const()[name = tensor("op_4549_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_4549_end_mask_0 = const()[name = tensor("op_4549_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_4549_cast_fp16 = slice_by_index(begin = var_4549_begin_0, end = var_4549_end_0, end_mask = var_4549_end_mask_0, x = matrix_bd_95_cast_fp16)[name = tensor("op_4549_cast_fp16")]; - tensor var_4550_to_fp16 = const()[name = tensor("op_4550_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_23_cast_fp16 = mul(x = var_4549_cast_fp16, y = var_4550_to_fp16)[name = tensor("qk_mask_23_cast_fp16")]; - tensor var_4554 = const()[name = tensor("op_4554"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_23_cast_fp16 = reshape(shape = var_4554, x = query_47_cast_fp16)[name = tensor("mh_q_23_cast_fp16")]; - tensor var_4556_to_fp16 = const()[name = tensor("op_4556_to_fp16"), val = tensor(0x1p-3)]; - tensor var_4557_cast_fp16 = mul(x = mh_q_23_cast_fp16, y = var_4556_to_fp16)[name = tensor("op_4557_cast_fp16")]; - tensor var_4560 = const()[name = tensor("op_4560"), val = tensor([1, 8, 64, 188])]; - tensor var_4561_cast_fp16 = reshape(shape = var_4560, x = key_23_cast_fp16)[name = tensor("op_4561_cast_fp16")]; - tensor mh_w_45_transpose_x_0 = const()[name = tensor("mh_w_45_transpose_x_0"), val = tensor(true)]; - tensor mh_w_45_transpose_y_0 = const()[name = tensor("mh_w_45_transpose_y_0"), val = tensor(false)]; - tensor mh_w_45_cast_fp16 = matmul(transpose_x = mh_w_45_transpose_x_0, transpose_y = mh_w_45_transpose_y_0, x = var_4557_cast_fp16, y = var_4561_cast_fp16)[name = tensor("mh_w_45_cast_fp16")]; - tensor mh_w_47_cast_fp16 = add(x = mh_w_45_cast_fp16, y = qk_mask_23_cast_fp16)[name = tensor("mh_w_47_cast_fp16")]; - tensor var_4565_cast_fp16 = softmax(axis = var_4352, x = mh_w_47_cast_fp16)[name = tensor("op_4565_cast_fp16")]; - tensor var_4566 = const()[name = tensor("op_4566"), val = tensor([1, 8, 64, 188])]; - tensor var_4567_cast_fp16 = reshape(shape = var_4566, x = value_23_cast_fp16)[name = tensor("op_4567_cast_fp16")]; - tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; - tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; - tensor attn_23_cast_fp16 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_4567_cast_fp16, y = var_4565_cast_fp16)[name = tensor("attn_23_cast_fp16")]; - tensor var_4570 = const()[name = tensor("op_4570"), val = tensor([1, 512, 1, 188])]; - tensor input_309_cast_fp16 = reshape(shape = var_4570, x = attn_23_cast_fp16)[name = tensor("input_309_cast_fp16")]; - tensor var_4580_pad_type_0 = const()[name = tensor("op_4580_pad_type_0"), val = tensor("valid")]; - tensor var_4580_strides_0 = const()[name = tensor("op_4580_strides_0"), val = tensor([1, 1])]; - tensor var_4580_pad_0 = const()[name = tensor("op_4580_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4580_dilations_0 = const()[name = tensor("op_4580_dilations_0"), val = tensor([1, 1])]; - tensor var_4580_groups_0 = const()[name = tensor("op_4580_groups_0"), val = tensor(1)]; - tensor layers_11_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68211136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68407808))), name = tensor("layers_11_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_11_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_11_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68408000)))]; - tensor var_4580_cast_fp16 = conv(bias = layers_11_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_4580_dilations_0, groups = var_4580_groups_0, pad = var_4580_pad_0, pad_type = var_4580_pad_type_0, strides = var_4580_strides_0, weight = layers_11_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_309_cast_fp16)[name = tensor("op_4580_cast_fp16")]; - tensor var_4586_pad_type_0 = const()[name = tensor("op_4586_pad_type_0"), val = tensor("valid")]; - tensor var_4586_strides_0 = const()[name = tensor("op_4586_strides_0"), val = tensor([1, 1])]; - tensor var_4586_pad_0 = const()[name = tensor("op_4586_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4586_dilations_0 = const()[name = tensor("op_4586_dilations_0"), val = tensor([1, 1])]; - tensor var_4586_groups_0 = const()[name = tensor("op_4586_groups_0"), val = tensor(1)]; - tensor layers_11_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68418048))), name = tensor("layers_11_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68409088))), shape = tensor([512, 512, 1, 1])]; - tensor var_4586_cast_fp16 = conv(dilations = var_4586_dilations_0, groups = var_4586_groups_0, pad = var_4586_pad_0, pad_type = var_4586_pad_type_0, strides = var_4586_strides_0, weight = layers_11_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_309_cast_fp16)[name = tensor("op_4586_cast_fp16")]; - tensor obj_49_cast_fp16 = add(x = var_4580_cast_fp16, y = var_4586_cast_fp16)[name = tensor("obj_49_cast_fp16")]; - tensor inputs_115_cast_fp16 = add(x = inputs_113_cast_fp16, y = obj_49_cast_fp16)[name = tensor("inputs_115_cast_fp16")]; - tensor out_115_axes_0 = const()[name = tensor("out_115_axes_0"), val = tensor([1])]; - tensor var_4597_to_fp16 = const()[name = tensor("op_4597_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_115_cast_fp16 = layer_norm(axes = out_115_axes_0, epsilon = var_4597_to_fp16, x = inputs_115_cast_fp16)[name = tensor("out_115_cast_fp16")]; - tensor input_311_gamma_0_to_fp16 = const()[name = tensor("input_311_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68450880)))]; - tensor input_311_beta_0_to_fp16 = const()[name = tensor("input_311_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68451968)))]; - tensor input_311_epsilon_0_to_fp16 = const()[name = tensor("input_311_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_311_cast_fp16 = batch_norm(beta = input_311_beta_0_to_fp16, epsilon = input_311_epsilon_0_to_fp16, gamma = input_311_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_115_cast_fp16)[name = tensor("input_311_cast_fp16")]; - tensor var_4619_pad_type_0 = const()[name = tensor("op_4619_pad_type_0"), val = tensor("valid")]; - tensor var_4619_strides_0 = const()[name = tensor("op_4619_strides_0"), val = tensor([1, 1])]; - tensor var_4619_pad_0 = const()[name = tensor("op_4619_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4619_dilations_0 = const()[name = tensor("op_4619_dilations_0"), val = tensor([1, 1])]; - tensor var_4619_groups_0 = const()[name = tensor("op_4619_groups_0"), val = tensor(1)]; - tensor layers_11_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68453056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68846336))), name = tensor("layers_11_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_11_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_11_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68846528)))]; - tensor var_4619_cast_fp16 = conv(bias = layers_11_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_4619_dilations_0, groups = var_4619_groups_0, pad = var_4619_pad_0, pad_type = var_4619_pad_type_0, strides = var_4619_strides_0, weight = layers_11_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = tensor("op_4619_cast_fp16")]; - tensor var_4625_pad_type_0 = const()[name = tensor("op_4625_pad_type_0"), val = tensor("valid")]; - tensor var_4625_strides_0 = const()[name = tensor("op_4625_strides_0"), val = tensor([1, 1])]; - tensor var_4625_pad_0 = const()[name = tensor("op_4625_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4625_dilations_0 = const()[name = tensor("op_4625_dilations_0"), val = tensor([1, 1])]; - tensor var_4625_groups_0 = const()[name = tensor("op_4625_groups_0"), val = tensor(1)]; - tensor layers_11_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68866112))), name = tensor("layers_11_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68848640))), shape = tensor([1024, 512, 1, 1])]; - tensor var_4625_cast_fp16 = conv(dilations = var_4625_dilations_0, groups = var_4625_groups_0, pad = var_4625_pad_0, pad_type = var_4625_pad_type_0, strides = var_4625_strides_0, weight = layers_11_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_311_cast_fp16)[name = tensor("op_4625_cast_fp16")]; - tensor input_313_cast_fp16 = add(x = var_4619_cast_fp16, y = var_4625_cast_fp16)[name = tensor("input_313_cast_fp16")]; - tensor input_315_split_num_splits_0 = const()[name = tensor("input_315_split_num_splits_0"), val = tensor(2)]; - tensor input_315_split_axis_0 = const()[name = tensor("input_315_split_axis_0"), val = tensor(1)]; - tensor input_315_split_cast_fp16_0, tensor input_315_split_cast_fp16_1 = split(axis = input_315_split_axis_0, num_splits = input_315_split_num_splits_0, x = input_313_cast_fp16)[name = tensor("input_315_split_cast_fp16")]; - tensor input_315_split_1_sigmoid_cast_fp16 = sigmoid(x = input_315_split_cast_fp16_1)[name = tensor("input_315_split_1_sigmoid_cast_fp16")]; - tensor input_315_cast_fp16 = mul(x = input_315_split_cast_fp16_0, y = input_315_split_1_sigmoid_cast_fp16)[name = tensor("input_315_cast_fp16")]; - tensor input_317_pad_type_0 = const()[name = tensor("input_317_pad_type_0"), val = tensor("custom")]; - tensor input_317_pad_0 = const()[name = tensor("input_317_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_317_groups_0 = const()[name = tensor("input_317_groups_0"), val = tensor(512)]; - tensor input_317_strides_0 = const()[name = tensor("input_317_strides_0"), val = tensor([1, 1])]; - tensor input_317_dilations_0 = const()[name = tensor("input_317_dilations_0"), val = tensor([1, 1])]; - tensor const_213_to_fp16 = const()[name = tensor("const_213_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68931712)))]; - tensor const_214_to_fp16 = const()[name = tensor("const_214_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68940992)))]; - tensor input_319_cast_fp16 = conv(bias = const_214_to_fp16, dilations = input_317_dilations_0, groups = input_317_groups_0, pad = input_317_pad_0, pad_type = input_317_pad_type_0, strides = input_317_strides_0, weight = const_213_to_fp16, x = input_315_cast_fp16)[name = tensor("input_319_cast_fp16")]; - tensor input_321_cast_fp16 = silu(x = input_319_cast_fp16)[name = tensor("input_321_cast_fp16")]; - tensor var_4649_pad_type_0 = const()[name = tensor("op_4649_pad_type_0"), val = tensor("valid")]; - tensor var_4649_strides_0 = const()[name = tensor("op_4649_strides_0"), val = tensor([1, 1])]; - tensor var_4649_pad_0 = const()[name = tensor("op_4649_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4649_dilations_0 = const()[name = tensor("op_4649_dilations_0"), val = tensor([1, 1])]; - tensor var_4649_groups_0 = const()[name = tensor("op_4649_groups_0"), val = tensor(1)]; - tensor layers_11_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68942080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69138752))), name = tensor("layers_11_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_11_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_11_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69138944)))]; - tensor var_4649_cast_fp16 = conv(bias = layers_11_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_4649_dilations_0, groups = var_4649_groups_0, pad = var_4649_pad_0, pad_type = var_4649_pad_type_0, strides = var_4649_strides_0, weight = layers_11_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_321_cast_fp16)[name = tensor("op_4649_cast_fp16")]; - tensor var_4655_pad_type_0 = const()[name = tensor("op_4655_pad_type_0"), val = tensor("valid")]; - tensor var_4655_strides_0 = const()[name = tensor("op_4655_strides_0"), val = tensor([1, 1])]; - tensor var_4655_pad_0 = const()[name = tensor("op_4655_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4655_dilations_0 = const()[name = tensor("op_4655_dilations_0"), val = tensor([1, 1])]; - tensor var_4655_groups_0 = const()[name = tensor("op_4655_groups_0"), val = tensor(1)]; - tensor layers_11_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69149120))), name = tensor("layers_11_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69140032))), shape = tensor([512, 512, 1, 1])]; - tensor var_4655_cast_fp16 = conv(dilations = var_4655_dilations_0, groups = var_4655_groups_0, pad = var_4655_pad_0, pad_type = var_4655_pad_type_0, strides = var_4655_strides_0, weight = layers_11_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_321_cast_fp16)[name = tensor("op_4655_cast_fp16")]; - tensor x_71_cast_fp16 = add(x = var_4649_cast_fp16, y = var_4655_cast_fp16)[name = tensor("x_71_cast_fp16")]; - tensor inputs_117_cast_fp16 = add(x = inputs_115_cast_fp16, y = x_71_cast_fp16)[name = tensor("inputs_117_cast_fp16")]; - tensor out_117_axes_0 = const()[name = tensor("out_117_axes_0"), val = tensor([1])]; - tensor var_4666_to_fp16 = const()[name = tensor("op_4666_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_117_cast_fp16 = layer_norm(axes = out_117_axes_0, epsilon = var_4666_to_fp16, x = inputs_117_cast_fp16)[name = tensor("out_117_cast_fp16")]; - tensor input_323_gamma_0_to_fp16 = const()[name = tensor("input_323_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69181952)))]; - tensor input_323_beta_0_to_fp16 = const()[name = tensor("input_323_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69183040)))]; - tensor input_323_epsilon_0_to_fp16 = const()[name = tensor("input_323_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_323_cast_fp16 = batch_norm(beta = input_323_beta_0_to_fp16, epsilon = input_323_epsilon_0_to_fp16, gamma = input_323_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_117_cast_fp16)[name = tensor("input_323_cast_fp16")]; - tensor var_4686_pad_type_0 = const()[name = tensor("op_4686_pad_type_0"), val = tensor("valid")]; - tensor var_4686_strides_0 = const()[name = tensor("op_4686_strides_0"), val = tensor([1, 1])]; - tensor var_4686_pad_0 = const()[name = tensor("op_4686_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4686_dilations_0 = const()[name = tensor("op_4686_dilations_0"), val = tensor([1, 1])]; - tensor var_4686_groups_0 = const()[name = tensor("op_4686_groups_0"), val = tensor(1)]; - tensor layers_11_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69184128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69970624))), name = tensor("layers_11_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_11_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_11_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69970816)))]; - tensor var_4686_cast_fp16 = conv(bias = layers_11_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_4686_dilations_0, groups = var_4686_groups_0, pad = var_4686_pad_0, pad_type = var_4686_pad_type_0, strides = var_4686_strides_0, weight = layers_11_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = tensor("op_4686_cast_fp16")]; - tensor var_4692_pad_type_0 = const()[name = tensor("op_4692_pad_type_0"), val = tensor("valid")]; - tensor var_4692_strides_0 = const()[name = tensor("op_4692_strides_0"), val = tensor([1, 1])]; - tensor var_4692_pad_0 = const()[name = tensor("op_4692_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4692_dilations_0 = const()[name = tensor("op_4692_dilations_0"), val = tensor([1, 1])]; - tensor var_4692_groups_0 = const()[name = tensor("op_4692_groups_0"), val = tensor(1)]; - tensor layers_11_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70009856))), name = tensor("layers_11_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69974976))), shape = tensor([2048, 512, 1, 1])]; - tensor var_4692_cast_fp16 = conv(dilations = var_4692_dilations_0, groups = var_4692_groups_0, pad = var_4692_pad_0, pad_type = var_4692_pad_type_0, strides = var_4692_strides_0, weight = layers_11_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_323_cast_fp16)[name = tensor("op_4692_cast_fp16")]; - tensor input_325_cast_fp16 = add(x = var_4686_cast_fp16, y = var_4692_cast_fp16)[name = tensor("input_325_cast_fp16")]; - tensor input_327_cast_fp16 = silu(x = input_325_cast_fp16)[name = tensor("input_327_cast_fp16")]; - tensor var_4703_pad_type_0 = const()[name = tensor("op_4703_pad_type_0"), val = tensor("valid")]; - tensor var_4703_strides_0 = const()[name = tensor("op_4703_strides_0"), val = tensor([1, 1])]; - tensor var_4703_pad_0 = const()[name = tensor("op_4703_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4703_dilations_0 = const()[name = tensor("op_4703_dilations_0"), val = tensor([1, 1])]; - tensor var_4703_groups_0 = const()[name = tensor("op_4703_groups_0"), val = tensor(1)]; - tensor layers_11_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70140992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70927488))), name = tensor("layers_11_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_11_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_11_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70927680)))]; - tensor var_4703_cast_fp16 = conv(bias = layers_11_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_4703_dilations_0, groups = var_4703_groups_0, pad = var_4703_pad_0, pad_type = var_4703_pad_type_0, strides = var_4703_strides_0, weight = layers_11_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_327_cast_fp16)[name = tensor("op_4703_cast_fp16")]; - tensor var_4709_pad_type_0 = const()[name = tensor("op_4709_pad_type_0"), val = tensor("valid")]; - tensor var_4709_strides_0 = const()[name = tensor("op_4709_strides_0"), val = tensor([1, 1])]; - tensor var_4709_pad_0 = const()[name = tensor("op_4709_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4709_dilations_0 = const()[name = tensor("op_4709_dilations_0"), val = tensor([1, 1])]; - tensor var_4709_groups_0 = const()[name = tensor("op_4709_groups_0"), val = tensor(1)]; - tensor layers_11_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70970688))), name = tensor("layers_11_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70928768))), shape = tensor([512, 2048, 1, 1])]; - tensor var_4709_cast_fp16 = conv(dilations = var_4709_dilations_0, groups = var_4709_groups_0, pad = var_4709_pad_0, pad_type = var_4709_pad_type_0, strides = var_4709_strides_0, weight = layers_11_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_327_cast_fp16)[name = tensor("op_4709_cast_fp16")]; - tensor x_73_cast_fp16 = add(x = var_4703_cast_fp16, y = var_4709_cast_fp16)[name = tensor("x_73_cast_fp16")]; - tensor var_4711_to_fp16 = const()[name = tensor("op_4711_to_fp16"), val = tensor(0x1p-1)]; - tensor var_4712_cast_fp16 = mul(x = x_73_cast_fp16, y = var_4711_to_fp16)[name = tensor("op_4712_cast_fp16")]; - tensor inputs_119_cast_fp16 = add(x = inputs_117_cast_fp16, y = var_4712_cast_fp16)[name = tensor("inputs_119_cast_fp16")]; - tensor out_119_axes_0 = const()[name = tensor("out_119_axes_0"), val = tensor([1])]; - tensor var_4722_to_fp16 = const()[name = tensor("op_4722_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_119_cast_fp16 = layer_norm(axes = out_119_axes_0, epsilon = var_4722_to_fp16, x = inputs_119_cast_fp16)[name = tensor("out_119_cast_fp16")]; - tensor inputs_121_gamma_0_to_fp16 = const()[name = tensor("inputs_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71101824)))]; - tensor inputs_121_beta_0_to_fp16 = const()[name = tensor("inputs_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71102912)))]; - tensor inputs_121_epsilon_0_to_fp16 = const()[name = tensor("inputs_121_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_121_cast_fp16 = batch_norm(beta = inputs_121_beta_0_to_fp16, epsilon = inputs_121_epsilon_0_to_fp16, gamma = inputs_121_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_119_cast_fp16)[name = tensor("inputs_121_cast_fp16")]; - tensor var_4736 = const()[name = tensor("op_4736"), val = tensor(3)]; - tensor out_121_axes_0 = const()[name = tensor("out_121_axes_0"), val = tensor([1])]; - tensor var_4767_to_fp16 = const()[name = tensor("op_4767_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_121_cast_fp16 = layer_norm(axes = out_121_axes_0, epsilon = var_4767_to_fp16, x = inputs_121_cast_fp16)[name = tensor("out_121_cast_fp16")]; - tensor input_329_gamma_0_to_fp16 = const()[name = tensor("input_329_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71104000)))]; - tensor input_329_beta_0_to_fp16 = const()[name = tensor("input_329_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71105088)))]; - tensor input_329_epsilon_0_to_fp16 = const()[name = tensor("input_329_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_329_cast_fp16 = batch_norm(beta = input_329_beta_0_to_fp16, epsilon = input_329_epsilon_0_to_fp16, gamma = input_329_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_121_cast_fp16)[name = tensor("input_329_cast_fp16")]; - tensor var_4787_pad_type_0 = const()[name = tensor("op_4787_pad_type_0"), val = tensor("valid")]; - tensor var_4787_strides_0 = const()[name = tensor("op_4787_strides_0"), val = tensor([1, 1])]; - tensor var_4787_pad_0 = const()[name = tensor("op_4787_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4787_dilations_0 = const()[name = tensor("op_4787_dilations_0"), val = tensor([1, 1])]; - tensor var_4787_groups_0 = const()[name = tensor("op_4787_groups_0"), val = tensor(1)]; - tensor layers_12_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71106176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71892672))), name = tensor("layers_12_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_12_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_12_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71892864)))]; - tensor var_4787_cast_fp16 = conv(bias = layers_12_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_4787_dilations_0, groups = var_4787_groups_0, pad = var_4787_pad_0, pad_type = var_4787_pad_type_0, strides = var_4787_strides_0, weight = layers_12_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_329_cast_fp16)[name = tensor("op_4787_cast_fp16")]; - tensor var_4793_pad_type_0 = const()[name = tensor("op_4793_pad_type_0"), val = tensor("valid")]; - tensor var_4793_strides_0 = const()[name = tensor("op_4793_strides_0"), val = tensor([1, 1])]; - tensor var_4793_pad_0 = const()[name = tensor("op_4793_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4793_dilations_0 = const()[name = tensor("op_4793_dilations_0"), val = tensor([1, 1])]; - tensor var_4793_groups_0 = const()[name = tensor("op_4793_groups_0"), val = tensor(1)]; - tensor layers_12_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71931776))), name = tensor("layers_12_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71897024))), shape = tensor([2048, 512, 1, 1])]; - tensor var_4793_cast_fp16 = conv(dilations = var_4793_dilations_0, groups = var_4793_groups_0, pad = var_4793_pad_0, pad_type = var_4793_pad_type_0, strides = var_4793_strides_0, weight = layers_12_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_329_cast_fp16)[name = tensor("op_4793_cast_fp16")]; - tensor input_331_cast_fp16 = add(x = var_4787_cast_fp16, y = var_4793_cast_fp16)[name = tensor("input_331_cast_fp16")]; - tensor input_333_cast_fp16 = silu(x = input_331_cast_fp16)[name = tensor("input_333_cast_fp16")]; - tensor var_4804_pad_type_0 = const()[name = tensor("op_4804_pad_type_0"), val = tensor("valid")]; - tensor var_4804_strides_0 = const()[name = tensor("op_4804_strides_0"), val = tensor([1, 1])]; - tensor var_4804_pad_0 = const()[name = tensor("op_4804_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4804_dilations_0 = const()[name = tensor("op_4804_dilations_0"), val = tensor([1, 1])]; - tensor var_4804_groups_0 = const()[name = tensor("op_4804_groups_0"), val = tensor(1)]; - tensor layers_12_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72062912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72849408))), name = tensor("layers_12_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_12_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_12_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72849600)))]; - tensor var_4804_cast_fp16 = conv(bias = layers_12_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_4804_dilations_0, groups = var_4804_groups_0, pad = var_4804_pad_0, pad_type = var_4804_pad_type_0, strides = var_4804_strides_0, weight = layers_12_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_333_cast_fp16)[name = tensor("op_4804_cast_fp16")]; - tensor var_4810_pad_type_0 = const()[name = tensor("op_4810_pad_type_0"), val = tensor("valid")]; - tensor var_4810_strides_0 = const()[name = tensor("op_4810_strides_0"), val = tensor([1, 1])]; - tensor var_4810_pad_0 = const()[name = tensor("op_4810_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4810_dilations_0 = const()[name = tensor("op_4810_dilations_0"), val = tensor([1, 1])]; - tensor var_4810_groups_0 = const()[name = tensor("op_4810_groups_0"), val = tensor(1)]; - tensor layers_12_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72890432))), name = tensor("layers_12_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72850688))), shape = tensor([512, 2048, 1, 1])]; - tensor var_4810_cast_fp16 = conv(dilations = var_4810_dilations_0, groups = var_4810_groups_0, pad = var_4810_pad_0, pad_type = var_4810_pad_type_0, strides = var_4810_strides_0, weight = layers_12_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_333_cast_fp16)[name = tensor("op_4810_cast_fp16")]; - tensor x_75_cast_fp16 = add(x = var_4804_cast_fp16, y = var_4810_cast_fp16)[name = tensor("x_75_cast_fp16")]; - tensor var_4812_to_fp16 = const()[name = tensor("op_4812_to_fp16"), val = tensor(0x1p-1)]; - tensor var_4813_cast_fp16 = mul(x = x_75_cast_fp16, y = var_4812_to_fp16)[name = tensor("op_4813_cast_fp16")]; - tensor inputs_123_cast_fp16 = add(x = inputs_121_cast_fp16, y = var_4813_cast_fp16)[name = tensor("inputs_123_cast_fp16")]; - tensor out_123_axes_0 = const()[name = tensor("out_123_axes_0"), val = tensor([1])]; - tensor var_4823_to_fp16 = const()[name = tensor("op_4823_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_123_cast_fp16 = layer_norm(axes = out_123_axes_0, epsilon = var_4823_to_fp16, x = inputs_123_cast_fp16)[name = tensor("out_123_cast_fp16")]; - tensor obj_51_gamma_0_to_fp16 = const()[name = tensor("obj_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73021568)))]; - tensor obj_51_beta_0_to_fp16 = const()[name = tensor("obj_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73022656)))]; - tensor obj_51_epsilon_0_to_fp16 = const()[name = tensor("obj_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_51_cast_fp16 = batch_norm(beta = obj_51_beta_0_to_fp16, epsilon = obj_51_epsilon_0_to_fp16, gamma = obj_51_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_123_cast_fp16)[name = tensor("obj_51_cast_fp16")]; - tensor var_4848_pad_type_0 = const()[name = tensor("op_4848_pad_type_0"), val = tensor("valid")]; - tensor var_4848_strides_0 = const()[name = tensor("op_4848_strides_0"), val = tensor([1, 1])]; - tensor var_4848_pad_0 = const()[name = tensor("op_4848_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4848_dilations_0 = const()[name = tensor("op_4848_dilations_0"), val = tensor([1, 1])]; - tensor var_4848_groups_0 = const()[name = tensor("op_4848_groups_0"), val = tensor(1)]; - tensor layers_12_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73023744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73220416))), name = tensor("layers_12_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_12_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_12_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73220608)))]; - tensor var_4848_cast_fp16 = conv(bias = layers_12_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_4848_dilations_0, groups = var_4848_groups_0, pad = var_4848_pad_0, pad_type = var_4848_pad_type_0, strides = var_4848_strides_0, weight = layers_12_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_51_cast_fp16)[name = tensor("op_4848_cast_fp16")]; - tensor var_4854_pad_type_0 = const()[name = tensor("op_4854_pad_type_0"), val = tensor("valid")]; - tensor var_4854_strides_0 = const()[name = tensor("op_4854_strides_0"), val = tensor([1, 1])]; - tensor var_4854_pad_0 = const()[name = tensor("op_4854_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4854_dilations_0 = const()[name = tensor("op_4854_dilations_0"), val = tensor([1, 1])]; - tensor var_4854_groups_0 = const()[name = tensor("op_4854_groups_0"), val = tensor(1)]; - tensor layers_12_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73230784))), name = tensor("layers_12_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73221696))), shape = tensor([512, 512, 1, 1])]; - tensor var_4854_cast_fp16 = conv(dilations = var_4854_dilations_0, groups = var_4854_groups_0, pad = var_4854_pad_0, pad_type = var_4854_pad_type_0, strides = var_4854_strides_0, weight = layers_12_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_51_cast_fp16)[name = tensor("op_4854_cast_fp16")]; - tensor query_49_cast_fp16 = add(x = var_4848_cast_fp16, y = var_4854_cast_fp16)[name = tensor("query_49_cast_fp16")]; - tensor var_4863_pad_type_0 = const()[name = tensor("op_4863_pad_type_0"), val = tensor("valid")]; - tensor var_4863_strides_0 = const()[name = tensor("op_4863_strides_0"), val = tensor([1, 1])]; - tensor var_4863_pad_0 = const()[name = tensor("op_4863_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4863_dilations_0 = const()[name = tensor("op_4863_dilations_0"), val = tensor([1, 1])]; - tensor var_4863_groups_0 = const()[name = tensor("op_4863_groups_0"), val = tensor(1)]; - tensor layers_12_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73263616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73460288))), name = tensor("layers_12_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_4863_cast_fp16 = conv(dilations = var_4863_dilations_0, groups = var_4863_groups_0, pad = var_4863_pad_0, pad_type = var_4863_pad_type_0, strides = var_4863_strides_0, weight = layers_12_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_51_cast_fp16)[name = tensor("op_4863_cast_fp16")]; - tensor var_4869_pad_type_0 = const()[name = tensor("op_4869_pad_type_0"), val = tensor("valid")]; - tensor var_4869_strides_0 = const()[name = tensor("op_4869_strides_0"), val = tensor([1, 1])]; - tensor var_4869_pad_0 = const()[name = tensor("op_4869_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4869_dilations_0 = const()[name = tensor("op_4869_dilations_0"), val = tensor([1, 1])]; - tensor var_4869_groups_0 = const()[name = tensor("op_4869_groups_0"), val = tensor(1)]; - tensor layers_12_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73468160))), name = tensor("layers_12_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73460480))), shape = tensor([512, 512, 1, 1])]; - tensor var_4869_cast_fp16 = conv(dilations = var_4869_dilations_0, groups = var_4869_groups_0, pad = var_4869_pad_0, pad_type = var_4869_pad_type_0, strides = var_4869_strides_0, weight = layers_12_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_51_cast_fp16)[name = tensor("op_4869_cast_fp16")]; - tensor key_25_cast_fp16 = add(x = var_4863_cast_fp16, y = var_4869_cast_fp16)[name = tensor("key_25_cast_fp16")]; - tensor var_4879_pad_type_0 = const()[name = tensor("op_4879_pad_type_0"), val = tensor("valid")]; - tensor var_4879_strides_0 = const()[name = tensor("op_4879_strides_0"), val = tensor([1, 1])]; - tensor var_4879_pad_0 = const()[name = tensor("op_4879_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4879_dilations_0 = const()[name = tensor("op_4879_dilations_0"), val = tensor([1, 1])]; - tensor var_4879_groups_0 = const()[name = tensor("op_4879_groups_0"), val = tensor(1)]; - tensor layers_12_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73500992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73697664))), name = tensor("layers_12_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_12_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_12_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73697856)))]; - tensor var_4879_cast_fp16 = conv(bias = layers_12_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_4879_dilations_0, groups = var_4879_groups_0, pad = var_4879_pad_0, pad_type = var_4879_pad_type_0, strides = var_4879_strides_0, weight = layers_12_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_51_cast_fp16)[name = tensor("op_4879_cast_fp16")]; - tensor var_4885_pad_type_0 = const()[name = tensor("op_4885_pad_type_0"), val = tensor("valid")]; - tensor var_4885_strides_0 = const()[name = tensor("op_4885_strides_0"), val = tensor([1, 1])]; - tensor var_4885_pad_0 = const()[name = tensor("op_4885_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4885_dilations_0 = const()[name = tensor("op_4885_dilations_0"), val = tensor([1, 1])]; - tensor var_4885_groups_0 = const()[name = tensor("op_4885_groups_0"), val = tensor(1)]; - tensor layers_12_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73707712))), name = tensor("layers_12_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73698944))), shape = tensor([512, 512, 1, 1])]; - tensor var_4885_cast_fp16 = conv(dilations = var_4885_dilations_0, groups = var_4885_groups_0, pad = var_4885_pad_0, pad_type = var_4885_pad_type_0, strides = var_4885_strides_0, weight = layers_12_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_51_cast_fp16)[name = tensor("op_4885_cast_fp16")]; - tensor value_25_cast_fp16 = add(x = var_4879_cast_fp16, y = var_4885_cast_fp16)[name = tensor("value_25_cast_fp16")]; - tensor var_4888_to_fp16 = const()[name = tensor("op_4888_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73740544)))]; - tensor query_51_cast_fp16 = add(x = query_49_cast_fp16, y = var_4888_to_fp16)[name = tensor("query_51_cast_fp16")]; - tensor var_4891_to_fp16 = const()[name = tensor("op_4891_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73741632)))]; - tensor q_with_bias_v_25_cast_fp16 = add(x = query_49_cast_fp16, y = var_4891_to_fp16)[name = tensor("q_with_bias_v_25_cast_fp16")]; - tensor var_4901_pad_type_0 = const()[name = tensor("op_4901_pad_type_0"), val = tensor("valid")]; - tensor var_4901_strides_0 = const()[name = tensor("op_4901_strides_0"), val = tensor([1, 1])]; - tensor var_4901_pad_0 = const()[name = tensor("op_4901_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4901_dilations_0 = const()[name = tensor("op_4901_dilations_0"), val = tensor([1, 1])]; - tensor var_4901_groups_0 = const()[name = tensor("op_4901_groups_0"), val = tensor(1)]; - tensor layers_12_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73742720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73939392))), name = tensor("layers_12_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_4901_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_4901_dilations_0, groups = var_4901_groups_0, pad = var_4901_pad_0, pad_type = var_4901_pad_type_0, strides = var_4901_strides_0, weight = layers_12_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_4901_cast_fp16")]; - tensor var_4907_pad_type_0 = const()[name = tensor("op_4907_pad_type_0"), val = tensor("valid")]; - tensor var_4907_strides_0 = const()[name = tensor("op_4907_strides_0"), val = tensor([1, 1])]; - tensor var_4907_pad_0 = const()[name = tensor("op_4907_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4907_dilations_0 = const()[name = tensor("op_4907_dilations_0"), val = tensor([1, 1])]; - tensor var_4907_groups_0 = const()[name = tensor("op_4907_groups_0"), val = tensor(1)]; - tensor layers_12_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73961280))), name = tensor("layers_12_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73939584))), shape = tensor([512, 512, 1, 1])]; - tensor var_4907_cast_fp16 = conv(dilations = var_4907_dilations_0, groups = var_4907_groups_0, pad = var_4907_pad_0, pad_type = var_4907_pad_type_0, strides = var_4907_strides_0, weight = layers_12_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_4907_cast_fp16")]; - tensor p_25_cast_fp16 = add(x = var_4901_cast_fp16, y = var_4907_cast_fp16)[name = tensor("p_25_cast_fp16")]; - tensor var_4911 = const()[name = tensor("op_4911"), val = tensor([1, 8, 64, 188])]; - tensor var_4912_cast_fp16 = reshape(shape = var_4911, x = q_with_bias_v_25_cast_fp16)[name = tensor("op_4912_cast_fp16")]; - tensor var_4913 = const()[name = tensor("op_4913"), val = tensor([1, 8, 64, -1])]; - tensor var_4914_cast_fp16 = reshape(shape = var_4913, x = p_25_cast_fp16)[name = tensor("op_4914_cast_fp16")]; - tensor matrix_bd_97_transpose_x_0 = const()[name = tensor("matrix_bd_97_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_97_transpose_y_0 = const()[name = tensor("matrix_bd_97_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_97_cast_fp16 = matmul(transpose_x = matrix_bd_97_transpose_x_0, transpose_y = matrix_bd_97_transpose_y_0, x = var_4912_cast_fp16, y = var_4914_cast_fp16)[name = tensor("matrix_bd_97_cast_fp16")]; - tensor matrix_bd_99_pad_0 = const()[name = tensor("matrix_bd_99_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_99_mode_0 = const()[name = tensor("matrix_bd_99_mode_0"), val = tensor("constant")]; - tensor const_142_to_fp16 = const()[name = tensor("const_142_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_99_cast_fp16 = pad(constant_val = const_142_to_fp16, mode = matrix_bd_99_mode_0, pad = matrix_bd_99_pad_0, x = matrix_bd_97_cast_fp16)[name = tensor("matrix_bd_99_cast_fp16")]; - tensor var_4923 = const()[name = tensor("op_4923"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_101_cast_fp16 = reshape(shape = var_4923, x = matrix_bd_99_cast_fp16)[name = tensor("matrix_bd_101_cast_fp16")]; - tensor var_4927_begin_0 = const()[name = tensor("op_4927_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_4927_end_0 = const()[name = tensor("op_4927_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_4927_end_mask_0 = const()[name = tensor("op_4927_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_4927_cast_fp16 = slice_by_index(begin = var_4927_begin_0, end = var_4927_end_0, end_mask = var_4927_end_mask_0, x = matrix_bd_101_cast_fp16)[name = tensor("op_4927_cast_fp16")]; - tensor var_4928 = const()[name = tensor("op_4928"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_103_cast_fp16 = reshape(shape = var_4928, x = var_4927_cast_fp16)[name = tensor("matrix_bd_103_cast_fp16")]; - tensor var_4933_begin_0 = const()[name = tensor("op_4933_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4933_end_0 = const()[name = tensor("op_4933_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_4933_end_mask_0 = const()[name = tensor("op_4933_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_4933_cast_fp16 = slice_by_index(begin = var_4933_begin_0, end = var_4933_end_0, end_mask = var_4933_end_mask_0, x = matrix_bd_103_cast_fp16)[name = tensor("op_4933_cast_fp16")]; - tensor var_4934_to_fp16 = const()[name = tensor("op_4934_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_25_cast_fp16 = mul(x = var_4933_cast_fp16, y = var_4934_to_fp16)[name = tensor("qk_mask_25_cast_fp16")]; - tensor var_4938 = const()[name = tensor("op_4938"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_25_cast_fp16 = reshape(shape = var_4938, x = query_51_cast_fp16)[name = tensor("mh_q_25_cast_fp16")]; - tensor var_4940_to_fp16 = const()[name = tensor("op_4940_to_fp16"), val = tensor(0x1p-3)]; - tensor var_4941_cast_fp16 = mul(x = mh_q_25_cast_fp16, y = var_4940_to_fp16)[name = tensor("op_4941_cast_fp16")]; - tensor var_4944 = const()[name = tensor("op_4944"), val = tensor([1, 8, 64, 188])]; - tensor var_4945_cast_fp16 = reshape(shape = var_4944, x = key_25_cast_fp16)[name = tensor("op_4945_cast_fp16")]; - tensor mh_w_49_transpose_x_0 = const()[name = tensor("mh_w_49_transpose_x_0"), val = tensor(true)]; - tensor mh_w_49_transpose_y_0 = const()[name = tensor("mh_w_49_transpose_y_0"), val = tensor(false)]; - tensor mh_w_49_cast_fp16 = matmul(transpose_x = mh_w_49_transpose_x_0, transpose_y = mh_w_49_transpose_y_0, x = var_4941_cast_fp16, y = var_4945_cast_fp16)[name = tensor("mh_w_49_cast_fp16")]; - tensor mh_w_51_cast_fp16 = add(x = mh_w_49_cast_fp16, y = qk_mask_25_cast_fp16)[name = tensor("mh_w_51_cast_fp16")]; - tensor var_4949_cast_fp16 = softmax(axis = var_4736, x = mh_w_51_cast_fp16)[name = tensor("op_4949_cast_fp16")]; - tensor var_4950 = const()[name = tensor("op_4950"), val = tensor([1, 8, 64, 188])]; - tensor var_4951_cast_fp16 = reshape(shape = var_4950, x = value_25_cast_fp16)[name = tensor("op_4951_cast_fp16")]; - tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; - tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; - tensor attn_25_cast_fp16 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_4951_cast_fp16, y = var_4949_cast_fp16)[name = tensor("attn_25_cast_fp16")]; - tensor var_4954 = const()[name = tensor("op_4954"), val = tensor([1, 512, 1, 188])]; - tensor input_335_cast_fp16 = reshape(shape = var_4954, x = attn_25_cast_fp16)[name = tensor("input_335_cast_fp16")]; - tensor var_4964_pad_type_0 = const()[name = tensor("op_4964_pad_type_0"), val = tensor("valid")]; - tensor var_4964_strides_0 = const()[name = tensor("op_4964_strides_0"), val = tensor([1, 1])]; - tensor var_4964_pad_0 = const()[name = tensor("op_4964_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4964_dilations_0 = const()[name = tensor("op_4964_dilations_0"), val = tensor([1, 1])]; - tensor var_4964_groups_0 = const()[name = tensor("op_4964_groups_0"), val = tensor(1)]; - tensor layers_12_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73994112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74190784))), name = tensor("layers_12_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_12_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_12_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74190976)))]; - tensor var_4964_cast_fp16 = conv(bias = layers_12_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_4964_dilations_0, groups = var_4964_groups_0, pad = var_4964_pad_0, pad_type = var_4964_pad_type_0, strides = var_4964_strides_0, weight = layers_12_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = tensor("op_4964_cast_fp16")]; - tensor var_4970_pad_type_0 = const()[name = tensor("op_4970_pad_type_0"), val = tensor("valid")]; - tensor var_4970_strides_0 = const()[name = tensor("op_4970_strides_0"), val = tensor([1, 1])]; - tensor var_4970_pad_0 = const()[name = tensor("op_4970_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4970_dilations_0 = const()[name = tensor("op_4970_dilations_0"), val = tensor([1, 1])]; - tensor var_4970_groups_0 = const()[name = tensor("op_4970_groups_0"), val = tensor(1)]; - tensor layers_12_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74201984))), name = tensor("layers_12_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74192064))), shape = tensor([512, 512, 1, 1])]; - tensor var_4970_cast_fp16 = conv(dilations = var_4970_dilations_0, groups = var_4970_groups_0, pad = var_4970_pad_0, pad_type = var_4970_pad_type_0, strides = var_4970_strides_0, weight = layers_12_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_335_cast_fp16)[name = tensor("op_4970_cast_fp16")]; - tensor obj_53_cast_fp16 = add(x = var_4964_cast_fp16, y = var_4970_cast_fp16)[name = tensor("obj_53_cast_fp16")]; - tensor inputs_125_cast_fp16 = add(x = inputs_123_cast_fp16, y = obj_53_cast_fp16)[name = tensor("inputs_125_cast_fp16")]; - tensor out_125_axes_0 = const()[name = tensor("out_125_axes_0"), val = tensor([1])]; - tensor var_4981_to_fp16 = const()[name = tensor("op_4981_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_125_cast_fp16 = layer_norm(axes = out_125_axes_0, epsilon = var_4981_to_fp16, x = inputs_125_cast_fp16)[name = tensor("out_125_cast_fp16")]; - tensor input_337_gamma_0_to_fp16 = const()[name = tensor("input_337_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74234816)))]; - tensor input_337_beta_0_to_fp16 = const()[name = tensor("input_337_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74235904)))]; - tensor input_337_epsilon_0_to_fp16 = const()[name = tensor("input_337_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_337_cast_fp16 = batch_norm(beta = input_337_beta_0_to_fp16, epsilon = input_337_epsilon_0_to_fp16, gamma = input_337_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_125_cast_fp16)[name = tensor("input_337_cast_fp16")]; - tensor var_5003_pad_type_0 = const()[name = tensor("op_5003_pad_type_0"), val = tensor("valid")]; - tensor var_5003_strides_0 = const()[name = tensor("op_5003_strides_0"), val = tensor([1, 1])]; - tensor var_5003_pad_0 = const()[name = tensor("op_5003_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5003_dilations_0 = const()[name = tensor("op_5003_dilations_0"), val = tensor([1, 1])]; - tensor var_5003_groups_0 = const()[name = tensor("op_5003_groups_0"), val = tensor(1)]; - tensor layers_12_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74236992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74630272))), name = tensor("layers_12_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_12_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_12_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74630464)))]; - tensor var_5003_cast_fp16 = conv(bias = layers_12_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_5003_dilations_0, groups = var_5003_groups_0, pad = var_5003_pad_0, pad_type = var_5003_pad_type_0, strides = var_5003_strides_0, weight = layers_12_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_337_cast_fp16)[name = tensor("op_5003_cast_fp16")]; - tensor var_5009_pad_type_0 = const()[name = tensor("op_5009_pad_type_0"), val = tensor("valid")]; - tensor var_5009_strides_0 = const()[name = tensor("op_5009_strides_0"), val = tensor([1, 1])]; - tensor var_5009_pad_0 = const()[name = tensor("op_5009_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5009_dilations_0 = const()[name = tensor("op_5009_dilations_0"), val = tensor([1, 1])]; - tensor var_5009_groups_0 = const()[name = tensor("op_5009_groups_0"), val = tensor(1)]; - tensor layers_12_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74651328))), name = tensor("layers_12_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74632576))), shape = tensor([1024, 512, 1, 1])]; - tensor var_5009_cast_fp16 = conv(dilations = var_5009_dilations_0, groups = var_5009_groups_0, pad = var_5009_pad_0, pad_type = var_5009_pad_type_0, strides = var_5009_strides_0, weight = layers_12_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_337_cast_fp16)[name = tensor("op_5009_cast_fp16")]; - tensor input_339_cast_fp16 = add(x = var_5003_cast_fp16, y = var_5009_cast_fp16)[name = tensor("input_339_cast_fp16")]; - tensor input_341_split_num_splits_0 = const()[name = tensor("input_341_split_num_splits_0"), val = tensor(2)]; - tensor input_341_split_axis_0 = const()[name = tensor("input_341_split_axis_0"), val = tensor(1)]; - tensor input_341_split_cast_fp16_0, tensor input_341_split_cast_fp16_1 = split(axis = input_341_split_axis_0, num_splits = input_341_split_num_splits_0, x = input_339_cast_fp16)[name = tensor("input_341_split_cast_fp16")]; - tensor input_341_split_1_sigmoid_cast_fp16 = sigmoid(x = input_341_split_cast_fp16_1)[name = tensor("input_341_split_1_sigmoid_cast_fp16")]; - tensor input_341_cast_fp16 = mul(x = input_341_split_cast_fp16_0, y = input_341_split_1_sigmoid_cast_fp16)[name = tensor("input_341_cast_fp16")]; - tensor input_343_pad_type_0 = const()[name = tensor("input_343_pad_type_0"), val = tensor("custom")]; - tensor input_343_pad_0 = const()[name = tensor("input_343_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_343_groups_0 = const()[name = tensor("input_343_groups_0"), val = tensor(512)]; - tensor input_343_strides_0 = const()[name = tensor("input_343_strides_0"), val = tensor([1, 1])]; - tensor input_343_dilations_0 = const()[name = tensor("input_343_dilations_0"), val = tensor([1, 1])]; - tensor const_215_to_fp16 = const()[name = tensor("const_215_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74716928)))]; - tensor const_216_to_fp16 = const()[name = tensor("const_216_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74726208)))]; - tensor input_345_cast_fp16 = conv(bias = const_216_to_fp16, dilations = input_343_dilations_0, groups = input_343_groups_0, pad = input_343_pad_0, pad_type = input_343_pad_type_0, strides = input_343_strides_0, weight = const_215_to_fp16, x = input_341_cast_fp16)[name = tensor("input_345_cast_fp16")]; - tensor input_347_cast_fp16 = silu(x = input_345_cast_fp16)[name = tensor("input_347_cast_fp16")]; - tensor var_5033_pad_type_0 = const()[name = tensor("op_5033_pad_type_0"), val = tensor("valid")]; - tensor var_5033_strides_0 = const()[name = tensor("op_5033_strides_0"), val = tensor([1, 1])]; - tensor var_5033_pad_0 = const()[name = tensor("op_5033_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5033_dilations_0 = const()[name = tensor("op_5033_dilations_0"), val = tensor([1, 1])]; - tensor var_5033_groups_0 = const()[name = tensor("op_5033_groups_0"), val = tensor(1)]; - tensor layers_12_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74727296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74923968))), name = tensor("layers_12_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_12_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_12_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74924160)))]; - tensor var_5033_cast_fp16 = conv(bias = layers_12_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_5033_dilations_0, groups = var_5033_groups_0, pad = var_5033_pad_0, pad_type = var_5033_pad_type_0, strides = var_5033_strides_0, weight = layers_12_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_347_cast_fp16)[name = tensor("op_5033_cast_fp16")]; - tensor var_5039_pad_type_0 = const()[name = tensor("op_5039_pad_type_0"), val = tensor("valid")]; - tensor var_5039_strides_0 = const()[name = tensor("op_5039_strides_0"), val = tensor([1, 1])]; - tensor var_5039_pad_0 = const()[name = tensor("op_5039_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5039_dilations_0 = const()[name = tensor("op_5039_dilations_0"), val = tensor([1, 1])]; - tensor var_5039_groups_0 = const()[name = tensor("op_5039_groups_0"), val = tensor(1)]; - tensor layers_12_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74934784))), name = tensor("layers_12_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74925248))), shape = tensor([512, 512, 1, 1])]; - tensor var_5039_cast_fp16 = conv(dilations = var_5039_dilations_0, groups = var_5039_groups_0, pad = var_5039_pad_0, pad_type = var_5039_pad_type_0, strides = var_5039_strides_0, weight = layers_12_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_347_cast_fp16)[name = tensor("op_5039_cast_fp16")]; - tensor x_77_cast_fp16 = add(x = var_5033_cast_fp16, y = var_5039_cast_fp16)[name = tensor("x_77_cast_fp16")]; - tensor inputs_127_cast_fp16 = add(x = inputs_125_cast_fp16, y = x_77_cast_fp16)[name = tensor("inputs_127_cast_fp16")]; - tensor out_127_axes_0 = const()[name = tensor("out_127_axes_0"), val = tensor([1])]; - tensor var_5050_to_fp16 = const()[name = tensor("op_5050_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_127_cast_fp16 = layer_norm(axes = out_127_axes_0, epsilon = var_5050_to_fp16, x = inputs_127_cast_fp16)[name = tensor("out_127_cast_fp16")]; - tensor input_349_gamma_0_to_fp16 = const()[name = tensor("input_349_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74967616)))]; - tensor input_349_beta_0_to_fp16 = const()[name = tensor("input_349_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74968704)))]; - tensor input_349_epsilon_0_to_fp16 = const()[name = tensor("input_349_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_349_cast_fp16 = batch_norm(beta = input_349_beta_0_to_fp16, epsilon = input_349_epsilon_0_to_fp16, gamma = input_349_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_127_cast_fp16)[name = tensor("input_349_cast_fp16")]; - tensor var_5070_pad_type_0 = const()[name = tensor("op_5070_pad_type_0"), val = tensor("valid")]; - tensor var_5070_strides_0 = const()[name = tensor("op_5070_strides_0"), val = tensor([1, 1])]; - tensor var_5070_pad_0 = const()[name = tensor("op_5070_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5070_dilations_0 = const()[name = tensor("op_5070_dilations_0"), val = tensor([1, 1])]; - tensor var_5070_groups_0 = const()[name = tensor("op_5070_groups_0"), val = tensor(1)]; - tensor layers_12_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74969792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75756288))), name = tensor("layers_12_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_12_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_12_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75756480)))]; - tensor var_5070_cast_fp16 = conv(bias = layers_12_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_5070_dilations_0, groups = var_5070_groups_0, pad = var_5070_pad_0, pad_type = var_5070_pad_type_0, strides = var_5070_strides_0, weight = layers_12_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = tensor("op_5070_cast_fp16")]; - tensor var_5076_pad_type_0 = const()[name = tensor("op_5076_pad_type_0"), val = tensor("valid")]; - tensor var_5076_strides_0 = const()[name = tensor("op_5076_strides_0"), val = tensor([1, 1])]; - tensor var_5076_pad_0 = const()[name = tensor("op_5076_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5076_dilations_0 = const()[name = tensor("op_5076_dilations_0"), val = tensor([1, 1])]; - tensor var_5076_groups_0 = const()[name = tensor("op_5076_groups_0"), val = tensor(1)]; - tensor layers_12_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75797184))), name = tensor("layers_12_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75760640))), shape = tensor([2048, 512, 1, 1])]; - tensor var_5076_cast_fp16 = conv(dilations = var_5076_dilations_0, groups = var_5076_groups_0, pad = var_5076_pad_0, pad_type = var_5076_pad_type_0, strides = var_5076_strides_0, weight = layers_12_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_349_cast_fp16)[name = tensor("op_5076_cast_fp16")]; - tensor input_351_cast_fp16 = add(x = var_5070_cast_fp16, y = var_5076_cast_fp16)[name = tensor("input_351_cast_fp16")]; - tensor input_353_cast_fp16 = silu(x = input_351_cast_fp16)[name = tensor("input_353_cast_fp16")]; - tensor var_5087_pad_type_0 = const()[name = tensor("op_5087_pad_type_0"), val = tensor("valid")]; - tensor var_5087_strides_0 = const()[name = tensor("op_5087_strides_0"), val = tensor([1, 1])]; - tensor var_5087_pad_0 = const()[name = tensor("op_5087_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5087_dilations_0 = const()[name = tensor("op_5087_dilations_0"), val = tensor([1, 1])]; - tensor var_5087_groups_0 = const()[name = tensor("op_5087_groups_0"), val = tensor(1)]; - tensor layers_12_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75928320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76714816))), name = tensor("layers_12_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_12_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_12_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76715008)))]; - tensor var_5087_cast_fp16 = conv(bias = layers_12_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_5087_dilations_0, groups = var_5087_groups_0, pad = var_5087_pad_0, pad_type = var_5087_pad_type_0, strides = var_5087_strides_0, weight = layers_12_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = tensor("op_5087_cast_fp16")]; - tensor var_5093_pad_type_0 = const()[name = tensor("op_5093_pad_type_0"), val = tensor("valid")]; - tensor var_5093_strides_0 = const()[name = tensor("op_5093_strides_0"), val = tensor([1, 1])]; - tensor var_5093_pad_0 = const()[name = tensor("op_5093_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5093_dilations_0 = const()[name = tensor("op_5093_dilations_0"), val = tensor([1, 1])]; - tensor var_5093_groups_0 = const()[name = tensor("op_5093_groups_0"), val = tensor(1)]; - tensor layers_12_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76758592))), name = tensor("layers_12_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76716096))), shape = tensor([512, 2048, 1, 1])]; - tensor var_5093_cast_fp16 = conv(dilations = var_5093_dilations_0, groups = var_5093_groups_0, pad = var_5093_pad_0, pad_type = var_5093_pad_type_0, strides = var_5093_strides_0, weight = layers_12_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_353_cast_fp16)[name = tensor("op_5093_cast_fp16")]; - tensor x_79_cast_fp16 = add(x = var_5087_cast_fp16, y = var_5093_cast_fp16)[name = tensor("x_79_cast_fp16")]; - tensor var_5095_to_fp16 = const()[name = tensor("op_5095_to_fp16"), val = tensor(0x1p-1)]; - tensor var_5096_cast_fp16 = mul(x = x_79_cast_fp16, y = var_5095_to_fp16)[name = tensor("op_5096_cast_fp16")]; - tensor inputs_129_cast_fp16 = add(x = inputs_127_cast_fp16, y = var_5096_cast_fp16)[name = tensor("inputs_129_cast_fp16")]; - tensor out_129_axes_0 = const()[name = tensor("out_129_axes_0"), val = tensor([1])]; - tensor var_5106_to_fp16 = const()[name = tensor("op_5106_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_129_cast_fp16 = layer_norm(axes = out_129_axes_0, epsilon = var_5106_to_fp16, x = inputs_129_cast_fp16)[name = tensor("out_129_cast_fp16")]; - tensor inputs_131_gamma_0_to_fp16 = const()[name = tensor("inputs_131_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76889728)))]; - tensor inputs_131_beta_0_to_fp16 = const()[name = tensor("inputs_131_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76890816)))]; - tensor inputs_131_epsilon_0_to_fp16 = const()[name = tensor("inputs_131_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_131_cast_fp16 = batch_norm(beta = inputs_131_beta_0_to_fp16, epsilon = inputs_131_epsilon_0_to_fp16, gamma = inputs_131_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_129_cast_fp16)[name = tensor("inputs_131_cast_fp16")]; - tensor var_5120 = const()[name = tensor("op_5120"), val = tensor(3)]; - tensor out_131_axes_0 = const()[name = tensor("out_131_axes_0"), val = tensor([1])]; - tensor var_5151_to_fp16 = const()[name = tensor("op_5151_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_131_cast_fp16 = layer_norm(axes = out_131_axes_0, epsilon = var_5151_to_fp16, x = inputs_131_cast_fp16)[name = tensor("out_131_cast_fp16")]; - tensor input_355_gamma_0_to_fp16 = const()[name = tensor("input_355_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76891904)))]; - tensor input_355_beta_0_to_fp16 = const()[name = tensor("input_355_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76892992)))]; - tensor input_355_epsilon_0_to_fp16 = const()[name = tensor("input_355_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_355_cast_fp16 = batch_norm(beta = input_355_beta_0_to_fp16, epsilon = input_355_epsilon_0_to_fp16, gamma = input_355_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_131_cast_fp16)[name = tensor("input_355_cast_fp16")]; - tensor var_5171_pad_type_0 = const()[name = tensor("op_5171_pad_type_0"), val = tensor("valid")]; - tensor var_5171_strides_0 = const()[name = tensor("op_5171_strides_0"), val = tensor([1, 1])]; - tensor var_5171_pad_0 = const()[name = tensor("op_5171_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5171_dilations_0 = const()[name = tensor("op_5171_dilations_0"), val = tensor([1, 1])]; - tensor var_5171_groups_0 = const()[name = tensor("op_5171_groups_0"), val = tensor(1)]; - tensor layers_13_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76894080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77680576))), name = tensor("layers_13_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_13_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_13_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77680768)))]; - tensor var_5171_cast_fp16 = conv(bias = layers_13_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_5171_dilations_0, groups = var_5171_groups_0, pad = var_5171_pad_0, pad_type = var_5171_pad_type_0, strides = var_5171_strides_0, weight = layers_13_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_355_cast_fp16)[name = tensor("op_5171_cast_fp16")]; - tensor var_5177_pad_type_0 = const()[name = tensor("op_5177_pad_type_0"), val = tensor("valid")]; - tensor var_5177_strides_0 = const()[name = tensor("op_5177_strides_0"), val = tensor([1, 1])]; - tensor var_5177_pad_0 = const()[name = tensor("op_5177_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5177_dilations_0 = const()[name = tensor("op_5177_dilations_0"), val = tensor([1, 1])]; - tensor var_5177_groups_0 = const()[name = tensor("op_5177_groups_0"), val = tensor(1)]; - tensor layers_13_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77722240))), name = tensor("layers_13_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77684928))), shape = tensor([2048, 512, 1, 1])]; - tensor var_5177_cast_fp16 = conv(dilations = var_5177_dilations_0, groups = var_5177_groups_0, pad = var_5177_pad_0, pad_type = var_5177_pad_type_0, strides = var_5177_strides_0, weight = layers_13_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_355_cast_fp16)[name = tensor("op_5177_cast_fp16")]; - tensor input_357_cast_fp16 = add(x = var_5171_cast_fp16, y = var_5177_cast_fp16)[name = tensor("input_357_cast_fp16")]; - tensor input_359_cast_fp16 = silu(x = input_357_cast_fp16)[name = tensor("input_359_cast_fp16")]; - tensor var_5188_pad_type_0 = const()[name = tensor("op_5188_pad_type_0"), val = tensor("valid")]; - tensor var_5188_strides_0 = const()[name = tensor("op_5188_strides_0"), val = tensor([1, 1])]; - tensor var_5188_pad_0 = const()[name = tensor("op_5188_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5188_dilations_0 = const()[name = tensor("op_5188_dilations_0"), val = tensor([1, 1])]; - tensor var_5188_groups_0 = const()[name = tensor("op_5188_groups_0"), val = tensor(1)]; - tensor layers_13_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77853376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78639872))), name = tensor("layers_13_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_13_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_13_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78640064)))]; - tensor var_5188_cast_fp16 = conv(bias = layers_13_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_5188_dilations_0, groups = var_5188_groups_0, pad = var_5188_pad_0, pad_type = var_5188_pad_type_0, strides = var_5188_strides_0, weight = layers_13_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_359_cast_fp16)[name = tensor("op_5188_cast_fp16")]; - tensor var_5194_pad_type_0 = const()[name = tensor("op_5194_pad_type_0"), val = tensor("valid")]; - tensor var_5194_strides_0 = const()[name = tensor("op_5194_strides_0"), val = tensor([1, 1])]; - tensor var_5194_pad_0 = const()[name = tensor("op_5194_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5194_dilations_0 = const()[name = tensor("op_5194_dilations_0"), val = tensor([1, 1])]; - tensor var_5194_groups_0 = const()[name = tensor("op_5194_groups_0"), val = tensor(1)]; - tensor layers_13_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78683136))), name = tensor("layers_13_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78641152))), shape = tensor([512, 2048, 1, 1])]; - tensor var_5194_cast_fp16 = conv(dilations = var_5194_dilations_0, groups = var_5194_groups_0, pad = var_5194_pad_0, pad_type = var_5194_pad_type_0, strides = var_5194_strides_0, weight = layers_13_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_359_cast_fp16)[name = tensor("op_5194_cast_fp16")]; - tensor x_81_cast_fp16 = add(x = var_5188_cast_fp16, y = var_5194_cast_fp16)[name = tensor("x_81_cast_fp16")]; - tensor var_5196_to_fp16 = const()[name = tensor("op_5196_to_fp16"), val = tensor(0x1p-1)]; - tensor var_5197_cast_fp16 = mul(x = x_81_cast_fp16, y = var_5196_to_fp16)[name = tensor("op_5197_cast_fp16")]; - tensor inputs_133_cast_fp16 = add(x = inputs_131_cast_fp16, y = var_5197_cast_fp16)[name = tensor("inputs_133_cast_fp16")]; - tensor out_133_axes_0 = const()[name = tensor("out_133_axes_0"), val = tensor([1])]; - tensor var_5207_to_fp16 = const()[name = tensor("op_5207_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_133_cast_fp16 = layer_norm(axes = out_133_axes_0, epsilon = var_5207_to_fp16, x = inputs_133_cast_fp16)[name = tensor("out_133_cast_fp16")]; - tensor obj_55_gamma_0_to_fp16 = const()[name = tensor("obj_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78814272)))]; - tensor obj_55_beta_0_to_fp16 = const()[name = tensor("obj_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78815360)))]; - tensor obj_55_epsilon_0_to_fp16 = const()[name = tensor("obj_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_55_cast_fp16 = batch_norm(beta = obj_55_beta_0_to_fp16, epsilon = obj_55_epsilon_0_to_fp16, gamma = obj_55_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_133_cast_fp16)[name = tensor("obj_55_cast_fp16")]; - tensor var_5232_pad_type_0 = const()[name = tensor("op_5232_pad_type_0"), val = tensor("valid")]; - tensor var_5232_strides_0 = const()[name = tensor("op_5232_strides_0"), val = tensor([1, 1])]; - tensor var_5232_pad_0 = const()[name = tensor("op_5232_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5232_dilations_0 = const()[name = tensor("op_5232_dilations_0"), val = tensor([1, 1])]; - tensor var_5232_groups_0 = const()[name = tensor("op_5232_groups_0"), val = tensor(1)]; - tensor layers_13_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78816448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79013120))), name = tensor("layers_13_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_13_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_13_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79013312)))]; - tensor var_5232_cast_fp16 = conv(bias = layers_13_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_5232_dilations_0, groups = var_5232_groups_0, pad = var_5232_pad_0, pad_type = var_5232_pad_type_0, strides = var_5232_strides_0, weight = layers_13_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_55_cast_fp16)[name = tensor("op_5232_cast_fp16")]; - tensor var_5238_pad_type_0 = const()[name = tensor("op_5238_pad_type_0"), val = tensor("valid")]; - tensor var_5238_strides_0 = const()[name = tensor("op_5238_strides_0"), val = tensor([1, 1])]; - tensor var_5238_pad_0 = const()[name = tensor("op_5238_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5238_dilations_0 = const()[name = tensor("op_5238_dilations_0"), val = tensor([1, 1])]; - tensor var_5238_groups_0 = const()[name = tensor("op_5238_groups_0"), val = tensor(1)]; - tensor layers_13_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79024320))), name = tensor("layers_13_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79014400))), shape = tensor([512, 512, 1, 1])]; - tensor var_5238_cast_fp16 = conv(dilations = var_5238_dilations_0, groups = var_5238_groups_0, pad = var_5238_pad_0, pad_type = var_5238_pad_type_0, strides = var_5238_strides_0, weight = layers_13_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_55_cast_fp16)[name = tensor("op_5238_cast_fp16")]; - tensor query_53_cast_fp16 = add(x = var_5232_cast_fp16, y = var_5238_cast_fp16)[name = tensor("query_53_cast_fp16")]; - tensor var_5247_pad_type_0 = const()[name = tensor("op_5247_pad_type_0"), val = tensor("valid")]; - tensor var_5247_strides_0 = const()[name = tensor("op_5247_strides_0"), val = tensor([1, 1])]; - tensor var_5247_pad_0 = const()[name = tensor("op_5247_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5247_dilations_0 = const()[name = tensor("op_5247_dilations_0"), val = tensor([1, 1])]; - tensor var_5247_groups_0 = const()[name = tensor("op_5247_groups_0"), val = tensor(1)]; - tensor layers_13_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79057152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79253824))), name = tensor("layers_13_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_5247_cast_fp16 = conv(dilations = var_5247_dilations_0, groups = var_5247_groups_0, pad = var_5247_pad_0, pad_type = var_5247_pad_type_0, strides = var_5247_strides_0, weight = layers_13_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_55_cast_fp16)[name = tensor("op_5247_cast_fp16")]; - tensor var_5253_pad_type_0 = const()[name = tensor("op_5253_pad_type_0"), val = tensor("valid")]; - tensor var_5253_strides_0 = const()[name = tensor("op_5253_strides_0"), val = tensor([1, 1])]; - tensor var_5253_pad_0 = const()[name = tensor("op_5253_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5253_dilations_0 = const()[name = tensor("op_5253_dilations_0"), val = tensor([1, 1])]; - tensor var_5253_groups_0 = const()[name = tensor("op_5253_groups_0"), val = tensor(1)]; - tensor layers_13_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79263680))), name = tensor("layers_13_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79254016))), shape = tensor([512, 512, 1, 1])]; - tensor var_5253_cast_fp16 = conv(dilations = var_5253_dilations_0, groups = var_5253_groups_0, pad = var_5253_pad_0, pad_type = var_5253_pad_type_0, strides = var_5253_strides_0, weight = layers_13_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_55_cast_fp16)[name = tensor("op_5253_cast_fp16")]; - tensor key_27_cast_fp16 = add(x = var_5247_cast_fp16, y = var_5253_cast_fp16)[name = tensor("key_27_cast_fp16")]; - tensor var_5263_pad_type_0 = const()[name = tensor("op_5263_pad_type_0"), val = tensor("valid")]; - tensor var_5263_strides_0 = const()[name = tensor("op_5263_strides_0"), val = tensor([1, 1])]; - tensor var_5263_pad_0 = const()[name = tensor("op_5263_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5263_dilations_0 = const()[name = tensor("op_5263_dilations_0"), val = tensor([1, 1])]; - tensor var_5263_groups_0 = const()[name = tensor("op_5263_groups_0"), val = tensor(1)]; - tensor layers_13_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79296512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79493184))), name = tensor("layers_13_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_13_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_13_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79493376)))]; - tensor var_5263_cast_fp16 = conv(bias = layers_13_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_5263_dilations_0, groups = var_5263_groups_0, pad = var_5263_pad_0, pad_type = var_5263_pad_type_0, strides = var_5263_strides_0, weight = layers_13_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_55_cast_fp16)[name = tensor("op_5263_cast_fp16")]; - tensor var_5269_pad_type_0 = const()[name = tensor("op_5269_pad_type_0"), val = tensor("valid")]; - tensor var_5269_strides_0 = const()[name = tensor("op_5269_strides_0"), val = tensor([1, 1])]; - tensor var_5269_pad_0 = const()[name = tensor("op_5269_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5269_dilations_0 = const()[name = tensor("op_5269_dilations_0"), val = tensor([1, 1])]; - tensor var_5269_groups_0 = const()[name = tensor("op_5269_groups_0"), val = tensor(1)]; - tensor layers_13_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79502784))), name = tensor("layers_13_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79494464))), shape = tensor([512, 512, 1, 1])]; - tensor var_5269_cast_fp16 = conv(dilations = var_5269_dilations_0, groups = var_5269_groups_0, pad = var_5269_pad_0, pad_type = var_5269_pad_type_0, strides = var_5269_strides_0, weight = layers_13_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_55_cast_fp16)[name = tensor("op_5269_cast_fp16")]; - tensor value_27_cast_fp16 = add(x = var_5263_cast_fp16, y = var_5269_cast_fp16)[name = tensor("value_27_cast_fp16")]; - tensor var_5272_to_fp16 = const()[name = tensor("op_5272_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79535616)))]; - tensor query_55_cast_fp16 = add(x = query_53_cast_fp16, y = var_5272_to_fp16)[name = tensor("query_55_cast_fp16")]; - tensor var_5275_to_fp16 = const()[name = tensor("op_5275_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79536704)))]; - tensor q_with_bias_v_27_cast_fp16 = add(x = query_53_cast_fp16, y = var_5275_to_fp16)[name = tensor("q_with_bias_v_27_cast_fp16")]; - tensor var_5285_pad_type_0 = const()[name = tensor("op_5285_pad_type_0"), val = tensor("valid")]; - tensor var_5285_strides_0 = const()[name = tensor("op_5285_strides_0"), val = tensor([1, 1])]; - tensor var_5285_pad_0 = const()[name = tensor("op_5285_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5285_dilations_0 = const()[name = tensor("op_5285_dilations_0"), val = tensor([1, 1])]; - tensor var_5285_groups_0 = const()[name = tensor("op_5285_groups_0"), val = tensor(1)]; - tensor layers_13_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79537792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79734464))), name = tensor("layers_13_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_5285_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_5285_dilations_0, groups = var_5285_groups_0, pad = var_5285_pad_0, pad_type = var_5285_pad_type_0, strides = var_5285_strides_0, weight = layers_13_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_5285_cast_fp16")]; - tensor var_5291_pad_type_0 = const()[name = tensor("op_5291_pad_type_0"), val = tensor("valid")]; - tensor var_5291_strides_0 = const()[name = tensor("op_5291_strides_0"), val = tensor([1, 1])]; - tensor var_5291_pad_0 = const()[name = tensor("op_5291_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5291_dilations_0 = const()[name = tensor("op_5291_dilations_0"), val = tensor([1, 1])]; - tensor var_5291_groups_0 = const()[name = tensor("op_5291_groups_0"), val = tensor(1)]; - tensor layers_13_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79756544))), name = tensor("layers_13_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79734656))), shape = tensor([512, 512, 1, 1])]; - tensor var_5291_cast_fp16 = conv(dilations = var_5291_dilations_0, groups = var_5291_groups_0, pad = var_5291_pad_0, pad_type = var_5291_pad_type_0, strides = var_5291_strides_0, weight = layers_13_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_5291_cast_fp16")]; - tensor p_27_cast_fp16 = add(x = var_5285_cast_fp16, y = var_5291_cast_fp16)[name = tensor("p_27_cast_fp16")]; - tensor var_5295 = const()[name = tensor("op_5295"), val = tensor([1, 8, 64, 188])]; - tensor var_5296_cast_fp16 = reshape(shape = var_5295, x = q_with_bias_v_27_cast_fp16)[name = tensor("op_5296_cast_fp16")]; - tensor var_5297 = const()[name = tensor("op_5297"), val = tensor([1, 8, 64, -1])]; - tensor var_5298_cast_fp16 = reshape(shape = var_5297, x = p_27_cast_fp16)[name = tensor("op_5298_cast_fp16")]; - tensor matrix_bd_105_transpose_x_0 = const()[name = tensor("matrix_bd_105_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_105_transpose_y_0 = const()[name = tensor("matrix_bd_105_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_105_cast_fp16 = matmul(transpose_x = matrix_bd_105_transpose_x_0, transpose_y = matrix_bd_105_transpose_y_0, x = var_5296_cast_fp16, y = var_5298_cast_fp16)[name = tensor("matrix_bd_105_cast_fp16")]; - tensor matrix_bd_107_pad_0 = const()[name = tensor("matrix_bd_107_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_107_mode_0 = const()[name = tensor("matrix_bd_107_mode_0"), val = tensor("constant")]; - tensor const_153_to_fp16 = const()[name = tensor("const_153_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_107_cast_fp16 = pad(constant_val = const_153_to_fp16, mode = matrix_bd_107_mode_0, pad = matrix_bd_107_pad_0, x = matrix_bd_105_cast_fp16)[name = tensor("matrix_bd_107_cast_fp16")]; - tensor var_5307 = const()[name = tensor("op_5307"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_109_cast_fp16 = reshape(shape = var_5307, x = matrix_bd_107_cast_fp16)[name = tensor("matrix_bd_109_cast_fp16")]; - tensor var_5311_begin_0 = const()[name = tensor("op_5311_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_5311_end_0 = const()[name = tensor("op_5311_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_5311_end_mask_0 = const()[name = tensor("op_5311_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_5311_cast_fp16 = slice_by_index(begin = var_5311_begin_0, end = var_5311_end_0, end_mask = var_5311_end_mask_0, x = matrix_bd_109_cast_fp16)[name = tensor("op_5311_cast_fp16")]; - tensor var_5312 = const()[name = tensor("op_5312"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_111_cast_fp16 = reshape(shape = var_5312, x = var_5311_cast_fp16)[name = tensor("matrix_bd_111_cast_fp16")]; - tensor var_5317_begin_0 = const()[name = tensor("op_5317_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5317_end_0 = const()[name = tensor("op_5317_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_5317_end_mask_0 = const()[name = tensor("op_5317_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_5317_cast_fp16 = slice_by_index(begin = var_5317_begin_0, end = var_5317_end_0, end_mask = var_5317_end_mask_0, x = matrix_bd_111_cast_fp16)[name = tensor("op_5317_cast_fp16")]; - tensor var_5318_to_fp16 = const()[name = tensor("op_5318_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_27_cast_fp16 = mul(x = var_5317_cast_fp16, y = var_5318_to_fp16)[name = tensor("qk_mask_27_cast_fp16")]; - tensor var_5322 = const()[name = tensor("op_5322"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_27_cast_fp16 = reshape(shape = var_5322, x = query_55_cast_fp16)[name = tensor("mh_q_27_cast_fp16")]; - tensor var_5324_to_fp16 = const()[name = tensor("op_5324_to_fp16"), val = tensor(0x1p-3)]; - tensor var_5325_cast_fp16 = mul(x = mh_q_27_cast_fp16, y = var_5324_to_fp16)[name = tensor("op_5325_cast_fp16")]; - tensor var_5328 = const()[name = tensor("op_5328"), val = tensor([1, 8, 64, 188])]; - tensor var_5329_cast_fp16 = reshape(shape = var_5328, x = key_27_cast_fp16)[name = tensor("op_5329_cast_fp16")]; - tensor mh_w_53_transpose_x_0 = const()[name = tensor("mh_w_53_transpose_x_0"), val = tensor(true)]; - tensor mh_w_53_transpose_y_0 = const()[name = tensor("mh_w_53_transpose_y_0"), val = tensor(false)]; - tensor mh_w_53_cast_fp16 = matmul(transpose_x = mh_w_53_transpose_x_0, transpose_y = mh_w_53_transpose_y_0, x = var_5325_cast_fp16, y = var_5329_cast_fp16)[name = tensor("mh_w_53_cast_fp16")]; - tensor mh_w_55_cast_fp16 = add(x = mh_w_53_cast_fp16, y = qk_mask_27_cast_fp16)[name = tensor("mh_w_55_cast_fp16")]; - tensor var_5333_cast_fp16 = softmax(axis = var_5120, x = mh_w_55_cast_fp16)[name = tensor("op_5333_cast_fp16")]; - tensor var_5334 = const()[name = tensor("op_5334"), val = tensor([1, 8, 64, 188])]; - tensor var_5335_cast_fp16 = reshape(shape = var_5334, x = value_27_cast_fp16)[name = tensor("op_5335_cast_fp16")]; - tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; - tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; - tensor attn_27_cast_fp16 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_5335_cast_fp16, y = var_5333_cast_fp16)[name = tensor("attn_27_cast_fp16")]; - tensor var_5338 = const()[name = tensor("op_5338"), val = tensor([1, 512, 1, 188])]; - tensor input_361_cast_fp16 = reshape(shape = var_5338, x = attn_27_cast_fp16)[name = tensor("input_361_cast_fp16")]; - tensor var_5348_pad_type_0 = const()[name = tensor("op_5348_pad_type_0"), val = tensor("valid")]; - tensor var_5348_strides_0 = const()[name = tensor("op_5348_strides_0"), val = tensor([1, 1])]; - tensor var_5348_pad_0 = const()[name = tensor("op_5348_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5348_dilations_0 = const()[name = tensor("op_5348_dilations_0"), val = tensor([1, 1])]; - tensor var_5348_groups_0 = const()[name = tensor("op_5348_groups_0"), val = tensor(1)]; - tensor layers_13_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79789376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79986048))), name = tensor("layers_13_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_13_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_13_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79986240)))]; - tensor var_5348_cast_fp16 = conv(bias = layers_13_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_5348_dilations_0, groups = var_5348_groups_0, pad = var_5348_pad_0, pad_type = var_5348_pad_type_0, strides = var_5348_strides_0, weight = layers_13_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = tensor("op_5348_cast_fp16")]; - tensor var_5354_pad_type_0 = const()[name = tensor("op_5354_pad_type_0"), val = tensor("valid")]; - tensor var_5354_strides_0 = const()[name = tensor("op_5354_strides_0"), val = tensor([1, 1])]; - tensor var_5354_pad_0 = const()[name = tensor("op_5354_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5354_dilations_0 = const()[name = tensor("op_5354_dilations_0"), val = tensor([1, 1])]; - tensor var_5354_groups_0 = const()[name = tensor("op_5354_groups_0"), val = tensor(1)]; - tensor layers_13_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79996608))), name = tensor("layers_13_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79987328))), shape = tensor([512, 512, 1, 1])]; - tensor var_5354_cast_fp16 = conv(dilations = var_5354_dilations_0, groups = var_5354_groups_0, pad = var_5354_pad_0, pad_type = var_5354_pad_type_0, strides = var_5354_strides_0, weight = layers_13_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_361_cast_fp16)[name = tensor("op_5354_cast_fp16")]; - tensor obj_57_cast_fp16 = add(x = var_5348_cast_fp16, y = var_5354_cast_fp16)[name = tensor("obj_57_cast_fp16")]; - tensor inputs_135_cast_fp16 = add(x = inputs_133_cast_fp16, y = obj_57_cast_fp16)[name = tensor("inputs_135_cast_fp16")]; - tensor out_135_axes_0 = const()[name = tensor("out_135_axes_0"), val = tensor([1])]; - tensor var_5365_to_fp16 = const()[name = tensor("op_5365_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_135_cast_fp16 = layer_norm(axes = out_135_axes_0, epsilon = var_5365_to_fp16, x = inputs_135_cast_fp16)[name = tensor("out_135_cast_fp16")]; - tensor input_363_gamma_0_to_fp16 = const()[name = tensor("input_363_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80029440)))]; - tensor input_363_beta_0_to_fp16 = const()[name = tensor("input_363_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80030528)))]; - tensor input_363_epsilon_0_to_fp16 = const()[name = tensor("input_363_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_363_cast_fp16 = batch_norm(beta = input_363_beta_0_to_fp16, epsilon = input_363_epsilon_0_to_fp16, gamma = input_363_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_135_cast_fp16)[name = tensor("input_363_cast_fp16")]; - tensor var_5387_pad_type_0 = const()[name = tensor("op_5387_pad_type_0"), val = tensor("valid")]; - tensor var_5387_strides_0 = const()[name = tensor("op_5387_strides_0"), val = tensor([1, 1])]; - tensor var_5387_pad_0 = const()[name = tensor("op_5387_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5387_dilations_0 = const()[name = tensor("op_5387_dilations_0"), val = tensor([1, 1])]; - tensor var_5387_groups_0 = const()[name = tensor("op_5387_groups_0"), val = tensor(1)]; - tensor layers_13_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80031616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80424896))), name = tensor("layers_13_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_13_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_13_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80425088)))]; - tensor var_5387_cast_fp16 = conv(bias = layers_13_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_5387_dilations_0, groups = var_5387_groups_0, pad = var_5387_pad_0, pad_type = var_5387_pad_type_0, strides = var_5387_strides_0, weight = layers_13_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_363_cast_fp16)[name = tensor("op_5387_cast_fp16")]; - tensor var_5393_pad_type_0 = const()[name = tensor("op_5393_pad_type_0"), val = tensor("valid")]; - tensor var_5393_strides_0 = const()[name = tensor("op_5393_strides_0"), val = tensor([1, 1])]; - tensor var_5393_pad_0 = const()[name = tensor("op_5393_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5393_dilations_0 = const()[name = tensor("op_5393_dilations_0"), val = tensor([1, 1])]; - tensor var_5393_groups_0 = const()[name = tensor("op_5393_groups_0"), val = tensor(1)]; - tensor layers_13_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80446784))), name = tensor("layers_13_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80427200))), shape = tensor([1024, 512, 1, 1])]; - tensor var_5393_cast_fp16 = conv(dilations = var_5393_dilations_0, groups = var_5393_groups_0, pad = var_5393_pad_0, pad_type = var_5393_pad_type_0, strides = var_5393_strides_0, weight = layers_13_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_363_cast_fp16)[name = tensor("op_5393_cast_fp16")]; - tensor input_365_cast_fp16 = add(x = var_5387_cast_fp16, y = var_5393_cast_fp16)[name = tensor("input_365_cast_fp16")]; - tensor input_367_split_num_splits_0 = const()[name = tensor("input_367_split_num_splits_0"), val = tensor(2)]; - tensor input_367_split_axis_0 = const()[name = tensor("input_367_split_axis_0"), val = tensor(1)]; - tensor input_367_split_cast_fp16_0, tensor input_367_split_cast_fp16_1 = split(axis = input_367_split_axis_0, num_splits = input_367_split_num_splits_0, x = input_365_cast_fp16)[name = tensor("input_367_split_cast_fp16")]; - tensor input_367_split_1_sigmoid_cast_fp16 = sigmoid(x = input_367_split_cast_fp16_1)[name = tensor("input_367_split_1_sigmoid_cast_fp16")]; - tensor input_367_cast_fp16 = mul(x = input_367_split_cast_fp16_0, y = input_367_split_1_sigmoid_cast_fp16)[name = tensor("input_367_cast_fp16")]; - tensor input_369_pad_type_0 = const()[name = tensor("input_369_pad_type_0"), val = tensor("custom")]; - tensor input_369_pad_0 = const()[name = tensor("input_369_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_369_groups_0 = const()[name = tensor("input_369_groups_0"), val = tensor(512)]; - tensor input_369_strides_0 = const()[name = tensor("input_369_strides_0"), val = tensor([1, 1])]; - tensor input_369_dilations_0 = const()[name = tensor("input_369_dilations_0"), val = tensor([1, 1])]; - tensor const_217_to_fp16 = const()[name = tensor("const_217_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80512384)))]; - tensor const_218_to_fp16 = const()[name = tensor("const_218_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80521664)))]; - tensor input_371_cast_fp16 = conv(bias = const_218_to_fp16, dilations = input_369_dilations_0, groups = input_369_groups_0, pad = input_369_pad_0, pad_type = input_369_pad_type_0, strides = input_369_strides_0, weight = const_217_to_fp16, x = input_367_cast_fp16)[name = tensor("input_371_cast_fp16")]; - tensor input_373_cast_fp16 = silu(x = input_371_cast_fp16)[name = tensor("input_373_cast_fp16")]; - tensor var_5417_pad_type_0 = const()[name = tensor("op_5417_pad_type_0"), val = tensor("valid")]; - tensor var_5417_strides_0 = const()[name = tensor("op_5417_strides_0"), val = tensor([1, 1])]; - tensor var_5417_pad_0 = const()[name = tensor("op_5417_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5417_dilations_0 = const()[name = tensor("op_5417_dilations_0"), val = tensor([1, 1])]; - tensor var_5417_groups_0 = const()[name = tensor("op_5417_groups_0"), val = tensor(1)]; - tensor layers_13_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80522752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80719424))), name = tensor("layers_13_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_13_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_13_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80719616)))]; - tensor var_5417_cast_fp16 = conv(bias = layers_13_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_5417_dilations_0, groups = var_5417_groups_0, pad = var_5417_pad_0, pad_type = var_5417_pad_type_0, strides = var_5417_strides_0, weight = layers_13_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = tensor("op_5417_cast_fp16")]; - tensor var_5423_pad_type_0 = const()[name = tensor("op_5423_pad_type_0"), val = tensor("valid")]; - tensor var_5423_strides_0 = const()[name = tensor("op_5423_strides_0"), val = tensor([1, 1])]; - tensor var_5423_pad_0 = const()[name = tensor("op_5423_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5423_dilations_0 = const()[name = tensor("op_5423_dilations_0"), val = tensor([1, 1])]; - tensor var_5423_groups_0 = const()[name = tensor("op_5423_groups_0"), val = tensor(1)]; - tensor layers_13_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80730112))), name = tensor("layers_13_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80720704))), shape = tensor([512, 512, 1, 1])]; - tensor var_5423_cast_fp16 = conv(dilations = var_5423_dilations_0, groups = var_5423_groups_0, pad = var_5423_pad_0, pad_type = var_5423_pad_type_0, strides = var_5423_strides_0, weight = layers_13_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_373_cast_fp16)[name = tensor("op_5423_cast_fp16")]; - tensor x_83_cast_fp16 = add(x = var_5417_cast_fp16, y = var_5423_cast_fp16)[name = tensor("x_83_cast_fp16")]; - tensor inputs_137_cast_fp16 = add(x = inputs_135_cast_fp16, y = x_83_cast_fp16)[name = tensor("inputs_137_cast_fp16")]; - tensor out_137_axes_0 = const()[name = tensor("out_137_axes_0"), val = tensor([1])]; - tensor var_5434_to_fp16 = const()[name = tensor("op_5434_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_137_cast_fp16 = layer_norm(axes = out_137_axes_0, epsilon = var_5434_to_fp16, x = inputs_137_cast_fp16)[name = tensor("out_137_cast_fp16")]; - tensor input_375_gamma_0_to_fp16 = const()[name = tensor("input_375_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80762944)))]; - tensor input_375_beta_0_to_fp16 = const()[name = tensor("input_375_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80764032)))]; - tensor input_375_epsilon_0_to_fp16 = const()[name = tensor("input_375_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_375_cast_fp16 = batch_norm(beta = input_375_beta_0_to_fp16, epsilon = input_375_epsilon_0_to_fp16, gamma = input_375_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_137_cast_fp16)[name = tensor("input_375_cast_fp16")]; - tensor var_5454_pad_type_0 = const()[name = tensor("op_5454_pad_type_0"), val = tensor("valid")]; - tensor var_5454_strides_0 = const()[name = tensor("op_5454_strides_0"), val = tensor([1, 1])]; - tensor var_5454_pad_0 = const()[name = tensor("op_5454_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5454_dilations_0 = const()[name = tensor("op_5454_dilations_0"), val = tensor([1, 1])]; - tensor var_5454_groups_0 = const()[name = tensor("op_5454_groups_0"), val = tensor(1)]; - tensor layers_13_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80765120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81551616))), name = tensor("layers_13_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_13_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_13_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81551808)))]; - tensor var_5454_cast_fp16 = conv(bias = layers_13_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_5454_dilations_0, groups = var_5454_groups_0, pad = var_5454_pad_0, pad_type = var_5454_pad_type_0, strides = var_5454_strides_0, weight = layers_13_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_375_cast_fp16)[name = tensor("op_5454_cast_fp16")]; - tensor var_5460_pad_type_0 = const()[name = tensor("op_5460_pad_type_0"), val = tensor("valid")]; - tensor var_5460_strides_0 = const()[name = tensor("op_5460_strides_0"), val = tensor([1, 1])]; - tensor var_5460_pad_0 = const()[name = tensor("op_5460_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5460_dilations_0 = const()[name = tensor("op_5460_dilations_0"), val = tensor([1, 1])]; - tensor var_5460_groups_0 = const()[name = tensor("op_5460_groups_0"), val = tensor(1)]; - tensor layers_13_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81591104))), name = tensor("layers_13_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81555968))), shape = tensor([2048, 512, 1, 1])]; - tensor var_5460_cast_fp16 = conv(dilations = var_5460_dilations_0, groups = var_5460_groups_0, pad = var_5460_pad_0, pad_type = var_5460_pad_type_0, strides = var_5460_strides_0, weight = layers_13_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_375_cast_fp16)[name = tensor("op_5460_cast_fp16")]; - tensor input_377_cast_fp16 = add(x = var_5454_cast_fp16, y = var_5460_cast_fp16)[name = tensor("input_377_cast_fp16")]; - tensor input_379_cast_fp16 = silu(x = input_377_cast_fp16)[name = tensor("input_379_cast_fp16")]; - tensor var_5471_pad_type_0 = const()[name = tensor("op_5471_pad_type_0"), val = tensor("valid")]; - tensor var_5471_strides_0 = const()[name = tensor("op_5471_strides_0"), val = tensor([1, 1])]; - tensor var_5471_pad_0 = const()[name = tensor("op_5471_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5471_dilations_0 = const()[name = tensor("op_5471_dilations_0"), val = tensor([1, 1])]; - tensor var_5471_groups_0 = const()[name = tensor("op_5471_groups_0"), val = tensor(1)]; - tensor layers_13_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81722240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82508736))), name = tensor("layers_13_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_13_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_13_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82508928)))]; - tensor var_5471_cast_fp16 = conv(bias = layers_13_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_5471_dilations_0, groups = var_5471_groups_0, pad = var_5471_pad_0, pad_type = var_5471_pad_type_0, strides = var_5471_strides_0, weight = layers_13_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_379_cast_fp16)[name = tensor("op_5471_cast_fp16")]; - tensor var_5477_pad_type_0 = const()[name = tensor("op_5477_pad_type_0"), val = tensor("valid")]; - tensor var_5477_strides_0 = const()[name = tensor("op_5477_strides_0"), val = tensor([1, 1])]; - tensor var_5477_pad_0 = const()[name = tensor("op_5477_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5477_dilations_0 = const()[name = tensor("op_5477_dilations_0"), val = tensor([1, 1])]; - tensor var_5477_groups_0 = const()[name = tensor("op_5477_groups_0"), val = tensor(1)]; - tensor layers_13_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82552320))), name = tensor("layers_13_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82510016))), shape = tensor([512, 2048, 1, 1])]; - tensor var_5477_cast_fp16 = conv(dilations = var_5477_dilations_0, groups = var_5477_groups_0, pad = var_5477_pad_0, pad_type = var_5477_pad_type_0, strides = var_5477_strides_0, weight = layers_13_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_379_cast_fp16)[name = tensor("op_5477_cast_fp16")]; - tensor x_85_cast_fp16 = add(x = var_5471_cast_fp16, y = var_5477_cast_fp16)[name = tensor("x_85_cast_fp16")]; - tensor var_5479_to_fp16 = const()[name = tensor("op_5479_to_fp16"), val = tensor(0x1p-1)]; - tensor var_5480_cast_fp16 = mul(x = x_85_cast_fp16, y = var_5479_to_fp16)[name = tensor("op_5480_cast_fp16")]; - tensor inputs_139_cast_fp16 = add(x = inputs_137_cast_fp16, y = var_5480_cast_fp16)[name = tensor("inputs_139_cast_fp16")]; - tensor out_139_axes_0 = const()[name = tensor("out_139_axes_0"), val = tensor([1])]; - tensor var_5490_to_fp16 = const()[name = tensor("op_5490_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_139_cast_fp16 = layer_norm(axes = out_139_axes_0, epsilon = var_5490_to_fp16, x = inputs_139_cast_fp16)[name = tensor("out_139_cast_fp16")]; - tensor inputs_141_gamma_0_to_fp16 = const()[name = tensor("inputs_141_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82683456)))]; - tensor inputs_141_beta_0_to_fp16 = const()[name = tensor("inputs_141_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82684544)))]; - tensor inputs_141_epsilon_0_to_fp16 = const()[name = tensor("inputs_141_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_141_cast_fp16 = batch_norm(beta = inputs_141_beta_0_to_fp16, epsilon = inputs_141_epsilon_0_to_fp16, gamma = inputs_141_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_139_cast_fp16)[name = tensor("inputs_141_cast_fp16")]; - tensor var_5504 = const()[name = tensor("op_5504"), val = tensor(3)]; - tensor out_141_axes_0 = const()[name = tensor("out_141_axes_0"), val = tensor([1])]; - tensor var_5535_to_fp16 = const()[name = tensor("op_5535_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_141_cast_fp16 = layer_norm(axes = out_141_axes_0, epsilon = var_5535_to_fp16, x = inputs_141_cast_fp16)[name = tensor("out_141_cast_fp16")]; - tensor input_381_gamma_0_to_fp16 = const()[name = tensor("input_381_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82685632)))]; - tensor input_381_beta_0_to_fp16 = const()[name = tensor("input_381_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82686720)))]; - tensor input_381_epsilon_0_to_fp16 = const()[name = tensor("input_381_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_381_cast_fp16 = batch_norm(beta = input_381_beta_0_to_fp16, epsilon = input_381_epsilon_0_to_fp16, gamma = input_381_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_141_cast_fp16)[name = tensor("input_381_cast_fp16")]; - tensor var_5555_pad_type_0 = const()[name = tensor("op_5555_pad_type_0"), val = tensor("valid")]; - tensor var_5555_strides_0 = const()[name = tensor("op_5555_strides_0"), val = tensor([1, 1])]; - tensor var_5555_pad_0 = const()[name = tensor("op_5555_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5555_dilations_0 = const()[name = tensor("op_5555_dilations_0"), val = tensor([1, 1])]; - tensor var_5555_groups_0 = const()[name = tensor("op_5555_groups_0"), val = tensor(1)]; - tensor layers_14_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82687808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83474304))), name = tensor("layers_14_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_14_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_14_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83474496)))]; - tensor var_5555_cast_fp16 = conv(bias = layers_14_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_5555_dilations_0, groups = var_5555_groups_0, pad = var_5555_pad_0, pad_type = var_5555_pad_type_0, strides = var_5555_strides_0, weight = layers_14_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = tensor("op_5555_cast_fp16")]; - tensor var_5561_pad_type_0 = const()[name = tensor("op_5561_pad_type_0"), val = tensor("valid")]; - tensor var_5561_strides_0 = const()[name = tensor("op_5561_strides_0"), val = tensor([1, 1])]; - tensor var_5561_pad_0 = const()[name = tensor("op_5561_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5561_dilations_0 = const()[name = tensor("op_5561_dilations_0"), val = tensor([1, 1])]; - tensor var_5561_groups_0 = const()[name = tensor("op_5561_groups_0"), val = tensor(1)]; - tensor layers_14_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83513984))), name = tensor("layers_14_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83478656))), shape = tensor([2048, 512, 1, 1])]; - tensor var_5561_cast_fp16 = conv(dilations = var_5561_dilations_0, groups = var_5561_groups_0, pad = var_5561_pad_0, pad_type = var_5561_pad_type_0, strides = var_5561_strides_0, weight = layers_14_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_381_cast_fp16)[name = tensor("op_5561_cast_fp16")]; - tensor input_383_cast_fp16 = add(x = var_5555_cast_fp16, y = var_5561_cast_fp16)[name = tensor("input_383_cast_fp16")]; - tensor input_385_cast_fp16 = silu(x = input_383_cast_fp16)[name = tensor("input_385_cast_fp16")]; - tensor var_5572_pad_type_0 = const()[name = tensor("op_5572_pad_type_0"), val = tensor("valid")]; - tensor var_5572_strides_0 = const()[name = tensor("op_5572_strides_0"), val = tensor([1, 1])]; - tensor var_5572_pad_0 = const()[name = tensor("op_5572_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5572_dilations_0 = const()[name = tensor("op_5572_dilations_0"), val = tensor([1, 1])]; - tensor var_5572_groups_0 = const()[name = tensor("op_5572_groups_0"), val = tensor(1)]; - tensor layers_14_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83645120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84431616))), name = tensor("layers_14_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_14_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_14_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84431808)))]; - tensor var_5572_cast_fp16 = conv(bias = layers_14_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_5572_dilations_0, groups = var_5572_groups_0, pad = var_5572_pad_0, pad_type = var_5572_pad_type_0, strides = var_5572_strides_0, weight = layers_14_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_385_cast_fp16)[name = tensor("op_5572_cast_fp16")]; - tensor var_5578_pad_type_0 = const()[name = tensor("op_5578_pad_type_0"), val = tensor("valid")]; - tensor var_5578_strides_0 = const()[name = tensor("op_5578_strides_0"), val = tensor([1, 1])]; - tensor var_5578_pad_0 = const()[name = tensor("op_5578_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5578_dilations_0 = const()[name = tensor("op_5578_dilations_0"), val = tensor([1, 1])]; - tensor var_5578_groups_0 = const()[name = tensor("op_5578_groups_0"), val = tensor(1)]; - tensor layers_14_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84471232))), name = tensor("layers_14_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84432896))), shape = tensor([512, 2048, 1, 1])]; - tensor var_5578_cast_fp16 = conv(dilations = var_5578_dilations_0, groups = var_5578_groups_0, pad = var_5578_pad_0, pad_type = var_5578_pad_type_0, strides = var_5578_strides_0, weight = layers_14_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_385_cast_fp16)[name = tensor("op_5578_cast_fp16")]; - tensor x_87_cast_fp16 = add(x = var_5572_cast_fp16, y = var_5578_cast_fp16)[name = tensor("x_87_cast_fp16")]; - tensor var_5580_to_fp16 = const()[name = tensor("op_5580_to_fp16"), val = tensor(0x1p-1)]; - tensor var_5581_cast_fp16 = mul(x = x_87_cast_fp16, y = var_5580_to_fp16)[name = tensor("op_5581_cast_fp16")]; - tensor inputs_143_cast_fp16 = add(x = inputs_141_cast_fp16, y = var_5581_cast_fp16)[name = tensor("inputs_143_cast_fp16")]; - tensor out_143_axes_0 = const()[name = tensor("out_143_axes_0"), val = tensor([1])]; - tensor var_5591_to_fp16 = const()[name = tensor("op_5591_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_143_cast_fp16 = layer_norm(axes = out_143_axes_0, epsilon = var_5591_to_fp16, x = inputs_143_cast_fp16)[name = tensor("out_143_cast_fp16")]; - tensor obj_59_gamma_0_to_fp16 = const()[name = tensor("obj_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84602368)))]; - tensor obj_59_beta_0_to_fp16 = const()[name = tensor("obj_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84603456)))]; - tensor obj_59_epsilon_0_to_fp16 = const()[name = tensor("obj_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_59_cast_fp16 = batch_norm(beta = obj_59_beta_0_to_fp16, epsilon = obj_59_epsilon_0_to_fp16, gamma = obj_59_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_143_cast_fp16)[name = tensor("obj_59_cast_fp16")]; - tensor var_5616_pad_type_0 = const()[name = tensor("op_5616_pad_type_0"), val = tensor("valid")]; - tensor var_5616_strides_0 = const()[name = tensor("op_5616_strides_0"), val = tensor([1, 1])]; - tensor var_5616_pad_0 = const()[name = tensor("op_5616_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5616_dilations_0 = const()[name = tensor("op_5616_dilations_0"), val = tensor([1, 1])]; - tensor var_5616_groups_0 = const()[name = tensor("op_5616_groups_0"), val = tensor(1)]; - tensor layers_14_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84604544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84801216))), name = tensor("layers_14_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_14_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_14_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84801408)))]; - tensor var_5616_cast_fp16 = conv(bias = layers_14_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_5616_dilations_0, groups = var_5616_groups_0, pad = var_5616_pad_0, pad_type = var_5616_pad_type_0, strides = var_5616_strides_0, weight = layers_14_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_59_cast_fp16)[name = tensor("op_5616_cast_fp16")]; - tensor var_5622_pad_type_0 = const()[name = tensor("op_5622_pad_type_0"), val = tensor("valid")]; - tensor var_5622_strides_0 = const()[name = tensor("op_5622_strides_0"), val = tensor([1, 1])]; - tensor var_5622_pad_0 = const()[name = tensor("op_5622_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5622_dilations_0 = const()[name = tensor("op_5622_dilations_0"), val = tensor([1, 1])]; - tensor var_5622_groups_0 = const()[name = tensor("op_5622_groups_0"), val = tensor(1)]; - tensor layers_14_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84809984))), name = tensor("layers_14_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84802496))), shape = tensor([512, 512, 1, 1])]; - tensor var_5622_cast_fp16 = conv(dilations = var_5622_dilations_0, groups = var_5622_groups_0, pad = var_5622_pad_0, pad_type = var_5622_pad_type_0, strides = var_5622_strides_0, weight = layers_14_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_59_cast_fp16)[name = tensor("op_5622_cast_fp16")]; - tensor query_57_cast_fp16 = add(x = var_5616_cast_fp16, y = var_5622_cast_fp16)[name = tensor("query_57_cast_fp16")]; - tensor var_5631_pad_type_0 = const()[name = tensor("op_5631_pad_type_0"), val = tensor("valid")]; - tensor var_5631_strides_0 = const()[name = tensor("op_5631_strides_0"), val = tensor([1, 1])]; - tensor var_5631_pad_0 = const()[name = tensor("op_5631_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5631_dilations_0 = const()[name = tensor("op_5631_dilations_0"), val = tensor([1, 1])]; - tensor var_5631_groups_0 = const()[name = tensor("op_5631_groups_0"), val = tensor(1)]; - tensor layers_14_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84842816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85039488))), name = tensor("layers_14_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_5631_cast_fp16 = conv(dilations = var_5631_dilations_0, groups = var_5631_groups_0, pad = var_5631_pad_0, pad_type = var_5631_pad_type_0, strides = var_5631_strides_0, weight = layers_14_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_59_cast_fp16)[name = tensor("op_5631_cast_fp16")]; - tensor var_5637_pad_type_0 = const()[name = tensor("op_5637_pad_type_0"), val = tensor("valid")]; - tensor var_5637_strides_0 = const()[name = tensor("op_5637_strides_0"), val = tensor([1, 1])]; - tensor var_5637_pad_0 = const()[name = tensor("op_5637_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5637_dilations_0 = const()[name = tensor("op_5637_dilations_0"), val = tensor([1, 1])]; - tensor var_5637_groups_0 = const()[name = tensor("op_5637_groups_0"), val = tensor(1)]; - tensor layers_14_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85047232))), name = tensor("layers_14_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85039680))), shape = tensor([512, 512, 1, 1])]; - tensor var_5637_cast_fp16 = conv(dilations = var_5637_dilations_0, groups = var_5637_groups_0, pad = var_5637_pad_0, pad_type = var_5637_pad_type_0, strides = var_5637_strides_0, weight = layers_14_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_59_cast_fp16)[name = tensor("op_5637_cast_fp16")]; - tensor key_29_cast_fp16 = add(x = var_5631_cast_fp16, y = var_5637_cast_fp16)[name = tensor("key_29_cast_fp16")]; - tensor var_5647_pad_type_0 = const()[name = tensor("op_5647_pad_type_0"), val = tensor("valid")]; - tensor var_5647_strides_0 = const()[name = tensor("op_5647_strides_0"), val = tensor([1, 1])]; - tensor var_5647_pad_0 = const()[name = tensor("op_5647_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5647_dilations_0 = const()[name = tensor("op_5647_dilations_0"), val = tensor([1, 1])]; - tensor var_5647_groups_0 = const()[name = tensor("op_5647_groups_0"), val = tensor(1)]; - tensor layers_14_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85080064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85276736))), name = tensor("layers_14_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_14_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_14_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85276928)))]; - tensor var_5647_cast_fp16 = conv(bias = layers_14_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_5647_dilations_0, groups = var_5647_groups_0, pad = var_5647_pad_0, pad_type = var_5647_pad_type_0, strides = var_5647_strides_0, weight = layers_14_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_59_cast_fp16)[name = tensor("op_5647_cast_fp16")]; - tensor var_5653_pad_type_0 = const()[name = tensor("op_5653_pad_type_0"), val = tensor("valid")]; - tensor var_5653_strides_0 = const()[name = tensor("op_5653_strides_0"), val = tensor([1, 1])]; - tensor var_5653_pad_0 = const()[name = tensor("op_5653_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5653_dilations_0 = const()[name = tensor("op_5653_dilations_0"), val = tensor([1, 1])]; - tensor var_5653_groups_0 = const()[name = tensor("op_5653_groups_0"), val = tensor(1)]; - tensor layers_14_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85285760))), name = tensor("layers_14_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85278016))), shape = tensor([512, 512, 1, 1])]; - tensor var_5653_cast_fp16 = conv(dilations = var_5653_dilations_0, groups = var_5653_groups_0, pad = var_5653_pad_0, pad_type = var_5653_pad_type_0, strides = var_5653_strides_0, weight = layers_14_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_59_cast_fp16)[name = tensor("op_5653_cast_fp16")]; - tensor value_29_cast_fp16 = add(x = var_5647_cast_fp16, y = var_5653_cast_fp16)[name = tensor("value_29_cast_fp16")]; - tensor var_5656_to_fp16 = const()[name = tensor("op_5656_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85318592)))]; - tensor query_59_cast_fp16 = add(x = query_57_cast_fp16, y = var_5656_to_fp16)[name = tensor("query_59_cast_fp16")]; - tensor var_5659_to_fp16 = const()[name = tensor("op_5659_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85319680)))]; - tensor q_with_bias_v_29_cast_fp16 = add(x = query_57_cast_fp16, y = var_5659_to_fp16)[name = tensor("q_with_bias_v_29_cast_fp16")]; - tensor var_5669_pad_type_0 = const()[name = tensor("op_5669_pad_type_0"), val = tensor("valid")]; - tensor var_5669_strides_0 = const()[name = tensor("op_5669_strides_0"), val = tensor([1, 1])]; - tensor var_5669_pad_0 = const()[name = tensor("op_5669_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5669_dilations_0 = const()[name = tensor("op_5669_dilations_0"), val = tensor([1, 1])]; - tensor var_5669_groups_0 = const()[name = tensor("op_5669_groups_0"), val = tensor(1)]; - tensor layers_14_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85320768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85517440))), name = tensor("layers_14_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_5669_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_5669_dilations_0, groups = var_5669_groups_0, pad = var_5669_pad_0, pad_type = var_5669_pad_type_0, strides = var_5669_strides_0, weight = layers_14_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_5669_cast_fp16")]; - tensor var_5675_pad_type_0 = const()[name = tensor("op_5675_pad_type_0"), val = tensor("valid")]; - tensor var_5675_strides_0 = const()[name = tensor("op_5675_strides_0"), val = tensor([1, 1])]; - tensor var_5675_pad_0 = const()[name = tensor("op_5675_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5675_dilations_0 = const()[name = tensor("op_5675_dilations_0"), val = tensor([1, 1])]; - tensor var_5675_groups_0 = const()[name = tensor("op_5675_groups_0"), val = tensor(1)]; - tensor layers_14_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85539840))), name = tensor("layers_14_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85517632))), shape = tensor([512, 512, 1, 1])]; - tensor var_5675_cast_fp16 = conv(dilations = var_5675_dilations_0, groups = var_5675_groups_0, pad = var_5675_pad_0, pad_type = var_5675_pad_type_0, strides = var_5675_strides_0, weight = layers_14_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_5675_cast_fp16")]; - tensor p_29_cast_fp16 = add(x = var_5669_cast_fp16, y = var_5675_cast_fp16)[name = tensor("p_29_cast_fp16")]; - tensor var_5679 = const()[name = tensor("op_5679"), val = tensor([1, 8, 64, 188])]; - tensor var_5680_cast_fp16 = reshape(shape = var_5679, x = q_with_bias_v_29_cast_fp16)[name = tensor("op_5680_cast_fp16")]; - tensor var_5681 = const()[name = tensor("op_5681"), val = tensor([1, 8, 64, -1])]; - tensor var_5682_cast_fp16 = reshape(shape = var_5681, x = p_29_cast_fp16)[name = tensor("op_5682_cast_fp16")]; - tensor matrix_bd_113_transpose_x_0 = const()[name = tensor("matrix_bd_113_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_113_transpose_y_0 = const()[name = tensor("matrix_bd_113_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_113_cast_fp16 = matmul(transpose_x = matrix_bd_113_transpose_x_0, transpose_y = matrix_bd_113_transpose_y_0, x = var_5680_cast_fp16, y = var_5682_cast_fp16)[name = tensor("matrix_bd_113_cast_fp16")]; - tensor matrix_bd_115_pad_0 = const()[name = tensor("matrix_bd_115_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_115_mode_0 = const()[name = tensor("matrix_bd_115_mode_0"), val = tensor("constant")]; - tensor const_164_to_fp16 = const()[name = tensor("const_164_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_115_cast_fp16 = pad(constant_val = const_164_to_fp16, mode = matrix_bd_115_mode_0, pad = matrix_bd_115_pad_0, x = matrix_bd_113_cast_fp16)[name = tensor("matrix_bd_115_cast_fp16")]; - tensor var_5691 = const()[name = tensor("op_5691"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_117_cast_fp16 = reshape(shape = var_5691, x = matrix_bd_115_cast_fp16)[name = tensor("matrix_bd_117_cast_fp16")]; - tensor var_5695_begin_0 = const()[name = tensor("op_5695_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_5695_end_0 = const()[name = tensor("op_5695_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_5695_end_mask_0 = const()[name = tensor("op_5695_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_5695_cast_fp16 = slice_by_index(begin = var_5695_begin_0, end = var_5695_end_0, end_mask = var_5695_end_mask_0, x = matrix_bd_117_cast_fp16)[name = tensor("op_5695_cast_fp16")]; - tensor var_5696 = const()[name = tensor("op_5696"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_119_cast_fp16 = reshape(shape = var_5696, x = var_5695_cast_fp16)[name = tensor("matrix_bd_119_cast_fp16")]; - tensor var_5701_begin_0 = const()[name = tensor("op_5701_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5701_end_0 = const()[name = tensor("op_5701_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_5701_end_mask_0 = const()[name = tensor("op_5701_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_5701_cast_fp16 = slice_by_index(begin = var_5701_begin_0, end = var_5701_end_0, end_mask = var_5701_end_mask_0, x = matrix_bd_119_cast_fp16)[name = tensor("op_5701_cast_fp16")]; - tensor var_5702_to_fp16 = const()[name = tensor("op_5702_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_29_cast_fp16 = mul(x = var_5701_cast_fp16, y = var_5702_to_fp16)[name = tensor("qk_mask_29_cast_fp16")]; - tensor var_5706 = const()[name = tensor("op_5706"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_29_cast_fp16 = reshape(shape = var_5706, x = query_59_cast_fp16)[name = tensor("mh_q_29_cast_fp16")]; - tensor var_5708_to_fp16 = const()[name = tensor("op_5708_to_fp16"), val = tensor(0x1p-3)]; - tensor var_5709_cast_fp16 = mul(x = mh_q_29_cast_fp16, y = var_5708_to_fp16)[name = tensor("op_5709_cast_fp16")]; - tensor var_5712 = const()[name = tensor("op_5712"), val = tensor([1, 8, 64, 188])]; - tensor var_5713_cast_fp16 = reshape(shape = var_5712, x = key_29_cast_fp16)[name = tensor("op_5713_cast_fp16")]; - tensor mh_w_57_transpose_x_0 = const()[name = tensor("mh_w_57_transpose_x_0"), val = tensor(true)]; - tensor mh_w_57_transpose_y_0 = const()[name = tensor("mh_w_57_transpose_y_0"), val = tensor(false)]; - tensor mh_w_57_cast_fp16 = matmul(transpose_x = mh_w_57_transpose_x_0, transpose_y = mh_w_57_transpose_y_0, x = var_5709_cast_fp16, y = var_5713_cast_fp16)[name = tensor("mh_w_57_cast_fp16")]; - tensor mh_w_59_cast_fp16 = add(x = mh_w_57_cast_fp16, y = qk_mask_29_cast_fp16)[name = tensor("mh_w_59_cast_fp16")]; - tensor var_5717_cast_fp16 = softmax(axis = var_5504, x = mh_w_59_cast_fp16)[name = tensor("op_5717_cast_fp16")]; - tensor var_5718 = const()[name = tensor("op_5718"), val = tensor([1, 8, 64, 188])]; - tensor var_5719_cast_fp16 = reshape(shape = var_5718, x = value_29_cast_fp16)[name = tensor("op_5719_cast_fp16")]; - tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; - tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; - tensor attn_29_cast_fp16 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_5719_cast_fp16, y = var_5717_cast_fp16)[name = tensor("attn_29_cast_fp16")]; - tensor var_5722 = const()[name = tensor("op_5722"), val = tensor([1, 512, 1, 188])]; - tensor input_387_cast_fp16 = reshape(shape = var_5722, x = attn_29_cast_fp16)[name = tensor("input_387_cast_fp16")]; - tensor var_5732_pad_type_0 = const()[name = tensor("op_5732_pad_type_0"), val = tensor("valid")]; - tensor var_5732_strides_0 = const()[name = tensor("op_5732_strides_0"), val = tensor([1, 1])]; - tensor var_5732_pad_0 = const()[name = tensor("op_5732_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5732_dilations_0 = const()[name = tensor("op_5732_dilations_0"), val = tensor([1, 1])]; - tensor var_5732_groups_0 = const()[name = tensor("op_5732_groups_0"), val = tensor(1)]; - tensor layers_14_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85572672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85769344))), name = tensor("layers_14_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_14_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_14_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85769536)))]; - tensor var_5732_cast_fp16 = conv(bias = layers_14_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_5732_dilations_0, groups = var_5732_groups_0, pad = var_5732_pad_0, pad_type = var_5732_pad_type_0, strides = var_5732_strides_0, weight = layers_14_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_387_cast_fp16)[name = tensor("op_5732_cast_fp16")]; - tensor var_5738_pad_type_0 = const()[name = tensor("op_5738_pad_type_0"), val = tensor("valid")]; - tensor var_5738_strides_0 = const()[name = tensor("op_5738_strides_0"), val = tensor([1, 1])]; - tensor var_5738_pad_0 = const()[name = tensor("op_5738_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5738_dilations_0 = const()[name = tensor("op_5738_dilations_0"), val = tensor([1, 1])]; - tensor var_5738_groups_0 = const()[name = tensor("op_5738_groups_0"), val = tensor(1)]; - tensor layers_14_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85779392))), name = tensor("layers_14_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85770624))), shape = tensor([512, 512, 1, 1])]; - tensor var_5738_cast_fp16 = conv(dilations = var_5738_dilations_0, groups = var_5738_groups_0, pad = var_5738_pad_0, pad_type = var_5738_pad_type_0, strides = var_5738_strides_0, weight = layers_14_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_387_cast_fp16)[name = tensor("op_5738_cast_fp16")]; - tensor obj_61_cast_fp16 = add(x = var_5732_cast_fp16, y = var_5738_cast_fp16)[name = tensor("obj_61_cast_fp16")]; - tensor inputs_145_cast_fp16 = add(x = inputs_143_cast_fp16, y = obj_61_cast_fp16)[name = tensor("inputs_145_cast_fp16")]; - tensor out_145_axes_0 = const()[name = tensor("out_145_axes_0"), val = tensor([1])]; - tensor var_5749_to_fp16 = const()[name = tensor("op_5749_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_145_cast_fp16 = layer_norm(axes = out_145_axes_0, epsilon = var_5749_to_fp16, x = inputs_145_cast_fp16)[name = tensor("out_145_cast_fp16")]; - tensor input_389_gamma_0_to_fp16 = const()[name = tensor("input_389_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85812224)))]; - tensor input_389_beta_0_to_fp16 = const()[name = tensor("input_389_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85813312)))]; - tensor input_389_epsilon_0_to_fp16 = const()[name = tensor("input_389_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_389_cast_fp16 = batch_norm(beta = input_389_beta_0_to_fp16, epsilon = input_389_epsilon_0_to_fp16, gamma = input_389_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_145_cast_fp16)[name = tensor("input_389_cast_fp16")]; - tensor var_5771_pad_type_0 = const()[name = tensor("op_5771_pad_type_0"), val = tensor("valid")]; - tensor var_5771_strides_0 = const()[name = tensor("op_5771_strides_0"), val = tensor([1, 1])]; - tensor var_5771_pad_0 = const()[name = tensor("op_5771_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5771_dilations_0 = const()[name = tensor("op_5771_dilations_0"), val = tensor([1, 1])]; - tensor var_5771_groups_0 = const()[name = tensor("op_5771_groups_0"), val = tensor(1)]; - tensor layers_14_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85814400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86207680))), name = tensor("layers_14_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_14_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_14_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86207872)))]; - tensor var_5771_cast_fp16 = conv(bias = layers_14_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_5771_dilations_0, groups = var_5771_groups_0, pad = var_5771_pad_0, pad_type = var_5771_pad_type_0, strides = var_5771_strides_0, weight = layers_14_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = tensor("op_5771_cast_fp16")]; - tensor var_5777_pad_type_0 = const()[name = tensor("op_5777_pad_type_0"), val = tensor("valid")]; - tensor var_5777_strides_0 = const()[name = tensor("op_5777_strides_0"), val = tensor([1, 1])]; - tensor var_5777_pad_0 = const()[name = tensor("op_5777_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5777_dilations_0 = const()[name = tensor("op_5777_dilations_0"), val = tensor([1, 1])]; - tensor var_5777_groups_0 = const()[name = tensor("op_5777_groups_0"), val = tensor(1)]; - tensor layers_14_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86229248))), name = tensor("layers_14_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86209984))), shape = tensor([1024, 512, 1, 1])]; - tensor var_5777_cast_fp16 = conv(dilations = var_5777_dilations_0, groups = var_5777_groups_0, pad = var_5777_pad_0, pad_type = var_5777_pad_type_0, strides = var_5777_strides_0, weight = layers_14_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_389_cast_fp16)[name = tensor("op_5777_cast_fp16")]; - tensor input_391_cast_fp16 = add(x = var_5771_cast_fp16, y = var_5777_cast_fp16)[name = tensor("input_391_cast_fp16")]; - tensor input_393_split_num_splits_0 = const()[name = tensor("input_393_split_num_splits_0"), val = tensor(2)]; - tensor input_393_split_axis_0 = const()[name = tensor("input_393_split_axis_0"), val = tensor(1)]; - tensor input_393_split_cast_fp16_0, tensor input_393_split_cast_fp16_1 = split(axis = input_393_split_axis_0, num_splits = input_393_split_num_splits_0, x = input_391_cast_fp16)[name = tensor("input_393_split_cast_fp16")]; - tensor input_393_split_1_sigmoid_cast_fp16 = sigmoid(x = input_393_split_cast_fp16_1)[name = tensor("input_393_split_1_sigmoid_cast_fp16")]; - tensor input_393_cast_fp16 = mul(x = input_393_split_cast_fp16_0, y = input_393_split_1_sigmoid_cast_fp16)[name = tensor("input_393_cast_fp16")]; - tensor input_395_pad_type_0 = const()[name = tensor("input_395_pad_type_0"), val = tensor("custom")]; - tensor input_395_pad_0 = const()[name = tensor("input_395_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_395_groups_0 = const()[name = tensor("input_395_groups_0"), val = tensor(512)]; - tensor input_395_strides_0 = const()[name = tensor("input_395_strides_0"), val = tensor([1, 1])]; - tensor input_395_dilations_0 = const()[name = tensor("input_395_dilations_0"), val = tensor([1, 1])]; - tensor const_219_to_fp16 = const()[name = tensor("const_219_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86294848)))]; - tensor const_220_to_fp16 = const()[name = tensor("const_220_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86304128)))]; - tensor input_397_cast_fp16 = conv(bias = const_220_to_fp16, dilations = input_395_dilations_0, groups = input_395_groups_0, pad = input_395_pad_0, pad_type = input_395_pad_type_0, strides = input_395_strides_0, weight = const_219_to_fp16, x = input_393_cast_fp16)[name = tensor("input_397_cast_fp16")]; - tensor input_399_cast_fp16 = silu(x = input_397_cast_fp16)[name = tensor("input_399_cast_fp16")]; - tensor var_5801_pad_type_0 = const()[name = tensor("op_5801_pad_type_0"), val = tensor("valid")]; - tensor var_5801_strides_0 = const()[name = tensor("op_5801_strides_0"), val = tensor([1, 1])]; - tensor var_5801_pad_0 = const()[name = tensor("op_5801_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5801_dilations_0 = const()[name = tensor("op_5801_dilations_0"), val = tensor([1, 1])]; - tensor var_5801_groups_0 = const()[name = tensor("op_5801_groups_0"), val = tensor(1)]; - tensor layers_14_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86305216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86501888))), name = tensor("layers_14_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_14_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_14_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86502080)))]; - tensor var_5801_cast_fp16 = conv(bias = layers_14_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_5801_dilations_0, groups = var_5801_groups_0, pad = var_5801_pad_0, pad_type = var_5801_pad_type_0, strides = var_5801_strides_0, weight = layers_14_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_399_cast_fp16)[name = tensor("op_5801_cast_fp16")]; - tensor var_5807_pad_type_0 = const()[name = tensor("op_5807_pad_type_0"), val = tensor("valid")]; - tensor var_5807_strides_0 = const()[name = tensor("op_5807_strides_0"), val = tensor([1, 1])]; - tensor var_5807_pad_0 = const()[name = tensor("op_5807_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5807_dilations_0 = const()[name = tensor("op_5807_dilations_0"), val = tensor([1, 1])]; - tensor var_5807_groups_0 = const()[name = tensor("op_5807_groups_0"), val = tensor(1)]; - tensor layers_14_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86511872))), name = tensor("layers_14_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86503168))), shape = tensor([512, 512, 1, 1])]; - tensor var_5807_cast_fp16 = conv(dilations = var_5807_dilations_0, groups = var_5807_groups_0, pad = var_5807_pad_0, pad_type = var_5807_pad_type_0, strides = var_5807_strides_0, weight = layers_14_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_399_cast_fp16)[name = tensor("op_5807_cast_fp16")]; - tensor x_89_cast_fp16 = add(x = var_5801_cast_fp16, y = var_5807_cast_fp16)[name = tensor("x_89_cast_fp16")]; - tensor inputs_147_cast_fp16 = add(x = inputs_145_cast_fp16, y = x_89_cast_fp16)[name = tensor("inputs_147_cast_fp16")]; - tensor out_147_axes_0 = const()[name = tensor("out_147_axes_0"), val = tensor([1])]; - tensor var_5818_to_fp16 = const()[name = tensor("op_5818_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_147_cast_fp16 = layer_norm(axes = out_147_axes_0, epsilon = var_5818_to_fp16, x = inputs_147_cast_fp16)[name = tensor("out_147_cast_fp16")]; - tensor input_401_gamma_0_to_fp16 = const()[name = tensor("input_401_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86544704)))]; - tensor input_401_beta_0_to_fp16 = const()[name = tensor("input_401_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86545792)))]; - tensor input_401_epsilon_0_to_fp16 = const()[name = tensor("input_401_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_401_cast_fp16 = batch_norm(beta = input_401_beta_0_to_fp16, epsilon = input_401_epsilon_0_to_fp16, gamma = input_401_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_147_cast_fp16)[name = tensor("input_401_cast_fp16")]; - tensor var_5838_pad_type_0 = const()[name = tensor("op_5838_pad_type_0"), val = tensor("valid")]; - tensor var_5838_strides_0 = const()[name = tensor("op_5838_strides_0"), val = tensor([1, 1])]; - tensor var_5838_pad_0 = const()[name = tensor("op_5838_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5838_dilations_0 = const()[name = tensor("op_5838_dilations_0"), val = tensor([1, 1])]; - tensor var_5838_groups_0 = const()[name = tensor("op_5838_groups_0"), val = tensor(1)]; - tensor layers_14_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86546880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87333376))), name = tensor("layers_14_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_14_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_14_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87333568)))]; - tensor var_5838_cast_fp16 = conv(bias = layers_14_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_5838_dilations_0, groups = var_5838_groups_0, pad = var_5838_pad_0, pad_type = var_5838_pad_type_0, strides = var_5838_strides_0, weight = layers_14_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = tensor("op_5838_cast_fp16")]; - tensor var_5844_pad_type_0 = const()[name = tensor("op_5844_pad_type_0"), val = tensor("valid")]; - tensor var_5844_strides_0 = const()[name = tensor("op_5844_strides_0"), val = tensor([1, 1])]; - tensor var_5844_pad_0 = const()[name = tensor("op_5844_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5844_dilations_0 = const()[name = tensor("op_5844_dilations_0"), val = tensor([1, 1])]; - tensor var_5844_groups_0 = const()[name = tensor("op_5844_groups_0"), val = tensor(1)]; - tensor layers_14_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87370176))), name = tensor("layers_14_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87337728))), shape = tensor([2048, 512, 1, 1])]; - tensor var_5844_cast_fp16 = conv(dilations = var_5844_dilations_0, groups = var_5844_groups_0, pad = var_5844_pad_0, pad_type = var_5844_pad_type_0, strides = var_5844_strides_0, weight = layers_14_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_401_cast_fp16)[name = tensor("op_5844_cast_fp16")]; - tensor input_403_cast_fp16 = add(x = var_5838_cast_fp16, y = var_5844_cast_fp16)[name = tensor("input_403_cast_fp16")]; - tensor input_405_cast_fp16 = silu(x = input_403_cast_fp16)[name = tensor("input_405_cast_fp16")]; - tensor var_5855_pad_type_0 = const()[name = tensor("op_5855_pad_type_0"), val = tensor("valid")]; - tensor var_5855_strides_0 = const()[name = tensor("op_5855_strides_0"), val = tensor([1, 1])]; - tensor var_5855_pad_0 = const()[name = tensor("op_5855_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5855_dilations_0 = const()[name = tensor("op_5855_dilations_0"), val = tensor([1, 1])]; - tensor var_5855_groups_0 = const()[name = tensor("op_5855_groups_0"), val = tensor(1)]; - tensor layers_14_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87501312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88287808))), name = tensor("layers_14_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_14_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_14_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88288000)))]; - tensor var_5855_cast_fp16 = conv(bias = layers_14_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_5855_dilations_0, groups = var_5855_groups_0, pad = var_5855_pad_0, pad_type = var_5855_pad_type_0, strides = var_5855_strides_0, weight = layers_14_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = tensor("op_5855_cast_fp16")]; - tensor var_5861_pad_type_0 = const()[name = tensor("op_5861_pad_type_0"), val = tensor("valid")]; - tensor var_5861_strides_0 = const()[name = tensor("op_5861_strides_0"), val = tensor([1, 1])]; - tensor var_5861_pad_0 = const()[name = tensor("op_5861_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5861_dilations_0 = const()[name = tensor("op_5861_dilations_0"), val = tensor([1, 1])]; - tensor var_5861_groups_0 = const()[name = tensor("op_5861_groups_0"), val = tensor(1)]; - tensor layers_14_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88327936))), name = tensor("layers_14_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88289088))), shape = tensor([512, 2048, 1, 1])]; - tensor var_5861_cast_fp16 = conv(dilations = var_5861_dilations_0, groups = var_5861_groups_0, pad = var_5861_pad_0, pad_type = var_5861_pad_type_0, strides = var_5861_strides_0, weight = layers_14_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_405_cast_fp16)[name = tensor("op_5861_cast_fp16")]; - tensor x_91_cast_fp16 = add(x = var_5855_cast_fp16, y = var_5861_cast_fp16)[name = tensor("x_91_cast_fp16")]; - tensor var_5863_to_fp16 = const()[name = tensor("op_5863_to_fp16"), val = tensor(0x1p-1)]; - tensor var_5864_cast_fp16 = mul(x = x_91_cast_fp16, y = var_5863_to_fp16)[name = tensor("op_5864_cast_fp16")]; - tensor inputs_149_cast_fp16 = add(x = inputs_147_cast_fp16, y = var_5864_cast_fp16)[name = tensor("inputs_149_cast_fp16")]; - tensor out_149_axes_0 = const()[name = tensor("out_149_axes_0"), val = tensor([1])]; - tensor var_5874_to_fp16 = const()[name = tensor("op_5874_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_149_cast_fp16 = layer_norm(axes = out_149_axes_0, epsilon = var_5874_to_fp16, x = inputs_149_cast_fp16)[name = tensor("out_149_cast_fp16")]; - tensor inputs_151_gamma_0_to_fp16 = const()[name = tensor("inputs_151_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88459072)))]; - tensor inputs_151_beta_0_to_fp16 = const()[name = tensor("inputs_151_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88460160)))]; - tensor inputs_151_epsilon_0_to_fp16 = const()[name = tensor("inputs_151_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_151_cast_fp16 = batch_norm(beta = inputs_151_beta_0_to_fp16, epsilon = inputs_151_epsilon_0_to_fp16, gamma = inputs_151_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_149_cast_fp16)[name = tensor("inputs_151_cast_fp16")]; - tensor var_5888 = const()[name = tensor("op_5888"), val = tensor(3)]; - tensor out_151_axes_0 = const()[name = tensor("out_151_axes_0"), val = tensor([1])]; - tensor var_5919_to_fp16 = const()[name = tensor("op_5919_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_151_cast_fp16 = layer_norm(axes = out_151_axes_0, epsilon = var_5919_to_fp16, x = inputs_151_cast_fp16)[name = tensor("out_151_cast_fp16")]; - tensor input_407_gamma_0_to_fp16 = const()[name = tensor("input_407_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88461248)))]; - tensor input_407_beta_0_to_fp16 = const()[name = tensor("input_407_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88462336)))]; - tensor input_407_epsilon_0_to_fp16 = const()[name = tensor("input_407_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_407_cast_fp16 = batch_norm(beta = input_407_beta_0_to_fp16, epsilon = input_407_epsilon_0_to_fp16, gamma = input_407_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_151_cast_fp16)[name = tensor("input_407_cast_fp16")]; - tensor var_5939_pad_type_0 = const()[name = tensor("op_5939_pad_type_0"), val = tensor("valid")]; - tensor var_5939_strides_0 = const()[name = tensor("op_5939_strides_0"), val = tensor([1, 1])]; - tensor var_5939_pad_0 = const()[name = tensor("op_5939_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5939_dilations_0 = const()[name = tensor("op_5939_dilations_0"), val = tensor([1, 1])]; - tensor var_5939_groups_0 = const()[name = tensor("op_5939_groups_0"), val = tensor(1)]; - tensor layers_15_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88463424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89249920))), name = tensor("layers_15_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_15_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_15_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89250112)))]; - tensor var_5939_cast_fp16 = conv(bias = layers_15_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_5939_dilations_0, groups = var_5939_groups_0, pad = var_5939_pad_0, pad_type = var_5939_pad_type_0, strides = var_5939_strides_0, weight = layers_15_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_407_cast_fp16)[name = tensor("op_5939_cast_fp16")]; - tensor var_5945_pad_type_0 = const()[name = tensor("op_5945_pad_type_0"), val = tensor("valid")]; - tensor var_5945_strides_0 = const()[name = tensor("op_5945_strides_0"), val = tensor([1, 1])]; - tensor var_5945_pad_0 = const()[name = tensor("op_5945_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5945_dilations_0 = const()[name = tensor("op_5945_dilations_0"), val = tensor([1, 1])]; - tensor var_5945_groups_0 = const()[name = tensor("op_5945_groups_0"), val = tensor(1)]; - tensor layers_15_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89285760))), name = tensor("layers_15_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89254272))), shape = tensor([2048, 512, 1, 1])]; - tensor var_5945_cast_fp16 = conv(dilations = var_5945_dilations_0, groups = var_5945_groups_0, pad = var_5945_pad_0, pad_type = var_5945_pad_type_0, strides = var_5945_strides_0, weight = layers_15_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_407_cast_fp16)[name = tensor("op_5945_cast_fp16")]; - tensor input_409_cast_fp16 = add(x = var_5939_cast_fp16, y = var_5945_cast_fp16)[name = tensor("input_409_cast_fp16")]; - tensor input_411_cast_fp16 = silu(x = input_409_cast_fp16)[name = tensor("input_411_cast_fp16")]; - tensor var_5956_pad_type_0 = const()[name = tensor("op_5956_pad_type_0"), val = tensor("valid")]; - tensor var_5956_strides_0 = const()[name = tensor("op_5956_strides_0"), val = tensor([1, 1])]; - tensor var_5956_pad_0 = const()[name = tensor("op_5956_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5956_dilations_0 = const()[name = tensor("op_5956_dilations_0"), val = tensor([1, 1])]; - tensor var_5956_groups_0 = const()[name = tensor("op_5956_groups_0"), val = tensor(1)]; - tensor layers_15_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89416896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90203392))), name = tensor("layers_15_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_15_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_15_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90203584)))]; - tensor var_5956_cast_fp16 = conv(bias = layers_15_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_5956_dilations_0, groups = var_5956_groups_0, pad = var_5956_pad_0, pad_type = var_5956_pad_type_0, strides = var_5956_strides_0, weight = layers_15_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_411_cast_fp16)[name = tensor("op_5956_cast_fp16")]; - tensor var_5962_pad_type_0 = const()[name = tensor("op_5962_pad_type_0"), val = tensor("valid")]; - tensor var_5962_strides_0 = const()[name = tensor("op_5962_strides_0"), val = tensor([1, 1])]; - tensor var_5962_pad_0 = const()[name = tensor("op_5962_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5962_dilations_0 = const()[name = tensor("op_5962_dilations_0"), val = tensor([1, 1])]; - tensor var_5962_groups_0 = const()[name = tensor("op_5962_groups_0"), val = tensor(1)]; - tensor layers_15_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90244800))), name = tensor("layers_15_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90204672))), shape = tensor([512, 2048, 1, 1])]; - tensor var_5962_cast_fp16 = conv(dilations = var_5962_dilations_0, groups = var_5962_groups_0, pad = var_5962_pad_0, pad_type = var_5962_pad_type_0, strides = var_5962_strides_0, weight = layers_15_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_411_cast_fp16)[name = tensor("op_5962_cast_fp16")]; - tensor x_93_cast_fp16 = add(x = var_5956_cast_fp16, y = var_5962_cast_fp16)[name = tensor("x_93_cast_fp16")]; - tensor var_5964_to_fp16 = const()[name = tensor("op_5964_to_fp16"), val = tensor(0x1p-1)]; - tensor var_5965_cast_fp16 = mul(x = x_93_cast_fp16, y = var_5964_to_fp16)[name = tensor("op_5965_cast_fp16")]; - tensor inputs_153_cast_fp16 = add(x = inputs_151_cast_fp16, y = var_5965_cast_fp16)[name = tensor("inputs_153_cast_fp16")]; - tensor out_153_axes_0 = const()[name = tensor("out_153_axes_0"), val = tensor([1])]; - tensor var_5975_to_fp16 = const()[name = tensor("op_5975_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_153_cast_fp16 = layer_norm(axes = out_153_axes_0, epsilon = var_5975_to_fp16, x = inputs_153_cast_fp16)[name = tensor("out_153_cast_fp16")]; - tensor obj_63_gamma_0_to_fp16 = const()[name = tensor("obj_63_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90375936)))]; - tensor obj_63_beta_0_to_fp16 = const()[name = tensor("obj_63_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90377024)))]; - tensor obj_63_epsilon_0_to_fp16 = const()[name = tensor("obj_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_63_cast_fp16 = batch_norm(beta = obj_63_beta_0_to_fp16, epsilon = obj_63_epsilon_0_to_fp16, gamma = obj_63_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_153_cast_fp16)[name = tensor("obj_63_cast_fp16")]; - tensor var_6000_pad_type_0 = const()[name = tensor("op_6000_pad_type_0"), val = tensor("valid")]; - tensor var_6000_strides_0 = const()[name = tensor("op_6000_strides_0"), val = tensor([1, 1])]; - tensor var_6000_pad_0 = const()[name = tensor("op_6000_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6000_dilations_0 = const()[name = tensor("op_6000_dilations_0"), val = tensor([1, 1])]; - tensor var_6000_groups_0 = const()[name = tensor("op_6000_groups_0"), val = tensor(1)]; - tensor layers_15_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90378112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90574784))), name = tensor("layers_15_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_15_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_15_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90574976)))]; - tensor var_6000_cast_fp16 = conv(bias = layers_15_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_6000_dilations_0, groups = var_6000_groups_0, pad = var_6000_pad_0, pad_type = var_6000_pad_type_0, strides = var_6000_strides_0, weight = layers_15_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_63_cast_fp16)[name = tensor("op_6000_cast_fp16")]; - tensor var_6006_pad_type_0 = const()[name = tensor("op_6006_pad_type_0"), val = tensor("valid")]; - tensor var_6006_strides_0 = const()[name = tensor("op_6006_strides_0"), val = tensor([1, 1])]; - tensor var_6006_pad_0 = const()[name = tensor("op_6006_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6006_dilations_0 = const()[name = tensor("op_6006_dilations_0"), val = tensor([1, 1])]; - tensor var_6006_groups_0 = const()[name = tensor("op_6006_groups_0"), val = tensor(1)]; - tensor layers_15_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90583680))), name = tensor("layers_15_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90576064))), shape = tensor([512, 512, 1, 1])]; - tensor var_6006_cast_fp16 = conv(dilations = var_6006_dilations_0, groups = var_6006_groups_0, pad = var_6006_pad_0, pad_type = var_6006_pad_type_0, strides = var_6006_strides_0, weight = layers_15_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_63_cast_fp16)[name = tensor("op_6006_cast_fp16")]; - tensor query_61_cast_fp16 = add(x = var_6000_cast_fp16, y = var_6006_cast_fp16)[name = tensor("query_61_cast_fp16")]; - tensor var_6015_pad_type_0 = const()[name = tensor("op_6015_pad_type_0"), val = tensor("valid")]; - tensor var_6015_strides_0 = const()[name = tensor("op_6015_strides_0"), val = tensor([1, 1])]; - tensor var_6015_pad_0 = const()[name = tensor("op_6015_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6015_dilations_0 = const()[name = tensor("op_6015_dilations_0"), val = tensor([1, 1])]; - tensor var_6015_groups_0 = const()[name = tensor("op_6015_groups_0"), val = tensor(1)]; - tensor layers_15_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90616512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90813184))), name = tensor("layers_15_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_6015_cast_fp16 = conv(dilations = var_6015_dilations_0, groups = var_6015_groups_0, pad = var_6015_pad_0, pad_type = var_6015_pad_type_0, strides = var_6015_strides_0, weight = layers_15_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_63_cast_fp16)[name = tensor("op_6015_cast_fp16")]; - tensor var_6021_pad_type_0 = const()[name = tensor("op_6021_pad_type_0"), val = tensor("valid")]; - tensor var_6021_strides_0 = const()[name = tensor("op_6021_strides_0"), val = tensor([1, 1])]; - tensor var_6021_pad_0 = const()[name = tensor("op_6021_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6021_dilations_0 = const()[name = tensor("op_6021_dilations_0"), val = tensor([1, 1])]; - tensor var_6021_groups_0 = const()[name = tensor("op_6021_groups_0"), val = tensor(1)]; - tensor layers_15_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90821184))), name = tensor("layers_15_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90813376))), shape = tensor([512, 512, 1, 1])]; - tensor var_6021_cast_fp16 = conv(dilations = var_6021_dilations_0, groups = var_6021_groups_0, pad = var_6021_pad_0, pad_type = var_6021_pad_type_0, strides = var_6021_strides_0, weight = layers_15_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_63_cast_fp16)[name = tensor("op_6021_cast_fp16")]; - tensor key_31_cast_fp16 = add(x = var_6015_cast_fp16, y = var_6021_cast_fp16)[name = tensor("key_31_cast_fp16")]; - tensor var_6031_pad_type_0 = const()[name = tensor("op_6031_pad_type_0"), val = tensor("valid")]; - tensor var_6031_strides_0 = const()[name = tensor("op_6031_strides_0"), val = tensor([1, 1])]; - tensor var_6031_pad_0 = const()[name = tensor("op_6031_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6031_dilations_0 = const()[name = tensor("op_6031_dilations_0"), val = tensor([1, 1])]; - tensor var_6031_groups_0 = const()[name = tensor("op_6031_groups_0"), val = tensor(1)]; - tensor layers_15_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90854016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91050688))), name = tensor("layers_15_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_15_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_15_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91050880)))]; - tensor var_6031_cast_fp16 = conv(bias = layers_15_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_6031_dilations_0, groups = var_6031_groups_0, pad = var_6031_pad_0, pad_type = var_6031_pad_type_0, strides = var_6031_strides_0, weight = layers_15_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_63_cast_fp16)[name = tensor("op_6031_cast_fp16")]; - tensor var_6037_pad_type_0 = const()[name = tensor("op_6037_pad_type_0"), val = tensor("valid")]; - tensor var_6037_strides_0 = const()[name = tensor("op_6037_strides_0"), val = tensor([1, 1])]; - tensor var_6037_pad_0 = const()[name = tensor("op_6037_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6037_dilations_0 = const()[name = tensor("op_6037_dilations_0"), val = tensor([1, 1])]; - tensor var_6037_groups_0 = const()[name = tensor("op_6037_groups_0"), val = tensor(1)]; - tensor layers_15_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91060672))), name = tensor("layers_15_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91051968))), shape = tensor([512, 512, 1, 1])]; - tensor var_6037_cast_fp16 = conv(dilations = var_6037_dilations_0, groups = var_6037_groups_0, pad = var_6037_pad_0, pad_type = var_6037_pad_type_0, strides = var_6037_strides_0, weight = layers_15_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_63_cast_fp16)[name = tensor("op_6037_cast_fp16")]; - tensor value_31_cast_fp16 = add(x = var_6031_cast_fp16, y = var_6037_cast_fp16)[name = tensor("value_31_cast_fp16")]; - tensor var_6040_to_fp16 = const()[name = tensor("op_6040_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91093504)))]; - tensor query_63_cast_fp16 = add(x = query_61_cast_fp16, y = var_6040_to_fp16)[name = tensor("query_63_cast_fp16")]; - tensor var_6043_to_fp16 = const()[name = tensor("op_6043_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91094592)))]; - tensor q_with_bias_v_31_cast_fp16 = add(x = query_61_cast_fp16, y = var_6043_to_fp16)[name = tensor("q_with_bias_v_31_cast_fp16")]; - tensor var_6053_pad_type_0 = const()[name = tensor("op_6053_pad_type_0"), val = tensor("valid")]; - tensor var_6053_strides_0 = const()[name = tensor("op_6053_strides_0"), val = tensor([1, 1])]; - tensor var_6053_pad_0 = const()[name = tensor("op_6053_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6053_dilations_0 = const()[name = tensor("op_6053_dilations_0"), val = tensor([1, 1])]; - tensor var_6053_groups_0 = const()[name = tensor("op_6053_groups_0"), val = tensor(1)]; - tensor layers_15_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91095680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91292352))), name = tensor("layers_15_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_6053_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_6053_dilations_0, groups = var_6053_groups_0, pad = var_6053_pad_0, pad_type = var_6053_pad_type_0, strides = var_6053_strides_0, weight = layers_15_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_6053_cast_fp16")]; - tensor var_6059_pad_type_0 = const()[name = tensor("op_6059_pad_type_0"), val = tensor("valid")]; - tensor var_6059_strides_0 = const()[name = tensor("op_6059_strides_0"), val = tensor([1, 1])]; - tensor var_6059_pad_0 = const()[name = tensor("op_6059_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6059_dilations_0 = const()[name = tensor("op_6059_dilations_0"), val = tensor([1, 1])]; - tensor var_6059_groups_0 = const()[name = tensor("op_6059_groups_0"), val = tensor(1)]; - tensor layers_15_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91315264))), name = tensor("layers_15_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91292544))), shape = tensor([512, 512, 1, 1])]; - tensor var_6059_cast_fp16 = conv(dilations = var_6059_dilations_0, groups = var_6059_groups_0, pad = var_6059_pad_0, pad_type = var_6059_pad_type_0, strides = var_6059_strides_0, weight = layers_15_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_6059_cast_fp16")]; - tensor p_31_cast_fp16 = add(x = var_6053_cast_fp16, y = var_6059_cast_fp16)[name = tensor("p_31_cast_fp16")]; - tensor var_6063 = const()[name = tensor("op_6063"), val = tensor([1, 8, 64, 188])]; - tensor var_6064_cast_fp16 = reshape(shape = var_6063, x = q_with_bias_v_31_cast_fp16)[name = tensor("op_6064_cast_fp16")]; - tensor var_6065 = const()[name = tensor("op_6065"), val = tensor([1, 8, 64, -1])]; - tensor var_6066_cast_fp16 = reshape(shape = var_6065, x = p_31_cast_fp16)[name = tensor("op_6066_cast_fp16")]; - tensor matrix_bd_121_transpose_x_0 = const()[name = tensor("matrix_bd_121_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_121_transpose_y_0 = const()[name = tensor("matrix_bd_121_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_121_cast_fp16 = matmul(transpose_x = matrix_bd_121_transpose_x_0, transpose_y = matrix_bd_121_transpose_y_0, x = var_6064_cast_fp16, y = var_6066_cast_fp16)[name = tensor("matrix_bd_121_cast_fp16")]; - tensor matrix_bd_123_pad_0 = const()[name = tensor("matrix_bd_123_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_123_mode_0 = const()[name = tensor("matrix_bd_123_mode_0"), val = tensor("constant")]; - tensor const_175_to_fp16 = const()[name = tensor("const_175_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_123_cast_fp16 = pad(constant_val = const_175_to_fp16, mode = matrix_bd_123_mode_0, pad = matrix_bd_123_pad_0, x = matrix_bd_121_cast_fp16)[name = tensor("matrix_bd_123_cast_fp16")]; - tensor var_6075 = const()[name = tensor("op_6075"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_125_cast_fp16 = reshape(shape = var_6075, x = matrix_bd_123_cast_fp16)[name = tensor("matrix_bd_125_cast_fp16")]; - tensor var_6079_begin_0 = const()[name = tensor("op_6079_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_6079_end_0 = const()[name = tensor("op_6079_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_6079_end_mask_0 = const()[name = tensor("op_6079_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_6079_cast_fp16 = slice_by_index(begin = var_6079_begin_0, end = var_6079_end_0, end_mask = var_6079_end_mask_0, x = matrix_bd_125_cast_fp16)[name = tensor("op_6079_cast_fp16")]; - tensor var_6080 = const()[name = tensor("op_6080"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_127_cast_fp16 = reshape(shape = var_6080, x = var_6079_cast_fp16)[name = tensor("matrix_bd_127_cast_fp16")]; - tensor var_6085_begin_0 = const()[name = tensor("op_6085_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6085_end_0 = const()[name = tensor("op_6085_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_6085_end_mask_0 = const()[name = tensor("op_6085_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_6085_cast_fp16 = slice_by_index(begin = var_6085_begin_0, end = var_6085_end_0, end_mask = var_6085_end_mask_0, x = matrix_bd_127_cast_fp16)[name = tensor("op_6085_cast_fp16")]; - tensor var_6086_to_fp16 = const()[name = tensor("op_6086_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_31_cast_fp16 = mul(x = var_6085_cast_fp16, y = var_6086_to_fp16)[name = tensor("qk_mask_31_cast_fp16")]; - tensor var_6090 = const()[name = tensor("op_6090"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_31_cast_fp16 = reshape(shape = var_6090, x = query_63_cast_fp16)[name = tensor("mh_q_31_cast_fp16")]; - tensor var_6092_to_fp16 = const()[name = tensor("op_6092_to_fp16"), val = tensor(0x1p-3)]; - tensor var_6093_cast_fp16 = mul(x = mh_q_31_cast_fp16, y = var_6092_to_fp16)[name = tensor("op_6093_cast_fp16")]; - tensor var_6096 = const()[name = tensor("op_6096"), val = tensor([1, 8, 64, 188])]; - tensor var_6097_cast_fp16 = reshape(shape = var_6096, x = key_31_cast_fp16)[name = tensor("op_6097_cast_fp16")]; - tensor mh_w_61_transpose_x_0 = const()[name = tensor("mh_w_61_transpose_x_0"), val = tensor(true)]; - tensor mh_w_61_transpose_y_0 = const()[name = tensor("mh_w_61_transpose_y_0"), val = tensor(false)]; - tensor mh_w_61_cast_fp16 = matmul(transpose_x = mh_w_61_transpose_x_0, transpose_y = mh_w_61_transpose_y_0, x = var_6093_cast_fp16, y = var_6097_cast_fp16)[name = tensor("mh_w_61_cast_fp16")]; - tensor mh_w_63_cast_fp16 = add(x = mh_w_61_cast_fp16, y = qk_mask_31_cast_fp16)[name = tensor("mh_w_63_cast_fp16")]; - tensor var_6101_cast_fp16 = softmax(axis = var_5888, x = mh_w_63_cast_fp16)[name = tensor("op_6101_cast_fp16")]; - tensor var_6102 = const()[name = tensor("op_6102"), val = tensor([1, 8, 64, 188])]; - tensor var_6103_cast_fp16 = reshape(shape = var_6102, x = value_31_cast_fp16)[name = tensor("op_6103_cast_fp16")]; - tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; - tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; - tensor attn_31_cast_fp16 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_6103_cast_fp16, y = var_6101_cast_fp16)[name = tensor("attn_31_cast_fp16")]; - tensor var_6106 = const()[name = tensor("op_6106"), val = tensor([1, 512, 1, 188])]; - tensor input_413_cast_fp16 = reshape(shape = var_6106, x = attn_31_cast_fp16)[name = tensor("input_413_cast_fp16")]; - tensor var_6116_pad_type_0 = const()[name = tensor("op_6116_pad_type_0"), val = tensor("valid")]; - tensor var_6116_strides_0 = const()[name = tensor("op_6116_strides_0"), val = tensor([1, 1])]; - tensor var_6116_pad_0 = const()[name = tensor("op_6116_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6116_dilations_0 = const()[name = tensor("op_6116_dilations_0"), val = tensor([1, 1])]; - tensor var_6116_groups_0 = const()[name = tensor("op_6116_groups_0"), val = tensor(1)]; - tensor layers_15_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91348096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91544768))), name = tensor("layers_15_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_15_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_15_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91544960)))]; - tensor var_6116_cast_fp16 = conv(bias = layers_15_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_6116_dilations_0, groups = var_6116_groups_0, pad = var_6116_pad_0, pad_type = var_6116_pad_type_0, strides = var_6116_strides_0, weight = layers_15_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = tensor("op_6116_cast_fp16")]; - tensor var_6122_pad_type_0 = const()[name = tensor("op_6122_pad_type_0"), val = tensor("valid")]; - tensor var_6122_strides_0 = const()[name = tensor("op_6122_strides_0"), val = tensor([1, 1])]; - tensor var_6122_pad_0 = const()[name = tensor("op_6122_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6122_dilations_0 = const()[name = tensor("op_6122_dilations_0"), val = tensor([1, 1])]; - tensor var_6122_groups_0 = const()[name = tensor("op_6122_groups_0"), val = tensor(1)]; - tensor layers_15_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91554496))), name = tensor("layers_15_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91546048))), shape = tensor([512, 512, 1, 1])]; - tensor var_6122_cast_fp16 = conv(dilations = var_6122_dilations_0, groups = var_6122_groups_0, pad = var_6122_pad_0, pad_type = var_6122_pad_type_0, strides = var_6122_strides_0, weight = layers_15_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_413_cast_fp16)[name = tensor("op_6122_cast_fp16")]; - tensor obj_65_cast_fp16 = add(x = var_6116_cast_fp16, y = var_6122_cast_fp16)[name = tensor("obj_65_cast_fp16")]; - tensor inputs_155_cast_fp16 = add(x = inputs_153_cast_fp16, y = obj_65_cast_fp16)[name = tensor("inputs_155_cast_fp16")]; - tensor out_155_axes_0 = const()[name = tensor("out_155_axes_0"), val = tensor([1])]; - tensor var_6133_to_fp16 = const()[name = tensor("op_6133_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_155_cast_fp16 = layer_norm(axes = out_155_axes_0, epsilon = var_6133_to_fp16, x = inputs_155_cast_fp16)[name = tensor("out_155_cast_fp16")]; - tensor input_415_gamma_0_to_fp16 = const()[name = tensor("input_415_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91587328)))]; - tensor input_415_beta_0_to_fp16 = const()[name = tensor("input_415_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91588416)))]; - tensor input_415_epsilon_0_to_fp16 = const()[name = tensor("input_415_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_415_cast_fp16 = batch_norm(beta = input_415_beta_0_to_fp16, epsilon = input_415_epsilon_0_to_fp16, gamma = input_415_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_155_cast_fp16)[name = tensor("input_415_cast_fp16")]; - tensor var_6155_pad_type_0 = const()[name = tensor("op_6155_pad_type_0"), val = tensor("valid")]; - tensor var_6155_strides_0 = const()[name = tensor("op_6155_strides_0"), val = tensor([1, 1])]; - tensor var_6155_pad_0 = const()[name = tensor("op_6155_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6155_dilations_0 = const()[name = tensor("op_6155_dilations_0"), val = tensor([1, 1])]; - tensor var_6155_groups_0 = const()[name = tensor("op_6155_groups_0"), val = tensor(1)]; - tensor layers_15_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91589504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91982784))), name = tensor("layers_15_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_15_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_15_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91982976)))]; - tensor var_6155_cast_fp16 = conv(bias = layers_15_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_6155_dilations_0, groups = var_6155_groups_0, pad = var_6155_pad_0, pad_type = var_6155_pad_type_0, strides = var_6155_strides_0, weight = layers_15_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_415_cast_fp16)[name = tensor("op_6155_cast_fp16")]; - tensor var_6161_pad_type_0 = const()[name = tensor("op_6161_pad_type_0"), val = tensor("valid")]; - tensor var_6161_strides_0 = const()[name = tensor("op_6161_strides_0"), val = tensor([1, 1])]; - tensor var_6161_pad_0 = const()[name = tensor("op_6161_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6161_dilations_0 = const()[name = tensor("op_6161_dilations_0"), val = tensor([1, 1])]; - tensor var_6161_groups_0 = const()[name = tensor("op_6161_groups_0"), val = tensor(1)]; - tensor layers_15_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92003456))), name = tensor("layers_15_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91985088))), shape = tensor([1024, 512, 1, 1])]; - tensor var_6161_cast_fp16 = conv(dilations = var_6161_dilations_0, groups = var_6161_groups_0, pad = var_6161_pad_0, pad_type = var_6161_pad_type_0, strides = var_6161_strides_0, weight = layers_15_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_415_cast_fp16)[name = tensor("op_6161_cast_fp16")]; - tensor input_417_cast_fp16 = add(x = var_6155_cast_fp16, y = var_6161_cast_fp16)[name = tensor("input_417_cast_fp16")]; - tensor input_419_split_num_splits_0 = const()[name = tensor("input_419_split_num_splits_0"), val = tensor(2)]; - tensor input_419_split_axis_0 = const()[name = tensor("input_419_split_axis_0"), val = tensor(1)]; - tensor input_419_split_cast_fp16_0, tensor input_419_split_cast_fp16_1 = split(axis = input_419_split_axis_0, num_splits = input_419_split_num_splits_0, x = input_417_cast_fp16)[name = tensor("input_419_split_cast_fp16")]; - tensor input_419_split_1_sigmoid_cast_fp16 = sigmoid(x = input_419_split_cast_fp16_1)[name = tensor("input_419_split_1_sigmoid_cast_fp16")]; - tensor input_419_cast_fp16 = mul(x = input_419_split_cast_fp16_0, y = input_419_split_1_sigmoid_cast_fp16)[name = tensor("input_419_cast_fp16")]; - tensor input_421_pad_type_0 = const()[name = tensor("input_421_pad_type_0"), val = tensor("custom")]; - tensor input_421_pad_0 = const()[name = tensor("input_421_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_421_groups_0 = const()[name = tensor("input_421_groups_0"), val = tensor(512)]; - tensor input_421_strides_0 = const()[name = tensor("input_421_strides_0"), val = tensor([1, 1])]; - tensor input_421_dilations_0 = const()[name = tensor("input_421_dilations_0"), val = tensor([1, 1])]; - tensor const_221_to_fp16 = const()[name = tensor("const_221_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92069056)))]; - tensor const_222_to_fp16 = const()[name = tensor("const_222_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92078336)))]; - tensor input_423_cast_fp16 = conv(bias = const_222_to_fp16, dilations = input_421_dilations_0, groups = input_421_groups_0, pad = input_421_pad_0, pad_type = input_421_pad_type_0, strides = input_421_strides_0, weight = const_221_to_fp16, x = input_419_cast_fp16)[name = tensor("input_423_cast_fp16")]; - tensor input_425_cast_fp16 = silu(x = input_423_cast_fp16)[name = tensor("input_425_cast_fp16")]; - tensor var_6185_pad_type_0 = const()[name = tensor("op_6185_pad_type_0"), val = tensor("valid")]; - tensor var_6185_strides_0 = const()[name = tensor("op_6185_strides_0"), val = tensor([1, 1])]; - tensor var_6185_pad_0 = const()[name = tensor("op_6185_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6185_dilations_0 = const()[name = tensor("op_6185_dilations_0"), val = tensor([1, 1])]; - tensor var_6185_groups_0 = const()[name = tensor("op_6185_groups_0"), val = tensor(1)]; - tensor layers_15_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92079424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92276096))), name = tensor("layers_15_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_15_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_15_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92276288)))]; - tensor var_6185_cast_fp16 = conv(bias = layers_15_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_6185_dilations_0, groups = var_6185_groups_0, pad = var_6185_pad_0, pad_type = var_6185_pad_type_0, strides = var_6185_strides_0, weight = layers_15_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = tensor("op_6185_cast_fp16")]; - tensor var_6191_pad_type_0 = const()[name = tensor("op_6191_pad_type_0"), val = tensor("valid")]; - tensor var_6191_strides_0 = const()[name = tensor("op_6191_strides_0"), val = tensor([1, 1])]; - tensor var_6191_pad_0 = const()[name = tensor("op_6191_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6191_dilations_0 = const()[name = tensor("op_6191_dilations_0"), val = tensor([1, 1])]; - tensor var_6191_groups_0 = const()[name = tensor("op_6191_groups_0"), val = tensor(1)]; - tensor layers_15_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92285568))), name = tensor("layers_15_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92277376))), shape = tensor([512, 512, 1, 1])]; - tensor var_6191_cast_fp16 = conv(dilations = var_6191_dilations_0, groups = var_6191_groups_0, pad = var_6191_pad_0, pad_type = var_6191_pad_type_0, strides = var_6191_strides_0, weight = layers_15_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_425_cast_fp16)[name = tensor("op_6191_cast_fp16")]; - tensor x_95_cast_fp16 = add(x = var_6185_cast_fp16, y = var_6191_cast_fp16)[name = tensor("x_95_cast_fp16")]; - tensor inputs_157_cast_fp16 = add(x = inputs_155_cast_fp16, y = x_95_cast_fp16)[name = tensor("inputs_157_cast_fp16")]; - tensor out_157_axes_0 = const()[name = tensor("out_157_axes_0"), val = tensor([1])]; - tensor var_6202_to_fp16 = const()[name = tensor("op_6202_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_157_cast_fp16 = layer_norm(axes = out_157_axes_0, epsilon = var_6202_to_fp16, x = inputs_157_cast_fp16)[name = tensor("out_157_cast_fp16")]; - tensor input_427_gamma_0_to_fp16 = const()[name = tensor("input_427_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92318400)))]; - tensor input_427_beta_0_to_fp16 = const()[name = tensor("input_427_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92319488)))]; - tensor input_427_epsilon_0_to_fp16 = const()[name = tensor("input_427_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_427_cast_fp16 = batch_norm(beta = input_427_beta_0_to_fp16, epsilon = input_427_epsilon_0_to_fp16, gamma = input_427_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_157_cast_fp16)[name = tensor("input_427_cast_fp16")]; - tensor var_6222_pad_type_0 = const()[name = tensor("op_6222_pad_type_0"), val = tensor("valid")]; - tensor var_6222_strides_0 = const()[name = tensor("op_6222_strides_0"), val = tensor([1, 1])]; - tensor var_6222_pad_0 = const()[name = tensor("op_6222_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6222_dilations_0 = const()[name = tensor("op_6222_dilations_0"), val = tensor([1, 1])]; - tensor var_6222_groups_0 = const()[name = tensor("op_6222_groups_0"), val = tensor(1)]; - tensor layers_15_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92320576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93107072))), name = tensor("layers_15_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_15_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_15_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93107264)))]; - tensor var_6222_cast_fp16 = conv(bias = layers_15_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_6222_dilations_0, groups = var_6222_groups_0, pad = var_6222_pad_0, pad_type = var_6222_pad_type_0, strides = var_6222_strides_0, weight = layers_15_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_427_cast_fp16)[name = tensor("op_6222_cast_fp16")]; - tensor var_6228_pad_type_0 = const()[name = tensor("op_6228_pad_type_0"), val = tensor("valid")]; - tensor var_6228_strides_0 = const()[name = tensor("op_6228_strides_0"), val = tensor([1, 1])]; - tensor var_6228_pad_0 = const()[name = tensor("op_6228_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6228_dilations_0 = const()[name = tensor("op_6228_dilations_0"), val = tensor([1, 1])]; - tensor var_6228_groups_0 = const()[name = tensor("op_6228_groups_0"), val = tensor(1)]; - tensor layers_15_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93144704))), name = tensor("layers_15_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93111424))), shape = tensor([2048, 512, 1, 1])]; - tensor var_6228_cast_fp16 = conv(dilations = var_6228_dilations_0, groups = var_6228_groups_0, pad = var_6228_pad_0, pad_type = var_6228_pad_type_0, strides = var_6228_strides_0, weight = layers_15_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_427_cast_fp16)[name = tensor("op_6228_cast_fp16")]; - tensor input_429_cast_fp16 = add(x = var_6222_cast_fp16, y = var_6228_cast_fp16)[name = tensor("input_429_cast_fp16")]; - tensor input_431_cast_fp16 = silu(x = input_429_cast_fp16)[name = tensor("input_431_cast_fp16")]; - tensor var_6239_pad_type_0 = const()[name = tensor("op_6239_pad_type_0"), val = tensor("valid")]; - tensor var_6239_strides_0 = const()[name = tensor("op_6239_strides_0"), val = tensor([1, 1])]; - tensor var_6239_pad_0 = const()[name = tensor("op_6239_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6239_dilations_0 = const()[name = tensor("op_6239_dilations_0"), val = tensor([1, 1])]; - tensor var_6239_groups_0 = const()[name = tensor("op_6239_groups_0"), val = tensor(1)]; - tensor layers_15_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93275840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94062336))), name = tensor("layers_15_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_15_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_15_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94062528)))]; - tensor var_6239_cast_fp16 = conv(bias = layers_15_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_6239_dilations_0, groups = var_6239_groups_0, pad = var_6239_pad_0, pad_type = var_6239_pad_type_0, strides = var_6239_strides_0, weight = layers_15_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_431_cast_fp16)[name = tensor("op_6239_cast_fp16")]; - tensor var_6245_pad_type_0 = const()[name = tensor("op_6245_pad_type_0"), val = tensor("valid")]; - tensor var_6245_strides_0 = const()[name = tensor("op_6245_strides_0"), val = tensor([1, 1])]; - tensor var_6245_pad_0 = const()[name = tensor("op_6245_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6245_dilations_0 = const()[name = tensor("op_6245_dilations_0"), val = tensor([1, 1])]; - tensor var_6245_groups_0 = const()[name = tensor("op_6245_groups_0"), val = tensor(1)]; - tensor layers_15_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94104576))), name = tensor("layers_15_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94063616))), shape = tensor([512, 2048, 1, 1])]; - tensor var_6245_cast_fp16 = conv(dilations = var_6245_dilations_0, groups = var_6245_groups_0, pad = var_6245_pad_0, pad_type = var_6245_pad_type_0, strides = var_6245_strides_0, weight = layers_15_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_431_cast_fp16)[name = tensor("op_6245_cast_fp16")]; - tensor x_97_cast_fp16 = add(x = var_6239_cast_fp16, y = var_6245_cast_fp16)[name = tensor("x_97_cast_fp16")]; - tensor var_6247_to_fp16 = const()[name = tensor("op_6247_to_fp16"), val = tensor(0x1p-1)]; - tensor var_6248_cast_fp16 = mul(x = x_97_cast_fp16, y = var_6247_to_fp16)[name = tensor("op_6248_cast_fp16")]; - tensor inputs_159_cast_fp16 = add(x = inputs_157_cast_fp16, y = var_6248_cast_fp16)[name = tensor("inputs_159_cast_fp16")]; - tensor out_159_axes_0 = const()[name = tensor("out_159_axes_0"), val = tensor([1])]; - tensor var_6258_to_fp16 = const()[name = tensor("op_6258_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_159_cast_fp16 = layer_norm(axes = out_159_axes_0, epsilon = var_6258_to_fp16, x = inputs_159_cast_fp16)[name = tensor("out_159_cast_fp16")]; - tensor inputs_161_gamma_0_to_fp16 = const()[name = tensor("inputs_161_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94235712)))]; - tensor inputs_161_beta_0_to_fp16 = const()[name = tensor("inputs_161_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94236800)))]; - tensor inputs_161_epsilon_0_to_fp16 = const()[name = tensor("inputs_161_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor inputs_161_cast_fp16 = batch_norm(beta = inputs_161_beta_0_to_fp16, epsilon = inputs_161_epsilon_0_to_fp16, gamma = inputs_161_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_159_cast_fp16)[name = tensor("inputs_161_cast_fp16")]; - tensor var_6272 = const()[name = tensor("op_6272"), val = tensor(3)]; - tensor out_161_axes_0 = const()[name = tensor("out_161_axes_0"), val = tensor([1])]; - tensor var_6303_to_fp16 = const()[name = tensor("op_6303_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_161_cast_fp16 = layer_norm(axes = out_161_axes_0, epsilon = var_6303_to_fp16, x = inputs_161_cast_fp16)[name = tensor("out_161_cast_fp16")]; - tensor input_433_gamma_0_to_fp16 = const()[name = tensor("input_433_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94237888)))]; - tensor input_433_beta_0_to_fp16 = const()[name = tensor("input_433_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94238976)))]; - tensor input_433_epsilon_0_to_fp16 = const()[name = tensor("input_433_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_433_cast_fp16 = batch_norm(beta = input_433_beta_0_to_fp16, epsilon = input_433_epsilon_0_to_fp16, gamma = input_433_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_161_cast_fp16)[name = tensor("input_433_cast_fp16")]; - tensor var_6323_pad_type_0 = const()[name = tensor("op_6323_pad_type_0"), val = tensor("valid")]; - tensor var_6323_strides_0 = const()[name = tensor("op_6323_strides_0"), val = tensor([1, 1])]; - tensor var_6323_pad_0 = const()[name = tensor("op_6323_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6323_dilations_0 = const()[name = tensor("op_6323_dilations_0"), val = tensor([1, 1])]; - tensor var_6323_groups_0 = const()[name = tensor("op_6323_groups_0"), val = tensor(1)]; - tensor layers_16_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94240064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95026560))), name = tensor("layers_16_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_16_feed_forward1_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_16_feed_forward1_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95026752)))]; - tensor var_6323_cast_fp16 = conv(bias = layers_16_feed_forward1_fc1_inlier_module_bias_to_fp16, dilations = var_6323_dilations_0, groups = var_6323_groups_0, pad = var_6323_pad_0, pad_type = var_6323_pad_type_0, strides = var_6323_strides_0, weight = layers_16_feed_forward1_fc1_inlier_module_weight_to_fp16_palettized, x = input_433_cast_fp16)[name = tensor("op_6323_cast_fp16")]; - tensor var_6329_pad_type_0 = const()[name = tensor("op_6329_pad_type_0"), val = tensor("valid")]; - tensor var_6329_strides_0 = const()[name = tensor("op_6329_strides_0"), val = tensor([1, 1])]; - tensor var_6329_pad_0 = const()[name = tensor("op_6329_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6329_dilations_0 = const()[name = tensor("op_6329_dilations_0"), val = tensor([1, 1])]; - tensor var_6329_groups_0 = const()[name = tensor("op_6329_groups_0"), val = tensor(1)]; - tensor layers_16_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95064576))), name = tensor("layers_16_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95030912))), shape = tensor([2048, 512, 1, 1])]; - tensor var_6329_cast_fp16 = conv(dilations = var_6329_dilations_0, groups = var_6329_groups_0, pad = var_6329_pad_0, pad_type = var_6329_pad_type_0, strides = var_6329_strides_0, weight = layers_16_feed_forward1_fc1_outlier_module_weight_to_fp16_sparsified, x = input_433_cast_fp16)[name = tensor("op_6329_cast_fp16")]; - tensor input_435_cast_fp16 = add(x = var_6323_cast_fp16, y = var_6329_cast_fp16)[name = tensor("input_435_cast_fp16")]; - tensor input_437_cast_fp16 = silu(x = input_435_cast_fp16)[name = tensor("input_437_cast_fp16")]; - tensor var_6340_pad_type_0 = const()[name = tensor("op_6340_pad_type_0"), val = tensor("valid")]; - tensor var_6340_strides_0 = const()[name = tensor("op_6340_strides_0"), val = tensor([1, 1])]; - tensor var_6340_pad_0 = const()[name = tensor("op_6340_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6340_dilations_0 = const()[name = tensor("op_6340_dilations_0"), val = tensor([1, 1])]; - tensor var_6340_groups_0 = const()[name = tensor("op_6340_groups_0"), val = tensor(1)]; - tensor layers_16_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95195712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95982208))), name = tensor("layers_16_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_16_feed_forward1_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_16_feed_forward1_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95982400)))]; - tensor var_6340_cast_fp16 = conv(bias = layers_16_feed_forward1_fc2_inlier_module_bias_to_fp16, dilations = var_6340_dilations_0, groups = var_6340_groups_0, pad = var_6340_pad_0, pad_type = var_6340_pad_type_0, strides = var_6340_strides_0, weight = layers_16_feed_forward1_fc2_inlier_module_weight_to_fp16_palettized, x = input_437_cast_fp16)[name = tensor("op_6340_cast_fp16")]; - tensor var_6346_pad_type_0 = const()[name = tensor("op_6346_pad_type_0"), val = tensor("valid")]; - tensor var_6346_strides_0 = const()[name = tensor("op_6346_strides_0"), val = tensor([1, 1])]; - tensor var_6346_pad_0 = const()[name = tensor("op_6346_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6346_dilations_0 = const()[name = tensor("op_6346_dilations_0"), val = tensor([1, 1])]; - tensor var_6346_groups_0 = const()[name = tensor("op_6346_groups_0"), val = tensor(1)]; - tensor layers_16_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96026752))), name = tensor("layers_16_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95983488))), shape = tensor([512, 2048, 1, 1])]; - tensor var_6346_cast_fp16 = conv(dilations = var_6346_dilations_0, groups = var_6346_groups_0, pad = var_6346_pad_0, pad_type = var_6346_pad_type_0, strides = var_6346_strides_0, weight = layers_16_feed_forward1_fc2_outlier_module_weight_to_fp16_sparsified, x = input_437_cast_fp16)[name = tensor("op_6346_cast_fp16")]; - tensor x_99_cast_fp16 = add(x = var_6340_cast_fp16, y = var_6346_cast_fp16)[name = tensor("x_99_cast_fp16")]; - tensor var_6348_to_fp16 = const()[name = tensor("op_6348_to_fp16"), val = tensor(0x1p-1)]; - tensor var_6349_cast_fp16 = mul(x = x_99_cast_fp16, y = var_6348_to_fp16)[name = tensor("op_6349_cast_fp16")]; - tensor inputs_163_cast_fp16 = add(x = inputs_161_cast_fp16, y = var_6349_cast_fp16)[name = tensor("inputs_163_cast_fp16")]; - tensor out_163_axes_0 = const()[name = tensor("out_163_axes_0"), val = tensor([1])]; - tensor var_6359_to_fp16 = const()[name = tensor("op_6359_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_163_cast_fp16 = layer_norm(axes = out_163_axes_0, epsilon = var_6359_to_fp16, x = inputs_163_cast_fp16)[name = tensor("out_163_cast_fp16")]; - tensor obj_67_gamma_0_to_fp16 = const()[name = tensor("obj_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96157888)))]; - tensor obj_67_beta_0_to_fp16 = const()[name = tensor("obj_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96158976)))]; - tensor obj_67_epsilon_0_to_fp16 = const()[name = tensor("obj_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor obj_67_cast_fp16 = batch_norm(beta = obj_67_beta_0_to_fp16, epsilon = obj_67_epsilon_0_to_fp16, gamma = obj_67_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_163_cast_fp16)[name = tensor("obj_67_cast_fp16")]; - tensor var_6384_pad_type_0 = const()[name = tensor("op_6384_pad_type_0"), val = tensor("valid")]; - tensor var_6384_strides_0 = const()[name = tensor("op_6384_strides_0"), val = tensor([1, 1])]; - tensor var_6384_pad_0 = const()[name = tensor("op_6384_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6384_dilations_0 = const()[name = tensor("op_6384_dilations_0"), val = tensor([1, 1])]; - tensor var_6384_groups_0 = const()[name = tensor("op_6384_groups_0"), val = tensor(1)]; - tensor layers_16_self_attn_q_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96160064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96356736))), name = tensor("layers_16_self_attn_q_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_16_self_attn_q_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_16_self_attn_q_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96356928)))]; - tensor var_6384_cast_fp16 = conv(bias = layers_16_self_attn_q_proj_inlier_module_bias_to_fp16, dilations = var_6384_dilations_0, groups = var_6384_groups_0, pad = var_6384_pad_0, pad_type = var_6384_pad_type_0, strides = var_6384_strides_0, weight = layers_16_self_attn_q_proj_inlier_module_weight_to_fp16_palettized, x = obj_67_cast_fp16)[name = tensor("op_6384_cast_fp16")]; - tensor var_6390_pad_type_0 = const()[name = tensor("op_6390_pad_type_0"), val = tensor("valid")]; - tensor var_6390_strides_0 = const()[name = tensor("op_6390_strides_0"), val = tensor([1, 1])]; - tensor var_6390_pad_0 = const()[name = tensor("op_6390_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6390_dilations_0 = const()[name = tensor("op_6390_dilations_0"), val = tensor([1, 1])]; - tensor var_6390_groups_0 = const()[name = tensor("op_6390_groups_0"), val = tensor(1)]; - tensor layers_16_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96365312))), name = tensor("layers_16_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96358016))), shape = tensor([512, 512, 1, 1])]; - tensor var_6390_cast_fp16 = conv(dilations = var_6390_dilations_0, groups = var_6390_groups_0, pad = var_6390_pad_0, pad_type = var_6390_pad_type_0, strides = var_6390_strides_0, weight = layers_16_self_attn_q_proj_outlier_module_weight_to_fp16_sparsified, x = obj_67_cast_fp16)[name = tensor("op_6390_cast_fp16")]; - tensor query_65_cast_fp16 = add(x = var_6384_cast_fp16, y = var_6390_cast_fp16)[name = tensor("query_65_cast_fp16")]; - tensor var_6399_pad_type_0 = const()[name = tensor("op_6399_pad_type_0"), val = tensor("valid")]; - tensor var_6399_strides_0 = const()[name = tensor("op_6399_strides_0"), val = tensor([1, 1])]; - tensor var_6399_pad_0 = const()[name = tensor("op_6399_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6399_dilations_0 = const()[name = tensor("op_6399_dilations_0"), val = tensor([1, 1])]; - tensor var_6399_groups_0 = const()[name = tensor("op_6399_groups_0"), val = tensor(1)]; - tensor layers_16_self_attn_k_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96398144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96594816))), name = tensor("layers_16_self_attn_k_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_6399_cast_fp16 = conv(dilations = var_6399_dilations_0, groups = var_6399_groups_0, pad = var_6399_pad_0, pad_type = var_6399_pad_type_0, strides = var_6399_strides_0, weight = layers_16_self_attn_k_proj_inlier_module_weight_to_fp16_palettized, x = obj_67_cast_fp16)[name = tensor("op_6399_cast_fp16")]; - tensor var_6405_pad_type_0 = const()[name = tensor("op_6405_pad_type_0"), val = tensor("valid")]; - tensor var_6405_strides_0 = const()[name = tensor("op_6405_strides_0"), val = tensor([1, 1])]; - tensor var_6405_pad_0 = const()[name = tensor("op_6405_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6405_dilations_0 = const()[name = tensor("op_6405_dilations_0"), val = tensor([1, 1])]; - tensor var_6405_groups_0 = const()[name = tensor("op_6405_groups_0"), val = tensor(1)]; - tensor layers_16_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96602560))), name = tensor("layers_16_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96595008))), shape = tensor([512, 512, 1, 1])]; - tensor var_6405_cast_fp16 = conv(dilations = var_6405_dilations_0, groups = var_6405_groups_0, pad = var_6405_pad_0, pad_type = var_6405_pad_type_0, strides = var_6405_strides_0, weight = layers_16_self_attn_k_proj_outlier_module_weight_to_fp16_sparsified, x = obj_67_cast_fp16)[name = tensor("op_6405_cast_fp16")]; - tensor key_cast_fp16 = add(x = var_6399_cast_fp16, y = var_6405_cast_fp16)[name = tensor("key_cast_fp16")]; - tensor var_6415_pad_type_0 = const()[name = tensor("op_6415_pad_type_0"), val = tensor("valid")]; - tensor var_6415_strides_0 = const()[name = tensor("op_6415_strides_0"), val = tensor([1, 1])]; - tensor var_6415_pad_0 = const()[name = tensor("op_6415_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6415_dilations_0 = const()[name = tensor("op_6415_dilations_0"), val = tensor([1, 1])]; - tensor var_6415_groups_0 = const()[name = tensor("op_6415_groups_0"), val = tensor(1)]; - tensor layers_16_self_attn_v_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96635392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96832064))), name = tensor("layers_16_self_attn_v_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_16_self_attn_v_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_16_self_attn_v_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96832256)))]; - tensor var_6415_cast_fp16 = conv(bias = layers_16_self_attn_v_proj_inlier_module_bias_to_fp16, dilations = var_6415_dilations_0, groups = var_6415_groups_0, pad = var_6415_pad_0, pad_type = var_6415_pad_type_0, strides = var_6415_strides_0, weight = layers_16_self_attn_v_proj_inlier_module_weight_to_fp16_palettized, x = obj_67_cast_fp16)[name = tensor("op_6415_cast_fp16")]; - tensor var_6421_pad_type_0 = const()[name = tensor("op_6421_pad_type_0"), val = tensor("valid")]; - tensor var_6421_strides_0 = const()[name = tensor("op_6421_strides_0"), val = tensor([1, 1])]; - tensor var_6421_pad_0 = const()[name = tensor("op_6421_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6421_dilations_0 = const()[name = tensor("op_6421_dilations_0"), val = tensor([1, 1])]; - tensor var_6421_groups_0 = const()[name = tensor("op_6421_groups_0"), val = tensor(1)]; - tensor layers_16_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96841664))), name = tensor("layers_16_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96833344))), shape = tensor([512, 512, 1, 1])]; - tensor var_6421_cast_fp16 = conv(dilations = var_6421_dilations_0, groups = var_6421_groups_0, pad = var_6421_pad_0, pad_type = var_6421_pad_type_0, strides = var_6421_strides_0, weight = layers_16_self_attn_v_proj_outlier_module_weight_to_fp16_sparsified, x = obj_67_cast_fp16)[name = tensor("op_6421_cast_fp16")]; - tensor value_cast_fp16 = add(x = var_6415_cast_fp16, y = var_6421_cast_fp16)[name = tensor("value_cast_fp16")]; - tensor var_6424_to_fp16 = const()[name = tensor("op_6424_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96874496)))]; - tensor query_cast_fp16 = add(x = query_65_cast_fp16, y = var_6424_to_fp16)[name = tensor("query_cast_fp16")]; - tensor var_6427_to_fp16 = const()[name = tensor("op_6427_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96875584)))]; - tensor q_with_bias_v_cast_fp16 = add(x = query_65_cast_fp16, y = var_6427_to_fp16)[name = tensor("q_with_bias_v_cast_fp16")]; - tensor var_6437_pad_type_0 = const()[name = tensor("op_6437_pad_type_0"), val = tensor("valid")]; - tensor var_6437_strides_0 = const()[name = tensor("op_6437_strides_0"), val = tensor([1, 1])]; - tensor var_6437_pad_0 = const()[name = tensor("op_6437_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6437_dilations_0 = const()[name = tensor("op_6437_dilations_0"), val = tensor([1, 1])]; - tensor var_6437_groups_0 = const()[name = tensor("op_6437_groups_0"), val = tensor(1)]; - tensor layers_16_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96876672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97073344))), name = tensor("layers_16_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor var_6437_cast_fp16 = conv(bias = input_17_mean_0_to_fp16, dilations = var_6437_dilations_0, groups = var_6437_groups_0, pad = var_6437_pad_0, pad_type = var_6437_pad_type_0, strides = var_6437_strides_0, weight = layers_16_self_attn_linear_pos_inlier_module_weight_to_fp16_palettized, x = obj_3_cast_fp16)[name = tensor("op_6437_cast_fp16")]; - tensor var_6443_pad_type_0 = const()[name = tensor("op_6443_pad_type_0"), val = tensor("valid")]; - tensor var_6443_strides_0 = const()[name = tensor("op_6443_strides_0"), val = tensor([1, 1])]; - tensor var_6443_pad_0 = const()[name = tensor("op_6443_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6443_dilations_0 = const()[name = tensor("op_6443_dilations_0"), val = tensor([1, 1])]; - tensor var_6443_groups_0 = const()[name = tensor("op_6443_groups_0"), val = tensor(1)]; - tensor layers_16_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97094016))), name = tensor("layers_16_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97073536))), shape = tensor([512, 512, 1, 1])]; - tensor var_6443_cast_fp16 = conv(dilations = var_6443_dilations_0, groups = var_6443_groups_0, pad = var_6443_pad_0, pad_type = var_6443_pad_type_0, strides = var_6443_strides_0, weight = layers_16_self_attn_linear_pos_outlier_module_weight_to_fp16_sparsified, x = obj_3_cast_fp16)[name = tensor("op_6443_cast_fp16")]; - tensor p_cast_fp16 = add(x = var_6437_cast_fp16, y = var_6443_cast_fp16)[name = tensor("p_cast_fp16")]; - tensor var_6447 = const()[name = tensor("op_6447"), val = tensor([1, 8, 64, 188])]; - tensor var_6448_cast_fp16 = reshape(shape = var_6447, x = q_with_bias_v_cast_fp16)[name = tensor("op_6448_cast_fp16")]; - tensor var_6449 = const()[name = tensor("op_6449"), val = tensor([1, 8, 64, -1])]; - tensor var_6450_cast_fp16 = reshape(shape = var_6449, x = p_cast_fp16)[name = tensor("op_6450_cast_fp16")]; - tensor matrix_bd_129_transpose_x_0 = const()[name = tensor("matrix_bd_129_transpose_x_0"), val = tensor(true)]; - tensor matrix_bd_129_transpose_y_0 = const()[name = tensor("matrix_bd_129_transpose_y_0"), val = tensor(false)]; - tensor matrix_bd_129_cast_fp16 = matmul(transpose_x = matrix_bd_129_transpose_x_0, transpose_y = matrix_bd_129_transpose_y_0, x = var_6448_cast_fp16, y = var_6450_cast_fp16)[name = tensor("matrix_bd_129_cast_fp16")]; - tensor matrix_bd_131_pad_0 = const()[name = tensor("matrix_bd_131_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor matrix_bd_131_mode_0 = const()[name = tensor("matrix_bd_131_mode_0"), val = tensor("constant")]; - tensor const_186_to_fp16 = const()[name = tensor("const_186_to_fp16"), val = tensor(0x0p+0)]; - tensor matrix_bd_131_cast_fp16 = pad(constant_val = const_186_to_fp16, mode = matrix_bd_131_mode_0, pad = matrix_bd_131_pad_0, x = matrix_bd_129_cast_fp16)[name = tensor("matrix_bd_131_cast_fp16")]; - tensor var_6459 = const()[name = tensor("op_6459"), val = tensor([1, 8, -1, 188])]; - tensor matrix_bd_133_cast_fp16 = reshape(shape = var_6459, x = matrix_bd_131_cast_fp16)[name = tensor("matrix_bd_133_cast_fp16")]; - tensor var_6463_begin_0 = const()[name = tensor("op_6463_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_6463_end_0 = const()[name = tensor("op_6463_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_6463_end_mask_0 = const()[name = tensor("op_6463_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_6463_cast_fp16 = slice_by_index(begin = var_6463_begin_0, end = var_6463_end_0, end_mask = var_6463_end_mask_0, x = matrix_bd_133_cast_fp16)[name = tensor("op_6463_cast_fp16")]; - tensor var_6464 = const()[name = tensor("op_6464"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_cast_fp16 = reshape(shape = var_6464, x = var_6463_cast_fp16)[name = tensor("matrix_bd_cast_fp16")]; - tensor var_6469_begin_0 = const()[name = tensor("op_6469_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6469_end_0 = const()[name = tensor("op_6469_end_0"), val = tensor([1, 8, 188, 188])]; - tensor var_6469_end_mask_0 = const()[name = tensor("op_6469_end_mask_0"), val = tensor([true, true, true, false])]; - tensor var_6469_cast_fp16 = slice_by_index(begin = var_6469_begin_0, end = var_6469_end_0, end_mask = var_6469_end_mask_0, x = matrix_bd_cast_fp16)[name = tensor("op_6469_cast_fp16")]; - tensor var_6470_to_fp16 = const()[name = tensor("op_6470_to_fp16"), val = tensor(0x1p-3)]; - tensor qk_mask_cast_fp16 = mul(x = var_6469_cast_fp16, y = var_6470_to_fp16)[name = tensor("qk_mask_cast_fp16")]; - tensor var_6474 = const()[name = tensor("op_6474"), val = tensor([1, 8, 64, 188])]; - tensor mh_q_cast_fp16 = reshape(shape = var_6474, x = query_cast_fp16)[name = tensor("mh_q_cast_fp16")]; - tensor var_6476_to_fp16 = const()[name = tensor("op_6476_to_fp16"), val = tensor(0x1p-3)]; - tensor var_6477_cast_fp16 = mul(x = mh_q_cast_fp16, y = var_6476_to_fp16)[name = tensor("op_6477_cast_fp16")]; - tensor var_6480 = const()[name = tensor("op_6480"), val = tensor([1, 8, 64, 188])]; - tensor var_6481_cast_fp16 = reshape(shape = var_6480, x = key_cast_fp16)[name = tensor("op_6481_cast_fp16")]; - tensor mh_w_65_transpose_x_0 = const()[name = tensor("mh_w_65_transpose_x_0"), val = tensor(true)]; - tensor mh_w_65_transpose_y_0 = const()[name = tensor("mh_w_65_transpose_y_0"), val = tensor(false)]; - tensor mh_w_65_cast_fp16 = matmul(transpose_x = mh_w_65_transpose_x_0, transpose_y = mh_w_65_transpose_y_0, x = var_6477_cast_fp16, y = var_6481_cast_fp16)[name = tensor("mh_w_65_cast_fp16")]; - tensor mh_w_cast_fp16 = add(x = mh_w_65_cast_fp16, y = qk_mask_cast_fp16)[name = tensor("mh_w_cast_fp16")]; - tensor var_6485_cast_fp16 = softmax(axis = var_6272, x = mh_w_cast_fp16)[name = tensor("op_6485_cast_fp16")]; - tensor var_6486 = const()[name = tensor("op_6486"), val = tensor([1, 8, 64, 188])]; - tensor var_6487_cast_fp16 = reshape(shape = var_6486, x = value_cast_fp16)[name = tensor("op_6487_cast_fp16")]; - tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; - tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; - tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_6487_cast_fp16, y = var_6485_cast_fp16)[name = tensor("attn_cast_fp16")]; - tensor var_6490 = const()[name = tensor("op_6490"), val = tensor([1, 512, 1, 188])]; - tensor input_439_cast_fp16 = reshape(shape = var_6490, x = attn_cast_fp16)[name = tensor("input_439_cast_fp16")]; - tensor var_6500_pad_type_0 = const()[name = tensor("op_6500_pad_type_0"), val = tensor("valid")]; - tensor var_6500_strides_0 = const()[name = tensor("op_6500_strides_0"), val = tensor([1, 1])]; - tensor var_6500_pad_0 = const()[name = tensor("op_6500_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6500_dilations_0 = const()[name = tensor("op_6500_dilations_0"), val = tensor([1, 1])]; - tensor var_6500_groups_0 = const()[name = tensor("op_6500_groups_0"), val = tensor(1)]; - tensor layers_16_self_attn_o_proj_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97126848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97323520))), name = tensor("layers_16_self_attn_o_proj_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_16_self_attn_o_proj_inlier_module_bias_to_fp16 = const()[name = tensor("layers_16_self_attn_o_proj_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97323712)))]; - tensor var_6500_cast_fp16 = conv(bias = layers_16_self_attn_o_proj_inlier_module_bias_to_fp16, dilations = var_6500_dilations_0, groups = var_6500_groups_0, pad = var_6500_pad_0, pad_type = var_6500_pad_type_0, strides = var_6500_strides_0, weight = layers_16_self_attn_o_proj_inlier_module_weight_to_fp16_palettized, x = input_439_cast_fp16)[name = tensor("op_6500_cast_fp16")]; - tensor var_6506_pad_type_0 = const()[name = tensor("op_6506_pad_type_0"), val = tensor("valid")]; - tensor var_6506_strides_0 = const()[name = tensor("op_6506_strides_0"), val = tensor([1, 1])]; - tensor var_6506_pad_0 = const()[name = tensor("op_6506_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6506_dilations_0 = const()[name = tensor("op_6506_dilations_0"), val = tensor([1, 1])]; - tensor var_6506_groups_0 = const()[name = tensor("op_6506_groups_0"), val = tensor(1)]; - tensor layers_16_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97333248))), name = tensor("layers_16_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97324800))), shape = tensor([512, 512, 1, 1])]; - tensor var_6506_cast_fp16 = conv(dilations = var_6506_dilations_0, groups = var_6506_groups_0, pad = var_6506_pad_0, pad_type = var_6506_pad_type_0, strides = var_6506_strides_0, weight = layers_16_self_attn_o_proj_outlier_module_weight_to_fp16_sparsified, x = input_439_cast_fp16)[name = tensor("op_6506_cast_fp16")]; - tensor obj_cast_fp16 = add(x = var_6500_cast_fp16, y = var_6506_cast_fp16)[name = tensor("obj_cast_fp16")]; - tensor inputs_165_cast_fp16 = add(x = inputs_163_cast_fp16, y = obj_cast_fp16)[name = tensor("inputs_165_cast_fp16")]; - tensor out_165_axes_0 = const()[name = tensor("out_165_axes_0"), val = tensor([1])]; - tensor var_6517_to_fp16 = const()[name = tensor("op_6517_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_165_cast_fp16 = layer_norm(axes = out_165_axes_0, epsilon = var_6517_to_fp16, x = inputs_165_cast_fp16)[name = tensor("out_165_cast_fp16")]; - tensor input_441_gamma_0_to_fp16 = const()[name = tensor("input_441_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97366080)))]; - tensor input_441_beta_0_to_fp16 = const()[name = tensor("input_441_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97367168)))]; - tensor input_441_epsilon_0_to_fp16 = const()[name = tensor("input_441_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_441_cast_fp16 = batch_norm(beta = input_441_beta_0_to_fp16, epsilon = input_441_epsilon_0_to_fp16, gamma = input_441_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_165_cast_fp16)[name = tensor("input_441_cast_fp16")]; - tensor var_6539_pad_type_0 = const()[name = tensor("op_6539_pad_type_0"), val = tensor("valid")]; - tensor var_6539_strides_0 = const()[name = tensor("op_6539_strides_0"), val = tensor([1, 1])]; - tensor var_6539_pad_0 = const()[name = tensor("op_6539_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6539_dilations_0 = const()[name = tensor("op_6539_dilations_0"), val = tensor([1, 1])]; - tensor var_6539_groups_0 = const()[name = tensor("op_6539_groups_0"), val = tensor(1)]; - tensor layers_16_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97368256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97761536))), name = tensor("layers_16_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized"), shape = tensor([1024, 512, 1, 1])]; - tensor layers_16_conv_pointwise_conv1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_16_conv_pointwise_conv1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97761728)))]; - tensor var_6539_cast_fp16 = conv(bias = layers_16_conv_pointwise_conv1_inlier_module_bias_to_fp16, dilations = var_6539_dilations_0, groups = var_6539_groups_0, pad = var_6539_pad_0, pad_type = var_6539_pad_type_0, strides = var_6539_strides_0, weight = layers_16_conv_pointwise_conv1_inlier_module_weight_to_fp16_palettized, x = input_441_cast_fp16)[name = tensor("op_6539_cast_fp16")]; - tensor var_6545_pad_type_0 = const()[name = tensor("op_6545_pad_type_0"), val = tensor("valid")]; - tensor var_6545_strides_0 = const()[name = tensor("op_6545_strides_0"), val = tensor([1, 1])]; - tensor var_6545_pad_0 = const()[name = tensor("op_6545_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6545_dilations_0 = const()[name = tensor("op_6545_dilations_0"), val = tensor([1, 1])]; - tensor var_6545_groups_0 = const()[name = tensor("op_6545_groups_0"), val = tensor(1)]; - tensor layers_16_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97782720))), name = tensor("layers_16_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97763840))), shape = tensor([1024, 512, 1, 1])]; - tensor var_6545_cast_fp16 = conv(dilations = var_6545_dilations_0, groups = var_6545_groups_0, pad = var_6545_pad_0, pad_type = var_6545_pad_type_0, strides = var_6545_strides_0, weight = layers_16_conv_pointwise_conv1_outlier_module_weight_to_fp16_sparsified, x = input_441_cast_fp16)[name = tensor("op_6545_cast_fp16")]; - tensor input_443_cast_fp16 = add(x = var_6539_cast_fp16, y = var_6545_cast_fp16)[name = tensor("input_443_cast_fp16")]; - tensor input_445_split_num_splits_0 = const()[name = tensor("input_445_split_num_splits_0"), val = tensor(2)]; - tensor input_445_split_axis_0 = const()[name = tensor("input_445_split_axis_0"), val = tensor(1)]; - tensor input_445_split_cast_fp16_0, tensor input_445_split_cast_fp16_1 = split(axis = input_445_split_axis_0, num_splits = input_445_split_num_splits_0, x = input_443_cast_fp16)[name = tensor("input_445_split_cast_fp16")]; - tensor input_445_split_1_sigmoid_cast_fp16 = sigmoid(x = input_445_split_cast_fp16_1)[name = tensor("input_445_split_1_sigmoid_cast_fp16")]; - tensor input_445_cast_fp16 = mul(x = input_445_split_cast_fp16_0, y = input_445_split_1_sigmoid_cast_fp16)[name = tensor("input_445_cast_fp16")]; - tensor input_447_pad_type_0 = const()[name = tensor("input_447_pad_type_0"), val = tensor("custom")]; - tensor input_447_pad_0 = const()[name = tensor("input_447_pad_0"), val = tensor([0, 0, 4, 4])]; - tensor input_447_groups_0 = const()[name = tensor("input_447_groups_0"), val = tensor(512)]; - tensor input_447_strides_0 = const()[name = tensor("input_447_strides_0"), val = tensor([1, 1])]; - tensor input_447_dilations_0 = const()[name = tensor("input_447_dilations_0"), val = tensor([1, 1])]; - tensor const_223_to_fp16 = const()[name = tensor("const_223_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97848320)))]; - tensor const_224_to_fp16 = const()[name = tensor("const_224_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97857600)))]; - tensor input_449_cast_fp16 = conv(bias = const_224_to_fp16, dilations = input_447_dilations_0, groups = input_447_groups_0, pad = input_447_pad_0, pad_type = input_447_pad_type_0, strides = input_447_strides_0, weight = const_223_to_fp16, x = input_445_cast_fp16)[name = tensor("input_449_cast_fp16")]; - tensor input_451_cast_fp16 = silu(x = input_449_cast_fp16)[name = tensor("input_451_cast_fp16")]; - tensor var_6569_pad_type_0 = const()[name = tensor("op_6569_pad_type_0"), val = tensor("valid")]; - tensor var_6569_strides_0 = const()[name = tensor("op_6569_strides_0"), val = tensor([1, 1])]; - tensor var_6569_pad_0 = const()[name = tensor("op_6569_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6569_dilations_0 = const()[name = tensor("op_6569_dilations_0"), val = tensor([1, 1])]; - tensor var_6569_groups_0 = const()[name = tensor("op_6569_groups_0"), val = tensor(1)]; - tensor layers_16_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97858688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98055360))), name = tensor("layers_16_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 512, 1, 1])]; - tensor layers_16_conv_pointwise_conv2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_16_conv_pointwise_conv2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98055552)))]; - tensor var_6569_cast_fp16 = conv(bias = layers_16_conv_pointwise_conv2_inlier_module_bias_to_fp16, dilations = var_6569_dilations_0, groups = var_6569_groups_0, pad = var_6569_pad_0, pad_type = var_6569_pad_type_0, strides = var_6569_strides_0, weight = layers_16_conv_pointwise_conv2_inlier_module_weight_to_fp16_palettized, x = input_451_cast_fp16)[name = tensor("op_6569_cast_fp16")]; - tensor var_6575_pad_type_0 = const()[name = tensor("op_6575_pad_type_0"), val = tensor("valid")]; - tensor var_6575_strides_0 = const()[name = tensor("op_6575_strides_0"), val = tensor([1, 1])]; - tensor var_6575_pad_0 = const()[name = tensor("op_6575_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6575_dilations_0 = const()[name = tensor("op_6575_dilations_0"), val = tensor([1, 1])]; - tensor var_6575_groups_0 = const()[name = tensor("op_6575_groups_0"), val = tensor(1)]; - tensor layers_16_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98064256))), name = tensor("layers_16_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98056640))), shape = tensor([512, 512, 1, 1])]; - tensor var_6575_cast_fp16 = conv(dilations = var_6575_dilations_0, groups = var_6575_groups_0, pad = var_6575_pad_0, pad_type = var_6575_pad_type_0, strides = var_6575_strides_0, weight = layers_16_conv_pointwise_conv2_outlier_module_weight_to_fp16_sparsified, x = input_451_cast_fp16)[name = tensor("op_6575_cast_fp16")]; - tensor x_101_cast_fp16 = add(x = var_6569_cast_fp16, y = var_6575_cast_fp16)[name = tensor("x_101_cast_fp16")]; - tensor inputs_167_cast_fp16 = add(x = inputs_165_cast_fp16, y = x_101_cast_fp16)[name = tensor("inputs_167_cast_fp16")]; - tensor out_167_axes_0 = const()[name = tensor("out_167_axes_0"), val = tensor([1])]; - tensor var_6586_to_fp16 = const()[name = tensor("op_6586_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_167_cast_fp16 = layer_norm(axes = out_167_axes_0, epsilon = var_6586_to_fp16, x = inputs_167_cast_fp16)[name = tensor("out_167_cast_fp16")]; - tensor input_453_gamma_0_to_fp16 = const()[name = tensor("input_453_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98097088)))]; - tensor input_453_beta_0_to_fp16 = const()[name = tensor("input_453_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98098176)))]; - tensor input_453_epsilon_0_to_fp16 = const()[name = tensor("input_453_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_453_cast_fp16 = batch_norm(beta = input_453_beta_0_to_fp16, epsilon = input_453_epsilon_0_to_fp16, gamma = input_453_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_167_cast_fp16)[name = tensor("input_453_cast_fp16")]; - tensor var_6606_pad_type_0 = const()[name = tensor("op_6606_pad_type_0"), val = tensor("valid")]; - tensor var_6606_strides_0 = const()[name = tensor("op_6606_strides_0"), val = tensor([1, 1])]; - tensor var_6606_pad_0 = const()[name = tensor("op_6606_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6606_dilations_0 = const()[name = tensor("op_6606_dilations_0"), val = tensor([1, 1])]; - tensor var_6606_groups_0 = const()[name = tensor("op_6606_groups_0"), val = tensor(1)]; - tensor layers_16_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98099264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98885760))), name = tensor("layers_16_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized"), shape = tensor([2048, 512, 1, 1])]; - tensor layers_16_feed_forward2_fc1_inlier_module_bias_to_fp16 = const()[name = tensor("layers_16_feed_forward2_fc1_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98885952)))]; - tensor var_6606_cast_fp16 = conv(bias = layers_16_feed_forward2_fc1_inlier_module_bias_to_fp16, dilations = var_6606_dilations_0, groups = var_6606_groups_0, pad = var_6606_pad_0, pad_type = var_6606_pad_type_0, strides = var_6606_strides_0, weight = layers_16_feed_forward2_fc1_inlier_module_weight_to_fp16_palettized, x = input_453_cast_fp16)[name = tensor("op_6606_cast_fp16")]; - tensor var_6612_pad_type_0 = const()[name = tensor("op_6612_pad_type_0"), val = tensor("valid")]; - tensor var_6612_strides_0 = const()[name = tensor("op_6612_strides_0"), val = tensor([1, 1])]; - tensor var_6612_pad_0 = const()[name = tensor("op_6612_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6612_dilations_0 = const()[name = tensor("op_6612_dilations_0"), val = tensor([1, 1])]; - tensor var_6612_groups_0 = const()[name = tensor("op_6612_groups_0"), val = tensor(1)]; - tensor layers_16_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98919552))), name = tensor("layers_16_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98890112))), shape = tensor([2048, 512, 1, 1])]; - tensor var_6612_cast_fp16 = conv(dilations = var_6612_dilations_0, groups = var_6612_groups_0, pad = var_6612_pad_0, pad_type = var_6612_pad_type_0, strides = var_6612_strides_0, weight = layers_16_feed_forward2_fc1_outlier_module_weight_to_fp16_sparsified, x = input_453_cast_fp16)[name = tensor("op_6612_cast_fp16")]; - tensor input_455_cast_fp16 = add(x = var_6606_cast_fp16, y = var_6612_cast_fp16)[name = tensor("input_455_cast_fp16")]; - tensor input_457_cast_fp16 = silu(x = input_455_cast_fp16)[name = tensor("input_457_cast_fp16")]; - tensor var_6623_pad_type_0 = const()[name = tensor("op_6623_pad_type_0"), val = tensor("valid")]; - tensor var_6623_strides_0 = const()[name = tensor("op_6623_strides_0"), val = tensor([1, 1])]; - tensor var_6623_pad_0 = const()[name = tensor("op_6623_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6623_dilations_0 = const()[name = tensor("op_6623_dilations_0"), val = tensor([1, 1])]; - tensor var_6623_groups_0 = const()[name = tensor("op_6623_groups_0"), val = tensor(1)]; - tensor layers_16_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99050688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99837184))), name = tensor("layers_16_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized"), shape = tensor([512, 2048, 1, 1])]; - tensor layers_16_feed_forward2_fc2_inlier_module_bias_to_fp16 = const()[name = tensor("layers_16_feed_forward2_fc2_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99837376)))]; - tensor var_6623_cast_fp16 = conv(bias = layers_16_feed_forward2_fc2_inlier_module_bias_to_fp16, dilations = var_6623_dilations_0, groups = var_6623_groups_0, pad = var_6623_pad_0, pad_type = var_6623_pad_type_0, strides = var_6623_strides_0, weight = layers_16_feed_forward2_fc2_inlier_module_weight_to_fp16_palettized, x = input_457_cast_fp16)[name = tensor("op_6623_cast_fp16")]; - tensor var_6629_pad_type_0 = const()[name = tensor("op_6629_pad_type_0"), val = tensor("valid")]; - tensor var_6629_strides_0 = const()[name = tensor("op_6629_strides_0"), val = tensor([1, 1])]; - tensor var_6629_pad_0 = const()[name = tensor("op_6629_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6629_dilations_0 = const()[name = tensor("op_6629_dilations_0"), val = tensor([1, 1])]; - tensor var_6629_groups_0 = const()[name = tensor("op_6629_groups_0"), val = tensor(1)]; - tensor layers_16_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99870784))), name = tensor("layers_16_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99838464))), shape = tensor([512, 2048, 1, 1])]; - tensor var_6629_cast_fp16 = conv(dilations = var_6629_dilations_0, groups = var_6629_groups_0, pad = var_6629_pad_0, pad_type = var_6629_pad_type_0, strides = var_6629_strides_0, weight = layers_16_feed_forward2_fc2_outlier_module_weight_to_fp16_sparsified, x = input_457_cast_fp16)[name = tensor("op_6629_cast_fp16")]; - tensor x_cast_fp16 = add(x = var_6623_cast_fp16, y = var_6629_cast_fp16)[name = tensor("x_cast_fp16")]; - tensor var_6631_to_fp16 = const()[name = tensor("op_6631_to_fp16"), val = tensor(0x1p-1)]; - tensor var_6632_cast_fp16 = mul(x = x_cast_fp16, y = var_6631_to_fp16)[name = tensor("op_6632_cast_fp16")]; - tensor inputs_cast_fp16 = add(x = inputs_167_cast_fp16, y = var_6632_cast_fp16)[name = tensor("inputs_cast_fp16")]; - tensor out_169_axes_0 = const()[name = tensor("out_169_axes_0"), val = tensor([1])]; - tensor var_6642_to_fp16 = const()[name = tensor("op_6642_to_fp16"), val = tensor(0x1.5p-17)]; - tensor out_169_cast_fp16 = layer_norm(axes = out_169_axes_0, epsilon = var_6642_to_fp16, x = inputs_cast_fp16)[name = tensor("out_169_cast_fp16")]; - tensor encoder_output_embeds_type_fp32_gamma_0_to_fp16 = const()[name = tensor("encoder_output_embeds_type_fp32_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100001920)))]; - tensor encoder_output_embeds_type_fp32_beta_0_to_fp16 = const()[name = tensor("encoder_output_embeds_type_fp32_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100003008)))]; - tensor encoder_output_embeds_type_fp32_epsilon_0_to_fp16 = const()[name = tensor("encoder_output_embeds_type_fp32_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; - tensor encoder_output_embeds = batch_norm(beta = encoder_output_embeds_type_fp32_beta_0_to_fp16, epsilon = encoder_output_embeds_type_fp32_epsilon_0_to_fp16, gamma = encoder_output_embeds_type_fp32_gamma_0_to_fp16, mean = input_17_mean_0_to_fp16, variance = input_17_variance_0_to_fp16, x = out_169_cast_fp16)[name = tensor("encoder_output_embeds_type_fp32_cast_fp16")]; - tensor var_6665_pad_type_0 = const()[name = tensor("op_6665_pad_type_0"), val = tensor("valid")]; - tensor var_6665_strides_0 = const()[name = tensor("op_6665_strides_0"), val = tensor([1, 1])]; - tensor var_6665_pad_0 = const()[name = tensor("op_6665_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6665_dilations_0 = const()[name = tensor("op_6665_dilations_0"), val = tensor([1, 1])]; - tensor var_6665_groups_0 = const()[name = tensor("op_6665_groups_0"), val = tensor(1)]; - tensor out_projection_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100004096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100249920))), name = tensor("out_projection_inlier_module_weight_to_fp16_palettized"), shape = tensor([640, 512, 1, 1])]; - tensor out_projection_inlier_module_bias_to_fp16 = const()[name = tensor("out_projection_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100250112)))]; - tensor var_6665_cast_fp16 = conv(bias = out_projection_inlier_module_bias_to_fp16, dilations = var_6665_dilations_0, groups = var_6665_groups_0, pad = var_6665_pad_0, pad_type = var_6665_pad_type_0, strides = var_6665_strides_0, weight = out_projection_inlier_module_weight_to_fp16_palettized, x = encoder_output_embeds)[name = tensor("op_6665_cast_fp16")]; - tensor var_6671_pad_type_0 = const()[name = tensor("op_6671_pad_type_0"), val = tensor("valid")]; - tensor var_6671_strides_0 = const()[name = tensor("op_6671_strides_0"), val = tensor([1, 1])]; - tensor var_6671_pad_0 = const()[name = tensor("op_6671_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6671_dilations_0 = const()[name = tensor("op_6671_dilations_0"), val = tensor([1, 1])]; - tensor var_6671_groups_0 = const()[name = tensor("op_6671_groups_0"), val = tensor(1)]; - tensor out_projection_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100261568))), name = tensor("out_projection_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100251456))), shape = tensor([640, 512, 1, 1])]; - tensor var_6671_cast_fp16 = conv(dilations = var_6671_dilations_0, groups = var_6671_groups_0, pad = var_6671_pad_0, pad_type = var_6671_pad_type_0, strides = var_6671_strides_0, weight = out_projection_outlier_module_weight_to_fp16_sparsified, x = encoder_output_embeds)[name = tensor("op_6671_cast_fp16")]; - tensor joint_projected_encoder_output_embeds = add(x = var_6665_cast_fp16, y = var_6671_cast_fp16)[name = tensor("op_6672_cast_fp16")]; - tensor var_6686_pad_type_0 = const()[name = tensor("op_6686_pad_type_0"), val = tensor("valid")]; - tensor var_6686_strides_0 = const()[name = tensor("op_6686_strides_0"), val = tensor([1, 1])]; - tensor var_6686_pad_0 = const()[name = tensor("op_6686_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6686_dilations_0 = const()[name = tensor("op_6686_dilations_0"), val = tensor([1, 1])]; - tensor var_6686_groups_0 = const()[name = tensor("op_6686_groups_0"), val = tensor(1)]; - tensor ctc_head_inlier_module_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100302592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100696256))), name = tensor("ctc_head_inlier_module_weight_to_fp16_palettized"), shape = tensor([1025, 512, 1, 1])]; - tensor ctc_head_inlier_module_bias_to_fp16 = const()[name = tensor("ctc_head_inlier_module_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100696448)))]; - tensor var_6686_cast_fp16 = conv(bias = ctc_head_inlier_module_bias_to_fp16, dilations = var_6686_dilations_0, groups = var_6686_groups_0, pad = var_6686_pad_0, pad_type = var_6686_pad_type_0, strides = var_6686_strides_0, weight = ctc_head_inlier_module_weight_to_fp16_palettized, x = encoder_output_embeds)[name = tensor("op_6686_cast_fp16")]; - tensor var_6692_pad_type_0 = const()[name = tensor("op_6692_pad_type_0"), val = tensor("valid")]; - tensor var_6692_strides_0 = const()[name = tensor("op_6692_strides_0"), val = tensor([1, 1])]; - tensor var_6692_pad_0 = const()[name = tensor("op_6692_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6692_dilations_0 = const()[name = tensor("op_6692_dilations_0"), val = tensor([1, 1])]; - tensor var_6692_groups_0 = const()[name = tensor("op_6692_groups_0"), val = tensor(1)]; - tensor ctc_head_outlier_module_weight_to_fp16_sparsified = constexpr_sparse_to_dense()[mask = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100712640))), name = tensor("ctc_head_outlier_module_weight_to_fp16_sparsified"), nonzero_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100698624))), shape = tensor([1025, 512, 1, 1])]; - tensor var_6692_cast_fp16 = conv(dilations = var_6692_dilations_0, groups = var_6692_groups_0, pad = var_6692_pad_0, pad_type = var_6692_pad_type_0, strides = var_6692_strides_0, weight = ctc_head_outlier_module_weight_to_fp16_sparsified, x = encoder_output_embeds)[name = tensor("op_6692_cast_fp16")]; - tensor ctc_head_raw_output = add(x = var_6686_cast_fp16, y = var_6692_cast_fp16)[name = tensor("op_6693_cast_fp16")]; - tensor var_6715 = const()[name = tensor("op_6715"), val = tensor(1)]; - tensor var_6717_softmax_cast_fp16 = softmax(axis = var_6715, x = ctc_head_raw_output)[name = tensor("op_6717_softmax_cast_fp16")]; - tensor var_6717_epsilon_0 = const()[name = tensor("op_6717_epsilon_0"), val = tensor(0x1p-149)]; - tensor ctc_head_output = log(epsilon = var_6717_epsilon_0, x = var_6717_softmax_cast_fp16)[name = tensor("op_6717_cast_fp16")]; - } -> (encoder_output_embeds, joint_projected_encoder_output_embeds, ctc_head_raw_output, ctc_head_output); -} \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/weights/weight.bin b/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/weights/weight.bin deleted file mode 100644 index 5a2bb38f8adc0042481f17648abc50b67e583d27..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/AudioEncoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:af0734b4a5d7465ad9e8bb170f0c53c5e6b91ebb75a9bdf88d3f59ae4ad6aebd -size 100778304 diff --git a/parakeet-ctc-110m-coreml/CTCHead.mlmodelc/analytics/coremldata.bin 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67b3b5f87ce33caed71f9233828f90775bb8ac9d..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/CTCHead.mlmodelc/model.mil +++ /dev/null @@ -1,24 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor encoder_output) { - tensor var_4 = const()[name = tensor("op_4"), val = tensor(-1)]; - tensor var_18_pad_type_0 = const()[name = tensor("op_18_pad_type_0"), val = tensor("valid")]; - tensor var_18_strides_0 = const()[name = tensor("op_18_strides_0"), val = tensor([1])]; - tensor var_18_pad_0 = const()[name = tensor("op_18_pad_0"), val = tensor([0, 0])]; - tensor var_18_dilations_0 = const()[name = tensor("op_18_dilations_0"), val = tensor([1])]; - tensor var_18_groups_0 = const()[name = tensor("op_18_groups_0"), val = tensor(1)]; - tensor encoder_output_to_fp16_dtype_0 = const()[name = tensor("encoder_output_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor module_decoder_layers_0_weight_to_fp16 = const()[name = tensor("module_decoder_layers_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor module_decoder_layers_0_bias_to_fp16 = const()[name = tensor("module_decoder_layers_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049728)))]; - tensor encoder_output_to_fp16 = cast(dtype = encoder_output_to_fp16_dtype_0, x = encoder_output)[name = tensor("cast_1")]; - tensor var_18_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = var_18_dilations_0, groups = var_18_groups_0, pad = var_18_pad_0, pad_type = var_18_pad_type_0, strides = var_18_strides_0, weight = module_decoder_layers_0_weight_to_fp16, x = encoder_output_to_fp16)[name = tensor("op_18_cast_fp16")]; - tensor input_perm_0 = const()[name = tensor("input_perm_0"), val = tensor([0, 2, 1])]; - tensor input_cast_fp16 = transpose(perm = input_perm_0, x = var_18_cast_fp16)[name = tensor("transpose_0")]; - tensor out_objects_softmax_cast_fp16 = softmax(axis = var_4, x = input_cast_fp16)[name = tensor("out_objects_softmax_cast_fp16")]; - tensor out_objects_epsilon_0 = const()[name = tensor("out_objects_epsilon_0"), val = tensor(0x1p-149)]; - tensor out_objects_cast_fp16 = log(epsilon = out_objects_epsilon_0, x = out_objects_softmax_cast_fp16)[name = tensor("out_objects_cast_fp16")]; - tensor out_objects_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("out_objects_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor ctc_logits = cast(dtype = out_objects_cast_fp16_to_fp32_dtype_0, x = out_objects_cast_fp16)[name = tensor("cast_0")]; - } -> (ctc_logits); -} \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/CTCHead.mlmodelc/weights/weight.bin 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"isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1]", - "name" : "targets", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "target_length", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "h_in", - "type" : "MultiArray" - }, - { - 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{"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor c_in, tensor h_in, tensor target_length, tensor targets) { - tensor y_axis_0 = const()[name = tensor("y_axis_0"), val = tensor(0)]; - tensor y_batch_dims_0 = const()[name = tensor("y_batch_dims_0"), val = tensor(0)]; - tensor y_validate_indices_0 = const()[name = tensor("y_validate_indices_0"), val = tensor(false)]; - tensor module_prediction_embed_weight_to_fp16 = const()[name = tensor("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor targets_to_int16_dtype_0 = const()[name = tensor("targets_to_int16_dtype_0"), val = tensor("int16")]; - tensor targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor("cast_8")]; - tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = targets_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = tensor("y_cast_fp16_cast_uint16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([1, 0, 2])]; - tensor input_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; - tensor h_in_to_fp16_dtype_0 = const()[name = tensor("h_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor("cast_7")]; - tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = h_in_to_fp16)[name = tensor("input_lstm_h0_squeeze_cast_fp16")]; - tensor input_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; - tensor c_in_to_fp16_dtype_0 = const()[name = tensor("c_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor("cast_6")]; - tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = c_in_to_fp16)[name = tensor("input_lstm_c0_squeeze_cast_fp16")]; - tensor input_direction_0 = const()[name = tensor("input_direction_0"), val = tensor("forward")]; - tensor input_output_sequence_0 = const()[name = tensor("input_output_sequence_0"), val = tensor(true)]; - tensor input_recurrent_activation_0 = const()[name = tensor("input_recurrent_activation_0"), val = tensor("sigmoid")]; - tensor input_cell_activation_0 = const()[name = tensor("input_cell_activation_0"), val = tensor("tanh")]; - tensor input_activation_0 = const()[name = tensor("input_activation_0"), val = tensor("tanh")]; - tensor concat_1_to_fp16 = const()[name = tensor("concat_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312128)))]; - tensor concat_2_to_fp16 = const()[name = tensor("concat_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4588992)))]; - tensor concat_0_to_fp16 = const()[name = tensor("concat_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7865856)))]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = tensor("transpose_2")]; - tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_0_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor obj_3_axes_0 = const()[name = tensor("obj_3_axes_0"), val = tensor([0])]; - tensor obj_3_cast_fp16 = expand_dims(axes = obj_3_axes_0, x = input_cast_fp16_1)[name = tensor("obj_3_cast_fp16")]; - tensor obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_3_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_axes_0 = const()[name = tensor("obj_axes_0"), val = tensor([0])]; - tensor obj_cast_fp16 = expand_dims(axes = obj_axes_0, x = input_cast_fp16_2)[name = tensor("obj_cast_fp16")]; - tensor obj_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([1, 2, 0])]; - tensor transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("transpose_0_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor("transpose_1")]; - tensor decoder = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = tensor("cast_3")]; - tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = tensor("cast_4")]; - tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = tensor("cast_5")]; - tensor target_length_tmp = identity(x = target_length)[name = tensor("target_length_tmp")]; - } -> (decoder, h_out, c_out); -} \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/Decoder.mlmodelc/weights/weight.bin b/parakeet-ctc-110m-coreml/Decoder.mlmodelc/weights/weight.bin deleted file mode 100644 index 4543ad4ea89b21ebed0de0d5ec3975d07eace7fb..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/Decoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:dd90b58597ee2c172c672dffe13b1110898ba07394c1a15efc96cc8c6b18411b -size 7871040 diff --git a/parakeet-ctc-110m-coreml/Encoder.mlmodelc/analytics/coremldata.bin 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: 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.logicalAnd" : 2, - "Ios17.reshape" : 103, - "Ios16.softmax" : 17, - "Ios17.matmul" : 51, - "Ios17.transpose" : 123, - "Split" : 17, - "Ios17.expandDims" : 17, - "Select" : 51, - "Ios17.add" : 128, - "Tile" : 8, - "Ios17.sliceByIndex" : 34, - "Ios16.sigmoid" : 17, - "Pad" : 34, - "Ios17.logicalNot" : 2, - "Ios17.layerNorm" : 85, - "Ios16.silu" : 51, - "Ios17.less" : 5, - "Ios17.sub" : 3, - "Ios17.conv" : 56, - "Ios16.relu" : 3, - "Ios17.linear" : 137, - "Ios17.cast" : 11, - "Ios17.floorDiv" : 3, - "Ios17.mul" : 77 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 80 × 1501)", - "shortDescription" : "", - "shape" : "[1, 80, 1501]", - "name" : "mel_features", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "generatedClassName" : "parakeet_encoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/Encoder.mlmodelc/model.mil b/parakeet-ctc-110m-coreml/Encoder.mlmodelc/model.mil deleted file mode 100644 index 344bd1abf313c03c9ed9287c00893739f5d361bd..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/Encoder.mlmodelc/model.mil +++ /dev/null @@ -1,2690 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor mel_features, tensor mel_length) { - tensor var_23 = const()[name = tensor("op_23"), val = tensor(-1)]; - tensor x_1_perm_0 = const()[name = tensor("x_1_perm_0"), val = tensor([0, 2, 1])]; - tensor mel_features_to_fp16_dtype_0 = const()[name = tensor("mel_features_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor tensor_1_axes_0 = const()[name = tensor("tensor_1_axes_0"), val = tensor([1])]; - tensor mel_features_to_fp16 = cast(dtype = mel_features_to_fp16_dtype_0, x = mel_features)[name = tensor("cast_182")]; - tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_features_to_fp16)[name = tensor("transpose_207")]; - tensor tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_1_cast_fp16)[name = tensor("tensor_1_cast_fp16")]; - tensor expand_dims_0 = const()[name = tensor("expand_dims_0"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 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1494, 1495, 1496, 1497, 1498, 1499, 1500]])]; - tensor var_87_axes_0 = const()[name = tensor("op_87_axes_0"), val = tensor([1])]; - tensor var_87 = expand_dims(axes = var_87_axes_0, x = mel_length)[name = tensor("op_87")]; - tensor time_mask_1 = less(x = expand_dims_0, y = var_87)[name = tensor("time_mask_1")]; - tensor var_89_axes_0 = const()[name = tensor("op_89_axes_0"), val = tensor([-1])]; - tensor var_89 = expand_dims(axes = var_89_axes_0, x = time_mask_1)[name = tensor("op_89")]; - tensor var_91_reps_0 = const()[name = tensor("op_91_reps_0"), val = tensor([1, 1, 80])]; - tensor var_91 = tile(reps = var_91_reps_0, x = var_89)[name = tensor("op_91")]; - tensor var_97_axes_0 = const()[name = tensor("op_97_axes_0"), val = tensor([1])]; - tensor cast_3_to_fp16_dtype_0 = const()[name = tensor("cast_3_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_91_to_fp16 = cast(dtype = cast_3_to_fp16_dtype_0, x = var_91)[name = tensor("cast_181")]; - tensor var_97_cast_fp16 = expand_dims(axes = var_97_axes_0, x = var_91_to_fp16)[name = tensor("op_97_cast_fp16")]; - tensor input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_97_cast_fp16)[name = tensor("input_1_cast_fp16")]; - tensor tensor_3_pad_type_0 = const()[name = tensor("tensor_3_pad_type_0"), val = tensor("custom")]; - tensor tensor_3_pad_0 = const()[name = tensor("tensor_3_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_3_strides_0 = const()[name = tensor("tensor_3_strides_0"), val = tensor([2, 2])]; - tensor tensor_3_dilations_0 = const()[name = tensor("tensor_3_dilations_0"), val = tensor([1, 1])]; - tensor tensor_3_groups_0 = const()[name = tensor("tensor_3_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_0_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor module_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4736)))]; - tensor tensor_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("tensor_3_cast_fp16")]; - tensor cast_1_to_fp16_dtype_0 = const()[name = tensor("cast_1_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_108_promoted_to_fp16 = const()[name = tensor("op_108_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor mel_length_to_fp16 = cast(dtype = cast_1_to_fp16_dtype_0, x = mel_length)[name = tensor("cast_180")]; - tensor var_109_cast_fp16 = add(x = mel_length_to_fp16, y = var_108_promoted_to_fp16)[name = tensor("op_109_cast_fp16")]; - tensor var_110_promoted_to_fp16 = const()[name = tensor("op_110_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_111_cast_fp16 = add(x = var_109_cast_fp16, y = var_110_promoted_to_fp16)[name = tensor("op_111_cast_fp16")]; - tensor var_112_promoted_to_fp16 = const()[name = tensor("op_112_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_113_cast_fp16 = sub(x = var_111_cast_fp16, y = var_112_promoted_to_fp16)[name = tensor("op_113_cast_fp16")]; - tensor var_21_promoted_to_fp16 = const()[name = tensor("op_21_promoted_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_0_cast_fp16 = floor_div(x = var_113_cast_fp16, y = var_21_promoted_to_fp16)[name = tensor("floor_div_0_cast_fp16")]; - tensor var_115_promoted_to_fp16 = const()[name = tensor("op_115_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_115_promoted_to_fp16)[name = tensor("current_lengths_3_cast_fp16")]; - tensor cast_4_dtype_0 = const()[name = tensor("cast_4_dtype_0"), val = tensor("int32")]; - tensor expand_dims_1 = const()[name = tensor("expand_dims_1"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750]])]; - tensor var_124_axes_0 = const()[name = tensor("op_124_axes_0"), val = tensor([1])]; - tensor current_lengths_3_cast_fp16_to_int32 = cast(dtype = cast_4_dtype_0, x = current_lengths_3_cast_fp16)[name = tensor("cast_179")]; - tensor var_124 = expand_dims(axes = var_124_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = tensor("op_124")]; - tensor time_mask_3 = less(x = expand_dims_1, y = var_124)[name = tensor("time_mask_3")]; - tensor var_126_axes_0 = const()[name = tensor("op_126_axes_0"), val = tensor([-1])]; - tensor var_126 = expand_dims(axes = var_126_axes_0, x = time_mask_3)[name = tensor("op_126")]; - tensor var_128_reps_0 = const()[name = tensor("op_128_reps_0"), val = tensor([1, 1, 40])]; - tensor var_128 = tile(reps = var_128_reps_0, x = var_126)[name = tensor("op_128")]; - tensor var_134_axes_0 = const()[name = tensor("op_134_axes_0"), val = tensor([1])]; - tensor cast_5_to_fp16_dtype_0 = const()[name = tensor("cast_5_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_128_to_fp16 = cast(dtype = cast_5_to_fp16_dtype_0, x = var_128)[name = tensor("cast_178")]; - tensor var_134_cast_fp16 = expand_dims(axes = var_134_axes_0, x = var_128_to_fp16)[name = tensor("op_134_cast_fp16")]; - tensor expanded_mask_3_reps_0 = const()[name = tensor("expanded_mask_3_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_134_cast_fp16)[name = tensor("expanded_mask_3_cast_fp16")]; - tensor input_3_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor tensor_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor("tensor_5_cast_fp16")]; - tensor input_5_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor tensor_7_pad_type_0 = const()[name = tensor("tensor_7_pad_type_0"), val = tensor("custom")]; - tensor tensor_7_pad_0 = const()[name = tensor("tensor_7_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_7_strides_0 = const()[name = tensor("tensor_7_strides_0"), val = tensor([2, 2])]; - tensor tensor_7_groups_0 = const()[name = tensor("tensor_7_groups_0"), val = tensor(256)]; - tensor tensor_7_dilations_0 = const()[name = tensor("tensor_7_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_2_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5312)))]; - tensor module_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9984)))]; - tensor tensor_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16, x = input_5_cast_fp16)[name = tensor("tensor_7_cast_fp16")]; - tensor var_154_promoted_to_fp16 = const()[name = tensor("op_154_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_155_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_154_promoted_to_fp16)[name = tensor("op_155_cast_fp16")]; - tensor var_156_promoted_to_fp16 = const()[name = tensor("op_156_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_157_cast_fp16 = add(x = var_155_cast_fp16, y = var_156_promoted_to_fp16)[name = tensor("op_157_cast_fp16")]; - tensor var_158_promoted_to_fp16 = const()[name = tensor("op_158_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_159_cast_fp16 = sub(x = var_157_cast_fp16, y = var_158_promoted_to_fp16)[name = tensor("op_159_cast_fp16")]; - tensor var_21_promoted_1_to_fp16 = const()[name = tensor("op_21_promoted_1_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_1_cast_fp16 = floor_div(x = var_159_cast_fp16, y = var_21_promoted_1_to_fp16)[name = tensor("floor_div_1_cast_fp16")]; - tensor var_161_promoted_to_fp16 = const()[name = tensor("op_161_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_161_promoted_to_fp16)[name = tensor("current_lengths_5_cast_fp16")]; - tensor cast_6_dtype_0 = const()[name = tensor("cast_6_dtype_0"), val = tensor("int32")]; - tensor expand_dims_2 = const()[name = tensor("expand_dims_2"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375]])]; - tensor var_170_axes_0 = const()[name = tensor("op_170_axes_0"), val = tensor([1])]; - tensor current_lengths_5_cast_fp16_to_int32 = cast(dtype = cast_6_dtype_0, x = current_lengths_5_cast_fp16)[name = tensor("cast_177")]; - tensor var_170 = expand_dims(axes = var_170_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = tensor("op_170")]; - tensor time_mask_5 = less(x = expand_dims_2, y = var_170)[name = tensor("time_mask_5")]; - tensor var_172_axes_0 = const()[name = tensor("op_172_axes_0"), val = tensor([-1])]; - tensor var_172 = expand_dims(axes = var_172_axes_0, x = time_mask_5)[name = tensor("op_172")]; - tensor var_174_reps_0 = const()[name = tensor("op_174_reps_0"), val = tensor([1, 1, 20])]; - tensor var_174 = tile(reps = var_174_reps_0, x = var_172)[name = tensor("op_174")]; - tensor var_180_axes_0 = const()[name = tensor("op_180_axes_0"), val = tensor([1])]; - tensor cast_7_to_fp16_dtype_0 = const()[name = tensor("cast_7_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_174_to_fp16 = cast(dtype = cast_7_to_fp16_dtype_0, x = var_174)[name = tensor("cast_176")]; - tensor var_180_cast_fp16 = expand_dims(axes = var_180_axes_0, x = var_174_to_fp16)[name = tensor("op_180_cast_fp16")]; - tensor expanded_mask_7_reps_0 = const()[name = tensor("expanded_mask_7_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_180_cast_fp16)[name = tensor("expanded_mask_7_cast_fp16")]; - tensor input_7_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor tensor_9_pad_type_0 = const()[name = tensor("tensor_9_pad_type_0"), val = tensor("valid")]; - tensor tensor_9_strides_0 = const()[name = tensor("tensor_9_strides_0"), val = tensor([1, 1])]; - tensor tensor_9_pad_0 = const()[name = tensor("tensor_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor tensor_9_dilations_0 = const()[name = tensor("tensor_9_dilations_0"), val = tensor([1, 1])]; - tensor tensor_9_groups_0 = const()[name = tensor("tensor_9_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_3_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_3_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10560)))]; - tensor module_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141696)))]; - tensor tensor_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("tensor_9_cast_fp16")]; - tensor input_9_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; - tensor tensor_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor("tensor_11_cast_fp16")]; - tensor input_11_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_11_cast_fp16")]; - tensor tensor_13_pad_type_0 = const()[name = tensor("tensor_13_pad_type_0"), val = tensor("custom")]; - tensor tensor_13_pad_0 = const()[name = tensor("tensor_13_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_13_strides_0 = const()[name = tensor("tensor_13_strides_0"), val = tensor([2, 2])]; - tensor tensor_13_groups_0 = const()[name = tensor("tensor_13_groups_0"), val = tensor(256)]; - tensor tensor_13_dilations_0 = const()[name = tensor("tensor_13_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_5_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_5_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142272)))]; - tensor module_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146944)))]; - tensor tensor_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("tensor_13_cast_fp16")]; - tensor var_215_promoted_to_fp16 = const()[name = tensor("op_215_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_216_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_215_promoted_to_fp16)[name = tensor("op_216_cast_fp16")]; - tensor var_217_promoted_to_fp16 = const()[name = tensor("op_217_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_218_cast_fp16 = add(x = var_216_cast_fp16, y = var_217_promoted_to_fp16)[name = tensor("op_218_cast_fp16")]; - tensor var_219_promoted_to_fp16 = const()[name = tensor("op_219_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_220_cast_fp16 = sub(x = var_218_cast_fp16, y = var_219_promoted_to_fp16)[name = tensor("op_220_cast_fp16")]; - tensor var_21_promoted_2_to_fp16 = const()[name = tensor("op_21_promoted_2_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_2_cast_fp16 = floor_div(x = var_220_cast_fp16, y = var_21_promoted_2_to_fp16)[name = tensor("floor_div_2_cast_fp16")]; - tensor var_222_promoted_to_fp16 = const()[name = tensor("op_222_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_222_promoted_to_fp16)[name = tensor("current_lengths_cast_fp16")]; - tensor cast_8_dtype_0 = const()[name = tensor("cast_8_dtype_0"), val = tensor("int32")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187]])]; - tensor var_231_axes_0 = const()[name = tensor("op_231_axes_0"), val = tensor([1])]; - tensor current_lengths_cast_fp16_to_int32 = cast(dtype = cast_8_dtype_0, x = current_lengths_cast_fp16)[name = tensor("cast_175")]; - tensor var_231 = expand_dims(axes = var_231_axes_0, x = current_lengths_cast_fp16_to_int32)[name = tensor("op_231")]; - tensor time_mask = less(x = expand_dims_3, y = var_231)[name = tensor("time_mask")]; - tensor var_233_axes_0 = const()[name = tensor("op_233_axes_0"), val = tensor([-1])]; - tensor var_233 = expand_dims(axes = var_233_axes_0, x = time_mask)[name = tensor("op_233")]; - tensor var_235_reps_0 = const()[name = tensor("op_235_reps_0"), val = tensor([1, 1, 10])]; - tensor var_235 = tile(reps = var_235_reps_0, x = var_233)[name = tensor("op_235")]; - tensor var_241_axes_0 = const()[name = tensor("op_241_axes_0"), val = tensor([1])]; - tensor cast_9_to_fp16_dtype_0 = const()[name = tensor("cast_9_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_235_to_fp16 = cast(dtype = cast_9_to_fp16_dtype_0, x = var_235)[name = tensor("cast_174")]; - tensor var_241_cast_fp16 = expand_dims(axes = var_241_axes_0, x = var_235_to_fp16)[name = tensor("op_241_cast_fp16")]; - tensor expanded_mask_13_reps_0 = const()[name = tensor("expanded_mask_13_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_241_cast_fp16)[name = tensor("expanded_mask_13_cast_fp16")]; - tensor input_13_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("input_13_cast_fp16")]; - tensor tensor_15_pad_type_0 = const()[name = tensor("tensor_15_pad_type_0"), val = tensor("valid")]; - tensor tensor_15_strides_0 = const()[name = tensor("tensor_15_strides_0"), val = tensor([1, 1])]; - tensor tensor_15_pad_0 = const()[name = tensor("tensor_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor tensor_15_dilations_0 = const()[name = tensor("tensor_15_dilations_0"), val = tensor([1, 1])]; - tensor tensor_15_groups_0 = const()[name = tensor("tensor_15_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_6_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_6_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147520)))]; - tensor module_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278656)))]; - tensor tensor_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16, x = input_13_cast_fp16)[name = tensor("tensor_15_cast_fp16")]; - tensor input_15_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; - tensor tensor_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor("tensor_cast_fp16")]; - tensor x_3_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("x_3_cast_fp16")]; - tensor var_275_perm_0 = const()[name = tensor("op_275_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_276 = const()[name = tensor("op_276"), val = tensor([1, 188, -1])]; - tensor var_275_cast_fp16 = transpose(perm = var_275_perm_0, x = x_3_cast_fp16)[name = tensor("transpose_206")]; - tensor input_17_cast_fp16 = reshape(shape = var_276, x = var_275_cast_fp16)[name = tensor("input_17_cast_fp16")]; - tensor module_pre_encode_out_weight_to_fp16 = const()[name = tensor("module_pre_encode_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279232)))]; - tensor module_pre_encode_out_bias_to_fp16 = const()[name = tensor("module_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2900736)))]; - tensor linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16, x = input_17_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor cast_12_dtype_0 = const()[name = tensor("cast_12_dtype_0"), val = tensor("int32")]; - tensor var_314_axes_0 = const()[name = tensor("op_314_axes_0"), val = tensor([-1])]; - tensor encoder_length = cast(dtype = cast_12_dtype_0, x = current_lengths_cast_fp16)[name = tensor("cast_173")]; - tensor var_314 = expand_dims(axes = var_314_axes_0, x = encoder_length)[name = tensor("op_314")]; - tensor pad_mask_1 = less(x = expand_dims_3, y = var_314)[name = tensor("pad_mask_1")]; - tensor var_316_axes_0 = const()[name = tensor("op_316_axes_0"), val = tensor([1])]; - tensor var_316 = expand_dims(axes = var_316_axes_0, x = pad_mask_1)[name = tensor("op_316")]; - tensor var_317 = const()[name = tensor("op_317"), val = tensor([1, 188, 1])]; - tensor pad_mask_for_att_mask_1 = tile(reps = var_317, x = var_316)[name = tensor("pad_mask_for_att_mask_1")]; - tensor var_319_perm_0 = const()[name = tensor("op_319_perm_0"), val = tensor([0, 2, 1])]; - tensor var_319 = transpose(perm = var_319_perm_0, x = pad_mask_for_att_mask_1)[name = tensor("transpose_205")]; - tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_319)[name = tensor("pad_mask_for_att_mask")]; - tensor const_63 = const()[name = tensor("const_63"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; - tensor att_mask = logical_and(x = pad_mask_for_att_mask, y = const_63)[name = tensor("att_mask")]; - tensor mask_9 = logical_not(x = att_mask)[name = tensor("mask_9")]; - tensor pad_mask = logical_not(x = pad_mask_1)[name = tensor("pad_mask")]; - tensor input_21_axes_0 = const()[name = tensor("input_21_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2901824)))]; - tensor module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2902912)))]; - tensor var_9_to_fp16 = const()[name = tensor("op_9_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = tensor("input_21_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2904000)))]; - tensor module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001216)))]; - tensor linear_1_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16, x = input_21_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor("input_25_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5005376)))]; - tensor module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7102592)))]; - tensor linear_2_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16, x = input_25_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor var_352_to_fp16 = const()[name = tensor("op_352_to_fp16"), val = tensor(0x1p-1)]; - tensor var_353_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_352_to_fp16)[name = tensor("op_353_cast_fp16")]; - tensor input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_353_cast_fp16)[name = tensor("input_31_cast_fp16")]; - tensor query_1_axes_0 = const()[name = tensor("query_1_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7103680)))]; - tensor module_layers_0_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7104768)))]; - tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("query_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7105856)))]; - tensor module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7630208)))]; - tensor linear_3_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_q_bias_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_3_cast_fp16")]; - tensor var_370 = const()[name = tensor("op_370"), val = tensor([1, -1, 8, 64])]; - tensor q_1_cast_fp16 = reshape(shape = var_370, x = linear_3_cast_fp16)[name = tensor("q_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7631296)))]; - tensor module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8155648)))]; - tensor linear_4_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_k_bias_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_4_cast_fp16")]; - tensor var_375 = const()[name = tensor("op_375"), val = tensor([1, -1, 8, 64])]; - tensor k_1_cast_fp16 = reshape(shape = var_375, x = linear_4_cast_fp16)[name = tensor("k_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8156736)))]; - tensor module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8681088)))]; - tensor linear_5_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_v_bias_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_5_cast_fp16")]; - tensor var_380 = const()[name = tensor("op_380"), val = tensor([1, -1, 8, 64])]; - tensor v_1_cast_fp16 = reshape(shape = var_380, x = linear_5_cast_fp16)[name = tensor("v_1_cast_fp16")]; - tensor value_3_perm_0 = const()[name = tensor("value_3_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8682176)))]; - tensor var_392_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = tensor("op_392_cast_fp16")]; - tensor module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8683264)))]; - tensor var_394_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = tensor("op_394_cast_fp16")]; - tensor q_with_bias_v_1_perm_0 = const()[name = tensor("q_with_bias_v_1_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_7_transpose_x_0 = const()[name = tensor("x_7_transpose_x_0"), val = tensor(false)]; - tensor x_7_transpose_y_0 = const()[name = tensor("x_7_transpose_y_0"), val = tensor(false)]; - tensor var_396_to_fp16 = const()[name = tensor("op_396_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8684352)))]; - tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_394_cast_fp16)[name = tensor("transpose_203")]; - tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = var_396_to_fp16)[name = tensor("x_7_cast_fp16")]; - tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_9_mode_0 = const()[name = tensor("x_9_mode_0"), val = tensor("constant")]; - tensor const_70_to_fp16 = const()[name = tensor("const_70_to_fp16"), val = tensor(0x0p+0)]; - tensor x_9_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = tensor("x_9_cast_fp16")]; - tensor var_404 = const()[name = tensor("op_404"), val = tensor([1, 8, -1, 188])]; - tensor x_11_cast_fp16 = reshape(shape = var_404, x = x_9_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor var_408_begin_0 = const()[name = tensor("op_408_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_408_end_0 = const()[name = tensor("op_408_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_408_end_mask_0 = const()[name = tensor("op_408_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_408_cast_fp16 = slice_by_index(begin = var_408_begin_0, end = var_408_end_0, end_mask = var_408_end_mask_0, x = x_11_cast_fp16)[name = tensor("op_408_cast_fp16")]; - tensor var_409 = const()[name = tensor("op_409"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_1_cast_fp16 = reshape(shape = var_409, x = var_408_cast_fp16)[name = tensor("matrix_bd_1_cast_fp16")]; - tensor matrix_ac_1_transpose_x_0 = const()[name = tensor("matrix_ac_1_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_1_transpose_y_0 = const()[name = tensor("matrix_ac_1_transpose_y_0"), val = tensor(false)]; - tensor transpose_51_perm_0 = const()[name = tensor("transpose_51_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_52_perm_0 = const()[name = tensor("transpose_52_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = k_1_cast_fp16)[name = tensor("transpose_201")]; - tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_392_cast_fp16)[name = tensor("transpose_202")]; - tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_51, y = transpose_52)[name = tensor("matrix_ac_1_cast_fp16")]; - tensor matrix_bd_3_begin_0 = const()[name = tensor("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_3_end_0 = const()[name = tensor("matrix_bd_3_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_3_end_mask_0 = const()[name = tensor("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = tensor("matrix_bd_3_cast_fp16")]; - tensor var_418_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = tensor("op_418_cast_fp16")]; - tensor _inversed_scores_1_y_0_to_fp16 = const()[name = tensor("_inversed_scores_1_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_1_cast_fp16 = mul(x = var_418_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = tensor("_inversed_scores_1_cast_fp16")]; - tensor mask_11_axes_0 = const()[name = tensor("mask_11_axes_0"), val = tensor([1])]; - tensor mask_11 = expand_dims(axes = mask_11_axes_0, x = mask_9)[name = tensor("mask_11")]; - tensor var_12_to_fp16 = const()[name = tensor("op_12_to_fp16"), val = tensor(-0x1.388p+13)]; - tensor scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_11)[name = tensor("scores_3_cast_fp16")]; - tensor var_424_cast_fp16 = softmax(axis = var_23, x = scores_3_cast_fp16)[name = tensor("op_424_cast_fp16")]; - tensor var_11_to_fp16 = const()[name = tensor("op_11_to_fp16"), val = tensor(0x0p+0)]; - tensor input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_424_cast_fp16, cond = mask_11)[name = tensor("input_33_cast_fp16")]; - tensor x_13_transpose_x_0 = const()[name = tensor("x_13_transpose_x_0"), val = tensor(false)]; - tensor x_13_transpose_y_0 = const()[name = tensor("x_13_transpose_y_0"), val = tensor(false)]; - tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = tensor("transpose_204")]; - tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_3_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_428_perm_0 = const()[name = tensor("op_428_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_429 = const()[name = tensor("op_429"), val = tensor([1, -1, 512])]; - tensor var_428_cast_fp16 = transpose(perm = var_428_perm_0, x = x_13_cast_fp16)[name = tensor("transpose_200")]; - tensor input_35_cast_fp16 = reshape(shape = var_429, x = var_428_cast_fp16)[name = tensor("input_35_cast_fp16")]; - tensor module_layers_0_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9068416)))]; - tensor module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9592768)))]; - tensor linear_7_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_out_bias_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16, x = input_35_cast_fp16)[name = tensor("linear_7_cast_fp16")]; - tensor input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = tensor("input_39_cast_fp16")]; - tensor x_17_axes_0 = const()[name = tensor("x_17_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9593856)))]; - tensor module_layers_0_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9594944)))]; - tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("x_17_cast_fp16")]; - tensor input_41_perm_0 = const()[name = tensor("input_41_perm_0"), val = tensor([0, 2, 1])]; - tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; - tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([1])]; - tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0])]; - tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1])]; - tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9596032)))]; - tensor module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10644672)))]; - tensor input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = tensor("transpose_199")]; - tensor input_43_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv1_bias_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16, x = input_41_cast_fp16)[name = tensor("input_43_cast_fp16")]; - tensor x_19_split_num_splits_0 = const()[name = tensor("x_19_split_num_splits_0"), val = tensor(2)]; - tensor x_19_split_axis_0 = const()[name = tensor("x_19_split_axis_0"), val = tensor(1)]; - tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = tensor("x_19_split_cast_fp16")]; - tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = tensor("x_19_split_1_sigmoid_cast_fp16")]; - tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = tensor("x_19_cast_fp16")]; - tensor var_453_axes_0 = const()[name = tensor("op_453_axes_0"), val = tensor([1])]; - tensor var_453 = expand_dims(axes = var_453_axes_0, x = pad_mask)[name = tensor("op_453")]; - tensor input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_453)[name = tensor("input_45_cast_fp16")]; - tensor input_47_pad_0 = const()[name = tensor("input_47_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_47_mode_0 = const()[name = tensor("input_47_mode_0"), val = tensor("constant")]; - tensor const_73_to_fp16 = const()[name = tensor("const_73_to_fp16"), val = tensor(0x0p+0)]; - tensor input_47_cast_fp16 = pad(constant_val = const_73_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = tensor("input_47_cast_fp16")]; - tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("valid")]; - tensor input_49_groups_0 = const()[name = tensor("input_49_groups_0"), val = tensor(512)]; - tensor input_49_strides_0 = const()[name = tensor("input_49_strides_0"), val = tensor([1])]; - tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([0, 0])]; - tensor input_49_dilations_0 = const()[name = tensor("input_49_dilations_0"), val = tensor([1])]; - tensor const_234_to_fp16 = const()[name = tensor("const_234_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10646784)))]; - tensor const_235_to_fp16 = const()[name = tensor("const_235_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10656064)))]; - tensor input_51_cast_fp16 = conv(bias = const_235_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_234_to_fp16, x = input_47_cast_fp16)[name = tensor("input_51_cast_fp16")]; - tensor input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = tensor("input_53_cast_fp16")]; - tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("valid")]; - tensor x_21_strides_0 = const()[name = tensor("x_21_strides_0"), val = tensor([1])]; - tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0])]; - tensor x_21_dilations_0 = const()[name = tensor("x_21_dilations_0"), val = tensor([1])]; - tensor x_21_groups_0 = const()[name = tensor("x_21_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10657152)))]; - tensor module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11181504)))]; - tensor x_21_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv2_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_layers_0_conv_pointwise_conv2_weight_to_fp16, x = input_53_cast_fp16)[name = tensor("x_21_cast_fp16")]; - tensor input_55_perm_0 = const()[name = tensor("input_55_perm_0"), val = tensor([0, 2, 1])]; - tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = tensor("transpose_198")]; - tensor input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; - tensor input_59_axes_0 = const()[name = tensor("input_59_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11182592)))]; - tensor module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11183680)))]; - tensor input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("input_59_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11184768)))]; - tensor module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13281984)))]; - tensor linear_8_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("linear_8_cast_fp16")]; - tensor input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = tensor("input_63_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13286144)))]; - tensor module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15383360)))]; - tensor linear_9_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16, x = input_63_cast_fp16)[name = tensor("linear_9_cast_fp16")]; - tensor var_495_to_fp16 = const()[name = tensor("op_495_to_fp16"), val = tensor(0x1p-1)]; - tensor var_496_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_495_to_fp16)[name = tensor("op_496_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_496_cast_fp16)[name = tensor("input_69_cast_fp16")]; - tensor input_71_axes_0 = const()[name = tensor("input_71_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15384448)))]; - tensor module_layers_0_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15385536)))]; - tensor input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = tensor("input_71_cast_fp16")]; - tensor input_73_axes_0 = const()[name = tensor("input_73_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15386624)))]; - tensor module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15387712)))]; - tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = tensor("input_73_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15388800)))]; - tensor module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17486016)))]; - tensor linear_10_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16, x = input_73_cast_fp16)[name = tensor("linear_10_cast_fp16")]; - tensor input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor("input_77_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17490176)))]; - tensor module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19587392)))]; - tensor linear_11_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16, x = input_77_cast_fp16)[name = tensor("linear_11_cast_fp16")]; - tensor var_526_to_fp16 = const()[name = tensor("op_526_to_fp16"), val = tensor(0x1p-1)]; - tensor var_527_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_526_to_fp16)[name = tensor("op_527_cast_fp16")]; - tensor input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_527_cast_fp16)[name = tensor("input_83_cast_fp16")]; - tensor query_3_axes_0 = const()[name = tensor("query_3_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19588480)))]; - tensor module_layers_1_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19589568)))]; - tensor query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = tensor("query_3_cast_fp16")]; - tensor module_layers_1_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19590656)))]; - tensor module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20115008)))]; - tensor linear_12_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_q_bias_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_12_cast_fp16")]; - tensor var_544 = const()[name = tensor("op_544"), val = tensor([1, -1, 8, 64])]; - tensor q_7_cast_fp16 = reshape(shape = var_544, x = linear_12_cast_fp16)[name = tensor("q_7_cast_fp16")]; - tensor module_layers_1_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20116096)))]; - tensor module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20640448)))]; - tensor linear_13_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_k_bias_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_13_cast_fp16")]; - tensor var_549 = const()[name = tensor("op_549"), val = tensor([1, -1, 8, 64])]; - tensor k_5_cast_fp16 = reshape(shape = var_549, x = linear_13_cast_fp16)[name = tensor("k_5_cast_fp16")]; - tensor module_layers_1_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20641536)))]; - tensor module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21165888)))]; - tensor linear_14_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_v_bias_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_14_cast_fp16")]; - tensor var_554 = const()[name = tensor("op_554"), val = tensor([1, -1, 8, 64])]; - tensor v_3_cast_fp16 = reshape(shape = var_554, x = linear_14_cast_fp16)[name = tensor("v_3_cast_fp16")]; - tensor value_5_perm_0 = const()[name = tensor("value_5_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21166976)))]; - tensor var_566_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = tensor("op_566_cast_fp16")]; - tensor module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21168064)))]; - tensor var_568_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = tensor("op_568_cast_fp16")]; - tensor q_with_bias_v_3_perm_0 = const()[name = tensor("q_with_bias_v_3_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_29_transpose_x_0 = const()[name = tensor("x_29_transpose_x_0"), val = tensor(false)]; - tensor x_29_transpose_y_0 = const()[name = tensor("x_29_transpose_y_0"), val = tensor(false)]; - tensor var_570_to_fp16 = const()[name = tensor("op_570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21169152)))]; - tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_568_cast_fp16)[name = tensor("transpose_196")]; - tensor x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = var_570_to_fp16)[name = tensor("x_29_cast_fp16")]; - tensor x_31_pad_0 = const()[name = tensor("x_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_31_mode_0 = const()[name = tensor("x_31_mode_0"), val = tensor("constant")]; - tensor const_80_to_fp16 = const()[name = tensor("const_80_to_fp16"), val = tensor(0x0p+0)]; - tensor x_31_cast_fp16 = pad(constant_val = const_80_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = tensor("x_31_cast_fp16")]; - tensor var_578 = const()[name = tensor("op_578"), val = tensor([1, 8, -1, 188])]; - tensor x_33_cast_fp16 = reshape(shape = var_578, x = x_31_cast_fp16)[name = tensor("x_33_cast_fp16")]; - tensor var_582_begin_0 = const()[name = tensor("op_582_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_582_end_0 = const()[name = tensor("op_582_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_582_end_mask_0 = const()[name = tensor("op_582_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_582_cast_fp16 = slice_by_index(begin = var_582_begin_0, end = var_582_end_0, end_mask = var_582_end_mask_0, x = x_33_cast_fp16)[name = tensor("op_582_cast_fp16")]; - tensor var_583 = const()[name = tensor("op_583"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_5_cast_fp16 = reshape(shape = var_583, x = var_582_cast_fp16)[name = tensor("matrix_bd_5_cast_fp16")]; - tensor matrix_ac_3_transpose_x_0 = const()[name = tensor("matrix_ac_3_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_3_transpose_y_0 = const()[name = tensor("matrix_ac_3_transpose_y_0"), val = tensor(false)]; - tensor transpose_53_perm_0 = const()[name = tensor("transpose_53_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_54_perm_0 = const()[name = tensor("transpose_54_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = k_5_cast_fp16)[name = tensor("transpose_194")]; - tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = var_566_cast_fp16)[name = tensor("transpose_195")]; - tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_53, y = transpose_54)[name = tensor("matrix_ac_3_cast_fp16")]; - tensor matrix_bd_7_begin_0 = const()[name = tensor("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_7_end_0 = const()[name = tensor("matrix_bd_7_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_7_end_mask_0 = const()[name = tensor("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = tensor("matrix_bd_7_cast_fp16")]; - tensor var_592_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = tensor("op_592_cast_fp16")]; - tensor _inversed_scores_5_y_0_to_fp16 = const()[name = tensor("_inversed_scores_5_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_5_cast_fp16 = mul(x = var_592_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = tensor("_inversed_scores_5_cast_fp16")]; - tensor scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_11)[name = tensor("scores_7_cast_fp16")]; - tensor var_598_cast_fp16 = softmax(axis = var_23, x = scores_7_cast_fp16)[name = tensor("op_598_cast_fp16")]; - tensor input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_598_cast_fp16, cond = mask_11)[name = tensor("input_85_cast_fp16")]; - tensor x_35_transpose_x_0 = const()[name = tensor("x_35_transpose_x_0"), val = tensor(false)]; - tensor x_35_transpose_y_0 = const()[name = tensor("x_35_transpose_y_0"), val = tensor(false)]; - tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = tensor("transpose_197")]; - tensor x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_5_cast_fp16)[name = tensor("x_35_cast_fp16")]; - tensor var_602_perm_0 = const()[name = tensor("op_602_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_603 = const()[name = tensor("op_603"), val = tensor([1, -1, 512])]; - tensor var_602_cast_fp16 = transpose(perm = var_602_perm_0, x = x_35_cast_fp16)[name = tensor("transpose_193")]; - tensor input_87_cast_fp16 = reshape(shape = var_603, x = var_602_cast_fp16)[name = tensor("input_87_cast_fp16")]; - tensor module_layers_1_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21553216)))]; - tensor module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22077568)))]; - tensor linear_16_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_out_bias_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16, x = input_87_cast_fp16)[name = tensor("linear_16_cast_fp16")]; - tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = tensor("input_91_cast_fp16")]; - tensor x_39_axes_0 = const()[name = tensor("x_39_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22078656)))]; - tensor module_layers_1_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22079744)))]; - tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = tensor("x_39_cast_fp16")]; - tensor input_93_perm_0 = const()[name = tensor("input_93_perm_0"), val = tensor([0, 2, 1])]; - tensor input_95_pad_type_0 = const()[name = tensor("input_95_pad_type_0"), val = tensor("valid")]; - tensor input_95_strides_0 = const()[name = tensor("input_95_strides_0"), val = tensor([1])]; - tensor input_95_pad_0 = const()[name = tensor("input_95_pad_0"), val = tensor([0, 0])]; - tensor input_95_dilations_0 = const()[name = tensor("input_95_dilations_0"), val = tensor([1])]; - tensor input_95_groups_0 = const()[name = tensor("input_95_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22080832)))]; - tensor module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23129472)))]; - tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = tensor("transpose_192")]; - tensor input_95_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv1_bias_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16, x = input_93_cast_fp16)[name = tensor("input_95_cast_fp16")]; - tensor x_41_split_num_splits_0 = const()[name = tensor("x_41_split_num_splits_0"), val = tensor(2)]; - tensor x_41_split_axis_0 = const()[name = tensor("x_41_split_axis_0"), val = tensor(1)]; - tensor x_41_split_cast_fp16_0, tensor x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = tensor("x_41_split_cast_fp16")]; - tensor x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = tensor("x_41_split_1_sigmoid_cast_fp16")]; - tensor x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = tensor("x_41_cast_fp16")]; - tensor input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_453)[name = tensor("input_97_cast_fp16")]; - tensor input_99_pad_0 = const()[name = tensor("input_99_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_99_mode_0 = const()[name = tensor("input_99_mode_0"), val = tensor("constant")]; - tensor const_83_to_fp16 = const()[name = tensor("const_83_to_fp16"), val = tensor(0x0p+0)]; - tensor input_99_cast_fp16 = pad(constant_val = const_83_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = tensor("input_99_cast_fp16")]; - tensor input_101_pad_type_0 = const()[name = tensor("input_101_pad_type_0"), val = tensor("valid")]; - tensor input_101_groups_0 = const()[name = tensor("input_101_groups_0"), val = tensor(512)]; - tensor input_101_strides_0 = const()[name = tensor("input_101_strides_0"), val = tensor([1])]; - tensor input_101_pad_0 = const()[name = tensor("input_101_pad_0"), val = tensor([0, 0])]; - tensor input_101_dilations_0 = const()[name = tensor("input_101_dilations_0"), val = tensor([1])]; - tensor const_236_to_fp16 = const()[name = tensor("const_236_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23131584)))]; - tensor const_237_to_fp16 = const()[name = tensor("const_237_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23140864)))]; - tensor input_103_cast_fp16 = conv(bias = const_237_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_236_to_fp16, x = input_99_cast_fp16)[name = tensor("input_103_cast_fp16")]; - tensor input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = tensor("input_105_cast_fp16")]; - tensor x_43_pad_type_0 = const()[name = tensor("x_43_pad_type_0"), val = tensor("valid")]; - tensor x_43_strides_0 = const()[name = tensor("x_43_strides_0"), val = tensor([1])]; - tensor x_43_pad_0 = const()[name = tensor("x_43_pad_0"), val = tensor([0, 0])]; - tensor x_43_dilations_0 = const()[name = tensor("x_43_dilations_0"), val = tensor([1])]; - tensor x_43_groups_0 = const()[name = tensor("x_43_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23141952)))]; - tensor module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23666304)))]; - tensor x_43_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv2_bias_to_fp16, dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = module_layers_1_conv_pointwise_conv2_weight_to_fp16, x = input_105_cast_fp16)[name = tensor("x_43_cast_fp16")]; - tensor input_107_perm_0 = const()[name = tensor("input_107_perm_0"), val = tensor([0, 2, 1])]; - tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = tensor("transpose_191")]; - tensor input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = tensor("input_109_cast_fp16")]; - tensor input_111_axes_0 = const()[name = tensor("input_111_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23667392)))]; - tensor module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23668480)))]; - tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("input_111_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23669568)))]; - tensor module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25766784)))]; - tensor linear_17_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16, x = input_111_cast_fp16)[name = tensor("linear_17_cast_fp16")]; - tensor input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = tensor("input_115_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25770944)))]; - tensor module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27868160)))]; - tensor linear_18_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16, x = input_115_cast_fp16)[name = tensor("linear_18_cast_fp16")]; - tensor var_669_to_fp16 = const()[name = tensor("op_669_to_fp16"), val = tensor(0x1p-1)]; - tensor var_670_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_669_to_fp16)[name = tensor("op_670_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_670_cast_fp16)[name = tensor("input_121_cast_fp16")]; - tensor input_123_axes_0 = const()[name = tensor("input_123_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27869248)))]; - tensor module_layers_1_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27870336)))]; - tensor input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("input_123_cast_fp16")]; - tensor input_125_axes_0 = const()[name = tensor("input_125_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27871424)))]; - tensor module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27872512)))]; - tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor("input_125_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27873600)))]; - tensor module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29970816)))]; - tensor linear_19_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16, x = input_125_cast_fp16)[name = tensor("linear_19_cast_fp16")]; - tensor input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor("input_129_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29974976)))]; - tensor module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32072192)))]; - tensor linear_20_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("linear_20_cast_fp16")]; - tensor var_700_to_fp16 = const()[name = tensor("op_700_to_fp16"), val = tensor(0x1p-1)]; - tensor var_701_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_700_to_fp16)[name = tensor("op_701_cast_fp16")]; - tensor input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_701_cast_fp16)[name = tensor("input_135_cast_fp16")]; - tensor query_5_axes_0 = const()[name = tensor("query_5_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32073280)))]; - tensor module_layers_2_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32074368)))]; - tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = tensor("query_5_cast_fp16")]; - tensor module_layers_2_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32075456)))]; - tensor module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32599808)))]; - tensor linear_21_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_q_bias_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_21_cast_fp16")]; - tensor var_718 = const()[name = tensor("op_718"), val = tensor([1, -1, 8, 64])]; - tensor q_13_cast_fp16 = reshape(shape = var_718, x = linear_21_cast_fp16)[name = tensor("q_13_cast_fp16")]; - tensor module_layers_2_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32600896)))]; - tensor module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33125248)))]; - tensor linear_22_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_k_bias_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_22_cast_fp16")]; - tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, -1, 8, 64])]; - tensor k_9_cast_fp16 = reshape(shape = var_723, x = linear_22_cast_fp16)[name = tensor("k_9_cast_fp16")]; - tensor module_layers_2_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33126336)))]; - tensor module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33650688)))]; - tensor linear_23_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_v_bias_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_23_cast_fp16")]; - tensor var_728 = const()[name = tensor("op_728"), val = tensor([1, -1, 8, 64])]; - tensor v_5_cast_fp16 = reshape(shape = var_728, x = linear_23_cast_fp16)[name = tensor("v_5_cast_fp16")]; - tensor value_7_perm_0 = const()[name = tensor("value_7_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33651776)))]; - tensor var_740_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = tensor("op_740_cast_fp16")]; - tensor module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33652864)))]; - tensor var_742_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = tensor("op_742_cast_fp16")]; - tensor q_with_bias_v_5_perm_0 = const()[name = tensor("q_with_bias_v_5_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_51_transpose_x_0 = const()[name = tensor("x_51_transpose_x_0"), val = tensor(false)]; - tensor x_51_transpose_y_0 = const()[name = tensor("x_51_transpose_y_0"), val = tensor(false)]; - tensor var_744_to_fp16 = const()[name = tensor("op_744_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33653952)))]; - tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_742_cast_fp16)[name = tensor("transpose_189")]; - tensor x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = var_744_to_fp16)[name = tensor("x_51_cast_fp16")]; - tensor x_53_pad_0 = const()[name = tensor("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_53_mode_0 = const()[name = tensor("x_53_mode_0"), val = tensor("constant")]; - tensor const_90_to_fp16 = const()[name = tensor("const_90_to_fp16"), val = tensor(0x0p+0)]; - tensor x_53_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = tensor("x_53_cast_fp16")]; - tensor var_752 = const()[name = tensor("op_752"), val = tensor([1, 8, -1, 188])]; - tensor x_55_cast_fp16 = reshape(shape = var_752, x = x_53_cast_fp16)[name = tensor("x_55_cast_fp16")]; - tensor var_756_begin_0 = const()[name = tensor("op_756_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_756_end_0 = const()[name = tensor("op_756_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_756_end_mask_0 = const()[name = tensor("op_756_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_756_cast_fp16 = slice_by_index(begin = var_756_begin_0, end = var_756_end_0, end_mask = var_756_end_mask_0, x = x_55_cast_fp16)[name = tensor("op_756_cast_fp16")]; - tensor var_757 = const()[name = tensor("op_757"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_9_cast_fp16 = reshape(shape = var_757, x = var_756_cast_fp16)[name = tensor("matrix_bd_9_cast_fp16")]; - tensor matrix_ac_5_transpose_x_0 = const()[name = tensor("matrix_ac_5_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_5_transpose_y_0 = const()[name = tensor("matrix_ac_5_transpose_y_0"), val = tensor(false)]; - tensor transpose_55_perm_0 = const()[name = tensor("transpose_55_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_56_perm_0 = const()[name = tensor("transpose_56_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = k_9_cast_fp16)[name = tensor("transpose_187")]; - tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_740_cast_fp16)[name = tensor("transpose_188")]; - tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_55, y = transpose_56)[name = tensor("matrix_ac_5_cast_fp16")]; - tensor matrix_bd_11_begin_0 = const()[name = tensor("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_11_end_0 = const()[name = tensor("matrix_bd_11_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_11_end_mask_0 = const()[name = tensor("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = tensor("matrix_bd_11_cast_fp16")]; - tensor var_766_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = tensor("op_766_cast_fp16")]; - tensor _inversed_scores_9_y_0_to_fp16 = const()[name = tensor("_inversed_scores_9_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_9_cast_fp16 = mul(x = var_766_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = tensor("_inversed_scores_9_cast_fp16")]; - tensor scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_11)[name = tensor("scores_11_cast_fp16")]; - tensor var_772_cast_fp16 = softmax(axis = var_23, x = scores_11_cast_fp16)[name = tensor("op_772_cast_fp16")]; - tensor input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_772_cast_fp16, cond = mask_11)[name = tensor("input_137_cast_fp16")]; - tensor x_57_transpose_x_0 = const()[name = tensor("x_57_transpose_x_0"), val = tensor(false)]; - tensor x_57_transpose_y_0 = const()[name = tensor("x_57_transpose_y_0"), val = tensor(false)]; - tensor value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = tensor("transpose_190")]; - tensor x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_7_cast_fp16)[name = tensor("x_57_cast_fp16")]; - tensor var_776_perm_0 = const()[name = tensor("op_776_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_777 = const()[name = tensor("op_777"), val = tensor([1, -1, 512])]; - tensor var_776_cast_fp16 = transpose(perm = var_776_perm_0, x = x_57_cast_fp16)[name = tensor("transpose_186")]; - tensor input_139_cast_fp16 = reshape(shape = var_777, x = var_776_cast_fp16)[name = tensor("input_139_cast_fp16")]; - tensor module_layers_2_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34038016)))]; - tensor module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34562368)))]; - tensor linear_25_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_out_bias_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("linear_25_cast_fp16")]; - tensor input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = tensor("input_143_cast_fp16")]; - tensor x_61_axes_0 = const()[name = tensor("x_61_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34563456)))]; - tensor module_layers_2_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34564544)))]; - tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = tensor("x_61_cast_fp16")]; - tensor input_145_perm_0 = const()[name = tensor("input_145_perm_0"), val = tensor([0, 2, 1])]; - tensor input_147_pad_type_0 = const()[name = tensor("input_147_pad_type_0"), val = tensor("valid")]; - tensor input_147_strides_0 = const()[name = tensor("input_147_strides_0"), val = tensor([1])]; - tensor input_147_pad_0 = const()[name = tensor("input_147_pad_0"), val = tensor([0, 0])]; - tensor input_147_dilations_0 = const()[name = tensor("input_147_dilations_0"), val = tensor([1])]; - tensor input_147_groups_0 = const()[name = tensor("input_147_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34565632)))]; - tensor module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35614272)))]; - tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = tensor("transpose_185")]; - tensor input_147_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16, x = input_145_cast_fp16)[name = tensor("input_147_cast_fp16")]; - tensor x_63_split_num_splits_0 = const()[name = tensor("x_63_split_num_splits_0"), val = tensor(2)]; - tensor x_63_split_axis_0 = const()[name = tensor("x_63_split_axis_0"), val = tensor(1)]; - tensor x_63_split_cast_fp16_0, tensor x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = tensor("x_63_split_cast_fp16")]; - tensor x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = tensor("x_63_split_1_sigmoid_cast_fp16")]; - tensor x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = tensor("x_63_cast_fp16")]; - tensor input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_453)[name = tensor("input_149_cast_fp16")]; - tensor input_151_pad_0 = const()[name = tensor("input_151_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_151_mode_0 = const()[name = tensor("input_151_mode_0"), val = tensor("constant")]; - tensor const_93_to_fp16 = const()[name = tensor("const_93_to_fp16"), val = tensor(0x0p+0)]; - tensor input_151_cast_fp16 = pad(constant_val = const_93_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = tensor("input_151_cast_fp16")]; - tensor input_153_pad_type_0 = const()[name = tensor("input_153_pad_type_0"), val = tensor("valid")]; - tensor input_153_groups_0 = const()[name = tensor("input_153_groups_0"), val = tensor(512)]; - tensor input_153_strides_0 = const()[name = tensor("input_153_strides_0"), val = tensor([1])]; - tensor input_153_pad_0 = const()[name = tensor("input_153_pad_0"), val = tensor([0, 0])]; - tensor input_153_dilations_0 = const()[name = tensor("input_153_dilations_0"), val = tensor([1])]; - tensor const_238_to_fp16 = const()[name = tensor("const_238_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35616384)))]; - tensor const_239_to_fp16 = const()[name = tensor("const_239_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35625664)))]; - tensor input_155_cast_fp16 = conv(bias = const_239_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_238_to_fp16, x = input_151_cast_fp16)[name = tensor("input_155_cast_fp16")]; - tensor input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = tensor("input_157_cast_fp16")]; - tensor x_65_pad_type_0 = const()[name = tensor("x_65_pad_type_0"), val = tensor("valid")]; - tensor x_65_strides_0 = const()[name = tensor("x_65_strides_0"), val = tensor([1])]; - tensor x_65_pad_0 = const()[name = tensor("x_65_pad_0"), val = tensor([0, 0])]; - tensor x_65_dilations_0 = const()[name = tensor("x_65_dilations_0"), val = tensor([1])]; - tensor x_65_groups_0 = const()[name = tensor("x_65_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35626752)))]; - tensor module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36151104)))]; - tensor x_65_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv2_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_layers_2_conv_pointwise_conv2_weight_to_fp16, x = input_157_cast_fp16)[name = tensor("x_65_cast_fp16")]; - tensor input_159_perm_0 = const()[name = tensor("input_159_perm_0"), val = tensor([0, 2, 1])]; - tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = tensor("transpose_184")]; - tensor input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = tensor("input_161_cast_fp16")]; - tensor input_163_axes_0 = const()[name = tensor("input_163_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36152192)))]; - tensor module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36153280)))]; - tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("input_163_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36154368)))]; - tensor module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38251584)))]; - tensor linear_26_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16, x = input_163_cast_fp16)[name = tensor("linear_26_cast_fp16")]; - tensor input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = tensor("input_167_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38255744)))]; - tensor module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40352960)))]; - tensor linear_27_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16, x = input_167_cast_fp16)[name = tensor("linear_27_cast_fp16")]; - tensor var_843_to_fp16 = const()[name = tensor("op_843_to_fp16"), val = tensor(0x1p-1)]; - tensor var_844_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_843_to_fp16)[name = tensor("op_844_cast_fp16")]; - tensor input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_844_cast_fp16)[name = tensor("input_173_cast_fp16")]; - tensor input_175_axes_0 = const()[name = tensor("input_175_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40354048)))]; - tensor module_layers_2_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40355136)))]; - tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = tensor("input_175_cast_fp16")]; - tensor input_177_axes_0 = const()[name = tensor("input_177_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40356224)))]; - tensor module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40357312)))]; - tensor input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor("input_177_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40358400)))]; - tensor module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42455616)))]; - tensor linear_28_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16, x = input_177_cast_fp16)[name = tensor("linear_28_cast_fp16")]; - tensor input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = tensor("input_181_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42459776)))]; - tensor module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44556992)))]; - tensor linear_29_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16, x = input_181_cast_fp16)[name = tensor("linear_29_cast_fp16")]; - tensor var_874_to_fp16 = const()[name = tensor("op_874_to_fp16"), val = tensor(0x1p-1)]; - tensor var_875_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_874_to_fp16)[name = tensor("op_875_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_875_cast_fp16)[name = tensor("input_187_cast_fp16")]; - tensor query_7_axes_0 = const()[name = tensor("query_7_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44558080)))]; - tensor module_layers_3_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44559168)))]; - tensor query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = tensor("query_7_cast_fp16")]; - tensor module_layers_3_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44560256)))]; - tensor module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45084608)))]; - tensor linear_30_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_q_bias_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_30_cast_fp16")]; - tensor var_892 = const()[name = tensor("op_892"), val = tensor([1, -1, 8, 64])]; - tensor q_19_cast_fp16 = reshape(shape = var_892, x = linear_30_cast_fp16)[name = tensor("q_19_cast_fp16")]; - tensor module_layers_3_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45085696)))]; - tensor module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45610048)))]; - tensor linear_31_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_k_bias_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_31_cast_fp16")]; - tensor var_897 = const()[name = tensor("op_897"), val = tensor([1, -1, 8, 64])]; - tensor k_13_cast_fp16 = reshape(shape = var_897, x = linear_31_cast_fp16)[name = tensor("k_13_cast_fp16")]; - tensor module_layers_3_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45611136)))]; - tensor module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46135488)))]; - tensor linear_32_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_v_bias_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_32_cast_fp16")]; - tensor var_902 = const()[name = tensor("op_902"), val = tensor([1, -1, 8, 64])]; - tensor v_7_cast_fp16 = reshape(shape = var_902, x = linear_32_cast_fp16)[name = tensor("v_7_cast_fp16")]; - tensor value_9_perm_0 = const()[name = tensor("value_9_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46136576)))]; - tensor var_914_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = tensor("op_914_cast_fp16")]; - tensor module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46137664)))]; - tensor var_916_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = tensor("op_916_cast_fp16")]; - tensor q_with_bias_v_7_perm_0 = const()[name = tensor("q_with_bias_v_7_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_73_transpose_x_0 = const()[name = tensor("x_73_transpose_x_0"), val = tensor(false)]; - tensor x_73_transpose_y_0 = const()[name = tensor("x_73_transpose_y_0"), val = tensor(false)]; - tensor var_918_to_fp16 = const()[name = tensor("op_918_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46138752)))]; - tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_916_cast_fp16)[name = tensor("transpose_182")]; - tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = var_918_to_fp16)[name = tensor("x_73_cast_fp16")]; - tensor x_75_pad_0 = const()[name = tensor("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_75_mode_0 = const()[name = tensor("x_75_mode_0"), val = tensor("constant")]; - tensor const_100_to_fp16 = const()[name = tensor("const_100_to_fp16"), val = tensor(0x0p+0)]; - tensor x_75_cast_fp16 = pad(constant_val = const_100_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = tensor("x_75_cast_fp16")]; - tensor var_926 = const()[name = tensor("op_926"), val = tensor([1, 8, -1, 188])]; - tensor x_77_cast_fp16 = reshape(shape = var_926, x = x_75_cast_fp16)[name = tensor("x_77_cast_fp16")]; - tensor var_930_begin_0 = const()[name = tensor("op_930_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_930_end_0 = const()[name = tensor("op_930_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_930_end_mask_0 = const()[name = tensor("op_930_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_930_cast_fp16 = slice_by_index(begin = var_930_begin_0, end = var_930_end_0, end_mask = var_930_end_mask_0, x = x_77_cast_fp16)[name = tensor("op_930_cast_fp16")]; - tensor var_931 = const()[name = tensor("op_931"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_13_cast_fp16 = reshape(shape = var_931, x = var_930_cast_fp16)[name = tensor("matrix_bd_13_cast_fp16")]; - tensor matrix_ac_7_transpose_x_0 = const()[name = tensor("matrix_ac_7_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_7_transpose_y_0 = const()[name = tensor("matrix_ac_7_transpose_y_0"), val = tensor(false)]; - tensor transpose_57_perm_0 = const()[name = tensor("transpose_57_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_58_perm_0 = const()[name = tensor("transpose_58_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = k_13_cast_fp16)[name = tensor("transpose_180")]; - tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_914_cast_fp16)[name = tensor("transpose_181")]; - tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_57, y = transpose_58)[name = tensor("matrix_ac_7_cast_fp16")]; - tensor matrix_bd_15_begin_0 = const()[name = tensor("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_15_end_0 = const()[name = tensor("matrix_bd_15_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_15_end_mask_0 = const()[name = tensor("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = tensor("matrix_bd_15_cast_fp16")]; - tensor var_940_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = tensor("op_940_cast_fp16")]; - tensor _inversed_scores_13_y_0_to_fp16 = const()[name = tensor("_inversed_scores_13_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_13_cast_fp16 = mul(x = var_940_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = tensor("_inversed_scores_13_cast_fp16")]; - tensor scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_11)[name = tensor("scores_15_cast_fp16")]; - tensor var_946_cast_fp16 = softmax(axis = var_23, x = scores_15_cast_fp16)[name = tensor("op_946_cast_fp16")]; - tensor input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_946_cast_fp16, cond = mask_11)[name = tensor("input_189_cast_fp16")]; - tensor x_79_transpose_x_0 = const()[name = tensor("x_79_transpose_x_0"), val = tensor(false)]; - tensor x_79_transpose_y_0 = const()[name = tensor("x_79_transpose_y_0"), val = tensor(false)]; - tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = tensor("transpose_183")]; - tensor x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_9_cast_fp16)[name = tensor("x_79_cast_fp16")]; - tensor var_950_perm_0 = const()[name = tensor("op_950_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_951 = const()[name = tensor("op_951"), val = tensor([1, -1, 512])]; - tensor var_950_cast_fp16 = transpose(perm = var_950_perm_0, x = x_79_cast_fp16)[name = tensor("transpose_179")]; - tensor input_191_cast_fp16 = reshape(shape = var_951, x = var_950_cast_fp16)[name = tensor("input_191_cast_fp16")]; - tensor module_layers_3_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46522816)))]; - tensor module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47047168)))]; - tensor linear_34_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_out_bias_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16, x = input_191_cast_fp16)[name = tensor("linear_34_cast_fp16")]; - tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = tensor("input_195_cast_fp16")]; - tensor x_83_axes_0 = const()[name = tensor("x_83_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47048256)))]; - tensor module_layers_3_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47049344)))]; - tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = tensor("x_83_cast_fp16")]; - tensor input_197_perm_0 = const()[name = tensor("input_197_perm_0"), val = tensor([0, 2, 1])]; - tensor input_199_pad_type_0 = const()[name = tensor("input_199_pad_type_0"), val = tensor("valid")]; - tensor input_199_strides_0 = const()[name = tensor("input_199_strides_0"), val = tensor([1])]; - tensor input_199_pad_0 = const()[name = tensor("input_199_pad_0"), val = tensor([0, 0])]; - tensor input_199_dilations_0 = const()[name = tensor("input_199_dilations_0"), val = tensor([1])]; - tensor input_199_groups_0 = const()[name = tensor("input_199_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47050432)))]; - tensor module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48099072)))]; - tensor input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = tensor("transpose_178")]; - tensor input_199_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv1_bias_to_fp16, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16, x = input_197_cast_fp16)[name = tensor("input_199_cast_fp16")]; - tensor x_85_split_num_splits_0 = const()[name = tensor("x_85_split_num_splits_0"), val = tensor(2)]; - tensor x_85_split_axis_0 = const()[name = tensor("x_85_split_axis_0"), val = tensor(1)]; - tensor x_85_split_cast_fp16_0, tensor x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = tensor("x_85_split_cast_fp16")]; - tensor x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = tensor("x_85_split_1_sigmoid_cast_fp16")]; - tensor x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = tensor("x_85_cast_fp16")]; - tensor input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_453)[name = tensor("input_201_cast_fp16")]; - tensor input_203_pad_0 = const()[name = tensor("input_203_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_203_mode_0 = const()[name = tensor("input_203_mode_0"), val = tensor("constant")]; - tensor const_103_to_fp16 = const()[name = tensor("const_103_to_fp16"), val = tensor(0x0p+0)]; - tensor input_203_cast_fp16 = pad(constant_val = const_103_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = tensor("input_203_cast_fp16")]; - tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("valid")]; - tensor input_205_groups_0 = const()[name = tensor("input_205_groups_0"), val = tensor(512)]; - tensor input_205_strides_0 = const()[name = tensor("input_205_strides_0"), val = tensor([1])]; - tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([0, 0])]; - tensor input_205_dilations_0 = const()[name = tensor("input_205_dilations_0"), val = tensor([1])]; - tensor const_240_to_fp16 = const()[name = tensor("const_240_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48101184)))]; - tensor const_241_to_fp16 = const()[name = tensor("const_241_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48110464)))]; - tensor input_207_cast_fp16 = conv(bias = const_241_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_240_to_fp16, x = input_203_cast_fp16)[name = tensor("input_207_cast_fp16")]; - tensor input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = tensor("input_209_cast_fp16")]; - tensor x_87_pad_type_0 = const()[name = tensor("x_87_pad_type_0"), val = tensor("valid")]; - tensor x_87_strides_0 = const()[name = tensor("x_87_strides_0"), val = tensor([1])]; - tensor x_87_pad_0 = const()[name = tensor("x_87_pad_0"), val = tensor([0, 0])]; - tensor x_87_dilations_0 = const()[name = tensor("x_87_dilations_0"), val = tensor([1])]; - tensor x_87_groups_0 = const()[name = tensor("x_87_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48111552)))]; - tensor module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48635904)))]; - tensor x_87_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv2_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16, x = input_209_cast_fp16)[name = tensor("x_87_cast_fp16")]; - tensor input_211_perm_0 = const()[name = tensor("input_211_perm_0"), val = tensor([0, 2, 1])]; - tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = tensor("transpose_177")]; - tensor input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = tensor("input_213_cast_fp16")]; - tensor input_215_axes_0 = const()[name = tensor("input_215_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48636992)))]; - tensor module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48638080)))]; - tensor input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = tensor("input_215_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48639168)))]; - tensor module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50736384)))]; - tensor linear_35_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16, x = input_215_cast_fp16)[name = tensor("linear_35_cast_fp16")]; - tensor input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = tensor("input_219_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50740544)))]; - tensor module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52837760)))]; - tensor linear_36_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16, x = input_219_cast_fp16)[name = tensor("linear_36_cast_fp16")]; - tensor var_1017_to_fp16 = const()[name = tensor("op_1017_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1018_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1017_to_fp16)[name = tensor("op_1018_cast_fp16")]; - tensor input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_1018_cast_fp16)[name = tensor("input_225_cast_fp16")]; - tensor input_227_axes_0 = const()[name = tensor("input_227_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52838848)))]; - tensor module_layers_3_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52839936)))]; - tensor input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = tensor("input_227_cast_fp16")]; - tensor input_229_axes_0 = const()[name = tensor("input_229_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52841024)))]; - tensor module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52842112)))]; - tensor input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = tensor("input_229_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52843200)))]; - tensor module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54940416)))]; - tensor linear_37_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16, x = input_229_cast_fp16)[name = tensor("linear_37_cast_fp16")]; - tensor input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = tensor("input_233_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54944576)))]; - tensor module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57041792)))]; - tensor linear_38_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16, x = input_233_cast_fp16)[name = tensor("linear_38_cast_fp16")]; - tensor var_1048_to_fp16 = const()[name = tensor("op_1048_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1049_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1048_to_fp16)[name = tensor("op_1049_cast_fp16")]; - tensor input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_1049_cast_fp16)[name = tensor("input_239_cast_fp16")]; - tensor query_9_axes_0 = const()[name = tensor("query_9_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57042880)))]; - tensor module_layers_4_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57043968)))]; - tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = tensor("query_9_cast_fp16")]; - tensor module_layers_4_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57045056)))]; - tensor module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57569408)))]; - tensor linear_39_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_q_bias_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_39_cast_fp16")]; - tensor var_1066 = const()[name = tensor("op_1066"), val = tensor([1, -1, 8, 64])]; - tensor q_25_cast_fp16 = reshape(shape = var_1066, x = linear_39_cast_fp16)[name = tensor("q_25_cast_fp16")]; - tensor module_layers_4_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57570496)))]; - tensor module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58094848)))]; - tensor linear_40_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_k_bias_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_40_cast_fp16")]; - tensor var_1071 = const()[name = tensor("op_1071"), val = tensor([1, -1, 8, 64])]; - tensor k_17_cast_fp16 = reshape(shape = var_1071, x = linear_40_cast_fp16)[name = tensor("k_17_cast_fp16")]; - tensor module_layers_4_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58095936)))]; - tensor module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58620288)))]; - tensor linear_41_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_v_bias_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_41_cast_fp16")]; - tensor var_1076 = const()[name = tensor("op_1076"), val = tensor([1, -1, 8, 64])]; - tensor v_9_cast_fp16 = reshape(shape = var_1076, x = linear_41_cast_fp16)[name = tensor("v_9_cast_fp16")]; - tensor value_11_perm_0 = const()[name = tensor("value_11_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58621376)))]; - tensor var_1088_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1088_cast_fp16")]; - tensor module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58622464)))]; - tensor var_1090_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1090_cast_fp16")]; - tensor q_with_bias_v_9_perm_0 = const()[name = tensor("q_with_bias_v_9_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_95_transpose_x_0 = const()[name = tensor("x_95_transpose_x_0"), val = tensor(false)]; - tensor x_95_transpose_y_0 = const()[name = tensor("x_95_transpose_y_0"), val = tensor(false)]; - tensor var_1092_to_fp16 = const()[name = tensor("op_1092_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58623552)))]; - tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1090_cast_fp16)[name = tensor("transpose_175")]; - tensor x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = var_1092_to_fp16)[name = tensor("x_95_cast_fp16")]; - tensor x_97_pad_0 = const()[name = tensor("x_97_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_97_mode_0 = const()[name = tensor("x_97_mode_0"), val = tensor("constant")]; - tensor const_110_to_fp16 = const()[name = tensor("const_110_to_fp16"), val = tensor(0x0p+0)]; - tensor x_97_cast_fp16 = pad(constant_val = const_110_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = tensor("x_97_cast_fp16")]; - tensor var_1100 = const()[name = tensor("op_1100"), val = tensor([1, 8, -1, 188])]; - tensor x_99_cast_fp16 = reshape(shape = var_1100, x = x_97_cast_fp16)[name = tensor("x_99_cast_fp16")]; - tensor var_1104_begin_0 = const()[name = tensor("op_1104_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1104_end_0 = const()[name = tensor("op_1104_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1104_end_mask_0 = const()[name = tensor("op_1104_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1104_cast_fp16 = slice_by_index(begin = var_1104_begin_0, end = var_1104_end_0, end_mask = var_1104_end_mask_0, x = x_99_cast_fp16)[name = tensor("op_1104_cast_fp16")]; - tensor var_1105 = const()[name = tensor("op_1105"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_17_cast_fp16 = reshape(shape = var_1105, x = var_1104_cast_fp16)[name = tensor("matrix_bd_17_cast_fp16")]; - tensor matrix_ac_9_transpose_x_0 = const()[name = tensor("matrix_ac_9_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_9_transpose_y_0 = const()[name = tensor("matrix_ac_9_transpose_y_0"), val = tensor(false)]; - tensor transpose_59_perm_0 = const()[name = tensor("transpose_59_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_60_perm_0 = const()[name = tensor("transpose_60_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = k_17_cast_fp16)[name = tensor("transpose_173")]; - tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = var_1088_cast_fp16)[name = tensor("transpose_174")]; - tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_59, y = transpose_60)[name = tensor("matrix_ac_9_cast_fp16")]; - tensor matrix_bd_19_begin_0 = const()[name = tensor("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_19_end_0 = const()[name = tensor("matrix_bd_19_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_19_end_mask_0 = const()[name = tensor("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = tensor("matrix_bd_19_cast_fp16")]; - tensor var_1114_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = tensor("op_1114_cast_fp16")]; - tensor _inversed_scores_17_y_0_to_fp16 = const()[name = tensor("_inversed_scores_17_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_17_cast_fp16 = mul(x = var_1114_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = tensor("_inversed_scores_17_cast_fp16")]; - tensor scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_11)[name = tensor("scores_19_cast_fp16")]; - tensor var_1120_cast_fp16 = softmax(axis = var_23, x = scores_19_cast_fp16)[name = tensor("op_1120_cast_fp16")]; - tensor input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_1120_cast_fp16, cond = mask_11)[name = tensor("input_241_cast_fp16")]; - tensor x_101_transpose_x_0 = const()[name = tensor("x_101_transpose_x_0"), val = tensor(false)]; - tensor x_101_transpose_y_0 = const()[name = tensor("x_101_transpose_y_0"), val = tensor(false)]; - tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = tensor("transpose_176")]; - tensor x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_11_cast_fp16)[name = tensor("x_101_cast_fp16")]; - tensor var_1124_perm_0 = const()[name = tensor("op_1124_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1125 = const()[name = tensor("op_1125"), val = tensor([1, -1, 512])]; - tensor var_1124_cast_fp16 = transpose(perm = var_1124_perm_0, x = x_101_cast_fp16)[name = tensor("transpose_172")]; - tensor input_243_cast_fp16 = reshape(shape = var_1125, x = var_1124_cast_fp16)[name = tensor("input_243_cast_fp16")]; - tensor module_layers_4_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59007616)))]; - tensor module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59531968)))]; - tensor linear_43_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_out_bias_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16, x = input_243_cast_fp16)[name = tensor("linear_43_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = tensor("input_247_cast_fp16")]; - tensor x_105_axes_0 = const()[name = tensor("x_105_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59533056)))]; - tensor module_layers_4_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59534144)))]; - tensor x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = tensor("x_105_cast_fp16")]; - tensor input_249_perm_0 = const()[name = tensor("input_249_perm_0"), val = tensor([0, 2, 1])]; - tensor input_251_pad_type_0 = const()[name = tensor("input_251_pad_type_0"), val = tensor("valid")]; - tensor input_251_strides_0 = const()[name = tensor("input_251_strides_0"), val = tensor([1])]; - tensor input_251_pad_0 = const()[name = tensor("input_251_pad_0"), val = tensor([0, 0])]; - tensor input_251_dilations_0 = const()[name = tensor("input_251_dilations_0"), val = tensor([1])]; - tensor input_251_groups_0 = const()[name = tensor("input_251_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59535232)))]; - tensor module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60583872)))]; - tensor input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = tensor("transpose_171")]; - tensor input_251_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv1_bias_to_fp16, dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16, x = input_249_cast_fp16)[name = tensor("input_251_cast_fp16")]; - tensor x_107_split_num_splits_0 = const()[name = tensor("x_107_split_num_splits_0"), val = tensor(2)]; - tensor x_107_split_axis_0 = const()[name = tensor("x_107_split_axis_0"), val = tensor(1)]; - tensor x_107_split_cast_fp16_0, tensor x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = tensor("x_107_split_cast_fp16")]; - tensor x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = tensor("x_107_split_1_sigmoid_cast_fp16")]; - tensor x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = tensor("x_107_cast_fp16")]; - tensor input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_453)[name = tensor("input_253_cast_fp16")]; - tensor input_255_pad_0 = const()[name = tensor("input_255_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_255_mode_0 = const()[name = tensor("input_255_mode_0"), val = tensor("constant")]; - tensor const_113_to_fp16 = const()[name = tensor("const_113_to_fp16"), val = tensor(0x0p+0)]; - tensor input_255_cast_fp16 = pad(constant_val = const_113_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = tensor("input_255_cast_fp16")]; - tensor input_257_pad_type_0 = const()[name = tensor("input_257_pad_type_0"), val = tensor("valid")]; - tensor input_257_groups_0 = const()[name = tensor("input_257_groups_0"), val = tensor(512)]; - tensor input_257_strides_0 = const()[name = tensor("input_257_strides_0"), val = tensor([1])]; - tensor input_257_pad_0 = const()[name = tensor("input_257_pad_0"), val = tensor([0, 0])]; - tensor input_257_dilations_0 = const()[name = tensor("input_257_dilations_0"), val = tensor([1])]; - tensor const_242_to_fp16 = const()[name = tensor("const_242_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60585984)))]; - tensor const_243_to_fp16 = const()[name = tensor("const_243_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60595264)))]; - tensor input_259_cast_fp16 = conv(bias = const_243_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_242_to_fp16, x = input_255_cast_fp16)[name = tensor("input_259_cast_fp16")]; - tensor input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = tensor("input_261_cast_fp16")]; - tensor x_109_pad_type_0 = const()[name = tensor("x_109_pad_type_0"), val = tensor("valid")]; - tensor x_109_strides_0 = const()[name = tensor("x_109_strides_0"), val = tensor([1])]; - tensor x_109_pad_0 = const()[name = tensor("x_109_pad_0"), val = tensor([0, 0])]; - tensor x_109_dilations_0 = const()[name = tensor("x_109_dilations_0"), val = tensor([1])]; - tensor x_109_groups_0 = const()[name = tensor("x_109_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60596352)))]; - tensor module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61120704)))]; - tensor x_109_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv2_bias_to_fp16, dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16, x = input_261_cast_fp16)[name = tensor("x_109_cast_fp16")]; - tensor input_263_perm_0 = const()[name = tensor("input_263_perm_0"), val = tensor([0, 2, 1])]; - tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = tensor("transpose_170")]; - tensor input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = tensor("input_265_cast_fp16")]; - tensor input_267_axes_0 = const()[name = tensor("input_267_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61121792)))]; - tensor module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61122880)))]; - tensor input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor("input_267_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61123968)))]; - tensor module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63221184)))]; - tensor linear_44_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16, x = input_267_cast_fp16)[name = tensor("linear_44_cast_fp16")]; - tensor input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = tensor("input_271_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63225344)))]; - tensor module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65322560)))]; - tensor linear_45_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16, x = input_271_cast_fp16)[name = tensor("linear_45_cast_fp16")]; - tensor var_1191_to_fp16 = const()[name = tensor("op_1191_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1192_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1191_to_fp16)[name = tensor("op_1192_cast_fp16")]; - tensor input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1192_cast_fp16)[name = tensor("input_277_cast_fp16")]; - tensor input_279_axes_0 = const()[name = tensor("input_279_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65323648)))]; - tensor module_layers_4_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65324736)))]; - tensor input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = tensor("input_279_cast_fp16")]; - tensor input_281_axes_0 = const()[name = tensor("input_281_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65325824)))]; - tensor module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65326912)))]; - tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = tensor("input_281_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65328000)))]; - tensor module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67425216)))]; - tensor linear_46_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16, x = input_281_cast_fp16)[name = tensor("linear_46_cast_fp16")]; - tensor input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = tensor("input_285_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67429376)))]; - tensor module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69526592)))]; - tensor linear_47_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16, x = input_285_cast_fp16)[name = tensor("linear_47_cast_fp16")]; - tensor var_1222_to_fp16 = const()[name = tensor("op_1222_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1223_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1222_to_fp16)[name = tensor("op_1223_cast_fp16")]; - tensor input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1223_cast_fp16)[name = tensor("input_291_cast_fp16")]; - tensor query_11_axes_0 = const()[name = tensor("query_11_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69527680)))]; - tensor module_layers_5_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69528768)))]; - tensor query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = tensor("query_11_cast_fp16")]; - tensor module_layers_5_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69529856)))]; - tensor module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70054208)))]; - tensor linear_48_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_q_bias_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_48_cast_fp16")]; - tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1, -1, 8, 64])]; - tensor q_31_cast_fp16 = reshape(shape = var_1240, x = linear_48_cast_fp16)[name = tensor("q_31_cast_fp16")]; - tensor module_layers_5_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70055296)))]; - tensor module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70579648)))]; - tensor linear_49_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_k_bias_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_49_cast_fp16")]; - tensor var_1245 = const()[name = tensor("op_1245"), val = tensor([1, -1, 8, 64])]; - tensor k_21_cast_fp16 = reshape(shape = var_1245, x = linear_49_cast_fp16)[name = tensor("k_21_cast_fp16")]; - tensor module_layers_5_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70580736)))]; - tensor module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71105088)))]; - tensor linear_50_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_v_bias_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_50_cast_fp16")]; - tensor var_1250 = const()[name = tensor("op_1250"), val = tensor([1, -1, 8, 64])]; - tensor v_11_cast_fp16 = reshape(shape = var_1250, x = linear_50_cast_fp16)[name = tensor("v_11_cast_fp16")]; - tensor value_13_perm_0 = const()[name = tensor("value_13_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71106176)))]; - tensor var_1262_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1262_cast_fp16")]; - tensor module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71107264)))]; - tensor var_1264_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1264_cast_fp16")]; - tensor q_with_bias_v_11_perm_0 = const()[name = tensor("q_with_bias_v_11_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_117_transpose_x_0 = const()[name = tensor("x_117_transpose_x_0"), val = tensor(false)]; - tensor x_117_transpose_y_0 = const()[name = tensor("x_117_transpose_y_0"), val = tensor(false)]; - tensor var_1266_to_fp16 = const()[name = tensor("op_1266_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71108352)))]; - tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1264_cast_fp16)[name = tensor("transpose_168")]; - tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = var_1266_to_fp16)[name = tensor("x_117_cast_fp16")]; - tensor x_119_pad_0 = const()[name = tensor("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_119_mode_0 = const()[name = tensor("x_119_mode_0"), val = tensor("constant")]; - tensor const_120_to_fp16 = const()[name = tensor("const_120_to_fp16"), val = tensor(0x0p+0)]; - tensor x_119_cast_fp16 = pad(constant_val = const_120_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = tensor("x_119_cast_fp16")]; - tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([1, 8, -1, 188])]; - tensor x_121_cast_fp16 = reshape(shape = var_1274, x = x_119_cast_fp16)[name = tensor("x_121_cast_fp16")]; - tensor var_1278_begin_0 = const()[name = tensor("op_1278_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1278_end_0 = const()[name = tensor("op_1278_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1278_end_mask_0 = const()[name = tensor("op_1278_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1278_cast_fp16 = slice_by_index(begin = var_1278_begin_0, end = var_1278_end_0, end_mask = var_1278_end_mask_0, x = x_121_cast_fp16)[name = tensor("op_1278_cast_fp16")]; - tensor var_1279 = const()[name = tensor("op_1279"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1279, x = var_1278_cast_fp16)[name = tensor("matrix_bd_21_cast_fp16")]; - tensor matrix_ac_11_transpose_x_0 = const()[name = tensor("matrix_ac_11_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_11_transpose_y_0 = const()[name = tensor("matrix_ac_11_transpose_y_0"), val = tensor(false)]; - tensor transpose_61_perm_0 = const()[name = tensor("transpose_61_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_62_perm_0 = const()[name = tensor("transpose_62_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = k_21_cast_fp16)[name = tensor("transpose_166")]; - tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_1262_cast_fp16)[name = tensor("transpose_167")]; - tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_61, y = transpose_62)[name = tensor("matrix_ac_11_cast_fp16")]; - tensor matrix_bd_23_begin_0 = const()[name = tensor("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_23_end_0 = const()[name = tensor("matrix_bd_23_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_23_end_mask_0 = const()[name = tensor("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = tensor("matrix_bd_23_cast_fp16")]; - tensor var_1288_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = tensor("op_1288_cast_fp16")]; - tensor _inversed_scores_21_y_0_to_fp16 = const()[name = tensor("_inversed_scores_21_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_21_cast_fp16 = mul(x = var_1288_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = tensor("_inversed_scores_21_cast_fp16")]; - tensor scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_11)[name = tensor("scores_23_cast_fp16")]; - tensor var_1294_cast_fp16 = softmax(axis = var_23, x = scores_23_cast_fp16)[name = tensor("op_1294_cast_fp16")]; - tensor input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1294_cast_fp16, cond = mask_11)[name = tensor("input_293_cast_fp16")]; - tensor x_123_transpose_x_0 = const()[name = tensor("x_123_transpose_x_0"), val = tensor(false)]; - tensor x_123_transpose_y_0 = const()[name = tensor("x_123_transpose_y_0"), val = tensor(false)]; - tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = tensor("transpose_169")]; - tensor x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_13_cast_fp16)[name = tensor("x_123_cast_fp16")]; - tensor var_1298_perm_0 = const()[name = tensor("op_1298_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1299 = const()[name = tensor("op_1299"), val = tensor([1, -1, 512])]; - tensor var_1298_cast_fp16 = transpose(perm = var_1298_perm_0, x = x_123_cast_fp16)[name = tensor("transpose_165")]; - tensor input_295_cast_fp16 = reshape(shape = var_1299, x = var_1298_cast_fp16)[name = tensor("input_295_cast_fp16")]; - tensor module_layers_5_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71492416)))]; - tensor module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72016768)))]; - tensor linear_52_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_out_bias_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16, x = input_295_cast_fp16)[name = tensor("linear_52_cast_fp16")]; - tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = tensor("input_299_cast_fp16")]; - tensor x_127_axes_0 = const()[name = tensor("x_127_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72017856)))]; - tensor module_layers_5_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72018944)))]; - tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = tensor("x_127_cast_fp16")]; - tensor input_301_perm_0 = const()[name = tensor("input_301_perm_0"), val = tensor([0, 2, 1])]; - tensor input_303_pad_type_0 = const()[name = tensor("input_303_pad_type_0"), val = tensor("valid")]; - tensor input_303_strides_0 = const()[name = tensor("input_303_strides_0"), val = tensor([1])]; - tensor input_303_pad_0 = const()[name = tensor("input_303_pad_0"), val = tensor([0, 0])]; - tensor input_303_dilations_0 = const()[name = tensor("input_303_dilations_0"), val = tensor([1])]; - tensor input_303_groups_0 = const()[name = tensor("input_303_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72020032)))]; - tensor module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73068672)))]; - tensor input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = tensor("transpose_164")]; - tensor input_303_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv1_bias_to_fp16, dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16, x = input_301_cast_fp16)[name = tensor("input_303_cast_fp16")]; - tensor x_129_split_num_splits_0 = const()[name = tensor("x_129_split_num_splits_0"), val = tensor(2)]; - tensor x_129_split_axis_0 = const()[name = tensor("x_129_split_axis_0"), val = tensor(1)]; - tensor x_129_split_cast_fp16_0, tensor x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = tensor("x_129_split_cast_fp16")]; - tensor x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = tensor("x_129_split_1_sigmoid_cast_fp16")]; - tensor x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = tensor("x_129_cast_fp16")]; - tensor input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_453)[name = tensor("input_305_cast_fp16")]; - tensor input_307_pad_0 = const()[name = tensor("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_307_mode_0 = const()[name = tensor("input_307_mode_0"), val = tensor("constant")]; - tensor const_123_to_fp16 = const()[name = tensor("const_123_to_fp16"), val = tensor(0x0p+0)]; - tensor input_307_cast_fp16 = pad(constant_val = const_123_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = tensor("input_307_cast_fp16")]; - tensor input_309_pad_type_0 = const()[name = tensor("input_309_pad_type_0"), val = tensor("valid")]; - tensor input_309_groups_0 = const()[name = tensor("input_309_groups_0"), val = tensor(512)]; - tensor input_309_strides_0 = const()[name = tensor("input_309_strides_0"), val = tensor([1])]; - tensor input_309_pad_0 = const()[name = tensor("input_309_pad_0"), val = tensor([0, 0])]; - tensor input_309_dilations_0 = const()[name = tensor("input_309_dilations_0"), val = tensor([1])]; - tensor const_244_to_fp16 = const()[name = tensor("const_244_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73070784)))]; - tensor const_245_to_fp16 = const()[name = tensor("const_245_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73080064)))]; - tensor input_311_cast_fp16 = conv(bias = const_245_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_244_to_fp16, x = input_307_cast_fp16)[name = tensor("input_311_cast_fp16")]; - tensor input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = tensor("input_313_cast_fp16")]; - tensor x_131_pad_type_0 = const()[name = tensor("x_131_pad_type_0"), val = tensor("valid")]; - tensor x_131_strides_0 = const()[name = tensor("x_131_strides_0"), val = tensor([1])]; - tensor x_131_pad_0 = const()[name = tensor("x_131_pad_0"), val = tensor([0, 0])]; - tensor x_131_dilations_0 = const()[name = tensor("x_131_dilations_0"), val = tensor([1])]; - tensor x_131_groups_0 = const()[name = tensor("x_131_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73081152)))]; - tensor module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73605504)))]; - tensor x_131_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv2_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_layers_5_conv_pointwise_conv2_weight_to_fp16, x = input_313_cast_fp16)[name = tensor("x_131_cast_fp16")]; - tensor input_315_perm_0 = const()[name = tensor("input_315_perm_0"), val = tensor([0, 2, 1])]; - tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = tensor("transpose_163")]; - tensor input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = tensor("input_317_cast_fp16")]; - tensor input_319_axes_0 = const()[name = tensor("input_319_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73606592)))]; - tensor module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73607680)))]; - tensor input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor("input_319_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73608768)))]; - tensor module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75705984)))]; - tensor linear_53_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16, x = input_319_cast_fp16)[name = tensor("linear_53_cast_fp16")]; - tensor input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = tensor("input_323_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75710144)))]; - tensor module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77807360)))]; - tensor linear_54_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16, x = input_323_cast_fp16)[name = tensor("linear_54_cast_fp16")]; - tensor var_1365_to_fp16 = const()[name = tensor("op_1365_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1366_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1365_to_fp16)[name = tensor("op_1366_cast_fp16")]; - tensor input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1366_cast_fp16)[name = tensor("input_329_cast_fp16")]; - tensor input_331_axes_0 = const()[name = tensor("input_331_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77808448)))]; - tensor module_layers_5_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77809536)))]; - tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("input_331_cast_fp16")]; - tensor input_333_axes_0 = const()[name = tensor("input_333_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77810624)))]; - tensor module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77811712)))]; - tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("input_333_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77812800)))]; - tensor module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79910016)))]; - tensor linear_55_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16, x = input_333_cast_fp16)[name = tensor("linear_55_cast_fp16")]; - tensor input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = tensor("input_337_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79914176)))]; - tensor module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82011392)))]; - tensor linear_56_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16, x = input_337_cast_fp16)[name = tensor("linear_56_cast_fp16")]; - tensor var_1396_to_fp16 = const()[name = tensor("op_1396_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1397_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1396_to_fp16)[name = tensor("op_1397_cast_fp16")]; - tensor input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1397_cast_fp16)[name = tensor("input_343_cast_fp16")]; - tensor query_13_axes_0 = const()[name = tensor("query_13_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82012480)))]; - tensor module_layers_6_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82013568)))]; - tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = tensor("query_13_cast_fp16")]; - tensor module_layers_6_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82014656)))]; - tensor module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82539008)))]; - tensor linear_57_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_q_bias_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_57_cast_fp16")]; - tensor var_1414 = const()[name = tensor("op_1414"), val = tensor([1, -1, 8, 64])]; - tensor q_37_cast_fp16 = reshape(shape = var_1414, x = linear_57_cast_fp16)[name = tensor("q_37_cast_fp16")]; - tensor module_layers_6_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82540096)))]; - tensor module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83064448)))]; - tensor linear_58_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_k_bias_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_58_cast_fp16")]; - tensor var_1419 = const()[name = tensor("op_1419"), val = tensor([1, -1, 8, 64])]; - tensor k_25_cast_fp16 = reshape(shape = var_1419, x = linear_58_cast_fp16)[name = tensor("k_25_cast_fp16")]; - tensor module_layers_6_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83065536)))]; - tensor module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83589888)))]; - tensor linear_59_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_v_bias_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_59_cast_fp16")]; - tensor var_1424 = const()[name = tensor("op_1424"), val = tensor([1, -1, 8, 64])]; - tensor v_13_cast_fp16 = reshape(shape = var_1424, x = linear_59_cast_fp16)[name = tensor("v_13_cast_fp16")]; - tensor value_15_perm_0 = const()[name = tensor("value_15_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83590976)))]; - tensor var_1436_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1436_cast_fp16")]; - tensor module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83592064)))]; - tensor var_1438_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1438_cast_fp16")]; - tensor q_with_bias_v_13_perm_0 = const()[name = tensor("q_with_bias_v_13_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_139_transpose_x_0 = const()[name = tensor("x_139_transpose_x_0"), val = tensor(false)]; - tensor x_139_transpose_y_0 = const()[name = tensor("x_139_transpose_y_0"), val = tensor(false)]; - tensor var_1440_to_fp16 = const()[name = tensor("op_1440_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83593152)))]; - tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1438_cast_fp16)[name = tensor("transpose_161")]; - tensor x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = var_1440_to_fp16)[name = tensor("x_139_cast_fp16")]; - tensor x_141_pad_0 = const()[name = tensor("x_141_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_141_mode_0 = const()[name = tensor("x_141_mode_0"), val = tensor("constant")]; - tensor const_130_to_fp16 = const()[name = tensor("const_130_to_fp16"), val = tensor(0x0p+0)]; - tensor x_141_cast_fp16 = pad(constant_val = const_130_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = tensor("x_141_cast_fp16")]; - tensor var_1448 = const()[name = tensor("op_1448"), val = tensor([1, 8, -1, 188])]; - tensor x_143_cast_fp16 = reshape(shape = var_1448, x = x_141_cast_fp16)[name = tensor("x_143_cast_fp16")]; - tensor var_1452_begin_0 = const()[name = tensor("op_1452_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1452_end_0 = const()[name = tensor("op_1452_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1452_end_mask_0 = const()[name = tensor("op_1452_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1452_cast_fp16 = slice_by_index(begin = var_1452_begin_0, end = var_1452_end_0, end_mask = var_1452_end_mask_0, x = x_143_cast_fp16)[name = tensor("op_1452_cast_fp16")]; - tensor var_1453 = const()[name = tensor("op_1453"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1453, x = var_1452_cast_fp16)[name = tensor("matrix_bd_25_cast_fp16")]; - tensor matrix_ac_13_transpose_x_0 = const()[name = tensor("matrix_ac_13_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_13_transpose_y_0 = const()[name = tensor("matrix_ac_13_transpose_y_0"), val = tensor(false)]; - tensor transpose_63_perm_0 = const()[name = tensor("transpose_63_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_64_perm_0 = const()[name = tensor("transpose_64_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = k_25_cast_fp16)[name = tensor("transpose_159")]; - tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_1436_cast_fp16)[name = tensor("transpose_160")]; - tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_63, y = transpose_64)[name = tensor("matrix_ac_13_cast_fp16")]; - tensor matrix_bd_27_begin_0 = const()[name = tensor("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_27_end_0 = const()[name = tensor("matrix_bd_27_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_27_end_mask_0 = const()[name = tensor("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = tensor("matrix_bd_27_cast_fp16")]; - tensor var_1462_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = tensor("op_1462_cast_fp16")]; - tensor _inversed_scores_25_y_0_to_fp16 = const()[name = tensor("_inversed_scores_25_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_25_cast_fp16 = mul(x = var_1462_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = tensor("_inversed_scores_25_cast_fp16")]; - tensor scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_11)[name = tensor("scores_27_cast_fp16")]; - tensor var_1468_cast_fp16 = softmax(axis = var_23, x = scores_27_cast_fp16)[name = tensor("op_1468_cast_fp16")]; - tensor input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1468_cast_fp16, cond = mask_11)[name = tensor("input_345_cast_fp16")]; - tensor x_145_transpose_x_0 = const()[name = tensor("x_145_transpose_x_0"), val = tensor(false)]; - tensor x_145_transpose_y_0 = const()[name = tensor("x_145_transpose_y_0"), val = tensor(false)]; - tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = tensor("transpose_162")]; - tensor x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_15_cast_fp16)[name = tensor("x_145_cast_fp16")]; - tensor var_1472_perm_0 = const()[name = tensor("op_1472_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1473 = const()[name = tensor("op_1473"), val = tensor([1, -1, 512])]; - tensor var_1472_cast_fp16 = transpose(perm = var_1472_perm_0, x = x_145_cast_fp16)[name = tensor("transpose_158")]; - tensor input_347_cast_fp16 = reshape(shape = var_1473, x = var_1472_cast_fp16)[name = tensor("input_347_cast_fp16")]; - tensor module_layers_6_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83977216)))]; - tensor module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84501568)))]; - tensor linear_61_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_out_bias_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16, x = input_347_cast_fp16)[name = tensor("linear_61_cast_fp16")]; - tensor input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = tensor("input_351_cast_fp16")]; - tensor x_149_axes_0 = const()[name = tensor("x_149_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84502656)))]; - tensor module_layers_6_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84503744)))]; - tensor x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = tensor("x_149_cast_fp16")]; - tensor input_353_perm_0 = const()[name = tensor("input_353_perm_0"), val = tensor([0, 2, 1])]; - tensor input_355_pad_type_0 = const()[name = tensor("input_355_pad_type_0"), val = tensor("valid")]; - tensor input_355_strides_0 = const()[name = tensor("input_355_strides_0"), val = tensor([1])]; - tensor input_355_pad_0 = const()[name = tensor("input_355_pad_0"), val = tensor([0, 0])]; - tensor input_355_dilations_0 = const()[name = tensor("input_355_dilations_0"), val = tensor([1])]; - tensor input_355_groups_0 = const()[name = tensor("input_355_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84504832)))]; - tensor module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85553472)))]; - tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = tensor("transpose_157")]; - tensor input_355_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv1_bias_to_fp16, dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16, x = input_353_cast_fp16)[name = tensor("input_355_cast_fp16")]; - tensor x_151_split_num_splits_0 = const()[name = tensor("x_151_split_num_splits_0"), val = tensor(2)]; - tensor x_151_split_axis_0 = const()[name = tensor("x_151_split_axis_0"), val = tensor(1)]; - tensor x_151_split_cast_fp16_0, tensor x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = tensor("x_151_split_cast_fp16")]; - tensor x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = tensor("x_151_split_1_sigmoid_cast_fp16")]; - tensor x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = tensor("x_151_cast_fp16")]; - tensor input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_453)[name = tensor("input_357_cast_fp16")]; - tensor input_359_pad_0 = const()[name = tensor("input_359_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_359_mode_0 = const()[name = tensor("input_359_mode_0"), val = tensor("constant")]; - tensor const_133_to_fp16 = const()[name = tensor("const_133_to_fp16"), val = tensor(0x0p+0)]; - tensor input_359_cast_fp16 = pad(constant_val = const_133_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = tensor("input_359_cast_fp16")]; - tensor input_361_pad_type_0 = const()[name = tensor("input_361_pad_type_0"), val = tensor("valid")]; - tensor input_361_groups_0 = const()[name = tensor("input_361_groups_0"), val = tensor(512)]; - tensor input_361_strides_0 = const()[name = tensor("input_361_strides_0"), val = tensor([1])]; - tensor input_361_pad_0 = const()[name = tensor("input_361_pad_0"), val = tensor([0, 0])]; - tensor input_361_dilations_0 = const()[name = tensor("input_361_dilations_0"), val = tensor([1])]; - tensor const_246_to_fp16 = const()[name = tensor("const_246_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85555584)))]; - tensor const_247_to_fp16 = const()[name = tensor("const_247_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85564864)))]; - tensor input_363_cast_fp16 = conv(bias = const_247_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_246_to_fp16, x = input_359_cast_fp16)[name = tensor("input_363_cast_fp16")]; - tensor input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; - tensor x_153_pad_type_0 = const()[name = tensor("x_153_pad_type_0"), val = tensor("valid")]; - tensor x_153_strides_0 = const()[name = tensor("x_153_strides_0"), val = tensor([1])]; - tensor x_153_pad_0 = const()[name = tensor("x_153_pad_0"), val = tensor([0, 0])]; - tensor x_153_dilations_0 = const()[name = tensor("x_153_dilations_0"), val = tensor([1])]; - tensor x_153_groups_0 = const()[name = tensor("x_153_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85565952)))]; - tensor module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86090304)))]; - tensor x_153_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv2_bias_to_fp16, dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = module_layers_6_conv_pointwise_conv2_weight_to_fp16, x = input_365_cast_fp16)[name = tensor("x_153_cast_fp16")]; - tensor input_367_perm_0 = const()[name = tensor("input_367_perm_0"), val = tensor([0, 2, 1])]; - tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = tensor("transpose_156")]; - tensor input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = tensor("input_369_cast_fp16")]; - tensor input_371_axes_0 = const()[name = tensor("input_371_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86091392)))]; - tensor module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86092480)))]; - tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor("input_371_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86093568)))]; - tensor module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88190784)))]; - tensor linear_62_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16, x = input_371_cast_fp16)[name = tensor("linear_62_cast_fp16")]; - tensor input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = tensor("input_375_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88194944)))]; - tensor module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90292160)))]; - tensor linear_63_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16, x = input_375_cast_fp16)[name = tensor("linear_63_cast_fp16")]; - tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1540_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1539_to_fp16)[name = tensor("op_1540_cast_fp16")]; - tensor input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1540_cast_fp16)[name = tensor("input_381_cast_fp16")]; - tensor input_383_axes_0 = const()[name = tensor("input_383_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90293248)))]; - tensor module_layers_6_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90294336)))]; - tensor input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = tensor("input_383_cast_fp16")]; - tensor input_385_axes_0 = const()[name = tensor("input_385_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90295424)))]; - tensor module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90296512)))]; - tensor input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = tensor("input_385_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90297600)))]; - tensor module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92394816)))]; - tensor linear_64_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16, x = input_385_cast_fp16)[name = tensor("linear_64_cast_fp16")]; - tensor input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = tensor("input_389_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92398976)))]; - tensor module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94496192)))]; - tensor linear_65_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16, x = input_389_cast_fp16)[name = tensor("linear_65_cast_fp16")]; - tensor var_1570_to_fp16 = const()[name = tensor("op_1570_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1571_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1570_to_fp16)[name = tensor("op_1571_cast_fp16")]; - tensor input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1571_cast_fp16)[name = tensor("input_395_cast_fp16")]; - tensor query_15_axes_0 = const()[name = tensor("query_15_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94497280)))]; - tensor module_layers_7_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94498368)))]; - tensor query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = tensor("query_15_cast_fp16")]; - tensor module_layers_7_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94499456)))]; - tensor module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95023808)))]; - tensor linear_66_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_q_bias_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_66_cast_fp16")]; - tensor var_1588 = const()[name = tensor("op_1588"), val = tensor([1, -1, 8, 64])]; - tensor q_43_cast_fp16 = reshape(shape = var_1588, x = linear_66_cast_fp16)[name = tensor("q_43_cast_fp16")]; - tensor module_layers_7_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95024896)))]; - tensor module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95549248)))]; - tensor linear_67_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_k_bias_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_67_cast_fp16")]; - tensor var_1593 = const()[name = tensor("op_1593"), val = tensor([1, -1, 8, 64])]; - tensor k_29_cast_fp16 = reshape(shape = var_1593, x = linear_67_cast_fp16)[name = tensor("k_29_cast_fp16")]; - tensor module_layers_7_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95550336)))]; - tensor module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96074688)))]; - tensor linear_68_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_v_bias_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_68_cast_fp16")]; - tensor var_1598 = const()[name = tensor("op_1598"), val = tensor([1, -1, 8, 64])]; - tensor v_15_cast_fp16 = reshape(shape = var_1598, x = linear_68_cast_fp16)[name = tensor("v_15_cast_fp16")]; - tensor value_17_perm_0 = const()[name = tensor("value_17_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96075776)))]; - tensor var_1610_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1610_cast_fp16")]; - tensor module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96076864)))]; - tensor var_1612_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1612_cast_fp16")]; - tensor q_with_bias_v_15_perm_0 = const()[name = tensor("q_with_bias_v_15_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_161_transpose_x_0 = const()[name = tensor("x_161_transpose_x_0"), val = tensor(false)]; - tensor x_161_transpose_y_0 = const()[name = tensor("x_161_transpose_y_0"), val = tensor(false)]; - tensor var_1614_to_fp16 = const()[name = tensor("op_1614_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96077952)))]; - tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1612_cast_fp16)[name = tensor("transpose_154")]; - tensor x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = var_1614_to_fp16)[name = tensor("x_161_cast_fp16")]; - tensor x_163_pad_0 = const()[name = tensor("x_163_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_163_mode_0 = const()[name = tensor("x_163_mode_0"), val = tensor("constant")]; - tensor const_140_to_fp16 = const()[name = tensor("const_140_to_fp16"), val = tensor(0x0p+0)]; - tensor x_163_cast_fp16 = pad(constant_val = const_140_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = tensor("x_163_cast_fp16")]; - tensor var_1622 = const()[name = tensor("op_1622"), val = tensor([1, 8, -1, 188])]; - tensor x_165_cast_fp16 = reshape(shape = var_1622, x = x_163_cast_fp16)[name = tensor("x_165_cast_fp16")]; - tensor var_1626_begin_0 = const()[name = tensor("op_1626_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1626_end_0 = const()[name = tensor("op_1626_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1626_end_mask_0 = const()[name = tensor("op_1626_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1626_cast_fp16 = slice_by_index(begin = var_1626_begin_0, end = var_1626_end_0, end_mask = var_1626_end_mask_0, x = x_165_cast_fp16)[name = tensor("op_1626_cast_fp16")]; - tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1627, x = var_1626_cast_fp16)[name = tensor("matrix_bd_29_cast_fp16")]; - tensor matrix_ac_15_transpose_x_0 = const()[name = tensor("matrix_ac_15_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_15_transpose_y_0 = const()[name = tensor("matrix_ac_15_transpose_y_0"), val = tensor(false)]; - tensor transpose_65_perm_0 = const()[name = tensor("transpose_65_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_66_perm_0 = const()[name = tensor("transpose_66_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = k_29_cast_fp16)[name = tensor("transpose_152")]; - tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = var_1610_cast_fp16)[name = tensor("transpose_153")]; - tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_65, y = transpose_66)[name = tensor("matrix_ac_15_cast_fp16")]; - tensor matrix_bd_31_begin_0 = const()[name = tensor("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_31_end_0 = const()[name = tensor("matrix_bd_31_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_31_end_mask_0 = const()[name = tensor("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = tensor("matrix_bd_31_cast_fp16")]; - tensor var_1636_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = tensor("op_1636_cast_fp16")]; - tensor _inversed_scores_29_y_0_to_fp16 = const()[name = tensor("_inversed_scores_29_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_29_cast_fp16 = mul(x = var_1636_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = tensor("_inversed_scores_29_cast_fp16")]; - tensor scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_11)[name = tensor("scores_31_cast_fp16")]; - tensor var_1642_cast_fp16 = softmax(axis = var_23, x = scores_31_cast_fp16)[name = tensor("op_1642_cast_fp16")]; - tensor input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1642_cast_fp16, cond = mask_11)[name = tensor("input_397_cast_fp16")]; - tensor x_167_transpose_x_0 = const()[name = tensor("x_167_transpose_x_0"), val = tensor(false)]; - tensor x_167_transpose_y_0 = const()[name = tensor("x_167_transpose_y_0"), val = tensor(false)]; - tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = tensor("transpose_155")]; - tensor x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_17_cast_fp16)[name = tensor("x_167_cast_fp16")]; - tensor var_1646_perm_0 = const()[name = tensor("op_1646_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1647 = const()[name = tensor("op_1647"), val = tensor([1, -1, 512])]; - tensor var_1646_cast_fp16 = transpose(perm = var_1646_perm_0, x = x_167_cast_fp16)[name = tensor("transpose_151")]; - tensor input_399_cast_fp16 = reshape(shape = var_1647, x = var_1646_cast_fp16)[name = tensor("input_399_cast_fp16")]; - tensor module_layers_7_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96462016)))]; - tensor module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96986368)))]; - tensor linear_70_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_out_bias_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16, x = input_399_cast_fp16)[name = tensor("linear_70_cast_fp16")]; - tensor input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = tensor("input_403_cast_fp16")]; - tensor x_171_axes_0 = const()[name = tensor("x_171_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96987456)))]; - tensor module_layers_7_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96988544)))]; - tensor x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = tensor("x_171_cast_fp16")]; - tensor input_405_perm_0 = const()[name = tensor("input_405_perm_0"), val = tensor([0, 2, 1])]; - tensor input_407_pad_type_0 = const()[name = tensor("input_407_pad_type_0"), val = tensor("valid")]; - tensor input_407_strides_0 = const()[name = tensor("input_407_strides_0"), val = tensor([1])]; - tensor input_407_pad_0 = const()[name = tensor("input_407_pad_0"), val = tensor([0, 0])]; - tensor input_407_dilations_0 = const()[name = tensor("input_407_dilations_0"), val = tensor([1])]; - tensor input_407_groups_0 = const()[name = tensor("input_407_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96989632)))]; - tensor module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98038272)))]; - tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = tensor("transpose_150")]; - tensor input_407_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv1_bias_to_fp16, dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16, x = input_405_cast_fp16)[name = tensor("input_407_cast_fp16")]; - tensor x_173_split_num_splits_0 = const()[name = tensor("x_173_split_num_splits_0"), val = tensor(2)]; - tensor x_173_split_axis_0 = const()[name = tensor("x_173_split_axis_0"), val = tensor(1)]; - tensor x_173_split_cast_fp16_0, tensor x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = tensor("x_173_split_cast_fp16")]; - tensor x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = tensor("x_173_split_1_sigmoid_cast_fp16")]; - tensor x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = tensor("x_173_cast_fp16")]; - tensor input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_453)[name = tensor("input_409_cast_fp16")]; - tensor input_411_pad_0 = const()[name = tensor("input_411_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_411_mode_0 = const()[name = tensor("input_411_mode_0"), val = tensor("constant")]; - tensor const_143_to_fp16 = const()[name = tensor("const_143_to_fp16"), val = tensor(0x0p+0)]; - tensor input_411_cast_fp16 = pad(constant_val = const_143_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = tensor("input_411_cast_fp16")]; - tensor input_413_pad_type_0 = const()[name = tensor("input_413_pad_type_0"), val = tensor("valid")]; - tensor input_413_groups_0 = const()[name = tensor("input_413_groups_0"), val = tensor(512)]; - tensor input_413_strides_0 = const()[name = tensor("input_413_strides_0"), val = tensor([1])]; - tensor input_413_pad_0 = const()[name = tensor("input_413_pad_0"), val = tensor([0, 0])]; - tensor input_413_dilations_0 = const()[name = tensor("input_413_dilations_0"), val = tensor([1])]; - tensor const_248_to_fp16 = const()[name = tensor("const_248_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98040384)))]; - tensor const_249_to_fp16 = const()[name = tensor("const_249_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98049664)))]; - tensor input_415_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_248_to_fp16, x = input_411_cast_fp16)[name = tensor("input_415_cast_fp16")]; - tensor input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = tensor("input_417_cast_fp16")]; - tensor x_175_pad_type_0 = const()[name = tensor("x_175_pad_type_0"), val = tensor("valid")]; - tensor x_175_strides_0 = const()[name = tensor("x_175_strides_0"), val = tensor([1])]; - tensor x_175_pad_0 = const()[name = tensor("x_175_pad_0"), val = tensor([0, 0])]; - tensor x_175_dilations_0 = const()[name = tensor("x_175_dilations_0"), val = tensor([1])]; - tensor x_175_groups_0 = const()[name = tensor("x_175_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98050752)))]; - tensor module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98575104)))]; - tensor x_175_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv2_bias_to_fp16, dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = module_layers_7_conv_pointwise_conv2_weight_to_fp16, x = input_417_cast_fp16)[name = tensor("x_175_cast_fp16")]; - tensor input_419_perm_0 = const()[name = tensor("input_419_perm_0"), val = tensor([0, 2, 1])]; - tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = tensor("transpose_149")]; - tensor input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = tensor("input_421_cast_fp16")]; - tensor input_423_axes_0 = const()[name = tensor("input_423_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98576192)))]; - tensor module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98577280)))]; - tensor input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = tensor("input_423_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98578368)))]; - tensor module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100675584)))]; - tensor linear_71_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor("linear_71_cast_fp16")]; - tensor input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = tensor("input_427_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100679744)))]; - tensor module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102776960)))]; - tensor linear_72_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16, x = input_427_cast_fp16)[name = tensor("linear_72_cast_fp16")]; - tensor var_1713_to_fp16 = const()[name = tensor("op_1713_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1714_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1713_to_fp16)[name = tensor("op_1714_cast_fp16")]; - tensor input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1714_cast_fp16)[name = tensor("input_433_cast_fp16")]; - tensor input_435_axes_0 = const()[name = tensor("input_435_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102778048)))]; - tensor module_layers_7_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102779136)))]; - tensor input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = tensor("input_435_cast_fp16")]; - tensor input_437_axes_0 = const()[name = tensor("input_437_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102780224)))]; - tensor module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102781312)))]; - tensor input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = tensor("input_437_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102782400)))]; - tensor module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104879616)))]; - tensor linear_73_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16, x = input_437_cast_fp16)[name = tensor("linear_73_cast_fp16")]; - tensor input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = tensor("input_441_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104883776)))]; - tensor module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106980992)))]; - tensor linear_74_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16, x = input_441_cast_fp16)[name = tensor("linear_74_cast_fp16")]; - tensor var_1744_to_fp16 = const()[name = tensor("op_1744_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1745_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1744_to_fp16)[name = tensor("op_1745_cast_fp16")]; - tensor input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1745_cast_fp16)[name = tensor("input_447_cast_fp16")]; - tensor query_17_axes_0 = const()[name = tensor("query_17_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106982080)))]; - tensor module_layers_8_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106983168)))]; - tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = tensor("query_17_cast_fp16")]; - tensor module_layers_8_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106984256)))]; - tensor module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107508608)))]; - tensor linear_75_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_q_bias_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_75_cast_fp16")]; - tensor var_1762 = const()[name = tensor("op_1762"), val = tensor([1, -1, 8, 64])]; - tensor q_49_cast_fp16 = reshape(shape = var_1762, x = linear_75_cast_fp16)[name = tensor("q_49_cast_fp16")]; - tensor module_layers_8_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107509696)))]; - tensor module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108034048)))]; - tensor linear_76_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_k_bias_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_76_cast_fp16")]; - tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, -1, 8, 64])]; - tensor k_33_cast_fp16 = reshape(shape = var_1767, x = linear_76_cast_fp16)[name = tensor("k_33_cast_fp16")]; - tensor module_layers_8_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108035136)))]; - tensor module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108559488)))]; - tensor linear_77_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_v_bias_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_77_cast_fp16")]; - tensor var_1772 = const()[name = tensor("op_1772"), val = tensor([1, -1, 8, 64])]; - tensor v_17_cast_fp16 = reshape(shape = var_1772, x = linear_77_cast_fp16)[name = tensor("v_17_cast_fp16")]; - tensor value_19_perm_0 = const()[name = tensor("value_19_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108560576)))]; - tensor var_1784_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1784_cast_fp16")]; - tensor module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108561664)))]; - tensor var_1786_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1786_cast_fp16")]; - tensor q_with_bias_v_17_perm_0 = const()[name = tensor("q_with_bias_v_17_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_183_transpose_x_0 = const()[name = tensor("x_183_transpose_x_0"), val = tensor(false)]; - tensor x_183_transpose_y_0 = const()[name = tensor("x_183_transpose_y_0"), val = tensor(false)]; - tensor var_1788_to_fp16 = const()[name = tensor("op_1788_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108562752)))]; - tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1786_cast_fp16)[name = tensor("transpose_147")]; - tensor x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = var_1788_to_fp16)[name = tensor("x_183_cast_fp16")]; - tensor x_185_pad_0 = const()[name = tensor("x_185_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_185_mode_0 = const()[name = tensor("x_185_mode_0"), val = tensor("constant")]; - tensor const_150_to_fp16 = const()[name = tensor("const_150_to_fp16"), val = tensor(0x0p+0)]; - tensor x_185_cast_fp16 = pad(constant_val = const_150_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = tensor("x_185_cast_fp16")]; - tensor var_1796 = const()[name = tensor("op_1796"), val = tensor([1, 8, -1, 188])]; - tensor x_187_cast_fp16 = reshape(shape = var_1796, x = x_185_cast_fp16)[name = tensor("x_187_cast_fp16")]; - tensor var_1800_begin_0 = const()[name = tensor("op_1800_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1800_end_0 = const()[name = tensor("op_1800_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1800_end_mask_0 = const()[name = tensor("op_1800_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1800_cast_fp16 = slice_by_index(begin = var_1800_begin_0, end = var_1800_end_0, end_mask = var_1800_end_mask_0, x = x_187_cast_fp16)[name = tensor("op_1800_cast_fp16")]; - tensor var_1801 = const()[name = tensor("op_1801"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_33_cast_fp16 = reshape(shape = var_1801, x = var_1800_cast_fp16)[name = tensor("matrix_bd_33_cast_fp16")]; - tensor matrix_ac_17_transpose_x_0 = const()[name = tensor("matrix_ac_17_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_17_transpose_y_0 = const()[name = tensor("matrix_ac_17_transpose_y_0"), val = tensor(false)]; - tensor transpose_67_perm_0 = const()[name = tensor("transpose_67_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_68_perm_0 = const()[name = tensor("transpose_68_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = k_33_cast_fp16)[name = tensor("transpose_145")]; - tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_1784_cast_fp16)[name = tensor("transpose_146")]; - tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_67, y = transpose_68)[name = tensor("matrix_ac_17_cast_fp16")]; - tensor matrix_bd_35_begin_0 = const()[name = tensor("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_35_end_0 = const()[name = tensor("matrix_bd_35_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_35_end_mask_0 = const()[name = tensor("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = tensor("matrix_bd_35_cast_fp16")]; - tensor var_1810_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = tensor("op_1810_cast_fp16")]; - tensor _inversed_scores_33_y_0_to_fp16 = const()[name = tensor("_inversed_scores_33_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_33_cast_fp16 = mul(x = var_1810_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = tensor("_inversed_scores_33_cast_fp16")]; - tensor scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_11)[name = tensor("scores_35_cast_fp16")]; - tensor var_1816_cast_fp16 = softmax(axis = var_23, x = scores_35_cast_fp16)[name = tensor("op_1816_cast_fp16")]; - tensor input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1816_cast_fp16, cond = mask_11)[name = tensor("input_449_cast_fp16")]; - tensor x_189_transpose_x_0 = const()[name = tensor("x_189_transpose_x_0"), val = tensor(false)]; - tensor x_189_transpose_y_0 = const()[name = tensor("x_189_transpose_y_0"), val = tensor(false)]; - tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = tensor("transpose_148")]; - tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_19_cast_fp16)[name = tensor("x_189_cast_fp16")]; - tensor var_1820_perm_0 = const()[name = tensor("op_1820_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1821 = const()[name = tensor("op_1821"), val = tensor([1, -1, 512])]; - tensor var_1820_cast_fp16 = transpose(perm = var_1820_perm_0, x = x_189_cast_fp16)[name = tensor("transpose_144")]; - tensor input_451_cast_fp16 = reshape(shape = var_1821, x = var_1820_cast_fp16)[name = tensor("input_451_cast_fp16")]; - tensor module_layers_8_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108946816)))]; - tensor module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109471168)))]; - tensor linear_79_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_out_bias_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16, x = input_451_cast_fp16)[name = tensor("linear_79_cast_fp16")]; - tensor input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = tensor("input_455_cast_fp16")]; - tensor x_193_axes_0 = const()[name = tensor("x_193_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109472256)))]; - tensor module_layers_8_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109473344)))]; - tensor x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = tensor("x_193_cast_fp16")]; - tensor input_457_perm_0 = const()[name = tensor("input_457_perm_0"), val = tensor([0, 2, 1])]; - tensor input_459_pad_type_0 = const()[name = tensor("input_459_pad_type_0"), val = tensor("valid")]; - tensor input_459_strides_0 = const()[name = tensor("input_459_strides_0"), val = tensor([1])]; - tensor input_459_pad_0 = const()[name = tensor("input_459_pad_0"), val = tensor([0, 0])]; - tensor input_459_dilations_0 = const()[name = tensor("input_459_dilations_0"), val = tensor([1])]; - tensor input_459_groups_0 = const()[name = tensor("input_459_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109474432)))]; - tensor module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110523072)))]; - tensor input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = tensor("transpose_143")]; - tensor input_459_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv1_bias_to_fp16, dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16, x = input_457_cast_fp16)[name = tensor("input_459_cast_fp16")]; - tensor x_195_split_num_splits_0 = const()[name = tensor("x_195_split_num_splits_0"), val = tensor(2)]; - tensor x_195_split_axis_0 = const()[name = tensor("x_195_split_axis_0"), val = tensor(1)]; - tensor x_195_split_cast_fp16_0, tensor x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = tensor("x_195_split_cast_fp16")]; - tensor x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = tensor("x_195_split_1_sigmoid_cast_fp16")]; - tensor x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = tensor("x_195_cast_fp16")]; - tensor input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_453)[name = tensor("input_461_cast_fp16")]; - tensor input_463_pad_0 = const()[name = tensor("input_463_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_463_mode_0 = const()[name = tensor("input_463_mode_0"), val = tensor("constant")]; - tensor const_153_to_fp16 = const()[name = tensor("const_153_to_fp16"), val = tensor(0x0p+0)]; - tensor input_463_cast_fp16 = pad(constant_val = const_153_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = tensor("input_463_cast_fp16")]; - tensor input_465_pad_type_0 = const()[name = tensor("input_465_pad_type_0"), val = tensor("valid")]; - tensor input_465_groups_0 = const()[name = tensor("input_465_groups_0"), val = tensor(512)]; - tensor input_465_strides_0 = const()[name = tensor("input_465_strides_0"), val = tensor([1])]; - tensor input_465_pad_0 = const()[name = tensor("input_465_pad_0"), val = tensor([0, 0])]; - tensor input_465_dilations_0 = const()[name = tensor("input_465_dilations_0"), val = tensor([1])]; - tensor const_250_to_fp16 = const()[name = tensor("const_250_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110525184)))]; - tensor const_251_to_fp16 = const()[name = tensor("const_251_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110534464)))]; - tensor input_467_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_250_to_fp16, x = input_463_cast_fp16)[name = tensor("input_467_cast_fp16")]; - tensor input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = tensor("input_469_cast_fp16")]; - tensor x_197_pad_type_0 = const()[name = tensor("x_197_pad_type_0"), val = tensor("valid")]; - tensor x_197_strides_0 = const()[name = tensor("x_197_strides_0"), val = tensor([1])]; - tensor x_197_pad_0 = const()[name = tensor("x_197_pad_0"), val = tensor([0, 0])]; - tensor x_197_dilations_0 = const()[name = tensor("x_197_dilations_0"), val = tensor([1])]; - tensor x_197_groups_0 = const()[name = tensor("x_197_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110535552)))]; - tensor module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111059904)))]; - tensor x_197_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv2_bias_to_fp16, dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = module_layers_8_conv_pointwise_conv2_weight_to_fp16, x = input_469_cast_fp16)[name = tensor("x_197_cast_fp16")]; - tensor input_471_perm_0 = const()[name = tensor("input_471_perm_0"), val = tensor([0, 2, 1])]; - tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = tensor("transpose_142")]; - tensor input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = tensor("input_473_cast_fp16")]; - tensor input_475_axes_0 = const()[name = tensor("input_475_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111060992)))]; - tensor module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111062080)))]; - tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = tensor("input_475_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111063168)))]; - tensor module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113160384)))]; - tensor linear_80_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16, x = input_475_cast_fp16)[name = tensor("linear_80_cast_fp16")]; - tensor input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = tensor("input_479_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113164544)))]; - tensor module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115261760)))]; - tensor linear_81_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16, x = input_479_cast_fp16)[name = tensor("linear_81_cast_fp16")]; - tensor var_1887_to_fp16 = const()[name = tensor("op_1887_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1888_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1887_to_fp16)[name = tensor("op_1888_cast_fp16")]; - tensor input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1888_cast_fp16)[name = tensor("input_485_cast_fp16")]; - tensor input_487_axes_0 = const()[name = tensor("input_487_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115262848)))]; - tensor module_layers_8_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115263936)))]; - tensor input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = tensor("input_487_cast_fp16")]; - tensor input_489_axes_0 = const()[name = tensor("input_489_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115265024)))]; - tensor module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115266112)))]; - tensor input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = tensor("input_489_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115267200)))]; - tensor module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117364416)))]; - tensor linear_82_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16, x = input_489_cast_fp16)[name = tensor("linear_82_cast_fp16")]; - tensor input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = tensor("input_493_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117368576)))]; - tensor module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119465792)))]; - tensor linear_83_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16, x = input_493_cast_fp16)[name = tensor("linear_83_cast_fp16")]; - tensor var_1918_to_fp16 = const()[name = tensor("op_1918_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1919_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1918_to_fp16)[name = tensor("op_1919_cast_fp16")]; - tensor input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1919_cast_fp16)[name = tensor("input_499_cast_fp16")]; - tensor query_19_axes_0 = const()[name = tensor("query_19_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119466880)))]; - tensor module_layers_9_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119467968)))]; - tensor query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = tensor("query_19_cast_fp16")]; - tensor module_layers_9_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119469056)))]; - tensor module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119993408)))]; - tensor linear_84_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_q_bias_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_84_cast_fp16")]; - tensor var_1936 = const()[name = tensor("op_1936"), val = tensor([1, -1, 8, 64])]; - tensor q_55_cast_fp16 = reshape(shape = var_1936, x = linear_84_cast_fp16)[name = tensor("q_55_cast_fp16")]; - tensor module_layers_9_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119994496)))]; - tensor module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120518848)))]; - tensor linear_85_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_k_bias_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_85_cast_fp16")]; - tensor var_1941 = const()[name = tensor("op_1941"), val = tensor([1, -1, 8, 64])]; - tensor k_37_cast_fp16 = reshape(shape = var_1941, x = linear_85_cast_fp16)[name = tensor("k_37_cast_fp16")]; - tensor module_layers_9_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120519936)))]; - tensor module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121044288)))]; - tensor linear_86_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_v_bias_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_86_cast_fp16")]; - tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([1, -1, 8, 64])]; - tensor v_19_cast_fp16 = reshape(shape = var_1946, x = linear_86_cast_fp16)[name = tensor("v_19_cast_fp16")]; - tensor value_21_perm_0 = const()[name = tensor("value_21_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121045376)))]; - tensor var_1958_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1958_cast_fp16")]; - tensor module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121046464)))]; - tensor var_1960_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1960_cast_fp16")]; - tensor q_with_bias_v_19_perm_0 = const()[name = tensor("q_with_bias_v_19_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_205_transpose_x_0 = const()[name = tensor("x_205_transpose_x_0"), val = tensor(false)]; - tensor x_205_transpose_y_0 = const()[name = tensor("x_205_transpose_y_0"), val = tensor(false)]; - tensor var_1962_to_fp16 = const()[name = tensor("op_1962_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121047552)))]; - tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1960_cast_fp16)[name = tensor("transpose_140")]; - tensor x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = var_1962_to_fp16)[name = tensor("x_205_cast_fp16")]; - tensor x_207_pad_0 = const()[name = tensor("x_207_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_207_mode_0 = const()[name = tensor("x_207_mode_0"), val = tensor("constant")]; - tensor const_160_to_fp16 = const()[name = tensor("const_160_to_fp16"), val = tensor(0x0p+0)]; - tensor x_207_cast_fp16 = pad(constant_val = const_160_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = tensor("x_207_cast_fp16")]; - tensor var_1970 = const()[name = tensor("op_1970"), val = tensor([1, 8, -1, 188])]; - tensor x_209_cast_fp16 = reshape(shape = var_1970, x = x_207_cast_fp16)[name = tensor("x_209_cast_fp16")]; - tensor var_1974_begin_0 = const()[name = tensor("op_1974_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1974_end_0 = const()[name = tensor("op_1974_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1974_end_mask_0 = const()[name = tensor("op_1974_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1974_cast_fp16 = slice_by_index(begin = var_1974_begin_0, end = var_1974_end_0, end_mask = var_1974_end_mask_0, x = x_209_cast_fp16)[name = tensor("op_1974_cast_fp16")]; - tensor var_1975 = const()[name = tensor("op_1975"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1975, x = var_1974_cast_fp16)[name = tensor("matrix_bd_37_cast_fp16")]; - tensor matrix_ac_19_transpose_x_0 = const()[name = tensor("matrix_ac_19_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_19_transpose_y_0 = const()[name = tensor("matrix_ac_19_transpose_y_0"), val = tensor(false)]; - tensor transpose_69_perm_0 = const()[name = tensor("transpose_69_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_70_perm_0 = const()[name = tensor("transpose_70_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = k_37_cast_fp16)[name = tensor("transpose_138")]; - tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1958_cast_fp16)[name = tensor("transpose_139")]; - tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_69, y = transpose_70)[name = tensor("matrix_ac_19_cast_fp16")]; - tensor matrix_bd_39_begin_0 = const()[name = tensor("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_39_end_0 = const()[name = tensor("matrix_bd_39_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_39_end_mask_0 = const()[name = tensor("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = tensor("matrix_bd_39_cast_fp16")]; - tensor var_1984_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = tensor("op_1984_cast_fp16")]; - tensor _inversed_scores_37_y_0_to_fp16 = const()[name = tensor("_inversed_scores_37_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_37_cast_fp16 = mul(x = var_1984_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = tensor("_inversed_scores_37_cast_fp16")]; - tensor scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_11)[name = tensor("scores_39_cast_fp16")]; - tensor var_1990_cast_fp16 = softmax(axis = var_23, x = scores_39_cast_fp16)[name = tensor("op_1990_cast_fp16")]; - tensor input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1990_cast_fp16, cond = mask_11)[name = tensor("input_501_cast_fp16")]; - tensor x_211_transpose_x_0 = const()[name = tensor("x_211_transpose_x_0"), val = tensor(false)]; - tensor x_211_transpose_y_0 = const()[name = tensor("x_211_transpose_y_0"), val = tensor(false)]; - tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = tensor("transpose_141")]; - tensor x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_21_cast_fp16)[name = tensor("x_211_cast_fp16")]; - tensor var_1994_perm_0 = const()[name = tensor("op_1994_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1995 = const()[name = tensor("op_1995"), val = tensor([1, -1, 512])]; - tensor var_1994_cast_fp16 = transpose(perm = var_1994_perm_0, x = x_211_cast_fp16)[name = tensor("transpose_137")]; - tensor input_503_cast_fp16 = reshape(shape = var_1995, x = var_1994_cast_fp16)[name = tensor("input_503_cast_fp16")]; - tensor module_layers_9_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121431616)))]; - tensor module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121955968)))]; - tensor linear_88_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_out_bias_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16, x = input_503_cast_fp16)[name = tensor("linear_88_cast_fp16")]; - tensor input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = tensor("input_507_cast_fp16")]; - tensor x_215_axes_0 = const()[name = tensor("x_215_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121957056)))]; - tensor module_layers_9_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121958144)))]; - tensor x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = tensor("x_215_cast_fp16")]; - tensor input_509_perm_0 = const()[name = tensor("input_509_perm_0"), val = tensor([0, 2, 1])]; - tensor input_511_pad_type_0 = const()[name = tensor("input_511_pad_type_0"), val = tensor("valid")]; - tensor input_511_strides_0 = const()[name = tensor("input_511_strides_0"), val = tensor([1])]; - tensor input_511_pad_0 = const()[name = tensor("input_511_pad_0"), val = tensor([0, 0])]; - tensor input_511_dilations_0 = const()[name = tensor("input_511_dilations_0"), val = tensor([1])]; - tensor input_511_groups_0 = const()[name = tensor("input_511_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121959232)))]; - tensor module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123007872)))]; - tensor input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = tensor("transpose_136")]; - tensor input_511_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv1_bias_to_fp16, dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16, x = input_509_cast_fp16)[name = tensor("input_511_cast_fp16")]; - tensor x_217_split_num_splits_0 = const()[name = tensor("x_217_split_num_splits_0"), val = tensor(2)]; - tensor x_217_split_axis_0 = const()[name = tensor("x_217_split_axis_0"), val = tensor(1)]; - tensor x_217_split_cast_fp16_0, tensor x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = tensor("x_217_split_cast_fp16")]; - tensor x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = tensor("x_217_split_1_sigmoid_cast_fp16")]; - tensor x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = tensor("x_217_cast_fp16")]; - tensor input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_453)[name = tensor("input_513_cast_fp16")]; - tensor input_515_pad_0 = const()[name = tensor("input_515_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_515_mode_0 = const()[name = tensor("input_515_mode_0"), val = tensor("constant")]; - tensor const_163_to_fp16 = const()[name = tensor("const_163_to_fp16"), val = tensor(0x0p+0)]; - tensor input_515_cast_fp16 = pad(constant_val = const_163_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = tensor("input_515_cast_fp16")]; - tensor input_517_pad_type_0 = const()[name = tensor("input_517_pad_type_0"), val = tensor("valid")]; - tensor input_517_groups_0 = const()[name = tensor("input_517_groups_0"), val = tensor(512)]; - tensor input_517_strides_0 = const()[name = tensor("input_517_strides_0"), val = tensor([1])]; - tensor input_517_pad_0 = const()[name = tensor("input_517_pad_0"), val = tensor([0, 0])]; - tensor input_517_dilations_0 = const()[name = tensor("input_517_dilations_0"), val = tensor([1])]; - tensor const_252_to_fp16 = const()[name = tensor("const_252_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123009984)))]; - tensor const_253_to_fp16 = const()[name = tensor("const_253_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123019264)))]; - tensor input_519_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_252_to_fp16, x = input_515_cast_fp16)[name = tensor("input_519_cast_fp16")]; - tensor input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = tensor("input_521_cast_fp16")]; - tensor x_219_pad_type_0 = const()[name = tensor("x_219_pad_type_0"), val = tensor("valid")]; - tensor x_219_strides_0 = const()[name = tensor("x_219_strides_0"), val = tensor([1])]; - tensor x_219_pad_0 = const()[name = tensor("x_219_pad_0"), val = tensor([0, 0])]; - tensor x_219_dilations_0 = const()[name = tensor("x_219_dilations_0"), val = tensor([1])]; - tensor x_219_groups_0 = const()[name = tensor("x_219_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123020352)))]; - tensor module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123544704)))]; - tensor x_219_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv2_bias_to_fp16, dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = module_layers_9_conv_pointwise_conv2_weight_to_fp16, x = input_521_cast_fp16)[name = tensor("x_219_cast_fp16")]; - tensor input_523_perm_0 = const()[name = tensor("input_523_perm_0"), val = tensor([0, 2, 1])]; - tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = tensor("transpose_135")]; - tensor input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = tensor("input_525_cast_fp16")]; - tensor input_527_axes_0 = const()[name = tensor("input_527_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123545792)))]; - tensor module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123546880)))]; - tensor input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = tensor("input_527_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123547968)))]; - tensor module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125645184)))]; - tensor linear_89_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16, x = input_527_cast_fp16)[name = tensor("linear_89_cast_fp16")]; - tensor input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = tensor("input_531_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125649344)))]; - tensor module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127746560)))]; - tensor linear_90_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16, x = input_531_cast_fp16)[name = tensor("linear_90_cast_fp16")]; - tensor var_2061_to_fp16 = const()[name = tensor("op_2061_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2062_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2061_to_fp16)[name = tensor("op_2062_cast_fp16")]; - tensor input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_2062_cast_fp16)[name = tensor("input_537_cast_fp16")]; - tensor input_539_axes_0 = const()[name = tensor("input_539_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127747648)))]; - tensor module_layers_9_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127748736)))]; - tensor input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = tensor("input_539_cast_fp16")]; - tensor input_541_axes_0 = const()[name = tensor("input_541_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127749824)))]; - tensor module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127750912)))]; - tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = tensor("input_541_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127752000)))]; - tensor module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129849216)))]; - tensor linear_91_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16, x = input_541_cast_fp16)[name = tensor("linear_91_cast_fp16")]; - tensor input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = tensor("input_545_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129853376)))]; - tensor module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131950592)))]; - tensor linear_92_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16, x = input_545_cast_fp16)[name = tensor("linear_92_cast_fp16")]; - tensor var_2092_to_fp16 = const()[name = tensor("op_2092_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2093_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2092_to_fp16)[name = tensor("op_2093_cast_fp16")]; - tensor input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_2093_cast_fp16)[name = tensor("input_551_cast_fp16")]; - tensor query_21_axes_0 = const()[name = tensor("query_21_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131951680)))]; - tensor module_layers_10_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131952768)))]; - tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = tensor("query_21_cast_fp16")]; - tensor module_layers_10_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131953856)))]; - tensor module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132478208)))]; - tensor linear_93_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_q_bias_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_93_cast_fp16")]; - tensor var_2110 = const()[name = tensor("op_2110"), val = tensor([1, -1, 8, 64])]; - tensor q_61_cast_fp16 = reshape(shape = var_2110, x = linear_93_cast_fp16)[name = tensor("q_61_cast_fp16")]; - tensor module_layers_10_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132479296)))]; - tensor module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133003648)))]; - tensor linear_94_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_k_bias_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_94_cast_fp16")]; - tensor var_2115 = const()[name = tensor("op_2115"), val = tensor([1, -1, 8, 64])]; - tensor k_41_cast_fp16 = reshape(shape = var_2115, x = linear_94_cast_fp16)[name = tensor("k_41_cast_fp16")]; - tensor module_layers_10_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133004736)))]; - tensor module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133529088)))]; - tensor linear_95_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_v_bias_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_95_cast_fp16")]; - tensor var_2120 = const()[name = tensor("op_2120"), val = tensor([1, -1, 8, 64])]; - tensor v_21_cast_fp16 = reshape(shape = var_2120, x = linear_95_cast_fp16)[name = tensor("v_21_cast_fp16")]; - tensor value_23_perm_0 = const()[name = tensor("value_23_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133530176)))]; - tensor var_2132_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2132_cast_fp16")]; - tensor module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133531264)))]; - tensor var_2134_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2134_cast_fp16")]; - tensor q_with_bias_v_21_perm_0 = const()[name = tensor("q_with_bias_v_21_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_227_transpose_x_0 = const()[name = tensor("x_227_transpose_x_0"), val = tensor(false)]; - tensor x_227_transpose_y_0 = const()[name = tensor("x_227_transpose_y_0"), val = tensor(false)]; - tensor var_2136_to_fp16 = const()[name = tensor("op_2136_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133532352)))]; - tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2134_cast_fp16)[name = tensor("transpose_133")]; - tensor x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = var_2136_to_fp16)[name = tensor("x_227_cast_fp16")]; - tensor x_229_pad_0 = const()[name = tensor("x_229_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_229_mode_0 = const()[name = tensor("x_229_mode_0"), val = tensor("constant")]; - tensor const_170_to_fp16 = const()[name = tensor("const_170_to_fp16"), val = tensor(0x0p+0)]; - tensor x_229_cast_fp16 = pad(constant_val = const_170_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = tensor("x_229_cast_fp16")]; - tensor var_2144 = const()[name = tensor("op_2144"), val = tensor([1, 8, -1, 188])]; - tensor x_231_cast_fp16 = reshape(shape = var_2144, x = x_229_cast_fp16)[name = tensor("x_231_cast_fp16")]; - tensor var_2148_begin_0 = const()[name = tensor("op_2148_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2148_end_0 = const()[name = tensor("op_2148_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2148_end_mask_0 = const()[name = tensor("op_2148_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2148_cast_fp16 = slice_by_index(begin = var_2148_begin_0, end = var_2148_end_0, end_mask = var_2148_end_mask_0, x = x_231_cast_fp16)[name = tensor("op_2148_cast_fp16")]; - tensor var_2149 = const()[name = tensor("op_2149"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_41_cast_fp16 = reshape(shape = var_2149, x = var_2148_cast_fp16)[name = tensor("matrix_bd_41_cast_fp16")]; - tensor matrix_ac_21_transpose_x_0 = const()[name = tensor("matrix_ac_21_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_21_transpose_y_0 = const()[name = tensor("matrix_ac_21_transpose_y_0"), val = tensor(false)]; - tensor transpose_71_perm_0 = const()[name = tensor("transpose_71_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_72_perm_0 = const()[name = tensor("transpose_72_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = k_41_cast_fp16)[name = tensor("transpose_131")]; - tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = var_2132_cast_fp16)[name = tensor("transpose_132")]; - tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_71, y = transpose_72)[name = tensor("matrix_ac_21_cast_fp16")]; - tensor matrix_bd_43_begin_0 = const()[name = tensor("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_43_end_0 = const()[name = tensor("matrix_bd_43_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_43_end_mask_0 = const()[name = tensor("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = tensor("matrix_bd_43_cast_fp16")]; - tensor var_2158_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = tensor("op_2158_cast_fp16")]; - tensor _inversed_scores_41_y_0_to_fp16 = const()[name = tensor("_inversed_scores_41_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_41_cast_fp16 = mul(x = var_2158_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = tensor("_inversed_scores_41_cast_fp16")]; - tensor scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_11)[name = tensor("scores_43_cast_fp16")]; - tensor var_2164_cast_fp16 = softmax(axis = var_23, x = scores_43_cast_fp16)[name = tensor("op_2164_cast_fp16")]; - tensor input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_2164_cast_fp16, cond = mask_11)[name = tensor("input_553_cast_fp16")]; - tensor x_233_transpose_x_0 = const()[name = tensor("x_233_transpose_x_0"), val = tensor(false)]; - tensor x_233_transpose_y_0 = const()[name = tensor("x_233_transpose_y_0"), val = tensor(false)]; - tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = tensor("transpose_134")]; - tensor x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_23_cast_fp16)[name = tensor("x_233_cast_fp16")]; - tensor var_2168_perm_0 = const()[name = tensor("op_2168_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2169 = const()[name = tensor("op_2169"), val = tensor([1, -1, 512])]; - tensor var_2168_cast_fp16 = transpose(perm = var_2168_perm_0, x = x_233_cast_fp16)[name = tensor("transpose_130")]; - tensor input_555_cast_fp16 = reshape(shape = var_2169, x = var_2168_cast_fp16)[name = tensor("input_555_cast_fp16")]; - tensor module_layers_10_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133916416)))]; - tensor module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134440768)))]; - tensor linear_97_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_out_bias_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16, x = input_555_cast_fp16)[name = tensor("linear_97_cast_fp16")]; - tensor input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = tensor("input_559_cast_fp16")]; - tensor x_237_axes_0 = const()[name = tensor("x_237_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134441856)))]; - tensor module_layers_10_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134442944)))]; - tensor x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = tensor("x_237_cast_fp16")]; - tensor input_561_perm_0 = const()[name = tensor("input_561_perm_0"), val = tensor([0, 2, 1])]; - tensor input_563_pad_type_0 = const()[name = tensor("input_563_pad_type_0"), val = tensor("valid")]; - tensor input_563_strides_0 = const()[name = tensor("input_563_strides_0"), val = tensor([1])]; - tensor input_563_pad_0 = const()[name = tensor("input_563_pad_0"), val = tensor([0, 0])]; - tensor input_563_dilations_0 = const()[name = tensor("input_563_dilations_0"), val = tensor([1])]; - tensor input_563_groups_0 = const()[name = tensor("input_563_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134444032)))]; - tensor module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135492672)))]; - tensor input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = tensor("transpose_129")]; - tensor input_563_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv1_bias_to_fp16, dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16, x = input_561_cast_fp16)[name = tensor("input_563_cast_fp16")]; - tensor x_239_split_num_splits_0 = const()[name = tensor("x_239_split_num_splits_0"), val = tensor(2)]; - tensor x_239_split_axis_0 = const()[name = tensor("x_239_split_axis_0"), val = tensor(1)]; - tensor x_239_split_cast_fp16_0, tensor x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = tensor("x_239_split_cast_fp16")]; - tensor x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = tensor("x_239_split_1_sigmoid_cast_fp16")]; - tensor x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = tensor("x_239_cast_fp16")]; - tensor input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_453)[name = tensor("input_565_cast_fp16")]; - tensor input_567_pad_0 = const()[name = tensor("input_567_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_567_mode_0 = const()[name = tensor("input_567_mode_0"), val = tensor("constant")]; - tensor const_173_to_fp16 = const()[name = tensor("const_173_to_fp16"), val = tensor(0x0p+0)]; - tensor input_567_cast_fp16 = pad(constant_val = const_173_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = tensor("input_567_cast_fp16")]; - tensor input_569_pad_type_0 = const()[name = tensor("input_569_pad_type_0"), val = tensor("valid")]; - tensor input_569_groups_0 = const()[name = tensor("input_569_groups_0"), val = tensor(512)]; - tensor input_569_strides_0 = const()[name = tensor("input_569_strides_0"), val = tensor([1])]; - tensor input_569_pad_0 = const()[name = tensor("input_569_pad_0"), val = tensor([0, 0])]; - tensor input_569_dilations_0 = const()[name = tensor("input_569_dilations_0"), val = tensor([1])]; - tensor const_254_to_fp16 = const()[name = tensor("const_254_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135494784)))]; - tensor const_255_to_fp16 = const()[name = tensor("const_255_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135504064)))]; - tensor input_571_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_254_to_fp16, x = input_567_cast_fp16)[name = tensor("input_571_cast_fp16")]; - tensor input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = tensor("input_573_cast_fp16")]; - tensor x_241_pad_type_0 = const()[name = tensor("x_241_pad_type_0"), val = tensor("valid")]; - tensor x_241_strides_0 = const()[name = tensor("x_241_strides_0"), val = tensor([1])]; - tensor x_241_pad_0 = const()[name = tensor("x_241_pad_0"), val = tensor([0, 0])]; - tensor x_241_dilations_0 = const()[name = tensor("x_241_dilations_0"), val = tensor([1])]; - tensor x_241_groups_0 = const()[name = tensor("x_241_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135505152)))]; - tensor module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136029504)))]; - tensor x_241_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv2_bias_to_fp16, dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = module_layers_10_conv_pointwise_conv2_weight_to_fp16, x = input_573_cast_fp16)[name = tensor("x_241_cast_fp16")]; - tensor input_575_perm_0 = const()[name = tensor("input_575_perm_0"), val = tensor([0, 2, 1])]; - tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = tensor("transpose_128")]; - tensor input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = tensor("input_577_cast_fp16")]; - tensor input_579_axes_0 = const()[name = tensor("input_579_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136030592)))]; - tensor module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136031680)))]; - tensor input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = tensor("input_579_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136032768)))]; - tensor module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138129984)))]; - tensor linear_98_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16, x = input_579_cast_fp16)[name = tensor("linear_98_cast_fp16")]; - tensor input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = tensor("input_583_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138134144)))]; - tensor module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140231360)))]; - tensor linear_99_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16, x = input_583_cast_fp16)[name = tensor("linear_99_cast_fp16")]; - tensor var_2235_to_fp16 = const()[name = tensor("op_2235_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2236_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2235_to_fp16)[name = tensor("op_2236_cast_fp16")]; - tensor input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_2236_cast_fp16)[name = tensor("input_589_cast_fp16")]; - tensor input_591_axes_0 = const()[name = tensor("input_591_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140232448)))]; - tensor module_layers_10_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140233536)))]; - tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = tensor("input_591_cast_fp16")]; - tensor input_593_axes_0 = const()[name = tensor("input_593_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140234624)))]; - tensor module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140235712)))]; - tensor input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = tensor("input_593_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140236800)))]; - tensor module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142334016)))]; - tensor linear_100_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16, x = input_593_cast_fp16)[name = tensor("linear_100_cast_fp16")]; - tensor input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = tensor("input_597_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142338176)))]; - tensor module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144435392)))]; - tensor linear_101_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16, x = input_597_cast_fp16)[name = tensor("linear_101_cast_fp16")]; - tensor var_2266_to_fp16 = const()[name = tensor("op_2266_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2267_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2266_to_fp16)[name = tensor("op_2267_cast_fp16")]; - tensor input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2267_cast_fp16)[name = tensor("input_603_cast_fp16")]; - tensor query_23_axes_0 = const()[name = tensor("query_23_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144436480)))]; - tensor module_layers_11_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144437568)))]; - tensor query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = tensor("query_23_cast_fp16")]; - tensor module_layers_11_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144438656)))]; - tensor module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144963008)))]; - tensor linear_102_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_q_bias_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_102_cast_fp16")]; - tensor var_2284 = const()[name = tensor("op_2284"), val = tensor([1, -1, 8, 64])]; - tensor q_67_cast_fp16 = reshape(shape = var_2284, x = linear_102_cast_fp16)[name = tensor("q_67_cast_fp16")]; - tensor module_layers_11_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144964096)))]; - tensor module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145488448)))]; - tensor linear_103_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_k_bias_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_103_cast_fp16")]; - tensor var_2289 = const()[name = tensor("op_2289"), val = tensor([1, -1, 8, 64])]; - tensor k_45_cast_fp16 = reshape(shape = var_2289, x = linear_103_cast_fp16)[name = tensor("k_45_cast_fp16")]; - tensor module_layers_11_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145489536)))]; - tensor module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146013888)))]; - tensor linear_104_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_v_bias_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_104_cast_fp16")]; - tensor var_2294 = const()[name = tensor("op_2294"), val = tensor([1, -1, 8, 64])]; - tensor v_23_cast_fp16 = reshape(shape = var_2294, x = linear_104_cast_fp16)[name = tensor("v_23_cast_fp16")]; - tensor value_25_perm_0 = const()[name = tensor("value_25_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146014976)))]; - tensor var_2306_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2306_cast_fp16")]; - tensor module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146016064)))]; - tensor var_2308_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2308_cast_fp16")]; - tensor q_with_bias_v_23_perm_0 = const()[name = tensor("q_with_bias_v_23_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_249_transpose_x_0 = const()[name = tensor("x_249_transpose_x_0"), val = tensor(false)]; - tensor x_249_transpose_y_0 = const()[name = tensor("x_249_transpose_y_0"), val = tensor(false)]; - tensor var_2310_to_fp16 = const()[name = tensor("op_2310_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146017152)))]; - tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2308_cast_fp16)[name = tensor("transpose_126")]; - tensor x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = var_2310_to_fp16)[name = tensor("x_249_cast_fp16")]; - tensor x_251_pad_0 = const()[name = tensor("x_251_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_251_mode_0 = const()[name = tensor("x_251_mode_0"), val = tensor("constant")]; - tensor const_180_to_fp16 = const()[name = tensor("const_180_to_fp16"), val = tensor(0x0p+0)]; - tensor x_251_cast_fp16 = pad(constant_val = const_180_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = tensor("x_251_cast_fp16")]; - tensor var_2318 = const()[name = tensor("op_2318"), val = tensor([1, 8, -1, 188])]; - tensor x_253_cast_fp16 = reshape(shape = var_2318, x = x_251_cast_fp16)[name = tensor("x_253_cast_fp16")]; - tensor var_2322_begin_0 = const()[name = tensor("op_2322_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2322_end_0 = const()[name = tensor("op_2322_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2322_end_mask_0 = const()[name = tensor("op_2322_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2322_cast_fp16 = slice_by_index(begin = var_2322_begin_0, end = var_2322_end_0, end_mask = var_2322_end_mask_0, x = x_253_cast_fp16)[name = tensor("op_2322_cast_fp16")]; - tensor var_2323 = const()[name = tensor("op_2323"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2323, x = var_2322_cast_fp16)[name = tensor("matrix_bd_45_cast_fp16")]; - tensor matrix_ac_23_transpose_x_0 = const()[name = tensor("matrix_ac_23_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_23_transpose_y_0 = const()[name = tensor("matrix_ac_23_transpose_y_0"), val = tensor(false)]; - tensor transpose_73_perm_0 = const()[name = tensor("transpose_73_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_74_perm_0 = const()[name = tensor("transpose_74_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = k_45_cast_fp16)[name = tensor("transpose_124")]; - tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_2306_cast_fp16)[name = tensor("transpose_125")]; - tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_73, y = transpose_74)[name = tensor("matrix_ac_23_cast_fp16")]; - tensor matrix_bd_47_begin_0 = const()[name = tensor("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_47_end_0 = const()[name = tensor("matrix_bd_47_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_47_end_mask_0 = const()[name = tensor("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = tensor("matrix_bd_47_cast_fp16")]; - tensor var_2332_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = tensor("op_2332_cast_fp16")]; - tensor _inversed_scores_45_y_0_to_fp16 = const()[name = tensor("_inversed_scores_45_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_45_cast_fp16 = mul(x = var_2332_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = tensor("_inversed_scores_45_cast_fp16")]; - tensor scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_11)[name = tensor("scores_47_cast_fp16")]; - tensor var_2338_cast_fp16 = softmax(axis = var_23, x = scores_47_cast_fp16)[name = tensor("op_2338_cast_fp16")]; - tensor input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2338_cast_fp16, cond = mask_11)[name = tensor("input_605_cast_fp16")]; - tensor x_255_transpose_x_0 = const()[name = tensor("x_255_transpose_x_0"), val = tensor(false)]; - tensor x_255_transpose_y_0 = const()[name = tensor("x_255_transpose_y_0"), val = tensor(false)]; - tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = tensor("transpose_127")]; - tensor x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_25_cast_fp16)[name = tensor("x_255_cast_fp16")]; - tensor var_2342_perm_0 = const()[name = tensor("op_2342_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2343 = const()[name = tensor("op_2343"), val = tensor([1, -1, 512])]; - tensor var_2342_cast_fp16 = transpose(perm = var_2342_perm_0, x = x_255_cast_fp16)[name = tensor("transpose_123")]; - tensor input_607_cast_fp16 = reshape(shape = var_2343, x = var_2342_cast_fp16)[name = tensor("input_607_cast_fp16")]; - tensor module_layers_11_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146401216)))]; - tensor module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146925568)))]; - tensor linear_106_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_out_bias_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16, x = input_607_cast_fp16)[name = tensor("linear_106_cast_fp16")]; - tensor input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = tensor("input_611_cast_fp16")]; - tensor x_259_axes_0 = const()[name = tensor("x_259_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146926656)))]; - tensor module_layers_11_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146927744)))]; - tensor x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = tensor("x_259_cast_fp16")]; - tensor input_613_perm_0 = const()[name = tensor("input_613_perm_0"), val = tensor([0, 2, 1])]; - tensor input_615_pad_type_0 = const()[name = tensor("input_615_pad_type_0"), val = tensor("valid")]; - tensor input_615_strides_0 = const()[name = tensor("input_615_strides_0"), val = tensor([1])]; - tensor input_615_pad_0 = const()[name = tensor("input_615_pad_0"), val = tensor([0, 0])]; - tensor input_615_dilations_0 = const()[name = tensor("input_615_dilations_0"), val = tensor([1])]; - tensor input_615_groups_0 = const()[name = tensor("input_615_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146928832)))]; - tensor module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147977472)))]; - tensor input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = tensor("transpose_122")]; - tensor input_615_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv1_bias_to_fp16, dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16, x = input_613_cast_fp16)[name = tensor("input_615_cast_fp16")]; - tensor x_261_split_num_splits_0 = const()[name = tensor("x_261_split_num_splits_0"), val = tensor(2)]; - tensor x_261_split_axis_0 = const()[name = tensor("x_261_split_axis_0"), val = tensor(1)]; - tensor x_261_split_cast_fp16_0, tensor x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = tensor("x_261_split_cast_fp16")]; - tensor x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = tensor("x_261_split_1_sigmoid_cast_fp16")]; - tensor x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = tensor("x_261_cast_fp16")]; - tensor input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_453)[name = tensor("input_617_cast_fp16")]; - tensor input_619_pad_0 = const()[name = tensor("input_619_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_619_mode_0 = const()[name = tensor("input_619_mode_0"), val = tensor("constant")]; - tensor const_183_to_fp16 = const()[name = tensor("const_183_to_fp16"), val = tensor(0x0p+0)]; - tensor input_619_cast_fp16 = pad(constant_val = const_183_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = tensor("input_619_cast_fp16")]; - tensor input_621_pad_type_0 = const()[name = tensor("input_621_pad_type_0"), val = tensor("valid")]; - tensor input_621_groups_0 = const()[name = tensor("input_621_groups_0"), val = tensor(512)]; - tensor input_621_strides_0 = const()[name = tensor("input_621_strides_0"), val = tensor([1])]; - tensor input_621_pad_0 = const()[name = tensor("input_621_pad_0"), val = tensor([0, 0])]; - tensor input_621_dilations_0 = const()[name = tensor("input_621_dilations_0"), val = tensor([1])]; - tensor const_256_to_fp16 = const()[name = tensor("const_256_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147979584)))]; - tensor const_257_to_fp16 = const()[name = tensor("const_257_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147988864)))]; - tensor input_623_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_256_to_fp16, x = input_619_cast_fp16)[name = tensor("input_623_cast_fp16")]; - tensor input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = tensor("input_625_cast_fp16")]; - tensor x_263_pad_type_0 = const()[name = tensor("x_263_pad_type_0"), val = tensor("valid")]; - tensor x_263_strides_0 = const()[name = tensor("x_263_strides_0"), val = tensor([1])]; - tensor x_263_pad_0 = const()[name = tensor("x_263_pad_0"), val = tensor([0, 0])]; - tensor x_263_dilations_0 = const()[name = tensor("x_263_dilations_0"), val = tensor([1])]; - tensor x_263_groups_0 = const()[name = tensor("x_263_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147989952)))]; - tensor module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148514304)))]; - tensor x_263_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv2_bias_to_fp16, dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = module_layers_11_conv_pointwise_conv2_weight_to_fp16, x = input_625_cast_fp16)[name = tensor("x_263_cast_fp16")]; - tensor input_627_perm_0 = const()[name = tensor("input_627_perm_0"), val = tensor([0, 2, 1])]; - tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = tensor("transpose_121")]; - tensor input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = tensor("input_629_cast_fp16")]; - tensor input_631_axes_0 = const()[name = tensor("input_631_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148515392)))]; - tensor module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148516480)))]; - tensor input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = tensor("input_631_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148517568)))]; - tensor module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150614784)))]; - tensor linear_107_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16, x = input_631_cast_fp16)[name = tensor("linear_107_cast_fp16")]; - tensor input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = tensor("input_635_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150618944)))]; - tensor module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152716160)))]; - tensor linear_108_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16, x = input_635_cast_fp16)[name = tensor("linear_108_cast_fp16")]; - tensor var_2409_to_fp16 = const()[name = tensor("op_2409_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2410_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2409_to_fp16)[name = tensor("op_2410_cast_fp16")]; - tensor input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2410_cast_fp16)[name = tensor("input_641_cast_fp16")]; - tensor input_643_axes_0 = const()[name = tensor("input_643_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152717248)))]; - tensor module_layers_11_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152718336)))]; - tensor input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = tensor("input_643_cast_fp16")]; - tensor input_645_axes_0 = const()[name = tensor("input_645_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152719424)))]; - tensor module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152720512)))]; - tensor input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = tensor("input_645_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152721600)))]; - tensor module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154818816)))]; - tensor linear_109_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16, x = input_645_cast_fp16)[name = tensor("linear_109_cast_fp16")]; - tensor input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = tensor("input_649_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154822976)))]; - tensor module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156920192)))]; - tensor linear_110_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16, x = input_649_cast_fp16)[name = tensor("linear_110_cast_fp16")]; - tensor var_2440_to_fp16 = const()[name = tensor("op_2440_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2441_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2440_to_fp16)[name = tensor("op_2441_cast_fp16")]; - tensor input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2441_cast_fp16)[name = tensor("input_655_cast_fp16")]; - tensor query_25_axes_0 = const()[name = tensor("query_25_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156921280)))]; - tensor module_layers_12_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156922368)))]; - tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = tensor("query_25_cast_fp16")]; - tensor module_layers_12_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156923456)))]; - tensor module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157447808)))]; - tensor linear_111_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_q_bias_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_111_cast_fp16")]; - tensor var_2458 = const()[name = tensor("op_2458"), val = tensor([1, -1, 8, 64])]; - tensor q_73_cast_fp16 = reshape(shape = var_2458, x = linear_111_cast_fp16)[name = tensor("q_73_cast_fp16")]; - tensor module_layers_12_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157448896)))]; - tensor module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157973248)))]; - tensor linear_112_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_k_bias_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_112_cast_fp16")]; - tensor var_2463 = const()[name = tensor("op_2463"), val = tensor([1, -1, 8, 64])]; - tensor k_49_cast_fp16 = reshape(shape = var_2463, x = linear_112_cast_fp16)[name = tensor("k_49_cast_fp16")]; - tensor module_layers_12_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157974336)))]; - tensor module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158498688)))]; - tensor linear_113_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_v_bias_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_113_cast_fp16")]; - tensor var_2468 = const()[name = tensor("op_2468"), val = tensor([1, -1, 8, 64])]; - tensor v_25_cast_fp16 = reshape(shape = var_2468, x = linear_113_cast_fp16)[name = tensor("v_25_cast_fp16")]; - tensor value_27_perm_0 = const()[name = tensor("value_27_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158499776)))]; - tensor var_2480_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2480_cast_fp16")]; - tensor module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158500864)))]; - tensor var_2482_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2482_cast_fp16")]; - tensor q_with_bias_v_25_perm_0 = const()[name = tensor("q_with_bias_v_25_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_271_transpose_x_0 = const()[name = tensor("x_271_transpose_x_0"), val = tensor(false)]; - tensor x_271_transpose_y_0 = const()[name = tensor("x_271_transpose_y_0"), val = tensor(false)]; - tensor var_2484_to_fp16 = const()[name = tensor("op_2484_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158501952)))]; - tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2482_cast_fp16)[name = tensor("transpose_119")]; - tensor x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = var_2484_to_fp16)[name = tensor("x_271_cast_fp16")]; - tensor x_273_pad_0 = const()[name = tensor("x_273_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_273_mode_0 = const()[name = tensor("x_273_mode_0"), val = tensor("constant")]; - tensor const_190_to_fp16 = const()[name = tensor("const_190_to_fp16"), val = tensor(0x0p+0)]; - tensor x_273_cast_fp16 = pad(constant_val = const_190_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = tensor("x_273_cast_fp16")]; - tensor var_2492 = const()[name = tensor("op_2492"), val = tensor([1, 8, -1, 188])]; - tensor x_275_cast_fp16 = reshape(shape = var_2492, x = x_273_cast_fp16)[name = tensor("x_275_cast_fp16")]; - tensor var_2496_begin_0 = const()[name = tensor("op_2496_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2496_end_0 = const()[name = tensor("op_2496_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2496_end_mask_0 = const()[name = tensor("op_2496_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2496_cast_fp16 = slice_by_index(begin = var_2496_begin_0, end = var_2496_end_0, end_mask = var_2496_end_mask_0, x = x_275_cast_fp16)[name = tensor("op_2496_cast_fp16")]; - tensor var_2497 = const()[name = tensor("op_2497"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_49_cast_fp16 = reshape(shape = var_2497, x = var_2496_cast_fp16)[name = tensor("matrix_bd_49_cast_fp16")]; - tensor matrix_ac_25_transpose_x_0 = const()[name = tensor("matrix_ac_25_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_25_transpose_y_0 = const()[name = tensor("matrix_ac_25_transpose_y_0"), val = tensor(false)]; - tensor transpose_75_perm_0 = const()[name = tensor("transpose_75_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_76_perm_0 = const()[name = tensor("transpose_76_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = k_49_cast_fp16)[name = tensor("transpose_117")]; - tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_2480_cast_fp16)[name = tensor("transpose_118")]; - tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_75, y = transpose_76)[name = tensor("matrix_ac_25_cast_fp16")]; - tensor matrix_bd_51_begin_0 = const()[name = tensor("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_51_end_0 = const()[name = tensor("matrix_bd_51_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_51_end_mask_0 = const()[name = tensor("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = tensor("matrix_bd_51_cast_fp16")]; - tensor var_2506_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = tensor("op_2506_cast_fp16")]; - tensor _inversed_scores_49_y_0_to_fp16 = const()[name = tensor("_inversed_scores_49_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_49_cast_fp16 = mul(x = var_2506_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = tensor("_inversed_scores_49_cast_fp16")]; - tensor scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_11)[name = tensor("scores_51_cast_fp16")]; - tensor var_2512_cast_fp16 = softmax(axis = var_23, x = scores_51_cast_fp16)[name = tensor("op_2512_cast_fp16")]; - tensor input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2512_cast_fp16, cond = mask_11)[name = tensor("input_657_cast_fp16")]; - tensor x_277_transpose_x_0 = const()[name = tensor("x_277_transpose_x_0"), val = tensor(false)]; - tensor x_277_transpose_y_0 = const()[name = tensor("x_277_transpose_y_0"), val = tensor(false)]; - tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = tensor("transpose_120")]; - tensor x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_27_cast_fp16)[name = tensor("x_277_cast_fp16")]; - tensor var_2516_perm_0 = const()[name = tensor("op_2516_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2517 = const()[name = tensor("op_2517"), val = tensor([1, -1, 512])]; - tensor var_2516_cast_fp16 = transpose(perm = var_2516_perm_0, x = x_277_cast_fp16)[name = tensor("transpose_116")]; - tensor input_659_cast_fp16 = reshape(shape = var_2517, x = var_2516_cast_fp16)[name = tensor("input_659_cast_fp16")]; - tensor module_layers_12_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158886016)))]; - tensor module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159410368)))]; - tensor linear_115_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_out_bias_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16, x = input_659_cast_fp16)[name = tensor("linear_115_cast_fp16")]; - tensor input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = tensor("input_663_cast_fp16")]; - tensor x_281_axes_0 = const()[name = tensor("x_281_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159411456)))]; - tensor module_layers_12_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159412544)))]; - tensor x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = tensor("x_281_cast_fp16")]; - tensor input_665_perm_0 = const()[name = tensor("input_665_perm_0"), val = tensor([0, 2, 1])]; - tensor input_667_pad_type_0 = const()[name = tensor("input_667_pad_type_0"), val = tensor("valid")]; - tensor input_667_strides_0 = const()[name = tensor("input_667_strides_0"), val = tensor([1])]; - tensor input_667_pad_0 = const()[name = tensor("input_667_pad_0"), val = tensor([0, 0])]; - tensor input_667_dilations_0 = const()[name = tensor("input_667_dilations_0"), val = tensor([1])]; - tensor input_667_groups_0 = const()[name = tensor("input_667_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159413632)))]; - tensor module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160462272)))]; - tensor input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = tensor("transpose_115")]; - tensor input_667_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv1_bias_to_fp16, dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16, x = input_665_cast_fp16)[name = tensor("input_667_cast_fp16")]; - tensor x_283_split_num_splits_0 = const()[name = tensor("x_283_split_num_splits_0"), val = tensor(2)]; - tensor x_283_split_axis_0 = const()[name = tensor("x_283_split_axis_0"), val = tensor(1)]; - tensor x_283_split_cast_fp16_0, tensor x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = tensor("x_283_split_cast_fp16")]; - tensor x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = tensor("x_283_split_1_sigmoid_cast_fp16")]; - tensor x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = tensor("x_283_cast_fp16")]; - tensor input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_453)[name = tensor("input_669_cast_fp16")]; - tensor input_671_pad_0 = const()[name = tensor("input_671_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_671_mode_0 = const()[name = tensor("input_671_mode_0"), val = tensor("constant")]; - tensor const_193_to_fp16 = const()[name = tensor("const_193_to_fp16"), val = tensor(0x0p+0)]; - tensor input_671_cast_fp16 = pad(constant_val = const_193_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = tensor("input_671_cast_fp16")]; - tensor input_673_pad_type_0 = const()[name = tensor("input_673_pad_type_0"), val = tensor("valid")]; - tensor input_673_groups_0 = const()[name = tensor("input_673_groups_0"), val = tensor(512)]; - tensor input_673_strides_0 = const()[name = tensor("input_673_strides_0"), val = tensor([1])]; - tensor input_673_pad_0 = const()[name = tensor("input_673_pad_0"), val = tensor([0, 0])]; - tensor input_673_dilations_0 = const()[name = tensor("input_673_dilations_0"), val = tensor([1])]; - tensor const_258_to_fp16 = const()[name = tensor("const_258_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160464384)))]; - tensor const_259_to_fp16 = const()[name = tensor("const_259_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160473664)))]; - tensor input_675_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_258_to_fp16, x = input_671_cast_fp16)[name = tensor("input_675_cast_fp16")]; - tensor input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = tensor("input_677_cast_fp16")]; - tensor x_285_pad_type_0 = const()[name = tensor("x_285_pad_type_0"), val = tensor("valid")]; - tensor x_285_strides_0 = const()[name = tensor("x_285_strides_0"), val = tensor([1])]; - tensor x_285_pad_0 = const()[name = tensor("x_285_pad_0"), val = tensor([0, 0])]; - tensor x_285_dilations_0 = const()[name = tensor("x_285_dilations_0"), val = tensor([1])]; - tensor x_285_groups_0 = const()[name = tensor("x_285_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160474752)))]; - tensor module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160999104)))]; - tensor x_285_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv2_bias_to_fp16, dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = module_layers_12_conv_pointwise_conv2_weight_to_fp16, x = input_677_cast_fp16)[name = tensor("x_285_cast_fp16")]; - tensor input_679_perm_0 = const()[name = tensor("input_679_perm_0"), val = tensor([0, 2, 1])]; - tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = tensor("transpose_114")]; - tensor input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = tensor("input_681_cast_fp16")]; - tensor input_683_axes_0 = const()[name = tensor("input_683_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161000192)))]; - tensor module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161001280)))]; - tensor input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = tensor("input_683_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161002368)))]; - tensor module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163099584)))]; - tensor linear_116_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16, x = input_683_cast_fp16)[name = tensor("linear_116_cast_fp16")]; - tensor input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = tensor("input_687_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163103744)))]; - tensor module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165200960)))]; - tensor linear_117_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16, x = input_687_cast_fp16)[name = tensor("linear_117_cast_fp16")]; - tensor var_2583_to_fp16 = const()[name = tensor("op_2583_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2584_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2583_to_fp16)[name = tensor("op_2584_cast_fp16")]; - tensor input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2584_cast_fp16)[name = tensor("input_693_cast_fp16")]; - tensor input_695_axes_0 = const()[name = tensor("input_695_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165202048)))]; - tensor module_layers_12_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165203136)))]; - tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = tensor("input_695_cast_fp16")]; - tensor input_697_axes_0 = const()[name = tensor("input_697_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165204224)))]; - tensor module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165205312)))]; - tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = tensor("input_697_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165206400)))]; - tensor module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167303616)))]; - tensor linear_118_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16, x = input_697_cast_fp16)[name = tensor("linear_118_cast_fp16")]; - tensor input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = tensor("input_701_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167307776)))]; - tensor module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169404992)))]; - tensor linear_119_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16, x = input_701_cast_fp16)[name = tensor("linear_119_cast_fp16")]; - tensor var_2614_to_fp16 = const()[name = tensor("op_2614_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2615_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2614_to_fp16)[name = tensor("op_2615_cast_fp16")]; - tensor input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2615_cast_fp16)[name = tensor("input_707_cast_fp16")]; - tensor query_27_axes_0 = const()[name = tensor("query_27_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169406080)))]; - tensor module_layers_13_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169407168)))]; - tensor query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = tensor("query_27_cast_fp16")]; - tensor module_layers_13_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169408256)))]; - tensor module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169932608)))]; - tensor linear_120_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_q_bias_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_120_cast_fp16")]; - tensor var_2632 = const()[name = tensor("op_2632"), val = tensor([1, -1, 8, 64])]; - tensor q_79_cast_fp16 = reshape(shape = var_2632, x = linear_120_cast_fp16)[name = tensor("q_79_cast_fp16")]; - tensor module_layers_13_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169933696)))]; - tensor module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170458048)))]; - tensor linear_121_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_k_bias_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_121_cast_fp16")]; - tensor var_2637 = const()[name = tensor("op_2637"), val = tensor([1, -1, 8, 64])]; - tensor k_53_cast_fp16 = reshape(shape = var_2637, x = linear_121_cast_fp16)[name = tensor("k_53_cast_fp16")]; - tensor module_layers_13_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170459136)))]; - tensor module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170983488)))]; - tensor linear_122_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_v_bias_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_122_cast_fp16")]; - tensor var_2642 = const()[name = tensor("op_2642"), val = tensor([1, -1, 8, 64])]; - tensor v_27_cast_fp16 = reshape(shape = var_2642, x = linear_122_cast_fp16)[name = tensor("v_27_cast_fp16")]; - tensor value_29_perm_0 = const()[name = tensor("value_29_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170984576)))]; - tensor var_2654_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2654_cast_fp16")]; - tensor module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170985664)))]; - tensor var_2656_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2656_cast_fp16")]; - tensor q_with_bias_v_27_perm_0 = const()[name = tensor("q_with_bias_v_27_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_293_transpose_x_0 = const()[name = tensor("x_293_transpose_x_0"), val = tensor(false)]; - tensor x_293_transpose_y_0 = const()[name = tensor("x_293_transpose_y_0"), val = tensor(false)]; - tensor var_2658_to_fp16 = const()[name = tensor("op_2658_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170986752)))]; - tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2656_cast_fp16)[name = tensor("transpose_112")]; - tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = var_2658_to_fp16)[name = tensor("x_293_cast_fp16")]; - tensor x_295_pad_0 = const()[name = tensor("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_295_mode_0 = const()[name = tensor("x_295_mode_0"), val = tensor("constant")]; - tensor const_200_to_fp16 = const()[name = tensor("const_200_to_fp16"), val = tensor(0x0p+0)]; - tensor x_295_cast_fp16 = pad(constant_val = const_200_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = tensor("x_295_cast_fp16")]; - tensor var_2666 = const()[name = tensor("op_2666"), val = tensor([1, 8, -1, 188])]; - tensor x_297_cast_fp16 = reshape(shape = var_2666, x = x_295_cast_fp16)[name = tensor("x_297_cast_fp16")]; - tensor var_2670_begin_0 = const()[name = tensor("op_2670_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2670_end_0 = const()[name = tensor("op_2670_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2670_end_mask_0 = const()[name = tensor("op_2670_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2670_cast_fp16 = slice_by_index(begin = var_2670_begin_0, end = var_2670_end_0, end_mask = var_2670_end_mask_0, x = x_297_cast_fp16)[name = tensor("op_2670_cast_fp16")]; - tensor var_2671 = const()[name = tensor("op_2671"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2671, x = var_2670_cast_fp16)[name = tensor("matrix_bd_53_cast_fp16")]; - tensor matrix_ac_27_transpose_x_0 = const()[name = tensor("matrix_ac_27_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_27_transpose_y_0 = const()[name = tensor("matrix_ac_27_transpose_y_0"), val = tensor(false)]; - tensor transpose_77_perm_0 = const()[name = tensor("transpose_77_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_78_perm_0 = const()[name = tensor("transpose_78_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = k_53_cast_fp16)[name = tensor("transpose_110")]; - tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = var_2654_cast_fp16)[name = tensor("transpose_111")]; - tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_77, y = transpose_78)[name = tensor("matrix_ac_27_cast_fp16")]; - tensor matrix_bd_55_begin_0 = const()[name = tensor("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_55_end_0 = const()[name = tensor("matrix_bd_55_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_55_end_mask_0 = const()[name = tensor("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = tensor("matrix_bd_55_cast_fp16")]; - tensor var_2680_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = tensor("op_2680_cast_fp16")]; - tensor _inversed_scores_53_y_0_to_fp16 = const()[name = tensor("_inversed_scores_53_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_53_cast_fp16 = mul(x = var_2680_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = tensor("_inversed_scores_53_cast_fp16")]; - tensor scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_11)[name = tensor("scores_55_cast_fp16")]; - tensor var_2686_cast_fp16 = softmax(axis = var_23, x = scores_55_cast_fp16)[name = tensor("op_2686_cast_fp16")]; - tensor input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2686_cast_fp16, cond = mask_11)[name = tensor("input_709_cast_fp16")]; - tensor x_299_transpose_x_0 = const()[name = tensor("x_299_transpose_x_0"), val = tensor(false)]; - tensor x_299_transpose_y_0 = const()[name = tensor("x_299_transpose_y_0"), val = tensor(false)]; - tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = tensor("transpose_113")]; - tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_29_cast_fp16)[name = tensor("x_299_cast_fp16")]; - tensor var_2690_perm_0 = const()[name = tensor("op_2690_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2691 = const()[name = tensor("op_2691"), val = tensor([1, -1, 512])]; - tensor var_2690_cast_fp16 = transpose(perm = var_2690_perm_0, x = x_299_cast_fp16)[name = tensor("transpose_109")]; - tensor input_711_cast_fp16 = reshape(shape = var_2691, x = var_2690_cast_fp16)[name = tensor("input_711_cast_fp16")]; - tensor module_layers_13_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171370816)))]; - tensor module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171895168)))]; - tensor linear_124_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_out_bias_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16, x = input_711_cast_fp16)[name = tensor("linear_124_cast_fp16")]; - tensor input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = tensor("input_715_cast_fp16")]; - tensor x_303_axes_0 = const()[name = tensor("x_303_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171896256)))]; - tensor module_layers_13_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171897344)))]; - tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = tensor("x_303_cast_fp16")]; - tensor input_717_perm_0 = const()[name = tensor("input_717_perm_0"), val = tensor([0, 2, 1])]; - tensor input_719_pad_type_0 = const()[name = tensor("input_719_pad_type_0"), val = tensor("valid")]; - tensor input_719_strides_0 = const()[name = tensor("input_719_strides_0"), val = tensor([1])]; - tensor input_719_pad_0 = const()[name = tensor("input_719_pad_0"), val = tensor([0, 0])]; - tensor input_719_dilations_0 = const()[name = tensor("input_719_dilations_0"), val = tensor([1])]; - tensor input_719_groups_0 = const()[name = tensor("input_719_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171898432)))]; - tensor module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172947072)))]; - tensor input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = tensor("transpose_108")]; - tensor input_719_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv1_bias_to_fp16, dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16, x = input_717_cast_fp16)[name = tensor("input_719_cast_fp16")]; - tensor x_305_split_num_splits_0 = const()[name = tensor("x_305_split_num_splits_0"), val = tensor(2)]; - tensor x_305_split_axis_0 = const()[name = tensor("x_305_split_axis_0"), val = tensor(1)]; - tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = tensor("x_305_split_cast_fp16")]; - tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = tensor("x_305_split_1_sigmoid_cast_fp16")]; - tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = tensor("x_305_cast_fp16")]; - tensor input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_453)[name = tensor("input_721_cast_fp16")]; - tensor input_723_pad_0 = const()[name = tensor("input_723_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_723_mode_0 = const()[name = tensor("input_723_mode_0"), val = tensor("constant")]; - tensor const_203_to_fp16 = const()[name = tensor("const_203_to_fp16"), val = tensor(0x0p+0)]; - tensor input_723_cast_fp16 = pad(constant_val = const_203_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = tensor("input_723_cast_fp16")]; - tensor input_725_pad_type_0 = const()[name = tensor("input_725_pad_type_0"), val = tensor("valid")]; - tensor input_725_groups_0 = const()[name = tensor("input_725_groups_0"), val = tensor(512)]; - tensor input_725_strides_0 = const()[name = tensor("input_725_strides_0"), val = tensor([1])]; - tensor input_725_pad_0 = const()[name = tensor("input_725_pad_0"), val = tensor([0, 0])]; - tensor input_725_dilations_0 = const()[name = tensor("input_725_dilations_0"), val = tensor([1])]; - tensor const_260_to_fp16 = const()[name = tensor("const_260_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172949184)))]; - tensor const_261_to_fp16 = const()[name = tensor("const_261_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172958464)))]; - tensor input_727_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_260_to_fp16, x = input_723_cast_fp16)[name = tensor("input_727_cast_fp16")]; - tensor input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = tensor("input_729_cast_fp16")]; - tensor x_307_pad_type_0 = const()[name = tensor("x_307_pad_type_0"), val = tensor("valid")]; - tensor x_307_strides_0 = const()[name = tensor("x_307_strides_0"), val = tensor([1])]; - tensor x_307_pad_0 = const()[name = tensor("x_307_pad_0"), val = tensor([0, 0])]; - tensor x_307_dilations_0 = const()[name = tensor("x_307_dilations_0"), val = tensor([1])]; - tensor x_307_groups_0 = const()[name = tensor("x_307_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172959552)))]; - tensor module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173483904)))]; - tensor x_307_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv2_bias_to_fp16, dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = module_layers_13_conv_pointwise_conv2_weight_to_fp16, x = input_729_cast_fp16)[name = tensor("x_307_cast_fp16")]; - tensor input_731_perm_0 = const()[name = tensor("input_731_perm_0"), val = tensor([0, 2, 1])]; - tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = tensor("transpose_107")]; - tensor input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = tensor("input_733_cast_fp16")]; - tensor input_735_axes_0 = const()[name = tensor("input_735_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173484992)))]; - tensor module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173486080)))]; - tensor input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = tensor("input_735_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173487168)))]; - tensor module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175584384)))]; - tensor linear_125_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16, x = input_735_cast_fp16)[name = tensor("linear_125_cast_fp16")]; - tensor input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = tensor("input_739_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175588544)))]; - tensor module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177685760)))]; - tensor linear_126_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16, x = input_739_cast_fp16)[name = tensor("linear_126_cast_fp16")]; - tensor var_2757_to_fp16 = const()[name = tensor("op_2757_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2758_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2757_to_fp16)[name = tensor("op_2758_cast_fp16")]; - tensor input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2758_cast_fp16)[name = tensor("input_745_cast_fp16")]; - tensor input_747_axes_0 = const()[name = tensor("input_747_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177686848)))]; - tensor module_layers_13_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177687936)))]; - tensor input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = tensor("input_747_cast_fp16")]; - tensor input_749_axes_0 = const()[name = tensor("input_749_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177689024)))]; - tensor module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177690112)))]; - tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = tensor("input_749_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177691200)))]; - tensor module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179788416)))]; - tensor linear_127_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16, x = input_749_cast_fp16)[name = tensor("linear_127_cast_fp16")]; - tensor input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = tensor("input_753_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179792576)))]; - tensor module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181889792)))]; - tensor linear_128_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16, x = input_753_cast_fp16)[name = tensor("linear_128_cast_fp16")]; - tensor var_2788_to_fp16 = const()[name = tensor("op_2788_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2789_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2788_to_fp16)[name = tensor("op_2789_cast_fp16")]; - tensor input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2789_cast_fp16)[name = tensor("input_759_cast_fp16")]; - tensor query_29_axes_0 = const()[name = tensor("query_29_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181890880)))]; - tensor module_layers_14_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181891968)))]; - tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = tensor("query_29_cast_fp16")]; - tensor module_layers_14_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181893056)))]; - tensor module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182417408)))]; - tensor linear_129_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_q_bias_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_129_cast_fp16")]; - tensor var_2806 = const()[name = tensor("op_2806"), val = tensor([1, -1, 8, 64])]; - tensor q_85_cast_fp16 = reshape(shape = var_2806, x = linear_129_cast_fp16)[name = tensor("q_85_cast_fp16")]; - tensor module_layers_14_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182418496)))]; - tensor module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182942848)))]; - tensor linear_130_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_k_bias_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_130_cast_fp16")]; - tensor var_2811 = const()[name = tensor("op_2811"), val = tensor([1, -1, 8, 64])]; - tensor k_57_cast_fp16 = reshape(shape = var_2811, x = linear_130_cast_fp16)[name = tensor("k_57_cast_fp16")]; - tensor module_layers_14_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182943936)))]; - tensor module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183468288)))]; - tensor linear_131_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_v_bias_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_131_cast_fp16")]; - tensor var_2816 = const()[name = tensor("op_2816"), val = tensor([1, -1, 8, 64])]; - tensor v_29_cast_fp16 = reshape(shape = var_2816, x = linear_131_cast_fp16)[name = tensor("v_29_cast_fp16")]; - tensor value_31_perm_0 = const()[name = tensor("value_31_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183469376)))]; - tensor var_2828_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2828_cast_fp16")]; - tensor module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183470464)))]; - tensor var_2830_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2830_cast_fp16")]; - tensor q_with_bias_v_29_perm_0 = const()[name = tensor("q_with_bias_v_29_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_315_transpose_x_0 = const()[name = tensor("x_315_transpose_x_0"), val = tensor(false)]; - tensor x_315_transpose_y_0 = const()[name = tensor("x_315_transpose_y_0"), val = tensor(false)]; - tensor var_2832_to_fp16 = const()[name = tensor("op_2832_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183471552)))]; - tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2830_cast_fp16)[name = tensor("transpose_105")]; - tensor x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = var_2832_to_fp16)[name = tensor("x_315_cast_fp16")]; - tensor x_317_pad_0 = const()[name = tensor("x_317_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_317_mode_0 = const()[name = tensor("x_317_mode_0"), val = tensor("constant")]; - tensor const_210_to_fp16 = const()[name = tensor("const_210_to_fp16"), val = tensor(0x0p+0)]; - tensor x_317_cast_fp16 = pad(constant_val = const_210_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = tensor("x_317_cast_fp16")]; - tensor var_2840 = const()[name = tensor("op_2840"), val = tensor([1, 8, -1, 188])]; - tensor x_319_cast_fp16 = reshape(shape = var_2840, x = x_317_cast_fp16)[name = tensor("x_319_cast_fp16")]; - tensor var_2844_begin_0 = const()[name = tensor("op_2844_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2844_end_0 = const()[name = tensor("op_2844_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2844_end_mask_0 = const()[name = tensor("op_2844_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2844_cast_fp16 = slice_by_index(begin = var_2844_begin_0, end = var_2844_end_0, end_mask = var_2844_end_mask_0, x = x_319_cast_fp16)[name = tensor("op_2844_cast_fp16")]; - tensor var_2845 = const()[name = tensor("op_2845"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_57_cast_fp16 = reshape(shape = var_2845, x = var_2844_cast_fp16)[name = tensor("matrix_bd_57_cast_fp16")]; - tensor matrix_ac_29_transpose_x_0 = const()[name = tensor("matrix_ac_29_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_29_transpose_y_0 = const()[name = tensor("matrix_ac_29_transpose_y_0"), val = tensor(false)]; - tensor transpose_79_perm_0 = const()[name = tensor("transpose_79_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_80_perm_0 = const()[name = tensor("transpose_80_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = k_57_cast_fp16)[name = tensor("transpose_103")]; - tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_2828_cast_fp16)[name = tensor("transpose_104")]; - tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_79, y = transpose_80)[name = tensor("matrix_ac_29_cast_fp16")]; - tensor matrix_bd_59_begin_0 = const()[name = tensor("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_59_end_0 = const()[name = tensor("matrix_bd_59_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_59_end_mask_0 = const()[name = tensor("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = tensor("matrix_bd_59_cast_fp16")]; - tensor var_2854_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = tensor("op_2854_cast_fp16")]; - tensor _inversed_scores_57_y_0_to_fp16 = const()[name = tensor("_inversed_scores_57_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_57_cast_fp16 = mul(x = var_2854_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = tensor("_inversed_scores_57_cast_fp16")]; - tensor scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_11)[name = tensor("scores_59_cast_fp16")]; - tensor var_2860_cast_fp16 = softmax(axis = var_23, x = scores_59_cast_fp16)[name = tensor("op_2860_cast_fp16")]; - tensor input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2860_cast_fp16, cond = mask_11)[name = tensor("input_761_cast_fp16")]; - tensor x_321_transpose_x_0 = const()[name = tensor("x_321_transpose_x_0"), val = tensor(false)]; - tensor x_321_transpose_y_0 = const()[name = tensor("x_321_transpose_y_0"), val = tensor(false)]; - tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = tensor("transpose_106")]; - tensor x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_31_cast_fp16)[name = tensor("x_321_cast_fp16")]; - tensor var_2864_perm_0 = const()[name = tensor("op_2864_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2865 = const()[name = tensor("op_2865"), val = tensor([1, -1, 512])]; - tensor var_2864_cast_fp16 = transpose(perm = var_2864_perm_0, x = x_321_cast_fp16)[name = tensor("transpose_102")]; - tensor input_763_cast_fp16 = reshape(shape = var_2865, x = var_2864_cast_fp16)[name = tensor("input_763_cast_fp16")]; - tensor module_layers_14_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183855616)))]; - tensor module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184379968)))]; - tensor linear_133_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_out_bias_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16, x = input_763_cast_fp16)[name = tensor("linear_133_cast_fp16")]; - tensor input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = tensor("input_767_cast_fp16")]; - tensor x_325_axes_0 = const()[name = tensor("x_325_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184381056)))]; - tensor module_layers_14_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184382144)))]; - tensor x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = tensor("x_325_cast_fp16")]; - tensor input_769_perm_0 = const()[name = tensor("input_769_perm_0"), val = tensor([0, 2, 1])]; - tensor input_771_pad_type_0 = const()[name = tensor("input_771_pad_type_0"), val = tensor("valid")]; - tensor input_771_strides_0 = const()[name = tensor("input_771_strides_0"), val = tensor([1])]; - tensor input_771_pad_0 = const()[name = tensor("input_771_pad_0"), val = tensor([0, 0])]; - tensor input_771_dilations_0 = const()[name = tensor("input_771_dilations_0"), val = tensor([1])]; - tensor input_771_groups_0 = const()[name = tensor("input_771_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184383232)))]; - tensor module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185431872)))]; - tensor input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = tensor("transpose_101")]; - tensor input_771_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv1_bias_to_fp16, dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16, x = input_769_cast_fp16)[name = tensor("input_771_cast_fp16")]; - tensor x_327_split_num_splits_0 = const()[name = tensor("x_327_split_num_splits_0"), val = tensor(2)]; - tensor x_327_split_axis_0 = const()[name = tensor("x_327_split_axis_0"), val = tensor(1)]; - tensor x_327_split_cast_fp16_0, tensor x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = tensor("x_327_split_cast_fp16")]; - tensor x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = tensor("x_327_split_1_sigmoid_cast_fp16")]; - tensor x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = tensor("x_327_cast_fp16")]; - tensor input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_453)[name = tensor("input_773_cast_fp16")]; - tensor input_775_pad_0 = const()[name = tensor("input_775_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_775_mode_0 = const()[name = tensor("input_775_mode_0"), val = tensor("constant")]; - tensor const_213_to_fp16 = const()[name = tensor("const_213_to_fp16"), val = tensor(0x0p+0)]; - tensor input_775_cast_fp16 = pad(constant_val = const_213_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = tensor("input_775_cast_fp16")]; - tensor input_777_pad_type_0 = const()[name = tensor("input_777_pad_type_0"), val = tensor("valid")]; - tensor input_777_groups_0 = const()[name = tensor("input_777_groups_0"), val = tensor(512)]; - tensor input_777_strides_0 = const()[name = tensor("input_777_strides_0"), val = tensor([1])]; - tensor input_777_pad_0 = const()[name = tensor("input_777_pad_0"), val = tensor([0, 0])]; - tensor input_777_dilations_0 = const()[name = tensor("input_777_dilations_0"), val = tensor([1])]; - tensor const_262_to_fp16 = const()[name = tensor("const_262_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185433984)))]; - tensor const_263_to_fp16 = const()[name = tensor("const_263_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185443264)))]; - tensor input_779_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_262_to_fp16, x = input_775_cast_fp16)[name = tensor("input_779_cast_fp16")]; - tensor input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = tensor("input_781_cast_fp16")]; - tensor x_329_pad_type_0 = const()[name = tensor("x_329_pad_type_0"), val = tensor("valid")]; - tensor x_329_strides_0 = const()[name = tensor("x_329_strides_0"), val = tensor([1])]; - tensor x_329_pad_0 = const()[name = tensor("x_329_pad_0"), val = tensor([0, 0])]; - tensor x_329_dilations_0 = const()[name = tensor("x_329_dilations_0"), val = tensor([1])]; - tensor x_329_groups_0 = const()[name = tensor("x_329_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185444352)))]; - tensor module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185968704)))]; - tensor x_329_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv2_bias_to_fp16, dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = module_layers_14_conv_pointwise_conv2_weight_to_fp16, x = input_781_cast_fp16)[name = tensor("x_329_cast_fp16")]; - tensor input_783_perm_0 = const()[name = tensor("input_783_perm_0"), val = tensor([0, 2, 1])]; - tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = tensor("transpose_100")]; - tensor input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = tensor("input_785_cast_fp16")]; - tensor input_787_axes_0 = const()[name = tensor("input_787_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185969792)))]; - tensor module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185970880)))]; - tensor input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = tensor("input_787_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185971968)))]; - tensor module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188069184)))]; - tensor linear_134_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16, x = input_787_cast_fp16)[name = tensor("linear_134_cast_fp16")]; - tensor input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = tensor("input_791_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188073344)))]; - tensor module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190170560)))]; - tensor linear_135_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16, x = input_791_cast_fp16)[name = tensor("linear_135_cast_fp16")]; - tensor var_2931_to_fp16 = const()[name = tensor("op_2931_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2932_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2931_to_fp16)[name = tensor("op_2932_cast_fp16")]; - tensor input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2932_cast_fp16)[name = tensor("input_797_cast_fp16")]; - tensor input_799_axes_0 = const()[name = tensor("input_799_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190171648)))]; - tensor module_layers_14_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190172736)))]; - tensor input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = tensor("input_799_cast_fp16")]; - tensor input_801_axes_0 = const()[name = tensor("input_801_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190173824)))]; - tensor module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190174912)))]; - tensor input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = tensor("input_801_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190176000)))]; - tensor module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192273216)))]; - tensor linear_136_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16, x = input_801_cast_fp16)[name = tensor("linear_136_cast_fp16")]; - tensor input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = tensor("input_805_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192277376)))]; - tensor module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194374592)))]; - tensor linear_137_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16, x = input_805_cast_fp16)[name = tensor("linear_137_cast_fp16")]; - tensor var_2962_to_fp16 = const()[name = tensor("op_2962_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2963_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2962_to_fp16)[name = tensor("op_2963_cast_fp16")]; - tensor input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2963_cast_fp16)[name = tensor("input_811_cast_fp16")]; - tensor query_31_axes_0 = const()[name = tensor("query_31_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194375680)))]; - tensor module_layers_15_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194376768)))]; - tensor query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = tensor("query_31_cast_fp16")]; - tensor module_layers_15_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194377856)))]; - tensor module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194902208)))]; - tensor linear_138_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_q_bias_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_138_cast_fp16")]; - tensor var_2980 = const()[name = tensor("op_2980"), val = tensor([1, -1, 8, 64])]; - tensor q_91_cast_fp16 = reshape(shape = var_2980, x = linear_138_cast_fp16)[name = tensor("q_91_cast_fp16")]; - tensor module_layers_15_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194903296)))]; - tensor module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195427648)))]; - tensor linear_139_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_k_bias_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_139_cast_fp16")]; - tensor var_2985 = const()[name = tensor("op_2985"), val = tensor([1, -1, 8, 64])]; - tensor k_61_cast_fp16 = reshape(shape = var_2985, x = linear_139_cast_fp16)[name = tensor("k_61_cast_fp16")]; - tensor module_layers_15_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195428736)))]; - tensor module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195953088)))]; - tensor linear_140_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_v_bias_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_140_cast_fp16")]; - tensor var_2990 = const()[name = tensor("op_2990"), val = tensor([1, -1, 8, 64])]; - tensor v_31_cast_fp16 = reshape(shape = var_2990, x = linear_140_cast_fp16)[name = tensor("v_31_cast_fp16")]; - tensor value_33_perm_0 = const()[name = tensor("value_33_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195954176)))]; - tensor var_3002_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3002_cast_fp16")]; - tensor module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195955264)))]; - tensor var_3004_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3004_cast_fp16")]; - tensor q_with_bias_v_31_perm_0 = const()[name = tensor("q_with_bias_v_31_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_337_transpose_x_0 = const()[name = tensor("x_337_transpose_x_0"), val = tensor(false)]; - tensor x_337_transpose_y_0 = const()[name = tensor("x_337_transpose_y_0"), val = tensor(false)]; - tensor var_3006_to_fp16 = const()[name = tensor("op_3006_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195956352)))]; - tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3004_cast_fp16)[name = tensor("transpose_98")]; - tensor x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = var_3006_to_fp16)[name = tensor("x_337_cast_fp16")]; - tensor x_339_pad_0 = const()[name = tensor("x_339_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_339_mode_0 = const()[name = tensor("x_339_mode_0"), val = tensor("constant")]; - tensor const_220_to_fp16 = const()[name = tensor("const_220_to_fp16"), val = tensor(0x0p+0)]; - tensor x_339_cast_fp16 = pad(constant_val = const_220_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = tensor("x_339_cast_fp16")]; - tensor var_3014 = const()[name = tensor("op_3014"), val = tensor([1, 8, -1, 188])]; - tensor x_341_cast_fp16 = reshape(shape = var_3014, x = x_339_cast_fp16)[name = tensor("x_341_cast_fp16")]; - tensor var_3018_begin_0 = const()[name = tensor("op_3018_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3018_end_0 = const()[name = tensor("op_3018_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3018_end_mask_0 = const()[name = tensor("op_3018_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3018_cast_fp16 = slice_by_index(begin = var_3018_begin_0, end = var_3018_end_0, end_mask = var_3018_end_mask_0, x = x_341_cast_fp16)[name = tensor("op_3018_cast_fp16")]; - tensor var_3019 = const()[name = tensor("op_3019"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_61_cast_fp16 = reshape(shape = var_3019, x = var_3018_cast_fp16)[name = tensor("matrix_bd_61_cast_fp16")]; - tensor matrix_ac_31_transpose_x_0 = const()[name = tensor("matrix_ac_31_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_31_transpose_y_0 = const()[name = tensor("matrix_ac_31_transpose_y_0"), val = tensor(false)]; - tensor transpose_81_perm_0 = const()[name = tensor("transpose_81_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_82_perm_0 = const()[name = tensor("transpose_82_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = k_61_cast_fp16)[name = tensor("transpose_96")]; - tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_3002_cast_fp16)[name = tensor("transpose_97")]; - tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_81, y = transpose_82)[name = tensor("matrix_ac_31_cast_fp16")]; - tensor matrix_bd_63_begin_0 = const()[name = tensor("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_63_end_0 = const()[name = tensor("matrix_bd_63_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_63_end_mask_0 = const()[name = tensor("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = tensor("matrix_bd_63_cast_fp16")]; - tensor var_3028_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = tensor("op_3028_cast_fp16")]; - tensor _inversed_scores_61_y_0_to_fp16 = const()[name = tensor("_inversed_scores_61_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_61_cast_fp16 = mul(x = var_3028_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = tensor("_inversed_scores_61_cast_fp16")]; - tensor scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_11)[name = tensor("scores_63_cast_fp16")]; - tensor var_3034_cast_fp16 = softmax(axis = var_23, x = scores_63_cast_fp16)[name = tensor("op_3034_cast_fp16")]; - tensor input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_3034_cast_fp16, cond = mask_11)[name = tensor("input_813_cast_fp16")]; - tensor x_343_transpose_x_0 = const()[name = tensor("x_343_transpose_x_0"), val = tensor(false)]; - tensor x_343_transpose_y_0 = const()[name = tensor("x_343_transpose_y_0"), val = tensor(false)]; - tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = tensor("transpose_99")]; - tensor x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_33_cast_fp16)[name = tensor("x_343_cast_fp16")]; - tensor var_3038_perm_0 = const()[name = tensor("op_3038_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3039 = const()[name = tensor("op_3039"), val = tensor([1, -1, 512])]; - tensor var_3038_cast_fp16 = transpose(perm = var_3038_perm_0, x = x_343_cast_fp16)[name = tensor("transpose_95")]; - tensor input_815_cast_fp16 = reshape(shape = var_3039, x = var_3038_cast_fp16)[name = tensor("input_815_cast_fp16")]; - tensor module_layers_15_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196340416)))]; - tensor module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196864768)))]; - tensor linear_142_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_out_bias_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16, x = input_815_cast_fp16)[name = tensor("linear_142_cast_fp16")]; - tensor input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = tensor("input_819_cast_fp16")]; - tensor x_347_axes_0 = const()[name = tensor("x_347_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196865856)))]; - tensor module_layers_15_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196866944)))]; - tensor x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = tensor("x_347_cast_fp16")]; - tensor input_821_perm_0 = const()[name = tensor("input_821_perm_0"), val = tensor([0, 2, 1])]; - tensor input_823_pad_type_0 = const()[name = tensor("input_823_pad_type_0"), val = tensor("valid")]; - tensor input_823_strides_0 = const()[name = tensor("input_823_strides_0"), val = tensor([1])]; - tensor input_823_pad_0 = const()[name = tensor("input_823_pad_0"), val = tensor([0, 0])]; - tensor input_823_dilations_0 = const()[name = tensor("input_823_dilations_0"), val = tensor([1])]; - tensor input_823_groups_0 = const()[name = tensor("input_823_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196868032)))]; - tensor module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197916672)))]; - tensor input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = tensor("transpose_94")]; - tensor input_823_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv1_bias_to_fp16, dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16, x = input_821_cast_fp16)[name = tensor("input_823_cast_fp16")]; - tensor x_349_split_num_splits_0 = const()[name = tensor("x_349_split_num_splits_0"), val = tensor(2)]; - tensor x_349_split_axis_0 = const()[name = tensor("x_349_split_axis_0"), val = tensor(1)]; - tensor x_349_split_cast_fp16_0, tensor x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = tensor("x_349_split_cast_fp16")]; - tensor x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = tensor("x_349_split_1_sigmoid_cast_fp16")]; - tensor x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = tensor("x_349_cast_fp16")]; - tensor input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_453)[name = tensor("input_825_cast_fp16")]; - tensor input_827_pad_0 = const()[name = tensor("input_827_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_827_mode_0 = const()[name = tensor("input_827_mode_0"), val = tensor("constant")]; - tensor const_223_to_fp16 = const()[name = tensor("const_223_to_fp16"), val = tensor(0x0p+0)]; - tensor input_827_cast_fp16 = pad(constant_val = const_223_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = tensor("input_827_cast_fp16")]; - tensor input_829_pad_type_0 = const()[name = tensor("input_829_pad_type_0"), val = tensor("valid")]; - tensor input_829_groups_0 = const()[name = tensor("input_829_groups_0"), val = tensor(512)]; - tensor input_829_strides_0 = const()[name = tensor("input_829_strides_0"), val = tensor([1])]; - tensor input_829_pad_0 = const()[name = tensor("input_829_pad_0"), val = tensor([0, 0])]; - tensor input_829_dilations_0 = const()[name = tensor("input_829_dilations_0"), val = tensor([1])]; - tensor const_264_to_fp16 = const()[name = tensor("const_264_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197918784)))]; - tensor const_265_to_fp16 = const()[name = tensor("const_265_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197928064)))]; - tensor input_831_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_264_to_fp16, x = input_827_cast_fp16)[name = tensor("input_831_cast_fp16")]; - tensor input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = tensor("input_833_cast_fp16")]; - tensor x_351_pad_type_0 = const()[name = tensor("x_351_pad_type_0"), val = tensor("valid")]; - tensor x_351_strides_0 = const()[name = tensor("x_351_strides_0"), val = tensor([1])]; - tensor x_351_pad_0 = const()[name = tensor("x_351_pad_0"), val = tensor([0, 0])]; - tensor x_351_dilations_0 = const()[name = tensor("x_351_dilations_0"), val = tensor([1])]; - tensor x_351_groups_0 = const()[name = tensor("x_351_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197929152)))]; - tensor module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198453504)))]; - tensor x_351_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv2_bias_to_fp16, dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = module_layers_15_conv_pointwise_conv2_weight_to_fp16, x = input_833_cast_fp16)[name = tensor("x_351_cast_fp16")]; - tensor input_835_perm_0 = const()[name = tensor("input_835_perm_0"), val = tensor([0, 2, 1])]; - tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = tensor("transpose_93")]; - tensor input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = tensor("input_837_cast_fp16")]; - tensor input_839_axes_0 = const()[name = tensor("input_839_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198454592)))]; - tensor module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198455680)))]; - tensor input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = tensor("input_839_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198456768)))]; - tensor module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200553984)))]; - tensor linear_143_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16, x = input_839_cast_fp16)[name = tensor("linear_143_cast_fp16")]; - tensor input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = tensor("input_843_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200558144)))]; - tensor module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202655360)))]; - tensor linear_144_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16, x = input_843_cast_fp16)[name = tensor("linear_144_cast_fp16")]; - tensor var_3105_to_fp16 = const()[name = tensor("op_3105_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3106_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3105_to_fp16)[name = tensor("op_3106_cast_fp16")]; - tensor input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_3106_cast_fp16)[name = tensor("input_849_cast_fp16")]; - tensor input_851_axes_0 = const()[name = tensor("input_851_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202656448)))]; - tensor module_layers_15_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202657536)))]; - tensor input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = tensor("input_851_cast_fp16")]; - tensor input_853_axes_0 = const()[name = tensor("input_853_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202658624)))]; - tensor module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202659712)))]; - tensor input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = tensor("input_853_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202660800)))]; - tensor module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204758016)))]; - tensor linear_145_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16, x = input_853_cast_fp16)[name = tensor("linear_145_cast_fp16")]; - tensor input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = tensor("input_857_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204762176)))]; - tensor module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206859392)))]; - tensor linear_146_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16, x = input_857_cast_fp16)[name = tensor("linear_146_cast_fp16")]; - tensor var_3136_to_fp16 = const()[name = tensor("op_3136_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3137_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3136_to_fp16)[name = tensor("op_3137_cast_fp16")]; - tensor input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_3137_cast_fp16)[name = tensor("input_863_cast_fp16")]; - tensor query_axes_0 = const()[name = tensor("query_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206860480)))]; - tensor module_layers_16_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206861568)))]; - tensor query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = tensor("query_cast_fp16")]; - tensor module_layers_16_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206862656)))]; - tensor module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207387008)))]; - tensor linear_147_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_q_bias_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_147_cast_fp16")]; - tensor var_3154 = const()[name = tensor("op_3154"), val = tensor([1, -1, 8, 64])]; - tensor q_97_cast_fp16 = reshape(shape = var_3154, x = linear_147_cast_fp16)[name = tensor("q_97_cast_fp16")]; - tensor module_layers_16_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207388096)))]; - tensor module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207912448)))]; - tensor linear_148_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_k_bias_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_148_cast_fp16")]; - tensor var_3159 = const()[name = tensor("op_3159"), val = tensor([1, -1, 8, 64])]; - tensor k_65_cast_fp16 = reshape(shape = var_3159, x = linear_148_cast_fp16)[name = tensor("k_65_cast_fp16")]; - tensor module_layers_16_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207913536)))]; - tensor module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208437888)))]; - tensor linear_149_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_v_bias_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_149_cast_fp16")]; - tensor var_3164 = const()[name = tensor("op_3164"), val = tensor([1, -1, 8, 64])]; - tensor v_cast_fp16 = reshape(shape = var_3164, x = linear_149_cast_fp16)[name = tensor("v_cast_fp16")]; - tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208438976)))]; - tensor var_3176_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3176_cast_fp16")]; - tensor module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208440064)))]; - tensor var_3178_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3178_cast_fp16")]; - tensor q_with_bias_v_perm_0 = const()[name = tensor("q_with_bias_v_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_359_transpose_x_0 = const()[name = tensor("x_359_transpose_x_0"), val = tensor(false)]; - tensor x_359_transpose_y_0 = const()[name = tensor("x_359_transpose_y_0"), val = tensor(false)]; - tensor var_3180_to_fp16 = const()[name = tensor("op_3180_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208441152)))]; - tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_3178_cast_fp16)[name = tensor("transpose_91")]; - tensor x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_cast_fp16, y = var_3180_to_fp16)[name = tensor("x_359_cast_fp16")]; - tensor x_361_pad_0 = const()[name = tensor("x_361_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_361_mode_0 = const()[name = tensor("x_361_mode_0"), val = tensor("constant")]; - tensor const_230_to_fp16 = const()[name = tensor("const_230_to_fp16"), val = tensor(0x0p+0)]; - tensor x_361_cast_fp16 = pad(constant_val = const_230_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = tensor("x_361_cast_fp16")]; - tensor var_3188 = const()[name = tensor("op_3188"), val = tensor([1, 8, -1, 188])]; - tensor x_363_cast_fp16 = reshape(shape = var_3188, x = x_361_cast_fp16)[name = tensor("x_363_cast_fp16")]; - tensor var_3192_begin_0 = const()[name = tensor("op_3192_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3192_end_0 = const()[name = tensor("op_3192_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3192_end_mask_0 = const()[name = tensor("op_3192_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3192_cast_fp16 = slice_by_index(begin = var_3192_begin_0, end = var_3192_end_0, end_mask = var_3192_end_mask_0, x = x_363_cast_fp16)[name = tensor("op_3192_cast_fp16")]; - tensor var_3193 = const()[name = tensor("op_3193"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_65_cast_fp16 = reshape(shape = var_3193, x = var_3192_cast_fp16)[name = tensor("matrix_bd_65_cast_fp16")]; - tensor matrix_ac_transpose_x_0 = const()[name = tensor("matrix_ac_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_transpose_y_0 = const()[name = tensor("matrix_ac_transpose_y_0"), val = tensor(false)]; - tensor transpose_83_perm_0 = const()[name = tensor("transpose_83_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_84_perm_0 = const()[name = tensor("transpose_84_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = k_65_cast_fp16)[name = tensor("transpose_89")]; - tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = var_3176_cast_fp16)[name = tensor("transpose_90")]; - tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_83, y = transpose_84)[name = tensor("matrix_ac_cast_fp16")]; - tensor matrix_bd_begin_0 = const()[name = tensor("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_end_0 = const()[name = tensor("matrix_bd_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_end_mask_0 = const()[name = tensor("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_65_cast_fp16)[name = tensor("matrix_bd_cast_fp16")]; - tensor var_3202_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = tensor("op_3202_cast_fp16")]; - tensor _inversed_scores_65_y_0_to_fp16 = const()[name = tensor("_inversed_scores_65_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_65_cast_fp16 = mul(x = var_3202_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = tensor("_inversed_scores_65_cast_fp16")]; - tensor scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_11)[name = tensor("scores_cast_fp16")]; - tensor var_3208_cast_fp16 = softmax(axis = var_23, x = scores_cast_fp16)[name = tensor("op_3208_cast_fp16")]; - tensor input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_3208_cast_fp16, cond = mask_11)[name = tensor("input_865_cast_fp16")]; - tensor x_365_transpose_x_0 = const()[name = tensor("x_365_transpose_x_0"), val = tensor(false)]; - tensor x_365_transpose_y_0 = const()[name = tensor("x_365_transpose_y_0"), val = tensor(false)]; - tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = tensor("transpose_92")]; - tensor x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_cast_fp16)[name = tensor("x_365_cast_fp16")]; - tensor var_3212_perm_0 = const()[name = tensor("op_3212_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3213 = const()[name = tensor("op_3213"), val = tensor([1, -1, 512])]; - tensor var_3212_cast_fp16 = transpose(perm = var_3212_perm_0, x = x_365_cast_fp16)[name = tensor("transpose_88")]; - tensor input_867_cast_fp16 = reshape(shape = var_3213, x = var_3212_cast_fp16)[name = tensor("input_867_cast_fp16")]; - tensor module_layers_16_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208825216)))]; - tensor module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209349568)))]; - tensor linear_151_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_out_bias_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16, x = input_867_cast_fp16)[name = tensor("linear_151_cast_fp16")]; - tensor input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = tensor("input_871_cast_fp16")]; - tensor x_369_axes_0 = const()[name = tensor("x_369_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209350656)))]; - tensor module_layers_16_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209351744)))]; - tensor x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = tensor("x_369_cast_fp16")]; - tensor input_873_perm_0 = const()[name = tensor("input_873_perm_0"), val = tensor([0, 2, 1])]; - tensor input_875_pad_type_0 = const()[name = tensor("input_875_pad_type_0"), val = tensor("valid")]; - tensor input_875_strides_0 = const()[name = tensor("input_875_strides_0"), val = tensor([1])]; - tensor input_875_pad_0 = const()[name = tensor("input_875_pad_0"), val = tensor([0, 0])]; - tensor input_875_dilations_0 = const()[name = tensor("input_875_dilations_0"), val = tensor([1])]; - tensor input_875_groups_0 = const()[name = tensor("input_875_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209352832)))]; - tensor module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210401472)))]; - tensor input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = tensor("transpose_87")]; - tensor input_875_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv1_bias_to_fp16, dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16, x = input_873_cast_fp16)[name = tensor("input_875_cast_fp16")]; - tensor x_371_split_num_splits_0 = const()[name = tensor("x_371_split_num_splits_0"), val = tensor(2)]; - tensor x_371_split_axis_0 = const()[name = tensor("x_371_split_axis_0"), val = tensor(1)]; - tensor x_371_split_cast_fp16_0, tensor x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = tensor("x_371_split_cast_fp16")]; - tensor x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = tensor("x_371_split_1_sigmoid_cast_fp16")]; - tensor x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = tensor("x_371_cast_fp16")]; - tensor input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_453)[name = tensor("input_877_cast_fp16")]; - tensor input_879_pad_0 = const()[name = tensor("input_879_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_879_mode_0 = const()[name = tensor("input_879_mode_0"), val = tensor("constant")]; - tensor const_233_to_fp16 = const()[name = tensor("const_233_to_fp16"), val = tensor(0x0p+0)]; - tensor input_879_cast_fp16 = pad(constant_val = const_233_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = tensor("input_879_cast_fp16")]; - tensor input_881_pad_type_0 = const()[name = tensor("input_881_pad_type_0"), val = tensor("valid")]; - tensor input_881_groups_0 = const()[name = tensor("input_881_groups_0"), val = tensor(512)]; - tensor input_881_strides_0 = const()[name = tensor("input_881_strides_0"), val = tensor([1])]; - tensor input_881_pad_0 = const()[name = tensor("input_881_pad_0"), val = tensor([0, 0])]; - tensor input_881_dilations_0 = const()[name = tensor("input_881_dilations_0"), val = tensor([1])]; - tensor const_266_to_fp16 = const()[name = tensor("const_266_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210403584)))]; - tensor const_267_to_fp16 = const()[name = tensor("const_267_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210412864)))]; - tensor input_883_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_266_to_fp16, x = input_879_cast_fp16)[name = tensor("input_883_cast_fp16")]; - tensor input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = tensor("input_885_cast_fp16")]; - tensor x_373_pad_type_0 = const()[name = tensor("x_373_pad_type_0"), val = tensor("valid")]; - tensor x_373_strides_0 = const()[name = tensor("x_373_strides_0"), val = tensor([1])]; - tensor x_373_pad_0 = const()[name = tensor("x_373_pad_0"), val = tensor([0, 0])]; - tensor x_373_dilations_0 = const()[name = tensor("x_373_dilations_0"), val = tensor([1])]; - tensor x_373_groups_0 = const()[name = tensor("x_373_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210413952)))]; - tensor module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210938304)))]; - tensor x_373_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv2_bias_to_fp16, dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = module_layers_16_conv_pointwise_conv2_weight_to_fp16, x = input_885_cast_fp16)[name = tensor("x_373_cast_fp16")]; - tensor input_887_perm_0 = const()[name = tensor("input_887_perm_0"), val = tensor([0, 2, 1])]; - tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = tensor("transpose_86")]; - tensor input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = tensor("input_889_cast_fp16")]; - tensor input_891_axes_0 = const()[name = tensor("input_891_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210939392)))]; - tensor module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210940480)))]; - tensor input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = tensor("input_891_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210941568)))]; - tensor module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213038784)))]; - tensor linear_152_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16, x = input_891_cast_fp16)[name = tensor("linear_152_cast_fp16")]; - tensor input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = tensor("input_895_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213042944)))]; - tensor module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215140160)))]; - tensor linear_153_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16, x = input_895_cast_fp16)[name = tensor("linear_153_cast_fp16")]; - tensor var_3279_to_fp16 = const()[name = tensor("op_3279_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3280_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3279_to_fp16)[name = tensor("op_3280_cast_fp16")]; - tensor input_cast_fp16 = add(x = input_889_cast_fp16, y = var_3280_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor audio_signal_axes_0 = const()[name = tensor("audio_signal_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215141248)))]; - tensor module_layers_16_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215142336)))]; - tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("audio_signal_cast_fp16")]; - tensor obj_1_perm_0 = const()[name = tensor("obj_1_perm_0"), val = tensor([0, 2, 1])]; - tensor obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_1_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = tensor("transpose_85")]; - tensor encoder_output = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = tensor("cast_172")]; - } -> (encoder_output, encoder_length); -} \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/Encoder.mlmodelc/weights/weight.bin b/parakeet-ctc-110m-coreml/Encoder.mlmodelc/weights/weight.bin deleted file mode 100644 index c5bffa9667270bb21c093e55898f4fb2e299e426..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/Encoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:cecf7994b2758397d992802a4f6e5d656e3a1aeb7bbedc2aa430b1316d62474c -size 215143424 diff --git a/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/analytics/coremldata.bin 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"formattedType" : "MultiArray (Int32 1 × 188 × 1)", - "shortDescription" : "", - "shape" : "[1, 188, 1]", - "name" : "duration", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.reduceArgmax" : 2, - "Ios17.squeeze" : 1, - "Ios17.cast" : 4, - "Ios17.linear" : 3, - "Ios17.transpose" : 2, - "Ios17.sliceByIndex" : 2, - "Ios17.add" : 1, - "Ios16.relu" : 1, - "Ios16.softmax" : 1, - "Ios17.gatherAlongAxis" : 1, - "Ios17.expandDims" : 3 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 512 × 188)", - "shortDescription" : "", - "shape" : "[1, 512, 188]", - "name" : "encoder", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.version" : "8.3.0", - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0" - }, - "generatedClassName" : "parakeet_joint_decision", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/model.mil b/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/model.mil deleted file mode 100644 index edfc5050f47ffbe1f5344799cef5dc0b21c392e0..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/model.mil +++ /dev/null @@ -1,58 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor decoder, tensor encoder) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_to_fp16_dtype_0 = const()[name = tensor("encoder_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_to_fp16_dtype_0 = const()[name = tensor("decoder_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488)))]; - tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = tensor("cast_6")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656832)))]; - tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476096)))]; - tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = tensor("cast_5")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; - tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; - tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; - tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1477440)))]; - tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2795904)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 188, 1, 1025])]; - tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; - tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 188, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; - tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; - tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; - tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; - tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; - tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; - tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; - tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; - tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_4")]; - tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; - tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; - tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; - tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; - tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; - tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; - tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_3")]; - } -> (token_id, token_prob, duration); -} \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/weights/weight.bin b/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/JointDecision.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b3f771cb65b190f1873e39629676ed79b65a8361522f451b37bdba8b1106e6ff -size 2798028 diff --git a/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/analytics/coremldata.bin b/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 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b/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/metadata.json deleted file mode 100644 index b96db3615aa1d5e110c61c1f4694def827cb0a1f..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/metadata.json +++ /dev/null @@ -1,123 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M single-step joint decision (current frame)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_id", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_prob", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : 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tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_step_to_fp16_dtype_0 = const()[name = tensor("encoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_step_to_fp16_dtype_0 = const()[name = tensor("decoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488)))]; - tensor encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor("cast_9")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656832)))]; - tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476096)))]; - tensor decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor("cast_8")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; - tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; - tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; - tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1477440)))]; - tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2795904)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 1, 1, 1025])]; - tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; - tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 1, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; - tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; - tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; - tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; - tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; - tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; - tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; - tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; - tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_7")]; - tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; - tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; - tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; - tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; - tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; - tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; - tensor var_72 = const()[name = tensor("op_72"), val = tensor(64)]; - tensor var_76_axis_0 = const()[name = tensor("op_76_axis_0"), val = tensor(-1)]; - tensor var_76_ascending_0 = const()[name = tensor("op_76_ascending_0"), val = tensor(false)]; - tensor var_76_sort_0 = const()[name = tensor("op_76_sort_0"), val = tensor(true)]; - tensor var_76_return_indices_0 = const()[name = tensor("op_76_return_indices_0"), val = tensor(true)]; - tensor var_76_cast_fp16_cast_int16_output_indices_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_output_indices_dtype_0"), val = tensor("uint16")]; - tensor var_76_cast_fp16_cast_int16_0, tensor var_76_cast_fp16_cast_int16_1 = topk(ascending = var_76_ascending_0, axis = var_76_axis_0, k = var_72, output_indices_dtype = var_76_cast_fp16_cast_int16_output_indices_dtype_0, return_indices = var_76_return_indices_0, sort = var_76_sort_0, x = token_logits_cast_fp16)[name = tensor("op_76_cast_fp16_cast_int16")]; - tensor var_76_cast_fp16_cast_int16_1_to_int32_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_1_to_int32_dtype_0"), val = tensor("int32")]; - tensor var_76_cast_fp16_0_to_fp32_dtype_0 = const()[name = tensor("op_76_cast_fp16_0_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor top_k_logits = cast(dtype = var_76_cast_fp16_0_to_fp32_dtype_0, x = var_76_cast_fp16_cast_int16_0)[name = tensor("cast_4")]; - tensor top_k_ids = cast(dtype = var_76_cast_fp16_cast_int16_1_to_int32_dtype_0, x = var_76_cast_fp16_cast_int16_1)[name = tensor("cast_5")]; - tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_6")]; - } -> (token_id, token_prob, duration, top_k_ids, top_k_logits); -} \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/weights/weight.bin b/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/JointDecisionSingleStep.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid 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https://git-lfs.github.com/spec/v1 -oid sha256:3a32ec67c76aa0aa2faef518413c311493e89aeb7fa11289fa4b8653ab8a160c -size 330 diff --git a/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/metadata.json b/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/metadata.json deleted file mode 100644 index 944c0c11d0e59dff561e97c44e5e47127a9e953b..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/metadata.json +++ /dev/null @@ -1,76 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "storagePrecision" : "Float16", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 1 × 1501 × 80)", - "shortDescription" : "", - "shape" : "[1, 1, 1501, 80]", - "name" : "melspectrogram_features", - "type" : "MultiArray" - } - ], - "modelParameters" : [ - - ], - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.mul" : 2, - "Ios17.sqrt" : 1, - "Ios17.square" : 3, - "Ios17.transpose" : 1, - "Ios17.sub" : 2, - "Ios17.matmul" : 1, - "Ios17.conv" : 2, - "Ios17.log" : 1, - "Ios17.sliceByIndex" : 2, - "Ios17.add" : 3, - "Ios16.reduceMean" : 2, - "Ios17.realDiv" : 1, - "Ios17.expandDims" : 4, - "Ios17.squeeze" : 2, - "Ios17.reshape" : 2, - "Identity" : 1, - "Pad" : 2 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.5.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 240000)", - "shortDescription" : "", - "shape" : "[240000]", - "name" : "audio", - "type" : "MultiArray" - } - ], - "generatedClassName" : "MelSpectrogram", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/model.mil b/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/model.mil deleted file mode 100644 index d1ef7d81e60edf7bc921ade3f5b602a471e8609b..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/model.mil +++ /dev/null @@ -1,82 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.5.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor audio) { - tensor var_8_begin_0 = const()[name = tensor("op_8_begin_0"), val = tensor([1])]; - tensor var_8_end_0 = const()[name = tensor("op_8_end_0"), val = tensor([240000])]; - tensor var_8_end_mask_0 = const()[name = tensor("op_8_end_mask_0"), val = tensor([true])]; - tensor var_8_cast_fp16 = slice_by_index(begin = var_8_begin_0, end = var_8_end_0, end_mask = var_8_end_mask_0, x = audio)[name = tensor("op_8_cast_fp16")]; - tensor var_13_begin_0 = const()[name = tensor("op_13_begin_0"), val = tensor([0])]; - tensor var_13_end_0 = const()[name = tensor("op_13_end_0"), val = tensor([239999])]; - tensor var_13_end_mask_0 = const()[name = tensor("op_13_end_mask_0"), val = tensor([false])]; - tensor var_13_cast_fp16 = slice_by_index(begin = var_13_begin_0, end = var_13_end_0, end_mask = var_13_end_mask_0, x = audio)[name = tensor("op_13_cast_fp16")]; - tensor var_14_to_fp16 = const()[name = tensor("op_14_to_fp16"), val = tensor(0x1.f0cp-1)]; - tensor var_15_cast_fp16 = mul(x = var_13_cast_fp16, y = var_14_to_fp16)[name = tensor("op_15_cast_fp16")]; - tensor input_1_cast_fp16 = sub(x = var_8_cast_fp16, y = var_15_cast_fp16)[name = tensor("input_1_cast_fp16")]; - tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 0])]; - tensor input_3_mode_0 = const()[name = tensor("input_3_mode_0"), val = tensor("constant")]; - tensor const_0_to_fp16 = const()[name = tensor("const_0_to_fp16"), val = tensor(0x0p+0)]; - tensor input_3_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor var_30 = const()[name = tensor("op_30"), val = tensor([1, 1, 240000])]; - tensor input_5_cast_fp16 = reshape(shape = var_30, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; - tensor input_7_mode_0 = const()[name = tensor("input_7_mode_0"), val = tensor("reflect")]; - tensor const_2_to_fp16 = const()[name = tensor("const_2_to_fp16"), val = tensor(0x0p+0)]; - tensor input_7_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = input_7_mode_0, pad = input_7_pad_0, x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor var_42 = const()[name = tensor("op_42"), val = tensor([240512])]; - tensor input_cast_fp16 = reshape(shape = var_42, x = input_7_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0])]; - tensor expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_cast_fp16)[name = tensor("expand_dims_0_cast_fp16")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([160])]; - tensor expand_dims_4_axes_0 = const()[name = tensor("expand_dims_4_axes_0"), val = tensor([1])]; - tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = expand_dims_0_cast_fp16)[name = tensor("expand_dims_4_cast_fp16")]; - tensor conv_0_pad_type_0 = const()[name = tensor("conv_0_pad_type_0"), val = tensor("valid")]; - tensor conv_0_pad_0 = const()[name = tensor("conv_0_pad_0"), val = tensor([0, 0])]; - tensor conv_0_dilations_0 = const()[name = tensor("conv_0_dilations_0"), val = tensor([1])]; - tensor conv_0_groups_0 = const()[name = tensor("conv_0_groups_0"), val = tensor(1)]; - tensor expand_dims_1_to_fp16 = const()[name = tensor("expand_dims_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_0_cast_fp16")]; - tensor conv_1_pad_type_0 = const()[name = tensor("conv_1_pad_type_0"), val = tensor("valid")]; - tensor conv_1_pad_0 = const()[name = tensor("conv_1_pad_0"), val = tensor([0, 0])]; - tensor conv_1_dilations_0 = const()[name = tensor("conv_1_dilations_0"), val = tensor([1])]; - tensor conv_1_groups_0 = const()[name = tensor("conv_1_groups_0"), val = tensor(1)]; - tensor expand_dims_2_to_fp16 = const()[name = tensor("expand_dims_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263296)))]; - tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_1_cast_fp16")]; - tensor squeeze_0_axes_0 = const()[name = tensor("squeeze_0_axes_0"), val = tensor([0])]; - tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = conv_0_cast_fp16)[name = tensor("squeeze_0_cast_fp16")]; - tensor squeeze_1_axes_0 = const()[name = tensor("squeeze_1_axes_0"), val = tensor([0])]; - tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = conv_1_cast_fp16)[name = tensor("squeeze_1_cast_fp16")]; - tensor square_1_cast_fp16 = square(x = squeeze_0_cast_fp16)[name = tensor("square_1_cast_fp16")]; - tensor square_2_cast_fp16 = square(x = squeeze_1_cast_fp16)[name = tensor("square_2_cast_fp16")]; - tensor add_1_cast_fp16 = add(x = square_1_cast_fp16, y = square_2_cast_fp16)[name = tensor("add_1_cast_fp16")]; - tensor magnitudes_cast_fp16 = identity(x = add_1_cast_fp16)[name = tensor("magnitudes_cast_fp16")]; - tensor mel_spec_1_transpose_x_0 = const()[name = tensor("mel_spec_1_transpose_x_0"), val = tensor(false)]; - tensor mel_spec_1_transpose_y_0 = const()[name = tensor("mel_spec_1_transpose_y_0"), val = tensor(false)]; - tensor mel_filters_to_fp16 = const()[name = tensor("mel_filters_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526528)))]; - tensor mel_spec_1_cast_fp16 = matmul(transpose_x = mel_spec_1_transpose_x_0, transpose_y = mel_spec_1_transpose_y_0, x = mel_filters_to_fp16, y = magnitudes_cast_fp16)[name = tensor("mel_spec_1_cast_fp16")]; - tensor var_56_to_fp16 = const()[name = tensor("op_56_to_fp16"), val = tensor(0x1p-24)]; - tensor mel_spec_3_cast_fp16 = add(x = mel_spec_1_cast_fp16, y = var_56_to_fp16)[name = tensor("mel_spec_3_cast_fp16")]; - tensor mel_spec_5_epsilon_0 = const()[name = tensor("mel_spec_5_epsilon_0"), val = tensor(0x1p-149)]; - tensor mel_spec_5_cast_fp16 = log(epsilon = mel_spec_5_epsilon_0, x = mel_spec_3_cast_fp16)[name = tensor("mel_spec_5_cast_fp16")]; - tensor per_feature_mean_axes_0 = const()[name = tensor("per_feature_mean_axes_0"), val = tensor([-1])]; - tensor per_feature_mean_keep_dims_0 = const()[name = tensor("per_feature_mean_keep_dims_0"), val = tensor(true)]; - tensor per_feature_mean_cast_fp16 = reduce_mean(axes = per_feature_mean_axes_0, keep_dims = per_feature_mean_keep_dims_0, x = mel_spec_5_cast_fp16)[name = tensor("per_feature_mean_cast_fp16")]; - tensor sub_0_cast_fp16 = sub(x = mel_spec_5_cast_fp16, y = per_feature_mean_cast_fp16)[name = tensor("sub_0_cast_fp16")]; - tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; - tensor reduce_mean_1_axes_0 = const()[name = tensor("reduce_mean_1_axes_0"), val = tensor([-1])]; - tensor reduce_mean_1_keep_dims_0 = const()[name = tensor("reduce_mean_1_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_1_cast_fp16 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_1_cast_fp16")]; - tensor real_div_0_to_fp16 = const()[name = tensor("real_div_0_to_fp16"), val = tensor(0x1.004p+0)]; - tensor mul_0_cast_fp16 = mul(x = reduce_mean_1_cast_fp16, y = real_div_0_to_fp16)[name = tensor("mul_0_cast_fp16")]; - tensor sqrt_0_cast_fp16 = sqrt(x = mul_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; - tensor var_70_to_fp16 = const()[name = tensor("op_70_to_fp16"), val = tensor(0x1.5p-17)]; - tensor per_feature_std_cast_fp16 = add(x = sqrt_0_cast_fp16, y = var_70_to_fp16)[name = tensor("per_feature_std_cast_fp16")]; - tensor mel_spec_cast_fp16 = real_div(x = sub_0_cast_fp16, y = per_feature_std_cast_fp16)[name = tensor("mel_spec_cast_fp16")]; - tensor var_75_perm_0 = const()[name = tensor("op_75_perm_0"), val = tensor([1, 0])]; - tensor var_77_axes_0 = const()[name = tensor("op_77_axes_0"), val = tensor([0])]; - tensor var_75_cast_fp16 = transpose(perm = var_75_perm_0, x = mel_spec_cast_fp16)[name = tensor("transpose_0")]; - tensor var_77_cast_fp16 = expand_dims(axes = var_77_axes_0, x = var_75_cast_fp16)[name = tensor("op_77_cast_fp16")]; - tensor var_79_axes_0 = const()[name = tensor("op_79_axes_0"), val = tensor([1])]; - tensor melspectrogram_features = expand_dims(axes = var_79_axes_0, x = var_77_cast_fp16)[name = tensor("op_79_cast_fp16")]; - } -> (melspectrogram_features); -} \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/weights/weight.bin b/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/weights/weight.bin deleted file mode 100644 index 09bc101712342c386fbfbcae32f80a96569376e3..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/MelSpectrogram.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0a89c055bfde9022029d3cc59a23e949385e063974460d8eaec3a7614c3eaaa8 -size 567712 diff --git a/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin b/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index d34457003aea6648633a14e7cd6134edc9c30f17..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a1ac15543fbb9301fba5f018b147e44d767479dec352aaa91dfe7bcf65949693 -size 243 diff --git a/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/coremldata.bin b/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/coremldata.bin deleted file mode 100644 index cd81f47cc6672a441cbe9556757a30ffc0ff0bc7..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4940877938cc1b6d8830bbdd68ac8a49377cc57d75b61308883da5235b6a1914 -size 439 diff --git a/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/metadata.json b/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/metadata.json deleted file mode 100644 index 6087d5e76f7dd060cf4f135f1f3ae0bbbeb2f30a..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/metadata.json +++ /dev/null @@ -1,112 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M preprocessor (15 s window)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32)", - "shortDescription" : "", - "shape" : "[]", - "name" : "mel_features", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Range1d" : 3, - "Ios17.equal" : 1, - "Ios17.notEqual" : 1, - "Ios17.reshape" : 2, - "Identity" : 1, - "Ios17.matmul" : 1, - "Select" : 6, - "Ios17.expandDims" : 12, - "Ios17.add" : 3, - "Tile" : 2, - "Ios17.sliceByIndex" : 3, - "Ios16.reduceSum" : 4, - "Shape" : 4, - "Ios17.gather" : 4, - "Ios17.logicalNot" : 1, - "Pad" : 1, - "Ios17.log" : 1, - "Ios17.less" : 2, - "Ios17.sub" : 4, - "Ios17.conv" : 2, - "Ios17.pow" : 2, - "Ios17.cast" : 10, - "Ios17.concat" : 3, - "Stack" : 1, - "Ios17.floorDiv" : 1, - "Ios17.realDiv" : 4, - "Ios17.sqrt" : 1, - "Ios17.greaterEqual" : 1, - "Ios17.mul" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32, UInt16)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "dataType" : "Float32", - "hasShapeFlexibility" : "1", - "isOptional" : "0", - "shapeFlexibility" : "1 × 1...240000", - "shapeRange" : "[[1, 1], [1, 240000]]", - "formattedType" : "MultiArray (Float32 1 × 1)", - "type" : "MultiArray", - "shape" : "[1, 1]", - "name" : "audio", - "shortDescription" : "" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "audio_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "generatedClassName" : "parakeet_preprocessor", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/model.mil b/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/model.mil deleted file mode 100644 index ae325b6071a8ee5dad74a2e516e60889d89771d8..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/model.mil +++ /dev/null @@ -1,191 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor audio, tensor audio_length) [FlexibleShapeInformation = tuple, dict, tensor>>, tuple, dict, list, ?>>>>((("DefaultShapes", {{"audio", [1, 1]}}), ("RangeDims", {{"audio", [[1, 1], [1, 240000]]}})))] { - tensor var_9 = const()[name = tensor("op_9"), val = tensor(1)]; - tensor var_10 = const()[name = tensor("op_10"), val = tensor(160)]; - tensor var_12 = const()[name = tensor("op_12"), val = tensor(0)]; - tensor var_34 = const()[name = tensor("op_34"), val = tensor(512)]; - tensor var_35 = add(x = audio_length, y = var_34)[name = tensor("op_35")]; - tensor var_36 = const()[name = tensor("op_36"), val = tensor(512)]; - tensor var_37 = sub(x = var_35, y = var_36)[name = tensor("op_37")]; - tensor floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor("floor_div_0")]; - tensor var_40 = equal(x = audio_length, y = var_12)[name = tensor("op_40")]; - tensor var_41 = const()[name = tensor("op_41"), val = tensor([0])]; - tensor mel_length = select(a = var_41, b = floor_div_0, cond = var_40)[name = tensor("seq_len")]; - tensor audio_to_fp16_dtype_0 = const()[name = tensor("audio_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = tensor("cast_27")]; - tensor var_43_shape_cast_fp16 = shape(x = audio_to_fp16)[name = tensor("op_43_shape_cast_fp16")]; - tensor gather_0_axis_0 = const()[name = tensor("gather_0_axis_0"), val = tensor(0)]; - tensor gather_0_batch_dims_0 = const()[name = tensor("gather_0_batch_dims_0"), val = tensor(0)]; - tensor gather_0_validate_indices_0 = const()[name = tensor("gather_0_validate_indices_0"), val = tensor(false)]; - tensor var_43_shape_cast_fp16_to_int16_dtype_0 = const()[name = tensor("op_43_shape_cast_fp16_to_int16_dtype_0"), val = tensor("int16")]; - tensor select_0_to_uint16 = const()[name = tensor("select_0_to_uint16"), val = tensor(1)]; - tensor var_43_shape_cast_fp16_to_int16 = cast(dtype = var_43_shape_cast_fp16_to_int16_dtype_0, x = var_43_shape_cast_fp16)[name = tensor("cast_26")]; - tensor gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_43_shape_cast_fp16_to_int16)[name = tensor("gather_0_cast_uint16")]; - tensor gather_0_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_0_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_0 = const()[name = tensor("const_0"), val = tensor(0)]; - tensor const_1 = const()[name = tensor("const_1"), val = tensor(1)]; - tensor gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = tensor("cast_25")]; - tensor var_44 = range_1d(end = gather_0_cast_uint16_to_int32, start = const_0, step = const_1)[name = tensor("op_44")]; - tensor var_45_axes_0 = const()[name = tensor("op_45_axes_0"), val = tensor([0])]; - tensor var_45 = expand_dims(axes = var_45_axes_0, x = var_44)[name = tensor("op_45")]; - tensor var_46_axes_0 = const()[name = tensor("op_46_axes_0"), val = tensor([1])]; - tensor var_46 = expand_dims(axes = var_46_axes_0, x = audio_length)[name = tensor("op_46")]; - tensor timemask = less(x = var_45, y = var_46)[name = tensor("timemask")]; - tensor var_49_begin_0 = const()[name = tensor("op_49_begin_0"), val = tensor([0, 0])]; - tensor var_49_end_0 = const()[name = tensor("op_49_end_0"), val = tensor([1, 1])]; - tensor var_49_end_mask_0 = const()[name = tensor("op_49_end_mask_0"), val = tensor([true, false])]; - tensor var_49_squeeze_mask_0 = const()[name = tensor("op_49_squeeze_mask_0"), val = tensor([false, true])]; - tensor var_49_cast_fp16 = slice_by_index(begin = var_49_begin_0, end = var_49_end_0, end_mask = var_49_end_mask_0, squeeze_mask = var_49_squeeze_mask_0, x = audio_to_fp16)[name = tensor("op_49_cast_fp16")]; - tensor var_50_axes_0 = const()[name = tensor("op_50_axes_0"), val = tensor([1])]; - tensor var_50_cast_fp16 = expand_dims(axes = var_50_axes_0, x = var_49_cast_fp16)[name = tensor("op_50_cast_fp16")]; - tensor var_52_begin_0 = const()[name = tensor("op_52_begin_0"), val = tensor([0, 1])]; - tensor var_52_end_0 = const()[name = tensor("op_52_end_0"), val = tensor([1, 0])]; - tensor var_52_end_mask_0 = const()[name = tensor("op_52_end_mask_0"), val = tensor([true, true])]; - tensor var_52_cast_fp16 = slice_by_index(begin = var_52_begin_0, end = var_52_end_0, end_mask = var_52_end_mask_0, x = audio_to_fp16)[name = tensor("op_52_cast_fp16")]; - tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 0])]; - tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([1, -1])]; - tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, false])]; - tensor var_54_cast_fp16 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = audio_to_fp16)[name = tensor("op_54_cast_fp16")]; - tensor var_55_to_fp16 = const()[name = tensor("op_55_to_fp16"), val = tensor(0x1.f0cp-1)]; - tensor var_56_cast_fp16 = mul(x = var_54_cast_fp16, y = var_55_to_fp16)[name = tensor("op_56_cast_fp16")]; - tensor var_57_cast_fp16 = sub(x = var_52_cast_fp16, y = var_56_cast_fp16)[name = tensor("op_57_cast_fp16")]; - tensor x_3_interleave_0 = const()[name = tensor("x_3_interleave_0"), val = tensor(false)]; - tensor x_3_cast_fp16 = concat(axis = var_9, interleave = x_3_interleave_0, values = (var_50_cast_fp16, var_57_cast_fp16))[name = tensor("x_3_cast_fp16")]; - tensor var_60 = logical_not(x = timemask)[name = tensor("op_60")]; - tensor var_16_to_fp16 = const()[name = tensor("op_16_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_60)[name = tensor("input_1_cast_fp16")]; - tensor concat_1x = const()[name = tensor("concat_1x"), val = tensor([1, 1, -1])]; - tensor input_3_cast_fp16 = reshape(shape = concat_1x, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; - tensor input_5_mode_0 = const()[name = tensor("input_5_mode_0"), val = tensor("constant")]; - tensor const_3_to_fp16 = const()[name = tensor("const_3_to_fp16"), val = tensor(0x0p+0)]; - tensor input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor concat_2x = const()[name = tensor("concat_2x"), val = tensor([1, -1])]; - tensor input_cast_fp16 = reshape(shape = concat_2x, x = input_5_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([160])]; - tensor expand_dims_4_axes_0 = const()[name = tensor("expand_dims_4_axes_0"), val = tensor([1])]; - tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = tensor("expand_dims_4_cast_fp16")]; - tensor conv_0_pad_type_0 = const()[name = tensor("conv_0_pad_type_0"), val = tensor("valid")]; - tensor conv_0_pad_0 = const()[name = tensor("conv_0_pad_0"), val = tensor([0, 0])]; - tensor conv_0_dilations_0 = const()[name = tensor("conv_0_dilations_0"), val = tensor([1])]; - tensor conv_0_groups_0 = const()[name = tensor("conv_0_groups_0"), val = tensor(1)]; - tensor expand_dims_1_to_fp16 = const()[name = tensor("expand_dims_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_0_cast_fp16")]; - tensor conv_1_pad_type_0 = const()[name = tensor("conv_1_pad_type_0"), val = tensor("valid")]; - tensor conv_1_pad_0 = const()[name = tensor("conv_1_pad_0"), val = tensor([0, 0])]; - tensor conv_1_dilations_0 = const()[name = tensor("conv_1_dilations_0"), val = tensor([1])]; - tensor conv_1_groups_0 = const()[name = tensor("conv_1_groups_0"), val = tensor(1)]; - tensor expand_dims_2_to_fp16 = const()[name = tensor("expand_dims_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263296)))]; - tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_1_cast_fp16")]; - tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(-1)]; - tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor("stack_0_cast_fp16")]; - tensor var_19_promoted_to_fp16 = const()[name = tensor("op_19_promoted_to_fp16"), val = tensor(0x1p+1)]; - tensor var_75_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_19_promoted_to_fp16)[name = tensor("op_75_cast_fp16")]; - tensor var_77_axes_0 = const()[name = tensor("op_77_axes_0"), val = tensor([-1])]; - tensor var_77_keep_dims_0 = const()[name = tensor("op_77_keep_dims_0"), val = tensor(false)]; - tensor var_77_cast_fp16 = reduce_sum(axes = var_77_axes_0, keep_dims = var_77_keep_dims_0, x = var_75_cast_fp16)[name = tensor("op_77_cast_fp16")]; - tensor x_11_cast_fp16 = identity(x = var_77_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor x_13_transpose_x_0 = const()[name = tensor("x_13_transpose_x_0"), val = tensor(false)]; - tensor x_13_transpose_y_0 = const()[name = tensor("x_13_transpose_y_0"), val = tensor(false)]; - tensor const_4_to_fp16 = const()[name = tensor("const_4_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526528)))]; - tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_4_to_fp16, y = x_11_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_84_to_fp16 = const()[name = tensor("op_84_to_fp16"), val = tensor(0x1p-24)]; - tensor var_85_cast_fp16 = add(x = x_13_cast_fp16, y = var_84_to_fp16)[name = tensor("op_85_cast_fp16")]; - tensor x_15_epsilon_0 = const()[name = tensor("x_15_epsilon_0"), val = tensor(0x1p-149)]; - tensor x_15_cast_fp16 = log(epsilon = x_15_epsilon_0, x = var_85_cast_fp16)[name = tensor("x_15_cast_fp16")]; - tensor var_87_shape_cast_fp16 = shape(x = x_15_cast_fp16)[name = tensor("op_87_shape_cast_fp16")]; - tensor gather_5 = const()[name = tensor("gather_5"), val = tensor(1)]; - tensor gather_6_axis_0 = const()[name = tensor("gather_6_axis_0"), val = tensor(0)]; - tensor gather_6_batch_dims_0 = const()[name = tensor("gather_6_batch_dims_0"), val = tensor(0)]; - tensor gather_6_validate_indices_0 = const()[name = tensor("gather_6_validate_indices_0"), val = tensor(false)]; - tensor var_87_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_87_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor select_6_to_uint16 = const()[name = tensor("select_6_to_uint16"), val = tensor(2)]; - tensor var_87_shape_cast_fp16_to_uint16 = cast(dtype = var_87_shape_cast_fp16_to_uint16_dtype_0, x = var_87_shape_cast_fp16)[name = tensor("cast_24")]; - tensor gather_6_cast_uint16 = gather(axis = gather_6_axis_0, batch_dims = gather_6_batch_dims_0, indices = select_6_to_uint16, validate_indices = gather_6_validate_indices_0, x = var_87_shape_cast_fp16_to_uint16)[name = tensor("gather_6_cast_uint16")]; - tensor gather_6_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_6_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_5 = const()[name = tensor("const_5"), val = tensor(0)]; - tensor const_6 = const()[name = tensor("const_6"), val = tensor(1)]; - tensor gather_6_cast_uint16_to_int32 = cast(dtype = gather_6_cast_uint16_to_int32_dtype_0, x = gather_6_cast_uint16)[name = tensor("cast_23")]; - tensor var_89 = range_1d(end = gather_6_cast_uint16_to_int32, start = const_5, step = const_6)[name = tensor("op_89")]; - tensor var_90_axes_0 = const()[name = tensor("op_90_axes_0"), val = tensor([0])]; - tensor var_90 = expand_dims(axes = var_90_axes_0, x = var_89)[name = tensor("op_90")]; - tensor concat_3_axis_0 = const()[name = tensor("concat_3_axis_0"), val = tensor(0)]; - tensor concat_3_interleave_0 = const()[name = tensor("concat_3_interleave_0"), val = tensor(false)]; - tensor concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (gather_5, gather_6_cast_uint16_to_int32))[name = tensor("concat_3")]; - tensor shape_8 = shape(x = var_90)[name = tensor("shape_8")]; - tensor real_div_0 = real_div(x = concat_3, y = shape_8)[name = tensor("real_div_0")]; - tensor time_steps = tile(reps = real_div_0, x = var_90)[name = tensor("time_steps")]; - tensor var_93_axes_0 = const()[name = tensor("op_93_axes_0"), val = tensor([1])]; - tensor var_93 = expand_dims(axes = var_93_axes_0, x = mel_length)[name = tensor("op_93")]; - tensor valid_mask = less(x = time_steps, y = var_93)[name = tensor("valid_mask")]; - tensor var_95_axes_0 = const()[name = tensor("op_95_axes_0"), val = tensor([1])]; - tensor var_95 = expand_dims(axes = var_95_axes_0, x = valid_mask)[name = tensor("op_95")]; - tensor var_96_cast_fp16 = select(a = x_15_cast_fp16, b = var_16_to_fp16, cond = var_95)[name = tensor("op_96_cast_fp16")]; - tensor x_mean_numerator_axes_0 = const()[name = tensor("x_mean_numerator_axes_0"), val = tensor([2])]; - tensor x_mean_numerator_keep_dims_0 = const()[name = tensor("x_mean_numerator_keep_dims_0"), val = tensor(false)]; - tensor x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_96_cast_fp16)[name = tensor("x_mean_numerator_cast_fp16")]; - tensor x_mean_denominator_axes_0 = const()[name = tensor("x_mean_denominator_axes_0"), val = tensor([1])]; - tensor x_mean_denominator_keep_dims_0 = const()[name = tensor("x_mean_denominator_keep_dims_0"), val = tensor(false)]; - tensor cast_6_to_fp16_dtype_0 = const()[name = tensor("cast_6_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor valid_mask_to_fp16 = cast(dtype = cast_6_to_fp16_dtype_0, x = valid_mask)[name = tensor("cast_22")]; - tensor x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor("x_mean_denominator_cast_fp16")]; - tensor var_101_axes_0 = const()[name = tensor("op_101_axes_0"), val = tensor([1])]; - tensor var_101_cast_fp16 = expand_dims(axes = var_101_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor("op_101_cast_fp16")]; - tensor x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_101_cast_fp16)[name = tensor("x_mean_cast_fp16")]; - tensor var_104_axes_0 = const()[name = tensor("op_104_axes_0"), val = tensor([2])]; - tensor var_104_cast_fp16 = expand_dims(axes = var_104_axes_0, x = x_mean_cast_fp16)[name = tensor("op_104_cast_fp16")]; - tensor var_105_cast_fp16 = sub(x = x_15_cast_fp16, y = var_104_cast_fp16)[name = tensor("op_105_cast_fp16")]; - tensor var_106_cast_fp16 = select(a = var_105_cast_fp16, b = var_16_to_fp16, cond = var_95)[name = tensor("op_106_cast_fp16")]; - tensor var_19_promoted_1_to_fp16 = const()[name = tensor("op_19_promoted_1_to_fp16"), val = tensor(0x1p+1)]; - tensor var_107_cast_fp16 = pow(x = var_106_cast_fp16, y = var_19_promoted_1_to_fp16)[name = tensor("op_107_cast_fp16")]; - tensor var_109_axes_0 = const()[name = tensor("op_109_axes_0"), val = tensor([2])]; - tensor var_109_keep_dims_0 = const()[name = tensor("op_109_keep_dims_0"), val = tensor(false)]; - tensor var_109_cast_fp16 = reduce_sum(axes = var_109_axes_0, keep_dims = var_109_keep_dims_0, x = var_107_cast_fp16)[name = tensor("op_109_cast_fp16")]; - tensor var_111_to_fp16 = const()[name = tensor("op_111_to_fp16"), val = tensor(0x1p+0)]; - tensor var_112_cast_fp16 = sub(x = var_101_cast_fp16, y = var_111_to_fp16)[name = tensor("op_112_cast_fp16")]; - tensor var_113_cast_fp16 = real_div(x = var_109_cast_fp16, y = var_112_cast_fp16)[name = tensor("op_113_cast_fp16")]; - tensor x_std_1_cast_fp16 = sqrt(x = var_113_cast_fp16)[name = tensor("x_std_1_cast_fp16")]; - tensor var_115_cast_fp16 = not_equal(x = x_std_1_cast_fp16, y = x_std_1_cast_fp16)[name = tensor("op_115_cast_fp16")]; - tensor x_std_3_cast_fp16 = select(a = var_16_to_fp16, b = x_std_1_cast_fp16, cond = var_115_cast_fp16)[name = tensor("x_std_3_cast_fp16")]; - tensor var_25_to_fp16 = const()[name = tensor("op_25_to_fp16"), val = tensor(0x1.5p-17)]; - tensor x_std_cast_fp16 = add(x = x_std_3_cast_fp16, y = var_25_to_fp16)[name = tensor("x_std_cast_fp16")]; - tensor var_120_axes_0 = const()[name = tensor("op_120_axes_0"), val = tensor([2])]; - tensor var_120_cast_fp16 = expand_dims(axes = var_120_axes_0, x = x_std_cast_fp16)[name = tensor("op_120_cast_fp16")]; - tensor x_cast_fp16 = real_div(x = var_105_cast_fp16, y = var_120_cast_fp16)[name = tensor("x_cast_fp16")]; - tensor var_122_shape_cast_fp16 = shape(x = x_cast_fp16)[name = tensor("op_122_shape_cast_fp16")]; - tensor gather_7_axis_0 = const()[name = tensor("gather_7_axis_0"), val = tensor(0)]; - tensor gather_7_batch_dims_0 = const()[name = tensor("gather_7_batch_dims_0"), val = tensor(0)]; - tensor gather_7_validate_indices_0 = const()[name = tensor("gather_7_validate_indices_0"), val = tensor(false)]; - tensor var_122_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_122_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor select_7_to_uint16 = const()[name = tensor("select_7_to_uint16"), val = tensor(2)]; - tensor var_122_shape_cast_fp16_to_uint16 = cast(dtype = var_122_shape_cast_fp16_to_uint16_dtype_0, x = var_122_shape_cast_fp16)[name = tensor("cast_21")]; - tensor gather_7_cast_uint16 = gather(axis = gather_7_axis_0, batch_dims = gather_7_batch_dims_0, indices = select_7_to_uint16, validate_indices = gather_7_validate_indices_0, x = var_122_shape_cast_fp16_to_uint16)[name = tensor("gather_7_cast_uint16")]; - tensor gather_7_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_7_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_7 = const()[name = tensor("const_7"), val = tensor(0)]; - tensor const_8 = const()[name = tensor("const_8"), val = tensor(1)]; - tensor gather_7_cast_uint16_to_int32 = cast(dtype = gather_7_cast_uint16_to_int32_dtype_0, x = gather_7_cast_uint16)[name = tensor("cast_20")]; - tensor mask_1 = range_1d(end = gather_7_cast_uint16_to_int32, start = const_7, step = const_8)[name = tensor("mask_1")]; - tensor gather_8_axis_0 = const()[name = tensor("gather_8_axis_0"), val = tensor(0)]; - tensor gather_8_batch_dims_0 = const()[name = tensor("gather_8_batch_dims_0"), val = tensor(0)]; - tensor gather_8_validate_indices_0 = const()[name = tensor("gather_8_validate_indices_0"), val = tensor(false)]; - tensor select_8_to_uint16 = const()[name = tensor("select_8_to_uint16"), val = tensor(0)]; - tensor gather_8_cast_uint16 = gather(axis = gather_8_axis_0, batch_dims = gather_8_batch_dims_0, indices = select_8_to_uint16, validate_indices = gather_8_validate_indices_0, x = var_122_shape_cast_fp16_to_uint16)[name = tensor("gather_8_cast_uint16")]; - tensor gather_8_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_8_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor concat_4_axis_0 = const()[name = tensor("concat_4_axis_0"), val = tensor(0)]; - tensor concat_4_interleave_0 = const()[name = tensor("concat_4_interleave_0"), val = tensor(false)]; - tensor gather_8_cast_uint16_to_int32 = cast(dtype = gather_8_cast_uint16_to_int32_dtype_0, x = gather_8_cast_uint16)[name = tensor("cast_19")]; - tensor concat_4 = concat(axis = concat_4_axis_0, interleave = concat_4_interleave_0, values = (gather_8_cast_uint16_to_int32, var_9))[name = tensor("concat_4")]; - tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0])]; - tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = tensor("expand_dims_0")]; - tensor var_126 = tile(reps = concat_4, x = expand_dims_0)[name = tensor("op_126")]; - tensor mask = greater_equal(x = var_126, y = var_93)[name = tensor("mask")]; - tensor var_129_axes_0 = const()[name = tensor("op_129_axes_0"), val = tensor([1])]; - tensor var_129 = expand_dims(axes = var_129_axes_0, x = mask)[name = tensor("op_129")]; - tensor cast_15_to_fp16 = const()[name = tensor("cast_15_to_fp16"), val = tensor(0x0p+0)]; - tensor processed_signal_cast_fp16 = select(a = cast_15_to_fp16, b = x_cast_fp16, cond = var_129)[name = tensor("processed_signal_cast_fp16")]; - tensor processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("processed_signal_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor mel_features = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = tensor("cast_18")]; - } -> (mel_features, mel_length); -} \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/weights/weight.bin b/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/weights/weight.bin deleted file mode 100644 index d0c6ac87e5f78315662149af9ab4ea3658497dbe..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/Preprocessor.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c062338de852a26607ce4101f74e6895de3a4134a57b07232bd72bfc6f1d7f1a -size 567712 diff --git a/parakeet-ctc-110m-coreml/config.json b/parakeet-ctc-110m-coreml/config.json deleted file mode 100644 index aaa7174ca014092519980c51451ac7ca3832a352..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/config.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "bos_token_id": 1, - "eos_token_id": 2, - "nemo_model_type": "parakeet", - "pad_token_id": 0, - "vocab_size": 1024, -} diff --git a/parakeet-ctc-110m-coreml/metadata.json b/parakeet-ctc-110m-coreml/metadata.json deleted file mode 100644 index 6db872ae8a7d463c95dc180210ace0fce019f4a6..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/metadata.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "model_id": "nvidia/parakeet-tdt_ctc-110m", - "model_type": "hybrid_rnnt_ctc", - "sample_rate": 16000, - "max_audio_seconds": 15.0, - "max_audio_samples": 240000, - "max_symbol_steps": 1, - "vocab_size": 1024, - "joint_extra_outputs": 5, - "encoder_dim": 512, - "decoder_dim": 640, - "decoder_hidden": 640, - "decoder_layers": 1, - "blank_id": 1024, - "checkpoint": { - "type": "pretrained", - "model_id": "nvidia/parakeet-tdt_ctc-110m" - }, - "coreml": { - "compute_units": "CPU_ONLY", - "compute_precision": "FLOAT32" - }, - "components": { - "preprocessor": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "path": "parakeet_preprocessor.mlpackage" - }, - "encoder": { - "inputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_encoder.mlpackage" - }, - "ctc_head": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ] - }, - "outputs": { - "log_probs": [ - 1, - 188, - 1025 - ] - }, - "path": "parakeet_ctc_head.mlpackage" - }, - "mel_encoder": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_mel_encoder.mlpackage" - }, - "decoder": { - "inputs": { - "targets": [ - 1, - 1 - ], - "target_length": [ - 1 - ], - "h_in": [ - 1, - 1, - 640 - ], - "c_in": [ - 1, - 1, - 640 - ] - }, - "outputs": { - "decoder": [ - 1, - 640, - 1 - ], - "h_out": [ - 1, - 1, - 640 - ], - "c_out": [ - 1, - 1, - 640 - ] - }, - "path": "parakeet_decoder.mlpackage" - }, - "joint": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "logits": [ - 1, - 188, - 1, - 1030 - ] - }, - "path": "parakeet_joint.mlpackage" - }, - "joint_decision": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 188, - 1 - ], - "token_prob": [ - 1, - 188, - 1 - ], - "duration": [ - 1, - 188, - 1 - ] - }, - "path": "parakeet_joint_decision.mlpackage" - }, - "joint_decision_single_step": { - "inputs": { - "encoder_step": [ - 1, - 512, - 1 - ], - "decoder_step": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 1, - 1 - ], - "token_prob": [ - 1, - 1, - 1 - ], - "duration": [ - 1, - 1, - 1 - ], - "top_k_ids": [ - 1, - 1, - 1, - 64 - ], - "top_k_logits": [ - 1, - 1, - 1, - 64 - ] - }, - "path": "parakeet_joint_decision_single_step.mlpackage" - } - } -} \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/preprocessor_config.json b/parakeet-ctc-110m-coreml/preprocessor_config.json deleted file mode 100644 index 54683c2afb1e841dfe59d03ad92c060a6cf50c0d..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/preprocessor_config.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "feature_extractor_type": "ParakeetFeatureExtractor", - "feature_size": 80, - "hop_length": 160, - "n_fft": 512, - "padding_side": "right", - "padding_value": 0.0, - "preemphasis": 0.97, - "processor_class": "ParakeetProcessor", - "return_attention_mask": true, - "sampling_rate": 16000, - "win_length": 400 -} diff --git a/parakeet-ctc-110m-coreml/special_tokens_map.json b/parakeet-ctc-110m-coreml/special_tokens_map.json deleted file mode 100644 index 815fbdc56abc60a0f29e51ff2373c998d7779805..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/special_tokens_map.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "pad_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "unk_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - } -} diff --git a/parakeet-ctc-110m-coreml/tokenizer.json b/parakeet-ctc-110m-coreml/tokenizer.json deleted file mode 100644 index 21e87f185d71820f5089456aa9c79c97c0a0ce8b..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/tokenizer.json +++ /dev/null @@ -1,2456 +0,0 @@ -{ - "version": "1.0", - "truncation": null, - "padding": null, - "added_tokens": [ - { - "id": 0, - "content": "", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 1024, - "content": "", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - } - ], - "normalizer": { - "type": "Sequence", - "normalizers": [ - { - "type": "Precompiled", - "precompiled_charsmap": 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- "in e", - "i ne", - "▁her e", - "▁he re", - "▁h ere", - "▁w ould", - "ca use", - "ri ght", - "r ight", - "s o", - "os t", - "o st", - "ur e", - "u re", - "▁ha s", - "▁h as", - "e ct", - "▁th ink", - "▁f e", - "on g", - "o ng", - "▁se e", - "▁s ee", - "▁wh en", - "▁wh o", - "▁we re", - "▁w ere", - "▁real ly", - "▁re ally", - "▁the ir", - "▁w ant", - "on e", - "o ne", - "op le", - "o ple", - "▁the n", - "▁th en", - "▁tim e", - "▁s a", - "a p", - "▁t e", - "▁ te", - "▁H e", - "▁y e", - "c k", - "▁he r", - "▁h er", - "▁ her", - "▁th ing", - "▁t hing", - "▁ thing", - "▁r ight", - "▁ right", - "▁wh ich", - "it t", - "ic e", - "i ce", - "ac t", - "a ct", - "▁pe ople", - "t y", - "▁tw o", - "▁ J", - "▁ im", - "th er", - "t her", - "c i", - "os e", - "o se", - "▁c l", - "▁ qu", - "▁m an", - "▁al so", - "re e", - "r ee", - "▁e n", - "▁ en", - "u d", - "▁h ow", - "re at", - "a k", - "h ing", - "a g", - "▁an y", - "▁ any", - "f f", - "ac e", - "a ce", - "pe r", - "p er", - "▁be cause", - "▁ very", - "ow n", - "o wn", - "▁a d", - "▁ ad", - "▁ac t", - "▁a ct", - "▁ act", - "▁be en", - "▁b een", - "▁no w", - "▁n ow", - "▁a g", - "▁ ag", - "▁int o", - "▁com p", - "ar s", - "ion s", - "i ons", - "ar e", - "a re", - "it e", - "i te", - "i v", - "▁the se", - "▁th ese", - "ay s", - "a ys", - "e p", - "▁Th is", - "▁sh e", - "▁s he", - "an s", - "a h", - "ee n", - "e en", - "▁o ver", - "r y", - "▁l o", - "ag e", - "a ge", - "▁p r", - "▁s p", - "u e", - "▁c o", - "▁ co", - "ic k", - "i ck", - "be r", - "b er", - "▁d id", - "i p", - "ac h", - "a ch", - "▁b ack", - "▁n o", - "▁con t", - "▁co nt", - "▁c ont", - "▁o ther", - "▁ other", - "▁ever y", - "▁e very", - "p t", - "▁ne ed", - "▁h im", - "▁ U", - "▁I n", - "▁wor k", - "ir st", - "▁p art", - "▁loo k", - "▁l ook", - "itt le", - "b le", - "i z", - "▁u n", - "▁ un", - "▁m ake", - "ome t", - "om et", - "nd er", - "n der", - "is h", - "n a", - "▁l ittle", - "▁of f", - "▁o ff", - "▁th an", - "▁go t", - "▁g ot", - "ual ly", - "u ally", - "▁pe r", - "▁p er", - "▁ per", - "▁go od", - "▁g ood", - "▁w ay", - "▁cou ld", - "▁c ould", - "▁a c", - "▁ ac", - "▁im p", - "ab le", - "a ble", - "▁wh ere", - "if f", - "i ff", - "▁Th at", - "▁re s", - "▁r es", - "▁ res", - "ou nt", - "p l", - "an ce", - "a nce", - "▁f irst", - "▁r o", - "▁ ro", - "▁pr e", - "▁p re", - "as s", - "▁sa y", - "▁s ay", - "in t", - "i nt", - "ate d", - "at ed", - "ir e", - "i re", - "uc h", - "u ch", - "as e", - "a se", - "▁some t", - "▁s omet", - "ou nd", - "o und", - "▁do wn", - "▁d own", - "▁d iff", - "se l", - "s el", - "▁g u", - "▁a m", - "▁ am", - "res s", - "r ess", - "▁lo t", - "▁l ot", - "en ce", - "e nce", - "▁d is", - "or m", - "i x", - "▁p o", - "v ing", - "ent y", - "en ty", - "▁ K", - "▁sp e", - "▁s pe", - "un d", - "u nd", - "h e", - "▁m uch", - "▁a r", - "▁ ar", - "rou nd", - "ro und", - "r ound", - "▁a pp", - "c o", - "ar k", - "▁ne w", - "▁n ew", - "ate r", - "at er", - "a ter", - "ul t", - "en d", - "e nd", - "▁ev en", - "▁e ven", - "▁st art", - "ation s", - "at ions", - "rou gh", - "r ough", - "il e", - "i le", - "ft er", - "f ter", - "▁we ll", - "▁w ell", - "b e", - "▁The y", - "▁th ree", - "ig n", - "il d", - "i ld", - "▁sa id", - "ou gh", - "an g", - "a ng", - "▁to o", - "▁t oo", - "ad e", - "▁b l", - "en s", - "▁in c", - "i a", - "▁th ose", - "▁m o", - "▁t ake", - "▁th rough", - "▁f l", - "▁k ind", - "▁thing s", - "▁th ings", - "▁be t", - "▁b et", - "▁on ly", - "▁S t", - "▁le t", - "▁l et", - "ces s", - "c ess", - "▁C h", - "ar y", - "a ry", - "ve l", - "v el", - "▁I f", - "x t", - "oth er", - "ot her", - "o ther", - "a v", - "ic al", - "or d", - "▁ag ain", - "▁somet hing", - "▁some thing", - "on na", - "f ore", - "▁m ay", - "t ing", - "▁b u", - "▁diff ere", - "ur n", - "▁g onna", - "▁do es", - "uc t", - "u ct", - "o g", - "▁tw enty", - "▁g r", - "▁ gr", - "▁Y e", - "w n", - "▁sh ould", - "▁com m", - "▁c omm", - "it ion", - "▁un der", - "▁u nder", - "▁he l", - "▁h el", - "or y", - "o ry", - "▁f o", - "▁us e", - "▁u se", - "▁ use", - "ig h", - "i gh", - "if e", - "▁act ually", - "▁t al", - "▁ca ll", - "▁c all", - "ent s", - "en ts", - "i ous", - "ul l", - "u ll", - "▁The re", - "▁Th ere", - "▁Ye ah", - "▁mo st", - "▁m ost", - "▁k e", - "▁ ke", - "or s", - "ve d", - "v ed", - "y s", - "▁s c", - "▁ha pp", - "op e", - "o pe", - "▁hel p", - "at ch", - "▁Wh at", - "▁re m", - "▁r em", - "pl e", - "p le", - "▁No w", - "▁N ow", - "▁b r", - "oo l", - "o ol", - "ot h", - "o th", - "▁fo ur", - "▁f our", - "sel f", - "▁st r", - "n e", - "th ing", - "t hing", - "▁p ut", - "ia l", - "i al", - "▁g reat", - "a il", - "u b", - "n ing", - "▁s m", - "▁fe el", - "▁f ive", - "od y", - "und red", - "is s", - "an k", - "ge t", - "g et", - "ak ing", - "a king", - "▁man y", - "▁m any", - "▁h undred", - "▁year s", - "▁ye ars", - "▁be ing", - "▁com e", - "▁c ome", - "▁me an", - "il y", - "i ly", - "▁differe nt", - "▁a fter", - "▁se r", - "▁s er", - "▁sh ow", - "f orm", - "f ul", - "o y", - "▁s ix", - "▁v ide", - "▁ V", - "▁it s", - "▁ its", - "▁po int", - "▁d ay", - "▁ day", - "▁de s", - "▁d es", - "on s", - "▁b it", - "▁be l", - "▁b el", - "▁be fore", - "▁a w", - "▁en d", - "▁e nd", - "▁ end", - "▁O h", - "▁st ill", - "at h", - "a th", - "▁lo ng", - "▁l ong", - "▁ '", - "is e", - "i se", - "o b", - "d ay", - "▁ad d", - "f t", - "ve s", - "v es", - "ce s", - "c es", - "ad y", - "▁c r", - "▁ar ound", - "▁a round", - "▁tr y", - "▁t ry", - "le s", - "l es", - "ver s", - "v ers", - "k ay", - "ia n", - "i an", - "ate s", - "at es", - "▁fin d", - "▁f ind", - "w ard", - "▁A s", - "▁e ight", - "li c", - "l ic", - "▁s ame", - "▁po s", - "▁p os", - "▁e m", - "▁ em", - "▁m ade", - "▁su pp", - "▁l ife", - "▁B e", - "pe ct", - "p ect", - "▁de c", - "▁pl ay", - "ang e", - "an ge", - "▁at t", - "▁per s", - "▁p ers", - "w ays", - "▁h igh", - "▁ha nd", - "▁h and", - "▁ne xt", - "▁con s", - "▁c ons", - "▁o wn", - "▁ own", - "▁in v", - "ow er", - "▁in d", - "▁ ind", - "er t", - "n g", - "av e", - "a ve", - "▁ye ar", - "▁b ig", - "at ing", - "a ting", - "▁wor ld", - "▁re l", - "▁r el", - "▁sur e", - "▁su re", - "▁s ure", - "▁tr a", - "▁t ra", - "e w", - "ere d", - "er ed", - "e red", - "▁f in", - "▁We ll", - "▁W ell", - "▁s l", - "▁do ing", - "b s", - "▁se t", - "▁s et", - "▁re c", - "u al", - "ci al", - "c ial", - "▁p h", - "▁ ph", - "er m", - "▁lo ve", - "p h", - "▁re al", - "▁la st", - "▁l ast", - "ic t", - "i ct", - "▁b o", - "▁r a", - "▁ ra", - "ib le", - "i ble", - "▁w r", - "m er", - "▁cou nt", - "▁c ount", - "it ies", - "▁al ways", - "ine t", - "in et", - "ment s", - "m ents", - "u c", - "▁m ight", - "▁int er", - "▁in ter", - "▁vide o", - "g in", - "▁te ll", - "▁t ell", - "▁ne ver", - "▁n ever", - "ven t", - "ve nt", - "v ent", - "▁imp ort", - "ie d", - "i ed", - "▁s y", - "▁H ow", - "ical ly", - "ic ally", - "ough t", - "ou ght", - "▁th ir", - "▁re p", - "▁r ep", - "k s", - "i b", - "▁f am", - "j ect", - "▁b as", - "▁S he", - "▁g ive", - "ake s", - "ak es", - "▁n inet", - "▁re g", - "▁m in", - "▁o p", - "▁ op", - "▁de f", - "▁did n", - "t e", - "▁cou r", - "▁co ur", - "▁c our", - "▁wh y", - "▁en t", - "▁e nt", - "▁ ent", - "▁pl ace", - "▁in s", - "▁ca r", - "▁c ar", - "ath er", - "at her", - "a ther", - "▁pers on", - "ul ar", - "▁ins t", - "▁in st", - "▁pro d", - "▁pr od", - "le ct", - "l ect", - "▁A l", - "▁to day", - "▁be c", - "▁su r", - "▁s ur", - "▁Al l", - "▁A ll", - "▁an other", - "▁bu s", - "▁b us", - "▁ke ep", - "el l", - "e ll", - "es e", - "e se", - "ri end", - "▁qu est", - "▁tal k", - "al s", - "ing s", - "▁mo n", - "▁m on", - "co nd", - "c ond", - "ol d", - "o ld", - "▁ac c", - "▁l a", - "▁n um", - "ide nt", - "id ent", - "▁ch e", - "▁c he", - "in ess", - "i ness", - "▁t urn", - "▁e ar", - "▁N o", - "ous and", - "▁bet ter", - "if ic", - "▁lo o", - "▁l oo", - "▁g l", - "o c", - "▁import ant", - "ite d", - "it ed", - "▁A n", - "▁th ousand", - "il ity", - "ll ow", - "▁use d", - "▁us ed", - "▁g en", - "▁s im", - "l i", - "▁happ en", - "▁U n", - "▁L et", - "a ir", - "oc k", - "o ck", - "ab ly", - "g g", - "▁w atch", - "▁F or", - "▁s w", - "re n", - "r en", - "ut e", - "u te", - "e ver", - "▁po l", - "▁p ol", - "▁sc h", - "▁s ch", - "▁Wh en", - "▁su ch", - "▁s uch", - "▁f if", - "▁h ome", - "▁cl e", - "▁c le", - "▁cont in", - "ous e", - "ou se", - "o use", - "▁f riend", - "ur ing", - "▁O kay", - "g r", - "▁ab le", - "▁a ble", - "▁ able", - "▁st ud", - "▁e ff", - "h ip", - "b ody", - "▁to p", - "▁t op", - "n ess", - "▁exp er", - "▁ex per", - "▁pre t", - "▁pr et", - "▁bo th", - "▁b oth", - "▁don e", - "▁do ne", - "▁d one", - "c ri", - "▁m ark", - "▁wh ile", - "▁o ld", - "▁ old", - "ro s", - "r os", - "on t", - "o nt", - "▁se cond", - "at ive", - "▁th ought", - "▁be st", - "▁b est", - "▁fo und", - "▁f ound", - "ie w", - "i ew", - "▁bel ie", - "▁e ach", - "ere st", - "er est", - "▁tr i", - "▁t ri", - "▁e as", - "▁c a", - "▁ ca", - "▁f act", - "▁car e", - "▁ca re", - "▁c are", - "▁f un", - "at ter", - "ure s", - "ur es", - "u res", - "▁he ad", - "▁le ar", - "▁w ater", - "▁h ard", - "▁fe w", - "▁f ew", - "▁s ide", - "w een", - "▁ex p", - "▁aw ay", - "it s", - "i ts", - "▁ex t", - "▁e xt", - "l ud", - "▁r un", - "▁tr ans", - "in ce", - "i nce", - "▁s k", - "▁op en", - "c us", - "▁bet ween", - "▁call ed", - "▁we e", - "▁w ee", - "▁pret ty", - "as on", - "▁f ar", - "em ber", - "om m", - "▁inter est", - "▁int erest", - "an y", - "ne r", - "n er", - "u ff", - "▁pre s", - "▁pr es", - "▁p res", - "▁c ur", - "▁ch ild", - "e e", - "▁to get", - "▁toget her", - "ol og", - "▁G od", - "on d", - "o nd", - "▁ch ar", - "▁look ing", - "▁loo king", - "st em", - "a z", - "ce nt", - "c ent", - "▁o b", - "▁ ob", - "▁as s", - "▁ ass", - "l and", - "▁does n", - "▁bus iness", - "▁cour se", - "▁te n", - "▁t en", - "▁ ten", - "p s", - "ar ch", - "ce d", - "c ed", - "m s", - "iz e", - "n ce", - "▁re f", - "▁n ame", - "ros s", - "▁gr ow", - "one y", - "▁we nt", - "▁w ent", - "ic s", - "te en", - "t een", - "▁co u", - "▁c ou", - "▁pro b", - "▁pr ob", - "▁re t", - "▁r et", - "▁gu ys", - "▁c ame", - "as h", - "le d", - "l ed", - "▁E ur", - "ue s", - "u es", - "▁ ide", - "g an", - "▁every thing", - "▁get ting", - "▁as k", - "▁co r", - "▁c or", - "▁bu ild", - "▁s ign", - "▁sm all", - "uc k", - "u ck", - "▁e l", - "▁ el", - "▁co l", - "▁c ol", - "▁I s", - "ation al", - "st and", - "c y", - "▁con f", - "d er", - "▁br e", - "▁b re", - "▁ca p", - "▁c ap", - "▁mo d", - "▁m od", - "et s", - "e ts", - "i ke", - "▁num ber", - "▁comp le", - "▁com ple", - "ert ain", - "▁ev er", - "▁e ver", - "▁ ever", - "▁col l", - "▁co ll", - "▁h um", - "▁Eur ope", - "▁cr e", - "▁c re", - "▁me t", - "▁m et", - "▁ex am", - "▁mo ve", - "▁p ass", - "▁le ft", - "▁sy stem", - "▁inc lud", - "▁Th ank", - "ce pt", - "▁w om", - "▁prod uct", - "te n", - "t en", - "▁res t", - "▁re st", - "▁r est", - "▁prob ably", - "▁dr i", - "▁d ri", - "▁D o", - "▁gen er", - "▁any thing", - "▁la r", - "▁l ar", - "▁M y", - "▁sch ool", - "▁le ad", - "▁su b", - "▁s ub", - "▁t y", - "▁ ty", - "▁pl an", - "▁see m", - "▁se em", - "▁who le", - "ire ct", - "ir ect", - "▁li ght", - "▁l ight", - "▁m ust", - "▁mo m", - "▁m om", - "▁op p", - "▁o pp", - "▁supp ort", - "▁fam ily", - "ice s", - "ic es", - "i ces", - "am p", - "▁prob le", - "▁pro ble", - "▁d r", - "re ady", - "▁us ing", - "ens e", - "en se", - "▁pro v", - "us h", - "a x", - "▁p ower", - "▁R e", - "al th", - "▁e v", - "▁st and", - "▁ stand", - "▁w ar", - "t s" - ] - } -} \ No newline at end of file diff --git a/parakeet-ctc-110m-coreml/tokenizer_config.json b/parakeet-ctc-110m-coreml/tokenizer_config.json deleted file mode 100644 index d9eaa0fb393ab6e3ac6890fe99efb6191cded97c..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/tokenizer_config.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "added_tokens_decoder": { - "0": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "1024": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - } - }, - "clean_up_tokenization_spaces": false, - "extra_special_tokens": {}, - "model_max_length": 1000000000000000019884624838656, - "pad_token": "", - "processor_class": "ParakeetProcessor", - "tokenizer_class": "PreTrainedTokenizer", - "unk_token": "" -} diff --git a/parakeet-ctc-110m-coreml/vocab.json b/parakeet-ctc-110m-coreml/vocab.json deleted file mode 100644 index 33b95831722563db5362641673b37885e25fb47a..0000000000000000000000000000000000000000 --- a/parakeet-ctc-110m-coreml/vocab.json +++ /dev/null @@ -1 +0,0 @@ -{"0": "", "1": "▁t", "2": "▁th", "3": "▁a", "4": "in", "5": "re", "6": "▁the", "7": "▁w", "8": "▁s", "9": "▁o", "10": "er", "11": "ou", "12": "at", "13": "nd", "14": "it", "15": "▁h", "16": "▁c", "17": "▁b", "18": "is", "19": "en", "20": "on", "21": "ing", "22": "▁f", "23": "▁to", "24": "▁m", "25": "es", "26": "▁p", "27": "or", "28": "an", "29": "▁d", "30": "ll", "31": "▁I", "32": "ed", "33": 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"▁are", "112": "▁sh", "113": "ust", "114": "ce", "115": "ally", "116": "ill", "117": "▁H", "118": "▁j", "119": "ter", "120": "▁go", "121": "▁And", "122": "ation", "123": "▁C", "124": "▁so", "125": "ome", "126": "▁not", "127": "op", "128": "il", "129": "ore", "130": "▁ne", "131": "▁can", "132": "▁me", "133": "▁at", "134": "ould", "135": "ant", "136": "▁M", "137": "▁like", "138": "ere", "139": "▁they", "140": "ra", "141": "ers", "142": "▁ab", "143": "▁de", "144": "▁kn", "145": "ge", "146": "▁Y", "147": "▁ch", "148": "ul", "149": "pp", "150": "▁or", "151": "▁al", "152": "▁con", "153": "▁com", "154": "ess", "155": "▁su", "156": "out", "157": "▁your", "158": "▁So", "159": "ate", "160": "▁one", "161": "▁all", "162": "▁ex", "163": "est", "164": "▁fr", "165": "▁just", "166": "▁pro", "167": "▁know", "168": "▁O", "169": "ain", "170": "▁but", "171": "ol", "172": "ive", "173": "▁v", "174": "use", "175": "very", "176": "art", "177": "qu", "178": "▁my", "179": "el", "180": "▁N", "181": "nt", "182": 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b/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/metadata.json deleted file mode 100644 index e3a08d21a5d1577db2bedd1749ea0ddae5d21b73..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/metadata.json +++ /dev/null @@ -1,123 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet decoder (RNNT prediction network)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 2 × 1 × 640)", - "shortDescription" : "", - "shape" : "[2, 1, 640]", - "name" : "h_out", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 2 × 1 × 640)", - "shortDescription" : "", - "shape" : "[2, 1, 640]", - "name" : "c_out", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Select" : 1, - "Ios17.squeeze" : 4, - "Ios17.gather" : 1, - "Ios17.cast" : 8, - "Ios17.lstm" : 2, - "Split" : 2, - "Ios17.add" : 1, - "Ios17.transpose" : 2, - "Ios17.greaterEqual" : 1, - "Identity" : 1, - "Stack" : 2 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1]", - "name" : "targets", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "target_length", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 2 × 1 × 640)", - "shortDescription" : "", - "shape" : "[2, 1, 640]", - "name" : "h_in", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 2 × 1 × 640)", - "shortDescription" : "", - "shape" : "[2, 1, 640]", - "name" : "c_in", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2025-09-25", - "com.github.apple.coremltools.source" : "torch==2.7.0", - "com.github.apple.coremltools.version" : "9.0b1", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "parakeet_decoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/model.mil b/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/model.mil deleted file mode 100644 index 167cc9db34dd7828e0e61eab40d6a09f8a234d30..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/model.mil +++ /dev/null @@ -1,73 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] -{ - func main(tensor c_in, tensor h_in, tensor target_length, tensor targets) { - tensor y_batch_dims_0 = const()[name = tensor("y_batch_dims_0"), val = tensor(0)]; - tensor y_validate_indices_0 = const()[name = tensor("y_validate_indices_0"), val = tensor(false)]; - tensor module_prediction_embed_weight_to_fp16 = const()[name = tensor("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor targets_to_int16_dtype_0 = const()[name = tensor("targets_to_int16_dtype_0"), val = tensor("int16")]; - tensor cast_1_dtype_0 = const()[name = tensor("cast_1_dtype_0"), val = tensor("int32")]; - tensor greater_equal_0_y_0 = const()[name = tensor("greater_equal_0_y_0"), val = tensor(0)]; - tensor targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor("cast_9")]; - tensor cast_1 = cast(dtype = cast_1_dtype_0, x = targets_to_int16)[name = tensor("cast_8")]; - tensor greater_equal_0 = greater_equal(x = cast_1, y = greater_equal_0_y_0)[name = tensor("greater_equal_0")]; - tensor slice_by_index_0 = const()[name = tensor("slice_by_index_0"), val = tensor(1025)]; - tensor add_2 = add(x = cast_1, y = slice_by_index_0)[name = tensor("add_2")]; - tensor select_0 = select(a = cast_1, b = add_2, cond = greater_equal_0)[name = tensor("select_0")]; - tensor y_cast_fp16_cast_uint16_axis_0 = const()[name = tensor("y_cast_fp16_cast_uint16_axis_0"), val = tensor(0)]; - tensor select_0_to_int16_dtype_0 = const()[name = tensor("select_0_to_int16_dtype_0"), val = tensor("int16")]; - tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor("cast_7")]; - tensor y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = tensor("y_cast_fp16_cast_uint16_cast_uint16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([1, 0, 2])]; - tensor split_0_num_splits_0 = const()[name = tensor("split_0_num_splits_0"), val = tensor(2)]; - tensor split_0_axis_0 = const()[name = tensor("split_0_axis_0"), val = tensor(0)]; - tensor h_in_to_fp16_dtype_0 = const()[name = tensor("h_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor("cast_6")]; - tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = tensor("split_0_cast_fp16")]; - tensor split_1_num_splits_0 = const()[name = tensor("split_1_num_splits_0"), val = tensor(2)]; - tensor split_1_axis_0 = const()[name = tensor("split_1_axis_0"), val = tensor(0)]; - tensor c_in_to_fp16_dtype_0 = const()[name = tensor("c_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor("cast_5")]; - tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = tensor("split_1_cast_fp16")]; - tensor input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; - tensor input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = tensor("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; - tensor input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; - tensor input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = tensor("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; - tensor input_lstm_layer_0_direction_0 = const()[name = tensor("input_lstm_layer_0_direction_0"), val = tensor("forward")]; - tensor input_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_lstm_layer_0_output_sequence_0"), val = tensor(true)]; - tensor input_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; - tensor input_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; - tensor input_lstm_layer_0_activation_0 = const()[name = tensor("input_lstm_layer_0_activation_0"), val = tensor("tanh")]; - tensor concat_1_to_fp16 = const()[name = tensor("concat_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312128)))]; - tensor concat_2_to_fp16 = const()[name = tensor("concat_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4588992)))]; - tensor concat_0_to_fp16 = const()[name = tensor("concat_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7865856)))]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = tensor("transpose_2")]; - tensor input_lstm_layer_0_cast_fp16_0, tensor input_lstm_layer_0_cast_fp16_1, tensor input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor("input_lstm_layer_0_cast_fp16")]; - tensor input_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; - tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = tensor("input_lstm_h0_squeeze_cast_fp16")]; - tensor input_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; - tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = tensor("input_lstm_c0_squeeze_cast_fp16")]; - tensor input_direction_0 = const()[name = tensor("input_direction_0"), val = tensor("forward")]; - tensor input_output_sequence_0 = const()[name = tensor("input_output_sequence_0"), val = tensor(true)]; - tensor input_recurrent_activation_0 = const()[name = tensor("input_recurrent_activation_0"), val = tensor("sigmoid")]; - tensor input_cell_activation_0 = const()[name = tensor("input_cell_activation_0"), val = tensor("tanh")]; - tensor input_activation_0 = const()[name = tensor("input_activation_0"), val = tensor("tanh")]; - tensor concat_4_to_fp16 = const()[name = tensor("concat_4_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7871040)))]; - tensor concat_5_to_fp16 = const()[name = tensor("concat_5_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11147904)))]; - tensor concat_3_to_fp16 = const()[name = tensor("concat_3_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14424768)))]; - tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = tensor("input_cast_fp16")]; - tensor obj_3_axis_0 = const()[name = tensor("obj_3_axis_0"), val = tensor(0)]; - tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = tensor("obj_3_cast_fp16")]; - tensor obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_3_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_axis_0 = const()[name = tensor("obj_axis_0"), val = tensor(0)]; - tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = tensor("obj_cast_fp16")]; - tensor obj_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([1, 2, 0])]; - tensor transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("transpose_0_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor("transpose_1")]; - tensor decoder = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = tensor("cast_2")]; - tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = tensor("cast_3")]; - tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = tensor("cast_4")]; - tensor target_length_tmp = identity(x = target_length)[name = tensor("target_length_tmp")]; - } -> (decoder, h_out, c_out); -} \ No newline at end of file diff --git a/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/weights/weight.bin b/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/weights/weight.bin deleted file mode 100644 index a895491b78de520abc21d90321ffd90c95483662..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Decoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:27d26890221d82322c1092fd99d7b40578e435d5cf4b83c887c42603caf97aba -size 14429952 diff --git a/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/analytics/coremldata.bin b/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 4738782a8e36389621ff4b3b90dd2a661b35146c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:42e638870d73f26b332918a3496ce36793fbb413a81cbd3d16ba01328637a105 -size 243 diff --git a/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/coremldata.bin b/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/coremldata.bin deleted file mode 100644 index e016ed32c20e1563399f15adad451a442644b3d7..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4def7aa848599ad0e17a8b9a982edcdbf33cf92e1f4b798de32e2ca0bc74b030 -size 485 diff --git a/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/metadata.json b/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/metadata.json deleted file mode 100644 index c87f9eb24e76b8e518dc1fbead46c8c0029d3925..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/metadata.json +++ /dev/null @@ -1,106 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "enc6bit-palettize quantized - encoder", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1024 × 188)", - "shortDescription" : "", - "shape" : "[1, 1024, 188]", - "name" : "encoder", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "encoder_length", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Mixed (Float16, Palettized (6 bits))", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.floor" : 3, - "Ios17.logicalAnd" : 2, - "Ios17.reshape" : 145, - "Ios16.softmax" : 24, - "Ios17.matmul" : 72, - "Ios17.transpose" : 172, - "Split" : 24, - "Ios17.expandDims" : 5, - "Select" : 72, - "Ios17.add" : 174, - "Tile" : 1, - "Ios17.sliceByIndex" : 48, - "Ios16.sigmoid" : 24, - "Pad" : 48, - "Ios17.logicalNot" : 2, - "Ios17.layerNorm" : 120, - "Ios16.silu" : 72, - "Ios17.less" : 1, - "Ios16.constexprLutToDense" : 294, - "Ios17.conv" : 77, - "Ios16.relu" : 3, - "Ios17.linear" : 193, - "Ios17.cast" : 4, - "Ios17.mul" : 99 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 128 × 1501)", - "shortDescription" : "", - "shape" : "[1, 128, 1501]", - "name" : "mel", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2025-09-25", - "com.github.apple.coremltools.source" : "torch==2.7.0", - "com.github.apple.coremltools.version" : "9.0b1", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "parakeet_encoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/model.mil b/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/model.mil deleted file mode 100644 index 93d1301ccde7c2ca447af6c01ab05bfda5a5dd2c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/model.mil +++ /dev/null @@ -1,3404 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})] -{ - func main(tensor mel, tensor mel_length) { - tensor var_30 = const()[name = tensor("op_30"), val = tensor(-1)]; - tensor x_1_perm_0 = const()[name = tensor("x_1_perm_0"), val = tensor([0, 2, 1])]; - tensor mel_to_fp16_dtype_0 = const()[name = tensor("mel_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_86_to_fp16_dtype_0 = const()[name = tensor("op_86_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_87_promoted_to_fp16 = const()[name = tensor("op_87_promoted_to_fp16"), val = tensor(-0x1p+0)]; - tensor mel_length_to_fp16 = cast(dtype = var_86_to_fp16_dtype_0, x = mel_length)[name = tensor("cast_3")]; - tensor var_88_cast_fp16 = add(x = mel_length_to_fp16, y = var_87_promoted_to_fp16)[name = tensor("op_88_cast_fp16")]; - tensor _inversed_90_y_0_to_fp16 = const()[name = tensor("_inversed_90_y_0_to_fp16"), val = tensor(0x1p-1)]; - tensor _inversed_90_cast_fp16 = mul(x = var_88_cast_fp16, y = _inversed_90_y_0_to_fp16)[name = tensor("_inversed_90_cast_fp16")]; - tensor var_91_to_fp16 = const()[name = tensor("op_91_to_fp16"), val = tensor(0x1p+0)]; - tensor lengths_1_cast_fp16 = add(x = _inversed_90_cast_fp16, y = var_91_to_fp16)[name = tensor("lengths_1_cast_fp16")]; - tensor lengths_3_cast_fp16 = floor(x = lengths_1_cast_fp16)[name = tensor("lengths_3_cast_fp16")]; - tensor var_95_promoted_to_fp16 = const()[name = tensor("op_95_promoted_to_fp16"), val = tensor(-0x1p+0)]; - tensor var_96_cast_fp16 = add(x = lengths_3_cast_fp16, y = var_95_promoted_to_fp16)[name = tensor("op_96_cast_fp16")]; - tensor _inversed_98_y_0_to_fp16 = const()[name = tensor("_inversed_98_y_0_to_fp16"), val = tensor(0x1p-1)]; - tensor _inversed_98_cast_fp16 = mul(x = var_96_cast_fp16, y = _inversed_98_y_0_to_fp16)[name = tensor("_inversed_98_cast_fp16")]; - tensor var_99_to_fp16 = const()[name = tensor("op_99_to_fp16"), val = tensor(0x1p+0)]; - tensor lengths_7_cast_fp16 = add(x = _inversed_98_cast_fp16, y = var_99_to_fp16)[name = tensor("lengths_7_cast_fp16")]; - tensor lengths_9_cast_fp16 = floor(x = lengths_7_cast_fp16)[name = tensor("lengths_9_cast_fp16")]; - tensor var_103_promoted_to_fp16 = const()[name = tensor("op_103_promoted_to_fp16"), val = tensor(-0x1p+0)]; - tensor var_104_cast_fp16 = add(x = lengths_9_cast_fp16, y = var_103_promoted_to_fp16)[name = tensor("op_104_cast_fp16")]; - tensor _inversed_106_y_0_to_fp16 = const()[name = tensor("_inversed_106_y_0_to_fp16"), val = tensor(0x1p-1)]; - tensor _inversed_106_cast_fp16 = mul(x = var_104_cast_fp16, y = _inversed_106_y_0_to_fp16)[name = tensor("_inversed_106_cast_fp16")]; - tensor var_107_to_fp16 = const()[name = tensor("op_107_to_fp16"), val = tensor(0x1p+0)]; - tensor lengths_13_cast_fp16 = add(x = _inversed_106_cast_fp16, y = var_107_to_fp16)[name = tensor("lengths_13_cast_fp16")]; - tensor lengths_cast_fp16 = floor(x = lengths_13_cast_fp16)[name = tensor("lengths_cast_fp16")]; - tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([1])]; - tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = tensor("cast_2")]; - tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = tensor("transpose_315")]; - tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = x_1_cast_fp16)[name = tensor("input_1_cast_fp16")]; - tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; - tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_3_strides_0 = const()[name = tensor("input_3_strides_0"), val = tensor([2, 2])]; - tensor input_3_dilations_0 = const()[name = tensor("input_3_dilations_0"), val = tensor([1, 1])]; - tensor input_3_groups_0 = const()[name = tensor("input_3_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1856))), name = tensor("module_pre_encode_conv_0_weight_to_fp16_palettized"), shape = tensor([256, 1, 3, 3])]; - tensor module_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2048)))]; - tensor input_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; - tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_7_strides_0 = const()[name = tensor("input_7_strides_0"), val = tensor([2, 2])]; - tensor input_7_groups_0 = const()[name = tensor("input_7_groups_0"), val = tensor(256)]; - tensor input_7_dilations_0 = const()[name = tensor("input_7_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4416))), name = tensor("module_pre_encode_conv_2_weight_to_fp16_palettized"), shape = tensor([256, 1, 3, 3])]; - tensor module_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4608)))]; - tensor input_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("valid")]; - tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([1, 1])]; - tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1, 1])]; - tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_3_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54400))), name = tensor("module_pre_encode_conv_3_weight_to_fp16_palettized"), shape = tensor([256, 256, 1, 1])]; - tensor module_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54592)))]; - tensor input_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16_palettized, x = input_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; - tensor input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor("input_11_cast_fp16")]; - tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; - tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_13_strides_0 = const()[name = tensor("input_13_strides_0"), val = tensor([2, 2])]; - tensor input_13_groups_0 = const()[name = tensor("input_13_groups_0"), val = tensor(256)]; - tensor input_13_dilations_0 = const()[name = tensor("input_13_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_5_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56960))), name = tensor("module_pre_encode_conv_5_weight_to_fp16_palettized"), shape = tensor([256, 1, 3, 3])]; - tensor module_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57152)))]; - tensor input_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = tensor("input_13_cast_fp16")]; - tensor input_15_pad_type_0 = const()[name = tensor("input_15_pad_type_0"), val = tensor("valid")]; - tensor input_15_strides_0 = const()[name = tensor("input_15_strides_0"), val = tensor([1, 1])]; - tensor input_15_pad_0 = const()[name = tensor("input_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_15_dilations_0 = const()[name = tensor("input_15_dilations_0"), val = tensor([1, 1])]; - tensor input_15_groups_0 = const()[name = tensor("input_15_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_6_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106944))), name = tensor("module_pre_encode_conv_6_weight_to_fp16_palettized"), shape = tensor([256, 256, 1, 1])]; - tensor module_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107136)))]; - tensor input_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16_palettized, x = input_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; - tensor x_3_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor("x_3_cast_fp16")]; - tensor var_157_perm_0 = const()[name = tensor("op_157_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_158 = const()[name = tensor("op_158"), val = tensor([1, 188, -1])]; - tensor var_157_cast_fp16 = transpose(perm = var_157_perm_0, x = x_3_cast_fp16)[name = tensor("transpose_314")]; - tensor input_17_cast_fp16 = reshape(shape = var_158, x = var_157_cast_fp16)[name = tensor("input_17_cast_fp16")]; - tensor module_pre_encode_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3253504))), name = tensor("module_pre_encode_out_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor module_pre_encode_out_bias_to_fp16 = const()[name = tensor("module_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3253696)))]; - tensor linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16_palettized, x = input_17_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor padding_length_dtype_0 = const()[name = tensor("padding_length_dtype_0"), val = tensor("int32")]; - tensor expand_dims_0 = const()[name = tensor("expand_dims_0"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187]])]; - tensor var_196_axes_0 = const()[name = tensor("op_196_axes_0"), val = tensor([-1])]; - tensor encoder_length = cast(dtype = padding_length_dtype_0, x = lengths_cast_fp16)[name = tensor("cast_1")]; - tensor var_196 = expand_dims(axes = var_196_axes_0, x = encoder_length)[name = tensor("op_196")]; - tensor pad_mask_1 = less(x = expand_dims_0, y = var_196)[name = tensor("pad_mask_1")]; - tensor var_198_axes_0 = const()[name = tensor("op_198_axes_0"), val = tensor([1])]; - tensor var_198 = expand_dims(axes = var_198_axes_0, x = pad_mask_1)[name = tensor("op_198")]; - tensor var_199 = const()[name = tensor("op_199"), val = tensor([1, 188, 1])]; - tensor pad_mask_for_att_mask_1 = tile(reps = var_199, x = var_198)[name = tensor("pad_mask_for_att_mask_1")]; - tensor var_201_perm_0 = const()[name = tensor("op_201_perm_0"), val = tensor([0, 2, 1])]; - tensor var_201 = transpose(perm = var_201_perm_0, x = pad_mask_for_att_mask_1)[name = tensor("transpose_313")]; - tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_201)[name = tensor("pad_mask_for_att_mask")]; - tensor const_7 = const()[name = tensor("const_7"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, 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true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; - tensor att_mask = logical_and(x = pad_mask_for_att_mask, y = const_7)[name = tensor("att_mask")]; - tensor mask_1 = logical_not(x = att_mask)[name = tensor("mask_1")]; - tensor pad_mask = logical_not(x = pad_mask_1)[name = tensor("pad_mask")]; - tensor input_21_axes_0 = const()[name = tensor("input_21_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3255808)))]; - tensor module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3257920)))]; - tensor var_9_to_fp16 = const()[name = tensor("op_9_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = tensor("input_21_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3260032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6405824))), name = tensor("module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_1_bias_0_to_fp16 = const()[name = tensor("linear_1_bias_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6406016)))]; - tensor linear_1_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor("input_25_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6414272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9560064))), name = tensor("module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_2_bias_0_to_fp16 = const()[name = tensor("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9560256)))]; - tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized, x = input_25_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor var_232_to_fp16 = const()[name = tensor("op_232_to_fp16"), val = tensor(0x1p-1)]; - tensor var_233_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_232_to_fp16)[name = tensor("op_233_cast_fp16")]; - tensor input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_233_cast_fp16)[name = tensor("input_31_cast_fp16")]; - tensor query_1_axes_0 = const()[name = tensor("query_1_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9562368)))]; - tensor module_layers_0_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9564480)))]; - tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("query_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9566592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10353088))), name = tensor("module_layers_0_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_3_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = tensor("linear_3_cast_fp16")]; - tensor var_249 = const()[name = tensor("op_249"), val = tensor([1, -1, 8, 128])]; - tensor q_1_cast_fp16 = reshape(shape = var_249, x = linear_3_cast_fp16)[name = tensor("q_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10353280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11139776))), name = tensor("module_layers_0_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_4_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = tensor("linear_4_cast_fp16")]; - tensor var_253 = const()[name = tensor("op_253"), val = tensor([1, -1, 8, 128])]; - tensor k_1_cast_fp16 = reshape(shape = var_253, x = linear_4_cast_fp16)[name = tensor("k_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11139968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11926464))), name = tensor("module_layers_0_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_5_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = tensor("linear_5_cast_fp16")]; - tensor var_257 = const()[name = tensor("op_257"), val = tensor([1, -1, 8, 128])]; - tensor v_1_cast_fp16 = reshape(shape = var_257, x = linear_5_cast_fp16)[name = tensor("v_1_cast_fp16")]; - tensor value_3_perm_0 = const()[name = tensor("value_3_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11926656)))]; - tensor var_269_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = tensor("op_269_cast_fp16")]; - tensor module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11928768)))]; - tensor var_271_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = tensor("op_271_cast_fp16")]; - tensor q_with_bias_v_1_perm_0 = const()[name = tensor("q_with_bias_v_1_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_7_transpose_x_0 = const()[name = tensor("x_7_transpose_x_0"), val = tensor(false)]; - tensor x_7_transpose_y_0 = const()[name = tensor("x_7_transpose_y_0"), val = tensor(false)]; - tensor op_273_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11930880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12218944))), name = tensor("op_273_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_271_cast_fp16)[name = tensor("transpose_312")]; - tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_273_to_fp16_palettized)[name = tensor("x_7_cast_fp16")]; - tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_9_mode_0 = const()[name = tensor("x_9_mode_0"), val = tensor("constant")]; - tensor const_14_to_fp16 = const()[name = tensor("const_14_to_fp16"), val = tensor(0x0p+0)]; - tensor x_9_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = tensor("x_9_cast_fp16")]; - tensor var_281 = const()[name = tensor("op_281"), val = tensor([1, 8, -1, 188])]; - tensor x_11_cast_fp16 = reshape(shape = var_281, x = x_9_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor var_285_begin_0 = const()[name = tensor("op_285_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_285_end_0 = const()[name = tensor("op_285_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_285_end_mask_0 = const()[name = tensor("op_285_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_285_cast_fp16 = slice_by_index(begin = var_285_begin_0, end = var_285_end_0, end_mask = var_285_end_mask_0, x = x_11_cast_fp16)[name = tensor("op_285_cast_fp16")]; - tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_1_cast_fp16 = reshape(shape = var_286, x = var_285_cast_fp16)[name = tensor("matrix_bd_1_cast_fp16")]; - tensor matrix_ac_1_transpose_x_0 = const()[name = tensor("matrix_ac_1_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_1_transpose_y_0 = const()[name = tensor("matrix_ac_1_transpose_y_0"), val = tensor(false)]; - tensor transpose_96_perm_0 = const()[name = tensor("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_97_perm_0 = const()[name = tensor("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = tensor("transpose_310")]; - tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_269_cast_fp16)[name = tensor("transpose_311")]; - tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = tensor("matrix_ac_1_cast_fp16")]; - tensor matrix_bd_3_begin_0 = const()[name = tensor("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_3_end_0 = const()[name = tensor("matrix_bd_3_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_3_end_mask_0 = const()[name = tensor("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = tensor("matrix_bd_3_cast_fp16")]; - tensor var_295_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = tensor("op_295_cast_fp16")]; - tensor _inversed_scores_1_y_0_to_fp16 = const()[name = tensor("_inversed_scores_1_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_1_cast_fp16 = mul(x = var_295_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = tensor("_inversed_scores_1_cast_fp16")]; - tensor mask_3_axes_0 = const()[name = tensor("mask_3_axes_0"), val = tensor([1])]; - tensor mask_3 = expand_dims(axes = mask_3_axes_0, x = mask_1)[name = tensor("mask_3")]; - tensor var_12_to_fp16 = const()[name = tensor("op_12_to_fp16"), val = tensor(-0x1.388p+13)]; - tensor scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_3)[name = tensor("scores_3_cast_fp16")]; - tensor var_301_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = tensor("op_301_cast_fp16")]; - tensor var_11_to_fp16 = const()[name = tensor("op_11_to_fp16"), val = tensor(0x0p+0)]; - tensor input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_301_cast_fp16, cond = mask_3)[name = tensor("input_33_cast_fp16")]; - tensor x_13_transpose_x_0 = const()[name = tensor("x_13_transpose_x_0"), val = tensor(false)]; - tensor x_13_transpose_y_0 = const()[name = tensor("x_13_transpose_y_0"), val = tensor(false)]; - tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = tensor("transpose_309")]; - tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_3_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_305_perm_0 = const()[name = tensor("op_305_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_306 = const()[name = tensor("op_306"), val = tensor([1, -1, 1024])]; - tensor var_305_cast_fp16 = transpose(perm = var_305_perm_0, x = x_13_cast_fp16)[name = tensor("transpose_308")]; - tensor input_35_cast_fp16 = reshape(shape = var_306, x = var_305_cast_fp16)[name = tensor("input_35_cast_fp16")]; - tensor module_layers_0_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12219136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13005632))), name = tensor("module_layers_0_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_7_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = tensor("linear_7_cast_fp16")]; - tensor input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = tensor("input_39_cast_fp16")]; - tensor x_17_axes_0 = const()[name = tensor("x_17_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13005824)))]; - tensor module_layers_0_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13007936)))]; - tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("x_17_cast_fp16")]; - tensor input_41_perm_0 = const()[name = tensor("input_41_perm_0"), val = tensor([0, 2, 1])]; - tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; - tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([1])]; - tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0])]; - tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1])]; - tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13010048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14582976))), name = tensor("module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = tensor("transpose_307")]; - tensor input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_41_cast_fp16)[name = tensor("input_43_cast_fp16")]; - tensor x_19_split_num_splits_0 = const()[name = tensor("x_19_split_num_splits_0"), val = tensor(2)]; - tensor x_19_split_axis_0 = const()[name = tensor("x_19_split_axis_0"), val = tensor(1)]; - tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = tensor("x_19_split_cast_fp16")]; - tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = tensor("x_19_split_1_sigmoid_cast_fp16")]; - tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = tensor("x_19_cast_fp16")]; - tensor var_328_axes_0 = const()[name = tensor("op_328_axes_0"), val = tensor([1])]; - tensor var_328 = expand_dims(axes = var_328_axes_0, x = pad_mask)[name = tensor("op_328")]; - tensor input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_328)[name = tensor("input_45_cast_fp16")]; - tensor input_47_pad_0 = const()[name = tensor("input_47_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_47_mode_0 = const()[name = tensor("input_47_mode_0"), val = tensor("constant")]; - tensor const_17_to_fp16 = const()[name = tensor("const_17_to_fp16"), val = tensor(0x0p+0)]; - tensor input_47_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = tensor("input_47_cast_fp16")]; - tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("valid")]; - tensor input_49_groups_0 = const()[name = tensor("input_49_groups_0"), val = tensor(1024)]; - tensor input_49_strides_0 = const()[name = tensor("input_49_strides_0"), val = tensor([1])]; - tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([0, 0])]; - tensor input_49_dilations_0 = const()[name = tensor("input_49_dilations_0"), val = tensor([1])]; - tensor const_248_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14583168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14590144))), name = tensor("const_248_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_249_to_fp16 = const()[name = tensor("const_249_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14590336)))]; - tensor input_51_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_248_to_fp16_palettized, x = input_47_cast_fp16)[name = tensor("input_51_cast_fp16")]; - tensor input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = tensor("input_53_cast_fp16")]; - tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("valid")]; - tensor x_21_strides_0 = const()[name = tensor("x_21_strides_0"), val = tensor([1])]; - tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0])]; - tensor x_21_dilations_0 = const()[name = tensor("x_21_dilations_0"), val = tensor([1])]; - tensor x_21_groups_0 = const()[name = tensor("x_21_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14592448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15378944))), name = tensor("module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_53_cast_fp16)[name = tensor("x_21_cast_fp16")]; - tensor input_55_perm_0 = const()[name = tensor("input_55_perm_0"), val = tensor([0, 2, 1])]; - tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = tensor("transpose_306")]; - tensor input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; - tensor input_59_axes_0 = const()[name = tensor("input_59_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15379136)))]; - tensor module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15381248)))]; - tensor input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("input_59_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15383360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18529152))), name = tensor("module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_8_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = tensor("linear_8_cast_fp16")]; - tensor input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = tensor("input_63_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18529344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21675136))), name = tensor("module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_9_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized, x = input_63_cast_fp16)[name = tensor("linear_9_cast_fp16")]; - tensor var_366_to_fp16 = const()[name = tensor("op_366_to_fp16"), val = tensor(0x1p-1)]; - tensor var_367_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_366_to_fp16)[name = tensor("op_367_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_367_cast_fp16)[name = tensor("input_69_cast_fp16")]; - tensor input_71_axes_0 = const()[name = tensor("input_71_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21675328)))]; - tensor module_layers_0_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21677440)))]; - tensor input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = tensor("input_71_cast_fp16")]; - tensor input_73_axes_0 = const()[name = tensor("input_73_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21679552)))]; - tensor module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21681664)))]; - tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = tensor("input_73_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21683776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24829568))), name = tensor("module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_10_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized, x = input_73_cast_fp16)[name = tensor("linear_10_cast_fp16")]; - tensor input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor("input_77_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24829760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27975552))), name = tensor("module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_11_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = tensor("linear_11_cast_fp16")]; - tensor var_395_to_fp16 = const()[name = tensor("op_395_to_fp16"), val = tensor(0x1p-1)]; - tensor var_396_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_395_to_fp16)[name = tensor("op_396_cast_fp16")]; - tensor input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_396_cast_fp16)[name = tensor("input_83_cast_fp16")]; - tensor query_3_axes_0 = const()[name = tensor("query_3_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27975744)))]; - tensor module_layers_1_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27977856)))]; - tensor query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = tensor("query_3_cast_fp16")]; - tensor module_layers_1_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27979968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28766464))), name = tensor("module_layers_1_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_12_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = tensor("linear_12_cast_fp16")]; - tensor var_412 = const()[name = tensor("op_412"), val = tensor([1, -1, 8, 128])]; - tensor q_7_cast_fp16 = reshape(shape = var_412, x = linear_12_cast_fp16)[name = tensor("q_7_cast_fp16")]; - tensor module_layers_1_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28766656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29553152))), name = tensor("module_layers_1_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_13_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = tensor("linear_13_cast_fp16")]; - tensor var_416 = const()[name = tensor("op_416"), val = tensor([1, -1, 8, 128])]; - tensor k_5_cast_fp16 = reshape(shape = var_416, x = linear_13_cast_fp16)[name = tensor("k_5_cast_fp16")]; - tensor module_layers_1_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29553344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30339840))), name = tensor("module_layers_1_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_14_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = tensor("linear_14_cast_fp16")]; - tensor var_420 = const()[name = tensor("op_420"), val = tensor([1, -1, 8, 128])]; - tensor v_3_cast_fp16 = reshape(shape = var_420, x = linear_14_cast_fp16)[name = tensor("v_3_cast_fp16")]; - tensor value_5_perm_0 = const()[name = tensor("value_5_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30340032)))]; - tensor var_432_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = tensor("op_432_cast_fp16")]; - tensor module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30342144)))]; - tensor var_434_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = tensor("op_434_cast_fp16")]; - tensor q_with_bias_v_3_perm_0 = const()[name = tensor("q_with_bias_v_3_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_29_transpose_x_0 = const()[name = tensor("x_29_transpose_x_0"), val = tensor(false)]; - tensor x_29_transpose_y_0 = const()[name = tensor("x_29_transpose_y_0"), val = tensor(false)]; - tensor op_436_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30344256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30632320))), name = tensor("op_436_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_434_cast_fp16)[name = tensor("transpose_305")]; - tensor x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_436_to_fp16_palettized)[name = tensor("x_29_cast_fp16")]; - tensor x_31_pad_0 = const()[name = tensor("x_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_31_mode_0 = const()[name = tensor("x_31_mode_0"), val = tensor("constant")]; - tensor const_24_to_fp16 = const()[name = tensor("const_24_to_fp16"), val = tensor(0x0p+0)]; - tensor x_31_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = tensor("x_31_cast_fp16")]; - tensor var_444 = const()[name = tensor("op_444"), val = tensor([1, 8, -1, 188])]; - tensor x_33_cast_fp16 = reshape(shape = var_444, x = x_31_cast_fp16)[name = tensor("x_33_cast_fp16")]; - tensor var_448_begin_0 = const()[name = tensor("op_448_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_448_end_0 = const()[name = tensor("op_448_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_448_end_mask_0 = const()[name = tensor("op_448_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_448_cast_fp16 = slice_by_index(begin = var_448_begin_0, end = var_448_end_0, end_mask = var_448_end_mask_0, x = x_33_cast_fp16)[name = tensor("op_448_cast_fp16")]; - tensor var_449 = const()[name = tensor("op_449"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_5_cast_fp16 = reshape(shape = var_449, x = var_448_cast_fp16)[name = tensor("matrix_bd_5_cast_fp16")]; - tensor matrix_ac_3_transpose_x_0 = const()[name = tensor("matrix_ac_3_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_3_transpose_y_0 = const()[name = tensor("matrix_ac_3_transpose_y_0"), val = tensor(false)]; - tensor transpose_98_perm_0 = const()[name = tensor("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_99_perm_0 = const()[name = tensor("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = tensor("transpose_303")]; - tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = var_432_cast_fp16)[name = tensor("transpose_304")]; - tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = tensor("matrix_ac_3_cast_fp16")]; - tensor matrix_bd_7_begin_0 = const()[name = tensor("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_7_end_0 = const()[name = tensor("matrix_bd_7_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_7_end_mask_0 = const()[name = tensor("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = tensor("matrix_bd_7_cast_fp16")]; - tensor var_458_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = tensor("op_458_cast_fp16")]; - tensor _inversed_scores_5_y_0_to_fp16 = const()[name = tensor("_inversed_scores_5_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_5_cast_fp16 = mul(x = var_458_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = tensor("_inversed_scores_5_cast_fp16")]; - tensor scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_3)[name = tensor("scores_7_cast_fp16")]; - tensor var_464_cast_fp16 = softmax(axis = var_30, x = scores_7_cast_fp16)[name = tensor("op_464_cast_fp16")]; - tensor input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_464_cast_fp16, cond = mask_3)[name = tensor("input_85_cast_fp16")]; - tensor x_35_transpose_x_0 = const()[name = tensor("x_35_transpose_x_0"), val = tensor(false)]; - tensor x_35_transpose_y_0 = const()[name = tensor("x_35_transpose_y_0"), val = tensor(false)]; - tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = tensor("transpose_302")]; - tensor x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_5_cast_fp16)[name = tensor("x_35_cast_fp16")]; - tensor var_468_perm_0 = const()[name = tensor("op_468_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_469 = const()[name = tensor("op_469"), val = tensor([1, -1, 1024])]; - tensor var_468_cast_fp16 = transpose(perm = var_468_perm_0, x = x_35_cast_fp16)[name = tensor("transpose_301")]; - tensor input_87_cast_fp16 = reshape(shape = var_469, x = var_468_cast_fp16)[name = tensor("input_87_cast_fp16")]; - tensor module_layers_1_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30632512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31419008))), name = tensor("module_layers_1_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_16_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16_palettized, x = input_87_cast_fp16)[name = tensor("linear_16_cast_fp16")]; - tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = tensor("input_91_cast_fp16")]; - tensor x_39_axes_0 = const()[name = tensor("x_39_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31419200)))]; - tensor module_layers_1_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31421312)))]; - tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = tensor("x_39_cast_fp16")]; - tensor input_93_perm_0 = const()[name = tensor("input_93_perm_0"), val = tensor([0, 2, 1])]; - tensor input_95_pad_type_0 = const()[name = tensor("input_95_pad_type_0"), val = tensor("valid")]; - tensor input_95_strides_0 = const()[name = tensor("input_95_strides_0"), val = tensor([1])]; - tensor input_95_pad_0 = const()[name = tensor("input_95_pad_0"), val = tensor([0, 0])]; - tensor input_95_dilations_0 = const()[name = tensor("input_95_dilations_0"), val = tensor([1])]; - tensor input_95_groups_0 = const()[name = tensor("input_95_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31423424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32996352))), name = tensor("module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = tensor("transpose_300")]; - tensor input_95_cast_fp16 = conv(dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = tensor("input_95_cast_fp16")]; - tensor x_41_split_num_splits_0 = const()[name = tensor("x_41_split_num_splits_0"), val = tensor(2)]; - tensor x_41_split_axis_0 = const()[name = tensor("x_41_split_axis_0"), val = tensor(1)]; - tensor x_41_split_cast_fp16_0, tensor x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = tensor("x_41_split_cast_fp16")]; - tensor x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = tensor("x_41_split_1_sigmoid_cast_fp16")]; - tensor x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = tensor("x_41_cast_fp16")]; - tensor input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_328)[name = tensor("input_97_cast_fp16")]; - tensor input_99_pad_0 = const()[name = tensor("input_99_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_99_mode_0 = const()[name = tensor("input_99_mode_0"), val = tensor("constant")]; - tensor const_27_to_fp16 = const()[name = tensor("const_27_to_fp16"), val = tensor(0x0p+0)]; - tensor input_99_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = tensor("input_99_cast_fp16")]; - tensor input_101_pad_type_0 = const()[name = tensor("input_101_pad_type_0"), val = tensor("valid")]; - tensor input_101_groups_0 = const()[name = tensor("input_101_groups_0"), val = tensor(1024)]; - tensor input_101_strides_0 = const()[name = tensor("input_101_strides_0"), val = tensor([1])]; - tensor input_101_pad_0 = const()[name = tensor("input_101_pad_0"), val = tensor([0, 0])]; - tensor input_101_dilations_0 = const()[name = tensor("input_101_dilations_0"), val = tensor([1])]; - tensor const_250_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32996544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33003520))), name = tensor("const_250_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_251_to_fp16 = const()[name = tensor("const_251_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33003712)))]; - tensor input_103_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_250_to_fp16_palettized, x = input_99_cast_fp16)[name = tensor("input_103_cast_fp16")]; - tensor input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = tensor("input_105_cast_fp16")]; - tensor x_43_pad_type_0 = const()[name = tensor("x_43_pad_type_0"), val = tensor("valid")]; - tensor x_43_strides_0 = const()[name = tensor("x_43_strides_0"), val = tensor([1])]; - tensor x_43_pad_0 = const()[name = tensor("x_43_pad_0"), val = tensor([0, 0])]; - tensor x_43_dilations_0 = const()[name = tensor("x_43_dilations_0"), val = tensor([1])]; - tensor x_43_groups_0 = const()[name = tensor("x_43_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33005824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33792320))), name = tensor("module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_43_cast_fp16 = conv(dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_105_cast_fp16)[name = tensor("x_43_cast_fp16")]; - tensor input_107_perm_0 = const()[name = tensor("input_107_perm_0"), val = tensor([0, 2, 1])]; - tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = tensor("transpose_299")]; - tensor input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = tensor("input_109_cast_fp16")]; - tensor input_111_axes_0 = const()[name = tensor("input_111_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33792512)))]; - tensor module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33794624)))]; - tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("input_111_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33796736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36942528))), name = tensor("module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_17_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = tensor("linear_17_cast_fp16")]; - tensor input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = tensor("input_115_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36942720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40088512))), name = tensor("module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_18_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized, x = input_115_cast_fp16)[name = tensor("linear_18_cast_fp16")]; - tensor var_529_to_fp16 = const()[name = tensor("op_529_to_fp16"), val = tensor(0x1p-1)]; - tensor var_530_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_529_to_fp16)[name = tensor("op_530_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_530_cast_fp16)[name = tensor("input_121_cast_fp16")]; - tensor input_123_axes_0 = const()[name = tensor("input_123_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40088704)))]; - tensor module_layers_1_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40090816)))]; - tensor input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("input_123_cast_fp16")]; - tensor input_125_axes_0 = const()[name = tensor("input_125_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40092928)))]; - tensor module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40095040)))]; - tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor("input_125_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40097152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43242944))), name = tensor("module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_19_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized, x = input_125_cast_fp16)[name = tensor("linear_19_cast_fp16")]; - tensor input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor("input_129_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43243136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46388928))), name = tensor("module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_20_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized, x = input_129_cast_fp16)[name = tensor("linear_20_cast_fp16")]; - tensor var_558_to_fp16 = const()[name = tensor("op_558_to_fp16"), val = tensor(0x1p-1)]; - tensor var_559_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_558_to_fp16)[name = tensor("op_559_cast_fp16")]; - tensor input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_559_cast_fp16)[name = tensor("input_135_cast_fp16")]; - tensor query_5_axes_0 = const()[name = tensor("query_5_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46389120)))]; - tensor module_layers_2_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46391232)))]; - tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = tensor("query_5_cast_fp16")]; - tensor module_layers_2_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46393344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47179840))), name = tensor("module_layers_2_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_21_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = tensor("linear_21_cast_fp16")]; - tensor var_575 = const()[name = tensor("op_575"), val = tensor([1, -1, 8, 128])]; - tensor q_13_cast_fp16 = reshape(shape = var_575, x = linear_21_cast_fp16)[name = tensor("q_13_cast_fp16")]; - tensor module_layers_2_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47180032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47966528))), name = tensor("module_layers_2_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_22_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = tensor("linear_22_cast_fp16")]; - tensor var_579 = const()[name = tensor("op_579"), val = tensor([1, -1, 8, 128])]; - tensor k_9_cast_fp16 = reshape(shape = var_579, x = linear_22_cast_fp16)[name = tensor("k_9_cast_fp16")]; - tensor module_layers_2_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47966720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48753216))), name = tensor("module_layers_2_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_23_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = tensor("linear_23_cast_fp16")]; - tensor var_583 = const()[name = tensor("op_583"), val = tensor([1, -1, 8, 128])]; - tensor v_5_cast_fp16 = reshape(shape = var_583, x = linear_23_cast_fp16)[name = tensor("v_5_cast_fp16")]; - tensor value_7_perm_0 = const()[name = tensor("value_7_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48753408)))]; - tensor var_595_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = tensor("op_595_cast_fp16")]; - tensor module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48755520)))]; - tensor var_597_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = tensor("op_597_cast_fp16")]; - tensor q_with_bias_v_5_perm_0 = const()[name = tensor("q_with_bias_v_5_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_51_transpose_x_0 = const()[name = tensor("x_51_transpose_x_0"), val = tensor(false)]; - tensor x_51_transpose_y_0 = const()[name = tensor("x_51_transpose_y_0"), val = tensor(false)]; - tensor op_599_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48757632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49045696))), name = tensor("op_599_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_597_cast_fp16)[name = tensor("transpose_298")]; - tensor x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_599_to_fp16_palettized)[name = tensor("x_51_cast_fp16")]; - tensor x_53_pad_0 = const()[name = tensor("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_53_mode_0 = const()[name = tensor("x_53_mode_0"), val = tensor("constant")]; - tensor const_34_to_fp16 = const()[name = tensor("const_34_to_fp16"), val = tensor(0x0p+0)]; - tensor x_53_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = tensor("x_53_cast_fp16")]; - tensor var_607 = const()[name = tensor("op_607"), val = tensor([1, 8, -1, 188])]; - tensor x_55_cast_fp16 = reshape(shape = var_607, x = x_53_cast_fp16)[name = tensor("x_55_cast_fp16")]; - tensor var_611_begin_0 = const()[name = tensor("op_611_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_611_end_0 = const()[name = tensor("op_611_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_611_end_mask_0 = const()[name = tensor("op_611_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_611_cast_fp16 = slice_by_index(begin = var_611_begin_0, end = var_611_end_0, end_mask = var_611_end_mask_0, x = x_55_cast_fp16)[name = tensor("op_611_cast_fp16")]; - tensor var_612 = const()[name = tensor("op_612"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_9_cast_fp16 = reshape(shape = var_612, x = var_611_cast_fp16)[name = tensor("matrix_bd_9_cast_fp16")]; - tensor matrix_ac_5_transpose_x_0 = const()[name = tensor("matrix_ac_5_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_5_transpose_y_0 = const()[name = tensor("matrix_ac_5_transpose_y_0"), val = tensor(false)]; - tensor transpose_100_perm_0 = const()[name = tensor("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_101_perm_0 = const()[name = tensor("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = tensor("transpose_296")]; - tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_595_cast_fp16)[name = tensor("transpose_297")]; - tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = tensor("matrix_ac_5_cast_fp16")]; - tensor matrix_bd_11_begin_0 = const()[name = tensor("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_11_end_0 = const()[name = tensor("matrix_bd_11_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_11_end_mask_0 = const()[name = tensor("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = tensor("matrix_bd_11_cast_fp16")]; - tensor var_621_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = tensor("op_621_cast_fp16")]; - tensor _inversed_scores_9_y_0_to_fp16 = const()[name = tensor("_inversed_scores_9_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_9_cast_fp16 = mul(x = var_621_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = tensor("_inversed_scores_9_cast_fp16")]; - tensor scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_3)[name = tensor("scores_11_cast_fp16")]; - tensor var_627_cast_fp16 = softmax(axis = var_30, x = scores_11_cast_fp16)[name = tensor("op_627_cast_fp16")]; - tensor input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_627_cast_fp16, cond = mask_3)[name = tensor("input_137_cast_fp16")]; - tensor x_57_transpose_x_0 = const()[name = tensor("x_57_transpose_x_0"), val = tensor(false)]; - tensor x_57_transpose_y_0 = const()[name = tensor("x_57_transpose_y_0"), val = tensor(false)]; - tensor value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = tensor("transpose_295")]; - tensor x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_7_cast_fp16)[name = tensor("x_57_cast_fp16")]; - tensor var_631_perm_0 = const()[name = tensor("op_631_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_632 = const()[name = tensor("op_632"), val = tensor([1, -1, 1024])]; - tensor var_631_cast_fp16 = transpose(perm = var_631_perm_0, x = x_57_cast_fp16)[name = tensor("transpose_294")]; - tensor input_139_cast_fp16 = reshape(shape = var_632, x = var_631_cast_fp16)[name = tensor("input_139_cast_fp16")]; - tensor module_layers_2_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49045888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49832384))), name = tensor("module_layers_2_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_25_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16_palettized, x = input_139_cast_fp16)[name = tensor("linear_25_cast_fp16")]; - tensor input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = tensor("input_143_cast_fp16")]; - tensor x_61_axes_0 = const()[name = tensor("x_61_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49832576)))]; - tensor module_layers_2_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49834688)))]; - tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = tensor("x_61_cast_fp16")]; - tensor input_145_perm_0 = const()[name = tensor("input_145_perm_0"), val = tensor([0, 2, 1])]; - tensor input_147_pad_type_0 = const()[name = tensor("input_147_pad_type_0"), val = tensor("valid")]; - tensor input_147_strides_0 = const()[name = tensor("input_147_strides_0"), val = tensor([1])]; - tensor input_147_pad_0 = const()[name = tensor("input_147_pad_0"), val = tensor([0, 0])]; - tensor input_147_dilations_0 = const()[name = tensor("input_147_dilations_0"), val = tensor([1])]; - tensor input_147_groups_0 = const()[name = tensor("input_147_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49836800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51409728))), name = tensor("module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = tensor("transpose_293")]; - tensor input_147_cast_fp16 = conv(dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_145_cast_fp16)[name = tensor("input_147_cast_fp16")]; - tensor x_63_split_num_splits_0 = const()[name = tensor("x_63_split_num_splits_0"), val = tensor(2)]; - tensor x_63_split_axis_0 = const()[name = tensor("x_63_split_axis_0"), val = tensor(1)]; - tensor x_63_split_cast_fp16_0, tensor x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = tensor("x_63_split_cast_fp16")]; - tensor x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = tensor("x_63_split_1_sigmoid_cast_fp16")]; - tensor x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = tensor("x_63_cast_fp16")]; - tensor input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_328)[name = tensor("input_149_cast_fp16")]; - tensor input_151_pad_0 = const()[name = tensor("input_151_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_151_mode_0 = const()[name = tensor("input_151_mode_0"), val = tensor("constant")]; - tensor const_37_to_fp16 = const()[name = tensor("const_37_to_fp16"), val = tensor(0x0p+0)]; - tensor input_151_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = tensor("input_151_cast_fp16")]; - tensor input_153_pad_type_0 = const()[name = tensor("input_153_pad_type_0"), val = tensor("valid")]; - tensor input_153_groups_0 = const()[name = tensor("input_153_groups_0"), val = tensor(1024)]; - tensor input_153_strides_0 = const()[name = tensor("input_153_strides_0"), val = tensor([1])]; - tensor input_153_pad_0 = const()[name = tensor("input_153_pad_0"), val = tensor([0, 0])]; - tensor input_153_dilations_0 = const()[name = tensor("input_153_dilations_0"), val = tensor([1])]; - tensor const_252_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51409920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51416896))), name = tensor("const_252_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_253_to_fp16 = const()[name = tensor("const_253_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51417088)))]; - tensor input_155_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_252_to_fp16_palettized, x = input_151_cast_fp16)[name = tensor("input_155_cast_fp16")]; - tensor input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = tensor("input_157_cast_fp16")]; - tensor x_65_pad_type_0 = const()[name = tensor("x_65_pad_type_0"), val = tensor("valid")]; - tensor x_65_strides_0 = const()[name = tensor("x_65_strides_0"), val = tensor([1])]; - tensor x_65_pad_0 = const()[name = tensor("x_65_pad_0"), val = tensor([0, 0])]; - tensor x_65_dilations_0 = const()[name = tensor("x_65_dilations_0"), val = tensor([1])]; - tensor x_65_groups_0 = const()[name = tensor("x_65_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51419200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52205696))), name = tensor("module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_65_cast_fp16 = conv(dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = tensor("x_65_cast_fp16")]; - tensor input_159_perm_0 = const()[name = tensor("input_159_perm_0"), val = tensor([0, 2, 1])]; - tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = tensor("transpose_292")]; - tensor input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = tensor("input_161_cast_fp16")]; - tensor input_163_axes_0 = const()[name = tensor("input_163_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52205888)))]; - tensor module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52208000)))]; - tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("input_163_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52210112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55355904))), name = tensor("module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_26_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized, x = input_163_cast_fp16)[name = tensor("linear_26_cast_fp16")]; - tensor input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = tensor("input_167_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55356096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58501888))), name = tensor("module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_27_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("linear_27_cast_fp16")]; - tensor var_692_to_fp16 = const()[name = tensor("op_692_to_fp16"), val = tensor(0x1p-1)]; - tensor var_693_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_692_to_fp16)[name = tensor("op_693_cast_fp16")]; - tensor input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_693_cast_fp16)[name = tensor("input_173_cast_fp16")]; - tensor input_175_axes_0 = const()[name = tensor("input_175_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58502080)))]; - tensor module_layers_2_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58504192)))]; - tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = tensor("input_175_cast_fp16")]; - tensor input_177_axes_0 = const()[name = tensor("input_177_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58506304)))]; - tensor module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58508416)))]; - tensor input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor("input_177_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58510528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61656320))), name = tensor("module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_28_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized, x = input_177_cast_fp16)[name = tensor("linear_28_cast_fp16")]; - tensor input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = tensor("input_181_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61656512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64802304))), name = tensor("module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_29_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = tensor("linear_29_cast_fp16")]; - tensor var_721_to_fp16 = const()[name = tensor("op_721_to_fp16"), val = tensor(0x1p-1)]; - tensor var_722_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_721_to_fp16)[name = tensor("op_722_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_722_cast_fp16)[name = tensor("input_187_cast_fp16")]; - tensor query_7_axes_0 = const()[name = tensor("query_7_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64802496)))]; - tensor module_layers_3_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64804608)))]; - tensor query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = tensor("query_7_cast_fp16")]; - tensor module_layers_3_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64806720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65593216))), name = tensor("module_layers_3_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_30_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = tensor("linear_30_cast_fp16")]; - tensor var_738 = const()[name = tensor("op_738"), val = tensor([1, -1, 8, 128])]; - tensor q_19_cast_fp16 = reshape(shape = var_738, x = linear_30_cast_fp16)[name = tensor("q_19_cast_fp16")]; - tensor module_layers_3_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65593408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66379904))), name = tensor("module_layers_3_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_31_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = tensor("linear_31_cast_fp16")]; - tensor var_742 = const()[name = tensor("op_742"), val = tensor([1, -1, 8, 128])]; - tensor k_13_cast_fp16 = reshape(shape = var_742, x = linear_31_cast_fp16)[name = tensor("k_13_cast_fp16")]; - tensor module_layers_3_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66380096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67166592))), name = tensor("module_layers_3_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_32_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = tensor("linear_32_cast_fp16")]; - tensor var_746 = const()[name = tensor("op_746"), val = tensor([1, -1, 8, 128])]; - tensor v_7_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = tensor("v_7_cast_fp16")]; - tensor value_9_perm_0 = const()[name = tensor("value_9_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67166784)))]; - tensor var_758_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = tensor("op_758_cast_fp16")]; - tensor module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67168896)))]; - tensor var_760_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = tensor("op_760_cast_fp16")]; - tensor q_with_bias_v_7_perm_0 = const()[name = tensor("q_with_bias_v_7_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_73_transpose_x_0 = const()[name = tensor("x_73_transpose_x_0"), val = tensor(false)]; - tensor x_73_transpose_y_0 = const()[name = tensor("x_73_transpose_y_0"), val = tensor(false)]; - tensor op_762_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67171008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67459072))), name = tensor("op_762_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_760_cast_fp16)[name = tensor("transpose_291")]; - tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_762_to_fp16_palettized)[name = tensor("x_73_cast_fp16")]; - tensor x_75_pad_0 = const()[name = tensor("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_75_mode_0 = const()[name = tensor("x_75_mode_0"), val = tensor("constant")]; - tensor const_44_to_fp16 = const()[name = tensor("const_44_to_fp16"), val = tensor(0x0p+0)]; - tensor x_75_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = tensor("x_75_cast_fp16")]; - tensor var_770 = const()[name = tensor("op_770"), val = tensor([1, 8, -1, 188])]; - tensor x_77_cast_fp16 = reshape(shape = var_770, x = x_75_cast_fp16)[name = tensor("x_77_cast_fp16")]; - tensor var_774_begin_0 = const()[name = tensor("op_774_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_774_end_0 = const()[name = tensor("op_774_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_774_end_mask_0 = const()[name = tensor("op_774_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_774_cast_fp16 = slice_by_index(begin = var_774_begin_0, end = var_774_end_0, end_mask = var_774_end_mask_0, x = x_77_cast_fp16)[name = tensor("op_774_cast_fp16")]; - tensor var_775 = const()[name = tensor("op_775"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_13_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = tensor("matrix_bd_13_cast_fp16")]; - tensor matrix_ac_7_transpose_x_0 = const()[name = tensor("matrix_ac_7_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_7_transpose_y_0 = const()[name = tensor("matrix_ac_7_transpose_y_0"), val = tensor(false)]; - tensor transpose_102_perm_0 = const()[name = tensor("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_103_perm_0 = const()[name = tensor("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = tensor("transpose_289")]; - tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_758_cast_fp16)[name = tensor("transpose_290")]; - tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = tensor("matrix_ac_7_cast_fp16")]; - tensor matrix_bd_15_begin_0 = const()[name = tensor("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_15_end_0 = const()[name = tensor("matrix_bd_15_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_15_end_mask_0 = const()[name = tensor("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = tensor("matrix_bd_15_cast_fp16")]; - tensor var_784_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = tensor("op_784_cast_fp16")]; - tensor _inversed_scores_13_y_0_to_fp16 = const()[name = tensor("_inversed_scores_13_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_13_cast_fp16 = mul(x = var_784_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = tensor("_inversed_scores_13_cast_fp16")]; - tensor scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_3)[name = tensor("scores_15_cast_fp16")]; - tensor var_790_cast_fp16 = softmax(axis = var_30, x = scores_15_cast_fp16)[name = tensor("op_790_cast_fp16")]; - tensor input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_790_cast_fp16, cond = mask_3)[name = tensor("input_189_cast_fp16")]; - tensor x_79_transpose_x_0 = const()[name = tensor("x_79_transpose_x_0"), val = tensor(false)]; - tensor x_79_transpose_y_0 = const()[name = tensor("x_79_transpose_y_0"), val = tensor(false)]; - tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = tensor("transpose_288")]; - tensor x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_9_cast_fp16)[name = tensor("x_79_cast_fp16")]; - tensor var_794_perm_0 = const()[name = tensor("op_794_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_795 = const()[name = tensor("op_795"), val = tensor([1, -1, 1024])]; - tensor var_794_cast_fp16 = transpose(perm = var_794_perm_0, x = x_79_cast_fp16)[name = tensor("transpose_287")]; - tensor input_191_cast_fp16 = reshape(shape = var_795, x = var_794_cast_fp16)[name = tensor("input_191_cast_fp16")]; - tensor module_layers_3_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67459264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68245760))), name = tensor("module_layers_3_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_34_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16_palettized, x = input_191_cast_fp16)[name = tensor("linear_34_cast_fp16")]; - tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = tensor("input_195_cast_fp16")]; - tensor x_83_axes_0 = const()[name = tensor("x_83_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68245952)))]; - tensor module_layers_3_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68248064)))]; - tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = tensor("x_83_cast_fp16")]; - tensor input_197_perm_0 = const()[name = tensor("input_197_perm_0"), val = tensor([0, 2, 1])]; - tensor input_199_pad_type_0 = const()[name = tensor("input_199_pad_type_0"), val = tensor("valid")]; - tensor input_199_strides_0 = const()[name = tensor("input_199_strides_0"), val = tensor([1])]; - tensor input_199_pad_0 = const()[name = tensor("input_199_pad_0"), val = tensor([0, 0])]; - tensor input_199_dilations_0 = const()[name = tensor("input_199_dilations_0"), val = tensor([1])]; - tensor input_199_groups_0 = const()[name = tensor("input_199_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68250176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69823104))), name = tensor("module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = tensor("transpose_286")]; - tensor input_199_cast_fp16 = conv(dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_197_cast_fp16)[name = tensor("input_199_cast_fp16")]; - tensor x_85_split_num_splits_0 = const()[name = tensor("x_85_split_num_splits_0"), val = tensor(2)]; - tensor x_85_split_axis_0 = const()[name = tensor("x_85_split_axis_0"), val = tensor(1)]; - tensor x_85_split_cast_fp16_0, tensor x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = tensor("x_85_split_cast_fp16")]; - tensor x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = tensor("x_85_split_1_sigmoid_cast_fp16")]; - tensor x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = tensor("x_85_cast_fp16")]; - tensor input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_328)[name = tensor("input_201_cast_fp16")]; - tensor input_203_pad_0 = const()[name = tensor("input_203_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_203_mode_0 = const()[name = tensor("input_203_mode_0"), val = tensor("constant")]; - tensor const_47_to_fp16 = const()[name = tensor("const_47_to_fp16"), val = tensor(0x0p+0)]; - tensor input_203_cast_fp16 = pad(constant_val = const_47_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = tensor("input_203_cast_fp16")]; - tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("valid")]; - tensor input_205_groups_0 = const()[name = tensor("input_205_groups_0"), val = tensor(1024)]; - tensor input_205_strides_0 = const()[name = tensor("input_205_strides_0"), val = tensor([1])]; - tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([0, 0])]; - tensor input_205_dilations_0 = const()[name = tensor("input_205_dilations_0"), val = tensor([1])]; - tensor const_254_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69823296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69830272))), name = tensor("const_254_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_255_to_fp16 = const()[name = tensor("const_255_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69830464)))]; - tensor input_207_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_254_to_fp16_palettized, x = input_203_cast_fp16)[name = tensor("input_207_cast_fp16")]; - tensor input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = tensor("input_209_cast_fp16")]; - tensor x_87_pad_type_0 = const()[name = tensor("x_87_pad_type_0"), val = tensor("valid")]; - tensor x_87_strides_0 = const()[name = tensor("x_87_strides_0"), val = tensor([1])]; - tensor x_87_pad_0 = const()[name = tensor("x_87_pad_0"), val = tensor([0, 0])]; - tensor x_87_dilations_0 = const()[name = tensor("x_87_dilations_0"), val = tensor([1])]; - tensor x_87_groups_0 = const()[name = tensor("x_87_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69832576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70619072))), name = tensor("module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_87_cast_fp16 = conv(dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_209_cast_fp16)[name = tensor("x_87_cast_fp16")]; - tensor input_211_perm_0 = const()[name = tensor("input_211_perm_0"), val = tensor([0, 2, 1])]; - tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = tensor("transpose_285")]; - tensor input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = tensor("input_213_cast_fp16")]; - tensor input_215_axes_0 = const()[name = tensor("input_215_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70619264)))]; - tensor module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70621376)))]; - tensor input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = tensor("input_215_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70623488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73769280))), name = tensor("module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_35_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized, x = input_215_cast_fp16)[name = tensor("linear_35_cast_fp16")]; - tensor input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = tensor("input_219_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73769472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76915264))), name = tensor("module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_36_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized, x = input_219_cast_fp16)[name = tensor("linear_36_cast_fp16")]; - tensor var_855_to_fp16 = const()[name = tensor("op_855_to_fp16"), val = tensor(0x1p-1)]; - tensor var_856_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_855_to_fp16)[name = tensor("op_856_cast_fp16")]; - tensor input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_856_cast_fp16)[name = tensor("input_225_cast_fp16")]; - tensor input_227_axes_0 = const()[name = tensor("input_227_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76915456)))]; - tensor module_layers_3_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76917568)))]; - tensor input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = tensor("input_227_cast_fp16")]; - tensor input_229_axes_0 = const()[name = tensor("input_229_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76919680)))]; - tensor module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76921792)))]; - tensor input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = tensor("input_229_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76923904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80069696))), name = tensor("module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_37_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = tensor("linear_37_cast_fp16")]; - tensor input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = tensor("input_233_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80069888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83215680))), name = tensor("module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_38_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized, x = input_233_cast_fp16)[name = tensor("linear_38_cast_fp16")]; - tensor var_884_to_fp16 = const()[name = tensor("op_884_to_fp16"), val = tensor(0x1p-1)]; - tensor var_885_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_884_to_fp16)[name = tensor("op_885_cast_fp16")]; - tensor input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_885_cast_fp16)[name = tensor("input_239_cast_fp16")]; - tensor query_9_axes_0 = const()[name = tensor("query_9_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83215872)))]; - tensor module_layers_4_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83217984)))]; - tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = tensor("query_9_cast_fp16")]; - tensor module_layers_4_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83220096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84006592))), name = tensor("module_layers_4_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_39_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = tensor("linear_39_cast_fp16")]; - tensor var_901 = const()[name = tensor("op_901"), val = tensor([1, -1, 8, 128])]; - tensor q_25_cast_fp16 = reshape(shape = var_901, x = linear_39_cast_fp16)[name = tensor("q_25_cast_fp16")]; - tensor module_layers_4_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84006784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84793280))), name = tensor("module_layers_4_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_40_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = tensor("linear_40_cast_fp16")]; - tensor var_905 = const()[name = tensor("op_905"), val = tensor([1, -1, 8, 128])]; - tensor k_17_cast_fp16 = reshape(shape = var_905, x = linear_40_cast_fp16)[name = tensor("k_17_cast_fp16")]; - tensor module_layers_4_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84793472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85579968))), name = tensor("module_layers_4_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_41_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = tensor("linear_41_cast_fp16")]; - tensor var_909 = const()[name = tensor("op_909"), val = tensor([1, -1, 8, 128])]; - tensor v_9_cast_fp16 = reshape(shape = var_909, x = linear_41_cast_fp16)[name = tensor("v_9_cast_fp16")]; - tensor value_11_perm_0 = const()[name = tensor("value_11_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85580160)))]; - tensor var_921_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = tensor("op_921_cast_fp16")]; - tensor module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85582272)))]; - tensor var_923_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = tensor("op_923_cast_fp16")]; - tensor q_with_bias_v_9_perm_0 = const()[name = tensor("q_with_bias_v_9_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_95_transpose_x_0 = const()[name = tensor("x_95_transpose_x_0"), val = tensor(false)]; - tensor x_95_transpose_y_0 = const()[name = tensor("x_95_transpose_y_0"), val = tensor(false)]; - tensor op_925_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85584384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85872448))), name = tensor("op_925_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_923_cast_fp16)[name = tensor("transpose_284")]; - tensor x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_925_to_fp16_palettized)[name = tensor("x_95_cast_fp16")]; - tensor x_97_pad_0 = const()[name = tensor("x_97_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_97_mode_0 = const()[name = tensor("x_97_mode_0"), val = tensor("constant")]; - tensor const_54_to_fp16 = const()[name = tensor("const_54_to_fp16"), val = tensor(0x0p+0)]; - tensor x_97_cast_fp16 = pad(constant_val = const_54_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = tensor("x_97_cast_fp16")]; - tensor var_933 = const()[name = tensor("op_933"), val = tensor([1, 8, -1, 188])]; - tensor x_99_cast_fp16 = reshape(shape = var_933, x = x_97_cast_fp16)[name = tensor("x_99_cast_fp16")]; - tensor var_937_begin_0 = const()[name = tensor("op_937_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_937_end_0 = const()[name = tensor("op_937_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_937_end_mask_0 = const()[name = tensor("op_937_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_937_cast_fp16 = slice_by_index(begin = var_937_begin_0, end = var_937_end_0, end_mask = var_937_end_mask_0, x = x_99_cast_fp16)[name = tensor("op_937_cast_fp16")]; - tensor var_938 = const()[name = tensor("op_938"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_17_cast_fp16 = reshape(shape = var_938, x = var_937_cast_fp16)[name = tensor("matrix_bd_17_cast_fp16")]; - tensor matrix_ac_9_transpose_x_0 = const()[name = tensor("matrix_ac_9_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_9_transpose_y_0 = const()[name = tensor("matrix_ac_9_transpose_y_0"), val = tensor(false)]; - tensor transpose_104_perm_0 = const()[name = tensor("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_105_perm_0 = const()[name = tensor("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = tensor("transpose_282")]; - tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = var_921_cast_fp16)[name = tensor("transpose_283")]; - tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = tensor("matrix_ac_9_cast_fp16")]; - tensor matrix_bd_19_begin_0 = const()[name = tensor("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_19_end_0 = const()[name = tensor("matrix_bd_19_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_19_end_mask_0 = const()[name = tensor("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = tensor("matrix_bd_19_cast_fp16")]; - tensor var_947_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = tensor("op_947_cast_fp16")]; - tensor _inversed_scores_17_y_0_to_fp16 = const()[name = tensor("_inversed_scores_17_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_17_cast_fp16 = mul(x = var_947_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = tensor("_inversed_scores_17_cast_fp16")]; - tensor scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_3)[name = tensor("scores_19_cast_fp16")]; - tensor var_953_cast_fp16 = softmax(axis = var_30, x = scores_19_cast_fp16)[name = tensor("op_953_cast_fp16")]; - tensor input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_953_cast_fp16, cond = mask_3)[name = tensor("input_241_cast_fp16")]; - tensor x_101_transpose_x_0 = const()[name = tensor("x_101_transpose_x_0"), val = tensor(false)]; - tensor x_101_transpose_y_0 = const()[name = tensor("x_101_transpose_y_0"), val = tensor(false)]; - tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = tensor("transpose_281")]; - tensor x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_11_cast_fp16)[name = tensor("x_101_cast_fp16")]; - tensor var_957_perm_0 = const()[name = tensor("op_957_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_958 = const()[name = tensor("op_958"), val = tensor([1, -1, 1024])]; - tensor var_957_cast_fp16 = transpose(perm = var_957_perm_0, x = x_101_cast_fp16)[name = tensor("transpose_280")]; - tensor input_243_cast_fp16 = reshape(shape = var_958, x = var_957_cast_fp16)[name = tensor("input_243_cast_fp16")]; - tensor module_layers_4_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85872640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86659136))), name = tensor("module_layers_4_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_43_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16_palettized, x = input_243_cast_fp16)[name = tensor("linear_43_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = tensor("input_247_cast_fp16")]; - tensor x_105_axes_0 = const()[name = tensor("x_105_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86659328)))]; - tensor module_layers_4_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86661440)))]; - tensor x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = tensor("x_105_cast_fp16")]; - tensor input_249_perm_0 = const()[name = tensor("input_249_perm_0"), val = tensor([0, 2, 1])]; - tensor input_251_pad_type_0 = const()[name = tensor("input_251_pad_type_0"), val = tensor("valid")]; - tensor input_251_strides_0 = const()[name = tensor("input_251_strides_0"), val = tensor([1])]; - tensor input_251_pad_0 = const()[name = tensor("input_251_pad_0"), val = tensor([0, 0])]; - tensor input_251_dilations_0 = const()[name = tensor("input_251_dilations_0"), val = tensor([1])]; - tensor input_251_groups_0 = const()[name = tensor("input_251_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86663552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88236480))), name = tensor("module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = tensor("transpose_279")]; - tensor input_251_cast_fp16 = conv(dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_249_cast_fp16)[name = tensor("input_251_cast_fp16")]; - tensor x_107_split_num_splits_0 = const()[name = tensor("x_107_split_num_splits_0"), val = tensor(2)]; - tensor x_107_split_axis_0 = const()[name = tensor("x_107_split_axis_0"), val = tensor(1)]; - tensor x_107_split_cast_fp16_0, tensor x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = tensor("x_107_split_cast_fp16")]; - tensor x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = tensor("x_107_split_1_sigmoid_cast_fp16")]; - tensor x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = tensor("x_107_cast_fp16")]; - tensor input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_328)[name = tensor("input_253_cast_fp16")]; - tensor input_255_pad_0 = const()[name = tensor("input_255_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_255_mode_0 = const()[name = tensor("input_255_mode_0"), val = tensor("constant")]; - tensor const_57_to_fp16 = const()[name = tensor("const_57_to_fp16"), val = tensor(0x0p+0)]; - tensor input_255_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = tensor("input_255_cast_fp16")]; - tensor input_257_pad_type_0 = const()[name = tensor("input_257_pad_type_0"), val = tensor("valid")]; - tensor input_257_groups_0 = const()[name = tensor("input_257_groups_0"), val = tensor(1024)]; - tensor input_257_strides_0 = const()[name = tensor("input_257_strides_0"), val = tensor([1])]; - tensor input_257_pad_0 = const()[name = tensor("input_257_pad_0"), val = tensor([0, 0])]; - tensor input_257_dilations_0 = const()[name = tensor("input_257_dilations_0"), val = tensor([1])]; - tensor const_256_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88236672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88243648))), name = tensor("const_256_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_257_to_fp16 = const()[name = tensor("const_257_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88243840)))]; - tensor input_259_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_256_to_fp16_palettized, x = input_255_cast_fp16)[name = tensor("input_259_cast_fp16")]; - tensor input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = tensor("input_261_cast_fp16")]; - tensor x_109_pad_type_0 = const()[name = tensor("x_109_pad_type_0"), val = tensor("valid")]; - tensor x_109_strides_0 = const()[name = tensor("x_109_strides_0"), val = tensor([1])]; - tensor x_109_pad_0 = const()[name = tensor("x_109_pad_0"), val = tensor([0, 0])]; - tensor x_109_dilations_0 = const()[name = tensor("x_109_dilations_0"), val = tensor([1])]; - tensor x_109_groups_0 = const()[name = tensor("x_109_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88245952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89032448))), name = tensor("module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_109_cast_fp16 = conv(dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_261_cast_fp16)[name = tensor("x_109_cast_fp16")]; - tensor input_263_perm_0 = const()[name = tensor("input_263_perm_0"), val = tensor([0, 2, 1])]; - tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = tensor("transpose_278")]; - tensor input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = tensor("input_265_cast_fp16")]; - tensor input_267_axes_0 = const()[name = tensor("input_267_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89032640)))]; - tensor module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89034752)))]; - tensor input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor("input_267_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89036864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92182656))), name = tensor("module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_44_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = tensor("linear_44_cast_fp16")]; - tensor input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = tensor("input_271_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92182848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95328640))), name = tensor("module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_45_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized, x = input_271_cast_fp16)[name = tensor("linear_45_cast_fp16")]; - tensor var_1018_to_fp16 = const()[name = tensor("op_1018_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1019_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1018_to_fp16)[name = tensor("op_1019_cast_fp16")]; - tensor input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1019_cast_fp16)[name = tensor("input_277_cast_fp16")]; - tensor input_279_axes_0 = const()[name = tensor("input_279_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95328832)))]; - tensor module_layers_4_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95330944)))]; - tensor input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = tensor("input_279_cast_fp16")]; - tensor input_281_axes_0 = const()[name = tensor("input_281_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95333056)))]; - tensor module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95335168)))]; - tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = tensor("input_281_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95337280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98483072))), name = tensor("module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_46_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized, x = input_281_cast_fp16)[name = tensor("linear_46_cast_fp16")]; - tensor input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = tensor("input_285_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98483264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101629056))), name = tensor("module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_47_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized, x = input_285_cast_fp16)[name = tensor("linear_47_cast_fp16")]; - tensor var_1047_to_fp16 = const()[name = tensor("op_1047_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1048_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1047_to_fp16)[name = tensor("op_1048_cast_fp16")]; - tensor input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1048_cast_fp16)[name = tensor("input_291_cast_fp16")]; - tensor query_11_axes_0 = const()[name = tensor("query_11_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101629248)))]; - tensor module_layers_5_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101631360)))]; - tensor query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = tensor("query_11_cast_fp16")]; - tensor module_layers_5_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101633472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102419968))), name = tensor("module_layers_5_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_48_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = tensor("linear_48_cast_fp16")]; - tensor var_1064 = const()[name = tensor("op_1064"), val = tensor([1, -1, 8, 128])]; - tensor q_31_cast_fp16 = reshape(shape = var_1064, x = linear_48_cast_fp16)[name = tensor("q_31_cast_fp16")]; - tensor module_layers_5_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102420160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103206656))), name = tensor("module_layers_5_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_49_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = tensor("linear_49_cast_fp16")]; - tensor var_1068 = const()[name = tensor("op_1068"), val = tensor([1, -1, 8, 128])]; - tensor k_21_cast_fp16 = reshape(shape = var_1068, x = linear_49_cast_fp16)[name = tensor("k_21_cast_fp16")]; - tensor module_layers_5_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103206848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103993344))), name = tensor("module_layers_5_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_50_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = tensor("linear_50_cast_fp16")]; - tensor var_1072 = const()[name = tensor("op_1072"), val = tensor([1, -1, 8, 128])]; - tensor v_11_cast_fp16 = reshape(shape = var_1072, x = linear_50_cast_fp16)[name = tensor("v_11_cast_fp16")]; - tensor value_13_perm_0 = const()[name = tensor("value_13_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103993536)))]; - tensor var_1084_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1084_cast_fp16")]; - tensor module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103995648)))]; - tensor var_1086_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1086_cast_fp16")]; - tensor q_with_bias_v_11_perm_0 = const()[name = tensor("q_with_bias_v_11_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_117_transpose_x_0 = const()[name = tensor("x_117_transpose_x_0"), val = tensor(false)]; - tensor x_117_transpose_y_0 = const()[name = tensor("x_117_transpose_y_0"), val = tensor(false)]; - tensor op_1088_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103997760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104285824))), name = tensor("op_1088_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1086_cast_fp16)[name = tensor("transpose_277")]; - tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = op_1088_to_fp16_palettized)[name = tensor("x_117_cast_fp16")]; - tensor x_119_pad_0 = const()[name = tensor("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_119_mode_0 = const()[name = tensor("x_119_mode_0"), val = tensor("constant")]; - tensor const_64_to_fp16 = const()[name = tensor("const_64_to_fp16"), val = tensor(0x0p+0)]; - tensor x_119_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = tensor("x_119_cast_fp16")]; - tensor var_1096 = const()[name = tensor("op_1096"), val = tensor([1, 8, -1, 188])]; - tensor x_121_cast_fp16 = reshape(shape = var_1096, x = x_119_cast_fp16)[name = tensor("x_121_cast_fp16")]; - tensor var_1100_begin_0 = const()[name = tensor("op_1100_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1100_end_0 = const()[name = tensor("op_1100_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1100_end_mask_0 = const()[name = tensor("op_1100_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1100_cast_fp16 = slice_by_index(begin = var_1100_begin_0, end = var_1100_end_0, end_mask = var_1100_end_mask_0, x = x_121_cast_fp16)[name = tensor("op_1100_cast_fp16")]; - tensor var_1101 = const()[name = tensor("op_1101"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1101, x = var_1100_cast_fp16)[name = tensor("matrix_bd_21_cast_fp16")]; - tensor matrix_ac_11_transpose_x_0 = const()[name = tensor("matrix_ac_11_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_11_transpose_y_0 = const()[name = tensor("matrix_ac_11_transpose_y_0"), val = tensor(false)]; - tensor transpose_106_perm_0 = const()[name = tensor("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_107_perm_0 = const()[name = tensor("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = tensor("transpose_275")]; - tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1084_cast_fp16)[name = tensor("transpose_276")]; - tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = tensor("matrix_ac_11_cast_fp16")]; - tensor matrix_bd_23_begin_0 = const()[name = tensor("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_23_end_0 = const()[name = tensor("matrix_bd_23_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_23_end_mask_0 = const()[name = tensor("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = tensor("matrix_bd_23_cast_fp16")]; - tensor var_1110_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = tensor("op_1110_cast_fp16")]; - tensor _inversed_scores_21_y_0_to_fp16 = const()[name = tensor("_inversed_scores_21_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_21_cast_fp16 = mul(x = var_1110_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = tensor("_inversed_scores_21_cast_fp16")]; - tensor scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_3)[name = tensor("scores_23_cast_fp16")]; - tensor var_1116_cast_fp16 = softmax(axis = var_30, x = scores_23_cast_fp16)[name = tensor("op_1116_cast_fp16")]; - tensor input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1116_cast_fp16, cond = mask_3)[name = tensor("input_293_cast_fp16")]; - tensor x_123_transpose_x_0 = const()[name = tensor("x_123_transpose_x_0"), val = tensor(false)]; - tensor x_123_transpose_y_0 = const()[name = tensor("x_123_transpose_y_0"), val = tensor(false)]; - tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = tensor("transpose_274")]; - tensor x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_13_cast_fp16)[name = tensor("x_123_cast_fp16")]; - tensor var_1120_perm_0 = const()[name = tensor("op_1120_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1121 = const()[name = tensor("op_1121"), val = tensor([1, -1, 1024])]; - tensor var_1120_cast_fp16 = transpose(perm = var_1120_perm_0, x = x_123_cast_fp16)[name = tensor("transpose_273")]; - tensor input_295_cast_fp16 = reshape(shape = var_1121, x = var_1120_cast_fp16)[name = tensor("input_295_cast_fp16")]; - tensor module_layers_5_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104286016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105072512))), name = tensor("module_layers_5_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_52_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = tensor("linear_52_cast_fp16")]; - tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = tensor("input_299_cast_fp16")]; - tensor x_127_axes_0 = const()[name = tensor("x_127_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105072704)))]; - tensor module_layers_5_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105074816)))]; - tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = tensor("x_127_cast_fp16")]; - tensor input_301_perm_0 = const()[name = tensor("input_301_perm_0"), val = tensor([0, 2, 1])]; - tensor input_303_pad_type_0 = const()[name = tensor("input_303_pad_type_0"), val = tensor("valid")]; - tensor input_303_strides_0 = const()[name = tensor("input_303_strides_0"), val = tensor([1])]; - tensor input_303_pad_0 = const()[name = tensor("input_303_pad_0"), val = tensor([0, 0])]; - tensor input_303_dilations_0 = const()[name = tensor("input_303_dilations_0"), val = tensor([1])]; - tensor input_303_groups_0 = const()[name = tensor("input_303_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105076928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106649856))), name = tensor("module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = tensor("transpose_272")]; - tensor input_303_cast_fp16 = conv(dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_301_cast_fp16)[name = tensor("input_303_cast_fp16")]; - tensor x_129_split_num_splits_0 = const()[name = tensor("x_129_split_num_splits_0"), val = tensor(2)]; - tensor x_129_split_axis_0 = const()[name = tensor("x_129_split_axis_0"), val = tensor(1)]; - tensor x_129_split_cast_fp16_0, tensor x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = tensor("x_129_split_cast_fp16")]; - tensor x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = tensor("x_129_split_1_sigmoid_cast_fp16")]; - tensor x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = tensor("x_129_cast_fp16")]; - tensor input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_328)[name = tensor("input_305_cast_fp16")]; - tensor input_307_pad_0 = const()[name = tensor("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_307_mode_0 = const()[name = tensor("input_307_mode_0"), val = tensor("constant")]; - tensor const_67_to_fp16 = const()[name = tensor("const_67_to_fp16"), val = tensor(0x0p+0)]; - tensor input_307_cast_fp16 = pad(constant_val = const_67_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = tensor("input_307_cast_fp16")]; - tensor input_309_pad_type_0 = const()[name = tensor("input_309_pad_type_0"), val = tensor("valid")]; - tensor input_309_groups_0 = const()[name = tensor("input_309_groups_0"), val = tensor(1024)]; - tensor input_309_strides_0 = const()[name = tensor("input_309_strides_0"), val = tensor([1])]; - tensor input_309_pad_0 = const()[name = tensor("input_309_pad_0"), val = tensor([0, 0])]; - tensor input_309_dilations_0 = const()[name = tensor("input_309_dilations_0"), val = tensor([1])]; - tensor const_258_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106650048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106657024))), name = tensor("const_258_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_259_to_fp16 = const()[name = tensor("const_259_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106657216)))]; - tensor input_311_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_258_to_fp16_palettized, x = input_307_cast_fp16)[name = tensor("input_311_cast_fp16")]; - tensor input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = tensor("input_313_cast_fp16")]; - tensor x_131_pad_type_0 = const()[name = tensor("x_131_pad_type_0"), val = tensor("valid")]; - tensor x_131_strides_0 = const()[name = tensor("x_131_strides_0"), val = tensor([1])]; - tensor x_131_pad_0 = const()[name = tensor("x_131_pad_0"), val = tensor([0, 0])]; - tensor x_131_dilations_0 = const()[name = tensor("x_131_dilations_0"), val = tensor([1])]; - tensor x_131_groups_0 = const()[name = tensor("x_131_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106659328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107445824))), name = tensor("module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_131_cast_fp16 = conv(dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_313_cast_fp16)[name = tensor("x_131_cast_fp16")]; - tensor input_315_perm_0 = const()[name = tensor("input_315_perm_0"), val = tensor([0, 2, 1])]; - tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = tensor("transpose_271")]; - tensor input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = tensor("input_317_cast_fp16")]; - tensor input_319_axes_0 = const()[name = tensor("input_319_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107446016)))]; - tensor module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107448128)))]; - tensor input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor("input_319_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107450240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110596032))), name = tensor("module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_53_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized, x = input_319_cast_fp16)[name = tensor("linear_53_cast_fp16")]; - tensor input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = tensor("input_323_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110596224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113742016))), name = tensor("module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_54_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = tensor("linear_54_cast_fp16")]; - tensor var_1181_to_fp16 = const()[name = tensor("op_1181_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1182_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1181_to_fp16)[name = tensor("op_1182_cast_fp16")]; - tensor input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1182_cast_fp16)[name = tensor("input_329_cast_fp16")]; - tensor input_331_axes_0 = const()[name = tensor("input_331_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113742208)))]; - tensor module_layers_5_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113744320)))]; - tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("input_331_cast_fp16")]; - tensor input_333_axes_0 = const()[name = tensor("input_333_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113746432)))]; - tensor module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113748544)))]; - tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("input_333_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113750656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(116896448))), name = tensor("module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_55_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized, x = input_333_cast_fp16)[name = tensor("linear_55_cast_fp16")]; - tensor input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = tensor("input_337_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(116896640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120042432))), name = tensor("module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_56_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized, x = input_337_cast_fp16)[name = tensor("linear_56_cast_fp16")]; - tensor var_1210_to_fp16 = const()[name = tensor("op_1210_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1211_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1210_to_fp16)[name = tensor("op_1211_cast_fp16")]; - tensor input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1211_cast_fp16)[name = tensor("input_343_cast_fp16")]; - tensor query_13_axes_0 = const()[name = tensor("query_13_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120042624)))]; - tensor module_layers_6_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120044736)))]; - tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = tensor("query_13_cast_fp16")]; - tensor module_layers_6_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120046848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120833344))), name = tensor("module_layers_6_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_57_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = tensor("linear_57_cast_fp16")]; - tensor var_1227 = const()[name = tensor("op_1227"), val = tensor([1, -1, 8, 128])]; - tensor q_37_cast_fp16 = reshape(shape = var_1227, x = linear_57_cast_fp16)[name = tensor("q_37_cast_fp16")]; - tensor module_layers_6_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120833536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121620032))), name = tensor("module_layers_6_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_58_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = tensor("linear_58_cast_fp16")]; - tensor var_1231 = const()[name = tensor("op_1231"), val = tensor([1, -1, 8, 128])]; - tensor k_25_cast_fp16 = reshape(shape = var_1231, x = linear_58_cast_fp16)[name = tensor("k_25_cast_fp16")]; - tensor module_layers_6_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121620224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122406720))), name = tensor("module_layers_6_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_59_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = tensor("linear_59_cast_fp16")]; - tensor var_1235 = const()[name = tensor("op_1235"), val = tensor([1, -1, 8, 128])]; - tensor v_13_cast_fp16 = reshape(shape = var_1235, x = linear_59_cast_fp16)[name = tensor("v_13_cast_fp16")]; - tensor value_15_perm_0 = const()[name = tensor("value_15_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122406912)))]; - tensor var_1247_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1247_cast_fp16")]; - tensor module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122409024)))]; - tensor var_1249_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1249_cast_fp16")]; - tensor q_with_bias_v_13_perm_0 = const()[name = tensor("q_with_bias_v_13_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_139_transpose_x_0 = const()[name = tensor("x_139_transpose_x_0"), val = tensor(false)]; - tensor x_139_transpose_y_0 = const()[name = tensor("x_139_transpose_y_0"), val = tensor(false)]; - tensor op_1251_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122411136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122699200))), name = tensor("op_1251_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1249_cast_fp16)[name = tensor("transpose_270")]; - tensor x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1251_to_fp16_palettized)[name = tensor("x_139_cast_fp16")]; - tensor x_141_pad_0 = const()[name = tensor("x_141_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_141_mode_0 = const()[name = tensor("x_141_mode_0"), val = tensor("constant")]; - tensor const_74_to_fp16 = const()[name = tensor("const_74_to_fp16"), val = tensor(0x0p+0)]; - tensor x_141_cast_fp16 = pad(constant_val = const_74_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = tensor("x_141_cast_fp16")]; - tensor var_1259 = const()[name = tensor("op_1259"), val = tensor([1, 8, -1, 188])]; - tensor x_143_cast_fp16 = reshape(shape = var_1259, x = x_141_cast_fp16)[name = tensor("x_143_cast_fp16")]; - tensor var_1263_begin_0 = const()[name = tensor("op_1263_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1263_end_0 = const()[name = tensor("op_1263_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1263_end_mask_0 = const()[name = tensor("op_1263_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1263_cast_fp16 = slice_by_index(begin = var_1263_begin_0, end = var_1263_end_0, end_mask = var_1263_end_mask_0, x = x_143_cast_fp16)[name = tensor("op_1263_cast_fp16")]; - tensor var_1264 = const()[name = tensor("op_1264"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1264, x = var_1263_cast_fp16)[name = tensor("matrix_bd_25_cast_fp16")]; - tensor matrix_ac_13_transpose_x_0 = const()[name = tensor("matrix_ac_13_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_13_transpose_y_0 = const()[name = tensor("matrix_ac_13_transpose_y_0"), val = tensor(false)]; - tensor transpose_108_perm_0 = const()[name = tensor("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_109_perm_0 = const()[name = tensor("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = tensor("transpose_268")]; - tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1247_cast_fp16)[name = tensor("transpose_269")]; - tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = tensor("matrix_ac_13_cast_fp16")]; - tensor matrix_bd_27_begin_0 = const()[name = tensor("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_27_end_0 = const()[name = tensor("matrix_bd_27_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_27_end_mask_0 = const()[name = tensor("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = tensor("matrix_bd_27_cast_fp16")]; - tensor var_1273_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = tensor("op_1273_cast_fp16")]; - tensor _inversed_scores_25_y_0_to_fp16 = const()[name = tensor("_inversed_scores_25_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_25_cast_fp16 = mul(x = var_1273_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = tensor("_inversed_scores_25_cast_fp16")]; - tensor scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_3)[name = tensor("scores_27_cast_fp16")]; - tensor var_1279_cast_fp16 = softmax(axis = var_30, x = scores_27_cast_fp16)[name = tensor("op_1279_cast_fp16")]; - tensor input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1279_cast_fp16, cond = mask_3)[name = tensor("input_345_cast_fp16")]; - tensor x_145_transpose_x_0 = const()[name = tensor("x_145_transpose_x_0"), val = tensor(false)]; - tensor x_145_transpose_y_0 = const()[name = tensor("x_145_transpose_y_0"), val = tensor(false)]; - tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = tensor("transpose_267")]; - tensor x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_15_cast_fp16)[name = tensor("x_145_cast_fp16")]; - tensor var_1283_perm_0 = const()[name = tensor("op_1283_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([1, -1, 1024])]; - tensor var_1283_cast_fp16 = transpose(perm = var_1283_perm_0, x = x_145_cast_fp16)[name = tensor("transpose_266")]; - tensor input_347_cast_fp16 = reshape(shape = var_1284, x = var_1283_cast_fp16)[name = tensor("input_347_cast_fp16")]; - tensor module_layers_6_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122699392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123485888))), name = tensor("module_layers_6_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_61_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16_palettized, x = input_347_cast_fp16)[name = tensor("linear_61_cast_fp16")]; - tensor input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = tensor("input_351_cast_fp16")]; - tensor x_149_axes_0 = const()[name = tensor("x_149_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123486080)))]; - tensor module_layers_6_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123488192)))]; - tensor x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = tensor("x_149_cast_fp16")]; - tensor input_353_perm_0 = const()[name = tensor("input_353_perm_0"), val = tensor([0, 2, 1])]; - tensor input_355_pad_type_0 = const()[name = tensor("input_355_pad_type_0"), val = tensor("valid")]; - tensor input_355_strides_0 = const()[name = tensor("input_355_strides_0"), val = tensor([1])]; - tensor input_355_pad_0 = const()[name = tensor("input_355_pad_0"), val = tensor([0, 0])]; - tensor input_355_dilations_0 = const()[name = tensor("input_355_dilations_0"), val = tensor([1])]; - tensor input_355_groups_0 = const()[name = tensor("input_355_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123490304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125063232))), name = tensor("module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = tensor("transpose_265")]; - tensor input_355_cast_fp16 = conv(dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = tensor("input_355_cast_fp16")]; - tensor x_151_split_num_splits_0 = const()[name = tensor("x_151_split_num_splits_0"), val = tensor(2)]; - tensor x_151_split_axis_0 = const()[name = tensor("x_151_split_axis_0"), val = tensor(1)]; - tensor x_151_split_cast_fp16_0, tensor x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = tensor("x_151_split_cast_fp16")]; - tensor x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = tensor("x_151_split_1_sigmoid_cast_fp16")]; - tensor x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = tensor("x_151_cast_fp16")]; - tensor input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_328)[name = tensor("input_357_cast_fp16")]; - tensor input_359_pad_0 = const()[name = tensor("input_359_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_359_mode_0 = const()[name = tensor("input_359_mode_0"), val = tensor("constant")]; - tensor const_77_to_fp16 = const()[name = tensor("const_77_to_fp16"), val = tensor(0x0p+0)]; - tensor input_359_cast_fp16 = pad(constant_val = const_77_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = tensor("input_359_cast_fp16")]; - tensor input_361_pad_type_0 = const()[name = tensor("input_361_pad_type_0"), val = tensor("valid")]; - tensor input_361_groups_0 = const()[name = tensor("input_361_groups_0"), val = tensor(1024)]; - tensor input_361_strides_0 = const()[name = tensor("input_361_strides_0"), val = tensor([1])]; - tensor input_361_pad_0 = const()[name = tensor("input_361_pad_0"), val = tensor([0, 0])]; - tensor input_361_dilations_0 = const()[name = tensor("input_361_dilations_0"), val = tensor([1])]; - tensor const_260_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125063424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125070400))), name = tensor("const_260_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_261_to_fp16 = const()[name = tensor("const_261_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125070592)))]; - tensor input_363_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_260_to_fp16_palettized, x = input_359_cast_fp16)[name = tensor("input_363_cast_fp16")]; - tensor input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; - tensor x_153_pad_type_0 = const()[name = tensor("x_153_pad_type_0"), val = tensor("valid")]; - tensor x_153_strides_0 = const()[name = tensor("x_153_strides_0"), val = tensor([1])]; - tensor x_153_pad_0 = const()[name = tensor("x_153_pad_0"), val = tensor([0, 0])]; - tensor x_153_dilations_0 = const()[name = tensor("x_153_dilations_0"), val = tensor([1])]; - tensor x_153_groups_0 = const()[name = tensor("x_153_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125072704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125859200))), name = tensor("module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_153_cast_fp16 = conv(dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_365_cast_fp16)[name = tensor("x_153_cast_fp16")]; - tensor input_367_perm_0 = const()[name = tensor("input_367_perm_0"), val = tensor([0, 2, 1])]; - tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = tensor("transpose_264")]; - tensor input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = tensor("input_369_cast_fp16")]; - tensor input_371_axes_0 = const()[name = tensor("input_371_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125859392)))]; - tensor module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125861504)))]; - tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor("input_371_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125863616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129009408))), name = tensor("module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_62_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized, x = input_371_cast_fp16)[name = tensor("linear_62_cast_fp16")]; - tensor input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = tensor("input_375_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129009600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132155392))), name = tensor("module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_63_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized, x = input_375_cast_fp16)[name = tensor("linear_63_cast_fp16")]; - tensor var_1344_to_fp16 = const()[name = tensor("op_1344_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1345_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1344_to_fp16)[name = tensor("op_1345_cast_fp16")]; - tensor input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1345_cast_fp16)[name = tensor("input_381_cast_fp16")]; - tensor input_383_axes_0 = const()[name = tensor("input_383_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132155584)))]; - tensor module_layers_6_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132157696)))]; - tensor input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = tensor("input_383_cast_fp16")]; - tensor input_385_axes_0 = const()[name = tensor("input_385_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132159808)))]; - tensor module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132161920)))]; - tensor input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = tensor("input_385_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132164032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135309824))), name = tensor("module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_64_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized, x = input_385_cast_fp16)[name = tensor("linear_64_cast_fp16")]; - tensor input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = tensor("input_389_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135310016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138455808))), name = tensor("module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_65_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = tensor("linear_65_cast_fp16")]; - tensor var_1373_to_fp16 = const()[name = tensor("op_1373_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1374_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1373_to_fp16)[name = tensor("op_1374_cast_fp16")]; - tensor input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1374_cast_fp16)[name = tensor("input_395_cast_fp16")]; - tensor query_15_axes_0 = const()[name = tensor("query_15_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138456000)))]; - tensor module_layers_7_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138458112)))]; - tensor query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = tensor("query_15_cast_fp16")]; - tensor module_layers_7_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138460224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139246720))), name = tensor("module_layers_7_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_66_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = tensor("linear_66_cast_fp16")]; - tensor var_1390 = const()[name = tensor("op_1390"), val = tensor([1, -1, 8, 128])]; - tensor q_43_cast_fp16 = reshape(shape = var_1390, x = linear_66_cast_fp16)[name = tensor("q_43_cast_fp16")]; - tensor module_layers_7_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139246912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140033408))), name = tensor("module_layers_7_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_67_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = tensor("linear_67_cast_fp16")]; - tensor var_1394 = const()[name = tensor("op_1394"), val = tensor([1, -1, 8, 128])]; - tensor k_29_cast_fp16 = reshape(shape = var_1394, x = linear_67_cast_fp16)[name = tensor("k_29_cast_fp16")]; - tensor module_layers_7_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140033600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140820096))), name = tensor("module_layers_7_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_68_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = tensor("linear_68_cast_fp16")]; - tensor var_1398 = const()[name = tensor("op_1398"), val = tensor([1, -1, 8, 128])]; - tensor v_15_cast_fp16 = reshape(shape = var_1398, x = linear_68_cast_fp16)[name = tensor("v_15_cast_fp16")]; - tensor value_17_perm_0 = const()[name = tensor("value_17_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140820288)))]; - tensor var_1410_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1410_cast_fp16")]; - tensor module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140822400)))]; - tensor var_1412_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1412_cast_fp16")]; - tensor q_with_bias_v_15_perm_0 = const()[name = tensor("q_with_bias_v_15_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_161_transpose_x_0 = const()[name = tensor("x_161_transpose_x_0"), val = tensor(false)]; - tensor x_161_transpose_y_0 = const()[name = tensor("x_161_transpose_y_0"), val = tensor(false)]; - tensor op_1414_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140824512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141112576))), name = tensor("op_1414_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1412_cast_fp16)[name = tensor("transpose_263")]; - tensor x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_1414_to_fp16_palettized)[name = tensor("x_161_cast_fp16")]; - tensor x_163_pad_0 = const()[name = tensor("x_163_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_163_mode_0 = const()[name = tensor("x_163_mode_0"), val = tensor("constant")]; - tensor const_84_to_fp16 = const()[name = tensor("const_84_to_fp16"), val = tensor(0x0p+0)]; - tensor x_163_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = tensor("x_163_cast_fp16")]; - tensor var_1422 = const()[name = tensor("op_1422"), val = tensor([1, 8, -1, 188])]; - tensor x_165_cast_fp16 = reshape(shape = var_1422, x = x_163_cast_fp16)[name = tensor("x_165_cast_fp16")]; - tensor var_1426_begin_0 = const()[name = tensor("op_1426_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1426_end_0 = const()[name = tensor("op_1426_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1426_end_mask_0 = const()[name = tensor("op_1426_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1426_cast_fp16 = slice_by_index(begin = var_1426_begin_0, end = var_1426_end_0, end_mask = var_1426_end_mask_0, x = x_165_cast_fp16)[name = tensor("op_1426_cast_fp16")]; - tensor var_1427 = const()[name = tensor("op_1427"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1427, x = var_1426_cast_fp16)[name = tensor("matrix_bd_29_cast_fp16")]; - tensor matrix_ac_15_transpose_x_0 = const()[name = tensor("matrix_ac_15_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_15_transpose_y_0 = const()[name = tensor("matrix_ac_15_transpose_y_0"), val = tensor(false)]; - tensor transpose_110_perm_0 = const()[name = tensor("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_111_perm_0 = const()[name = tensor("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = tensor("transpose_261")]; - tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1410_cast_fp16)[name = tensor("transpose_262")]; - tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = tensor("matrix_ac_15_cast_fp16")]; - tensor matrix_bd_31_begin_0 = const()[name = tensor("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_31_end_0 = const()[name = tensor("matrix_bd_31_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_31_end_mask_0 = const()[name = tensor("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = tensor("matrix_bd_31_cast_fp16")]; - tensor var_1436_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = tensor("op_1436_cast_fp16")]; - tensor _inversed_scores_29_y_0_to_fp16 = const()[name = tensor("_inversed_scores_29_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_29_cast_fp16 = mul(x = var_1436_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = tensor("_inversed_scores_29_cast_fp16")]; - tensor scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_3)[name = tensor("scores_31_cast_fp16")]; - tensor var_1442_cast_fp16 = softmax(axis = var_30, x = scores_31_cast_fp16)[name = tensor("op_1442_cast_fp16")]; - tensor input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1442_cast_fp16, cond = mask_3)[name = tensor("input_397_cast_fp16")]; - tensor x_167_transpose_x_0 = const()[name = tensor("x_167_transpose_x_0"), val = tensor(false)]; - tensor x_167_transpose_y_0 = const()[name = tensor("x_167_transpose_y_0"), val = tensor(false)]; - tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = tensor("transpose_260")]; - tensor x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_17_cast_fp16)[name = tensor("x_167_cast_fp16")]; - tensor var_1446_perm_0 = const()[name = tensor("op_1446_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1447 = const()[name = tensor("op_1447"), val = tensor([1, -1, 1024])]; - tensor var_1446_cast_fp16 = transpose(perm = var_1446_perm_0, x = x_167_cast_fp16)[name = tensor("transpose_259")]; - tensor input_399_cast_fp16 = reshape(shape = var_1447, x = var_1446_cast_fp16)[name = tensor("input_399_cast_fp16")]; - tensor module_layers_7_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141112768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141899264))), name = tensor("module_layers_7_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_70_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16_palettized, x = input_399_cast_fp16)[name = tensor("linear_70_cast_fp16")]; - tensor input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = tensor("input_403_cast_fp16")]; - tensor x_171_axes_0 = const()[name = tensor("x_171_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141899456)))]; - tensor module_layers_7_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141901568)))]; - tensor x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = tensor("x_171_cast_fp16")]; - tensor input_405_perm_0 = const()[name = tensor("input_405_perm_0"), val = tensor([0, 2, 1])]; - tensor input_407_pad_type_0 = const()[name = tensor("input_407_pad_type_0"), val = tensor("valid")]; - tensor input_407_strides_0 = const()[name = tensor("input_407_strides_0"), val = tensor([1])]; - tensor input_407_pad_0 = const()[name = tensor("input_407_pad_0"), val = tensor([0, 0])]; - tensor input_407_dilations_0 = const()[name = tensor("input_407_dilations_0"), val = tensor([1])]; - tensor input_407_groups_0 = const()[name = tensor("input_407_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141903680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143476608))), name = tensor("module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = tensor("transpose_258")]; - tensor input_407_cast_fp16 = conv(dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = tensor("input_407_cast_fp16")]; - tensor x_173_split_num_splits_0 = const()[name = tensor("x_173_split_num_splits_0"), val = tensor(2)]; - tensor x_173_split_axis_0 = const()[name = tensor("x_173_split_axis_0"), val = tensor(1)]; - tensor x_173_split_cast_fp16_0, tensor x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = tensor("x_173_split_cast_fp16")]; - tensor x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = tensor("x_173_split_1_sigmoid_cast_fp16")]; - tensor x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = tensor("x_173_cast_fp16")]; - tensor input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_328)[name = tensor("input_409_cast_fp16")]; - tensor input_411_pad_0 = const()[name = tensor("input_411_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_411_mode_0 = const()[name = tensor("input_411_mode_0"), val = tensor("constant")]; - tensor const_87_to_fp16 = const()[name = tensor("const_87_to_fp16"), val = tensor(0x0p+0)]; - tensor input_411_cast_fp16 = pad(constant_val = const_87_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = tensor("input_411_cast_fp16")]; - tensor input_413_pad_type_0 = const()[name = tensor("input_413_pad_type_0"), val = tensor("valid")]; - tensor input_413_groups_0 = const()[name = tensor("input_413_groups_0"), val = tensor(1024)]; - tensor input_413_strides_0 = const()[name = tensor("input_413_strides_0"), val = tensor([1])]; - tensor input_413_pad_0 = const()[name = tensor("input_413_pad_0"), val = tensor([0, 0])]; - tensor input_413_dilations_0 = const()[name = tensor("input_413_dilations_0"), val = tensor([1])]; - tensor const_262_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143476800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143483776))), name = tensor("const_262_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_263_to_fp16 = const()[name = tensor("const_263_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143483968)))]; - tensor input_415_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_262_to_fp16_palettized, x = input_411_cast_fp16)[name = tensor("input_415_cast_fp16")]; - tensor input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = tensor("input_417_cast_fp16")]; - tensor x_175_pad_type_0 = const()[name = tensor("x_175_pad_type_0"), val = tensor("valid")]; - tensor x_175_strides_0 = const()[name = tensor("x_175_strides_0"), val = tensor([1])]; - tensor x_175_pad_0 = const()[name = tensor("x_175_pad_0"), val = tensor([0, 0])]; - tensor x_175_dilations_0 = const()[name = tensor("x_175_dilations_0"), val = tensor([1])]; - tensor x_175_groups_0 = const()[name = tensor("x_175_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143486080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144272576))), name = tensor("module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_175_cast_fp16 = conv(dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = tensor("x_175_cast_fp16")]; - tensor input_419_perm_0 = const()[name = tensor("input_419_perm_0"), val = tensor([0, 2, 1])]; - tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = tensor("transpose_257")]; - tensor input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = tensor("input_421_cast_fp16")]; - tensor input_423_axes_0 = const()[name = tensor("input_423_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144272768)))]; - tensor module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144274880)))]; - tensor input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = tensor("input_423_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144276992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147422784))), name = tensor("module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_71_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized, x = input_423_cast_fp16)[name = tensor("linear_71_cast_fp16")]; - tensor input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = tensor("input_427_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147422976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150568768))), name = tensor("module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_72_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized, x = input_427_cast_fp16)[name = tensor("linear_72_cast_fp16")]; - tensor var_1507_to_fp16 = const()[name = tensor("op_1507_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1508_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1507_to_fp16)[name = tensor("op_1508_cast_fp16")]; - tensor input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1508_cast_fp16)[name = tensor("input_433_cast_fp16")]; - tensor input_435_axes_0 = const()[name = tensor("input_435_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150568960)))]; - tensor module_layers_7_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150571072)))]; - tensor input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = tensor("input_435_cast_fp16")]; - tensor input_437_axes_0 = const()[name = tensor("input_437_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150573184)))]; - tensor module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150575296)))]; - tensor input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = tensor("input_437_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150577408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153723200))), name = tensor("module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_73_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized, x = input_437_cast_fp16)[name = tensor("linear_73_cast_fp16")]; - tensor input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = tensor("input_441_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153723392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156869184))), name = tensor("module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_74_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized, x = input_441_cast_fp16)[name = tensor("linear_74_cast_fp16")]; - tensor var_1536_to_fp16 = const()[name = tensor("op_1536_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1537_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1536_to_fp16)[name = tensor("op_1537_cast_fp16")]; - tensor input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1537_cast_fp16)[name = tensor("input_447_cast_fp16")]; - tensor query_17_axes_0 = const()[name = tensor("query_17_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156869376)))]; - tensor module_layers_8_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156871488)))]; - tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = tensor("query_17_cast_fp16")]; - tensor module_layers_8_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156873600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157660096))), name = tensor("module_layers_8_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_75_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = tensor("linear_75_cast_fp16")]; - tensor var_1553 = const()[name = tensor("op_1553"), val = tensor([1, -1, 8, 128])]; - tensor q_49_cast_fp16 = reshape(shape = var_1553, x = linear_75_cast_fp16)[name = tensor("q_49_cast_fp16")]; - tensor module_layers_8_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157660288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158446784))), name = tensor("module_layers_8_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_76_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = tensor("linear_76_cast_fp16")]; - tensor var_1557 = const()[name = tensor("op_1557"), val = tensor([1, -1, 8, 128])]; - tensor k_33_cast_fp16 = reshape(shape = var_1557, x = linear_76_cast_fp16)[name = tensor("k_33_cast_fp16")]; - tensor module_layers_8_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158446976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159233472))), name = tensor("module_layers_8_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_77_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = tensor("linear_77_cast_fp16")]; - tensor var_1561 = const()[name = tensor("op_1561"), val = tensor([1, -1, 8, 128])]; - tensor v_17_cast_fp16 = reshape(shape = var_1561, x = linear_77_cast_fp16)[name = tensor("v_17_cast_fp16")]; - tensor value_19_perm_0 = const()[name = tensor("value_19_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159233664)))]; - tensor var_1573_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1573_cast_fp16")]; - tensor module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159235776)))]; - tensor var_1575_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1575_cast_fp16")]; - tensor q_with_bias_v_17_perm_0 = const()[name = tensor("q_with_bias_v_17_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_183_transpose_x_0 = const()[name = tensor("x_183_transpose_x_0"), val = tensor(false)]; - tensor x_183_transpose_y_0 = const()[name = tensor("x_183_transpose_y_0"), val = tensor(false)]; - tensor op_1577_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159237888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159525952))), name = tensor("op_1577_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1575_cast_fp16)[name = tensor("transpose_256")]; - tensor x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_1577_to_fp16_palettized)[name = tensor("x_183_cast_fp16")]; - tensor x_185_pad_0 = const()[name = tensor("x_185_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_185_mode_0 = const()[name = tensor("x_185_mode_0"), val = tensor("constant")]; - tensor const_94_to_fp16 = const()[name = tensor("const_94_to_fp16"), val = tensor(0x0p+0)]; - tensor x_185_cast_fp16 = pad(constant_val = const_94_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = tensor("x_185_cast_fp16")]; - tensor var_1585 = const()[name = tensor("op_1585"), val = tensor([1, 8, -1, 188])]; - tensor x_187_cast_fp16 = reshape(shape = var_1585, x = x_185_cast_fp16)[name = tensor("x_187_cast_fp16")]; - tensor var_1589_begin_0 = const()[name = tensor("op_1589_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1589_end_0 = const()[name = tensor("op_1589_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1589_end_mask_0 = const()[name = tensor("op_1589_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1589_cast_fp16 = slice_by_index(begin = var_1589_begin_0, end = var_1589_end_0, end_mask = var_1589_end_mask_0, x = x_187_cast_fp16)[name = tensor("op_1589_cast_fp16")]; - tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_33_cast_fp16 = reshape(shape = var_1590, x = var_1589_cast_fp16)[name = tensor("matrix_bd_33_cast_fp16")]; - tensor matrix_ac_17_transpose_x_0 = const()[name = tensor("matrix_ac_17_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_17_transpose_y_0 = const()[name = tensor("matrix_ac_17_transpose_y_0"), val = tensor(false)]; - tensor transpose_112_perm_0 = const()[name = tensor("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_113_perm_0 = const()[name = tensor("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = tensor("transpose_254")]; - tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1573_cast_fp16)[name = tensor("transpose_255")]; - tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = tensor("matrix_ac_17_cast_fp16")]; - tensor matrix_bd_35_begin_0 = const()[name = tensor("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_35_end_0 = const()[name = tensor("matrix_bd_35_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_35_end_mask_0 = const()[name = tensor("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = tensor("matrix_bd_35_cast_fp16")]; - tensor var_1599_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = tensor("op_1599_cast_fp16")]; - tensor _inversed_scores_33_y_0_to_fp16 = const()[name = tensor("_inversed_scores_33_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_33_cast_fp16 = mul(x = var_1599_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = tensor("_inversed_scores_33_cast_fp16")]; - tensor scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_3)[name = tensor("scores_35_cast_fp16")]; - tensor var_1605_cast_fp16 = softmax(axis = var_30, x = scores_35_cast_fp16)[name = tensor("op_1605_cast_fp16")]; - tensor input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1605_cast_fp16, cond = mask_3)[name = tensor("input_449_cast_fp16")]; - tensor x_189_transpose_x_0 = const()[name = tensor("x_189_transpose_x_0"), val = tensor(false)]; - tensor x_189_transpose_y_0 = const()[name = tensor("x_189_transpose_y_0"), val = tensor(false)]; - tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = tensor("transpose_253")]; - tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_19_cast_fp16)[name = tensor("x_189_cast_fp16")]; - tensor var_1609_perm_0 = const()[name = tensor("op_1609_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1610 = const()[name = tensor("op_1610"), val = tensor([1, -1, 1024])]; - tensor var_1609_cast_fp16 = transpose(perm = var_1609_perm_0, x = x_189_cast_fp16)[name = tensor("transpose_252")]; - tensor input_451_cast_fp16 = reshape(shape = var_1610, x = var_1609_cast_fp16)[name = tensor("input_451_cast_fp16")]; - tensor module_layers_8_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159526144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160312640))), name = tensor("module_layers_8_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_79_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16_palettized, x = input_451_cast_fp16)[name = tensor("linear_79_cast_fp16")]; - tensor input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = tensor("input_455_cast_fp16")]; - tensor x_193_axes_0 = const()[name = tensor("x_193_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160312832)))]; - tensor module_layers_8_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160314944)))]; - tensor x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = tensor("x_193_cast_fp16")]; - tensor input_457_perm_0 = const()[name = tensor("input_457_perm_0"), val = tensor([0, 2, 1])]; - tensor input_459_pad_type_0 = const()[name = tensor("input_459_pad_type_0"), val = tensor("valid")]; - tensor input_459_strides_0 = const()[name = tensor("input_459_strides_0"), val = tensor([1])]; - tensor input_459_pad_0 = const()[name = tensor("input_459_pad_0"), val = tensor([0, 0])]; - tensor input_459_dilations_0 = const()[name = tensor("input_459_dilations_0"), val = tensor([1])]; - tensor input_459_groups_0 = const()[name = tensor("input_459_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160317056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161889984))), name = tensor("module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = tensor("transpose_251")]; - tensor input_459_cast_fp16 = conv(dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_457_cast_fp16)[name = tensor("input_459_cast_fp16")]; - tensor x_195_split_num_splits_0 = const()[name = tensor("x_195_split_num_splits_0"), val = tensor(2)]; - tensor x_195_split_axis_0 = const()[name = tensor("x_195_split_axis_0"), val = tensor(1)]; - tensor x_195_split_cast_fp16_0, tensor x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = tensor("x_195_split_cast_fp16")]; - tensor x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = tensor("x_195_split_1_sigmoid_cast_fp16")]; - tensor x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = tensor("x_195_cast_fp16")]; - tensor input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_328)[name = tensor("input_461_cast_fp16")]; - tensor input_463_pad_0 = const()[name = tensor("input_463_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_463_mode_0 = const()[name = tensor("input_463_mode_0"), val = tensor("constant")]; - tensor const_97_to_fp16 = const()[name = tensor("const_97_to_fp16"), val = tensor(0x0p+0)]; - tensor input_463_cast_fp16 = pad(constant_val = const_97_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = tensor("input_463_cast_fp16")]; - tensor input_465_pad_type_0 = const()[name = tensor("input_465_pad_type_0"), val = tensor("valid")]; - tensor input_465_groups_0 = const()[name = tensor("input_465_groups_0"), val = tensor(1024)]; - tensor input_465_strides_0 = const()[name = tensor("input_465_strides_0"), val = tensor([1])]; - tensor input_465_pad_0 = const()[name = tensor("input_465_pad_0"), val = tensor([0, 0])]; - tensor input_465_dilations_0 = const()[name = tensor("input_465_dilations_0"), val = tensor([1])]; - tensor const_264_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161890176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161897152))), name = tensor("const_264_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_265_to_fp16 = const()[name = tensor("const_265_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161897344)))]; - tensor input_467_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_264_to_fp16_palettized, x = input_463_cast_fp16)[name = tensor("input_467_cast_fp16")]; - tensor input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = tensor("input_469_cast_fp16")]; - tensor x_197_pad_type_0 = const()[name = tensor("x_197_pad_type_0"), val = tensor("valid")]; - tensor x_197_strides_0 = const()[name = tensor("x_197_strides_0"), val = tensor([1])]; - tensor x_197_pad_0 = const()[name = tensor("x_197_pad_0"), val = tensor([0, 0])]; - tensor x_197_dilations_0 = const()[name = tensor("x_197_dilations_0"), val = tensor([1])]; - tensor x_197_groups_0 = const()[name = tensor("x_197_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161899456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162685952))), name = tensor("module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_197_cast_fp16 = conv(dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_469_cast_fp16)[name = tensor("x_197_cast_fp16")]; - tensor input_471_perm_0 = const()[name = tensor("input_471_perm_0"), val = tensor([0, 2, 1])]; - tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = tensor("transpose_250")]; - tensor input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = tensor("input_473_cast_fp16")]; - tensor input_475_axes_0 = const()[name = tensor("input_475_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162686144)))]; - tensor module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162688256)))]; - tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = tensor("input_475_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162690368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165836160))), name = tensor("module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_80_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized, x = input_475_cast_fp16)[name = tensor("linear_80_cast_fp16")]; - tensor input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = tensor("input_479_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165836352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168982144))), name = tensor("module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_81_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized, x = input_479_cast_fp16)[name = tensor("linear_81_cast_fp16")]; - tensor var_1670_to_fp16 = const()[name = tensor("op_1670_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1671_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1670_to_fp16)[name = tensor("op_1671_cast_fp16")]; - tensor input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1671_cast_fp16)[name = tensor("input_485_cast_fp16")]; - tensor input_487_axes_0 = const()[name = tensor("input_487_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168982336)))]; - tensor module_layers_8_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168984448)))]; - tensor input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = tensor("input_487_cast_fp16")]; - tensor input_489_axes_0 = const()[name = tensor("input_489_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168986560)))]; - tensor module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168988672)))]; - tensor input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = tensor("input_489_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168990784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172136576))), name = tensor("module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_82_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized, x = input_489_cast_fp16)[name = tensor("linear_82_cast_fp16")]; - tensor input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = tensor("input_493_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172136768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175282560))), name = tensor("module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_83_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized, x = input_493_cast_fp16)[name = tensor("linear_83_cast_fp16")]; - tensor var_1699_to_fp16 = const()[name = tensor("op_1699_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1700_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1699_to_fp16)[name = tensor("op_1700_cast_fp16")]; - tensor input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1700_cast_fp16)[name = tensor("input_499_cast_fp16")]; - tensor query_19_axes_0 = const()[name = tensor("query_19_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175282752)))]; - tensor module_layers_9_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175284864)))]; - tensor query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = tensor("query_19_cast_fp16")]; - tensor module_layers_9_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175286976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176073472))), name = tensor("module_layers_9_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_84_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = tensor("linear_84_cast_fp16")]; - tensor var_1716 = const()[name = tensor("op_1716"), val = tensor([1, -1, 8, 128])]; - tensor q_55_cast_fp16 = reshape(shape = var_1716, x = linear_84_cast_fp16)[name = tensor("q_55_cast_fp16")]; - tensor module_layers_9_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176073664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176860160))), name = tensor("module_layers_9_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_85_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = tensor("linear_85_cast_fp16")]; - tensor var_1720 = const()[name = tensor("op_1720"), val = tensor([1, -1, 8, 128])]; - tensor k_37_cast_fp16 = reshape(shape = var_1720, x = linear_85_cast_fp16)[name = tensor("k_37_cast_fp16")]; - tensor module_layers_9_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176860352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177646848))), name = tensor("module_layers_9_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_86_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = tensor("linear_86_cast_fp16")]; - tensor var_1724 = const()[name = tensor("op_1724"), val = tensor([1, -1, 8, 128])]; - tensor v_19_cast_fp16 = reshape(shape = var_1724, x = linear_86_cast_fp16)[name = tensor("v_19_cast_fp16")]; - tensor value_21_perm_0 = const()[name = tensor("value_21_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177647040)))]; - tensor var_1736_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1736_cast_fp16")]; - tensor module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177649152)))]; - tensor var_1738_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1738_cast_fp16")]; - tensor q_with_bias_v_19_perm_0 = const()[name = tensor("q_with_bias_v_19_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_205_transpose_x_0 = const()[name = tensor("x_205_transpose_x_0"), val = tensor(false)]; - tensor x_205_transpose_y_0 = const()[name = tensor("x_205_transpose_y_0"), val = tensor(false)]; - tensor op_1740_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177651264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177939328))), name = tensor("op_1740_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1738_cast_fp16)[name = tensor("transpose_249")]; - tensor x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_1740_to_fp16_palettized)[name = tensor("x_205_cast_fp16")]; - tensor x_207_pad_0 = const()[name = tensor("x_207_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_207_mode_0 = const()[name = tensor("x_207_mode_0"), val = tensor("constant")]; - tensor const_104_to_fp16 = const()[name = tensor("const_104_to_fp16"), val = tensor(0x0p+0)]; - tensor x_207_cast_fp16 = pad(constant_val = const_104_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = tensor("x_207_cast_fp16")]; - tensor var_1748 = const()[name = tensor("op_1748"), val = tensor([1, 8, -1, 188])]; - tensor x_209_cast_fp16 = reshape(shape = var_1748, x = x_207_cast_fp16)[name = tensor("x_209_cast_fp16")]; - tensor var_1752_begin_0 = const()[name = tensor("op_1752_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1752_end_0 = const()[name = tensor("op_1752_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1752_end_mask_0 = const()[name = tensor("op_1752_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1752_cast_fp16 = slice_by_index(begin = var_1752_begin_0, end = var_1752_end_0, end_mask = var_1752_end_mask_0, x = x_209_cast_fp16)[name = tensor("op_1752_cast_fp16")]; - tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1753, x = var_1752_cast_fp16)[name = tensor("matrix_bd_37_cast_fp16")]; - tensor matrix_ac_19_transpose_x_0 = const()[name = tensor("matrix_ac_19_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_19_transpose_y_0 = const()[name = tensor("matrix_ac_19_transpose_y_0"), val = tensor(false)]; - tensor transpose_114_perm_0 = const()[name = tensor("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_115_perm_0 = const()[name = tensor("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = tensor("transpose_247")]; - tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = var_1736_cast_fp16)[name = tensor("transpose_248")]; - tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = tensor("matrix_ac_19_cast_fp16")]; - tensor matrix_bd_39_begin_0 = const()[name = tensor("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_39_end_0 = const()[name = tensor("matrix_bd_39_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_39_end_mask_0 = const()[name = tensor("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = tensor("matrix_bd_39_cast_fp16")]; - tensor var_1762_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = tensor("op_1762_cast_fp16")]; - tensor _inversed_scores_37_y_0_to_fp16 = const()[name = tensor("_inversed_scores_37_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_37_cast_fp16 = mul(x = var_1762_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = tensor("_inversed_scores_37_cast_fp16")]; - tensor scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_3)[name = tensor("scores_39_cast_fp16")]; - tensor var_1768_cast_fp16 = softmax(axis = var_30, x = scores_39_cast_fp16)[name = tensor("op_1768_cast_fp16")]; - tensor input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1768_cast_fp16, cond = mask_3)[name = tensor("input_501_cast_fp16")]; - tensor x_211_transpose_x_0 = const()[name = tensor("x_211_transpose_x_0"), val = tensor(false)]; - tensor x_211_transpose_y_0 = const()[name = tensor("x_211_transpose_y_0"), val = tensor(false)]; - tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = tensor("transpose_246")]; - tensor x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_21_cast_fp16)[name = tensor("x_211_cast_fp16")]; - tensor var_1772_perm_0 = const()[name = tensor("op_1772_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1773 = const()[name = tensor("op_1773"), val = tensor([1, -1, 1024])]; - tensor var_1772_cast_fp16 = transpose(perm = var_1772_perm_0, x = x_211_cast_fp16)[name = tensor("transpose_245")]; - tensor input_503_cast_fp16 = reshape(shape = var_1773, x = var_1772_cast_fp16)[name = tensor("input_503_cast_fp16")]; - tensor module_layers_9_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177939520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178726016))), name = tensor("module_layers_9_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_88_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16_palettized, x = input_503_cast_fp16)[name = tensor("linear_88_cast_fp16")]; - tensor input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = tensor("input_507_cast_fp16")]; - tensor x_215_axes_0 = const()[name = tensor("x_215_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178726208)))]; - tensor module_layers_9_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178728320)))]; - tensor x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = tensor("x_215_cast_fp16")]; - tensor input_509_perm_0 = const()[name = tensor("input_509_perm_0"), val = tensor([0, 2, 1])]; - tensor input_511_pad_type_0 = const()[name = tensor("input_511_pad_type_0"), val = tensor("valid")]; - tensor input_511_strides_0 = const()[name = tensor("input_511_strides_0"), val = tensor([1])]; - tensor input_511_pad_0 = const()[name = tensor("input_511_pad_0"), val = tensor([0, 0])]; - tensor input_511_dilations_0 = const()[name = tensor("input_511_dilations_0"), val = tensor([1])]; - tensor input_511_groups_0 = const()[name = tensor("input_511_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178730432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180303360))), name = tensor("module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = tensor("transpose_244")]; - tensor input_511_cast_fp16 = conv(dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_509_cast_fp16)[name = tensor("input_511_cast_fp16")]; - tensor x_217_split_num_splits_0 = const()[name = tensor("x_217_split_num_splits_0"), val = tensor(2)]; - tensor x_217_split_axis_0 = const()[name = tensor("x_217_split_axis_0"), val = tensor(1)]; - tensor x_217_split_cast_fp16_0, tensor x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = tensor("x_217_split_cast_fp16")]; - tensor x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = tensor("x_217_split_1_sigmoid_cast_fp16")]; - tensor x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = tensor("x_217_cast_fp16")]; - tensor input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_328)[name = tensor("input_513_cast_fp16")]; - tensor input_515_pad_0 = const()[name = tensor("input_515_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_515_mode_0 = const()[name = tensor("input_515_mode_0"), val = tensor("constant")]; - tensor const_107_to_fp16 = const()[name = tensor("const_107_to_fp16"), val = tensor(0x0p+0)]; - tensor input_515_cast_fp16 = pad(constant_val = const_107_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = tensor("input_515_cast_fp16")]; - tensor input_517_pad_type_0 = const()[name = tensor("input_517_pad_type_0"), val = tensor("valid")]; - tensor input_517_groups_0 = const()[name = tensor("input_517_groups_0"), val = tensor(1024)]; - tensor input_517_strides_0 = const()[name = tensor("input_517_strides_0"), val = tensor([1])]; - tensor input_517_pad_0 = const()[name = tensor("input_517_pad_0"), val = tensor([0, 0])]; - tensor input_517_dilations_0 = const()[name = tensor("input_517_dilations_0"), val = tensor([1])]; - tensor const_266_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180303552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180310528))), name = tensor("const_266_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_267_to_fp16 = const()[name = tensor("const_267_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180310720)))]; - tensor input_519_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_266_to_fp16_palettized, x = input_515_cast_fp16)[name = tensor("input_519_cast_fp16")]; - tensor input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = tensor("input_521_cast_fp16")]; - tensor x_219_pad_type_0 = const()[name = tensor("x_219_pad_type_0"), val = tensor("valid")]; - tensor x_219_strides_0 = const()[name = tensor("x_219_strides_0"), val = tensor([1])]; - tensor x_219_pad_0 = const()[name = tensor("x_219_pad_0"), val = tensor([0, 0])]; - tensor x_219_dilations_0 = const()[name = tensor("x_219_dilations_0"), val = tensor([1])]; - tensor x_219_groups_0 = const()[name = tensor("x_219_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180312832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181099328))), name = tensor("module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_219_cast_fp16 = conv(dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_521_cast_fp16)[name = tensor("x_219_cast_fp16")]; - tensor input_523_perm_0 = const()[name = tensor("input_523_perm_0"), val = tensor([0, 2, 1])]; - tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = tensor("transpose_243")]; - tensor input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = tensor("input_525_cast_fp16")]; - tensor input_527_axes_0 = const()[name = tensor("input_527_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181099520)))]; - tensor module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181101632)))]; - tensor input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = tensor("input_527_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181103744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184249536))), name = tensor("module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_89_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized, x = input_527_cast_fp16)[name = tensor("linear_89_cast_fp16")]; - tensor input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = tensor("input_531_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184249728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187395520))), name = tensor("module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_90_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized, x = input_531_cast_fp16)[name = tensor("linear_90_cast_fp16")]; - tensor var_1833_to_fp16 = const()[name = tensor("op_1833_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1834_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_1833_to_fp16)[name = tensor("op_1834_cast_fp16")]; - tensor input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_1834_cast_fp16)[name = tensor("input_537_cast_fp16")]; - tensor input_539_axes_0 = const()[name = tensor("input_539_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187395712)))]; - tensor module_layers_9_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187397824)))]; - tensor input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = tensor("input_539_cast_fp16")]; - tensor input_541_axes_0 = const()[name = tensor("input_541_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187399936)))]; - tensor module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187402048)))]; - tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = tensor("input_541_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187404160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190549952))), name = tensor("module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_91_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized, x = input_541_cast_fp16)[name = tensor("linear_91_cast_fp16")]; - tensor input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = tensor("input_545_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190550144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193695936))), name = tensor("module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_92_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized, x = input_545_cast_fp16)[name = tensor("linear_92_cast_fp16")]; - tensor var_1862_to_fp16 = const()[name = tensor("op_1862_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1863_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_1862_to_fp16)[name = tensor("op_1863_cast_fp16")]; - tensor input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_1863_cast_fp16)[name = tensor("input_551_cast_fp16")]; - tensor query_21_axes_0 = const()[name = tensor("query_21_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193696128)))]; - tensor module_layers_10_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193698240)))]; - tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = tensor("query_21_cast_fp16")]; - tensor module_layers_10_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193700352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194486848))), name = tensor("module_layers_10_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_93_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = tensor("linear_93_cast_fp16")]; - tensor var_1879 = const()[name = tensor("op_1879"), val = tensor([1, -1, 8, 128])]; - tensor q_61_cast_fp16 = reshape(shape = var_1879, x = linear_93_cast_fp16)[name = tensor("q_61_cast_fp16")]; - tensor module_layers_10_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194487040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195273536))), name = tensor("module_layers_10_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_94_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = tensor("linear_94_cast_fp16")]; - tensor var_1883 = const()[name = tensor("op_1883"), val = tensor([1, -1, 8, 128])]; - tensor k_41_cast_fp16 = reshape(shape = var_1883, x = linear_94_cast_fp16)[name = tensor("k_41_cast_fp16")]; - tensor module_layers_10_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195273728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196060224))), name = tensor("module_layers_10_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_95_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = tensor("linear_95_cast_fp16")]; - tensor var_1887 = const()[name = tensor("op_1887"), val = tensor([1, -1, 8, 128])]; - tensor v_21_cast_fp16 = reshape(shape = var_1887, x = linear_95_cast_fp16)[name = tensor("v_21_cast_fp16")]; - tensor value_23_perm_0 = const()[name = tensor("value_23_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196060416)))]; - tensor var_1899_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1899_cast_fp16")]; - tensor module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196062528)))]; - tensor var_1901_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1901_cast_fp16")]; - tensor q_with_bias_v_21_perm_0 = const()[name = tensor("q_with_bias_v_21_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_227_transpose_x_0 = const()[name = tensor("x_227_transpose_x_0"), val = tensor(false)]; - tensor x_227_transpose_y_0 = const()[name = tensor("x_227_transpose_y_0"), val = tensor(false)]; - tensor op_1903_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196064640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196352704))), name = tensor("op_1903_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_1901_cast_fp16)[name = tensor("transpose_242")]; - tensor x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_1903_to_fp16_palettized)[name = tensor("x_227_cast_fp16")]; - tensor x_229_pad_0 = const()[name = tensor("x_229_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_229_mode_0 = const()[name = tensor("x_229_mode_0"), val = tensor("constant")]; - tensor const_114_to_fp16 = const()[name = tensor("const_114_to_fp16"), val = tensor(0x0p+0)]; - tensor x_229_cast_fp16 = pad(constant_val = const_114_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = tensor("x_229_cast_fp16")]; - tensor var_1911 = const()[name = tensor("op_1911"), val = tensor([1, 8, -1, 188])]; - tensor x_231_cast_fp16 = reshape(shape = var_1911, x = x_229_cast_fp16)[name = tensor("x_231_cast_fp16")]; - tensor var_1915_begin_0 = const()[name = tensor("op_1915_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1915_end_0 = const()[name = tensor("op_1915_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1915_end_mask_0 = const()[name = tensor("op_1915_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1915_cast_fp16 = slice_by_index(begin = var_1915_begin_0, end = var_1915_end_0, end_mask = var_1915_end_mask_0, x = x_231_cast_fp16)[name = tensor("op_1915_cast_fp16")]; - tensor var_1916 = const()[name = tensor("op_1916"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_41_cast_fp16 = reshape(shape = var_1916, x = var_1915_cast_fp16)[name = tensor("matrix_bd_41_cast_fp16")]; - tensor matrix_ac_21_transpose_x_0 = const()[name = tensor("matrix_ac_21_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_21_transpose_y_0 = const()[name = tensor("matrix_ac_21_transpose_y_0"), val = tensor(false)]; - tensor transpose_116_perm_0 = const()[name = tensor("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_117_perm_0 = const()[name = tensor("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = tensor("transpose_240")]; - tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = var_1899_cast_fp16)[name = tensor("transpose_241")]; - tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = tensor("matrix_ac_21_cast_fp16")]; - tensor matrix_bd_43_begin_0 = const()[name = tensor("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_43_end_0 = const()[name = tensor("matrix_bd_43_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_43_end_mask_0 = const()[name = tensor("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = tensor("matrix_bd_43_cast_fp16")]; - tensor var_1925_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = tensor("op_1925_cast_fp16")]; - tensor _inversed_scores_41_y_0_to_fp16 = const()[name = tensor("_inversed_scores_41_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_41_cast_fp16 = mul(x = var_1925_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = tensor("_inversed_scores_41_cast_fp16")]; - tensor scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_3)[name = tensor("scores_43_cast_fp16")]; - tensor var_1931_cast_fp16 = softmax(axis = var_30, x = scores_43_cast_fp16)[name = tensor("op_1931_cast_fp16")]; - tensor input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_1931_cast_fp16, cond = mask_3)[name = tensor("input_553_cast_fp16")]; - tensor x_233_transpose_x_0 = const()[name = tensor("x_233_transpose_x_0"), val = tensor(false)]; - tensor x_233_transpose_y_0 = const()[name = tensor("x_233_transpose_y_0"), val = tensor(false)]; - tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = tensor("transpose_239")]; - tensor x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_23_cast_fp16)[name = tensor("x_233_cast_fp16")]; - tensor var_1935_perm_0 = const()[name = tensor("op_1935_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1936 = const()[name = tensor("op_1936"), val = tensor([1, -1, 1024])]; - tensor var_1935_cast_fp16 = transpose(perm = var_1935_perm_0, x = x_233_cast_fp16)[name = tensor("transpose_238")]; - tensor input_555_cast_fp16 = reshape(shape = var_1936, x = var_1935_cast_fp16)[name = tensor("input_555_cast_fp16")]; - tensor module_layers_10_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196352896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197139392))), name = tensor("module_layers_10_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_97_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16_palettized, x = input_555_cast_fp16)[name = tensor("linear_97_cast_fp16")]; - tensor input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = tensor("input_559_cast_fp16")]; - tensor x_237_axes_0 = const()[name = tensor("x_237_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197139584)))]; - tensor module_layers_10_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197141696)))]; - tensor x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = tensor("x_237_cast_fp16")]; - tensor input_561_perm_0 = const()[name = tensor("input_561_perm_0"), val = tensor([0, 2, 1])]; - tensor input_563_pad_type_0 = const()[name = tensor("input_563_pad_type_0"), val = tensor("valid")]; - tensor input_563_strides_0 = const()[name = tensor("input_563_strides_0"), val = tensor([1])]; - tensor input_563_pad_0 = const()[name = tensor("input_563_pad_0"), val = tensor([0, 0])]; - tensor input_563_dilations_0 = const()[name = tensor("input_563_dilations_0"), val = tensor([1])]; - tensor input_563_groups_0 = const()[name = tensor("input_563_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197143808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198716736))), name = tensor("module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = tensor("transpose_237")]; - tensor input_563_cast_fp16 = conv(dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_561_cast_fp16)[name = tensor("input_563_cast_fp16")]; - tensor x_239_split_num_splits_0 = const()[name = tensor("x_239_split_num_splits_0"), val = tensor(2)]; - tensor x_239_split_axis_0 = const()[name = tensor("x_239_split_axis_0"), val = tensor(1)]; - tensor x_239_split_cast_fp16_0, tensor x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = tensor("x_239_split_cast_fp16")]; - tensor x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = tensor("x_239_split_1_sigmoid_cast_fp16")]; - tensor x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = tensor("x_239_cast_fp16")]; - tensor input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_328)[name = tensor("input_565_cast_fp16")]; - tensor input_567_pad_0 = const()[name = tensor("input_567_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_567_mode_0 = const()[name = tensor("input_567_mode_0"), val = tensor("constant")]; - tensor const_117_to_fp16 = const()[name = tensor("const_117_to_fp16"), val = tensor(0x0p+0)]; - tensor input_567_cast_fp16 = pad(constant_val = const_117_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = tensor("input_567_cast_fp16")]; - tensor input_569_pad_type_0 = const()[name = tensor("input_569_pad_type_0"), val = tensor("valid")]; - tensor input_569_groups_0 = const()[name = tensor("input_569_groups_0"), val = tensor(1024)]; - tensor input_569_strides_0 = const()[name = tensor("input_569_strides_0"), val = tensor([1])]; - tensor input_569_pad_0 = const()[name = tensor("input_569_pad_0"), val = tensor([0, 0])]; - tensor input_569_dilations_0 = const()[name = tensor("input_569_dilations_0"), val = tensor([1])]; - tensor const_268_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198716928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198723904))), name = tensor("const_268_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_269_to_fp16 = const()[name = tensor("const_269_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198724096)))]; - tensor input_571_cast_fp16 = conv(bias = const_269_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_268_to_fp16_palettized, x = input_567_cast_fp16)[name = tensor("input_571_cast_fp16")]; - tensor input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = tensor("input_573_cast_fp16")]; - tensor x_241_pad_type_0 = const()[name = tensor("x_241_pad_type_0"), val = tensor("valid")]; - tensor x_241_strides_0 = const()[name = tensor("x_241_strides_0"), val = tensor([1])]; - tensor x_241_pad_0 = const()[name = tensor("x_241_pad_0"), val = tensor([0, 0])]; - tensor x_241_dilations_0 = const()[name = tensor("x_241_dilations_0"), val = tensor([1])]; - tensor x_241_groups_0 = const()[name = tensor("x_241_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198726208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199512704))), name = tensor("module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_241_cast_fp16 = conv(dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_573_cast_fp16)[name = tensor("x_241_cast_fp16")]; - tensor input_575_perm_0 = const()[name = tensor("input_575_perm_0"), val = tensor([0, 2, 1])]; - tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = tensor("transpose_236")]; - tensor input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = tensor("input_577_cast_fp16")]; - tensor input_579_axes_0 = const()[name = tensor("input_579_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199512896)))]; - tensor module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199515008)))]; - tensor input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = tensor("input_579_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199517120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202662912))), name = tensor("module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_98_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized, x = input_579_cast_fp16)[name = tensor("linear_98_cast_fp16")]; - tensor input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = tensor("input_583_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202663104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205808896))), name = tensor("module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_99_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized, x = input_583_cast_fp16)[name = tensor("linear_99_cast_fp16")]; - tensor var_1996_to_fp16 = const()[name = tensor("op_1996_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1997_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_1996_to_fp16)[name = tensor("op_1997_cast_fp16")]; - tensor input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_1997_cast_fp16)[name = tensor("input_589_cast_fp16")]; - tensor input_591_axes_0 = const()[name = tensor("input_591_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205809088)))]; - tensor module_layers_10_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205811200)))]; - tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = tensor("input_591_cast_fp16")]; - tensor input_593_axes_0 = const()[name = tensor("input_593_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205813312)))]; - tensor module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205815424)))]; - tensor input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = tensor("input_593_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205817536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208963328))), name = tensor("module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_100_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized, x = input_593_cast_fp16)[name = tensor("linear_100_cast_fp16")]; - tensor input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = tensor("input_597_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208963520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212109312))), name = tensor("module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_101_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized, x = input_597_cast_fp16)[name = tensor("linear_101_cast_fp16")]; - tensor var_2025_to_fp16 = const()[name = tensor("op_2025_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2026_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2025_to_fp16)[name = tensor("op_2026_cast_fp16")]; - tensor input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2026_cast_fp16)[name = tensor("input_603_cast_fp16")]; - tensor query_23_axes_0 = const()[name = tensor("query_23_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212109504)))]; - tensor module_layers_11_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212111616)))]; - tensor query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = tensor("query_23_cast_fp16")]; - tensor module_layers_11_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212113728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212900224))), name = tensor("module_layers_11_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_102_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = tensor("linear_102_cast_fp16")]; - tensor var_2042 = const()[name = tensor("op_2042"), val = tensor([1, -1, 8, 128])]; - tensor q_67_cast_fp16 = reshape(shape = var_2042, x = linear_102_cast_fp16)[name = tensor("q_67_cast_fp16")]; - tensor module_layers_11_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212900416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213686912))), name = tensor("module_layers_11_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_103_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = tensor("linear_103_cast_fp16")]; - tensor var_2046 = const()[name = tensor("op_2046"), val = tensor([1, -1, 8, 128])]; - tensor k_45_cast_fp16 = reshape(shape = var_2046, x = linear_103_cast_fp16)[name = tensor("k_45_cast_fp16")]; - tensor module_layers_11_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213687104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214473600))), name = tensor("module_layers_11_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_104_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = tensor("linear_104_cast_fp16")]; - tensor var_2050 = const()[name = tensor("op_2050"), val = tensor([1, -1, 8, 128])]; - tensor v_23_cast_fp16 = reshape(shape = var_2050, x = linear_104_cast_fp16)[name = tensor("v_23_cast_fp16")]; - tensor value_25_perm_0 = const()[name = tensor("value_25_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214473792)))]; - tensor var_2062_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2062_cast_fp16")]; - tensor module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214475904)))]; - tensor var_2064_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2064_cast_fp16")]; - tensor q_with_bias_v_23_perm_0 = const()[name = tensor("q_with_bias_v_23_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_249_transpose_x_0 = const()[name = tensor("x_249_transpose_x_0"), val = tensor(false)]; - tensor x_249_transpose_y_0 = const()[name = tensor("x_249_transpose_y_0"), val = tensor(false)]; - tensor op_2066_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214478016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214766080))), name = tensor("op_2066_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2064_cast_fp16)[name = tensor("transpose_235")]; - tensor x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2066_to_fp16_palettized)[name = tensor("x_249_cast_fp16")]; - tensor x_251_pad_0 = const()[name = tensor("x_251_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_251_mode_0 = const()[name = tensor("x_251_mode_0"), val = tensor("constant")]; - tensor const_124_to_fp16 = const()[name = tensor("const_124_to_fp16"), val = tensor(0x0p+0)]; - tensor x_251_cast_fp16 = pad(constant_val = const_124_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = tensor("x_251_cast_fp16")]; - tensor var_2074 = const()[name = tensor("op_2074"), val = tensor([1, 8, -1, 188])]; - tensor x_253_cast_fp16 = reshape(shape = var_2074, x = x_251_cast_fp16)[name = tensor("x_253_cast_fp16")]; - tensor var_2078_begin_0 = const()[name = tensor("op_2078_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2078_end_0 = const()[name = tensor("op_2078_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2078_end_mask_0 = const()[name = tensor("op_2078_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2078_cast_fp16 = slice_by_index(begin = var_2078_begin_0, end = var_2078_end_0, end_mask = var_2078_end_mask_0, x = x_253_cast_fp16)[name = tensor("op_2078_cast_fp16")]; - tensor var_2079 = const()[name = tensor("op_2079"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2079, x = var_2078_cast_fp16)[name = tensor("matrix_bd_45_cast_fp16")]; - tensor matrix_ac_23_transpose_x_0 = const()[name = tensor("matrix_ac_23_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_23_transpose_y_0 = const()[name = tensor("matrix_ac_23_transpose_y_0"), val = tensor(false)]; - tensor transpose_118_perm_0 = const()[name = tensor("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_119_perm_0 = const()[name = tensor("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = tensor("transpose_233")]; - tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2062_cast_fp16)[name = tensor("transpose_234")]; - tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = tensor("matrix_ac_23_cast_fp16")]; - tensor matrix_bd_47_begin_0 = const()[name = tensor("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_47_end_0 = const()[name = tensor("matrix_bd_47_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_47_end_mask_0 = const()[name = tensor("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = tensor("matrix_bd_47_cast_fp16")]; - tensor var_2088_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = tensor("op_2088_cast_fp16")]; - tensor _inversed_scores_45_y_0_to_fp16 = const()[name = tensor("_inversed_scores_45_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_45_cast_fp16 = mul(x = var_2088_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = tensor("_inversed_scores_45_cast_fp16")]; - tensor scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_3)[name = tensor("scores_47_cast_fp16")]; - tensor var_2094_cast_fp16 = softmax(axis = var_30, x = scores_47_cast_fp16)[name = tensor("op_2094_cast_fp16")]; - tensor input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2094_cast_fp16, cond = mask_3)[name = tensor("input_605_cast_fp16")]; - tensor x_255_transpose_x_0 = const()[name = tensor("x_255_transpose_x_0"), val = tensor(false)]; - tensor x_255_transpose_y_0 = const()[name = tensor("x_255_transpose_y_0"), val = tensor(false)]; - tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = tensor("transpose_232")]; - tensor x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_25_cast_fp16)[name = tensor("x_255_cast_fp16")]; - tensor var_2098_perm_0 = const()[name = tensor("op_2098_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2099 = const()[name = tensor("op_2099"), val = tensor([1, -1, 1024])]; - tensor var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = x_255_cast_fp16)[name = tensor("transpose_231")]; - tensor input_607_cast_fp16 = reshape(shape = var_2099, x = var_2098_cast_fp16)[name = tensor("input_607_cast_fp16")]; - tensor module_layers_11_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214766272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215552768))), name = tensor("module_layers_11_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_106_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16_palettized, x = input_607_cast_fp16)[name = tensor("linear_106_cast_fp16")]; - tensor input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = tensor("input_611_cast_fp16")]; - tensor x_259_axes_0 = const()[name = tensor("x_259_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215552960)))]; - tensor module_layers_11_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215555072)))]; - tensor x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = tensor("x_259_cast_fp16")]; - tensor input_613_perm_0 = const()[name = tensor("input_613_perm_0"), val = tensor([0, 2, 1])]; - tensor input_615_pad_type_0 = const()[name = tensor("input_615_pad_type_0"), val = tensor("valid")]; - tensor input_615_strides_0 = const()[name = tensor("input_615_strides_0"), val = tensor([1])]; - tensor input_615_pad_0 = const()[name = tensor("input_615_pad_0"), val = tensor([0, 0])]; - tensor input_615_dilations_0 = const()[name = tensor("input_615_dilations_0"), val = tensor([1])]; - tensor input_615_groups_0 = const()[name = tensor("input_615_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215557184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217130112))), name = tensor("module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = tensor("transpose_230")]; - tensor input_615_cast_fp16 = conv(dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_613_cast_fp16)[name = tensor("input_615_cast_fp16")]; - tensor x_261_split_num_splits_0 = const()[name = tensor("x_261_split_num_splits_0"), val = tensor(2)]; - tensor x_261_split_axis_0 = const()[name = tensor("x_261_split_axis_0"), val = tensor(1)]; - tensor x_261_split_cast_fp16_0, tensor x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = tensor("x_261_split_cast_fp16")]; - tensor x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = tensor("x_261_split_1_sigmoid_cast_fp16")]; - tensor x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = tensor("x_261_cast_fp16")]; - tensor input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_328)[name = tensor("input_617_cast_fp16")]; - tensor input_619_pad_0 = const()[name = tensor("input_619_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_619_mode_0 = const()[name = tensor("input_619_mode_0"), val = tensor("constant")]; - tensor const_127_to_fp16 = const()[name = tensor("const_127_to_fp16"), val = tensor(0x0p+0)]; - tensor input_619_cast_fp16 = pad(constant_val = const_127_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = tensor("input_619_cast_fp16")]; - tensor input_621_pad_type_0 = const()[name = tensor("input_621_pad_type_0"), val = tensor("valid")]; - tensor input_621_groups_0 = const()[name = tensor("input_621_groups_0"), val = tensor(1024)]; - tensor input_621_strides_0 = const()[name = tensor("input_621_strides_0"), val = tensor([1])]; - tensor input_621_pad_0 = const()[name = tensor("input_621_pad_0"), val = tensor([0, 0])]; - tensor input_621_dilations_0 = const()[name = tensor("input_621_dilations_0"), val = tensor([1])]; - tensor const_270_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217130304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217137280))), name = tensor("const_270_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_271_to_fp16 = const()[name = tensor("const_271_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217137472)))]; - tensor input_623_cast_fp16 = conv(bias = const_271_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_270_to_fp16_palettized, x = input_619_cast_fp16)[name = tensor("input_623_cast_fp16")]; - tensor input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = tensor("input_625_cast_fp16")]; - tensor x_263_pad_type_0 = const()[name = tensor("x_263_pad_type_0"), val = tensor("valid")]; - tensor x_263_strides_0 = const()[name = tensor("x_263_strides_0"), val = tensor([1])]; - tensor x_263_pad_0 = const()[name = tensor("x_263_pad_0"), val = tensor([0, 0])]; - tensor x_263_dilations_0 = const()[name = tensor("x_263_dilations_0"), val = tensor([1])]; - tensor x_263_groups_0 = const()[name = tensor("x_263_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217139584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217926080))), name = tensor("module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_263_cast_fp16 = conv(dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_625_cast_fp16)[name = tensor("x_263_cast_fp16")]; - tensor input_627_perm_0 = const()[name = tensor("input_627_perm_0"), val = tensor([0, 2, 1])]; - tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = tensor("transpose_229")]; - tensor input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = tensor("input_629_cast_fp16")]; - tensor input_631_axes_0 = const()[name = tensor("input_631_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217926272)))]; - tensor module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217928384)))]; - tensor input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = tensor("input_631_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217930496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(221076288))), name = tensor("module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_107_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized, x = input_631_cast_fp16)[name = tensor("linear_107_cast_fp16")]; - tensor input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = tensor("input_635_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(221076480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224222272))), name = tensor("module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_108_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized, x = input_635_cast_fp16)[name = tensor("linear_108_cast_fp16")]; - tensor var_2159_to_fp16 = const()[name = tensor("op_2159_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2160_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2159_to_fp16)[name = tensor("op_2160_cast_fp16")]; - tensor input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2160_cast_fp16)[name = tensor("input_641_cast_fp16")]; - tensor input_643_axes_0 = const()[name = tensor("input_643_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224222464)))]; - tensor module_layers_11_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224224576)))]; - tensor input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = tensor("input_643_cast_fp16")]; - tensor input_645_axes_0 = const()[name = tensor("input_645_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224226688)))]; - tensor module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224228800)))]; - tensor input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = tensor("input_645_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224230912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227376704))), name = tensor("module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_109_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized, x = input_645_cast_fp16)[name = tensor("linear_109_cast_fp16")]; - tensor input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = tensor("input_649_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227376896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230522688))), name = tensor("module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_110_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized, x = input_649_cast_fp16)[name = tensor("linear_110_cast_fp16")]; - tensor var_2188_to_fp16 = const()[name = tensor("op_2188_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2189_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2188_to_fp16)[name = tensor("op_2189_cast_fp16")]; - tensor input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2189_cast_fp16)[name = tensor("input_655_cast_fp16")]; - tensor query_25_axes_0 = const()[name = tensor("query_25_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230522880)))]; - tensor module_layers_12_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230524992)))]; - tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = tensor("query_25_cast_fp16")]; - tensor module_layers_12_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230527104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231313600))), name = tensor("module_layers_12_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_111_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = tensor("linear_111_cast_fp16")]; - tensor var_2205 = const()[name = tensor("op_2205"), val = tensor([1, -1, 8, 128])]; - tensor q_73_cast_fp16 = reshape(shape = var_2205, x = linear_111_cast_fp16)[name = tensor("q_73_cast_fp16")]; - tensor module_layers_12_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231313792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232100288))), name = tensor("module_layers_12_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_112_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = tensor("linear_112_cast_fp16")]; - tensor var_2209 = const()[name = tensor("op_2209"), val = tensor([1, -1, 8, 128])]; - tensor k_49_cast_fp16 = reshape(shape = var_2209, x = linear_112_cast_fp16)[name = tensor("k_49_cast_fp16")]; - tensor module_layers_12_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232100480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232886976))), name = tensor("module_layers_12_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_113_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = tensor("linear_113_cast_fp16")]; - tensor var_2213 = const()[name = tensor("op_2213"), val = tensor([1, -1, 8, 128])]; - tensor v_25_cast_fp16 = reshape(shape = var_2213, x = linear_113_cast_fp16)[name = tensor("v_25_cast_fp16")]; - tensor value_27_perm_0 = const()[name = tensor("value_27_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232887168)))]; - tensor var_2225_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2225_cast_fp16")]; - tensor module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232889280)))]; - tensor var_2227_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2227_cast_fp16")]; - tensor q_with_bias_v_25_perm_0 = const()[name = tensor("q_with_bias_v_25_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_271_transpose_x_0 = const()[name = tensor("x_271_transpose_x_0"), val = tensor(false)]; - tensor x_271_transpose_y_0 = const()[name = tensor("x_271_transpose_y_0"), val = tensor(false)]; - tensor op_2229_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232891392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233179456))), name = tensor("op_2229_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2227_cast_fp16)[name = tensor("transpose_228")]; - tensor x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_2229_to_fp16_palettized)[name = tensor("x_271_cast_fp16")]; - tensor x_273_pad_0 = const()[name = tensor("x_273_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_273_mode_0 = const()[name = tensor("x_273_mode_0"), val = tensor("constant")]; - tensor const_134_to_fp16 = const()[name = tensor("const_134_to_fp16"), val = tensor(0x0p+0)]; - tensor x_273_cast_fp16 = pad(constant_val = const_134_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = tensor("x_273_cast_fp16")]; - tensor var_2237 = const()[name = tensor("op_2237"), val = tensor([1, 8, -1, 188])]; - tensor x_275_cast_fp16 = reshape(shape = var_2237, x = x_273_cast_fp16)[name = tensor("x_275_cast_fp16")]; - tensor var_2241_begin_0 = const()[name = tensor("op_2241_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2241_end_0 = const()[name = tensor("op_2241_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2241_end_mask_0 = const()[name = tensor("op_2241_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2241_cast_fp16 = slice_by_index(begin = var_2241_begin_0, end = var_2241_end_0, end_mask = var_2241_end_mask_0, x = x_275_cast_fp16)[name = tensor("op_2241_cast_fp16")]; - tensor var_2242 = const()[name = tensor("op_2242"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_49_cast_fp16 = reshape(shape = var_2242, x = var_2241_cast_fp16)[name = tensor("matrix_bd_49_cast_fp16")]; - tensor matrix_ac_25_transpose_x_0 = const()[name = tensor("matrix_ac_25_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_25_transpose_y_0 = const()[name = tensor("matrix_ac_25_transpose_y_0"), val = tensor(false)]; - tensor transpose_120_perm_0 = const()[name = tensor("transpose_120_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_121_perm_0 = const()[name = tensor("transpose_121_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = tensor("transpose_226")]; - tensor transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2225_cast_fp16)[name = tensor("transpose_227")]; - tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = tensor("matrix_ac_25_cast_fp16")]; - tensor matrix_bd_51_begin_0 = const()[name = tensor("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_51_end_0 = const()[name = tensor("matrix_bd_51_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_51_end_mask_0 = const()[name = tensor("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = tensor("matrix_bd_51_cast_fp16")]; - tensor var_2251_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = tensor("op_2251_cast_fp16")]; - tensor _inversed_scores_49_y_0_to_fp16 = const()[name = tensor("_inversed_scores_49_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_49_cast_fp16 = mul(x = var_2251_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = tensor("_inversed_scores_49_cast_fp16")]; - tensor scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_3)[name = tensor("scores_51_cast_fp16")]; - tensor var_2257_cast_fp16 = softmax(axis = var_30, x = scores_51_cast_fp16)[name = tensor("op_2257_cast_fp16")]; - tensor input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2257_cast_fp16, cond = mask_3)[name = tensor("input_657_cast_fp16")]; - tensor x_277_transpose_x_0 = const()[name = tensor("x_277_transpose_x_0"), val = tensor(false)]; - tensor x_277_transpose_y_0 = const()[name = tensor("x_277_transpose_y_0"), val = tensor(false)]; - tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = tensor("transpose_225")]; - tensor x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_27_cast_fp16)[name = tensor("x_277_cast_fp16")]; - tensor var_2261_perm_0 = const()[name = tensor("op_2261_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2262 = const()[name = tensor("op_2262"), val = tensor([1, -1, 1024])]; - tensor var_2261_cast_fp16 = transpose(perm = var_2261_perm_0, x = x_277_cast_fp16)[name = tensor("transpose_224")]; - tensor input_659_cast_fp16 = reshape(shape = var_2262, x = var_2261_cast_fp16)[name = tensor("input_659_cast_fp16")]; - tensor module_layers_12_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233179648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233966144))), name = tensor("module_layers_12_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_115_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16_palettized, x = input_659_cast_fp16)[name = tensor("linear_115_cast_fp16")]; - tensor input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = tensor("input_663_cast_fp16")]; - tensor x_281_axes_0 = const()[name = tensor("x_281_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233966336)))]; - tensor module_layers_12_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233968448)))]; - tensor x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = tensor("x_281_cast_fp16")]; - tensor input_665_perm_0 = const()[name = tensor("input_665_perm_0"), val = tensor([0, 2, 1])]; - tensor input_667_pad_type_0 = const()[name = tensor("input_667_pad_type_0"), val = tensor("valid")]; - tensor input_667_strides_0 = const()[name = tensor("input_667_strides_0"), val = tensor([1])]; - tensor input_667_pad_0 = const()[name = tensor("input_667_pad_0"), val = tensor([0, 0])]; - tensor input_667_dilations_0 = const()[name = tensor("input_667_dilations_0"), val = tensor([1])]; - tensor input_667_groups_0 = const()[name = tensor("input_667_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233970560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235543488))), name = tensor("module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = tensor("transpose_223")]; - tensor input_667_cast_fp16 = conv(dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_665_cast_fp16)[name = tensor("input_667_cast_fp16")]; - tensor x_283_split_num_splits_0 = const()[name = tensor("x_283_split_num_splits_0"), val = tensor(2)]; - tensor x_283_split_axis_0 = const()[name = tensor("x_283_split_axis_0"), val = tensor(1)]; - tensor x_283_split_cast_fp16_0, tensor x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = tensor("x_283_split_cast_fp16")]; - tensor x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = tensor("x_283_split_1_sigmoid_cast_fp16")]; - tensor x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = tensor("x_283_cast_fp16")]; - tensor input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_328)[name = tensor("input_669_cast_fp16")]; - tensor input_671_pad_0 = const()[name = tensor("input_671_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_671_mode_0 = const()[name = tensor("input_671_mode_0"), val = tensor("constant")]; - tensor const_137_to_fp16 = const()[name = tensor("const_137_to_fp16"), val = tensor(0x0p+0)]; - tensor input_671_cast_fp16 = pad(constant_val = const_137_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = tensor("input_671_cast_fp16")]; - tensor input_673_pad_type_0 = const()[name = tensor("input_673_pad_type_0"), val = tensor("valid")]; - tensor input_673_groups_0 = const()[name = tensor("input_673_groups_0"), val = tensor(1024)]; - tensor input_673_strides_0 = const()[name = tensor("input_673_strides_0"), val = tensor([1])]; - tensor input_673_pad_0 = const()[name = tensor("input_673_pad_0"), val = tensor([0, 0])]; - tensor input_673_dilations_0 = const()[name = tensor("input_673_dilations_0"), val = tensor([1])]; - tensor const_272_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235543680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235550656))), name = tensor("const_272_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_273_to_fp16 = const()[name = tensor("const_273_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235550848)))]; - tensor input_675_cast_fp16 = conv(bias = const_273_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_272_to_fp16_palettized, x = input_671_cast_fp16)[name = tensor("input_675_cast_fp16")]; - tensor input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = tensor("input_677_cast_fp16")]; - tensor x_285_pad_type_0 = const()[name = tensor("x_285_pad_type_0"), val = tensor("valid")]; - tensor x_285_strides_0 = const()[name = tensor("x_285_strides_0"), val = tensor([1])]; - tensor x_285_pad_0 = const()[name = tensor("x_285_pad_0"), val = tensor([0, 0])]; - tensor x_285_dilations_0 = const()[name = tensor("x_285_dilations_0"), val = tensor([1])]; - tensor x_285_groups_0 = const()[name = tensor("x_285_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235552960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236339456))), name = tensor("module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_677_cast_fp16)[name = tensor("x_285_cast_fp16")]; - tensor input_679_perm_0 = const()[name = tensor("input_679_perm_0"), val = tensor([0, 2, 1])]; - tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = tensor("transpose_222")]; - tensor input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = tensor("input_681_cast_fp16")]; - tensor input_683_axes_0 = const()[name = tensor("input_683_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236339648)))]; - tensor module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236341760)))]; - tensor input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = tensor("input_683_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236343872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239489664))), name = tensor("module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_116_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized, x = input_683_cast_fp16)[name = tensor("linear_116_cast_fp16")]; - tensor input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = tensor("input_687_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239489856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242635648))), name = tensor("module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_117_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized, x = input_687_cast_fp16)[name = tensor("linear_117_cast_fp16")]; - tensor var_2322_to_fp16 = const()[name = tensor("op_2322_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2323_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2322_to_fp16)[name = tensor("op_2323_cast_fp16")]; - tensor input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2323_cast_fp16)[name = tensor("input_693_cast_fp16")]; - tensor input_695_axes_0 = const()[name = tensor("input_695_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242635840)))]; - tensor module_layers_12_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242637952)))]; - tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = tensor("input_695_cast_fp16")]; - tensor input_697_axes_0 = const()[name = tensor("input_697_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242640064)))]; - tensor module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242642176)))]; - tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = tensor("input_697_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242644288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(245790080))), name = tensor("module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_118_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized, x = input_697_cast_fp16)[name = tensor("linear_118_cast_fp16")]; - tensor input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = tensor("input_701_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(245790272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248936064))), name = tensor("module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_119_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized, x = input_701_cast_fp16)[name = tensor("linear_119_cast_fp16")]; - tensor var_2351_to_fp16 = const()[name = tensor("op_2351_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2352_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2351_to_fp16)[name = tensor("op_2352_cast_fp16")]; - tensor input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2352_cast_fp16)[name = tensor("input_707_cast_fp16")]; - tensor query_27_axes_0 = const()[name = tensor("query_27_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248936256)))]; - tensor module_layers_13_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248938368)))]; - tensor query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = tensor("query_27_cast_fp16")]; - tensor module_layers_13_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248940480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249726976))), name = tensor("module_layers_13_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_120_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = tensor("linear_120_cast_fp16")]; - tensor var_2368 = const()[name = tensor("op_2368"), val = tensor([1, -1, 8, 128])]; - tensor q_79_cast_fp16 = reshape(shape = var_2368, x = linear_120_cast_fp16)[name = tensor("q_79_cast_fp16")]; - tensor module_layers_13_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249727168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250513664))), name = tensor("module_layers_13_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_121_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = tensor("linear_121_cast_fp16")]; - tensor var_2372 = const()[name = tensor("op_2372"), val = tensor([1, -1, 8, 128])]; - tensor k_53_cast_fp16 = reshape(shape = var_2372, x = linear_121_cast_fp16)[name = tensor("k_53_cast_fp16")]; - tensor module_layers_13_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250513856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251300352))), name = tensor("module_layers_13_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_122_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = tensor("linear_122_cast_fp16")]; - tensor var_2376 = const()[name = tensor("op_2376"), val = tensor([1, -1, 8, 128])]; - tensor v_27_cast_fp16 = reshape(shape = var_2376, x = linear_122_cast_fp16)[name = tensor("v_27_cast_fp16")]; - tensor value_29_perm_0 = const()[name = tensor("value_29_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251300544)))]; - tensor var_2388_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2388_cast_fp16")]; - tensor module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251302656)))]; - tensor var_2390_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2390_cast_fp16")]; - tensor q_with_bias_v_27_perm_0 = const()[name = tensor("q_with_bias_v_27_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_293_transpose_x_0 = const()[name = tensor("x_293_transpose_x_0"), val = tensor(false)]; - tensor x_293_transpose_y_0 = const()[name = tensor("x_293_transpose_y_0"), val = tensor(false)]; - tensor op_2392_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251304768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251592832))), name = tensor("op_2392_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2390_cast_fp16)[name = tensor("transpose_221")]; - tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_2392_to_fp16_palettized)[name = tensor("x_293_cast_fp16")]; - tensor x_295_pad_0 = const()[name = tensor("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_295_mode_0 = const()[name = tensor("x_295_mode_0"), val = tensor("constant")]; - tensor const_144_to_fp16 = const()[name = tensor("const_144_to_fp16"), val = tensor(0x0p+0)]; - tensor x_295_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = tensor("x_295_cast_fp16")]; - tensor var_2400 = const()[name = tensor("op_2400"), val = tensor([1, 8, -1, 188])]; - tensor x_297_cast_fp16 = reshape(shape = var_2400, x = x_295_cast_fp16)[name = tensor("x_297_cast_fp16")]; - tensor var_2404_begin_0 = const()[name = tensor("op_2404_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2404_end_0 = const()[name = tensor("op_2404_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2404_end_mask_0 = const()[name = tensor("op_2404_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2404_cast_fp16 = slice_by_index(begin = var_2404_begin_0, end = var_2404_end_0, end_mask = var_2404_end_mask_0, x = x_297_cast_fp16)[name = tensor("op_2404_cast_fp16")]; - tensor var_2405 = const()[name = tensor("op_2405"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2405, x = var_2404_cast_fp16)[name = tensor("matrix_bd_53_cast_fp16")]; - tensor matrix_ac_27_transpose_x_0 = const()[name = tensor("matrix_ac_27_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_27_transpose_y_0 = const()[name = tensor("matrix_ac_27_transpose_y_0"), val = tensor(false)]; - tensor transpose_122_perm_0 = const()[name = tensor("transpose_122_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_123_perm_0 = const()[name = tensor("transpose_123_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = tensor("transpose_219")]; - tensor transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2388_cast_fp16)[name = tensor("transpose_220")]; - tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = tensor("matrix_ac_27_cast_fp16")]; - tensor matrix_bd_55_begin_0 = const()[name = tensor("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_55_end_0 = const()[name = tensor("matrix_bd_55_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_55_end_mask_0 = const()[name = tensor("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = tensor("matrix_bd_55_cast_fp16")]; - tensor var_2414_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = tensor("op_2414_cast_fp16")]; - tensor _inversed_scores_53_y_0_to_fp16 = const()[name = tensor("_inversed_scores_53_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_53_cast_fp16 = mul(x = var_2414_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = tensor("_inversed_scores_53_cast_fp16")]; - tensor scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_3)[name = tensor("scores_55_cast_fp16")]; - tensor var_2420_cast_fp16 = softmax(axis = var_30, x = scores_55_cast_fp16)[name = tensor("op_2420_cast_fp16")]; - tensor input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2420_cast_fp16, cond = mask_3)[name = tensor("input_709_cast_fp16")]; - tensor x_299_transpose_x_0 = const()[name = tensor("x_299_transpose_x_0"), val = tensor(false)]; - tensor x_299_transpose_y_0 = const()[name = tensor("x_299_transpose_y_0"), val = tensor(false)]; - tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = tensor("transpose_218")]; - tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_29_cast_fp16)[name = tensor("x_299_cast_fp16")]; - tensor var_2424_perm_0 = const()[name = tensor("op_2424_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2425 = const()[name = tensor("op_2425"), val = tensor([1, -1, 1024])]; - tensor var_2424_cast_fp16 = transpose(perm = var_2424_perm_0, x = x_299_cast_fp16)[name = tensor("transpose_217")]; - tensor input_711_cast_fp16 = reshape(shape = var_2425, x = var_2424_cast_fp16)[name = tensor("input_711_cast_fp16")]; - tensor module_layers_13_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251593024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252379520))), name = tensor("module_layers_13_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_124_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16_palettized, x = input_711_cast_fp16)[name = tensor("linear_124_cast_fp16")]; - tensor input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = tensor("input_715_cast_fp16")]; - tensor x_303_axes_0 = const()[name = tensor("x_303_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252379712)))]; - tensor module_layers_13_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252381824)))]; - tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = tensor("x_303_cast_fp16")]; - tensor input_717_perm_0 = const()[name = tensor("input_717_perm_0"), val = tensor([0, 2, 1])]; - tensor input_719_pad_type_0 = const()[name = tensor("input_719_pad_type_0"), val = tensor("valid")]; - tensor input_719_strides_0 = const()[name = tensor("input_719_strides_0"), val = tensor([1])]; - tensor input_719_pad_0 = const()[name = tensor("input_719_pad_0"), val = tensor([0, 0])]; - tensor input_719_dilations_0 = const()[name = tensor("input_719_dilations_0"), val = tensor([1])]; - tensor input_719_groups_0 = const()[name = tensor("input_719_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252383936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253956864))), name = tensor("module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = tensor("transpose_216")]; - tensor input_719_cast_fp16 = conv(dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_717_cast_fp16)[name = tensor("input_719_cast_fp16")]; - tensor x_305_split_num_splits_0 = const()[name = tensor("x_305_split_num_splits_0"), val = tensor(2)]; - tensor x_305_split_axis_0 = const()[name = tensor("x_305_split_axis_0"), val = tensor(1)]; - tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = tensor("x_305_split_cast_fp16")]; - tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = tensor("x_305_split_1_sigmoid_cast_fp16")]; - tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = tensor("x_305_cast_fp16")]; - tensor input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_328)[name = tensor("input_721_cast_fp16")]; - tensor input_723_pad_0 = const()[name = tensor("input_723_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_723_mode_0 = const()[name = tensor("input_723_mode_0"), val = tensor("constant")]; - tensor const_147_to_fp16 = const()[name = tensor("const_147_to_fp16"), val = tensor(0x0p+0)]; - tensor input_723_cast_fp16 = pad(constant_val = const_147_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = tensor("input_723_cast_fp16")]; - tensor input_725_pad_type_0 = const()[name = tensor("input_725_pad_type_0"), val = tensor("valid")]; - tensor input_725_groups_0 = const()[name = tensor("input_725_groups_0"), val = tensor(1024)]; - tensor input_725_strides_0 = const()[name = tensor("input_725_strides_0"), val = tensor([1])]; - tensor input_725_pad_0 = const()[name = tensor("input_725_pad_0"), val = tensor([0, 0])]; - tensor input_725_dilations_0 = const()[name = tensor("input_725_dilations_0"), val = tensor([1])]; - tensor const_274_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253957056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253964032))), name = tensor("const_274_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_275_to_fp16 = const()[name = tensor("const_275_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253964224)))]; - tensor input_727_cast_fp16 = conv(bias = const_275_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_274_to_fp16_palettized, x = input_723_cast_fp16)[name = tensor("input_727_cast_fp16")]; - tensor input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = tensor("input_729_cast_fp16")]; - tensor x_307_pad_type_0 = const()[name = tensor("x_307_pad_type_0"), val = tensor("valid")]; - tensor x_307_strides_0 = const()[name = tensor("x_307_strides_0"), val = tensor([1])]; - tensor x_307_pad_0 = const()[name = tensor("x_307_pad_0"), val = tensor([0, 0])]; - tensor x_307_dilations_0 = const()[name = tensor("x_307_dilations_0"), val = tensor([1])]; - tensor x_307_groups_0 = const()[name = tensor("x_307_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253966336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254752832))), name = tensor("module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_729_cast_fp16)[name = tensor("x_307_cast_fp16")]; - tensor input_731_perm_0 = const()[name = tensor("input_731_perm_0"), val = tensor([0, 2, 1])]; - tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = tensor("transpose_215")]; - tensor input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = tensor("input_733_cast_fp16")]; - tensor input_735_axes_0 = const()[name = tensor("input_735_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254753024)))]; - tensor module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254755136)))]; - tensor input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = tensor("input_735_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254757248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257903040))), name = tensor("module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_125_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized, x = input_735_cast_fp16)[name = tensor("linear_125_cast_fp16")]; - tensor input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = tensor("input_739_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257903232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261049024))), name = tensor("module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_126_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized, x = input_739_cast_fp16)[name = tensor("linear_126_cast_fp16")]; - tensor var_2485_to_fp16 = const()[name = tensor("op_2485_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2486_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2485_to_fp16)[name = tensor("op_2486_cast_fp16")]; - tensor input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2486_cast_fp16)[name = tensor("input_745_cast_fp16")]; - tensor input_747_axes_0 = const()[name = tensor("input_747_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261049216)))]; - tensor module_layers_13_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261051328)))]; - tensor input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = tensor("input_747_cast_fp16")]; - tensor input_749_axes_0 = const()[name = tensor("input_749_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261053440)))]; - tensor module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261055552)))]; - tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = tensor("input_749_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261057664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264203456))), name = tensor("module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_127_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized, x = input_749_cast_fp16)[name = tensor("linear_127_cast_fp16")]; - tensor input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = tensor("input_753_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264203648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267349440))), name = tensor("module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_128_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized, x = input_753_cast_fp16)[name = tensor("linear_128_cast_fp16")]; - tensor var_2514_to_fp16 = const()[name = tensor("op_2514_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2515_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2514_to_fp16)[name = tensor("op_2515_cast_fp16")]; - tensor input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2515_cast_fp16)[name = tensor("input_759_cast_fp16")]; - tensor query_29_axes_0 = const()[name = tensor("query_29_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267349632)))]; - tensor module_layers_14_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267351744)))]; - tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = tensor("query_29_cast_fp16")]; - tensor module_layers_14_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267353856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268140352))), name = tensor("module_layers_14_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_129_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = tensor("linear_129_cast_fp16")]; - tensor var_2531 = const()[name = tensor("op_2531"), val = tensor([1, -1, 8, 128])]; - tensor q_85_cast_fp16 = reshape(shape = var_2531, x = linear_129_cast_fp16)[name = tensor("q_85_cast_fp16")]; - tensor module_layers_14_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268140544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268927040))), name = tensor("module_layers_14_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_130_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = tensor("linear_130_cast_fp16")]; - tensor var_2535 = const()[name = tensor("op_2535"), val = tensor([1, -1, 8, 128])]; - tensor k_57_cast_fp16 = reshape(shape = var_2535, x = linear_130_cast_fp16)[name = tensor("k_57_cast_fp16")]; - tensor module_layers_14_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268927232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269713728))), name = tensor("module_layers_14_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_131_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = tensor("linear_131_cast_fp16")]; - tensor var_2539 = const()[name = tensor("op_2539"), val = tensor([1, -1, 8, 128])]; - tensor v_29_cast_fp16 = reshape(shape = var_2539, x = linear_131_cast_fp16)[name = tensor("v_29_cast_fp16")]; - tensor value_31_perm_0 = const()[name = tensor("value_31_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269713920)))]; - tensor var_2551_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2551_cast_fp16")]; - tensor module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269716032)))]; - tensor var_2553_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2553_cast_fp16")]; - tensor q_with_bias_v_29_perm_0 = const()[name = tensor("q_with_bias_v_29_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_315_transpose_x_0 = const()[name = tensor("x_315_transpose_x_0"), val = tensor(false)]; - tensor x_315_transpose_y_0 = const()[name = tensor("x_315_transpose_y_0"), val = tensor(false)]; - tensor op_2555_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269718144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270006208))), name = tensor("op_2555_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2553_cast_fp16)[name = tensor("transpose_214")]; - tensor x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_2555_to_fp16_palettized)[name = tensor("x_315_cast_fp16")]; - tensor x_317_pad_0 = const()[name = tensor("x_317_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_317_mode_0 = const()[name = tensor("x_317_mode_0"), val = tensor("constant")]; - tensor const_154_to_fp16 = const()[name = tensor("const_154_to_fp16"), val = tensor(0x0p+0)]; - tensor x_317_cast_fp16 = pad(constant_val = const_154_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = tensor("x_317_cast_fp16")]; - tensor var_2563 = const()[name = tensor("op_2563"), val = tensor([1, 8, -1, 188])]; - tensor x_319_cast_fp16 = reshape(shape = var_2563, x = x_317_cast_fp16)[name = tensor("x_319_cast_fp16")]; - tensor var_2567_begin_0 = const()[name = tensor("op_2567_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2567_end_0 = const()[name = tensor("op_2567_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2567_end_mask_0 = const()[name = tensor("op_2567_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2567_cast_fp16 = slice_by_index(begin = var_2567_begin_0, end = var_2567_end_0, end_mask = var_2567_end_mask_0, x = x_319_cast_fp16)[name = tensor("op_2567_cast_fp16")]; - tensor var_2568 = const()[name = tensor("op_2568"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_57_cast_fp16 = reshape(shape = var_2568, x = var_2567_cast_fp16)[name = tensor("matrix_bd_57_cast_fp16")]; - tensor matrix_ac_29_transpose_x_0 = const()[name = tensor("matrix_ac_29_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_29_transpose_y_0 = const()[name = tensor("matrix_ac_29_transpose_y_0"), val = tensor(false)]; - tensor transpose_124_perm_0 = const()[name = tensor("transpose_124_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_125_perm_0 = const()[name = tensor("transpose_125_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = tensor("transpose_212")]; - tensor transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2551_cast_fp16)[name = tensor("transpose_213")]; - tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = tensor("matrix_ac_29_cast_fp16")]; - tensor matrix_bd_59_begin_0 = const()[name = tensor("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_59_end_0 = const()[name = tensor("matrix_bd_59_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_59_end_mask_0 = const()[name = tensor("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = tensor("matrix_bd_59_cast_fp16")]; - tensor var_2577_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = tensor("op_2577_cast_fp16")]; - tensor _inversed_scores_57_y_0_to_fp16 = const()[name = tensor("_inversed_scores_57_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_57_cast_fp16 = mul(x = var_2577_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = tensor("_inversed_scores_57_cast_fp16")]; - tensor scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_3)[name = tensor("scores_59_cast_fp16")]; - tensor var_2583_cast_fp16 = softmax(axis = var_30, x = scores_59_cast_fp16)[name = tensor("op_2583_cast_fp16")]; - tensor input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2583_cast_fp16, cond = mask_3)[name = tensor("input_761_cast_fp16")]; - tensor x_321_transpose_x_0 = const()[name = tensor("x_321_transpose_x_0"), val = tensor(false)]; - tensor x_321_transpose_y_0 = const()[name = tensor("x_321_transpose_y_0"), val = tensor(false)]; - tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = tensor("transpose_211")]; - tensor x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_31_cast_fp16)[name = tensor("x_321_cast_fp16")]; - tensor var_2587_perm_0 = const()[name = tensor("op_2587_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2588 = const()[name = tensor("op_2588"), val = tensor([1, -1, 1024])]; - tensor var_2587_cast_fp16 = transpose(perm = var_2587_perm_0, x = x_321_cast_fp16)[name = tensor("transpose_210")]; - tensor input_763_cast_fp16 = reshape(shape = var_2588, x = var_2587_cast_fp16)[name = tensor("input_763_cast_fp16")]; - tensor module_layers_14_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270006400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270792896))), name = tensor("module_layers_14_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_133_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16_palettized, x = input_763_cast_fp16)[name = tensor("linear_133_cast_fp16")]; - tensor input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = tensor("input_767_cast_fp16")]; - tensor x_325_axes_0 = const()[name = tensor("x_325_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270793088)))]; - tensor module_layers_14_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270795200)))]; - tensor x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = tensor("x_325_cast_fp16")]; - tensor input_769_perm_0 = const()[name = tensor("input_769_perm_0"), val = tensor([0, 2, 1])]; - tensor input_771_pad_type_0 = const()[name = tensor("input_771_pad_type_0"), val = tensor("valid")]; - tensor input_771_strides_0 = const()[name = tensor("input_771_strides_0"), val = tensor([1])]; - tensor input_771_pad_0 = const()[name = tensor("input_771_pad_0"), val = tensor([0, 0])]; - tensor input_771_dilations_0 = const()[name = tensor("input_771_dilations_0"), val = tensor([1])]; - tensor input_771_groups_0 = const()[name = tensor("input_771_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270797312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272370240))), name = tensor("module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = tensor("transpose_209")]; - tensor input_771_cast_fp16 = conv(dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_769_cast_fp16)[name = tensor("input_771_cast_fp16")]; - tensor x_327_split_num_splits_0 = const()[name = tensor("x_327_split_num_splits_0"), val = tensor(2)]; - tensor x_327_split_axis_0 = const()[name = tensor("x_327_split_axis_0"), val = tensor(1)]; - tensor x_327_split_cast_fp16_0, tensor x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = tensor("x_327_split_cast_fp16")]; - tensor x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = tensor("x_327_split_1_sigmoid_cast_fp16")]; - tensor x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = tensor("x_327_cast_fp16")]; - tensor input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_328)[name = tensor("input_773_cast_fp16")]; - tensor input_775_pad_0 = const()[name = tensor("input_775_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_775_mode_0 = const()[name = tensor("input_775_mode_0"), val = tensor("constant")]; - tensor const_157_to_fp16 = const()[name = tensor("const_157_to_fp16"), val = tensor(0x0p+0)]; - tensor input_775_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = tensor("input_775_cast_fp16")]; - tensor input_777_pad_type_0 = const()[name = tensor("input_777_pad_type_0"), val = tensor("valid")]; - tensor input_777_groups_0 = const()[name = tensor("input_777_groups_0"), val = tensor(1024)]; - tensor input_777_strides_0 = const()[name = tensor("input_777_strides_0"), val = tensor([1])]; - tensor input_777_pad_0 = const()[name = tensor("input_777_pad_0"), val = tensor([0, 0])]; - tensor input_777_dilations_0 = const()[name = tensor("input_777_dilations_0"), val = tensor([1])]; - tensor const_276_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272370432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272377408))), name = tensor("const_276_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_277_to_fp16 = const()[name = tensor("const_277_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272377600)))]; - tensor input_779_cast_fp16 = conv(bias = const_277_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_276_to_fp16_palettized, x = input_775_cast_fp16)[name = tensor("input_779_cast_fp16")]; - tensor input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = tensor("input_781_cast_fp16")]; - tensor x_329_pad_type_0 = const()[name = tensor("x_329_pad_type_0"), val = tensor("valid")]; - tensor x_329_strides_0 = const()[name = tensor("x_329_strides_0"), val = tensor([1])]; - tensor x_329_pad_0 = const()[name = tensor("x_329_pad_0"), val = tensor([0, 0])]; - tensor x_329_dilations_0 = const()[name = tensor("x_329_dilations_0"), val = tensor([1])]; - tensor x_329_groups_0 = const()[name = tensor("x_329_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272379712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273166208))), name = tensor("module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_329_cast_fp16 = conv(dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_781_cast_fp16)[name = tensor("x_329_cast_fp16")]; - tensor input_783_perm_0 = const()[name = tensor("input_783_perm_0"), val = tensor([0, 2, 1])]; - tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = tensor("transpose_208")]; - tensor input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = tensor("input_785_cast_fp16")]; - tensor input_787_axes_0 = const()[name = tensor("input_787_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273166400)))]; - tensor module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273168512)))]; - tensor input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = tensor("input_787_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273170624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(276316416))), name = tensor("module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_134_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized, x = input_787_cast_fp16)[name = tensor("linear_134_cast_fp16")]; - tensor input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = tensor("input_791_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(276316608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279462400))), name = tensor("module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_135_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized, x = input_791_cast_fp16)[name = tensor("linear_135_cast_fp16")]; - tensor var_2648_to_fp16 = const()[name = tensor("op_2648_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2649_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2648_to_fp16)[name = tensor("op_2649_cast_fp16")]; - tensor input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2649_cast_fp16)[name = tensor("input_797_cast_fp16")]; - tensor input_799_axes_0 = const()[name = tensor("input_799_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279462592)))]; - tensor module_layers_14_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279464704)))]; - tensor input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = tensor("input_799_cast_fp16")]; - tensor input_801_axes_0 = const()[name = tensor("input_801_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279466816)))]; - tensor module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279468928)))]; - tensor input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = tensor("input_801_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279471040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282616832))), name = tensor("module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_136_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized, x = input_801_cast_fp16)[name = tensor("linear_136_cast_fp16")]; - tensor input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = tensor("input_805_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282617024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285762816))), name = tensor("module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_137_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized, x = input_805_cast_fp16)[name = tensor("linear_137_cast_fp16")]; - tensor var_2677_to_fp16 = const()[name = tensor("op_2677_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2678_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2677_to_fp16)[name = tensor("op_2678_cast_fp16")]; - tensor input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2678_cast_fp16)[name = tensor("input_811_cast_fp16")]; - tensor query_31_axes_0 = const()[name = tensor("query_31_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285763008)))]; - tensor module_layers_15_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285765120)))]; - tensor query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = tensor("query_31_cast_fp16")]; - tensor module_layers_15_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285767232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286553728))), name = tensor("module_layers_15_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_138_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = tensor("linear_138_cast_fp16")]; - tensor var_2694 = const()[name = tensor("op_2694"), val = tensor([1, -1, 8, 128])]; - tensor q_91_cast_fp16 = reshape(shape = var_2694, x = linear_138_cast_fp16)[name = tensor("q_91_cast_fp16")]; - tensor module_layers_15_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286553920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287340416))), name = tensor("module_layers_15_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_139_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = tensor("linear_139_cast_fp16")]; - tensor var_2698 = const()[name = tensor("op_2698"), val = tensor([1, -1, 8, 128])]; - tensor k_61_cast_fp16 = reshape(shape = var_2698, x = linear_139_cast_fp16)[name = tensor("k_61_cast_fp16")]; - tensor module_layers_15_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287340608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288127104))), name = tensor("module_layers_15_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_140_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = tensor("linear_140_cast_fp16")]; - tensor var_2702 = const()[name = tensor("op_2702"), val = tensor([1, -1, 8, 128])]; - tensor v_31_cast_fp16 = reshape(shape = var_2702, x = linear_140_cast_fp16)[name = tensor("v_31_cast_fp16")]; - tensor value_33_perm_0 = const()[name = tensor("value_33_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288127296)))]; - tensor var_2714_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2714_cast_fp16")]; - tensor module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288129408)))]; - tensor var_2716_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2716_cast_fp16")]; - tensor q_with_bias_v_31_perm_0 = const()[name = tensor("q_with_bias_v_31_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_337_transpose_x_0 = const()[name = tensor("x_337_transpose_x_0"), val = tensor(false)]; - tensor x_337_transpose_y_0 = const()[name = tensor("x_337_transpose_y_0"), val = tensor(false)]; - tensor op_2718_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288131520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288419584))), name = tensor("op_2718_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2716_cast_fp16)[name = tensor("transpose_207")]; - tensor x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_2718_to_fp16_palettized)[name = tensor("x_337_cast_fp16")]; - tensor x_339_pad_0 = const()[name = tensor("x_339_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_339_mode_0 = const()[name = tensor("x_339_mode_0"), val = tensor("constant")]; - tensor const_164_to_fp16 = const()[name = tensor("const_164_to_fp16"), val = tensor(0x0p+0)]; - tensor x_339_cast_fp16 = pad(constant_val = const_164_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = tensor("x_339_cast_fp16")]; - tensor var_2726 = const()[name = tensor("op_2726"), val = tensor([1, 8, -1, 188])]; - tensor x_341_cast_fp16 = reshape(shape = var_2726, x = x_339_cast_fp16)[name = tensor("x_341_cast_fp16")]; - tensor var_2730_begin_0 = const()[name = tensor("op_2730_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2730_end_0 = const()[name = tensor("op_2730_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2730_end_mask_0 = const()[name = tensor("op_2730_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2730_cast_fp16 = slice_by_index(begin = var_2730_begin_0, end = var_2730_end_0, end_mask = var_2730_end_mask_0, x = x_341_cast_fp16)[name = tensor("op_2730_cast_fp16")]; - tensor var_2731 = const()[name = tensor("op_2731"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_61_cast_fp16 = reshape(shape = var_2731, x = var_2730_cast_fp16)[name = tensor("matrix_bd_61_cast_fp16")]; - tensor matrix_ac_31_transpose_x_0 = const()[name = tensor("matrix_ac_31_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_31_transpose_y_0 = const()[name = tensor("matrix_ac_31_transpose_y_0"), val = tensor(false)]; - tensor transpose_126_perm_0 = const()[name = tensor("transpose_126_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_127_perm_0 = const()[name = tensor("transpose_127_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = tensor("transpose_205")]; - tensor transpose_126 = transpose(perm = transpose_126_perm_0, x = var_2714_cast_fp16)[name = tensor("transpose_206")]; - tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = tensor("matrix_ac_31_cast_fp16")]; - tensor matrix_bd_63_begin_0 = const()[name = tensor("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_63_end_0 = const()[name = tensor("matrix_bd_63_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_63_end_mask_0 = const()[name = tensor("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = tensor("matrix_bd_63_cast_fp16")]; - tensor var_2740_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = tensor("op_2740_cast_fp16")]; - tensor _inversed_scores_61_y_0_to_fp16 = const()[name = tensor("_inversed_scores_61_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_61_cast_fp16 = mul(x = var_2740_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = tensor("_inversed_scores_61_cast_fp16")]; - tensor scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_3)[name = tensor("scores_63_cast_fp16")]; - tensor var_2746_cast_fp16 = softmax(axis = var_30, x = scores_63_cast_fp16)[name = tensor("op_2746_cast_fp16")]; - tensor input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_2746_cast_fp16, cond = mask_3)[name = tensor("input_813_cast_fp16")]; - tensor x_343_transpose_x_0 = const()[name = tensor("x_343_transpose_x_0"), val = tensor(false)]; - tensor x_343_transpose_y_0 = const()[name = tensor("x_343_transpose_y_0"), val = tensor(false)]; - tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = tensor("transpose_204")]; - tensor x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_33_cast_fp16)[name = tensor("x_343_cast_fp16")]; - tensor var_2750_perm_0 = const()[name = tensor("op_2750_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2751 = const()[name = tensor("op_2751"), val = tensor([1, -1, 1024])]; - tensor var_2750_cast_fp16 = transpose(perm = var_2750_perm_0, x = x_343_cast_fp16)[name = tensor("transpose_203")]; - tensor input_815_cast_fp16 = reshape(shape = var_2751, x = var_2750_cast_fp16)[name = tensor("input_815_cast_fp16")]; - tensor module_layers_15_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288419776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289206272))), name = tensor("module_layers_15_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_142_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16_palettized, x = input_815_cast_fp16)[name = tensor("linear_142_cast_fp16")]; - tensor input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = tensor("input_819_cast_fp16")]; - tensor x_347_axes_0 = const()[name = tensor("x_347_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289206464)))]; - tensor module_layers_15_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289208576)))]; - tensor x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = tensor("x_347_cast_fp16")]; - tensor input_821_perm_0 = const()[name = tensor("input_821_perm_0"), val = tensor([0, 2, 1])]; - tensor input_823_pad_type_0 = const()[name = tensor("input_823_pad_type_0"), val = tensor("valid")]; - tensor input_823_strides_0 = const()[name = tensor("input_823_strides_0"), val = tensor([1])]; - tensor input_823_pad_0 = const()[name = tensor("input_823_pad_0"), val = tensor([0, 0])]; - tensor input_823_dilations_0 = const()[name = tensor("input_823_dilations_0"), val = tensor([1])]; - tensor input_823_groups_0 = const()[name = tensor("input_823_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289210688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290783616))), name = tensor("module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = tensor("transpose_202")]; - tensor input_823_cast_fp16 = conv(dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_821_cast_fp16)[name = tensor("input_823_cast_fp16")]; - tensor x_349_split_num_splits_0 = const()[name = tensor("x_349_split_num_splits_0"), val = tensor(2)]; - tensor x_349_split_axis_0 = const()[name = tensor("x_349_split_axis_0"), val = tensor(1)]; - tensor x_349_split_cast_fp16_0, tensor x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = tensor("x_349_split_cast_fp16")]; - tensor x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = tensor("x_349_split_1_sigmoid_cast_fp16")]; - tensor x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = tensor("x_349_cast_fp16")]; - tensor input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_328)[name = tensor("input_825_cast_fp16")]; - tensor input_827_pad_0 = const()[name = tensor("input_827_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_827_mode_0 = const()[name = tensor("input_827_mode_0"), val = tensor("constant")]; - tensor const_167_to_fp16 = const()[name = tensor("const_167_to_fp16"), val = tensor(0x0p+0)]; - tensor input_827_cast_fp16 = pad(constant_val = const_167_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = tensor("input_827_cast_fp16")]; - tensor input_829_pad_type_0 = const()[name = tensor("input_829_pad_type_0"), val = tensor("valid")]; - tensor input_829_groups_0 = const()[name = tensor("input_829_groups_0"), val = tensor(1024)]; - tensor input_829_strides_0 = const()[name = tensor("input_829_strides_0"), val = tensor([1])]; - tensor input_829_pad_0 = const()[name = tensor("input_829_pad_0"), val = tensor([0, 0])]; - tensor input_829_dilations_0 = const()[name = tensor("input_829_dilations_0"), val = tensor([1])]; - tensor const_278_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290783808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290790784))), name = tensor("const_278_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_279_to_fp16 = const()[name = tensor("const_279_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290790976)))]; - tensor input_831_cast_fp16 = conv(bias = const_279_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_278_to_fp16_palettized, x = input_827_cast_fp16)[name = tensor("input_831_cast_fp16")]; - tensor input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = tensor("input_833_cast_fp16")]; - tensor x_351_pad_type_0 = const()[name = tensor("x_351_pad_type_0"), val = tensor("valid")]; - tensor x_351_strides_0 = const()[name = tensor("x_351_strides_0"), val = tensor([1])]; - tensor x_351_pad_0 = const()[name = tensor("x_351_pad_0"), val = tensor([0, 0])]; - tensor x_351_dilations_0 = const()[name = tensor("x_351_dilations_0"), val = tensor([1])]; - tensor x_351_groups_0 = const()[name = tensor("x_351_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290793088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291579584))), name = tensor("module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_351_cast_fp16 = conv(dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_833_cast_fp16)[name = tensor("x_351_cast_fp16")]; - tensor input_835_perm_0 = const()[name = tensor("input_835_perm_0"), val = tensor([0, 2, 1])]; - tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = tensor("transpose_201")]; - tensor input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = tensor("input_837_cast_fp16")]; - tensor input_839_axes_0 = const()[name = tensor("input_839_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291579776)))]; - tensor module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291581888)))]; - tensor input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = tensor("input_839_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291584000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294729792))), name = tensor("module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_143_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized, x = input_839_cast_fp16)[name = tensor("linear_143_cast_fp16")]; - tensor input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = tensor("input_843_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294729984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297875776))), name = tensor("module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_144_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized, x = input_843_cast_fp16)[name = tensor("linear_144_cast_fp16")]; - tensor var_2811_to_fp16 = const()[name = tensor("op_2811_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2812_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_2811_to_fp16)[name = tensor("op_2812_cast_fp16")]; - tensor input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_2812_cast_fp16)[name = tensor("input_849_cast_fp16")]; - tensor input_851_axes_0 = const()[name = tensor("input_851_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297875968)))]; - tensor module_layers_15_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297878080)))]; - tensor input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = tensor("input_851_cast_fp16")]; - tensor input_853_axes_0 = const()[name = tensor("input_853_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297880192)))]; - tensor module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297882304)))]; - tensor input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = tensor("input_853_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297884416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301030208))), name = tensor("module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_145_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized, x = input_853_cast_fp16)[name = tensor("linear_145_cast_fp16")]; - tensor input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = tensor("input_857_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301030400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304176192))), name = tensor("module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_146_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized, x = input_857_cast_fp16)[name = tensor("linear_146_cast_fp16")]; - tensor var_2840_to_fp16 = const()[name = tensor("op_2840_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2841_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_2840_to_fp16)[name = tensor("op_2841_cast_fp16")]; - tensor input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_2841_cast_fp16)[name = tensor("input_863_cast_fp16")]; - tensor query_33_axes_0 = const()[name = tensor("query_33_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304176384)))]; - tensor module_layers_16_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304178496)))]; - tensor query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = tensor("query_33_cast_fp16")]; - tensor module_layers_16_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304180608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304967104))), name = tensor("module_layers_16_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_147_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = tensor("linear_147_cast_fp16")]; - tensor var_2857 = const()[name = tensor("op_2857"), val = tensor([1, -1, 8, 128])]; - tensor q_97_cast_fp16 = reshape(shape = var_2857, x = linear_147_cast_fp16)[name = tensor("q_97_cast_fp16")]; - tensor module_layers_16_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304967296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(305753792))), name = tensor("module_layers_16_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_148_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = tensor("linear_148_cast_fp16")]; - tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, -1, 8, 128])]; - tensor k_65_cast_fp16 = reshape(shape = var_2861, x = linear_148_cast_fp16)[name = tensor("k_65_cast_fp16")]; - tensor module_layers_16_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(305753984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306540480))), name = tensor("module_layers_16_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_149_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = tensor("linear_149_cast_fp16")]; - tensor var_2865 = const()[name = tensor("op_2865"), val = tensor([1, -1, 8, 128])]; - tensor v_33_cast_fp16 = reshape(shape = var_2865, x = linear_149_cast_fp16)[name = tensor("v_33_cast_fp16")]; - tensor value_35_perm_0 = const()[name = tensor("value_35_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306540672)))]; - tensor var_2877_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2877_cast_fp16")]; - tensor module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306542784)))]; - tensor var_2879_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2879_cast_fp16")]; - tensor q_with_bias_v_33_perm_0 = const()[name = tensor("q_with_bias_v_33_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_359_transpose_x_0 = const()[name = tensor("x_359_transpose_x_0"), val = tensor(false)]; - tensor x_359_transpose_y_0 = const()[name = tensor("x_359_transpose_y_0"), val = tensor(false)]; - tensor op_2881_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306544896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306832960))), name = tensor("op_2881_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_2879_cast_fp16)[name = tensor("transpose_200")]; - tensor x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_2881_to_fp16_palettized)[name = tensor("x_359_cast_fp16")]; - tensor x_361_pad_0 = const()[name = tensor("x_361_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_361_mode_0 = const()[name = tensor("x_361_mode_0"), val = tensor("constant")]; - tensor const_174_to_fp16 = const()[name = tensor("const_174_to_fp16"), val = tensor(0x0p+0)]; - tensor x_361_cast_fp16 = pad(constant_val = const_174_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = tensor("x_361_cast_fp16")]; - tensor var_2889 = const()[name = tensor("op_2889"), val = tensor([1, 8, -1, 188])]; - tensor x_363_cast_fp16 = reshape(shape = var_2889, x = x_361_cast_fp16)[name = tensor("x_363_cast_fp16")]; - tensor var_2893_begin_0 = const()[name = tensor("op_2893_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2893_end_0 = const()[name = tensor("op_2893_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2893_end_mask_0 = const()[name = tensor("op_2893_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2893_cast_fp16 = slice_by_index(begin = var_2893_begin_0, end = var_2893_end_0, end_mask = var_2893_end_mask_0, x = x_363_cast_fp16)[name = tensor("op_2893_cast_fp16")]; - tensor var_2894 = const()[name = tensor("op_2894"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_65_cast_fp16 = reshape(shape = var_2894, x = var_2893_cast_fp16)[name = tensor("matrix_bd_65_cast_fp16")]; - tensor matrix_ac_33_transpose_x_0 = const()[name = tensor("matrix_ac_33_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_33_transpose_y_0 = const()[name = tensor("matrix_ac_33_transpose_y_0"), val = tensor(false)]; - tensor transpose_128_perm_0 = const()[name = tensor("transpose_128_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_129_perm_0 = const()[name = tensor("transpose_129_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = tensor("transpose_198")]; - tensor transpose_128 = transpose(perm = transpose_128_perm_0, x = var_2877_cast_fp16)[name = tensor("transpose_199")]; - tensor matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = tensor("matrix_ac_33_cast_fp16")]; - tensor matrix_bd_67_begin_0 = const()[name = tensor("matrix_bd_67_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_67_end_0 = const()[name = tensor("matrix_bd_67_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_67_end_mask_0 = const()[name = tensor("matrix_bd_67_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = tensor("matrix_bd_67_cast_fp16")]; - tensor var_2903_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = tensor("op_2903_cast_fp16")]; - tensor _inversed_scores_65_y_0_to_fp16 = const()[name = tensor("_inversed_scores_65_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_65_cast_fp16 = mul(x = var_2903_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = tensor("_inversed_scores_65_cast_fp16")]; - tensor scores_67_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_3)[name = tensor("scores_67_cast_fp16")]; - tensor var_2909_cast_fp16 = softmax(axis = var_30, x = scores_67_cast_fp16)[name = tensor("op_2909_cast_fp16")]; - tensor input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_2909_cast_fp16, cond = mask_3)[name = tensor("input_865_cast_fp16")]; - tensor x_365_transpose_x_0 = const()[name = tensor("x_365_transpose_x_0"), val = tensor(false)]; - tensor x_365_transpose_y_0 = const()[name = tensor("x_365_transpose_y_0"), val = tensor(false)]; - tensor value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_33_cast_fp16)[name = tensor("transpose_197")]; - tensor x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_35_cast_fp16)[name = tensor("x_365_cast_fp16")]; - tensor var_2913_perm_0 = const()[name = tensor("op_2913_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2914 = const()[name = tensor("op_2914"), val = tensor([1, -1, 1024])]; - tensor var_2913_cast_fp16 = transpose(perm = var_2913_perm_0, x = x_365_cast_fp16)[name = tensor("transpose_196")]; - tensor input_867_cast_fp16 = reshape(shape = var_2914, x = var_2913_cast_fp16)[name = tensor("input_867_cast_fp16")]; - tensor module_layers_16_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306833152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307619648))), name = tensor("module_layers_16_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_151_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16_palettized, x = input_867_cast_fp16)[name = tensor("linear_151_cast_fp16")]; - tensor input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = tensor("input_871_cast_fp16")]; - tensor x_369_axes_0 = const()[name = tensor("x_369_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307619840)))]; - tensor module_layers_16_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307621952)))]; - tensor x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = tensor("x_369_cast_fp16")]; - tensor input_873_perm_0 = const()[name = tensor("input_873_perm_0"), val = tensor([0, 2, 1])]; - tensor input_875_pad_type_0 = const()[name = tensor("input_875_pad_type_0"), val = tensor("valid")]; - tensor input_875_strides_0 = const()[name = tensor("input_875_strides_0"), val = tensor([1])]; - tensor input_875_pad_0 = const()[name = tensor("input_875_pad_0"), val = tensor([0, 0])]; - tensor input_875_dilations_0 = const()[name = tensor("input_875_dilations_0"), val = tensor([1])]; - tensor input_875_groups_0 = const()[name = tensor("input_875_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307624064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309196992))), name = tensor("module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = tensor("transpose_195")]; - tensor input_875_cast_fp16 = conv(dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_873_cast_fp16)[name = tensor("input_875_cast_fp16")]; - tensor x_371_split_num_splits_0 = const()[name = tensor("x_371_split_num_splits_0"), val = tensor(2)]; - tensor x_371_split_axis_0 = const()[name = tensor("x_371_split_axis_0"), val = tensor(1)]; - tensor x_371_split_cast_fp16_0, tensor x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = tensor("x_371_split_cast_fp16")]; - tensor x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = tensor("x_371_split_1_sigmoid_cast_fp16")]; - tensor x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = tensor("x_371_cast_fp16")]; - tensor input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_328)[name = tensor("input_877_cast_fp16")]; - tensor input_879_pad_0 = const()[name = tensor("input_879_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_879_mode_0 = const()[name = tensor("input_879_mode_0"), val = tensor("constant")]; - tensor const_177_to_fp16 = const()[name = tensor("const_177_to_fp16"), val = tensor(0x0p+0)]; - tensor input_879_cast_fp16 = pad(constant_val = const_177_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = tensor("input_879_cast_fp16")]; - tensor input_881_pad_type_0 = const()[name = tensor("input_881_pad_type_0"), val = tensor("valid")]; - tensor input_881_groups_0 = const()[name = tensor("input_881_groups_0"), val = tensor(1024)]; - tensor input_881_strides_0 = const()[name = tensor("input_881_strides_0"), val = tensor([1])]; - tensor input_881_pad_0 = const()[name = tensor("input_881_pad_0"), val = tensor([0, 0])]; - tensor input_881_dilations_0 = const()[name = tensor("input_881_dilations_0"), val = tensor([1])]; - tensor const_280_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309197184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309204160))), name = tensor("const_280_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_281_to_fp16 = const()[name = tensor("const_281_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309204352)))]; - tensor input_883_cast_fp16 = conv(bias = const_281_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_280_to_fp16_palettized, x = input_879_cast_fp16)[name = tensor("input_883_cast_fp16")]; - tensor input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = tensor("input_885_cast_fp16")]; - tensor x_373_pad_type_0 = const()[name = tensor("x_373_pad_type_0"), val = tensor("valid")]; - tensor x_373_strides_0 = const()[name = tensor("x_373_strides_0"), val = tensor([1])]; - tensor x_373_pad_0 = const()[name = tensor("x_373_pad_0"), val = tensor([0, 0])]; - tensor x_373_dilations_0 = const()[name = tensor("x_373_dilations_0"), val = tensor([1])]; - tensor x_373_groups_0 = const()[name = tensor("x_373_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309206464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309992960))), name = tensor("module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_373_cast_fp16 = conv(dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_885_cast_fp16)[name = tensor("x_373_cast_fp16")]; - tensor input_887_perm_0 = const()[name = tensor("input_887_perm_0"), val = tensor([0, 2, 1])]; - tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = tensor("transpose_194")]; - tensor input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = tensor("input_889_cast_fp16")]; - tensor input_891_axes_0 = const()[name = tensor("input_891_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309993152)))]; - tensor module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309995264)))]; - tensor input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = tensor("input_891_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309997376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313143168))), name = tensor("module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_152_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized, x = input_891_cast_fp16)[name = tensor("linear_152_cast_fp16")]; - tensor input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = tensor("input_895_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313143360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316289152))), name = tensor("module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_153_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized, x = input_895_cast_fp16)[name = tensor("linear_153_cast_fp16")]; - tensor var_2974_to_fp16 = const()[name = tensor("op_2974_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2975_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_2974_to_fp16)[name = tensor("op_2975_cast_fp16")]; - tensor input_901_cast_fp16 = add(x = input_889_cast_fp16, y = var_2975_cast_fp16)[name = tensor("input_901_cast_fp16")]; - tensor input_903_axes_0 = const()[name = tensor("input_903_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316289344)))]; - tensor module_layers_16_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316291456)))]; - tensor input_903_cast_fp16 = layer_norm(axes = input_903_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_901_cast_fp16)[name = tensor("input_903_cast_fp16")]; - tensor input_905_axes_0 = const()[name = tensor("input_905_axes_0"), val = tensor([-1])]; - tensor module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316293568)))]; - tensor module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316295680)))]; - tensor input_905_cast_fp16 = layer_norm(axes = input_905_axes_0, beta = module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_903_cast_fp16)[name = tensor("input_905_cast_fp16")]; - tensor module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316297792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319443584))), name = tensor("module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_154_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized, x = input_905_cast_fp16)[name = tensor("linear_154_cast_fp16")]; - tensor input_909_cast_fp16 = silu(x = linear_154_cast_fp16)[name = tensor("input_909_cast_fp16")]; - tensor module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319443776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322589568))), name = tensor("module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_155_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized, x = input_909_cast_fp16)[name = tensor("linear_155_cast_fp16")]; - tensor var_3003_to_fp16 = const()[name = tensor("op_3003_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3004_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3003_to_fp16)[name = tensor("op_3004_cast_fp16")]; - tensor input_915_cast_fp16 = add(x = input_903_cast_fp16, y = var_3004_cast_fp16)[name = tensor("input_915_cast_fp16")]; - tensor query_35_axes_0 = const()[name = tensor("query_35_axes_0"), val = tensor([-1])]; - tensor module_layers_17_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_17_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322589760)))]; - tensor module_layers_17_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_17_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322591872)))]; - tensor query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_self_att_weight_to_fp16, x = input_915_cast_fp16)[name = tensor("query_35_cast_fp16")]; - tensor module_layers_17_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322593984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323380480))), name = tensor("module_layers_17_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_156_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_q_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = tensor("linear_156_cast_fp16")]; - tensor var_3020 = const()[name = tensor("op_3020"), val = tensor([1, -1, 8, 128])]; - tensor q_103_cast_fp16 = reshape(shape = var_3020, x = linear_156_cast_fp16)[name = tensor("q_103_cast_fp16")]; - tensor module_layers_17_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323380672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324167168))), name = tensor("module_layers_17_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_157_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_k_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = tensor("linear_157_cast_fp16")]; - tensor var_3024 = const()[name = tensor("op_3024"), val = tensor([1, -1, 8, 128])]; - tensor k_69_cast_fp16 = reshape(shape = var_3024, x = linear_157_cast_fp16)[name = tensor("k_69_cast_fp16")]; - tensor module_layers_17_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324167360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324953856))), name = tensor("module_layers_17_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_158_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_v_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = tensor("linear_158_cast_fp16")]; - tensor var_3028 = const()[name = tensor("op_3028"), val = tensor([1, -1, 8, 128])]; - tensor v_35_cast_fp16 = reshape(shape = var_3028, x = linear_158_cast_fp16)[name = tensor("v_35_cast_fp16")]; - tensor value_37_perm_0 = const()[name = tensor("value_37_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324954048)))]; - tensor var_3040_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3040_cast_fp16")]; - tensor module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324956160)))]; - tensor var_3042_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3042_cast_fp16")]; - tensor q_with_bias_v_35_perm_0 = const()[name = tensor("q_with_bias_v_35_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_381_transpose_x_0 = const()[name = tensor("x_381_transpose_x_0"), val = tensor(false)]; - tensor x_381_transpose_y_0 = const()[name = tensor("x_381_transpose_y_0"), val = tensor(false)]; - tensor op_3044_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324958272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325246336))), name = tensor("op_3044_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3042_cast_fp16)[name = tensor("transpose_193")]; - tensor x_381_cast_fp16 = matmul(transpose_x = x_381_transpose_x_0, transpose_y = x_381_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_3044_to_fp16_palettized)[name = tensor("x_381_cast_fp16")]; - tensor x_383_pad_0 = const()[name = tensor("x_383_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_383_mode_0 = const()[name = tensor("x_383_mode_0"), val = tensor("constant")]; - tensor const_184_to_fp16 = const()[name = tensor("const_184_to_fp16"), val = tensor(0x0p+0)]; - tensor x_383_cast_fp16 = pad(constant_val = const_184_to_fp16, mode = x_383_mode_0, pad = x_383_pad_0, x = x_381_cast_fp16)[name = tensor("x_383_cast_fp16")]; - tensor var_3052 = const()[name = tensor("op_3052"), val = tensor([1, 8, -1, 188])]; - tensor x_385_cast_fp16 = reshape(shape = var_3052, x = x_383_cast_fp16)[name = tensor("x_385_cast_fp16")]; - tensor var_3056_begin_0 = const()[name = tensor("op_3056_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3056_end_0 = const()[name = tensor("op_3056_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3056_end_mask_0 = const()[name = tensor("op_3056_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3056_cast_fp16 = slice_by_index(begin = var_3056_begin_0, end = var_3056_end_0, end_mask = var_3056_end_mask_0, x = x_385_cast_fp16)[name = tensor("op_3056_cast_fp16")]; - tensor var_3057 = const()[name = tensor("op_3057"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_69_cast_fp16 = reshape(shape = var_3057, x = var_3056_cast_fp16)[name = tensor("matrix_bd_69_cast_fp16")]; - tensor matrix_ac_35_transpose_x_0 = const()[name = tensor("matrix_ac_35_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_35_transpose_y_0 = const()[name = tensor("matrix_ac_35_transpose_y_0"), val = tensor(false)]; - tensor transpose_130_perm_0 = const()[name = tensor("transpose_130_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_131_perm_0 = const()[name = tensor("transpose_131_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = tensor("transpose_191")]; - tensor transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3040_cast_fp16)[name = tensor("transpose_192")]; - tensor matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = tensor("matrix_ac_35_cast_fp16")]; - tensor matrix_bd_71_begin_0 = const()[name = tensor("matrix_bd_71_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_71_end_0 = const()[name = tensor("matrix_bd_71_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_71_end_mask_0 = const()[name = tensor("matrix_bd_71_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = tensor("matrix_bd_71_cast_fp16")]; - tensor var_3066_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = tensor("op_3066_cast_fp16")]; - tensor _inversed_scores_69_y_0_to_fp16 = const()[name = tensor("_inversed_scores_69_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_69_cast_fp16 = mul(x = var_3066_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = tensor("_inversed_scores_69_cast_fp16")]; - tensor scores_71_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_3)[name = tensor("scores_71_cast_fp16")]; - tensor var_3072_cast_fp16 = softmax(axis = var_30, x = scores_71_cast_fp16)[name = tensor("op_3072_cast_fp16")]; - tensor input_917_cast_fp16 = select(a = var_11_to_fp16, b = var_3072_cast_fp16, cond = mask_3)[name = tensor("input_917_cast_fp16")]; - tensor x_387_transpose_x_0 = const()[name = tensor("x_387_transpose_x_0"), val = tensor(false)]; - tensor x_387_transpose_y_0 = const()[name = tensor("x_387_transpose_y_0"), val = tensor(false)]; - tensor value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_35_cast_fp16)[name = tensor("transpose_190")]; - tensor x_387_cast_fp16 = matmul(transpose_x = x_387_transpose_x_0, transpose_y = x_387_transpose_y_0, x = input_917_cast_fp16, y = value_37_cast_fp16)[name = tensor("x_387_cast_fp16")]; - tensor var_3076_perm_0 = const()[name = tensor("op_3076_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3077 = const()[name = tensor("op_3077"), val = tensor([1, -1, 1024])]; - tensor var_3076_cast_fp16 = transpose(perm = var_3076_perm_0, x = x_387_cast_fp16)[name = tensor("transpose_189")]; - tensor input_919_cast_fp16 = reshape(shape = var_3077, x = var_3076_cast_fp16)[name = tensor("input_919_cast_fp16")]; - tensor module_layers_17_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325246528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(326033024))), name = tensor("module_layers_17_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_160_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_self_attn_linear_out_weight_to_fp16_palettized, x = input_919_cast_fp16)[name = tensor("linear_160_cast_fp16")]; - tensor input_923_cast_fp16 = add(x = input_915_cast_fp16, y = linear_160_cast_fp16)[name = tensor("input_923_cast_fp16")]; - tensor x_391_axes_0 = const()[name = tensor("x_391_axes_0"), val = tensor([-1])]; - tensor module_layers_17_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_17_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(326033216)))]; - tensor module_layers_17_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_17_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(326035328)))]; - tensor x_391_cast_fp16 = layer_norm(axes = x_391_axes_0, beta = module_layers_17_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_conv_weight_to_fp16, x = input_923_cast_fp16)[name = tensor("x_391_cast_fp16")]; - tensor input_925_perm_0 = const()[name = tensor("input_925_perm_0"), val = tensor([0, 2, 1])]; - tensor input_927_pad_type_0 = const()[name = tensor("input_927_pad_type_0"), val = tensor("valid")]; - tensor input_927_strides_0 = const()[name = tensor("input_927_strides_0"), val = tensor([1])]; - tensor input_927_pad_0 = const()[name = tensor("input_927_pad_0"), val = tensor([0, 0])]; - tensor input_927_dilations_0 = const()[name = tensor("input_927_dilations_0"), val = tensor([1])]; - tensor input_927_groups_0 = const()[name = tensor("input_927_groups_0"), val = tensor(1)]; - tensor module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(326037440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(327610368))), name = tensor("module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_925_cast_fp16 = transpose(perm = input_925_perm_0, x = x_391_cast_fp16)[name = tensor("transpose_188")]; - tensor input_927_cast_fp16 = conv(dilations = input_927_dilations_0, groups = input_927_groups_0, pad = input_927_pad_0, pad_type = input_927_pad_type_0, strides = input_927_strides_0, weight = module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_925_cast_fp16)[name = tensor("input_927_cast_fp16")]; - tensor x_393_split_num_splits_0 = const()[name = tensor("x_393_split_num_splits_0"), val = tensor(2)]; - tensor x_393_split_axis_0 = const()[name = tensor("x_393_split_axis_0"), val = tensor(1)]; - tensor x_393_split_cast_fp16_0, tensor x_393_split_cast_fp16_1 = split(axis = x_393_split_axis_0, num_splits = x_393_split_num_splits_0, x = input_927_cast_fp16)[name = tensor("x_393_split_cast_fp16")]; - tensor x_393_split_1_sigmoid_cast_fp16 = sigmoid(x = x_393_split_cast_fp16_1)[name = tensor("x_393_split_1_sigmoid_cast_fp16")]; - tensor x_393_cast_fp16 = mul(x = x_393_split_cast_fp16_0, y = x_393_split_1_sigmoid_cast_fp16)[name = tensor("x_393_cast_fp16")]; - tensor input_929_cast_fp16 = select(a = var_11_to_fp16, b = x_393_cast_fp16, cond = var_328)[name = tensor("input_929_cast_fp16")]; - tensor input_931_pad_0 = const()[name = tensor("input_931_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_931_mode_0 = const()[name = tensor("input_931_mode_0"), val = tensor("constant")]; - tensor const_187_to_fp16 = const()[name = tensor("const_187_to_fp16"), val = tensor(0x0p+0)]; - tensor input_931_cast_fp16 = pad(constant_val = const_187_to_fp16, mode = input_931_mode_0, pad = input_931_pad_0, x = input_929_cast_fp16)[name = tensor("input_931_cast_fp16")]; - tensor input_933_pad_type_0 = const()[name = tensor("input_933_pad_type_0"), val = tensor("valid")]; - tensor input_933_groups_0 = const()[name = tensor("input_933_groups_0"), val = tensor(1024)]; - tensor input_933_strides_0 = const()[name = tensor("input_933_strides_0"), val = tensor([1])]; - tensor input_933_pad_0 = const()[name = tensor("input_933_pad_0"), val = tensor([0, 0])]; - tensor input_933_dilations_0 = const()[name = tensor("input_933_dilations_0"), val = tensor([1])]; - tensor const_282_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(327610560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(327617536))), name = tensor("const_282_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_283_to_fp16 = const()[name = tensor("const_283_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(327617728)))]; - tensor input_935_cast_fp16 = conv(bias = const_283_to_fp16, dilations = input_933_dilations_0, groups = input_933_groups_0, pad = input_933_pad_0, pad_type = input_933_pad_type_0, strides = input_933_strides_0, weight = const_282_to_fp16_palettized, x = input_931_cast_fp16)[name = tensor("input_935_cast_fp16")]; - tensor input_937_cast_fp16 = silu(x = input_935_cast_fp16)[name = tensor("input_937_cast_fp16")]; - tensor x_395_pad_type_0 = const()[name = tensor("x_395_pad_type_0"), val = tensor("valid")]; - tensor x_395_strides_0 = const()[name = tensor("x_395_strides_0"), val = tensor([1])]; - tensor x_395_pad_0 = const()[name = tensor("x_395_pad_0"), val = tensor([0, 0])]; - tensor x_395_dilations_0 = const()[name = tensor("x_395_dilations_0"), val = tensor([1])]; - tensor x_395_groups_0 = const()[name = tensor("x_395_groups_0"), val = tensor(1)]; - tensor module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(327619840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328406336))), name = tensor("module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_395_cast_fp16 = conv(dilations = x_395_dilations_0, groups = x_395_groups_0, pad = x_395_pad_0, pad_type = x_395_pad_type_0, strides = x_395_strides_0, weight = module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_937_cast_fp16)[name = tensor("x_395_cast_fp16")]; - tensor input_939_perm_0 = const()[name = tensor("input_939_perm_0"), val = tensor([0, 2, 1])]; - tensor input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_395_cast_fp16)[name = tensor("transpose_187")]; - tensor input_941_cast_fp16 = add(x = input_923_cast_fp16, y = input_939_cast_fp16)[name = tensor("input_941_cast_fp16")]; - tensor input_943_axes_0 = const()[name = tensor("input_943_axes_0"), val = tensor([-1])]; - tensor module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328406528)))]; - tensor module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328408640)))]; - tensor input_943_cast_fp16 = layer_norm(axes = input_943_axes_0, beta = module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_941_cast_fp16)[name = tensor("input_943_cast_fp16")]; - tensor module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328410752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(331556544))), name = tensor("module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_161_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized, x = input_943_cast_fp16)[name = tensor("linear_161_cast_fp16")]; - tensor input_947_cast_fp16 = silu(x = linear_161_cast_fp16)[name = tensor("input_947_cast_fp16")]; - tensor module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(331556736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334702528))), name = tensor("module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_162_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized, x = input_947_cast_fp16)[name = tensor("linear_162_cast_fp16")]; - tensor var_3137_to_fp16 = const()[name = tensor("op_3137_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3138_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3137_to_fp16)[name = tensor("op_3138_cast_fp16")]; - tensor input_953_cast_fp16 = add(x = input_941_cast_fp16, y = var_3138_cast_fp16)[name = tensor("input_953_cast_fp16")]; - tensor input_955_axes_0 = const()[name = tensor("input_955_axes_0"), val = tensor([-1])]; - tensor module_layers_17_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_17_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334702720)))]; - tensor module_layers_17_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_17_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334704832)))]; - tensor input_955_cast_fp16 = layer_norm(axes = input_955_axes_0, beta = module_layers_17_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_out_weight_to_fp16, x = input_953_cast_fp16)[name = tensor("input_955_cast_fp16")]; - tensor input_957_axes_0 = const()[name = tensor("input_957_axes_0"), val = tensor([-1])]; - tensor module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334706944)))]; - tensor module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334709056)))]; - tensor input_957_cast_fp16 = layer_norm(axes = input_957_axes_0, beta = module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_955_cast_fp16)[name = tensor("input_957_cast_fp16")]; - tensor module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334711168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337856960))), name = tensor("module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_163_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized, x = input_957_cast_fp16)[name = tensor("linear_163_cast_fp16")]; - tensor input_961_cast_fp16 = silu(x = linear_163_cast_fp16)[name = tensor("input_961_cast_fp16")]; - tensor module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337857152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341002944))), name = tensor("module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_164_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized, x = input_961_cast_fp16)[name = tensor("linear_164_cast_fp16")]; - tensor var_3166_to_fp16 = const()[name = tensor("op_3166_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3167_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3166_to_fp16)[name = tensor("op_3167_cast_fp16")]; - tensor input_967_cast_fp16 = add(x = input_955_cast_fp16, y = var_3167_cast_fp16)[name = tensor("input_967_cast_fp16")]; - tensor query_37_axes_0 = const()[name = tensor("query_37_axes_0"), val = tensor([-1])]; - tensor module_layers_18_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_18_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341003136)))]; - tensor module_layers_18_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_18_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341005248)))]; - tensor query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_self_att_weight_to_fp16, x = input_967_cast_fp16)[name = tensor("query_37_cast_fp16")]; - tensor module_layers_18_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341007360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341793856))), name = tensor("module_layers_18_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_165_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_q_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = tensor("linear_165_cast_fp16")]; - tensor var_3183 = const()[name = tensor("op_3183"), val = tensor([1, -1, 8, 128])]; - tensor q_109_cast_fp16 = reshape(shape = var_3183, x = linear_165_cast_fp16)[name = tensor("q_109_cast_fp16")]; - tensor module_layers_18_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341794048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(342580544))), name = tensor("module_layers_18_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_166_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_k_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = tensor("linear_166_cast_fp16")]; - tensor var_3187 = const()[name = tensor("op_3187"), val = tensor([1, -1, 8, 128])]; - tensor k_73_cast_fp16 = reshape(shape = var_3187, x = linear_166_cast_fp16)[name = tensor("k_73_cast_fp16")]; - tensor module_layers_18_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(342580736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343367232))), name = tensor("module_layers_18_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_167_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_v_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = tensor("linear_167_cast_fp16")]; - tensor var_3191 = const()[name = tensor("op_3191"), val = tensor([1, -1, 8, 128])]; - tensor v_37_cast_fp16 = reshape(shape = var_3191, x = linear_167_cast_fp16)[name = tensor("v_37_cast_fp16")]; - tensor value_39_perm_0 = const()[name = tensor("value_39_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343367424)))]; - tensor var_3203_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3203_cast_fp16")]; - tensor module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343369536)))]; - tensor var_3205_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3205_cast_fp16")]; - tensor q_with_bias_v_37_perm_0 = const()[name = tensor("q_with_bias_v_37_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_403_transpose_x_0 = const()[name = tensor("x_403_transpose_x_0"), val = tensor(false)]; - tensor x_403_transpose_y_0 = const()[name = tensor("x_403_transpose_y_0"), val = tensor(false)]; - tensor op_3207_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343371648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343659712))), name = tensor("op_3207_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3205_cast_fp16)[name = tensor("transpose_186")]; - tensor x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_3207_to_fp16_palettized)[name = tensor("x_403_cast_fp16")]; - tensor x_405_pad_0 = const()[name = tensor("x_405_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_405_mode_0 = const()[name = tensor("x_405_mode_0"), val = tensor("constant")]; - tensor const_194_to_fp16 = const()[name = tensor("const_194_to_fp16"), val = tensor(0x0p+0)]; - tensor x_405_cast_fp16 = pad(constant_val = const_194_to_fp16, mode = x_405_mode_0, pad = x_405_pad_0, x = x_403_cast_fp16)[name = tensor("x_405_cast_fp16")]; - tensor var_3215 = const()[name = tensor("op_3215"), val = tensor([1, 8, -1, 188])]; - tensor x_407_cast_fp16 = reshape(shape = var_3215, x = x_405_cast_fp16)[name = tensor("x_407_cast_fp16")]; - tensor var_3219_begin_0 = const()[name = tensor("op_3219_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3219_end_0 = const()[name = tensor("op_3219_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3219_end_mask_0 = const()[name = tensor("op_3219_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3219_cast_fp16 = slice_by_index(begin = var_3219_begin_0, end = var_3219_end_0, end_mask = var_3219_end_mask_0, x = x_407_cast_fp16)[name = tensor("op_3219_cast_fp16")]; - tensor var_3220 = const()[name = tensor("op_3220"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_73_cast_fp16 = reshape(shape = var_3220, x = var_3219_cast_fp16)[name = tensor("matrix_bd_73_cast_fp16")]; - tensor matrix_ac_37_transpose_x_0 = const()[name = tensor("matrix_ac_37_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_37_transpose_y_0 = const()[name = tensor("matrix_ac_37_transpose_y_0"), val = tensor(false)]; - tensor transpose_132_perm_0 = const()[name = tensor("transpose_132_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_133_perm_0 = const()[name = tensor("transpose_133_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = tensor("transpose_184")]; - tensor transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3203_cast_fp16)[name = tensor("transpose_185")]; - tensor matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = tensor("matrix_ac_37_cast_fp16")]; - tensor matrix_bd_75_begin_0 = const()[name = tensor("matrix_bd_75_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_75_end_0 = const()[name = tensor("matrix_bd_75_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_75_end_mask_0 = const()[name = tensor("matrix_bd_75_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = tensor("matrix_bd_75_cast_fp16")]; - tensor var_3229_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = tensor("op_3229_cast_fp16")]; - tensor _inversed_scores_73_y_0_to_fp16 = const()[name = tensor("_inversed_scores_73_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_73_cast_fp16 = mul(x = var_3229_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = tensor("_inversed_scores_73_cast_fp16")]; - tensor scores_75_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_3)[name = tensor("scores_75_cast_fp16")]; - tensor var_3235_cast_fp16 = softmax(axis = var_30, x = scores_75_cast_fp16)[name = tensor("op_3235_cast_fp16")]; - tensor input_969_cast_fp16 = select(a = var_11_to_fp16, b = var_3235_cast_fp16, cond = mask_3)[name = tensor("input_969_cast_fp16")]; - tensor x_409_transpose_x_0 = const()[name = tensor("x_409_transpose_x_0"), val = tensor(false)]; - tensor x_409_transpose_y_0 = const()[name = tensor("x_409_transpose_y_0"), val = tensor(false)]; - tensor value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_37_cast_fp16)[name = tensor("transpose_183")]; - tensor x_409_cast_fp16 = matmul(transpose_x = x_409_transpose_x_0, transpose_y = x_409_transpose_y_0, x = input_969_cast_fp16, y = value_39_cast_fp16)[name = tensor("x_409_cast_fp16")]; - tensor var_3239_perm_0 = const()[name = tensor("op_3239_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3240 = const()[name = tensor("op_3240"), val = tensor([1, -1, 1024])]; - tensor var_3239_cast_fp16 = transpose(perm = var_3239_perm_0, x = x_409_cast_fp16)[name = tensor("transpose_182")]; - tensor input_971_cast_fp16 = reshape(shape = var_3240, x = var_3239_cast_fp16)[name = tensor("input_971_cast_fp16")]; - tensor module_layers_18_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343659904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344446400))), name = tensor("module_layers_18_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_169_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_self_attn_linear_out_weight_to_fp16_palettized, x = input_971_cast_fp16)[name = tensor("linear_169_cast_fp16")]; - tensor input_975_cast_fp16 = add(x = input_967_cast_fp16, y = linear_169_cast_fp16)[name = tensor("input_975_cast_fp16")]; - tensor x_413_axes_0 = const()[name = tensor("x_413_axes_0"), val = tensor([-1])]; - tensor module_layers_18_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_18_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344446592)))]; - tensor module_layers_18_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_18_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344448704)))]; - tensor x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = module_layers_18_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_conv_weight_to_fp16, x = input_975_cast_fp16)[name = tensor("x_413_cast_fp16")]; - tensor input_977_perm_0 = const()[name = tensor("input_977_perm_0"), val = tensor([0, 2, 1])]; - tensor input_979_pad_type_0 = const()[name = tensor("input_979_pad_type_0"), val = tensor("valid")]; - tensor input_979_strides_0 = const()[name = tensor("input_979_strides_0"), val = tensor([1])]; - tensor input_979_pad_0 = const()[name = tensor("input_979_pad_0"), val = tensor([0, 0])]; - tensor input_979_dilations_0 = const()[name = tensor("input_979_dilations_0"), val = tensor([1])]; - tensor input_979_groups_0 = const()[name = tensor("input_979_groups_0"), val = tensor(1)]; - tensor module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344450816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346023744))), name = tensor("module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_977_cast_fp16 = transpose(perm = input_977_perm_0, x = x_413_cast_fp16)[name = tensor("transpose_181")]; - tensor input_979_cast_fp16 = conv(dilations = input_979_dilations_0, groups = input_979_groups_0, pad = input_979_pad_0, pad_type = input_979_pad_type_0, strides = input_979_strides_0, weight = module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_977_cast_fp16)[name = tensor("input_979_cast_fp16")]; - tensor x_415_split_num_splits_0 = const()[name = tensor("x_415_split_num_splits_0"), val = tensor(2)]; - tensor x_415_split_axis_0 = const()[name = tensor("x_415_split_axis_0"), val = tensor(1)]; - tensor x_415_split_cast_fp16_0, tensor x_415_split_cast_fp16_1 = split(axis = x_415_split_axis_0, num_splits = x_415_split_num_splits_0, x = input_979_cast_fp16)[name = tensor("x_415_split_cast_fp16")]; - tensor x_415_split_1_sigmoid_cast_fp16 = sigmoid(x = x_415_split_cast_fp16_1)[name = tensor("x_415_split_1_sigmoid_cast_fp16")]; - tensor x_415_cast_fp16 = mul(x = x_415_split_cast_fp16_0, y = x_415_split_1_sigmoid_cast_fp16)[name = tensor("x_415_cast_fp16")]; - tensor input_981_cast_fp16 = select(a = var_11_to_fp16, b = x_415_cast_fp16, cond = var_328)[name = tensor("input_981_cast_fp16")]; - tensor input_983_pad_0 = const()[name = tensor("input_983_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_983_mode_0 = const()[name = tensor("input_983_mode_0"), val = tensor("constant")]; - tensor const_197_to_fp16 = const()[name = tensor("const_197_to_fp16"), val = tensor(0x0p+0)]; - tensor input_983_cast_fp16 = pad(constant_val = const_197_to_fp16, mode = input_983_mode_0, pad = input_983_pad_0, x = input_981_cast_fp16)[name = tensor("input_983_cast_fp16")]; - tensor input_985_pad_type_0 = const()[name = tensor("input_985_pad_type_0"), val = tensor("valid")]; - tensor input_985_groups_0 = const()[name = tensor("input_985_groups_0"), val = tensor(1024)]; - tensor input_985_strides_0 = const()[name = tensor("input_985_strides_0"), val = tensor([1])]; - tensor input_985_pad_0 = const()[name = tensor("input_985_pad_0"), val = tensor([0, 0])]; - tensor input_985_dilations_0 = const()[name = tensor("input_985_dilations_0"), val = tensor([1])]; - tensor const_284_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346023936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346030912))), name = tensor("const_284_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_285_to_fp16 = const()[name = tensor("const_285_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346031104)))]; - tensor input_987_cast_fp16 = conv(bias = const_285_to_fp16, dilations = input_985_dilations_0, groups = input_985_groups_0, pad = input_985_pad_0, pad_type = input_985_pad_type_0, strides = input_985_strides_0, weight = const_284_to_fp16_palettized, x = input_983_cast_fp16)[name = tensor("input_987_cast_fp16")]; - tensor input_989_cast_fp16 = silu(x = input_987_cast_fp16)[name = tensor("input_989_cast_fp16")]; - tensor x_417_pad_type_0 = const()[name = tensor("x_417_pad_type_0"), val = tensor("valid")]; - tensor x_417_strides_0 = const()[name = tensor("x_417_strides_0"), val = tensor([1])]; - tensor x_417_pad_0 = const()[name = tensor("x_417_pad_0"), val = tensor([0, 0])]; - tensor x_417_dilations_0 = const()[name = tensor("x_417_dilations_0"), val = tensor([1])]; - tensor x_417_groups_0 = const()[name = tensor("x_417_groups_0"), val = tensor(1)]; - tensor module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346033216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346819712))), name = tensor("module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_417_cast_fp16 = conv(dilations = x_417_dilations_0, groups = x_417_groups_0, pad = x_417_pad_0, pad_type = x_417_pad_type_0, strides = x_417_strides_0, weight = module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_989_cast_fp16)[name = tensor("x_417_cast_fp16")]; - tensor input_991_perm_0 = const()[name = tensor("input_991_perm_0"), val = tensor([0, 2, 1])]; - tensor input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_417_cast_fp16)[name = tensor("transpose_180")]; - tensor input_993_cast_fp16 = add(x = input_975_cast_fp16, y = input_991_cast_fp16)[name = tensor("input_993_cast_fp16")]; - tensor input_995_axes_0 = const()[name = tensor("input_995_axes_0"), val = tensor([-1])]; - tensor module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346819904)))]; - tensor module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346822016)))]; - tensor input_995_cast_fp16 = layer_norm(axes = input_995_axes_0, beta = module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_993_cast_fp16)[name = tensor("input_995_cast_fp16")]; - tensor module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346824128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(349969920))), name = tensor("module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_170_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized, x = input_995_cast_fp16)[name = tensor("linear_170_cast_fp16")]; - tensor input_999_cast_fp16 = silu(x = linear_170_cast_fp16)[name = tensor("input_999_cast_fp16")]; - tensor module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(349970112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353115904))), name = tensor("module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_171_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized, x = input_999_cast_fp16)[name = tensor("linear_171_cast_fp16")]; - tensor var_3300_to_fp16 = const()[name = tensor("op_3300_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3301_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3300_to_fp16)[name = tensor("op_3301_cast_fp16")]; - tensor input_1005_cast_fp16 = add(x = input_993_cast_fp16, y = var_3301_cast_fp16)[name = tensor("input_1005_cast_fp16")]; - tensor input_1007_axes_0 = const()[name = tensor("input_1007_axes_0"), val = tensor([-1])]; - tensor module_layers_18_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_18_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353116096)))]; - tensor module_layers_18_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_18_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353118208)))]; - tensor input_1007_cast_fp16 = layer_norm(axes = input_1007_axes_0, beta = module_layers_18_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_out_weight_to_fp16, x = input_1005_cast_fp16)[name = tensor("input_1007_cast_fp16")]; - tensor input_1009_axes_0 = const()[name = tensor("input_1009_axes_0"), val = tensor([-1])]; - tensor module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353120320)))]; - tensor module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353122432)))]; - tensor input_1009_cast_fp16 = layer_norm(axes = input_1009_axes_0, beta = module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1007_cast_fp16)[name = tensor("input_1009_cast_fp16")]; - tensor module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353124544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(356270336))), name = tensor("module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_172_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1009_cast_fp16)[name = tensor("linear_172_cast_fp16")]; - tensor input_1013_cast_fp16 = silu(x = linear_172_cast_fp16)[name = tensor("input_1013_cast_fp16")]; - tensor module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(356270528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359416320))), name = tensor("module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_173_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1013_cast_fp16)[name = tensor("linear_173_cast_fp16")]; - tensor var_3329_to_fp16 = const()[name = tensor("op_3329_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3330_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3329_to_fp16)[name = tensor("op_3330_cast_fp16")]; - tensor input_1019_cast_fp16 = add(x = input_1007_cast_fp16, y = var_3330_cast_fp16)[name = tensor("input_1019_cast_fp16")]; - tensor query_39_axes_0 = const()[name = tensor("query_39_axes_0"), val = tensor([-1])]; - tensor module_layers_19_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_19_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359416512)))]; - tensor module_layers_19_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_19_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359418624)))]; - tensor query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_self_att_weight_to_fp16, x = input_1019_cast_fp16)[name = tensor("query_39_cast_fp16")]; - tensor module_layers_19_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359420736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(360207232))), name = tensor("module_layers_19_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_174_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_q_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = tensor("linear_174_cast_fp16")]; - tensor var_3346 = const()[name = tensor("op_3346"), val = tensor([1, -1, 8, 128])]; - tensor q_115_cast_fp16 = reshape(shape = var_3346, x = linear_174_cast_fp16)[name = tensor("q_115_cast_fp16")]; - tensor module_layers_19_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(360207424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(360993920))), name = tensor("module_layers_19_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_175_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_k_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = tensor("linear_175_cast_fp16")]; - tensor var_3350 = const()[name = tensor("op_3350"), val = tensor([1, -1, 8, 128])]; - tensor k_77_cast_fp16 = reshape(shape = var_3350, x = linear_175_cast_fp16)[name = tensor("k_77_cast_fp16")]; - tensor module_layers_19_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(360994112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361780608))), name = tensor("module_layers_19_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_176_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_v_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = tensor("linear_176_cast_fp16")]; - tensor var_3354 = const()[name = tensor("op_3354"), val = tensor([1, -1, 8, 128])]; - tensor v_39_cast_fp16 = reshape(shape = var_3354, x = linear_176_cast_fp16)[name = tensor("v_39_cast_fp16")]; - tensor value_41_perm_0 = const()[name = tensor("value_41_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361780800)))]; - tensor var_3366_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3366_cast_fp16")]; - tensor module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361782912)))]; - tensor var_3368_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3368_cast_fp16")]; - tensor q_with_bias_v_39_perm_0 = const()[name = tensor("q_with_bias_v_39_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_425_transpose_x_0 = const()[name = tensor("x_425_transpose_x_0"), val = tensor(false)]; - tensor x_425_transpose_y_0 = const()[name = tensor("x_425_transpose_y_0"), val = tensor(false)]; - tensor op_3370_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361785024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362073088))), name = tensor("op_3370_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3368_cast_fp16)[name = tensor("transpose_179")]; - tensor x_425_cast_fp16 = matmul(transpose_x = x_425_transpose_x_0, transpose_y = x_425_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_3370_to_fp16_palettized)[name = tensor("x_425_cast_fp16")]; - tensor x_427_pad_0 = const()[name = tensor("x_427_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_427_mode_0 = const()[name = tensor("x_427_mode_0"), val = tensor("constant")]; - tensor const_204_to_fp16 = const()[name = tensor("const_204_to_fp16"), val = tensor(0x0p+0)]; - tensor x_427_cast_fp16 = pad(constant_val = const_204_to_fp16, mode = x_427_mode_0, pad = x_427_pad_0, x = x_425_cast_fp16)[name = tensor("x_427_cast_fp16")]; - tensor var_3378 = const()[name = tensor("op_3378"), val = tensor([1, 8, -1, 188])]; - tensor x_429_cast_fp16 = reshape(shape = var_3378, x = x_427_cast_fp16)[name = tensor("x_429_cast_fp16")]; - tensor var_3382_begin_0 = const()[name = tensor("op_3382_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3382_end_0 = const()[name = tensor("op_3382_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3382_end_mask_0 = const()[name = tensor("op_3382_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3382_cast_fp16 = slice_by_index(begin = var_3382_begin_0, end = var_3382_end_0, end_mask = var_3382_end_mask_0, x = x_429_cast_fp16)[name = tensor("op_3382_cast_fp16")]; - tensor var_3383 = const()[name = tensor("op_3383"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_77_cast_fp16 = reshape(shape = var_3383, x = var_3382_cast_fp16)[name = tensor("matrix_bd_77_cast_fp16")]; - tensor matrix_ac_39_transpose_x_0 = const()[name = tensor("matrix_ac_39_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_39_transpose_y_0 = const()[name = tensor("matrix_ac_39_transpose_y_0"), val = tensor(false)]; - tensor transpose_134_perm_0 = const()[name = tensor("transpose_134_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_135_perm_0 = const()[name = tensor("transpose_135_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = tensor("transpose_177")]; - tensor transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3366_cast_fp16)[name = tensor("transpose_178")]; - tensor matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = tensor("matrix_ac_39_cast_fp16")]; - tensor matrix_bd_79_begin_0 = const()[name = tensor("matrix_bd_79_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_79_end_0 = const()[name = tensor("matrix_bd_79_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_79_end_mask_0 = const()[name = tensor("matrix_bd_79_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = tensor("matrix_bd_79_cast_fp16")]; - tensor var_3392_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = tensor("op_3392_cast_fp16")]; - tensor _inversed_scores_77_y_0_to_fp16 = const()[name = tensor("_inversed_scores_77_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_77_cast_fp16 = mul(x = var_3392_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = tensor("_inversed_scores_77_cast_fp16")]; - tensor scores_79_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_3)[name = tensor("scores_79_cast_fp16")]; - tensor var_3398_cast_fp16 = softmax(axis = var_30, x = scores_79_cast_fp16)[name = tensor("op_3398_cast_fp16")]; - tensor input_1021_cast_fp16 = select(a = var_11_to_fp16, b = var_3398_cast_fp16, cond = mask_3)[name = tensor("input_1021_cast_fp16")]; - tensor x_431_transpose_x_0 = const()[name = tensor("x_431_transpose_x_0"), val = tensor(false)]; - tensor x_431_transpose_y_0 = const()[name = tensor("x_431_transpose_y_0"), val = tensor(false)]; - tensor value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_39_cast_fp16)[name = tensor("transpose_176")]; - tensor x_431_cast_fp16 = matmul(transpose_x = x_431_transpose_x_0, transpose_y = x_431_transpose_y_0, x = input_1021_cast_fp16, y = value_41_cast_fp16)[name = tensor("x_431_cast_fp16")]; - tensor var_3402_perm_0 = const()[name = tensor("op_3402_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3403 = const()[name = tensor("op_3403"), val = tensor([1, -1, 1024])]; - tensor var_3402_cast_fp16 = transpose(perm = var_3402_perm_0, x = x_431_cast_fp16)[name = tensor("transpose_175")]; - tensor input_1023_cast_fp16 = reshape(shape = var_3403, x = var_3402_cast_fp16)[name = tensor("input_1023_cast_fp16")]; - tensor module_layers_19_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362073280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362859776))), name = tensor("module_layers_19_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_178_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_self_attn_linear_out_weight_to_fp16_palettized, x = input_1023_cast_fp16)[name = tensor("linear_178_cast_fp16")]; - tensor input_1027_cast_fp16 = add(x = input_1019_cast_fp16, y = linear_178_cast_fp16)[name = tensor("input_1027_cast_fp16")]; - tensor x_435_axes_0 = const()[name = tensor("x_435_axes_0"), val = tensor([-1])]; - tensor module_layers_19_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_19_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362859968)))]; - tensor module_layers_19_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_19_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362862080)))]; - tensor x_435_cast_fp16 = layer_norm(axes = x_435_axes_0, beta = module_layers_19_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_conv_weight_to_fp16, x = input_1027_cast_fp16)[name = tensor("x_435_cast_fp16")]; - tensor input_1029_perm_0 = const()[name = tensor("input_1029_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1031_pad_type_0 = const()[name = tensor("input_1031_pad_type_0"), val = tensor("valid")]; - tensor input_1031_strides_0 = const()[name = tensor("input_1031_strides_0"), val = tensor([1])]; - tensor input_1031_pad_0 = const()[name = tensor("input_1031_pad_0"), val = tensor([0, 0])]; - tensor input_1031_dilations_0 = const()[name = tensor("input_1031_dilations_0"), val = tensor([1])]; - tensor input_1031_groups_0 = const()[name = tensor("input_1031_groups_0"), val = tensor(1)]; - tensor module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362864192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364437120))), name = tensor("module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_1029_cast_fp16 = transpose(perm = input_1029_perm_0, x = x_435_cast_fp16)[name = tensor("transpose_174")]; - tensor input_1031_cast_fp16 = conv(dilations = input_1031_dilations_0, groups = input_1031_groups_0, pad = input_1031_pad_0, pad_type = input_1031_pad_type_0, strides = input_1031_strides_0, weight = module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1029_cast_fp16)[name = tensor("input_1031_cast_fp16")]; - tensor x_437_split_num_splits_0 = const()[name = tensor("x_437_split_num_splits_0"), val = tensor(2)]; - tensor x_437_split_axis_0 = const()[name = tensor("x_437_split_axis_0"), val = tensor(1)]; - tensor x_437_split_cast_fp16_0, tensor x_437_split_cast_fp16_1 = split(axis = x_437_split_axis_0, num_splits = x_437_split_num_splits_0, x = input_1031_cast_fp16)[name = tensor("x_437_split_cast_fp16")]; - tensor x_437_split_1_sigmoid_cast_fp16 = sigmoid(x = x_437_split_cast_fp16_1)[name = tensor("x_437_split_1_sigmoid_cast_fp16")]; - tensor x_437_cast_fp16 = mul(x = x_437_split_cast_fp16_0, y = x_437_split_1_sigmoid_cast_fp16)[name = tensor("x_437_cast_fp16")]; - tensor input_1033_cast_fp16 = select(a = var_11_to_fp16, b = x_437_cast_fp16, cond = var_328)[name = tensor("input_1033_cast_fp16")]; - tensor input_1035_pad_0 = const()[name = tensor("input_1035_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_1035_mode_0 = const()[name = tensor("input_1035_mode_0"), val = tensor("constant")]; - tensor const_207_to_fp16 = const()[name = tensor("const_207_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1035_cast_fp16 = pad(constant_val = const_207_to_fp16, mode = input_1035_mode_0, pad = input_1035_pad_0, x = input_1033_cast_fp16)[name = tensor("input_1035_cast_fp16")]; - tensor input_1037_pad_type_0 = const()[name = tensor("input_1037_pad_type_0"), val = tensor("valid")]; - tensor input_1037_groups_0 = const()[name = tensor("input_1037_groups_0"), val = tensor(1024)]; - tensor input_1037_strides_0 = const()[name = tensor("input_1037_strides_0"), val = tensor([1])]; - tensor input_1037_pad_0 = const()[name = tensor("input_1037_pad_0"), val = tensor([0, 0])]; - tensor input_1037_dilations_0 = const()[name = tensor("input_1037_dilations_0"), val = tensor([1])]; - tensor const_286_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364437312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364444288))), name = tensor("const_286_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_287_to_fp16 = const()[name = tensor("const_287_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364444480)))]; - tensor input_1039_cast_fp16 = conv(bias = const_287_to_fp16, dilations = input_1037_dilations_0, groups = input_1037_groups_0, pad = input_1037_pad_0, pad_type = input_1037_pad_type_0, strides = input_1037_strides_0, weight = const_286_to_fp16_palettized, x = input_1035_cast_fp16)[name = tensor("input_1039_cast_fp16")]; - tensor input_1041_cast_fp16 = silu(x = input_1039_cast_fp16)[name = tensor("input_1041_cast_fp16")]; - tensor x_439_pad_type_0 = const()[name = tensor("x_439_pad_type_0"), val = tensor("valid")]; - tensor x_439_strides_0 = const()[name = tensor("x_439_strides_0"), val = tensor([1])]; - tensor x_439_pad_0 = const()[name = tensor("x_439_pad_0"), val = tensor([0, 0])]; - tensor x_439_dilations_0 = const()[name = tensor("x_439_dilations_0"), val = tensor([1])]; - tensor x_439_groups_0 = const()[name = tensor("x_439_groups_0"), val = tensor(1)]; - tensor module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364446592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365233088))), name = tensor("module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_439_cast_fp16 = conv(dilations = x_439_dilations_0, groups = x_439_groups_0, pad = x_439_pad_0, pad_type = x_439_pad_type_0, strides = x_439_strides_0, weight = module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1041_cast_fp16)[name = tensor("x_439_cast_fp16")]; - tensor input_1043_perm_0 = const()[name = tensor("input_1043_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_439_cast_fp16)[name = tensor("transpose_173")]; - tensor input_1045_cast_fp16 = add(x = input_1027_cast_fp16, y = input_1043_cast_fp16)[name = tensor("input_1045_cast_fp16")]; - tensor input_1047_axes_0 = const()[name = tensor("input_1047_axes_0"), val = tensor([-1])]; - tensor module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365233280)))]; - tensor module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365235392)))]; - tensor input_1047_cast_fp16 = layer_norm(axes = input_1047_axes_0, beta = module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1045_cast_fp16)[name = tensor("input_1047_cast_fp16")]; - tensor module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365237504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(368383296))), name = tensor("module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_179_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1047_cast_fp16)[name = tensor("linear_179_cast_fp16")]; - tensor input_1051_cast_fp16 = silu(x = linear_179_cast_fp16)[name = tensor("input_1051_cast_fp16")]; - tensor module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(368383488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371529280))), name = tensor("module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_180_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1051_cast_fp16)[name = tensor("linear_180_cast_fp16")]; - tensor var_3463_to_fp16 = const()[name = tensor("op_3463_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3464_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3463_to_fp16)[name = tensor("op_3464_cast_fp16")]; - tensor input_1057_cast_fp16 = add(x = input_1045_cast_fp16, y = var_3464_cast_fp16)[name = tensor("input_1057_cast_fp16")]; - tensor input_1059_axes_0 = const()[name = tensor("input_1059_axes_0"), val = tensor([-1])]; - tensor module_layers_19_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_19_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371529472)))]; - tensor module_layers_19_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_19_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371531584)))]; - tensor input_1059_cast_fp16 = layer_norm(axes = input_1059_axes_0, beta = module_layers_19_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_out_weight_to_fp16, x = input_1057_cast_fp16)[name = tensor("input_1059_cast_fp16")]; - tensor input_1061_axes_0 = const()[name = tensor("input_1061_axes_0"), val = tensor([-1])]; - tensor module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371533696)))]; - tensor module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371535808)))]; - tensor input_1061_cast_fp16 = layer_norm(axes = input_1061_axes_0, beta = module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1059_cast_fp16)[name = tensor("input_1061_cast_fp16")]; - tensor module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371537920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(374683712))), name = tensor("module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_181_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1061_cast_fp16)[name = tensor("linear_181_cast_fp16")]; - tensor input_1065_cast_fp16 = silu(x = linear_181_cast_fp16)[name = tensor("input_1065_cast_fp16")]; - tensor module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(374683904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(377829696))), name = tensor("module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_182_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1065_cast_fp16)[name = tensor("linear_182_cast_fp16")]; - tensor var_3492_to_fp16 = const()[name = tensor("op_3492_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3493_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3492_to_fp16)[name = tensor("op_3493_cast_fp16")]; - tensor input_1071_cast_fp16 = add(x = input_1059_cast_fp16, y = var_3493_cast_fp16)[name = tensor("input_1071_cast_fp16")]; - tensor query_41_axes_0 = const()[name = tensor("query_41_axes_0"), val = tensor([-1])]; - tensor module_layers_20_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_20_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(377829888)))]; - tensor module_layers_20_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_20_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(377832000)))]; - tensor query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_self_att_weight_to_fp16, x = input_1071_cast_fp16)[name = tensor("query_41_cast_fp16")]; - tensor module_layers_20_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(377834112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378620608))), name = tensor("module_layers_20_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_183_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_q_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = tensor("linear_183_cast_fp16")]; - tensor var_3509 = const()[name = tensor("op_3509"), val = tensor([1, -1, 8, 128])]; - tensor q_121_cast_fp16 = reshape(shape = var_3509, x = linear_183_cast_fp16)[name = tensor("q_121_cast_fp16")]; - tensor module_layers_20_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378620800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(379407296))), name = tensor("module_layers_20_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_184_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_k_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = tensor("linear_184_cast_fp16")]; - tensor var_3513 = const()[name = tensor("op_3513"), val = tensor([1, -1, 8, 128])]; - tensor k_81_cast_fp16 = reshape(shape = var_3513, x = linear_184_cast_fp16)[name = tensor("k_81_cast_fp16")]; - tensor module_layers_20_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(379407488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380193984))), name = tensor("module_layers_20_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_185_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_v_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = tensor("linear_185_cast_fp16")]; - tensor var_3517 = const()[name = tensor("op_3517"), val = tensor([1, -1, 8, 128])]; - tensor v_41_cast_fp16 = reshape(shape = var_3517, x = linear_185_cast_fp16)[name = tensor("v_41_cast_fp16")]; - tensor value_43_perm_0 = const()[name = tensor("value_43_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380194176)))]; - tensor var_3529_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3529_cast_fp16")]; - tensor module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380196288)))]; - tensor var_3531_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3531_cast_fp16")]; - tensor q_with_bias_v_41_perm_0 = const()[name = tensor("q_with_bias_v_41_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_447_transpose_x_0 = const()[name = tensor("x_447_transpose_x_0"), val = tensor(false)]; - tensor x_447_transpose_y_0 = const()[name = tensor("x_447_transpose_y_0"), val = tensor(false)]; - tensor op_3533_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380198400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380486464))), name = tensor("op_3533_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3531_cast_fp16)[name = tensor("transpose_172")]; - tensor x_447_cast_fp16 = matmul(transpose_x = x_447_transpose_x_0, transpose_y = x_447_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_3533_to_fp16_palettized)[name = tensor("x_447_cast_fp16")]; - tensor x_449_pad_0 = const()[name = tensor("x_449_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_449_mode_0 = const()[name = tensor("x_449_mode_0"), val = tensor("constant")]; - tensor const_214_to_fp16 = const()[name = tensor("const_214_to_fp16"), val = tensor(0x0p+0)]; - tensor x_449_cast_fp16 = pad(constant_val = const_214_to_fp16, mode = x_449_mode_0, pad = x_449_pad_0, x = x_447_cast_fp16)[name = tensor("x_449_cast_fp16")]; - tensor var_3541 = const()[name = tensor("op_3541"), val = tensor([1, 8, -1, 188])]; - tensor x_451_cast_fp16 = reshape(shape = var_3541, x = x_449_cast_fp16)[name = tensor("x_451_cast_fp16")]; - tensor var_3545_begin_0 = const()[name = tensor("op_3545_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3545_end_0 = const()[name = tensor("op_3545_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3545_end_mask_0 = const()[name = tensor("op_3545_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3545_cast_fp16 = slice_by_index(begin = var_3545_begin_0, end = var_3545_end_0, end_mask = var_3545_end_mask_0, x = x_451_cast_fp16)[name = tensor("op_3545_cast_fp16")]; - tensor var_3546 = const()[name = tensor("op_3546"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_81_cast_fp16 = reshape(shape = var_3546, x = var_3545_cast_fp16)[name = tensor("matrix_bd_81_cast_fp16")]; - tensor matrix_ac_41_transpose_x_0 = const()[name = tensor("matrix_ac_41_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_41_transpose_y_0 = const()[name = tensor("matrix_ac_41_transpose_y_0"), val = tensor(false)]; - tensor transpose_136_perm_0 = const()[name = tensor("transpose_136_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_137_perm_0 = const()[name = tensor("transpose_137_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = tensor("transpose_170")]; - tensor transpose_136 = transpose(perm = transpose_136_perm_0, x = var_3529_cast_fp16)[name = tensor("transpose_171")]; - tensor matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = tensor("matrix_ac_41_cast_fp16")]; - tensor matrix_bd_83_begin_0 = const()[name = tensor("matrix_bd_83_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_83_end_0 = const()[name = tensor("matrix_bd_83_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_83_end_mask_0 = const()[name = tensor("matrix_bd_83_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = tensor("matrix_bd_83_cast_fp16")]; - tensor var_3555_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = tensor("op_3555_cast_fp16")]; - tensor _inversed_scores_81_y_0_to_fp16 = const()[name = tensor("_inversed_scores_81_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_81_cast_fp16 = mul(x = var_3555_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = tensor("_inversed_scores_81_cast_fp16")]; - tensor scores_83_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_3)[name = tensor("scores_83_cast_fp16")]; - tensor var_3561_cast_fp16 = softmax(axis = var_30, x = scores_83_cast_fp16)[name = tensor("op_3561_cast_fp16")]; - tensor input_1073_cast_fp16 = select(a = var_11_to_fp16, b = var_3561_cast_fp16, cond = mask_3)[name = tensor("input_1073_cast_fp16")]; - tensor x_453_transpose_x_0 = const()[name = tensor("x_453_transpose_x_0"), val = tensor(false)]; - tensor x_453_transpose_y_0 = const()[name = tensor("x_453_transpose_y_0"), val = tensor(false)]; - tensor value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_41_cast_fp16)[name = tensor("transpose_169")]; - tensor x_453_cast_fp16 = matmul(transpose_x = x_453_transpose_x_0, transpose_y = x_453_transpose_y_0, x = input_1073_cast_fp16, y = value_43_cast_fp16)[name = tensor("x_453_cast_fp16")]; - tensor var_3565_perm_0 = const()[name = tensor("op_3565_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3566 = const()[name = tensor("op_3566"), val = tensor([1, -1, 1024])]; - tensor var_3565_cast_fp16 = transpose(perm = var_3565_perm_0, x = x_453_cast_fp16)[name = tensor("transpose_168")]; - tensor input_1075_cast_fp16 = reshape(shape = var_3566, x = var_3565_cast_fp16)[name = tensor("input_1075_cast_fp16")]; - tensor module_layers_20_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380486656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(381273152))), name = tensor("module_layers_20_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_187_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_self_attn_linear_out_weight_to_fp16_palettized, x = input_1075_cast_fp16)[name = tensor("linear_187_cast_fp16")]; - tensor input_1079_cast_fp16 = add(x = input_1071_cast_fp16, y = linear_187_cast_fp16)[name = tensor("input_1079_cast_fp16")]; - tensor x_457_axes_0 = const()[name = tensor("x_457_axes_0"), val = tensor([-1])]; - tensor module_layers_20_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_20_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(381273344)))]; - tensor module_layers_20_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_20_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(381275456)))]; - tensor x_457_cast_fp16 = layer_norm(axes = x_457_axes_0, beta = module_layers_20_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_conv_weight_to_fp16, x = input_1079_cast_fp16)[name = tensor("x_457_cast_fp16")]; - tensor input_1081_perm_0 = const()[name = tensor("input_1081_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1083_pad_type_0 = const()[name = tensor("input_1083_pad_type_0"), val = tensor("valid")]; - tensor input_1083_strides_0 = const()[name = tensor("input_1083_strides_0"), val = tensor([1])]; - tensor input_1083_pad_0 = const()[name = tensor("input_1083_pad_0"), val = tensor([0, 0])]; - tensor input_1083_dilations_0 = const()[name = tensor("input_1083_dilations_0"), val = tensor([1])]; - tensor input_1083_groups_0 = const()[name = tensor("input_1083_groups_0"), val = tensor(1)]; - tensor module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(381277568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382850496))), name = tensor("module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_1081_cast_fp16 = transpose(perm = input_1081_perm_0, x = x_457_cast_fp16)[name = tensor("transpose_167")]; - tensor input_1083_cast_fp16 = conv(dilations = input_1083_dilations_0, groups = input_1083_groups_0, pad = input_1083_pad_0, pad_type = input_1083_pad_type_0, strides = input_1083_strides_0, weight = module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1081_cast_fp16)[name = tensor("input_1083_cast_fp16")]; - tensor x_459_split_num_splits_0 = const()[name = tensor("x_459_split_num_splits_0"), val = tensor(2)]; - tensor x_459_split_axis_0 = const()[name = tensor("x_459_split_axis_0"), val = tensor(1)]; - tensor x_459_split_cast_fp16_0, tensor x_459_split_cast_fp16_1 = split(axis = x_459_split_axis_0, num_splits = x_459_split_num_splits_0, x = input_1083_cast_fp16)[name = tensor("x_459_split_cast_fp16")]; - tensor x_459_split_1_sigmoid_cast_fp16 = sigmoid(x = x_459_split_cast_fp16_1)[name = tensor("x_459_split_1_sigmoid_cast_fp16")]; - tensor x_459_cast_fp16 = mul(x = x_459_split_cast_fp16_0, y = x_459_split_1_sigmoid_cast_fp16)[name = tensor("x_459_cast_fp16")]; - tensor input_1085_cast_fp16 = select(a = var_11_to_fp16, b = x_459_cast_fp16, cond = var_328)[name = tensor("input_1085_cast_fp16")]; - tensor input_1087_pad_0 = const()[name = tensor("input_1087_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_1087_mode_0 = const()[name = tensor("input_1087_mode_0"), val = tensor("constant")]; - tensor const_217_to_fp16 = const()[name = tensor("const_217_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1087_cast_fp16 = pad(constant_val = const_217_to_fp16, mode = input_1087_mode_0, pad = input_1087_pad_0, x = input_1085_cast_fp16)[name = tensor("input_1087_cast_fp16")]; - tensor input_1089_pad_type_0 = const()[name = tensor("input_1089_pad_type_0"), val = tensor("valid")]; - tensor input_1089_groups_0 = const()[name = tensor("input_1089_groups_0"), val = tensor(1024)]; - tensor input_1089_strides_0 = const()[name = tensor("input_1089_strides_0"), val = tensor([1])]; - tensor input_1089_pad_0 = const()[name = tensor("input_1089_pad_0"), val = tensor([0, 0])]; - tensor input_1089_dilations_0 = const()[name = tensor("input_1089_dilations_0"), val = tensor([1])]; - tensor const_288_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382850688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382857664))), name = tensor("const_288_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_289_to_fp16 = const()[name = tensor("const_289_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382857856)))]; - tensor input_1091_cast_fp16 = conv(bias = const_289_to_fp16, dilations = input_1089_dilations_0, groups = input_1089_groups_0, pad = input_1089_pad_0, pad_type = input_1089_pad_type_0, strides = input_1089_strides_0, weight = const_288_to_fp16_palettized, x = input_1087_cast_fp16)[name = tensor("input_1091_cast_fp16")]; - tensor input_1093_cast_fp16 = silu(x = input_1091_cast_fp16)[name = tensor("input_1093_cast_fp16")]; - tensor x_461_pad_type_0 = const()[name = tensor("x_461_pad_type_0"), val = tensor("valid")]; - tensor x_461_strides_0 = const()[name = tensor("x_461_strides_0"), val = tensor([1])]; - tensor x_461_pad_0 = const()[name = tensor("x_461_pad_0"), val = tensor([0, 0])]; - tensor x_461_dilations_0 = const()[name = tensor("x_461_dilations_0"), val = tensor([1])]; - tensor x_461_groups_0 = const()[name = tensor("x_461_groups_0"), val = tensor(1)]; - tensor module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382859968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383646464))), name = tensor("module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_461_cast_fp16 = conv(dilations = x_461_dilations_0, groups = x_461_groups_0, pad = x_461_pad_0, pad_type = x_461_pad_type_0, strides = x_461_strides_0, weight = module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1093_cast_fp16)[name = tensor("x_461_cast_fp16")]; - tensor input_1095_perm_0 = const()[name = tensor("input_1095_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_461_cast_fp16)[name = tensor("transpose_166")]; - tensor input_1097_cast_fp16 = add(x = input_1079_cast_fp16, y = input_1095_cast_fp16)[name = tensor("input_1097_cast_fp16")]; - tensor input_1099_axes_0 = const()[name = tensor("input_1099_axes_0"), val = tensor([-1])]; - tensor module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383646656)))]; - tensor module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383648768)))]; - tensor input_1099_cast_fp16 = layer_norm(axes = input_1099_axes_0, beta = module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1097_cast_fp16)[name = tensor("input_1099_cast_fp16")]; - tensor module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383650880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(386796672))), name = tensor("module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_188_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1099_cast_fp16)[name = tensor("linear_188_cast_fp16")]; - tensor input_1103_cast_fp16 = silu(x = linear_188_cast_fp16)[name = tensor("input_1103_cast_fp16")]; - tensor module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(386796864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389942656))), name = tensor("module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_189_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1103_cast_fp16)[name = tensor("linear_189_cast_fp16")]; - tensor var_3626_to_fp16 = const()[name = tensor("op_3626_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3627_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_3626_to_fp16)[name = tensor("op_3627_cast_fp16")]; - tensor input_1109_cast_fp16 = add(x = input_1097_cast_fp16, y = var_3627_cast_fp16)[name = tensor("input_1109_cast_fp16")]; - tensor input_1111_axes_0 = const()[name = tensor("input_1111_axes_0"), val = tensor([-1])]; - tensor module_layers_20_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_20_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389942848)))]; - tensor module_layers_20_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_20_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389944960)))]; - tensor input_1111_cast_fp16 = layer_norm(axes = input_1111_axes_0, beta = module_layers_20_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_out_weight_to_fp16, x = input_1109_cast_fp16)[name = tensor("input_1111_cast_fp16")]; - tensor input_1113_axes_0 = const()[name = tensor("input_1113_axes_0"), val = tensor([-1])]; - tensor module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389947072)))]; - tensor module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389949184)))]; - tensor input_1113_cast_fp16 = layer_norm(axes = input_1113_axes_0, beta = module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1111_cast_fp16)[name = tensor("input_1113_cast_fp16")]; - tensor module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389951296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393097088))), name = tensor("module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_190_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1113_cast_fp16)[name = tensor("linear_190_cast_fp16")]; - tensor input_1117_cast_fp16 = silu(x = linear_190_cast_fp16)[name = tensor("input_1117_cast_fp16")]; - tensor module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393097280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(396243072))), name = tensor("module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_191_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1117_cast_fp16)[name = tensor("linear_191_cast_fp16")]; - tensor var_3655_to_fp16 = const()[name = tensor("op_3655_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3656_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_3655_to_fp16)[name = tensor("op_3656_cast_fp16")]; - tensor input_1123_cast_fp16 = add(x = input_1111_cast_fp16, y = var_3656_cast_fp16)[name = tensor("input_1123_cast_fp16")]; - tensor query_43_axes_0 = const()[name = tensor("query_43_axes_0"), val = tensor([-1])]; - tensor module_layers_21_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_21_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(396243264)))]; - tensor module_layers_21_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_21_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(396245376)))]; - tensor query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_self_att_weight_to_fp16, x = input_1123_cast_fp16)[name = tensor("query_43_cast_fp16")]; - tensor module_layers_21_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(396247488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397033984))), name = tensor("module_layers_21_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_192_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_q_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = tensor("linear_192_cast_fp16")]; - tensor var_3672 = const()[name = tensor("op_3672"), val = tensor([1, -1, 8, 128])]; - tensor q_127_cast_fp16 = reshape(shape = var_3672, x = linear_192_cast_fp16)[name = tensor("q_127_cast_fp16")]; - tensor module_layers_21_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397034176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397820672))), name = tensor("module_layers_21_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_193_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_k_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = tensor("linear_193_cast_fp16")]; - tensor var_3676 = const()[name = tensor("op_3676"), val = tensor([1, -1, 8, 128])]; - tensor k_85_cast_fp16 = reshape(shape = var_3676, x = linear_193_cast_fp16)[name = tensor("k_85_cast_fp16")]; - tensor module_layers_21_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397820864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398607360))), name = tensor("module_layers_21_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_194_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_v_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = tensor("linear_194_cast_fp16")]; - tensor var_3680 = const()[name = tensor("op_3680"), val = tensor([1, -1, 8, 128])]; - tensor v_43_cast_fp16 = reshape(shape = var_3680, x = linear_194_cast_fp16)[name = tensor("v_43_cast_fp16")]; - tensor value_45_perm_0 = const()[name = tensor("value_45_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398607552)))]; - tensor var_3692_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3692_cast_fp16")]; - tensor module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398609664)))]; - tensor var_3694_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3694_cast_fp16")]; - tensor q_with_bias_v_43_perm_0 = const()[name = tensor("q_with_bias_v_43_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_469_transpose_x_0 = const()[name = tensor("x_469_transpose_x_0"), val = tensor(false)]; - tensor x_469_transpose_y_0 = const()[name = tensor("x_469_transpose_y_0"), val = tensor(false)]; - tensor op_3696_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398611776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398899840))), name = tensor("op_3696_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3694_cast_fp16)[name = tensor("transpose_165")]; - tensor x_469_cast_fp16 = matmul(transpose_x = x_469_transpose_x_0, transpose_y = x_469_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_3696_to_fp16_palettized)[name = tensor("x_469_cast_fp16")]; - tensor x_471_pad_0 = const()[name = tensor("x_471_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_471_mode_0 = const()[name = tensor("x_471_mode_0"), val = tensor("constant")]; - tensor const_224_to_fp16 = const()[name = tensor("const_224_to_fp16"), val = tensor(0x0p+0)]; - tensor x_471_cast_fp16 = pad(constant_val = const_224_to_fp16, mode = x_471_mode_0, pad = x_471_pad_0, x = x_469_cast_fp16)[name = tensor("x_471_cast_fp16")]; - tensor var_3704 = const()[name = tensor("op_3704"), val = tensor([1, 8, -1, 188])]; - tensor x_473_cast_fp16 = reshape(shape = var_3704, x = x_471_cast_fp16)[name = tensor("x_473_cast_fp16")]; - tensor var_3708_begin_0 = const()[name = tensor("op_3708_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3708_end_0 = const()[name = tensor("op_3708_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3708_end_mask_0 = const()[name = tensor("op_3708_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3708_cast_fp16 = slice_by_index(begin = var_3708_begin_0, end = var_3708_end_0, end_mask = var_3708_end_mask_0, x = x_473_cast_fp16)[name = tensor("op_3708_cast_fp16")]; - tensor var_3709 = const()[name = tensor("op_3709"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_85_cast_fp16 = reshape(shape = var_3709, x = var_3708_cast_fp16)[name = tensor("matrix_bd_85_cast_fp16")]; - tensor matrix_ac_43_transpose_x_0 = const()[name = tensor("matrix_ac_43_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_43_transpose_y_0 = const()[name = tensor("matrix_ac_43_transpose_y_0"), val = tensor(false)]; - tensor transpose_138_perm_0 = const()[name = tensor("transpose_138_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_139_perm_0 = const()[name = tensor("transpose_139_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = tensor("transpose_163")]; - tensor transpose_138 = transpose(perm = transpose_138_perm_0, x = var_3692_cast_fp16)[name = tensor("transpose_164")]; - tensor matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = tensor("matrix_ac_43_cast_fp16")]; - tensor matrix_bd_87_begin_0 = const()[name = tensor("matrix_bd_87_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_87_end_0 = const()[name = tensor("matrix_bd_87_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_87_end_mask_0 = const()[name = tensor("matrix_bd_87_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = tensor("matrix_bd_87_cast_fp16")]; - tensor var_3718_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = tensor("op_3718_cast_fp16")]; - tensor _inversed_scores_85_y_0_to_fp16 = const()[name = tensor("_inversed_scores_85_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_85_cast_fp16 = mul(x = var_3718_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = tensor("_inversed_scores_85_cast_fp16")]; - tensor scores_87_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_3)[name = tensor("scores_87_cast_fp16")]; - tensor var_3724_cast_fp16 = softmax(axis = var_30, x = scores_87_cast_fp16)[name = tensor("op_3724_cast_fp16")]; - tensor input_1125_cast_fp16 = select(a = var_11_to_fp16, b = var_3724_cast_fp16, cond = mask_3)[name = tensor("input_1125_cast_fp16")]; - tensor x_475_transpose_x_0 = const()[name = tensor("x_475_transpose_x_0"), val = tensor(false)]; - tensor x_475_transpose_y_0 = const()[name = tensor("x_475_transpose_y_0"), val = tensor(false)]; - tensor value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_43_cast_fp16)[name = tensor("transpose_162")]; - tensor x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = input_1125_cast_fp16, y = value_45_cast_fp16)[name = tensor("x_475_cast_fp16")]; - tensor var_3728_perm_0 = const()[name = tensor("op_3728_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3729 = const()[name = tensor("op_3729"), val = tensor([1, -1, 1024])]; - tensor var_3728_cast_fp16 = transpose(perm = var_3728_perm_0, x = x_475_cast_fp16)[name = tensor("transpose_161")]; - tensor input_1127_cast_fp16 = reshape(shape = var_3729, x = var_3728_cast_fp16)[name = tensor("input_1127_cast_fp16")]; - tensor module_layers_21_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398900032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(399686528))), name = tensor("module_layers_21_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_196_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_self_attn_linear_out_weight_to_fp16_palettized, x = input_1127_cast_fp16)[name = tensor("linear_196_cast_fp16")]; - tensor input_1131_cast_fp16 = add(x = input_1123_cast_fp16, y = linear_196_cast_fp16)[name = tensor("input_1131_cast_fp16")]; - tensor x_479_axes_0 = const()[name = tensor("x_479_axes_0"), val = tensor([-1])]; - tensor module_layers_21_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_21_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(399686720)))]; - tensor module_layers_21_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_21_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(399688832)))]; - tensor x_479_cast_fp16 = layer_norm(axes = x_479_axes_0, beta = module_layers_21_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_conv_weight_to_fp16, x = input_1131_cast_fp16)[name = tensor("x_479_cast_fp16")]; - tensor input_1133_perm_0 = const()[name = tensor("input_1133_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1135_pad_type_0 = const()[name = tensor("input_1135_pad_type_0"), val = tensor("valid")]; - tensor input_1135_strides_0 = const()[name = tensor("input_1135_strides_0"), val = tensor([1])]; - tensor input_1135_pad_0 = const()[name = tensor("input_1135_pad_0"), val = tensor([0, 0])]; - tensor input_1135_dilations_0 = const()[name = tensor("input_1135_dilations_0"), val = tensor([1])]; - tensor input_1135_groups_0 = const()[name = tensor("input_1135_groups_0"), val = tensor(1)]; - tensor module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(399690944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401263872))), name = tensor("module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_1133_cast_fp16 = transpose(perm = input_1133_perm_0, x = x_479_cast_fp16)[name = tensor("transpose_160")]; - tensor input_1135_cast_fp16 = conv(dilations = input_1135_dilations_0, groups = input_1135_groups_0, pad = input_1135_pad_0, pad_type = input_1135_pad_type_0, strides = input_1135_strides_0, weight = module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1133_cast_fp16)[name = tensor("input_1135_cast_fp16")]; - tensor x_481_split_num_splits_0 = const()[name = tensor("x_481_split_num_splits_0"), val = tensor(2)]; - tensor x_481_split_axis_0 = const()[name = tensor("x_481_split_axis_0"), val = tensor(1)]; - tensor x_481_split_cast_fp16_0, tensor x_481_split_cast_fp16_1 = split(axis = x_481_split_axis_0, num_splits = x_481_split_num_splits_0, x = input_1135_cast_fp16)[name = tensor("x_481_split_cast_fp16")]; - tensor x_481_split_1_sigmoid_cast_fp16 = sigmoid(x = x_481_split_cast_fp16_1)[name = tensor("x_481_split_1_sigmoid_cast_fp16")]; - tensor x_481_cast_fp16 = mul(x = x_481_split_cast_fp16_0, y = x_481_split_1_sigmoid_cast_fp16)[name = tensor("x_481_cast_fp16")]; - tensor input_1137_cast_fp16 = select(a = var_11_to_fp16, b = x_481_cast_fp16, cond = var_328)[name = tensor("input_1137_cast_fp16")]; - tensor input_1139_pad_0 = const()[name = tensor("input_1139_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_1139_mode_0 = const()[name = tensor("input_1139_mode_0"), val = tensor("constant")]; - tensor const_227_to_fp16 = const()[name = tensor("const_227_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1139_cast_fp16 = pad(constant_val = const_227_to_fp16, mode = input_1139_mode_0, pad = input_1139_pad_0, x = input_1137_cast_fp16)[name = tensor("input_1139_cast_fp16")]; - tensor input_1141_pad_type_0 = const()[name = tensor("input_1141_pad_type_0"), val = tensor("valid")]; - tensor input_1141_groups_0 = const()[name = tensor("input_1141_groups_0"), val = tensor(1024)]; - tensor input_1141_strides_0 = const()[name = tensor("input_1141_strides_0"), val = tensor([1])]; - tensor input_1141_pad_0 = const()[name = tensor("input_1141_pad_0"), val = tensor([0, 0])]; - tensor input_1141_dilations_0 = const()[name = tensor("input_1141_dilations_0"), val = tensor([1])]; - tensor const_290_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401264064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401271040))), name = tensor("const_290_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_291_to_fp16 = const()[name = tensor("const_291_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401271232)))]; - tensor input_1143_cast_fp16 = conv(bias = const_291_to_fp16, dilations = input_1141_dilations_0, groups = input_1141_groups_0, pad = input_1141_pad_0, pad_type = input_1141_pad_type_0, strides = input_1141_strides_0, weight = const_290_to_fp16_palettized, x = input_1139_cast_fp16)[name = tensor("input_1143_cast_fp16")]; - tensor input_1145_cast_fp16 = silu(x = input_1143_cast_fp16)[name = tensor("input_1145_cast_fp16")]; - tensor x_483_pad_type_0 = const()[name = tensor("x_483_pad_type_0"), val = tensor("valid")]; - tensor x_483_strides_0 = const()[name = tensor("x_483_strides_0"), val = tensor([1])]; - tensor x_483_pad_0 = const()[name = tensor("x_483_pad_0"), val = tensor([0, 0])]; - tensor x_483_dilations_0 = const()[name = tensor("x_483_dilations_0"), val = tensor([1])]; - tensor x_483_groups_0 = const()[name = tensor("x_483_groups_0"), val = tensor(1)]; - tensor module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401273344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402059840))), name = tensor("module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_483_cast_fp16 = conv(dilations = x_483_dilations_0, groups = x_483_groups_0, pad = x_483_pad_0, pad_type = x_483_pad_type_0, strides = x_483_strides_0, weight = module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1145_cast_fp16)[name = tensor("x_483_cast_fp16")]; - tensor input_1147_perm_0 = const()[name = tensor("input_1147_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_483_cast_fp16)[name = tensor("transpose_159")]; - tensor input_1149_cast_fp16 = add(x = input_1131_cast_fp16, y = input_1147_cast_fp16)[name = tensor("input_1149_cast_fp16")]; - tensor input_1151_axes_0 = const()[name = tensor("input_1151_axes_0"), val = tensor([-1])]; - tensor module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402060032)))]; - tensor module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402062144)))]; - tensor input_1151_cast_fp16 = layer_norm(axes = input_1151_axes_0, beta = module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1149_cast_fp16)[name = tensor("input_1151_cast_fp16")]; - tensor module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402064256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405210048))), name = tensor("module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_197_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1151_cast_fp16)[name = tensor("linear_197_cast_fp16")]; - tensor input_1155_cast_fp16 = silu(x = linear_197_cast_fp16)[name = tensor("input_1155_cast_fp16")]; - tensor module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405210240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408356032))), name = tensor("module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_198_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1155_cast_fp16)[name = tensor("linear_198_cast_fp16")]; - tensor var_3789_to_fp16 = const()[name = tensor("op_3789_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3790_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_3789_to_fp16)[name = tensor("op_3790_cast_fp16")]; - tensor input_1161_cast_fp16 = add(x = input_1149_cast_fp16, y = var_3790_cast_fp16)[name = tensor("input_1161_cast_fp16")]; - tensor input_1163_axes_0 = const()[name = tensor("input_1163_axes_0"), val = tensor([-1])]; - tensor module_layers_21_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_21_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408356224)))]; - tensor module_layers_21_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_21_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408358336)))]; - tensor input_1163_cast_fp16 = layer_norm(axes = input_1163_axes_0, beta = module_layers_21_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_out_weight_to_fp16, x = input_1161_cast_fp16)[name = tensor("input_1163_cast_fp16")]; - tensor input_1165_axes_0 = const()[name = tensor("input_1165_axes_0"), val = tensor([-1])]; - tensor module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408360448)))]; - tensor module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408362560)))]; - tensor input_1165_cast_fp16 = layer_norm(axes = input_1165_axes_0, beta = module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1163_cast_fp16)[name = tensor("input_1165_cast_fp16")]; - tensor module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408364672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(411510464))), name = tensor("module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_199_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1165_cast_fp16)[name = tensor("linear_199_cast_fp16")]; - tensor input_1169_cast_fp16 = silu(x = linear_199_cast_fp16)[name = tensor("input_1169_cast_fp16")]; - tensor module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(411510656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414656448))), name = tensor("module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_200_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1169_cast_fp16)[name = tensor("linear_200_cast_fp16")]; - tensor var_3818_to_fp16 = const()[name = tensor("op_3818_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3819_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_3818_to_fp16)[name = tensor("op_3819_cast_fp16")]; - tensor input_1175_cast_fp16 = add(x = input_1163_cast_fp16, y = var_3819_cast_fp16)[name = tensor("input_1175_cast_fp16")]; - tensor query_45_axes_0 = const()[name = tensor("query_45_axes_0"), val = tensor([-1])]; - tensor module_layers_22_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_22_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414656640)))]; - tensor module_layers_22_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_22_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414658752)))]; - tensor query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_self_att_weight_to_fp16, x = input_1175_cast_fp16)[name = tensor("query_45_cast_fp16")]; - tensor module_layers_22_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414660864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415447360))), name = tensor("module_layers_22_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_201_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_q_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = tensor("linear_201_cast_fp16")]; - tensor var_3835 = const()[name = tensor("op_3835"), val = tensor([1, -1, 8, 128])]; - tensor q_133_cast_fp16 = reshape(shape = var_3835, x = linear_201_cast_fp16)[name = tensor("q_133_cast_fp16")]; - tensor module_layers_22_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415447552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416234048))), name = tensor("module_layers_22_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_202_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_k_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = tensor("linear_202_cast_fp16")]; - tensor var_3839 = const()[name = tensor("op_3839"), val = tensor([1, -1, 8, 128])]; - tensor k_89_cast_fp16 = reshape(shape = var_3839, x = linear_202_cast_fp16)[name = tensor("k_89_cast_fp16")]; - tensor module_layers_22_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416234240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417020736))), name = tensor("module_layers_22_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_203_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_v_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = tensor("linear_203_cast_fp16")]; - tensor var_3843 = const()[name = tensor("op_3843"), val = tensor([1, -1, 8, 128])]; - tensor v_45_cast_fp16 = reshape(shape = var_3843, x = linear_203_cast_fp16)[name = tensor("v_45_cast_fp16")]; - tensor value_47_perm_0 = const()[name = tensor("value_47_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417020928)))]; - tensor var_3855_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3855_cast_fp16")]; - tensor module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417023040)))]; - tensor var_3857_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3857_cast_fp16")]; - tensor q_with_bias_v_45_perm_0 = const()[name = tensor("q_with_bias_v_45_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_491_transpose_x_0 = const()[name = tensor("x_491_transpose_x_0"), val = tensor(false)]; - tensor x_491_transpose_y_0 = const()[name = tensor("x_491_transpose_y_0"), val = tensor(false)]; - tensor op_3859_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417025152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417313216))), name = tensor("op_3859_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_3857_cast_fp16)[name = tensor("transpose_158")]; - tensor x_491_cast_fp16 = matmul(transpose_x = x_491_transpose_x_0, transpose_y = x_491_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_3859_to_fp16_palettized)[name = tensor("x_491_cast_fp16")]; - tensor x_493_pad_0 = const()[name = tensor("x_493_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_493_mode_0 = const()[name = tensor("x_493_mode_0"), val = tensor("constant")]; - tensor const_234_to_fp16 = const()[name = tensor("const_234_to_fp16"), val = tensor(0x0p+0)]; - tensor x_493_cast_fp16 = pad(constant_val = const_234_to_fp16, mode = x_493_mode_0, pad = x_493_pad_0, x = x_491_cast_fp16)[name = tensor("x_493_cast_fp16")]; - tensor var_3867 = const()[name = tensor("op_3867"), val = tensor([1, 8, -1, 188])]; - tensor x_495_cast_fp16 = reshape(shape = var_3867, x = x_493_cast_fp16)[name = tensor("x_495_cast_fp16")]; - tensor var_3871_begin_0 = const()[name = tensor("op_3871_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3871_end_0 = const()[name = tensor("op_3871_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3871_end_mask_0 = const()[name = tensor("op_3871_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3871_cast_fp16 = slice_by_index(begin = var_3871_begin_0, end = var_3871_end_0, end_mask = var_3871_end_mask_0, x = x_495_cast_fp16)[name = tensor("op_3871_cast_fp16")]; - tensor var_3872 = const()[name = tensor("op_3872"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_89_cast_fp16 = reshape(shape = var_3872, x = var_3871_cast_fp16)[name = tensor("matrix_bd_89_cast_fp16")]; - tensor matrix_ac_45_transpose_x_0 = const()[name = tensor("matrix_ac_45_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_45_transpose_y_0 = const()[name = tensor("matrix_ac_45_transpose_y_0"), val = tensor(false)]; - tensor transpose_140_perm_0 = const()[name = tensor("transpose_140_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_141_perm_0 = const()[name = tensor("transpose_141_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = tensor("transpose_156")]; - tensor transpose_140 = transpose(perm = transpose_140_perm_0, x = var_3855_cast_fp16)[name = tensor("transpose_157")]; - tensor matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = tensor("matrix_ac_45_cast_fp16")]; - tensor matrix_bd_91_begin_0 = const()[name = tensor("matrix_bd_91_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_91_end_0 = const()[name = tensor("matrix_bd_91_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_91_end_mask_0 = const()[name = tensor("matrix_bd_91_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = tensor("matrix_bd_91_cast_fp16")]; - tensor var_3881_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = tensor("op_3881_cast_fp16")]; - tensor _inversed_scores_89_y_0_to_fp16 = const()[name = tensor("_inversed_scores_89_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_89_cast_fp16 = mul(x = var_3881_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = tensor("_inversed_scores_89_cast_fp16")]; - tensor scores_91_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_3)[name = tensor("scores_91_cast_fp16")]; - tensor var_3887_cast_fp16 = softmax(axis = var_30, x = scores_91_cast_fp16)[name = tensor("op_3887_cast_fp16")]; - tensor input_1177_cast_fp16 = select(a = var_11_to_fp16, b = var_3887_cast_fp16, cond = mask_3)[name = tensor("input_1177_cast_fp16")]; - tensor x_497_transpose_x_0 = const()[name = tensor("x_497_transpose_x_0"), val = tensor(false)]; - tensor x_497_transpose_y_0 = const()[name = tensor("x_497_transpose_y_0"), val = tensor(false)]; - tensor value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_45_cast_fp16)[name = tensor("transpose_155")]; - tensor x_497_cast_fp16 = matmul(transpose_x = x_497_transpose_x_0, transpose_y = x_497_transpose_y_0, x = input_1177_cast_fp16, y = value_47_cast_fp16)[name = tensor("x_497_cast_fp16")]; - tensor var_3891_perm_0 = const()[name = tensor("op_3891_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3892 = const()[name = tensor("op_3892"), val = tensor([1, -1, 1024])]; - tensor var_3891_cast_fp16 = transpose(perm = var_3891_perm_0, x = x_497_cast_fp16)[name = tensor("transpose_154")]; - tensor input_1179_cast_fp16 = reshape(shape = var_3892, x = var_3891_cast_fp16)[name = tensor("input_1179_cast_fp16")]; - tensor module_layers_22_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417313408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418099904))), name = tensor("module_layers_22_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_205_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_self_attn_linear_out_weight_to_fp16_palettized, x = input_1179_cast_fp16)[name = tensor("linear_205_cast_fp16")]; - tensor input_1183_cast_fp16 = add(x = input_1175_cast_fp16, y = linear_205_cast_fp16)[name = tensor("input_1183_cast_fp16")]; - tensor x_501_axes_0 = const()[name = tensor("x_501_axes_0"), val = tensor([-1])]; - tensor module_layers_22_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_22_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418100096)))]; - tensor module_layers_22_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_22_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418102208)))]; - tensor x_501_cast_fp16 = layer_norm(axes = x_501_axes_0, beta = module_layers_22_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_conv_weight_to_fp16, x = input_1183_cast_fp16)[name = tensor("x_501_cast_fp16")]; - tensor input_1185_perm_0 = const()[name = tensor("input_1185_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1187_pad_type_0 = const()[name = tensor("input_1187_pad_type_0"), val = tensor("valid")]; - tensor input_1187_strides_0 = const()[name = tensor("input_1187_strides_0"), val = tensor([1])]; - tensor input_1187_pad_0 = const()[name = tensor("input_1187_pad_0"), val = tensor([0, 0])]; - tensor input_1187_dilations_0 = const()[name = tensor("input_1187_dilations_0"), val = tensor([1])]; - tensor input_1187_groups_0 = const()[name = tensor("input_1187_groups_0"), val = tensor(1)]; - tensor module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418104320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419677248))), name = tensor("module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_1185_cast_fp16 = transpose(perm = input_1185_perm_0, x = x_501_cast_fp16)[name = tensor("transpose_153")]; - tensor input_1187_cast_fp16 = conv(dilations = input_1187_dilations_0, groups = input_1187_groups_0, pad = input_1187_pad_0, pad_type = input_1187_pad_type_0, strides = input_1187_strides_0, weight = module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1185_cast_fp16)[name = tensor("input_1187_cast_fp16")]; - tensor x_503_split_num_splits_0 = const()[name = tensor("x_503_split_num_splits_0"), val = tensor(2)]; - tensor x_503_split_axis_0 = const()[name = tensor("x_503_split_axis_0"), val = tensor(1)]; - tensor x_503_split_cast_fp16_0, tensor x_503_split_cast_fp16_1 = split(axis = x_503_split_axis_0, num_splits = x_503_split_num_splits_0, x = input_1187_cast_fp16)[name = tensor("x_503_split_cast_fp16")]; - tensor x_503_split_1_sigmoid_cast_fp16 = sigmoid(x = x_503_split_cast_fp16_1)[name = tensor("x_503_split_1_sigmoid_cast_fp16")]; - tensor x_503_cast_fp16 = mul(x = x_503_split_cast_fp16_0, y = x_503_split_1_sigmoid_cast_fp16)[name = tensor("x_503_cast_fp16")]; - tensor input_1189_cast_fp16 = select(a = var_11_to_fp16, b = x_503_cast_fp16, cond = var_328)[name = tensor("input_1189_cast_fp16")]; - tensor input_1191_pad_0 = const()[name = tensor("input_1191_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_1191_mode_0 = const()[name = tensor("input_1191_mode_0"), val = tensor("constant")]; - tensor const_237_to_fp16 = const()[name = tensor("const_237_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1191_cast_fp16 = pad(constant_val = const_237_to_fp16, mode = input_1191_mode_0, pad = input_1191_pad_0, x = input_1189_cast_fp16)[name = tensor("input_1191_cast_fp16")]; - tensor input_1193_pad_type_0 = const()[name = tensor("input_1193_pad_type_0"), val = tensor("valid")]; - tensor input_1193_groups_0 = const()[name = tensor("input_1193_groups_0"), val = tensor(1024)]; - tensor input_1193_strides_0 = const()[name = tensor("input_1193_strides_0"), val = tensor([1])]; - tensor input_1193_pad_0 = const()[name = tensor("input_1193_pad_0"), val = tensor([0, 0])]; - tensor input_1193_dilations_0 = const()[name = tensor("input_1193_dilations_0"), val = tensor([1])]; - tensor const_292_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419677440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419684416))), name = tensor("const_292_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_293_to_fp16 = const()[name = tensor("const_293_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419684608)))]; - tensor input_1195_cast_fp16 = conv(bias = const_293_to_fp16, dilations = input_1193_dilations_0, groups = input_1193_groups_0, pad = input_1193_pad_0, pad_type = input_1193_pad_type_0, strides = input_1193_strides_0, weight = const_292_to_fp16_palettized, x = input_1191_cast_fp16)[name = tensor("input_1195_cast_fp16")]; - tensor input_1197_cast_fp16 = silu(x = input_1195_cast_fp16)[name = tensor("input_1197_cast_fp16")]; - tensor x_505_pad_type_0 = const()[name = tensor("x_505_pad_type_0"), val = tensor("valid")]; - tensor x_505_strides_0 = const()[name = tensor("x_505_strides_0"), val = tensor([1])]; - tensor x_505_pad_0 = const()[name = tensor("x_505_pad_0"), val = tensor([0, 0])]; - tensor x_505_dilations_0 = const()[name = tensor("x_505_dilations_0"), val = tensor([1])]; - tensor x_505_groups_0 = const()[name = tensor("x_505_groups_0"), val = tensor(1)]; - tensor module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419686720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420473216))), name = tensor("module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_505_cast_fp16 = conv(dilations = x_505_dilations_0, groups = x_505_groups_0, pad = x_505_pad_0, pad_type = x_505_pad_type_0, strides = x_505_strides_0, weight = module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1197_cast_fp16)[name = tensor("x_505_cast_fp16")]; - tensor input_1199_perm_0 = const()[name = tensor("input_1199_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_505_cast_fp16)[name = tensor("transpose_152")]; - tensor input_1201_cast_fp16 = add(x = input_1183_cast_fp16, y = input_1199_cast_fp16)[name = tensor("input_1201_cast_fp16")]; - tensor input_1203_axes_0 = const()[name = tensor("input_1203_axes_0"), val = tensor([-1])]; - tensor module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420473408)))]; - tensor module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420475520)))]; - tensor input_1203_cast_fp16 = layer_norm(axes = input_1203_axes_0, beta = module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1201_cast_fp16)[name = tensor("input_1203_cast_fp16")]; - tensor module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420477632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(423623424))), name = tensor("module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_206_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1203_cast_fp16)[name = tensor("linear_206_cast_fp16")]; - tensor input_1207_cast_fp16 = silu(x = linear_206_cast_fp16)[name = tensor("input_1207_cast_fp16")]; - tensor module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(423623616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426769408))), name = tensor("module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_207_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1207_cast_fp16)[name = tensor("linear_207_cast_fp16")]; - tensor var_3952_to_fp16 = const()[name = tensor("op_3952_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3953_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_3952_to_fp16)[name = tensor("op_3953_cast_fp16")]; - tensor input_1213_cast_fp16 = add(x = input_1201_cast_fp16, y = var_3953_cast_fp16)[name = tensor("input_1213_cast_fp16")]; - tensor input_1215_axes_0 = const()[name = tensor("input_1215_axes_0"), val = tensor([-1])]; - tensor module_layers_22_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_22_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426769600)))]; - tensor module_layers_22_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_22_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426771712)))]; - tensor input_1215_cast_fp16 = layer_norm(axes = input_1215_axes_0, beta = module_layers_22_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_out_weight_to_fp16, x = input_1213_cast_fp16)[name = tensor("input_1215_cast_fp16")]; - tensor input_1217_axes_0 = const()[name = tensor("input_1217_axes_0"), val = tensor([-1])]; - tensor module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426773824)))]; - tensor module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426775936)))]; - tensor input_1217_cast_fp16 = layer_norm(axes = input_1217_axes_0, beta = module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1215_cast_fp16)[name = tensor("input_1217_cast_fp16")]; - tensor module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426778048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429923840))), name = tensor("module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_208_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1217_cast_fp16)[name = tensor("linear_208_cast_fp16")]; - tensor input_1221_cast_fp16 = silu(x = linear_208_cast_fp16)[name = tensor("input_1221_cast_fp16")]; - tensor module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429924032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433069824))), name = tensor("module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_209_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1221_cast_fp16)[name = tensor("linear_209_cast_fp16")]; - tensor var_3981_to_fp16 = const()[name = tensor("op_3981_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3982_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_3981_to_fp16)[name = tensor("op_3982_cast_fp16")]; - tensor input_1227_cast_fp16 = add(x = input_1215_cast_fp16, y = var_3982_cast_fp16)[name = tensor("input_1227_cast_fp16")]; - tensor query_axes_0 = const()[name = tensor("query_axes_0"), val = tensor([-1])]; - tensor module_layers_23_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_23_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433070016)))]; - tensor module_layers_23_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_23_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433072128)))]; - tensor query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_self_att_weight_to_fp16, x = input_1227_cast_fp16)[name = tensor("query_cast_fp16")]; - tensor module_layers_23_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433074240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433860736))), name = tensor("module_layers_23_self_attn_linear_q_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_210_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_q_weight_to_fp16_palettized, x = query_cast_fp16)[name = tensor("linear_210_cast_fp16")]; - tensor var_3998 = const()[name = tensor("op_3998"), val = tensor([1, -1, 8, 128])]; - tensor q_139_cast_fp16 = reshape(shape = var_3998, x = linear_210_cast_fp16)[name = tensor("q_139_cast_fp16")]; - tensor module_layers_23_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433860928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434647424))), name = tensor("module_layers_23_self_attn_linear_k_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_211_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_k_weight_to_fp16_palettized, x = query_cast_fp16)[name = tensor("linear_211_cast_fp16")]; - tensor var_4002 = const()[name = tensor("op_4002"), val = tensor([1, -1, 8, 128])]; - tensor k_93_cast_fp16 = reshape(shape = var_4002, x = linear_211_cast_fp16)[name = tensor("k_93_cast_fp16")]; - tensor module_layers_23_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434647616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435434112))), name = tensor("module_layers_23_self_attn_linear_v_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_212_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_v_weight_to_fp16_palettized, x = query_cast_fp16)[name = tensor("linear_212_cast_fp16")]; - tensor var_4006 = const()[name = tensor("op_4006"), val = tensor([1, -1, 8, 128])]; - tensor v_cast_fp16 = reshape(shape = var_4006, x = linear_212_cast_fp16)[name = tensor("v_cast_fp16")]; - tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435434304)))]; - tensor var_4018_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_u_to_fp16)[name = tensor("op_4018_cast_fp16")]; - tensor module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435436416)))]; - tensor var_4020_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_v_to_fp16)[name = tensor("op_4020_cast_fp16")]; - tensor q_with_bias_v_perm_0 = const()[name = tensor("q_with_bias_v_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor x_513_transpose_x_0 = const()[name = tensor("x_513_transpose_x_0"), val = tensor(false)]; - tensor x_513_transpose_y_0 = const()[name = tensor("x_513_transpose_y_0"), val = tensor(false)]; - tensor op_4022_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435438528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435726592))), name = tensor("op_4022_to_fp16_palettized"), shape = tensor([1, 8, 128, 375])]; - tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4020_cast_fp16)[name = tensor("transpose_151")]; - tensor x_513_cast_fp16 = matmul(transpose_x = x_513_transpose_x_0, transpose_y = x_513_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_4022_to_fp16_palettized)[name = tensor("x_513_cast_fp16")]; - tensor x_515_pad_0 = const()[name = tensor("x_515_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_515_mode_0 = const()[name = tensor("x_515_mode_0"), val = tensor("constant")]; - tensor const_244_to_fp16 = const()[name = tensor("const_244_to_fp16"), val = tensor(0x0p+0)]; - tensor x_515_cast_fp16 = pad(constant_val = const_244_to_fp16, mode = x_515_mode_0, pad = x_515_pad_0, x = x_513_cast_fp16)[name = tensor("x_515_cast_fp16")]; - tensor var_4030 = const()[name = tensor("op_4030"), val = tensor([1, 8, -1, 188])]; - tensor x_517_cast_fp16 = reshape(shape = var_4030, x = x_515_cast_fp16)[name = tensor("x_517_cast_fp16")]; - tensor var_4034_begin_0 = const()[name = tensor("op_4034_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_4034_end_0 = const()[name = tensor("op_4034_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_4034_end_mask_0 = const()[name = tensor("op_4034_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_4034_cast_fp16 = slice_by_index(begin = var_4034_begin_0, end = var_4034_end_0, end_mask = var_4034_end_mask_0, x = x_517_cast_fp16)[name = tensor("op_4034_cast_fp16")]; - tensor var_4035 = const()[name = tensor("op_4035"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_93_cast_fp16 = reshape(shape = var_4035, x = var_4034_cast_fp16)[name = tensor("matrix_bd_93_cast_fp16")]; - tensor matrix_ac_transpose_x_0 = const()[name = tensor("matrix_ac_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_transpose_y_0 = const()[name = tensor("matrix_ac_transpose_y_0"), val = tensor(false)]; - tensor transpose_142_perm_0 = const()[name = tensor("transpose_142_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_143_perm_0 = const()[name = tensor("transpose_143_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = tensor("transpose_149")]; - tensor transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4018_cast_fp16)[name = tensor("transpose_150")]; - tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = tensor("matrix_ac_cast_fp16")]; - tensor matrix_bd_begin_0 = const()[name = tensor("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_end_0 = const()[name = tensor("matrix_bd_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_end_mask_0 = const()[name = tensor("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = tensor("matrix_bd_cast_fp16")]; - tensor var_4044_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = tensor("op_4044_cast_fp16")]; - tensor _inversed_scores_93_y_0_to_fp16 = const()[name = tensor("_inversed_scores_93_y_0_to_fp16"), val = tensor(0x1.6ap-4)]; - tensor _inversed_scores_93_cast_fp16 = mul(x = var_4044_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = tensor("_inversed_scores_93_cast_fp16")]; - tensor scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_3)[name = tensor("scores_cast_fp16")]; - tensor var_4050_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = tensor("op_4050_cast_fp16")]; - tensor input_1229_cast_fp16 = select(a = var_11_to_fp16, b = var_4050_cast_fp16, cond = mask_3)[name = tensor("input_1229_cast_fp16")]; - tensor x_519_transpose_x_0 = const()[name = tensor("x_519_transpose_x_0"), val = tensor(false)]; - tensor x_519_transpose_y_0 = const()[name = tensor("x_519_transpose_y_0"), val = tensor(false)]; - tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = tensor("transpose_148")]; - tensor x_519_cast_fp16 = matmul(transpose_x = x_519_transpose_x_0, transpose_y = x_519_transpose_y_0, x = input_1229_cast_fp16, y = value_cast_fp16)[name = tensor("x_519_cast_fp16")]; - tensor var_4054_perm_0 = const()[name = tensor("op_4054_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_4055 = const()[name = tensor("op_4055"), val = tensor([1, -1, 1024])]; - tensor var_4054_cast_fp16 = transpose(perm = var_4054_perm_0, x = x_519_cast_fp16)[name = tensor("transpose_147")]; - tensor input_1231_cast_fp16 = reshape(shape = var_4055, x = var_4054_cast_fp16)[name = tensor("input_1231_cast_fp16")]; - tensor module_layers_23_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435726784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436513280))), name = tensor("module_layers_23_self_attn_linear_out_weight_to_fp16_palettized"), shape = tensor([1024, 1024])]; - tensor linear_214_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_self_attn_linear_out_weight_to_fp16_palettized, x = input_1231_cast_fp16)[name = tensor("linear_214_cast_fp16")]; - tensor input_1235_cast_fp16 = add(x = input_1227_cast_fp16, y = linear_214_cast_fp16)[name = tensor("input_1235_cast_fp16")]; - tensor x_523_axes_0 = const()[name = tensor("x_523_axes_0"), val = tensor([-1])]; - tensor module_layers_23_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_23_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436513472)))]; - tensor module_layers_23_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_23_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436515584)))]; - tensor x_523_cast_fp16 = layer_norm(axes = x_523_axes_0, beta = module_layers_23_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_conv_weight_to_fp16, x = input_1235_cast_fp16)[name = tensor("x_523_cast_fp16")]; - tensor input_1237_perm_0 = const()[name = tensor("input_1237_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1239_pad_type_0 = const()[name = tensor("input_1239_pad_type_0"), val = tensor("valid")]; - tensor input_1239_strides_0 = const()[name = tensor("input_1239_strides_0"), val = tensor([1])]; - tensor input_1239_pad_0 = const()[name = tensor("input_1239_pad_0"), val = tensor([0, 0])]; - tensor input_1239_dilations_0 = const()[name = tensor("input_1239_dilations_0"), val = tensor([1])]; - tensor input_1239_groups_0 = const()[name = tensor("input_1239_groups_0"), val = tensor(1)]; - tensor module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436517696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438090624))), name = tensor("module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized"), shape = tensor([2048, 1024, 1])]; - tensor input_1237_cast_fp16 = transpose(perm = input_1237_perm_0, x = x_523_cast_fp16)[name = tensor("transpose_146")]; - tensor input_1239_cast_fp16 = conv(dilations = input_1239_dilations_0, groups = input_1239_groups_0, pad = input_1239_pad_0, pad_type = input_1239_pad_type_0, strides = input_1239_strides_0, weight = module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1237_cast_fp16)[name = tensor("input_1239_cast_fp16")]; - tensor x_525_split_num_splits_0 = const()[name = tensor("x_525_split_num_splits_0"), val = tensor(2)]; - tensor x_525_split_axis_0 = const()[name = tensor("x_525_split_axis_0"), val = tensor(1)]; - tensor x_525_split_cast_fp16_0, tensor x_525_split_cast_fp16_1 = split(axis = x_525_split_axis_0, num_splits = x_525_split_num_splits_0, x = input_1239_cast_fp16)[name = tensor("x_525_split_cast_fp16")]; - tensor x_525_split_1_sigmoid_cast_fp16 = sigmoid(x = x_525_split_cast_fp16_1)[name = tensor("x_525_split_1_sigmoid_cast_fp16")]; - tensor x_525_cast_fp16 = mul(x = x_525_split_cast_fp16_0, y = x_525_split_1_sigmoid_cast_fp16)[name = tensor("x_525_cast_fp16")]; - tensor input_1241_cast_fp16 = select(a = var_11_to_fp16, b = x_525_cast_fp16, cond = var_328)[name = tensor("input_1241_cast_fp16")]; - tensor input_1243_pad_0 = const()[name = tensor("input_1243_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_1243_mode_0 = const()[name = tensor("input_1243_mode_0"), val = tensor("constant")]; - tensor const_247_to_fp16 = const()[name = tensor("const_247_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1243_cast_fp16 = pad(constant_val = const_247_to_fp16, mode = input_1243_mode_0, pad = input_1243_pad_0, x = input_1241_cast_fp16)[name = tensor("input_1243_cast_fp16")]; - tensor input_1245_pad_type_0 = const()[name = tensor("input_1245_pad_type_0"), val = tensor("valid")]; - tensor input_1245_groups_0 = const()[name = tensor("input_1245_groups_0"), val = tensor(1024)]; - tensor input_1245_strides_0 = const()[name = tensor("input_1245_strides_0"), val = tensor([1])]; - tensor input_1245_pad_0 = const()[name = tensor("input_1245_pad_0"), val = tensor([0, 0])]; - tensor input_1245_dilations_0 = const()[name = tensor("input_1245_dilations_0"), val = tensor([1])]; - tensor const_294_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438090816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438097792))), name = tensor("const_294_to_fp16_palettized"), shape = tensor([1024, 1, 9])]; - tensor const_295_to_fp16 = const()[name = tensor("const_295_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438097984)))]; - tensor input_1247_cast_fp16 = conv(bias = const_295_to_fp16, dilations = input_1245_dilations_0, groups = input_1245_groups_0, pad = input_1245_pad_0, pad_type = input_1245_pad_type_0, strides = input_1245_strides_0, weight = const_294_to_fp16_palettized, x = input_1243_cast_fp16)[name = tensor("input_1247_cast_fp16")]; - tensor input_1249_cast_fp16 = silu(x = input_1247_cast_fp16)[name = tensor("input_1249_cast_fp16")]; - tensor x_527_pad_type_0 = const()[name = tensor("x_527_pad_type_0"), val = tensor("valid")]; - tensor x_527_strides_0 = const()[name = tensor("x_527_strides_0"), val = tensor([1])]; - tensor x_527_pad_0 = const()[name = tensor("x_527_pad_0"), val = tensor([0, 0])]; - tensor x_527_dilations_0 = const()[name = tensor("x_527_dilations_0"), val = tensor([1])]; - tensor x_527_groups_0 = const()[name = tensor("x_527_groups_0"), val = tensor(1)]; - tensor module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438100096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438886592))), name = tensor("module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized"), shape = tensor([1024, 1024, 1])]; - tensor x_527_cast_fp16 = conv(dilations = x_527_dilations_0, groups = x_527_groups_0, pad = x_527_pad_0, pad_type = x_527_pad_type_0, strides = x_527_strides_0, weight = module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1249_cast_fp16)[name = tensor("x_527_cast_fp16")]; - tensor input_1251_perm_0 = const()[name = tensor("input_1251_perm_0"), val = tensor([0, 2, 1])]; - tensor input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_527_cast_fp16)[name = tensor("transpose_145")]; - tensor input_1253_cast_fp16 = add(x = input_1235_cast_fp16, y = input_1251_cast_fp16)[name = tensor("input_1253_cast_fp16")]; - tensor input_1255_axes_0 = const()[name = tensor("input_1255_axes_0"), val = tensor([-1])]; - tensor module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438886784)))]; - tensor module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438888896)))]; - tensor input_1255_cast_fp16 = layer_norm(axes = input_1255_axes_0, beta = module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1253_cast_fp16)[name = tensor("input_1255_cast_fp16")]; - tensor module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(438891008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(442036800))), name = tensor("module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized"), shape = tensor([4096, 1024])]; - tensor linear_215_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1255_cast_fp16)[name = tensor("linear_215_cast_fp16")]; - tensor input_1259_cast_fp16 = silu(x = linear_215_cast_fp16)[name = tensor("input_1259_cast_fp16")]; - tensor module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(442036992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445182784))), name = tensor("module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized"), shape = tensor([1024, 4096])]; - tensor linear_216_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1259_cast_fp16)[name = tensor("linear_216_cast_fp16")]; - tensor var_4115_to_fp16 = const()[name = tensor("op_4115_to_fp16"), val = tensor(0x1p-1)]; - tensor var_4116_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4115_to_fp16)[name = tensor("op_4116_cast_fp16")]; - tensor input_cast_fp16 = add(x = input_1253_cast_fp16, y = var_4116_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor audio_signal_axes_0 = const()[name = tensor("audio_signal_axes_0"), val = tensor([-1])]; - tensor module_layers_23_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_23_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445182976)))]; - tensor module_layers_23_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_23_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445185088)))]; - tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_23_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("audio_signal_cast_fp16")]; - tensor obj_1_perm_0 = const()[name = tensor("obj_1_perm_0"), val = tensor([0, 2, 1])]; - tensor obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_1_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = tensor("transpose_144")]; - tensor encoder = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = tensor("cast_0")]; - } -> (encoder, encoder_length); -} \ No newline at end of file diff --git a/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/weights/weight.bin b/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/weights/weight.bin deleted file mode 100644 index e6313f81d9d94af46347f28087f51a60eb28164c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Encoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4adc7ad44f9d05e1bffeb2b06d3bb02861a5c7602dff63a6b494aed3bf8a6c3e -size 445187200 diff --git a/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/analytics/coremldata.bin 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a/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/metadata.json b/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/metadata.json deleted file mode 100644 index db3b2bc33cfda6f7cd1073d21182446180e53f94..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/metadata.json +++ /dev/null @@ -1,103 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet single-step joint decision (current frame)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_id", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_prob", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" 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b/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/model.mil deleted file mode 100644 index 339c35b303b074de7fe047bb099311a4cc08cf53..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/model.mil +++ /dev/null @@ -1,58 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] -{ - func main(tensor decoder_step, tensor encoder_step) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_step_to_fp16_dtype_0 = const()[name = tensor("encoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_step_to_fp16_dtype_0 = const()[name = tensor("decoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1310848)))]; - tensor encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor("cast_3")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312192)))]; - tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2131456)))]; - tensor decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor("cast_2")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; - tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; - tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; - tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2132800)))]; - tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3451264)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 1, 1, 1025])]; - tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; - tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 1, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; - tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; - tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; - tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; - tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; - tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; - tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; - tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; - tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_1")]; - tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; - tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; - tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; - tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; - tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; - tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; - tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_0")]; - } -> (token_id, token_prob, duration); -} \ No newline at end of file diff --git a/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/weights/weight.bin b/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/weights/weight.bin deleted file mode 100644 index 8764a151a786b38079c36a409fe5144ddf8b95a9..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/JointDecision.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ca22a65903a05e64137677da608077578a8606090a598abf4875fa6199aaa19d -size 3453388 diff --git a/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin b/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 84e7c818c15d2650b6c55b20e618fba3e6289763..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:03ab3c1327a054c54c07a40325db967ec574f2c91dcc8192bfa44aa561bcf2d8 -size 243 diff --git a/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/coremldata.bin b/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/coremldata.bin deleted file mode 100644 index b53fff2bc93fbe4b468811db15aa17767f3cb33c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d88ea1fc349459c9e100d6a96688c5b29a1f0d865f544be103001724b986b6d6 -size 494 diff --git a/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/metadata.json b/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/metadata.json deleted file mode 100644 index 887f7d4adc6d7f933686f7b984e23a47b45f57e3..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/metadata.json +++ /dev/null @@ -1,110 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "int8-linear quantized - preprocessor", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32)", - "shortDescription" : "", - "shape" : "[]", - "name" : "mel", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Int8", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Range1d" : 2, - "Ios17.reshape" : 2, - "Ios17.matmul" : 1, - "Ios17.expandDims" : 10, - "Select" : 3, - "Ios17.add" : 4, - "Tile" : 2, - "Ios17.sliceByIndex" : 3, - "Ios16.reduceSum" : 4, - "Shape" : 3, - "Ios17.gather" : 3, - "Pad" : 1, - "Ios17.log" : 1, - "Ios16.constexprAffineDequantize" : 3, - "Ios17.conv" : 2, - "Ios17.sub" : 4, - "Ios17.pow" : 2, - "Ios17.cast" : 10, - "Ios17.realDiv" : 4, - "Stack" : 1, - "Ios17.concat" : 3, - "Ios17.floorDiv" : 1, - "Ios17.less" : 1, - "Ios17.sqrt" : 1, - "Ios17.greaterEqual" : 1, - "Ios17.mul" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32, UInt16)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "dataType" : "Float32", - "hasShapeFlexibility" : "1", - "isOptional" : "0", - "shapeFlexibility" : "1 × 1...240000", - "shapeRange" : "[[1, 1], [1, 240000]]", - "formattedType" : "MultiArray (Float32 1 × 1)", - "type" : "MultiArray", - "shape" : "[1, 1]", - "name" : "audio_signal", - "shortDescription" : "" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "audio_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2025-09-25", - "com.github.apple.coremltools.source" : "torch==2.7.0", - "com.github.apple.coremltools.version" : "9.0b1", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "parakeet_preprocessor", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/model.mil b/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/model.mil deleted file mode 100644 index e94cce4bc7fbf55e55788449bf067ffe09a5ca91..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/model.mil +++ /dev/null @@ -1,169 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})] -{ - func main(tensor audio_length, tensor audio_signal) [FlexibleShapeInformation = tuple, dict, tensor>>, tuple, dict, list, ?>>>>((("DefaultShapes", {{"audio_signal", [1, 1]}}), ("RangeDims", {{"audio_signal", [[1, 1], [1, 240000]]}})))] { - tensor var_9 = const()[name = tensor("op_9"), val = tensor(1)]; - tensor var_10 = const()[name = tensor("op_10"), val = tensor(160)]; - tensor var_34 = const()[name = tensor("op_34"), val = tensor(512)]; - tensor var_35 = add(x = audio_length, y = var_34)[name = tensor("op_35")]; - tensor var_36 = const()[name = tensor("op_36"), val = tensor(512)]; - tensor var_37 = sub(x = var_35, y = var_36)[name = tensor("op_37")]; - tensor floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor("floor_div_0")]; - tensor var_38_to_fp16_dtype_0 = const()[name = tensor("op_38_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_39_promoted_to_fp16 = const()[name = tensor("op_39_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor floor_div_0_to_fp16 = cast(dtype = var_38_to_fp16_dtype_0, x = floor_div_0)[name = tensor("cast_9")]; - tensor seq_len_1_cast_fp16 = add(x = floor_div_0_to_fp16, y = var_39_promoted_to_fp16)[name = tensor("seq_len_1_cast_fp16")]; - tensor seq_len_dtype_0 = const()[name = tensor("seq_len_dtype_0"), val = tensor("int32")]; - tensor var_43_begin_0 = const()[name = tensor("op_43_begin_0"), val = tensor([0, 0])]; - tensor var_43_end_0 = const()[name = tensor("op_43_end_0"), val = tensor([1, 1])]; - tensor var_43_end_mask_0 = const()[name = tensor("op_43_end_mask_0"), val = tensor([true, false])]; - tensor var_43_squeeze_mask_0 = const()[name = tensor("op_43_squeeze_mask_0"), val = tensor([false, true])]; - tensor audio_signal_to_fp16_dtype_0 = const()[name = tensor("audio_signal_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor("cast_8")]; - tensor var_43_cast_fp16 = slice_by_index(begin = var_43_begin_0, end = var_43_end_0, end_mask = var_43_end_mask_0, squeeze_mask = var_43_squeeze_mask_0, x = audio_signal_to_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_44_axes_0 = const()[name = tensor("op_44_axes_0"), val = tensor([1])]; - tensor var_44_cast_fp16 = expand_dims(axes = var_44_axes_0, x = var_43_cast_fp16)[name = tensor("op_44_cast_fp16")]; - tensor var_46_begin_0 = const()[name = tensor("op_46_begin_0"), val = tensor([0, 1])]; - tensor var_46_end_0 = const()[name = tensor("op_46_end_0"), val = tensor([1, 0])]; - tensor var_46_end_mask_0 = const()[name = tensor("op_46_end_mask_0"), val = tensor([true, true])]; - tensor var_46_cast_fp16 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_46_cast_fp16")]; - tensor var_48_begin_0 = const()[name = tensor("op_48_begin_0"), val = tensor([0, 0])]; - tensor var_48_end_0 = const()[name = tensor("op_48_end_0"), val = tensor([1, -1])]; - tensor var_48_end_mask_0 = const()[name = tensor("op_48_end_mask_0"), val = tensor([true, false])]; - tensor var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, x = audio_signal_to_fp16)[name = tensor("op_48_cast_fp16")]; - tensor var_49_to_fp16 = const()[name = tensor("op_49_to_fp16"), val = tensor(0x1.f0cp-1)]; - tensor var_50_cast_fp16 = mul(x = var_48_cast_fp16, y = var_49_to_fp16)[name = tensor("op_50_cast_fp16")]; - tensor var_51_cast_fp16 = sub(x = var_46_cast_fp16, y = var_50_cast_fp16)[name = tensor("op_51_cast_fp16")]; - tensor input_1_interleave_0 = const()[name = tensor("input_1_interleave_0"), val = tensor(false)]; - tensor input_1_cast_fp16 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44_cast_fp16, var_51_cast_fp16))[name = tensor("input_1_cast_fp16")]; - tensor concat_0x = const()[name = tensor("concat_0x"), val = tensor([1, 1, -1])]; - tensor input_3_cast_fp16 = reshape(shape = concat_0x, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; - tensor input_5_mode_0 = const()[name = tensor("input_5_mode_0"), val = tensor("reflect")]; - tensor const_1_to_fp16 = const()[name = tensor("const_1_to_fp16"), val = tensor(0x0p+0)]; - tensor input_5_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor concat_1x = const()[name = tensor("concat_1x"), val = tensor([1, -1])]; - tensor input_cast_fp16 = reshape(shape = concat_1x, x = input_5_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([160])]; - tensor expand_dims_4_axes_0 = const()[name = tensor("expand_dims_4_axes_0"), val = tensor([1])]; - tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = tensor("expand_dims_4_cast_fp16")]; - tensor conv_0_pad_type_0 = const()[name = tensor("conv_0_pad_type_0"), val = tensor("valid")]; - tensor conv_0_pad_0 = const()[name = tensor("conv_0_pad_0"), val = tensor([0, 0])]; - tensor conv_0_dilations_0 = const()[name = tensor("conv_0_dilations_0"), val = tensor([1])]; - tensor conv_0_groups_0 = const()[name = tensor("conv_0_groups_0"), val = tensor(1)]; - tensor expand_dims_1_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("expand_dims_1_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132096))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131712)))]; - tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16_quantized, x = expand_dims_4_cast_fp16)[name = tensor("conv_0_cast_fp16")]; - tensor conv_1_pad_type_0 = const()[name = tensor("conv_1_pad_type_0"), val = tensor("valid")]; - tensor conv_1_pad_0 = const()[name = tensor("conv_1_pad_0"), val = tensor([0, 0])]; - tensor conv_1_dilations_0 = const()[name = tensor("conv_1_dilations_0"), val = tensor([1])]; - tensor conv_1_groups_0 = const()[name = tensor("conv_1_groups_0"), val = tensor(1)]; - tensor expand_dims_2_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("expand_dims_2_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132736))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264768))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264384)))]; - tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16_quantized, x = expand_dims_4_cast_fp16)[name = tensor("conv_1_cast_fp16")]; - tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(-1)]; - tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor("stack_0_cast_fp16")]; - tensor var_17_promoted_to_fp16 = const()[name = tensor("op_17_promoted_to_fp16"), val = tensor(0x1p+1)]; - tensor var_67_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_17_promoted_to_fp16)[name = tensor("op_67_cast_fp16")]; - tensor var_69_axes_0 = const()[name = tensor("op_69_axes_0"), val = tensor([-1])]; - tensor var_69_keep_dims_0 = const()[name = tensor("op_69_keep_dims_0"), val = tensor(false)]; - tensor var_69_cast_fp16 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67_cast_fp16)[name = tensor("op_69_cast_fp16")]; - tensor x_11_transpose_x_0 = const()[name = tensor("x_11_transpose_x_0"), val = tensor(false)]; - tensor x_11_transpose_y_0 = const()[name = tensor("x_11_transpose_y_0"), val = tensor(false)]; - tensor const_2_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor(1), name = tensor("const_2_to_fp16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265408))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298560))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298368)))]; - tensor x_11_cast_fp16 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = const_2_to_fp16_quantized, y = var_69_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor var_76_to_fp16 = const()[name = tensor("op_76_to_fp16"), val = tensor(0x1p-24)]; - tensor var_77_cast_fp16 = add(x = x_11_cast_fp16, y = var_76_to_fp16)[name = tensor("op_77_cast_fp16")]; - tensor x_13_epsilon_0 = const()[name = tensor("x_13_epsilon_0"), val = tensor(0x1p-149)]; - tensor x_13_cast_fp16 = log(epsilon = x_13_epsilon_0, x = var_77_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_79_shape_cast_fp16 = shape(x = x_13_cast_fp16)[name = tensor("op_79_shape_cast_fp16")]; - tensor gather_4 = const()[name = tensor("gather_4"), val = tensor(1)]; - tensor gather_5_axis_0 = const()[name = tensor("gather_5_axis_0"), val = tensor(0)]; - tensor gather_5_batch_dims_0 = const()[name = tensor("gather_5_batch_dims_0"), val = tensor(0)]; - tensor gather_5_validate_indices_0 = const()[name = tensor("gather_5_validate_indices_0"), val = tensor(false)]; - tensor var_79_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_79_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor gather_5_indices_0_to_uint16 = const()[name = tensor("gather_5_indices_0_to_uint16"), val = tensor(2)]; - tensor var_79_shape_cast_fp16_to_uint16 = cast(dtype = var_79_shape_cast_fp16_to_uint16_dtype_0, x = var_79_shape_cast_fp16)[name = tensor("cast_7")]; - tensor gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = gather_5_indices_0_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_79_shape_cast_fp16_to_uint16)[name = tensor("gather_5_cast_uint16")]; - tensor gather_5_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_5_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_3 = const()[name = tensor("const_3"), val = tensor(0)]; - tensor const_4 = const()[name = tensor("const_4"), val = tensor(1)]; - tensor gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = tensor("cast_6")]; - tensor var_81 = range_1d(end = gather_5_cast_uint16_to_int32, start = const_3, step = const_4)[name = tensor("op_81")]; - tensor var_82_axes_0 = const()[name = tensor("op_82_axes_0"), val = tensor([0])]; - tensor var_82 = expand_dims(axes = var_82_axes_0, x = var_81)[name = tensor("op_82")]; - tensor concat_2_axis_0 = const()[name = tensor("concat_2_axis_0"), val = tensor(0)]; - tensor concat_2_interleave_0 = const()[name = tensor("concat_2_interleave_0"), val = tensor(false)]; - tensor concat_2 = concat(axis = concat_2_axis_0, interleave = concat_2_interleave_0, values = (gather_4, gather_5_cast_uint16_to_int32))[name = tensor("concat_2")]; - tensor shape_0 = shape(x = var_82)[name = tensor("shape_0")]; - tensor real_div_0 = real_div(x = concat_2, y = shape_0)[name = tensor("real_div_0")]; - tensor time_steps = tile(reps = real_div_0, x = var_82)[name = tensor("time_steps")]; - tensor var_85_axes_0 = const()[name = tensor("op_85_axes_0"), val = tensor([1])]; - tensor mel_length = cast(dtype = seq_len_dtype_0, x = seq_len_1_cast_fp16)[name = tensor("cast_5")]; - tensor var_85 = expand_dims(axes = var_85_axes_0, x = mel_length)[name = tensor("op_85")]; - tensor valid_mask = less(x = time_steps, y = var_85)[name = tensor("valid_mask")]; - tensor var_87_axes_0 = const()[name = tensor("op_87_axes_0"), val = tensor([1])]; - tensor var_87 = expand_dims(axes = var_87_axes_0, x = valid_mask)[name = tensor("op_87")]; - tensor var_24_to_fp16 = const()[name = tensor("op_24_to_fp16"), val = tensor(0x0p+0)]; - tensor var_88_cast_fp16 = select(a = x_13_cast_fp16, b = var_24_to_fp16, cond = var_87)[name = tensor("op_88_cast_fp16")]; - tensor x_mean_numerator_axes_0 = const()[name = tensor("x_mean_numerator_axes_0"), val = tensor([2])]; - tensor x_mean_numerator_keep_dims_0 = const()[name = tensor("x_mean_numerator_keep_dims_0"), val = tensor(false)]; - tensor x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_88_cast_fp16)[name = tensor("x_mean_numerator_cast_fp16")]; - tensor x_mean_denominator_axes_0 = const()[name = tensor("x_mean_denominator_axes_0"), val = tensor([1])]; - tensor x_mean_denominator_keep_dims_0 = const()[name = tensor("x_mean_denominator_keep_dims_0"), val = tensor(false)]; - tensor cast_3_to_fp16_dtype_0 = const()[name = tensor("cast_3_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor valid_mask_to_fp16 = cast(dtype = cast_3_to_fp16_dtype_0, x = valid_mask)[name = tensor("cast_4")]; - tensor x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor("x_mean_denominator_cast_fp16")]; - tensor var_93_axes_0 = const()[name = tensor("op_93_axes_0"), val = tensor([1])]; - tensor var_93_cast_fp16 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor("op_93_cast_fp16")]; - tensor x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_93_cast_fp16)[name = tensor("x_mean_cast_fp16")]; - tensor var_96_axes_0 = const()[name = tensor("op_96_axes_0"), val = tensor([2])]; - tensor var_96_cast_fp16 = expand_dims(axes = var_96_axes_0, x = x_mean_cast_fp16)[name = tensor("op_96_cast_fp16")]; - tensor var_97_cast_fp16 = sub(x = x_13_cast_fp16, y = var_96_cast_fp16)[name = tensor("op_97_cast_fp16")]; - tensor var_98_cast_fp16 = select(a = var_97_cast_fp16, b = var_24_to_fp16, cond = var_87)[name = tensor("op_98_cast_fp16")]; - tensor var_17_promoted_1_to_fp16 = const()[name = tensor("op_17_promoted_1_to_fp16"), val = tensor(0x1p+1)]; - tensor var_99_cast_fp16 = pow(x = var_98_cast_fp16, y = var_17_promoted_1_to_fp16)[name = tensor("op_99_cast_fp16")]; - tensor var_101_axes_0 = const()[name = tensor("op_101_axes_0"), val = tensor([2])]; - tensor var_101_keep_dims_0 = const()[name = tensor("op_101_keep_dims_0"), val = tensor(false)]; - tensor var_101_cast_fp16 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99_cast_fp16)[name = tensor("op_101_cast_fp16")]; - tensor var_103_to_fp16 = const()[name = tensor("op_103_to_fp16"), val = tensor(0x1p+0)]; - tensor var_104_cast_fp16 = sub(x = var_93_cast_fp16, y = var_103_to_fp16)[name = tensor("op_104_cast_fp16")]; - tensor var_105_cast_fp16 = real_div(x = var_101_cast_fp16, y = var_104_cast_fp16)[name = tensor("op_105_cast_fp16")]; - tensor x_std_1_cast_fp16 = sqrt(x = var_105_cast_fp16)[name = tensor("x_std_1_cast_fp16")]; - tensor var_25_to_fp16 = const()[name = tensor("op_25_to_fp16"), val = tensor(0x1.5p-17)]; - tensor x_std_cast_fp16 = add(x = x_std_1_cast_fp16, y = var_25_to_fp16)[name = tensor("x_std_cast_fp16")]; - tensor var_110_axes_0 = const()[name = tensor("op_110_axes_0"), val = tensor([2])]; - tensor var_110_cast_fp16 = expand_dims(axes = var_110_axes_0, x = x_std_cast_fp16)[name = tensor("op_110_cast_fp16")]; - tensor x_cast_fp16 = real_div(x = var_97_cast_fp16, y = var_110_cast_fp16)[name = tensor("x_cast_fp16")]; - tensor var_112_shape_cast_fp16 = shape(x = x_cast_fp16)[name = tensor("op_112_shape_cast_fp16")]; - tensor gather_6_batch_dims_0 = const()[name = tensor("gather_6_batch_dims_0"), val = tensor(0)]; - tensor gather_6_validate_indices_0 = const()[name = tensor("gather_6_validate_indices_0"), val = tensor(false)]; - tensor var_112_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_112_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor gather_6_cast_uint16_axis_0 = const()[name = tensor("gather_6_cast_uint16_axis_0"), val = tensor(0)]; - tensor select_0_to_uint16 = const()[name = tensor("select_0_to_uint16"), val = tensor(2)]; - tensor var_112_shape_cast_fp16_to_uint16 = cast(dtype = var_112_shape_cast_fp16_to_uint16_dtype_0, x = var_112_shape_cast_fp16)[name = tensor("cast_3")]; - tensor gather_6_cast_uint16_cast_uint16 = gather(axis = gather_6_cast_uint16_axis_0, batch_dims = gather_6_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_6_validate_indices_0, x = var_112_shape_cast_fp16_to_uint16)[name = tensor("gather_6_cast_uint16_cast_uint16")]; - tensor gather_6_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_6_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_5 = const()[name = tensor("const_5"), val = tensor(0)]; - tensor const_6 = const()[name = tensor("const_6"), val = tensor(1)]; - tensor gather_6_cast_uint16_to_int32 = cast(dtype = gather_6_cast_uint16_to_int32_dtype_0, x = gather_6_cast_uint16_cast_uint16)[name = tensor("cast_2")]; - tensor mask_1 = range_1d(end = gather_6_cast_uint16_to_int32, start = const_5, step = const_6)[name = tensor("mask_1")]; - tensor gather_7_axis_0 = const()[name = tensor("gather_7_axis_0"), val = tensor(0)]; - tensor gather_7_batch_dims_0 = const()[name = tensor("gather_7_batch_dims_0"), val = tensor(0)]; - tensor gather_7_validate_indices_0 = const()[name = tensor("gather_7_validate_indices_0"), val = tensor(false)]; - tensor gather_7_indices_0_to_uint16 = const()[name = tensor("gather_7_indices_0_to_uint16"), val = tensor(0)]; - tensor gather_7_cast_uint16 = gather(axis = gather_7_axis_0, batch_dims = gather_7_batch_dims_0, indices = gather_7_indices_0_to_uint16, validate_indices = gather_7_validate_indices_0, x = var_112_shape_cast_fp16_to_uint16)[name = tensor("gather_7_cast_uint16")]; - tensor gather_7_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_7_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor concat_3_axis_0 = const()[name = tensor("concat_3_axis_0"), val = tensor(0)]; - tensor concat_3_interleave_0 = const()[name = tensor("concat_3_interleave_0"), val = tensor(false)]; - tensor gather_7_cast_uint16_to_int32 = cast(dtype = gather_7_cast_uint16_to_int32_dtype_0, x = gather_7_cast_uint16)[name = tensor("cast_1")]; - tensor concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (gather_7_cast_uint16_to_int32, var_9))[name = tensor("concat_3")]; - tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0])]; - tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = tensor("expand_dims_0")]; - tensor var_116 = tile(reps = concat_3, x = expand_dims_0)[name = tensor("op_116")]; - tensor mask = greater_equal(x = var_116, y = var_85)[name = tensor("mask")]; - tensor var_119_axes_0 = const()[name = tensor("op_119_axes_0"), val = tensor([1])]; - tensor var_119 = expand_dims(axes = var_119_axes_0, x = mask)[name = tensor("op_119")]; - tensor processed_signal_cast_fp16 = select(a = var_24_to_fp16, b = x_cast_fp16, cond = var_119)[name = tensor("processed_signal_cast_fp16")]; - tensor processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("processed_signal_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor mel = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = tensor("cast_0")]; - } -> (mel, mel_length); -} \ No newline at end of file diff --git a/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/weights/weight.bin b/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/weights/weight.bin deleted file mode 100644 index a9af1ac1e407ef336eebc2fd4da9de57e6d172ad..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/Preprocessor.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a5f7df6c7f47147ae9486fe18cc7792f9a44d093ec3c6a11e91ef2dc363c48dc -size 298880 diff --git a/parakeet-tdt-0.6b-v2-coreml/config.json b/parakeet-tdt-0.6b-v2-coreml/config.json deleted file mode 100644 index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/config.json +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/parakeet-tdt-0.6b-v2-coreml/parakeet_vocab.json b/parakeet-tdt-0.6b-v2-coreml/parakeet_vocab.json deleted file mode 100644 index a111d3bd29d44781c4588447c3eac1ea549cb1f1..0000000000000000000000000000000000000000 --- a/parakeet-tdt-0.6b-v2-coreml/parakeet_vocab.json +++ /dev/null @@ -1,1033 +0,0 @@ -{ - "479" : "▁happ", - "493" : "ial", - "386" : "▁diff", - "398" : "▁little", - "168" : "▁ch", - "872" : "2", - "227" : "ie", - "251" : "▁R", - "488" : "▁day", - "38" : "ed", - "610" : "▁might", - "127" : "▁can", - "70" : "ct", - "903" : "Ă ", - "329" : "▁been", - "694" : "ren", - "1005" : "Ćœ", - "661" : "▁reg", - "597" : "▁around", - "442" : "ys", - "764" : "▁number", - "705" : "▁trans", - "275" : "▁had", - "271" : "ak", - "1024" : "▁warming", - "564" : "▁play", - "32" : "▁y", - "463" : "▁under", - "684" : "ute", - "529" : "▁quest", - "719" : "ock", - "235" : "her", - "861" : "q", - "987" : "Č", - "593" : "urn", - "555" : "ath", - "216" : "▁think", - "211" : "el", - "500" : "▁bit", - "353" : "ry", - "490" : "▁Yeah", - "750" : "▁sim", - "283" : "▁these", - "320" : "▁sp", - "945" : "ÎČ", - "179" : "ca", - "934" : "т", - "258" : "ear", - "870" : "R", - "974" : "Ί", - "157" : "▁H", - "280" : "ine", - "381" : "▁well", - "21" : "▁m", - "668" : "▁la", - "429" : "▁bo", - "812" : "▁ty", - "537" : "ual", - "743" : "ss", - "119" : "▁kn", - "863" : "P", - "855" : "0", - "333" : "ple", - "850" : "x", - "959" : "я", - "467" : "▁bu", - "262" : "▁would", - "835" : "g", - "254" : "▁who", - "233" : "▁here", - "993" : "ĆŒ", - "889" : "Z", - "231" : "ack", - "788" : "ution", - "466" : "ning", - "332" : "▁did", - "388" : "cc", - "260" : "▁E", - "839" : ",", - "582" : "▁three", - "1000" : "Ă”", - "73" : "ver", - "486" : "ign", - "541" : "▁ser", - "800" : "▁exper", - "192" : "um", - "199" : "▁from", - "223" : "▁because", - "68" : "ent", - "608" : "▁tra", - "383" : "▁pre", - "774" : "▁place", - "194" : "▁your", - "148" : "ra", - "955" : "Ü", - "158" : "▁fr", - "670" : "▁sub", - "906" : "π", - "829" : "l", - "202" : "pe", - "948" : "ÎŽ", - "480" : "ater", - "342" : "ree", - "958" : "ƛ", - "967" : "Á", - "171" : "ate", - "91" : "am", - "114" : "ation", - "390" : "int", - "1013" : "À", - "794" : "▁read", - "1011" : "ф", - "355" : "ble", - "921" : "Ć«", - "234" : "▁pe", - "573" : "▁id", - "785" : "gan", - "335" : "▁other", - "963" : "Ö", - "570" : "get", - "2" : "▁th", - "273" : "ide", - "574" : "▁Oh", - "807" : "▁open", - "155" : "▁The", - "506" : "vers", - "729" : "▁sort", - "318" : "▁got", - "550" : "ank", - "896" : "Ăł", - "563" : "ict", - "1010" : "Đ·", - "520" : "▁gonna", - "1020" : "Κ", - "548" : "ved", - "848" : "S", - "578" : "▁rel", - "828" : "r", - "361" : "▁cont", - "379" : "ish", - "259" : "▁tim", - "441" : "▁imp", - "44" : "as", - "620" : "ways", - "34" : "▁I", - "87" : "st", - "13" : "nd", - "350" : "▁every", - "866" : "D", - "545" : "▁V", - "182" : "if", - "615" : "cial", - "69" : "ke", - "276" : "so", - "732" : "ics", - "749" : "als", - "605" : "aking", - "857" : "-", - "446" : "▁down", - "41" : "ar", - "203" : "un", - "816" : "▁met", - "543" : "▁ind", - "926" : "ĂŠ", - "64" : "▁on", - "246" : "▁them", - "172" : "qu", - "229" : "▁tr", - "775" : "▁gener", - "592" : "ower", - "617" : "▁give", - "399" : "ical", - "319" : "ag", - "669" : "▁last", - "400" : "▁gr", - "389" : "ittle", - "533" : "▁fu", - "460" : "uct", - "825" : "i", - "992" : "Ç", - "432" : "▁much", - "638" : "ction", - "700" : "▁love", - "988" : "έ", - "658" : "▁inv", - "549" : "▁still", - "322" : "act", - "143" : "il", - "966" : "ĐČ", - "245" : "▁It", - "445" : "▁yeah", - "524" : "self", - "742" : "ata", - "380" : "kay", - "176" : "▁su", - "624" : "▁gen", - "766" : "▁saying", - "42" : "▁that", - "136" : "▁ab", - "224" : "▁by", - "811" : "▁getting", - "76" : "id", - "430" : "be", - "124" : "▁know", - "88" : "ch", - "420" : "▁she", - "822" : "o", - "991" : "Å", - "623" : "ily", - "230" : "▁then", - "65" : "▁T", - "86" : "▁st", - "873" : "J", - "464" : "▁br", - "165" : "pp", - "325" : "iz", - "237" : "▁F", - "939" : "č", - "250" : "▁say", - "423" : "ord", - "516" : "▁fl", - "1030" : "▁urge", - "977" : "Îč", - "358" : "▁kind", - "471" : "co", - "7" : "▁w", - "252" : "▁people", - "12" : "er", - "932" : "ψ", - "632" : "▁cour", - "440" : "▁comm", - "664" : "▁cr", - "637" : "▁num", - "588" : "▁To", - "286" : "▁sa", - "292" : "pt", - "772" : "▁better", - "396" : "▁him", - "1007" : "Γ", - "26" : "▁d", - "450" : "ens", - "780" : "cept", - "936" : "ω", - "123" : "▁And", - "349" : "▁need", - "536" : "we", - "882" : "8", - "546" : "he", - "854" : "?", - "875" : ":", - "712" : "▁exam", - "437" : "ary", - "394" : "ip", - "344" : "own", - "709" : "▁fin", - "457" : "▁K", - "996" : "χ", - "990" : "ы", - "89" : "▁li", - "878" : "3", - "503" : "fe", - "61" : "et", - "782" : "▁understand", - "126" : "op", - "135" : "▁at", - "831" : "u", - "456" : "ody", - "594" : "▁okay", - "695" : "erest", - "324" : "▁also", - "51" : "▁it", - "542" : "▁rem", - "174" : "▁ex", - "1006" : "ț", - "128" : "▁or", - "357" : "ue", - "641" : "▁um", - "802" : "▁ele", - "213" : "▁some", - "606" : "▁pos", - "929" : "Îœ", - "885" : "!", - "784" : "▁thought", - "716" : "▁stud", - "232" : "▁pl", - "522" : "ces", - "184" : "▁if", - "37" : "is", - "867" : "N", - "653" : "uc", - "413" : "ult", - "167" : "ess", - "22" : "en", - "439" : "ving", - "104" : "▁r", - "755" : "oc", - "63" : "▁re", - "622" : "ward", - "1003" : "Ɓ", - "468" : "▁use", - "879" : "K", - "838" : "p", - "11" : "ou", - "54" : "le", - "118" : "▁not", - "558" : "ft", - "858" : "M", - "579" : "▁before", - "652" : "les", - "98" : "▁do", - "1026" : "▁issue", - "360" : "▁back", - "790" : "ason", - "731" : "▁today", - "706" : "▁count", - "650" : "▁didn", - "348" : "▁where", - "371" : "ep", - "898" : "ĂŒ", - "384" : "▁two", - "121" : "▁B", - "720" : "▁used", - "120" : "ight", - "1022" : "Ο", - "345" : "▁tw", - "307" : "▁work", - "655" : "ating", - "745" : "ween", - "596" : "▁bel", - "852" : "B", - "986" : "ĂŹ", - "197" : "▁get", - "79" : "▁he", - "504" : "▁doing", - "644" : "▁own", - "791" : "▁problem", - "931" : "Îł", - "33" : "▁l", - "187" : "ab", - "771" : "▁between", - "783" : "▁fun", - "823" : "a", - "723" : "▁No", - "303" : "▁mo", - "482" : "ition", - "164" : "▁M", - "346" : "▁part", - "103" : "ad", - "902" : "ç", - "781" : "ull", - "411" : "▁actually", - "598" : "ful", - "1002" : "ħ", - "351" : "pl", - "343" : "▁into", - "908" : "Ăș", - "337" : "ite", - "894" : "ĂĄ", - "912" : "Ä«", - "864" : "z", - "925" : "τ", - "153" : "▁con", - "144" : "▁but", - "433" : "▁per", - "711" : "▁pol", - "631" : "▁sm", - "431" : "ount", - "999" : "Í", - "946" : "Ăž", - "141" : "use", - "77" : "▁for", - "733" : "▁vide", - "730" : "▁For", - "418" : "ations", - "693" : "▁always", - "642" : "ood", - "1015" : "Ā", - "154" : "▁all", - "659" : "ably", - "1027" : "▁stay", - "777" : "▁ins", - "740" : "▁keep", - "96" : "▁so", - "806" : "▁partic", - "313" : "▁im", - "507" : "av", - "880" : "4", - "792" : "▁doesn", - "426" : "▁am", - "1001" : "ě", - "526" : "▁If", - "465" : "▁take", - "436" : "vel", - "116" : "ere", - "576" : "ever", - "568" : "oth", - "166" : "▁com", - "151" : "ul", - "798" : "ah", - "779" : "cond", - "556" : "▁end", - "161" : "ea", - "39" : "▁g", - "765" : "ention", - "100" : "th", - "461" : "▁only", - "590" : "▁hel", - "505" : "▁St", - "957" : "Ƅ", - "876" : "5", - "580" : "▁feel", - "321" : "ans", - "972" : "ь", - "718" : "▁car", - "851" : "W", - "960" : "đ", - "469" : "▁Ch", - "291" : "one", - "83" : "ly", - "295" : "▁has", - "84" : "▁go", - "981" : "Ƒ", - "923" : "λ", - "52" : "▁be", - "821" : "t", - "323" : "▁te", - "50" : "al", - "760" : "ense", - "149" : "ore", - "306" : "▁le", - "403" : "▁thr", - "628" : "ob", - "299" : "▁look", - "406" : "▁This", - "0" : "", - "75" : "all", - "475" : "▁call", - "341" : "reat", - "519" : "ents", - "66" : "▁A", - "186" : "nt", - "928" : "ĐŸ", - "244" : "ople", - "393" : "ence", - "834" : "m", - "833" : "y", - "414" : "able", - "297" : "▁very", - "364" : "▁pr", - "865" : "L", - "35" : "it", - "339" : "omet", - "27" : "es", - "150" : "▁there", - "715" : "ell", - "677" : "▁import", - "681" : "▁ear", - "820" : "e", - "139" : "▁So", - "449" : "na", - "302" : "▁time", - "532" : "▁What", - "133" : "ck", - "970" : "Îż", - "773" : "cus", - "228" : "▁us", - "552" : "▁wr", - "680" : "▁made", - "871" : "E", - "874" : "U", - "895" : "ÂŁ", - "846" : "T", - "55" : "ion", - "492" : "ile", - "787" : "cy", - "105" : "ir", - "662" : "lic", - "629" : "▁tell", - "367" : "▁good", - "514" : "form", - "656" : "olog", - "334" : "ually", - "294" : "ong", - "485" : "ade", - "682" : "▁ac", - "106" : "▁was", - "1028" : "▁together", - "910" : "ĂŁ", - "142" : "ter", - "922" : "Δ", - "175" : "very", - "314" : "▁ag", - "327" : "▁That", - "826" : "s", - "183" : "ive", - "979" : "Đł", - "751" : "vern", - "997" : "э", - "278" : "eah", - "517" : "fter", - "311" : "per", - "535" : "▁show", - "918" : "^", - "954" : "ĆĄ", - "722" : "stand", - "6" : "re", - "93" : "ce", - "435" : "▁differe", - "746" : "▁stuff", - "915" : "ρ", - "602" : "▁supp", - "209" : "▁L", - "767" : "▁commun", - "769" : "akes", - "375" : "▁lot", - "859" : "H", - "397" : "▁make", - "340" : "ber", - "886" : "%", - "736" : "▁Al", - "600" : "ise", - "938" : "ć", - "478" : "ting", - "962" : "ĐŒ", - "138" : "ol", - "125" : "ome", - "309" : "are", - "673" : "▁inst", - "97" : "▁have", - "562" : "ject", - "678" : "ific", - "257" : "ect", - "17" : "on", - "953" : "с", - "933" : "ē", - "844" : "'", - "949" : "η", - "190" : "▁v", - "980" : "Đș", - "452" : "▁fo", - "247" : "ame", - "612" : "▁help", - "501" : "▁spe", - "604" : "ange", - "654" : "ib", - "842" : "k", - "815" : "ave", - "687" : "▁form", - "222" : "res", - "421" : "sel", - "477" : "other", - "308" : "▁their", - "212" : "▁N", - "737" : "▁important", - "90" : "▁u", - "630" : "▁Now", - "425" : "ia", - "14" : "▁i", - "315" : "▁J", - "331" : "▁fe", - "304" : "▁ar", - "29" : "ll", - "499" : "▁sc", - "919" : "€", - "301" : "itt", - "201" : "ri", - "137" : "ould", - "289" : "▁man", - "81" : "▁this", - "1008" : "П", - "312" : "ions", - "725" : "ks", - "837" : "f", - "458" : "▁through", - "714" : "▁maybe", - "487" : "thing", - "424" : "▁may", - "31" : "▁and", - "78" : "ro", - "961" : "Đ»", - "956" : "Ă„", - "198" : "cause", - "95" : "im", - "899" : "ñ", - "111" : "ally", - "523" : "▁There", - "538" : "ons", - "797" : "▁el", - "726" : "▁interest", - "53" : "▁wh", - "296" : "▁any", - "489" : "fore", - "911" : "φ", - "248" : "▁We", - "639" : "▁add", - "108" : "▁W", - "521" : "▁point", - "416" : "▁dis", - "739" : "▁run", - "747" : "ract", - "3" : "▁a", - "530" : "▁most", - "734" : "▁bec", - "338" : "age", - "544" : "▁pers", - "113" : "▁se", - "691" : "▁able", - "847" : "A", - "651" : "stem", - "115" : "od", - "527" : "▁same", - "575" : "ves", - "696" : "▁As", - "277" : "▁qu", - "728" : "ited", - "640" : "▁set", - "502" : "ub", - "497" : "▁try", - "881" : "V", - "219" : "▁G", - "561" : "ph", - "759" : "▁All", - "177" : "ain", - "515" : "ors", - "71" : "▁S", - "509" : "ian", - "803" : "▁cou", - "25" : "an", - "583" : "iss", - "417" : "▁first", - "840" : "b", - "768" : "▁An", - "419" : "▁something", - "569" : "▁acc", - "607" : "atch", - "534" : "ug", - "195" : "▁my", - "832" : "c", - "634" : "cess", - "809" : "▁everything", - "1" : "▁t", - "557" : "▁bas", - "481" : "▁inc", - "57" : "ot", - "518" : "ail", - "924" : "α", - "173" : "▁lo", - "905" : "ÎŒ", - "762" : "▁probably", - "626" : "▁dec", - "647" : "▁its", - "415" : "orm", - "917" : "ĂŽ", - "560" : "body", - "474" : "▁put", - "572" : "▁em", - "689" : "▁system", - "909" : "Ξ", - "408" : "▁res", - "862" : "1", - "830" : "d", - "748" : "▁question", - "285" : "▁now", - "717" : "▁prod", - "60" : "▁e", - "818" : "oney", - "814" : "▁Because", - "893" : "Ă­", - "587" : "▁uh", - "377" : "▁things", - "454" : "▁ro", - "205" : "▁up", - "849" : "j", - "152" : "out", - "994" : "ÎŻ", - "789" : "ope", - "1018" : "Ćș", - "973" : "ĆŸ", - "950" : "Đż", - "901" : "Ăš", - "288" : "▁Wh", - "710" : "▁prob", - "581" : "igh", - "1009" : "ĐŽ", - "47" : "us", - "756" : "ness", - "648" : "▁God", - "43" : "om", - "952" : "Đœ", - "744" : "▁never", - "688" : "▁guys", - "272" : "▁co", - "36" : "▁in", - "405" : "ated", - "741" : "▁fact", - "56" : "ut", - "290" : "ous", - "770" : "▁belie", - "943" : "ĂČ", - "284" : "▁how", - "697" : "▁mod", - "671" : "▁att", - "453" : "▁comp", - "690" : "ew", - "101" : "▁an", - "264" : "ven", - "200" : "▁don", - "279" : "▁were", - "99" : "ht", - "305" : "hing", - "1014" : "Î", - "525" : "▁many", - "738" : "▁such", - "940" : "Δ", - "995" : "ζ", - "491" : "ark", - "18" : "▁h", - "947" : "Ƃ", - "702" : "▁ask", - "49" : "ow", - "808" : "▁gl", - "484" : "▁should", - "907" : "Ă€", - "916" : "Ăą", - "447" : "ang", - "107" : "▁as", - "843" : "v", - "1025" : "▁global", - "267" : "▁really", - "892" : "\/", - "703" : "old", - "679" : "ix", - "601" : "▁ob", - "498" : "ious", - "298" : "▁But", - "300" : "iv", - "565" : "▁Is", - "282" : "ther", - "249" : "our", - "539" : "▁Be", - "356" : "ap", - "528" : "▁sy", - "470" : "xt", - "373" : "ick", - "853" : "C", - "215" : "and", - "869" : "F", - "10" : "at", - "1004" : "Ɠ", - "686" : "ative", - "553" : "ought", - "976" : "ę", - "473" : "ild", - "4" : "in", - "352" : "▁ad", - "951" : "Ă«", - "265" : "▁our", - "427" : "▁her", - "676" : "▁rep", - "982" : "ƚ", - "243" : "ies", - "48" : "ic", - "978" : "б", - "28" : "or", - "566" : "ates", - "965" : "ș", - "9" : "▁s", - "92" : "ur", - "16" : "▁c", - "30" : "▁of", - "15" : "▁b", - "547" : "▁str", - "645" : "▁life", - "24" : "▁p", - "310" : "▁his", - "472" : "ory", - "189" : "▁going", - "699" : "ings", - "1029" : "▁bipartisan", - "178" : "▁one", - "67" : "▁ha", - "824" : "n", - "422" : "▁let", - "888" : "$", - "649" : "pect", - "635" : "nds", - "540" : "ically", - "511" : "red", - "827" : "h", - "19" : "ing", - "969" : "Ăč", - "985" : "υ", - "225" : "ake", - "239" : "ard", - "618" : "ike", - "240" : "▁right", - "585" : "ne", - "378" : "▁In", - "616" : "▁world", - "130" : "▁me", - "434" : "▁even", - "599" : "te", - "897" : "ā", - "206" : "▁P", - "336" : "▁U", - "82" : "ld", - "877" : "9", - "368" : "▁than", - "1016" : "ė", - "181" : "ist", - "438" : "▁app", - "611" : "ert", - "567" : "▁ph", - "374" : "way", - "395" : "ase", - "621" : "▁min", - "188" : "▁about", - "663" : "▁stu", - "571" : "▁years", - "94" : "ith", - "758" : "ize", - "180" : "art", - "40" : "▁you", - "904" : "Âż", - "845" : "I", - "268" : "▁more", - "253" : "▁see", - "683" : "▁def", - "856" : "O", - "370" : "▁gu", - "551" : "▁rec", - "8" : "▁o", - "428" : "▁said", - "675" : "▁happen", - "102" : "▁with", - "776" : "▁ca", - "363" : "▁somet", - "757" : "arch", - "914" : "ĂȘ", - "208" : "ort", - "660" : "▁sure", - "207" : "▁out", - "613" : "ost", - "942" : "Đž", - "761" : "blem", - "117" : "▁like", - "129" : "▁sh", - "293" : "ff", - "407" : "▁off", - "595" : "▁long", - "366" : "ire", - "614" : "▁too", - "256" : "ure", - "328" : "▁cl", - "793" : "ational", - "724" : "▁mon", - "989" : "х", - "134" : "▁what", - "45" : "▁n", - "707" : "ility", - "62" : "ay", - "801" : "▁four", - "392" : "▁those", - "160" : "ge", - "369" : "ace", - "236" : "▁will", - "80" : "se", - "708" : "▁high", - "217" : "em", - "944" : "р", - "672" : "▁op", - "754" : "▁Of", - "58" : "▁we", - "131" : "ill", - "810" : "▁eff", - "146" : "▁ne", - "998" : "Æ", - "496" : "▁being", - "975" : "у", - "625" : "▁find", - "132" : "ant", - "884" : "7", - "382" : "▁could", - "459" : "▁start", - "147" : "▁de", - "448" : "▁mean", - "1019" : "Κ", - "241" : "▁thing", - "819" : "▁", - "698" : "▁done", - "444" : "ress", - "46" : "ve", - "971" : "ч", - "646" : "ities", - "713" : "▁pres", - "483" : "▁different", - "577" : "▁inter", - "372" : "og", - "786" : "iew", - "692" : "ied", - "362" : "iff", - "1021" : "ÎŹ", - "586" : "▁why", - "636" : "▁big", - "1023" : "ό", - "162" : "▁Y", - "685" : "▁next", - "193" : "ok", - "221" : "▁D", - "665" : "▁ev", - "159" : "▁pro", - "59" : "▁is", - "1017" : "Ć ", - "74" : "▁Th", - "813" : "▁Am", - "5" : "▁the", - "218" : "oug", - "376" : "▁un", - "270" : "ose", - "140" : "▁C", - "122" : "▁they", - "412" : "▁talk", - "913" : "σ", - "326" : "ice", - "451" : "▁does", - "559" : "erm", - "23" : "▁f", - "210" : "ment", - "778" : "▁ass", - "984" : "Îș", - "316" : "▁no", - "666" : "ments", - "494" : "▁come", - "887" : "Q", - "891" : "Ă©", - "404" : "uch", - "920" : "É", - "455" : "▁bl", - "170" : "est", - "72" : "ig", - "443" : "▁again", - "727" : "▁ent", - "983" : "Ω", - "410" : "ance", - "281" : "▁act", - "763" : "hip", - "220" : "os", - "261" : "▁You", - "214" : "ich", - "930" : "Ăź", - "805" : "▁called", - "156" : "ers", - "753" : "▁course", - "868" : "G", - "238" : "▁which", - "836" : "w", - "704" : "ered", - "513" : "▁ke", - "964" : "Ă»", - "589" : "▁cons", - "409" : "ac", - "609" : "gr", - "476" : "▁new", - "619" : "▁Okay", - "287" : "ud", - "603" : "ady", - "633" : "▁real", - "508" : "ty", - "1012" : "ÂĄ", - "401" : "▁year", - "110" : "ust", - "196" : "ind", - "242" : "▁want", - "817" : "▁Like", - "890" : "X", - "191" : "▁wor", - "402" : "ass", - "266" : "ci", - "347" : "alk", - "387" : "ach", - "804" : "ont", - "701" : "ism", - "841" : ".", - "317" : "▁en", - "385" : "irst", - "255" : "ast", - "510" : "onna", - "20" : "▁to", - "667" : "▁another", - "591" : "▁after", - "354" : "▁over", - "112" : "▁j", - "674" : "▁sl", - "935" : "ß", - "204" : "ity", - "735" : "▁Well", - "900" : "ö", - "163" : "▁O", - "169" : "▁al", - "531" : "▁great", - "185" : "ink", - "752" : "ather", - "795" : "▁trying", - "796" : "▁sch", - "721" : "oy", - "657" : "▁person", - "799" : "atter", - "554" : "day", - "274" : "ough", - "109" : "▁are", - "643" : "ible", - "968" : "Ø", - "269" : "ound", - "145" : "▁just", - "263" : "▁when", - "85" : "▁k", - "365" : "nder", - "359" : "▁po", - "937" : "ĂŻ", - "226" : "▁int", - "584" : "▁des", - "512" : "wn", - "495" : "▁They", - "391" : "▁He", - "883" : "6", - "627" : "ular", - "927" : "Đ°", - "941" : "Đ”", - "330" : "▁way", - "860" : "Y", - "462" : "▁bet" -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/convert_tdt_decoder.py b/parakeet-tdt-ctc-110m/convert_tdt_decoder.py deleted file mode 100644 index 4ed15a38318bb788204bfdbd2bc466cff9ff6e5c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/convert_tdt_decoder.py +++ /dev/null @@ -1,323 +0,0 @@ -#!/usr/bin/env python3 -""" -Convert Parakeet TDT-CTC 110M decoder components to CoreML. - -This script exports the TDT decoder (prediction network) and joint network -with the SAME format as the working 0.6B model: -- JointDecision outputs token_id, token_prob, duration (argmax done inside) -- Uses shape [1, dim, 1] for encoder/decoder steps -- Matches the interface expected by TdtDecoderV3 -""" - -import argparse -import os -import torch -import torch.nn.functional as F -import coremltools as ct -import numpy as np -from pathlib import Path - -# NeMo imports -import nemo.collections.asr as nemo_asr - - -def get_model_config(model): - """Extract model configuration.""" - encoder_dim = None - pred_hidden = 640 # Default for parakeet models - num_layers = 1 - vocab_size = 1024 - num_durations = 5 - - # Get encoder dimension - if hasattr(model, 'encoder'): - encoder = model.encoder - if hasattr(encoder, 'd_model'): - encoder_dim = encoder.d_model - elif hasattr(encoder, '_feat_out'): - encoder_dim = encoder._feat_out - - # Get decoder config - if hasattr(model, 'decoder'): - decoder = model.decoder - if hasattr(decoder, 'pred_hidden'): - pred_hidden = decoder.pred_hidden - if hasattr(decoder, 'pred_rnn_layers'): - num_layers = decoder.pred_rnn_layers - - # Get joint config - if hasattr(model, 'joint'): - joint = model.joint - if hasattr(joint, 'num_extra_outputs'): - num_durations = joint.num_extra_outputs - if hasattr(joint, 'num_classes'): - vocab_size = joint.num_classes - num_durations - - return { - 'encoder_dim': encoder_dim, - 'pred_hidden': pred_hidden, - 'num_layers': num_layers, - 'vocab_size': vocab_size, - 'num_durations': num_durations, - } - - -class DecoderWrapper(torch.nn.Module): - """ - Wrapper for the RNNT/TDT decoder (prediction network). - - Matches 0.6B format: - - Input: targets[1,1], target_lengths[1], h_in[num_layers,1,pred_hidden], c_in[...] - - Output: decoder_output[1,pred_hidden,2], h_out[...], c_out[...] - """ - - def __init__(self, decoder, pred_hidden): - super().__init__() - self.decoder = decoder - self.pred_hidden = pred_hidden - - def forward(self, targets, target_lengths, h_in, c_in): - """ - Args: - targets: [1, 1] - previous token ID - target_lengths: [1] - always 1 - h_in: [num_layers, 1, pred_hidden] - c_in: [num_layers, 1, pred_hidden] - Returns: - decoder_output: [1, pred_hidden, 2] - prediction network output (transposed) - h_out: [num_layers, 1, pred_hidden] - c_out: [num_layers, 1, pred_hidden] - """ - state = (h_in, c_in) - # pred_output shape: [batch, time, pred_hidden] = [1, 1, pred_hidden] - pred_output, new_state = self.decoder.predict(targets, state=state, add_sos=False) - h_out, c_out = new_state - - # Transpose to [batch, pred_hidden, time] and concat two time steps - # (0.6B outputs [1, 640, 2] - we match this by duplicating) - pred_transposed = pred_output.transpose(1, 2) # [1, pred_hidden, 1] - decoder_output = torch.cat([pred_transposed, pred_transposed], dim=2) # [1, pred_hidden, 2] - - return decoder_output, h_out, c_out - - -class JointWrapper(torch.nn.Module): - """ - Wrapper for the TDT joint network with internal argmax. - - Matches 0.6B format: - - Input: encoder_step[1,encoder_dim,1], decoder_step[1,pred_hidden,1] - - Output: token_id[1,1,1], token_prob[1,1,1], duration[1,1,1] - """ - - def __init__(self, joint, vocab_size, num_durations=5): - super().__init__() - self.joint = joint - self.vocab_size = vocab_size - self.num_durations = num_durations - - def forward(self, encoder_step, decoder_step): - """ - Args: - encoder_step: [1, encoder_dim, 1] - decoder_step: [1, pred_hidden, 1] - Returns: - token_id: [1, 1, 1] - argmax token ID - token_prob: [1, 1, 1] - probability of selected token - duration: [1, 1, 1] - argmax duration bin - """ - # Transpose to [batch, 1, dim] for joint network - enc = encoder_step.transpose(1, 2) # [1, 1, encoder_dim] - dec = decoder_step.transpose(1, 2) # [1, 1, pred_hidden] - - # Run joint network - # Joint output: [1, 1, 1, vocab_size + 1 (blank) + num_durations] - joint_out = self.joint.joint(enc, dec) - - # Debug: print shape on first call - if not hasattr(self, '_debug_printed'): - self._debug_printed = True - print(f" Joint output shape: {joint_out.shape}") - print(f" Expected: vocab={self.vocab_size} + blank=1 + durations={self.num_durations} = {self.vocab_size + 1 + self.num_durations}") - - # Split: token logits include vocab + blank, durations are separate - # vocab_size = 1024 tokens (0-1023), blank = index 1024, durations = indices 1025+ - num_tokens = self.vocab_size + 1 # Include blank at vocab_size - logits = joint_out[..., :num_tokens] # [1, 1, 1, vocab_size + 1] - duration_logits = joint_out[..., num_tokens:] # [1, 1, 1, num_durations] - - # Apply softmax and get probabilities - probs = F.softmax(logits, dim=-1) - - # Argmax for token - token_id = torch.argmax(logits, dim=-1, keepdim=True) # [1, 1, 1, 1] - token_id = token_id.squeeze(-1) # [1, 1, 1] - - # Get probability of selected token - token_prob = torch.gather(probs, -1, token_id.unsqueeze(-1)) # [1, 1, 1, 1] - token_prob = token_prob.squeeze(-1) # [1, 1, 1] - - # Argmax for duration - duration = torch.argmax(duration_logits, dim=-1, keepdim=False) # [1, 1, 1] - - return token_id.int(), token_prob, duration.int() - - -def convert_decoder(model, config, output_dir: Path): - """Convert decoder to CoreML.""" - print(f"Converting Decoder...") - print(f" pred_hidden={config['pred_hidden']}, num_layers={config['num_layers']}") - - wrapper = DecoderWrapper(model.decoder, config['pred_hidden']) - wrapper.eval() - - # Create example inputs - targets = torch.zeros(1, 1, dtype=torch.long) - target_lengths = torch.ones(1, dtype=torch.long) - h_in = torch.zeros(config['num_layers'], 1, config['pred_hidden']) - c_in = torch.zeros(config['num_layers'], 1, config['pred_hidden']) - - # Trace the model - with torch.no_grad(): - traced = torch.jit.trace(wrapper, (targets, target_lengths, h_in, c_in)) - - # Convert to CoreML - mlmodel = ct.convert( - traced, - inputs=[ - ct.TensorType(name="targets", shape=(1, 1), dtype=np.int32), - ct.TensorType(name="target_lengths", shape=(1,), dtype=np.int32), - ct.TensorType(name="h_in", shape=(config['num_layers'], 1, config['pred_hidden']), dtype=np.float32), - ct.TensorType(name="c_in", shape=(config['num_layers'], 1, config['pred_hidden']), dtype=np.float32), - ], - outputs=[ - ct.TensorType(name="decoder_output"), - ct.TensorType(name="h_out"), - ct.TensorType(name="c_out"), - ], - minimum_deployment_target=ct.target.iOS17, - compute_precision=ct.precision.FLOAT16, - ) - - # Add metadata - mlmodel.author = "Fluid Inference" - mlmodel.short_description = "Hybrid TDT Decoder (110M)" - - # Save - output_path = output_dir / "Decoder.mlpackage" - mlmodel.save(str(output_path)) - print(f" Saved to {output_path}") - - return mlmodel - - -def convert_joint(model, config, output_dir: Path): - """Convert joint network to CoreML.""" - print(f"Converting JointDecision...") - print(f" encoder_dim={config['encoder_dim']}, pred_hidden={config['pred_hidden']}") - print(f" vocab_size={config['vocab_size']}, num_durations={config['num_durations']}") - - wrapper = JointWrapper( - model.joint, - vocab_size=config['vocab_size'], - num_durations=config['num_durations'] - ) - wrapper.eval() - - # Create example inputs - shape [1, dim, 1] - encoder_step = torch.randn(1, config['encoder_dim'], 1) - decoder_step = torch.randn(1, config['pred_hidden'], 1) - - # Trace the model - with torch.no_grad(): - traced = torch.jit.trace(wrapper, (encoder_step, decoder_step)) - - # Convert to CoreML - mlmodel = ct.convert( - traced, - inputs=[ - ct.TensorType(name="encoder_step", shape=(1, config['encoder_dim'], 1), dtype=np.float32), - ct.TensorType(name="decoder_step", shape=(1, config['pred_hidden'], 1), dtype=np.float32), - ], - outputs=[ - ct.TensorType(name="token_id"), - ct.TensorType(name="token_prob"), - ct.TensorType(name="duration"), - ], - minimum_deployment_target=ct.target.iOS17, - compute_precision=ct.precision.FLOAT16, - ) - - # Add metadata - mlmodel.author = "Fluid Inference" - mlmodel.short_description = "Hybrid Joint Decision (110M)" - - # Save - output_path = output_dir / "JointDecision.mlpackage" - mlmodel.save(str(output_path)) - print(f" Saved to {output_path}") - - return mlmodel - - -def main(): - parser = argparse.ArgumentParser(description="Convert TDT decoder to CoreML (0.6B format)") - parser.add_argument( - "--model-name", - default="nvidia/parakeet-tdt_ctc-110m", - help="NeMo model name or path" - ) - parser.add_argument( - "--output-dir", - type=Path, - default=Path("./output"), - help="Output directory for CoreML models" - ) - args = parser.parse_args() - - # Create output directory - args.output_dir.mkdir(parents=True, exist_ok=True) - - # Load model - print(f"Loading model: {args.model_name}") - model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(args.model_name) - model.eval() - - # Get model configuration - config = get_model_config(model) - - # Auto-detect encoder dim if not found - if config['encoder_dim'] is None: - print("Auto-detecting encoder dimension...") - dummy_audio = torch.randn(1, 16000) - dummy_length = torch.tensor([16000]) - with torch.no_grad(): - enc_out, enc_len = model.encoder( - audio_signal=dummy_audio, - length=dummy_length - ) - config['encoder_dim'] = enc_out.shape[-1] - - print(f"\nModel config:") - for k, v in config.items(): - print(f" {k}: {v}") - - # Convert components - print() - convert_decoder(model, config, args.output_dir) - convert_joint(model, config, args.output_dir) - - print("\nConversion complete!") - print(f"Models saved to: {args.output_dir}") - print("\nNext steps:") - print("1. Compile to .mlmodelc:") - print(f" cd {args.output_dir}") - print(" xcrun coremlcompiler compile Decoder.mlpackage .") - print(" xcrun coremlcompiler compile JointDecision.mlpackage .") - print("2. Copy to model cache:") - print(" cp -r Decoder.mlmodelc JointDecision.mlmodelc ~/Library/Application\\ Support/FluidAudio/Models/parakeet-ctc-110m-coreml/") - print("3. Test with: swift run fluidaudio hybrid-earnings-benchmark --max-files 1") - - -if __name__ == "__main__": - main() diff --git a/parakeet-tdt-ctc-110m/coreml/audio/yc_first_minute_16k_15s.wav b/parakeet-tdt-ctc-110m/coreml/audio/yc_first_minute_16k_15s.wav deleted file mode 100644 index 0b8040f5f124d20a1d7e812c576c831a7573eddf..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/audio/yc_first_minute_16k_15s.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c79c8bc763b4efccb3e12f199ec0a59aa2edc5e9e4d21ca70fde8f36762d4147 -size 480078 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/analytics/coremldata.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 4093e2df453ec508a533d293a574198b2372845b..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fc681823d92eca3dbece3a30c975afa7251eedae0e718b07ffbf1a8b4313b87e -size 243 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/coremldata.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/coremldata.bin deleted file mode 100644 index db4ae8de920867977480465fffece41bc606c0a2..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2ebec8fc38c063de4b2159e21b1f981309fa5947c24d7e4883aca20f7c15fbb9 -size 377 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/metadata.json b/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/metadata.json deleted file mode 100644 index 4abe30bf0b35b72d2c9c013b61cc78b4f26a85e1..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/metadata.json +++ /dev/null @@ -1,66 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M CTC decoder head", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 188 × 1025)", - "shortDescription" : "", - "shape" : "[1, 188, 1025]", - "name" : "ctc_logits", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.cast" : 2, - "Ios17.conv" : 1, - "Ios17.transpose" : 1, - "Ios16.softmax" : 1, - "Ios17.log" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - 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a/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/model.mil +++ /dev/null @@ -1,24 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor encoder_output) { - tensor var_4 = const()[name = tensor("op_4"), val = tensor(-1)]; - tensor var_18_pad_type_0 = const()[name = tensor("op_18_pad_type_0"), val = tensor("valid")]; - tensor var_18_strides_0 = const()[name = tensor("op_18_strides_0"), val = tensor([1])]; - tensor var_18_pad_0 = const()[name = tensor("op_18_pad_0"), val = tensor([0, 0])]; - tensor var_18_dilations_0 = const()[name = tensor("op_18_dilations_0"), val = tensor([1])]; - tensor var_18_groups_0 = const()[name = tensor("op_18_groups_0"), val = tensor(1)]; - tensor encoder_output_to_fp16_dtype_0 = const()[name = tensor("encoder_output_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor module_decoder_layers_0_weight_to_fp16 = const()[name = tensor("module_decoder_layers_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor module_decoder_layers_0_bias_to_fp16 = const()[name = tensor("module_decoder_layers_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049728)))]; - tensor encoder_output_to_fp16 = cast(dtype = encoder_output_to_fp16_dtype_0, x = encoder_output)[name = tensor("cast_1")]; - tensor var_18_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = var_18_dilations_0, groups = var_18_groups_0, pad = var_18_pad_0, pad_type = var_18_pad_type_0, strides = var_18_strides_0, weight = module_decoder_layers_0_weight_to_fp16, x = encoder_output_to_fp16)[name = tensor("op_18_cast_fp16")]; - tensor input_perm_0 = const()[name = tensor("input_perm_0"), val = tensor([0, 2, 1])]; - tensor input_cast_fp16 = transpose(perm = input_perm_0, x = var_18_cast_fp16)[name = tensor("transpose_0")]; - tensor out_objects_softmax_cast_fp16 = softmax(axis = var_4, x = input_cast_fp16)[name = tensor("out_objects_softmax_cast_fp16")]; - tensor out_objects_epsilon_0 = const()[name = tensor("out_objects_epsilon_0"), val = tensor(0x1p-149)]; - tensor out_objects_cast_fp16 = log(epsilon = out_objects_epsilon_0, x = out_objects_softmax_cast_fp16)[name = tensor("out_objects_cast_fp16")]; - tensor out_objects_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("out_objects_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor ctc_logits = cast(dtype = out_objects_cast_fp16_to_fp32_dtype_0, x = out_objects_cast_fp16)[name = tensor("cast_0")]; - } -> (ctc_logits); -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/weights/weight.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/weights/weight.bin deleted file mode 100644 index 23cedd570125191064c6111997f08c48898245f7..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/CTCHead.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fb9bead064427ffcb7529c0e3f378e421b4dde8e6d81447b6d1ca3352ca850e1 -size 1051842 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/analytics/coremldata.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index dd015548a03cc453e6477a24374e8fee955e75ef..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:990455f6431342750254f66edf27bfb41be62a7ba17a18e1dd6afd4f5f56e9eb -size 243 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/coremldata.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/coremldata.bin deleted file mode 100644 index cbea0affef0c3bb63be750e134dc0c9ed70aadb1..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:29009727821ad8551ab5fe9271e93c597d92a9714f64b94aa533a9ceb6e22b93 -size 498 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/metadata.json b/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/metadata.json deleted file mode 100644 index 786f4bb2823b1aa7ebb55fe335c9385ee0065b7c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/metadata.json +++ /dev/null @@ -1,118 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M decoder (RNNT prediction network)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "h_out", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "c_out", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.squeeze" : 2, - "Ios17.gather" : 1, - "Ios17.cast" : 6, - "Ios17.lstm" : 1, - "Ios17.transpose" : 2, - "Identity" : 1, - "Ios17.expandDims" : 2 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1]", - "name" : "targets", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "target_length", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "h_in", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "c_in", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.version" : "8.3.0", - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0" - }, - "generatedClassName" : "parakeet_decoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/model.mil b/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/model.mil deleted file mode 100644 index f69c7247525bc9d95f9341acc4f11eca709c7d00..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/model.mil +++ /dev/null @@ -1,45 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor c_in, tensor h_in, tensor target_length, tensor targets) { - tensor y_axis_0 = const()[name = tensor("y_axis_0"), val = tensor(0)]; - tensor y_batch_dims_0 = const()[name = tensor("y_batch_dims_0"), val = tensor(0)]; - tensor y_validate_indices_0 = const()[name = tensor("y_validate_indices_0"), val = tensor(false)]; - tensor module_prediction_embed_weight_to_fp16 = const()[name = tensor("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor targets_to_int16_dtype_0 = const()[name = tensor("targets_to_int16_dtype_0"), val = tensor("int16")]; - tensor targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor("cast_8")]; - tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = targets_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = tensor("y_cast_fp16_cast_uint16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([1, 0, 2])]; - tensor input_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; - tensor h_in_to_fp16_dtype_0 = const()[name = tensor("h_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor("cast_7")]; - tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = h_in_to_fp16)[name = tensor("input_lstm_h0_squeeze_cast_fp16")]; - tensor input_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; - tensor c_in_to_fp16_dtype_0 = const()[name = tensor("c_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor("cast_6")]; - tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = c_in_to_fp16)[name = tensor("input_lstm_c0_squeeze_cast_fp16")]; - tensor input_direction_0 = const()[name = tensor("input_direction_0"), val = tensor("forward")]; - tensor input_output_sequence_0 = const()[name = tensor("input_output_sequence_0"), val = tensor(true)]; - tensor input_recurrent_activation_0 = const()[name = tensor("input_recurrent_activation_0"), val = tensor("sigmoid")]; - tensor input_cell_activation_0 = const()[name = tensor("input_cell_activation_0"), val = tensor("tanh")]; - tensor input_activation_0 = const()[name = tensor("input_activation_0"), val = tensor("tanh")]; - tensor concat_1_to_fp16 = const()[name = tensor("concat_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312128)))]; - tensor concat_2_to_fp16 = const()[name = tensor("concat_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4588992)))]; - tensor concat_0_to_fp16 = const()[name = tensor("concat_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7865856)))]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = tensor("transpose_2")]; - tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_0_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor obj_3_axes_0 = const()[name = tensor("obj_3_axes_0"), val = tensor([0])]; - tensor obj_3_cast_fp16 = expand_dims(axes = obj_3_axes_0, x = input_cast_fp16_1)[name = tensor("obj_3_cast_fp16")]; - tensor obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_3_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_axes_0 = const()[name = tensor("obj_axes_0"), val = tensor([0])]; - tensor obj_cast_fp16 = expand_dims(axes = obj_axes_0, x = input_cast_fp16_2)[name = tensor("obj_cast_fp16")]; - tensor obj_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([1, 2, 0])]; - tensor transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("transpose_0_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor("transpose_1")]; - tensor decoder = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = tensor("cast_3")]; - tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = tensor("cast_4")]; - tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = tensor("cast_5")]; - tensor target_length_tmp = identity(x = target_length)[name = tensor("target_length_tmp")]; - } -> (decoder, h_out, c_out); -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/weights/weight.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/weights/weight.bin deleted file mode 100644 index 4543ad4ea89b21ebed0de0d5ec3975d07eace7fb..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Decoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:dd90b58597ee2c172c672dffe13b1110898ba07394c1a15efc96cc8c6b18411b -size 7871040 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/analytics/coremldata.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index d2787d4e5376d7f894d2b0a5a09f497a9b486c73..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b7ae65e2af616df46066b7efca2d7c19941666ac0685f4ed005666890a052b0d -size 243 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/coremldata.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/coremldata.bin deleted file mode 100644 index 9d9cdbfd392e8f7391b47c1c96f0fcbaab440389..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0713c2d6ac5f8f6fb9582be250351ebd8efc925f71f4261191165f1406f2ee5d -size 437 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/metadata.json b/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/metadata.json deleted file mode 100644 index 8e3f6cc9ddca206205987b2c828d2e595e19bfc2..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/metadata.json +++ /dev/null @@ -1,105 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M encoder (15 s window)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 512 × 188)", - "shortDescription" : "", - "shape" : "[1, 512, 188]", - "name" : "encoder_output", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "encoder_length", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.logicalAnd" : 2, - "Ios17.reshape" : 103, - "Ios16.softmax" : 17, - "Ios17.matmul" : 51, - "Ios17.transpose" : 123, - "Split" : 17, - "Ios17.expandDims" : 17, - "Select" : 51, - "Ios17.add" : 128, - "Tile" : 8, - "Ios17.sliceByIndex" : 34, - "Ios16.sigmoid" : 17, - "Pad" : 34, - "Ios17.logicalNot" : 2, - "Ios17.layerNorm" : 85, - "Ios16.silu" : 51, - "Ios17.less" : 5, - "Ios17.sub" : 3, - "Ios17.conv" : 56, - "Ios16.relu" : 3, - "Ios17.linear" : 137, - "Ios17.cast" : 11, - "Ios17.floorDiv" : 3, - "Ios17.mul" : 77 - }, - "computePrecision" : "Mixed (Float16, Float32, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 80 × 1501)", - "shortDescription" : "", - "shape" : "[1, 80, 1501]", - "name" : "mel_features", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "generatedClassName" : "parakeet_encoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/model.mil b/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/model.mil deleted file mode 100644 index 344bd1abf313c03c9ed9287c00893739f5d361bd..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/model.mil +++ /dev/null @@ -1,2690 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor mel_features, tensor mel_length) { - tensor var_23 = const()[name = tensor("op_23"), val = tensor(-1)]; - tensor x_1_perm_0 = const()[name = tensor("x_1_perm_0"), val = tensor([0, 2, 1])]; - tensor mel_features_to_fp16_dtype_0 = const()[name = tensor("mel_features_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor tensor_1_axes_0 = const()[name = tensor("tensor_1_axes_0"), val = tensor([1])]; - tensor mel_features_to_fp16 = cast(dtype = mel_features_to_fp16_dtype_0, x = mel_features)[name = tensor("cast_182")]; - tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_features_to_fp16)[name = tensor("transpose_207")]; - tensor tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_1_cast_fp16)[name = tensor("tensor_1_cast_fp16")]; - tensor expand_dims_0 = const()[name = tensor("expand_dims_0"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 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1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500]])]; - tensor var_87_axes_0 = const()[name = tensor("op_87_axes_0"), val = tensor([1])]; - tensor var_87 = expand_dims(axes = var_87_axes_0, x = mel_length)[name = tensor("op_87")]; - tensor time_mask_1 = less(x = expand_dims_0, y = var_87)[name = tensor("time_mask_1")]; - tensor var_89_axes_0 = const()[name = tensor("op_89_axes_0"), val = tensor([-1])]; - tensor var_89 = expand_dims(axes = var_89_axes_0, x = time_mask_1)[name = tensor("op_89")]; - tensor var_91_reps_0 = const()[name = tensor("op_91_reps_0"), val = tensor([1, 1, 80])]; - tensor var_91 = tile(reps = var_91_reps_0, x = var_89)[name = tensor("op_91")]; - tensor var_97_axes_0 = const()[name = tensor("op_97_axes_0"), val = tensor([1])]; - tensor cast_3_to_fp16_dtype_0 = const()[name = tensor("cast_3_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_91_to_fp16 = cast(dtype = cast_3_to_fp16_dtype_0, x = var_91)[name = tensor("cast_181")]; - tensor var_97_cast_fp16 = expand_dims(axes = var_97_axes_0, x = var_91_to_fp16)[name = tensor("op_97_cast_fp16")]; - tensor input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_97_cast_fp16)[name = tensor("input_1_cast_fp16")]; - tensor tensor_3_pad_type_0 = const()[name = tensor("tensor_3_pad_type_0"), val = tensor("custom")]; - tensor tensor_3_pad_0 = const()[name = tensor("tensor_3_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_3_strides_0 = const()[name = tensor("tensor_3_strides_0"), val = tensor([2, 2])]; - tensor tensor_3_dilations_0 = const()[name = tensor("tensor_3_dilations_0"), val = tensor([1, 1])]; - tensor tensor_3_groups_0 = const()[name = tensor("tensor_3_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_0_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor module_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4736)))]; - tensor tensor_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("tensor_3_cast_fp16")]; - tensor cast_1_to_fp16_dtype_0 = const()[name = tensor("cast_1_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_108_promoted_to_fp16 = const()[name = tensor("op_108_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor mel_length_to_fp16 = cast(dtype = cast_1_to_fp16_dtype_0, x = mel_length)[name = tensor("cast_180")]; - tensor var_109_cast_fp16 = add(x = mel_length_to_fp16, y = var_108_promoted_to_fp16)[name = tensor("op_109_cast_fp16")]; - tensor var_110_promoted_to_fp16 = const()[name = tensor("op_110_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_111_cast_fp16 = add(x = var_109_cast_fp16, y = var_110_promoted_to_fp16)[name = tensor("op_111_cast_fp16")]; - tensor var_112_promoted_to_fp16 = const()[name = tensor("op_112_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_113_cast_fp16 = sub(x = var_111_cast_fp16, y = var_112_promoted_to_fp16)[name = tensor("op_113_cast_fp16")]; - tensor var_21_promoted_to_fp16 = const()[name = tensor("op_21_promoted_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_0_cast_fp16 = floor_div(x = var_113_cast_fp16, y = var_21_promoted_to_fp16)[name = tensor("floor_div_0_cast_fp16")]; - tensor var_115_promoted_to_fp16 = const()[name = tensor("op_115_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_115_promoted_to_fp16)[name = tensor("current_lengths_3_cast_fp16")]; - tensor cast_4_dtype_0 = const()[name = tensor("cast_4_dtype_0"), val = tensor("int32")]; - tensor expand_dims_1 = const()[name = tensor("expand_dims_1"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750]])]; - tensor var_124_axes_0 = const()[name = tensor("op_124_axes_0"), val = tensor([1])]; - tensor current_lengths_3_cast_fp16_to_int32 = cast(dtype = cast_4_dtype_0, x = current_lengths_3_cast_fp16)[name = tensor("cast_179")]; - tensor var_124 = expand_dims(axes = var_124_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = tensor("op_124")]; - tensor time_mask_3 = less(x = expand_dims_1, y = var_124)[name = tensor("time_mask_3")]; - tensor var_126_axes_0 = const()[name = tensor("op_126_axes_0"), val = tensor([-1])]; - tensor var_126 = expand_dims(axes = var_126_axes_0, x = time_mask_3)[name = tensor("op_126")]; - tensor var_128_reps_0 = const()[name = tensor("op_128_reps_0"), val = tensor([1, 1, 40])]; - tensor var_128 = tile(reps = var_128_reps_0, x = var_126)[name = tensor("op_128")]; - tensor var_134_axes_0 = const()[name = tensor("op_134_axes_0"), val = tensor([1])]; - tensor cast_5_to_fp16_dtype_0 = const()[name = tensor("cast_5_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_128_to_fp16 = cast(dtype = cast_5_to_fp16_dtype_0, x = var_128)[name = tensor("cast_178")]; - tensor var_134_cast_fp16 = expand_dims(axes = var_134_axes_0, x = var_128_to_fp16)[name = tensor("op_134_cast_fp16")]; - tensor expanded_mask_3_reps_0 = const()[name = tensor("expanded_mask_3_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_134_cast_fp16)[name = tensor("expanded_mask_3_cast_fp16")]; - tensor input_3_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor tensor_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor("tensor_5_cast_fp16")]; - tensor input_5_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor tensor_7_pad_type_0 = const()[name = tensor("tensor_7_pad_type_0"), val = tensor("custom")]; - tensor tensor_7_pad_0 = const()[name = tensor("tensor_7_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_7_strides_0 = const()[name = tensor("tensor_7_strides_0"), val = tensor([2, 2])]; - tensor tensor_7_groups_0 = const()[name = tensor("tensor_7_groups_0"), val = tensor(256)]; - tensor tensor_7_dilations_0 = const()[name = tensor("tensor_7_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_2_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5312)))]; - tensor module_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9984)))]; - tensor tensor_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16, x = input_5_cast_fp16)[name = tensor("tensor_7_cast_fp16")]; - tensor var_154_promoted_to_fp16 = const()[name = tensor("op_154_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_155_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_154_promoted_to_fp16)[name = tensor("op_155_cast_fp16")]; - tensor var_156_promoted_to_fp16 = const()[name = tensor("op_156_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_157_cast_fp16 = add(x = var_155_cast_fp16, y = var_156_promoted_to_fp16)[name = tensor("op_157_cast_fp16")]; - tensor var_158_promoted_to_fp16 = const()[name = tensor("op_158_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_159_cast_fp16 = sub(x = var_157_cast_fp16, y = var_158_promoted_to_fp16)[name = tensor("op_159_cast_fp16")]; - tensor var_21_promoted_1_to_fp16 = const()[name = tensor("op_21_promoted_1_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_1_cast_fp16 = floor_div(x = var_159_cast_fp16, y = var_21_promoted_1_to_fp16)[name = tensor("floor_div_1_cast_fp16")]; - tensor var_161_promoted_to_fp16 = const()[name = tensor("op_161_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_161_promoted_to_fp16)[name = tensor("current_lengths_5_cast_fp16")]; - tensor cast_6_dtype_0 = const()[name = tensor("cast_6_dtype_0"), val = tensor("int32")]; - tensor expand_dims_2 = const()[name = tensor("expand_dims_2"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375]])]; - tensor var_170_axes_0 = const()[name = tensor("op_170_axes_0"), val = tensor([1])]; - tensor current_lengths_5_cast_fp16_to_int32 = cast(dtype = cast_6_dtype_0, x = current_lengths_5_cast_fp16)[name = tensor("cast_177")]; - tensor var_170 = expand_dims(axes = var_170_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = tensor("op_170")]; - tensor time_mask_5 = less(x = expand_dims_2, y = var_170)[name = tensor("time_mask_5")]; - tensor var_172_axes_0 = const()[name = tensor("op_172_axes_0"), val = tensor([-1])]; - tensor var_172 = expand_dims(axes = var_172_axes_0, x = time_mask_5)[name = tensor("op_172")]; - tensor var_174_reps_0 = const()[name = tensor("op_174_reps_0"), val = tensor([1, 1, 20])]; - tensor var_174 = tile(reps = var_174_reps_0, x = var_172)[name = tensor("op_174")]; - tensor var_180_axes_0 = const()[name = tensor("op_180_axes_0"), val = tensor([1])]; - tensor cast_7_to_fp16_dtype_0 = const()[name = tensor("cast_7_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_174_to_fp16 = cast(dtype = cast_7_to_fp16_dtype_0, x = var_174)[name = tensor("cast_176")]; - tensor var_180_cast_fp16 = expand_dims(axes = var_180_axes_0, x = var_174_to_fp16)[name = tensor("op_180_cast_fp16")]; - tensor expanded_mask_7_reps_0 = const()[name = tensor("expanded_mask_7_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_180_cast_fp16)[name = tensor("expanded_mask_7_cast_fp16")]; - tensor input_7_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor tensor_9_pad_type_0 = const()[name = tensor("tensor_9_pad_type_0"), val = tensor("valid")]; - tensor tensor_9_strides_0 = const()[name = tensor("tensor_9_strides_0"), val = tensor([1, 1])]; - tensor tensor_9_pad_0 = const()[name = tensor("tensor_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor tensor_9_dilations_0 = const()[name = tensor("tensor_9_dilations_0"), val = tensor([1, 1])]; - tensor tensor_9_groups_0 = const()[name = tensor("tensor_9_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_3_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_3_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10560)))]; - tensor module_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141696)))]; - tensor tensor_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("tensor_9_cast_fp16")]; - tensor input_9_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; - tensor tensor_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor("tensor_11_cast_fp16")]; - tensor input_11_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor("input_11_cast_fp16")]; - tensor tensor_13_pad_type_0 = const()[name = tensor("tensor_13_pad_type_0"), val = tensor("custom")]; - tensor tensor_13_pad_0 = const()[name = tensor("tensor_13_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor tensor_13_strides_0 = const()[name = tensor("tensor_13_strides_0"), val = tensor([2, 2])]; - tensor tensor_13_groups_0 = const()[name = tensor("tensor_13_groups_0"), val = tensor(256)]; - tensor tensor_13_dilations_0 = const()[name = tensor("tensor_13_dilations_0"), val = tensor([1, 1])]; - tensor module_pre_encode_conv_5_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_5_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142272)))]; - tensor module_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146944)))]; - tensor tensor_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("tensor_13_cast_fp16")]; - tensor var_215_promoted_to_fp16 = const()[name = tensor("op_215_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_216_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_215_promoted_to_fp16)[name = tensor("op_216_cast_fp16")]; - tensor var_217_promoted_to_fp16 = const()[name = tensor("op_217_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor var_218_cast_fp16 = add(x = var_216_cast_fp16, y = var_217_promoted_to_fp16)[name = tensor("op_218_cast_fp16")]; - tensor var_219_promoted_to_fp16 = const()[name = tensor("op_219_promoted_to_fp16"), val = tensor(0x1.8p+1)]; - tensor var_220_cast_fp16 = sub(x = var_218_cast_fp16, y = var_219_promoted_to_fp16)[name = tensor("op_220_cast_fp16")]; - tensor var_21_promoted_2_to_fp16 = const()[name = tensor("op_21_promoted_2_to_fp16"), val = tensor(0x1p+1)]; - tensor floor_div_2_cast_fp16 = floor_div(x = var_220_cast_fp16, y = var_21_promoted_2_to_fp16)[name = tensor("floor_div_2_cast_fp16")]; - tensor var_222_promoted_to_fp16 = const()[name = tensor("op_222_promoted_to_fp16"), val = tensor(0x1p+0)]; - tensor current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_222_promoted_to_fp16)[name = tensor("current_lengths_cast_fp16")]; - tensor cast_8_dtype_0 = const()[name = tensor("cast_8_dtype_0"), val = tensor("int32")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187]])]; - tensor var_231_axes_0 = const()[name = tensor("op_231_axes_0"), val = tensor([1])]; - tensor current_lengths_cast_fp16_to_int32 = cast(dtype = cast_8_dtype_0, x = current_lengths_cast_fp16)[name = tensor("cast_175")]; - tensor var_231 = expand_dims(axes = var_231_axes_0, x = current_lengths_cast_fp16_to_int32)[name = tensor("op_231")]; - tensor time_mask = less(x = expand_dims_3, y = var_231)[name = tensor("time_mask")]; - tensor var_233_axes_0 = const()[name = tensor("op_233_axes_0"), val = tensor([-1])]; - tensor var_233 = expand_dims(axes = var_233_axes_0, x = time_mask)[name = tensor("op_233")]; - tensor var_235_reps_0 = const()[name = tensor("op_235_reps_0"), val = tensor([1, 1, 10])]; - tensor var_235 = tile(reps = var_235_reps_0, x = var_233)[name = tensor("op_235")]; - tensor var_241_axes_0 = const()[name = tensor("op_241_axes_0"), val = tensor([1])]; - tensor cast_9_to_fp16_dtype_0 = const()[name = tensor("cast_9_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor var_235_to_fp16 = cast(dtype = cast_9_to_fp16_dtype_0, x = var_235)[name = tensor("cast_174")]; - tensor var_241_cast_fp16 = expand_dims(axes = var_241_axes_0, x = var_235_to_fp16)[name = tensor("op_241_cast_fp16")]; - tensor expanded_mask_13_reps_0 = const()[name = tensor("expanded_mask_13_reps_0"), val = tensor([1, 256, 1, 1])]; - tensor expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_241_cast_fp16)[name = tensor("expanded_mask_13_cast_fp16")]; - tensor input_13_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("input_13_cast_fp16")]; - tensor tensor_15_pad_type_0 = const()[name = tensor("tensor_15_pad_type_0"), val = tensor("valid")]; - tensor tensor_15_strides_0 = const()[name = tensor("tensor_15_strides_0"), val = tensor([1, 1])]; - tensor tensor_15_pad_0 = const()[name = tensor("tensor_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor tensor_15_dilations_0 = const()[name = tensor("tensor_15_dilations_0"), val = tensor([1, 1])]; - tensor tensor_15_groups_0 = const()[name = tensor("tensor_15_groups_0"), val = tensor(1)]; - tensor module_pre_encode_conv_6_weight_to_fp16 = const()[name = tensor("module_pre_encode_conv_6_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147520)))]; - tensor module_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor("module_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278656)))]; - tensor tensor_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16, x = input_13_cast_fp16)[name = tensor("tensor_15_cast_fp16")]; - tensor input_15_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; - tensor tensor_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor("tensor_cast_fp16")]; - tensor x_3_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor("x_3_cast_fp16")]; - tensor var_275_perm_0 = const()[name = tensor("op_275_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_276 = const()[name = tensor("op_276"), val = tensor([1, 188, -1])]; - tensor var_275_cast_fp16 = transpose(perm = var_275_perm_0, x = x_3_cast_fp16)[name = tensor("transpose_206")]; - tensor input_17_cast_fp16 = reshape(shape = var_276, x = var_275_cast_fp16)[name = tensor("input_17_cast_fp16")]; - tensor module_pre_encode_out_weight_to_fp16 = const()[name = tensor("module_pre_encode_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279232)))]; - tensor module_pre_encode_out_bias_to_fp16 = const()[name = tensor("module_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2900736)))]; - tensor linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16, x = input_17_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor cast_12_dtype_0 = const()[name = tensor("cast_12_dtype_0"), val = tensor("int32")]; - tensor var_314_axes_0 = const()[name = tensor("op_314_axes_0"), val = tensor([-1])]; - tensor encoder_length = cast(dtype = cast_12_dtype_0, x = current_lengths_cast_fp16)[name = tensor("cast_173")]; - tensor var_314 = expand_dims(axes = var_314_axes_0, x = encoder_length)[name = tensor("op_314")]; - tensor pad_mask_1 = less(x = expand_dims_3, y = var_314)[name = tensor("pad_mask_1")]; - tensor var_316_axes_0 = const()[name = tensor("op_316_axes_0"), val = tensor([1])]; - tensor var_316 = expand_dims(axes = var_316_axes_0, x = pad_mask_1)[name = tensor("op_316")]; - tensor var_317 = const()[name = tensor("op_317"), val = tensor([1, 188, 1])]; - tensor pad_mask_for_att_mask_1 = tile(reps = var_317, x = var_316)[name = tensor("pad_mask_for_att_mask_1")]; - tensor var_319_perm_0 = const()[name = tensor("op_319_perm_0"), val = tensor([0, 2, 1])]; - tensor var_319 = transpose(perm = var_319_perm_0, x = pad_mask_for_att_mask_1)[name = tensor("transpose_205")]; - tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_319)[name = tensor("pad_mask_for_att_mask")]; - tensor const_63 = const()[name = tensor("const_63"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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input_21_axes_0 = const()[name = tensor("input_21_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2901824)))]; - tensor module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2902912)))]; - tensor var_9_to_fp16 = const()[name = tensor("op_9_to_fp16"), val = tensor(0x1.5p-17)]; - tensor input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = tensor("input_21_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2904000)))]; - tensor module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001216)))]; - tensor linear_1_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16, x = input_21_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor("input_25_cast_fp16")]; - tensor module_layers_0_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5005376)))]; - tensor module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7102592)))]; - tensor linear_2_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16, x = input_25_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor var_352_to_fp16 = const()[name = tensor("op_352_to_fp16"), val = tensor(0x1p-1)]; - tensor var_353_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_352_to_fp16)[name = tensor("op_353_cast_fp16")]; - tensor input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_353_cast_fp16)[name = tensor("input_31_cast_fp16")]; - tensor query_1_axes_0 = const()[name = tensor("query_1_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7103680)))]; - tensor module_layers_0_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7104768)))]; - tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("query_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7105856)))]; - tensor module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7630208)))]; - tensor linear_3_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_q_bias_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_3_cast_fp16")]; - tensor var_370 = const()[name = tensor("op_370"), val = tensor([1, -1, 8, 64])]; - tensor q_1_cast_fp16 = reshape(shape = var_370, x = linear_3_cast_fp16)[name = tensor("q_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7631296)))]; - tensor module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8155648)))]; - tensor linear_4_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_k_bias_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_4_cast_fp16")]; - tensor var_375 = const()[name = tensor("op_375"), val = tensor([1, -1, 8, 64])]; - tensor k_1_cast_fp16 = reshape(shape = var_375, x = linear_4_cast_fp16)[name = tensor("k_1_cast_fp16")]; - tensor module_layers_0_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8156736)))]; - tensor module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8681088)))]; - tensor linear_5_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_v_bias_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16, x = query_1_cast_fp16)[name = tensor("linear_5_cast_fp16")]; - tensor var_380 = const()[name = tensor("op_380"), val = tensor([1, -1, 8, 64])]; - tensor v_1_cast_fp16 = reshape(shape = var_380, x = linear_5_cast_fp16)[name = tensor("v_1_cast_fp16")]; - tensor value_3_perm_0 = const()[name = tensor("value_3_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8682176)))]; - tensor var_392_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = tensor("op_392_cast_fp16")]; - tensor module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8683264)))]; - tensor var_394_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = tensor("op_394_cast_fp16")]; - tensor q_with_bias_v_1_perm_0 = const()[name = tensor("q_with_bias_v_1_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_7_transpose_x_0 = const()[name = tensor("x_7_transpose_x_0"), val = tensor(false)]; - tensor x_7_transpose_y_0 = const()[name = tensor("x_7_transpose_y_0"), val = tensor(false)]; - tensor var_396_to_fp16 = const()[name = tensor("op_396_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8684352)))]; - tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_394_cast_fp16)[name = tensor("transpose_203")]; - tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = var_396_to_fp16)[name = tensor("x_7_cast_fp16")]; - tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_9_mode_0 = const()[name = tensor("x_9_mode_0"), val = tensor("constant")]; - tensor const_70_to_fp16 = const()[name = tensor("const_70_to_fp16"), val = tensor(0x0p+0)]; - tensor x_9_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = tensor("x_9_cast_fp16")]; - tensor var_404 = const()[name = tensor("op_404"), val = tensor([1, 8, -1, 188])]; - tensor x_11_cast_fp16 = reshape(shape = var_404, x = x_9_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor var_408_begin_0 = const()[name = tensor("op_408_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_408_end_0 = const()[name = tensor("op_408_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_408_end_mask_0 = const()[name = tensor("op_408_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_408_cast_fp16 = slice_by_index(begin = var_408_begin_0, end = var_408_end_0, end_mask = var_408_end_mask_0, x = x_11_cast_fp16)[name = tensor("op_408_cast_fp16")]; - tensor var_409 = const()[name = tensor("op_409"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_1_cast_fp16 = reshape(shape = var_409, x = var_408_cast_fp16)[name = tensor("matrix_bd_1_cast_fp16")]; - tensor matrix_ac_1_transpose_x_0 = const()[name = tensor("matrix_ac_1_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_1_transpose_y_0 = const()[name = tensor("matrix_ac_1_transpose_y_0"), val = tensor(false)]; - tensor transpose_51_perm_0 = const()[name = tensor("transpose_51_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_52_perm_0 = const()[name = tensor("transpose_52_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = k_1_cast_fp16)[name = tensor("transpose_201")]; - tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_392_cast_fp16)[name = tensor("transpose_202")]; - tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_51, y = transpose_52)[name = tensor("matrix_ac_1_cast_fp16")]; - tensor matrix_bd_3_begin_0 = const()[name = tensor("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_3_end_0 = const()[name = tensor("matrix_bd_3_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_3_end_mask_0 = const()[name = tensor("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = tensor("matrix_bd_3_cast_fp16")]; - tensor var_418_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = tensor("op_418_cast_fp16")]; - tensor _inversed_scores_1_y_0_to_fp16 = const()[name = tensor("_inversed_scores_1_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_1_cast_fp16 = mul(x = var_418_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = tensor("_inversed_scores_1_cast_fp16")]; - tensor mask_11_axes_0 = const()[name = tensor("mask_11_axes_0"), val = tensor([1])]; - tensor mask_11 = expand_dims(axes = mask_11_axes_0, x = mask_9)[name = tensor("mask_11")]; - tensor var_12_to_fp16 = const()[name = tensor("op_12_to_fp16"), val = tensor(-0x1.388p+13)]; - tensor scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_11)[name = tensor("scores_3_cast_fp16")]; - tensor var_424_cast_fp16 = softmax(axis = var_23, x = scores_3_cast_fp16)[name = tensor("op_424_cast_fp16")]; - tensor var_11_to_fp16 = const()[name = tensor("op_11_to_fp16"), val = tensor(0x0p+0)]; - tensor input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_424_cast_fp16, cond = mask_11)[name = tensor("input_33_cast_fp16")]; - tensor x_13_transpose_x_0 = const()[name = tensor("x_13_transpose_x_0"), val = tensor(false)]; - tensor x_13_transpose_y_0 = const()[name = tensor("x_13_transpose_y_0"), val = tensor(false)]; - tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = tensor("transpose_204")]; - tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_3_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_428_perm_0 = const()[name = tensor("op_428_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_429 = const()[name = tensor("op_429"), val = tensor([1, -1, 512])]; - tensor var_428_cast_fp16 = transpose(perm = var_428_perm_0, x = x_13_cast_fp16)[name = tensor("transpose_200")]; - tensor input_35_cast_fp16 = reshape(shape = var_429, x = var_428_cast_fp16)[name = tensor("input_35_cast_fp16")]; - tensor module_layers_0_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9068416)))]; - tensor module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9592768)))]; - tensor linear_7_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_out_bias_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16, x = input_35_cast_fp16)[name = tensor("linear_7_cast_fp16")]; - tensor input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = tensor("input_39_cast_fp16")]; - tensor x_17_axes_0 = const()[name = tensor("x_17_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9593856)))]; - tensor module_layers_0_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9594944)))]; - tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("x_17_cast_fp16")]; - tensor input_41_perm_0 = const()[name = tensor("input_41_perm_0"), val = tensor([0, 2, 1])]; - tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; - tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([1])]; - tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0])]; - tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1])]; - tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9596032)))]; - tensor module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10644672)))]; - tensor input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = tensor("transpose_199")]; - tensor input_43_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv1_bias_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16, x = input_41_cast_fp16)[name = tensor("input_43_cast_fp16")]; - tensor x_19_split_num_splits_0 = const()[name = tensor("x_19_split_num_splits_0"), val = tensor(2)]; - tensor x_19_split_axis_0 = const()[name = tensor("x_19_split_axis_0"), val = tensor(1)]; - tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = tensor("x_19_split_cast_fp16")]; - tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = tensor("x_19_split_1_sigmoid_cast_fp16")]; - tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = tensor("x_19_cast_fp16")]; - tensor var_453_axes_0 = const()[name = tensor("op_453_axes_0"), val = tensor([1])]; - tensor var_453 = expand_dims(axes = var_453_axes_0, x = pad_mask)[name = tensor("op_453")]; - tensor input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_453)[name = tensor("input_45_cast_fp16")]; - tensor input_47_pad_0 = const()[name = tensor("input_47_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_47_mode_0 = const()[name = tensor("input_47_mode_0"), val = tensor("constant")]; - tensor const_73_to_fp16 = const()[name = tensor("const_73_to_fp16"), val = tensor(0x0p+0)]; - tensor input_47_cast_fp16 = pad(constant_val = const_73_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = tensor("input_47_cast_fp16")]; - tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("valid")]; - tensor input_49_groups_0 = const()[name = tensor("input_49_groups_0"), val = tensor(512)]; - tensor input_49_strides_0 = const()[name = tensor("input_49_strides_0"), val = tensor([1])]; - tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([0, 0])]; - tensor input_49_dilations_0 = const()[name = tensor("input_49_dilations_0"), val = tensor([1])]; - tensor const_234_to_fp16 = const()[name = tensor("const_234_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10646784)))]; - tensor const_235_to_fp16 = const()[name = tensor("const_235_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10656064)))]; - tensor input_51_cast_fp16 = conv(bias = const_235_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_234_to_fp16, x = input_47_cast_fp16)[name = tensor("input_51_cast_fp16")]; - tensor input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = tensor("input_53_cast_fp16")]; - tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("valid")]; - tensor x_21_strides_0 = const()[name = tensor("x_21_strides_0"), val = tensor([1])]; - tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0])]; - tensor x_21_dilations_0 = const()[name = tensor("x_21_dilations_0"), val = tensor([1])]; - tensor x_21_groups_0 = const()[name = tensor("x_21_groups_0"), val = tensor(1)]; - tensor module_layers_0_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10657152)))]; - tensor module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11181504)))]; - tensor x_21_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv2_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_layers_0_conv_pointwise_conv2_weight_to_fp16, x = input_53_cast_fp16)[name = tensor("x_21_cast_fp16")]; - tensor input_55_perm_0 = const()[name = tensor("input_55_perm_0"), val = tensor([0, 2, 1])]; - tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = tensor("transpose_198")]; - tensor input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; - tensor input_59_axes_0 = const()[name = tensor("input_59_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11182592)))]; - tensor module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11183680)))]; - tensor input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("input_59_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11184768)))]; - tensor module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13281984)))]; - tensor linear_8_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("linear_8_cast_fp16")]; - tensor input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = tensor("input_63_cast_fp16")]; - tensor module_layers_0_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13286144)))]; - tensor module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15383360)))]; - tensor linear_9_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16, x = input_63_cast_fp16)[name = tensor("linear_9_cast_fp16")]; - tensor var_495_to_fp16 = const()[name = tensor("op_495_to_fp16"), val = tensor(0x1p-1)]; - tensor var_496_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_495_to_fp16)[name = tensor("op_496_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_496_cast_fp16)[name = tensor("input_69_cast_fp16")]; - tensor input_71_axes_0 = const()[name = tensor("input_71_axes_0"), val = tensor([-1])]; - tensor module_layers_0_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15384448)))]; - tensor module_layers_0_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15385536)))]; - tensor input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = tensor("input_71_cast_fp16")]; - tensor input_73_axes_0 = const()[name = tensor("input_73_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15386624)))]; - tensor module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15387712)))]; - tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = tensor("input_73_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15388800)))]; - tensor module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17486016)))]; - tensor linear_10_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16, x = input_73_cast_fp16)[name = tensor("linear_10_cast_fp16")]; - tensor input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor("input_77_cast_fp16")]; - tensor module_layers_1_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17490176)))]; - tensor module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19587392)))]; - tensor linear_11_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16, x = input_77_cast_fp16)[name = tensor("linear_11_cast_fp16")]; - tensor var_526_to_fp16 = const()[name = tensor("op_526_to_fp16"), val = tensor(0x1p-1)]; - tensor var_527_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_526_to_fp16)[name = tensor("op_527_cast_fp16")]; - tensor input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_527_cast_fp16)[name = tensor("input_83_cast_fp16")]; - tensor query_3_axes_0 = const()[name = tensor("query_3_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19588480)))]; - tensor module_layers_1_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19589568)))]; - tensor query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = tensor("query_3_cast_fp16")]; - tensor module_layers_1_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19590656)))]; - tensor module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20115008)))]; - tensor linear_12_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_q_bias_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_12_cast_fp16")]; - tensor var_544 = const()[name = tensor("op_544"), val = tensor([1, -1, 8, 64])]; - tensor q_7_cast_fp16 = reshape(shape = var_544, x = linear_12_cast_fp16)[name = tensor("q_7_cast_fp16")]; - tensor module_layers_1_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20116096)))]; - tensor module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20640448)))]; - tensor linear_13_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_k_bias_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_13_cast_fp16")]; - tensor var_549 = const()[name = tensor("op_549"), val = tensor([1, -1, 8, 64])]; - tensor k_5_cast_fp16 = reshape(shape = var_549, x = linear_13_cast_fp16)[name = tensor("k_5_cast_fp16")]; - tensor module_layers_1_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20641536)))]; - tensor module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21165888)))]; - tensor linear_14_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_v_bias_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16, x = query_3_cast_fp16)[name = tensor("linear_14_cast_fp16")]; - tensor var_554 = const()[name = tensor("op_554"), val = tensor([1, -1, 8, 64])]; - tensor v_3_cast_fp16 = reshape(shape = var_554, x = linear_14_cast_fp16)[name = tensor("v_3_cast_fp16")]; - tensor value_5_perm_0 = const()[name = tensor("value_5_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21166976)))]; - tensor var_566_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = tensor("op_566_cast_fp16")]; - tensor module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21168064)))]; - tensor var_568_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = tensor("op_568_cast_fp16")]; - tensor q_with_bias_v_3_perm_0 = const()[name = tensor("q_with_bias_v_3_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_29_transpose_x_0 = const()[name = tensor("x_29_transpose_x_0"), val = tensor(false)]; - tensor x_29_transpose_y_0 = const()[name = tensor("x_29_transpose_y_0"), val = tensor(false)]; - tensor var_570_to_fp16 = const()[name = tensor("op_570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21169152)))]; - tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_568_cast_fp16)[name = tensor("transpose_196")]; - tensor x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = var_570_to_fp16)[name = tensor("x_29_cast_fp16")]; - tensor x_31_pad_0 = const()[name = tensor("x_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_31_mode_0 = const()[name = tensor("x_31_mode_0"), val = tensor("constant")]; - tensor const_80_to_fp16 = const()[name = tensor("const_80_to_fp16"), val = tensor(0x0p+0)]; - tensor x_31_cast_fp16 = pad(constant_val = const_80_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = tensor("x_31_cast_fp16")]; - tensor var_578 = const()[name = tensor("op_578"), val = tensor([1, 8, -1, 188])]; - tensor x_33_cast_fp16 = reshape(shape = var_578, x = x_31_cast_fp16)[name = tensor("x_33_cast_fp16")]; - tensor var_582_begin_0 = const()[name = tensor("op_582_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_582_end_0 = const()[name = tensor("op_582_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_582_end_mask_0 = const()[name = tensor("op_582_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_582_cast_fp16 = slice_by_index(begin = var_582_begin_0, end = var_582_end_0, end_mask = var_582_end_mask_0, x = x_33_cast_fp16)[name = tensor("op_582_cast_fp16")]; - tensor var_583 = const()[name = tensor("op_583"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_5_cast_fp16 = reshape(shape = var_583, x = var_582_cast_fp16)[name = tensor("matrix_bd_5_cast_fp16")]; - tensor matrix_ac_3_transpose_x_0 = const()[name = tensor("matrix_ac_3_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_3_transpose_y_0 = const()[name = tensor("matrix_ac_3_transpose_y_0"), val = tensor(false)]; - tensor transpose_53_perm_0 = const()[name = tensor("transpose_53_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_54_perm_0 = const()[name = tensor("transpose_54_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = k_5_cast_fp16)[name = tensor("transpose_194")]; - tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = var_566_cast_fp16)[name = tensor("transpose_195")]; - tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_53, y = transpose_54)[name = tensor("matrix_ac_3_cast_fp16")]; - tensor matrix_bd_7_begin_0 = const()[name = tensor("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_7_end_0 = const()[name = tensor("matrix_bd_7_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_7_end_mask_0 = const()[name = tensor("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = tensor("matrix_bd_7_cast_fp16")]; - tensor var_592_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = tensor("op_592_cast_fp16")]; - tensor _inversed_scores_5_y_0_to_fp16 = const()[name = tensor("_inversed_scores_5_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_5_cast_fp16 = mul(x = var_592_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = tensor("_inversed_scores_5_cast_fp16")]; - tensor scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_11)[name = tensor("scores_7_cast_fp16")]; - tensor var_598_cast_fp16 = softmax(axis = var_23, x = scores_7_cast_fp16)[name = tensor("op_598_cast_fp16")]; - tensor input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_598_cast_fp16, cond = mask_11)[name = tensor("input_85_cast_fp16")]; - tensor x_35_transpose_x_0 = const()[name = tensor("x_35_transpose_x_0"), val = tensor(false)]; - tensor x_35_transpose_y_0 = const()[name = tensor("x_35_transpose_y_0"), val = tensor(false)]; - tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = tensor("transpose_197")]; - tensor x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_5_cast_fp16)[name = tensor("x_35_cast_fp16")]; - tensor var_602_perm_0 = const()[name = tensor("op_602_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_603 = const()[name = tensor("op_603"), val = tensor([1, -1, 512])]; - tensor var_602_cast_fp16 = transpose(perm = var_602_perm_0, x = x_35_cast_fp16)[name = tensor("transpose_193")]; - tensor input_87_cast_fp16 = reshape(shape = var_603, x = var_602_cast_fp16)[name = tensor("input_87_cast_fp16")]; - tensor module_layers_1_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21553216)))]; - tensor module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22077568)))]; - tensor linear_16_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_out_bias_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16, x = input_87_cast_fp16)[name = tensor("linear_16_cast_fp16")]; - tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = tensor("input_91_cast_fp16")]; - tensor x_39_axes_0 = const()[name = tensor("x_39_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22078656)))]; - tensor module_layers_1_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22079744)))]; - tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = tensor("x_39_cast_fp16")]; - tensor input_93_perm_0 = const()[name = tensor("input_93_perm_0"), val = tensor([0, 2, 1])]; - tensor input_95_pad_type_0 = const()[name = tensor("input_95_pad_type_0"), val = tensor("valid")]; - tensor input_95_strides_0 = const()[name = tensor("input_95_strides_0"), val = tensor([1])]; - tensor input_95_pad_0 = const()[name = tensor("input_95_pad_0"), val = tensor([0, 0])]; - tensor input_95_dilations_0 = const()[name = tensor("input_95_dilations_0"), val = tensor([1])]; - tensor input_95_groups_0 = const()[name = tensor("input_95_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22080832)))]; - tensor module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23129472)))]; - tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = tensor("transpose_192")]; - tensor input_95_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv1_bias_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16, x = input_93_cast_fp16)[name = tensor("input_95_cast_fp16")]; - tensor x_41_split_num_splits_0 = const()[name = tensor("x_41_split_num_splits_0"), val = tensor(2)]; - tensor x_41_split_axis_0 = const()[name = tensor("x_41_split_axis_0"), val = tensor(1)]; - tensor x_41_split_cast_fp16_0, tensor x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = tensor("x_41_split_cast_fp16")]; - tensor x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = tensor("x_41_split_1_sigmoid_cast_fp16")]; - tensor x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = tensor("x_41_cast_fp16")]; - tensor input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_453)[name = tensor("input_97_cast_fp16")]; - tensor input_99_pad_0 = const()[name = tensor("input_99_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_99_mode_0 = const()[name = tensor("input_99_mode_0"), val = tensor("constant")]; - tensor const_83_to_fp16 = const()[name = tensor("const_83_to_fp16"), val = tensor(0x0p+0)]; - tensor input_99_cast_fp16 = pad(constant_val = const_83_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = tensor("input_99_cast_fp16")]; - tensor input_101_pad_type_0 = const()[name = tensor("input_101_pad_type_0"), val = tensor("valid")]; - tensor input_101_groups_0 = const()[name = tensor("input_101_groups_0"), val = tensor(512)]; - tensor input_101_strides_0 = const()[name = tensor("input_101_strides_0"), val = tensor([1])]; - tensor input_101_pad_0 = const()[name = tensor("input_101_pad_0"), val = tensor([0, 0])]; - tensor input_101_dilations_0 = const()[name = tensor("input_101_dilations_0"), val = tensor([1])]; - tensor const_236_to_fp16 = const()[name = tensor("const_236_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23131584)))]; - tensor const_237_to_fp16 = const()[name = tensor("const_237_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23140864)))]; - tensor input_103_cast_fp16 = conv(bias = const_237_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_236_to_fp16, x = input_99_cast_fp16)[name = tensor("input_103_cast_fp16")]; - tensor input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = tensor("input_105_cast_fp16")]; - tensor x_43_pad_type_0 = const()[name = tensor("x_43_pad_type_0"), val = tensor("valid")]; - tensor x_43_strides_0 = const()[name = tensor("x_43_strides_0"), val = tensor([1])]; - tensor x_43_pad_0 = const()[name = tensor("x_43_pad_0"), val = tensor([0, 0])]; - tensor x_43_dilations_0 = const()[name = tensor("x_43_dilations_0"), val = tensor([1])]; - tensor x_43_groups_0 = const()[name = tensor("x_43_groups_0"), val = tensor(1)]; - tensor module_layers_1_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23141952)))]; - tensor module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23666304)))]; - tensor x_43_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv2_bias_to_fp16, dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = module_layers_1_conv_pointwise_conv2_weight_to_fp16, x = input_105_cast_fp16)[name = tensor("x_43_cast_fp16")]; - tensor input_107_perm_0 = const()[name = tensor("input_107_perm_0"), val = tensor([0, 2, 1])]; - tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = tensor("transpose_191")]; - tensor input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = tensor("input_109_cast_fp16")]; - tensor input_111_axes_0 = const()[name = tensor("input_111_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23667392)))]; - tensor module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23668480)))]; - tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("input_111_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23669568)))]; - tensor module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25766784)))]; - tensor linear_17_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16, x = input_111_cast_fp16)[name = tensor("linear_17_cast_fp16")]; - tensor input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = tensor("input_115_cast_fp16")]; - tensor module_layers_1_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25770944)))]; - tensor module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27868160)))]; - tensor linear_18_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16, x = input_115_cast_fp16)[name = tensor("linear_18_cast_fp16")]; - tensor var_669_to_fp16 = const()[name = tensor("op_669_to_fp16"), val = tensor(0x1p-1)]; - tensor var_670_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_669_to_fp16)[name = tensor("op_670_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_670_cast_fp16)[name = tensor("input_121_cast_fp16")]; - tensor input_123_axes_0 = const()[name = tensor("input_123_axes_0"), val = tensor([-1])]; - tensor module_layers_1_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27869248)))]; - tensor module_layers_1_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27870336)))]; - tensor input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("input_123_cast_fp16")]; - tensor input_125_axes_0 = const()[name = tensor("input_125_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27871424)))]; - tensor module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27872512)))]; - tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor("input_125_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27873600)))]; - tensor module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29970816)))]; - tensor linear_19_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16, x = input_125_cast_fp16)[name = tensor("linear_19_cast_fp16")]; - tensor input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor("input_129_cast_fp16")]; - tensor module_layers_2_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29974976)))]; - tensor module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32072192)))]; - tensor linear_20_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("linear_20_cast_fp16")]; - tensor var_700_to_fp16 = const()[name = tensor("op_700_to_fp16"), val = tensor(0x1p-1)]; - tensor var_701_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_700_to_fp16)[name = tensor("op_701_cast_fp16")]; - tensor input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_701_cast_fp16)[name = tensor("input_135_cast_fp16")]; - tensor query_5_axes_0 = const()[name = tensor("query_5_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32073280)))]; - tensor module_layers_2_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32074368)))]; - tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = tensor("query_5_cast_fp16")]; - tensor module_layers_2_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32075456)))]; - tensor module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32599808)))]; - tensor linear_21_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_q_bias_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_21_cast_fp16")]; - tensor var_718 = const()[name = tensor("op_718"), val = tensor([1, -1, 8, 64])]; - tensor q_13_cast_fp16 = reshape(shape = var_718, x = linear_21_cast_fp16)[name = tensor("q_13_cast_fp16")]; - tensor module_layers_2_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32600896)))]; - tensor module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33125248)))]; - tensor linear_22_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_k_bias_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_22_cast_fp16")]; - tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, -1, 8, 64])]; - tensor k_9_cast_fp16 = reshape(shape = var_723, x = linear_22_cast_fp16)[name = tensor("k_9_cast_fp16")]; - tensor module_layers_2_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33126336)))]; - tensor module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33650688)))]; - tensor linear_23_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_v_bias_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16, x = query_5_cast_fp16)[name = tensor("linear_23_cast_fp16")]; - tensor var_728 = const()[name = tensor("op_728"), val = tensor([1, -1, 8, 64])]; - tensor v_5_cast_fp16 = reshape(shape = var_728, x = linear_23_cast_fp16)[name = tensor("v_5_cast_fp16")]; - tensor value_7_perm_0 = const()[name = tensor("value_7_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33651776)))]; - tensor var_740_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = tensor("op_740_cast_fp16")]; - tensor module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33652864)))]; - tensor var_742_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = tensor("op_742_cast_fp16")]; - tensor q_with_bias_v_5_perm_0 = const()[name = tensor("q_with_bias_v_5_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_51_transpose_x_0 = const()[name = tensor("x_51_transpose_x_0"), val = tensor(false)]; - tensor x_51_transpose_y_0 = const()[name = tensor("x_51_transpose_y_0"), val = tensor(false)]; - tensor var_744_to_fp16 = const()[name = tensor("op_744_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33653952)))]; - tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_742_cast_fp16)[name = tensor("transpose_189")]; - tensor x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = var_744_to_fp16)[name = tensor("x_51_cast_fp16")]; - tensor x_53_pad_0 = const()[name = tensor("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_53_mode_0 = const()[name = tensor("x_53_mode_0"), val = tensor("constant")]; - tensor const_90_to_fp16 = const()[name = tensor("const_90_to_fp16"), val = tensor(0x0p+0)]; - tensor x_53_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = tensor("x_53_cast_fp16")]; - tensor var_752 = const()[name = tensor("op_752"), val = tensor([1, 8, -1, 188])]; - tensor x_55_cast_fp16 = reshape(shape = var_752, x = x_53_cast_fp16)[name = tensor("x_55_cast_fp16")]; - tensor var_756_begin_0 = const()[name = tensor("op_756_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_756_end_0 = const()[name = tensor("op_756_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_756_end_mask_0 = const()[name = tensor("op_756_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_756_cast_fp16 = slice_by_index(begin = var_756_begin_0, end = var_756_end_0, end_mask = var_756_end_mask_0, x = x_55_cast_fp16)[name = tensor("op_756_cast_fp16")]; - tensor var_757 = const()[name = tensor("op_757"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_9_cast_fp16 = reshape(shape = var_757, x = var_756_cast_fp16)[name = tensor("matrix_bd_9_cast_fp16")]; - tensor matrix_ac_5_transpose_x_0 = const()[name = tensor("matrix_ac_5_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_5_transpose_y_0 = const()[name = tensor("matrix_ac_5_transpose_y_0"), val = tensor(false)]; - tensor transpose_55_perm_0 = const()[name = tensor("transpose_55_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_56_perm_0 = const()[name = tensor("transpose_56_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = k_9_cast_fp16)[name = tensor("transpose_187")]; - tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_740_cast_fp16)[name = tensor("transpose_188")]; - tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_55, y = transpose_56)[name = tensor("matrix_ac_5_cast_fp16")]; - tensor matrix_bd_11_begin_0 = const()[name = tensor("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_11_end_0 = const()[name = tensor("matrix_bd_11_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_11_end_mask_0 = const()[name = tensor("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = tensor("matrix_bd_11_cast_fp16")]; - tensor var_766_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = tensor("op_766_cast_fp16")]; - tensor _inversed_scores_9_y_0_to_fp16 = const()[name = tensor("_inversed_scores_9_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_9_cast_fp16 = mul(x = var_766_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = tensor("_inversed_scores_9_cast_fp16")]; - tensor scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_11)[name = tensor("scores_11_cast_fp16")]; - tensor var_772_cast_fp16 = softmax(axis = var_23, x = scores_11_cast_fp16)[name = tensor("op_772_cast_fp16")]; - tensor input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_772_cast_fp16, cond = mask_11)[name = tensor("input_137_cast_fp16")]; - tensor x_57_transpose_x_0 = const()[name = tensor("x_57_transpose_x_0"), val = tensor(false)]; - tensor x_57_transpose_y_0 = const()[name = tensor("x_57_transpose_y_0"), val = tensor(false)]; - tensor value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = tensor("transpose_190")]; - tensor x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_7_cast_fp16)[name = tensor("x_57_cast_fp16")]; - tensor var_776_perm_0 = const()[name = tensor("op_776_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_777 = const()[name = tensor("op_777"), val = tensor([1, -1, 512])]; - tensor var_776_cast_fp16 = transpose(perm = var_776_perm_0, x = x_57_cast_fp16)[name = tensor("transpose_186")]; - tensor input_139_cast_fp16 = reshape(shape = var_777, x = var_776_cast_fp16)[name = tensor("input_139_cast_fp16")]; - tensor module_layers_2_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34038016)))]; - tensor module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34562368)))]; - tensor linear_25_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_out_bias_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("linear_25_cast_fp16")]; - tensor input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = tensor("input_143_cast_fp16")]; - tensor x_61_axes_0 = const()[name = tensor("x_61_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34563456)))]; - tensor module_layers_2_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34564544)))]; - tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = tensor("x_61_cast_fp16")]; - tensor input_145_perm_0 = const()[name = tensor("input_145_perm_0"), val = tensor([0, 2, 1])]; - tensor input_147_pad_type_0 = const()[name = tensor("input_147_pad_type_0"), val = tensor("valid")]; - tensor input_147_strides_0 = const()[name = tensor("input_147_strides_0"), val = tensor([1])]; - tensor input_147_pad_0 = const()[name = tensor("input_147_pad_0"), val = tensor([0, 0])]; - tensor input_147_dilations_0 = const()[name = tensor("input_147_dilations_0"), val = tensor([1])]; - tensor input_147_groups_0 = const()[name = tensor("input_147_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34565632)))]; - tensor module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35614272)))]; - tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = tensor("transpose_185")]; - tensor input_147_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16, x = input_145_cast_fp16)[name = tensor("input_147_cast_fp16")]; - tensor x_63_split_num_splits_0 = const()[name = tensor("x_63_split_num_splits_0"), val = tensor(2)]; - tensor x_63_split_axis_0 = const()[name = tensor("x_63_split_axis_0"), val = tensor(1)]; - tensor x_63_split_cast_fp16_0, tensor x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = tensor("x_63_split_cast_fp16")]; - tensor x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = tensor("x_63_split_1_sigmoid_cast_fp16")]; - tensor x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = tensor("x_63_cast_fp16")]; - tensor input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_453)[name = tensor("input_149_cast_fp16")]; - tensor input_151_pad_0 = const()[name = tensor("input_151_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_151_mode_0 = const()[name = tensor("input_151_mode_0"), val = tensor("constant")]; - tensor const_93_to_fp16 = const()[name = tensor("const_93_to_fp16"), val = tensor(0x0p+0)]; - tensor input_151_cast_fp16 = pad(constant_val = const_93_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = tensor("input_151_cast_fp16")]; - tensor input_153_pad_type_0 = const()[name = tensor("input_153_pad_type_0"), val = tensor("valid")]; - tensor input_153_groups_0 = const()[name = tensor("input_153_groups_0"), val = tensor(512)]; - tensor input_153_strides_0 = const()[name = tensor("input_153_strides_0"), val = tensor([1])]; - tensor input_153_pad_0 = const()[name = tensor("input_153_pad_0"), val = tensor([0, 0])]; - tensor input_153_dilations_0 = const()[name = tensor("input_153_dilations_0"), val = tensor([1])]; - tensor const_238_to_fp16 = const()[name = tensor("const_238_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35616384)))]; - tensor const_239_to_fp16 = const()[name = tensor("const_239_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35625664)))]; - tensor input_155_cast_fp16 = conv(bias = const_239_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_238_to_fp16, x = input_151_cast_fp16)[name = tensor("input_155_cast_fp16")]; - tensor input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = tensor("input_157_cast_fp16")]; - tensor x_65_pad_type_0 = const()[name = tensor("x_65_pad_type_0"), val = tensor("valid")]; - tensor x_65_strides_0 = const()[name = tensor("x_65_strides_0"), val = tensor([1])]; - tensor x_65_pad_0 = const()[name = tensor("x_65_pad_0"), val = tensor([0, 0])]; - tensor x_65_dilations_0 = const()[name = tensor("x_65_dilations_0"), val = tensor([1])]; - tensor x_65_groups_0 = const()[name = tensor("x_65_groups_0"), val = tensor(1)]; - tensor module_layers_2_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35626752)))]; - tensor module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36151104)))]; - tensor x_65_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv2_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_layers_2_conv_pointwise_conv2_weight_to_fp16, x = input_157_cast_fp16)[name = tensor("x_65_cast_fp16")]; - tensor input_159_perm_0 = const()[name = tensor("input_159_perm_0"), val = tensor([0, 2, 1])]; - tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = tensor("transpose_184")]; - tensor input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = tensor("input_161_cast_fp16")]; - tensor input_163_axes_0 = const()[name = tensor("input_163_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36152192)))]; - tensor module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36153280)))]; - tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("input_163_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36154368)))]; - tensor module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38251584)))]; - tensor linear_26_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16, x = input_163_cast_fp16)[name = tensor("linear_26_cast_fp16")]; - tensor input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = tensor("input_167_cast_fp16")]; - tensor module_layers_2_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38255744)))]; - tensor module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40352960)))]; - tensor linear_27_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16, x = input_167_cast_fp16)[name = tensor("linear_27_cast_fp16")]; - tensor var_843_to_fp16 = const()[name = tensor("op_843_to_fp16"), val = tensor(0x1p-1)]; - tensor var_844_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_843_to_fp16)[name = tensor("op_844_cast_fp16")]; - tensor input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_844_cast_fp16)[name = tensor("input_173_cast_fp16")]; - tensor input_175_axes_0 = const()[name = tensor("input_175_axes_0"), val = tensor([-1])]; - tensor module_layers_2_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40354048)))]; - tensor module_layers_2_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40355136)))]; - tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = tensor("input_175_cast_fp16")]; - tensor input_177_axes_0 = const()[name = tensor("input_177_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40356224)))]; - tensor module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40357312)))]; - tensor input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor("input_177_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40358400)))]; - tensor module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42455616)))]; - tensor linear_28_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16, x = input_177_cast_fp16)[name = tensor("linear_28_cast_fp16")]; - tensor input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = tensor("input_181_cast_fp16")]; - tensor module_layers_3_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42459776)))]; - tensor module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44556992)))]; - tensor linear_29_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16, x = input_181_cast_fp16)[name = tensor("linear_29_cast_fp16")]; - tensor var_874_to_fp16 = const()[name = tensor("op_874_to_fp16"), val = tensor(0x1p-1)]; - tensor var_875_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_874_to_fp16)[name = tensor("op_875_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_875_cast_fp16)[name = tensor("input_187_cast_fp16")]; - tensor query_7_axes_0 = const()[name = tensor("query_7_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44558080)))]; - tensor module_layers_3_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44559168)))]; - tensor query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = tensor("query_7_cast_fp16")]; - tensor module_layers_3_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44560256)))]; - tensor module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45084608)))]; - tensor linear_30_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_q_bias_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_30_cast_fp16")]; - tensor var_892 = const()[name = tensor("op_892"), val = tensor([1, -1, 8, 64])]; - tensor q_19_cast_fp16 = reshape(shape = var_892, x = linear_30_cast_fp16)[name = tensor("q_19_cast_fp16")]; - tensor module_layers_3_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45085696)))]; - tensor module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45610048)))]; - tensor linear_31_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_k_bias_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_31_cast_fp16")]; - tensor var_897 = const()[name = tensor("op_897"), val = tensor([1, -1, 8, 64])]; - tensor k_13_cast_fp16 = reshape(shape = var_897, x = linear_31_cast_fp16)[name = tensor("k_13_cast_fp16")]; - tensor module_layers_3_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45611136)))]; - tensor module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46135488)))]; - tensor linear_32_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_v_bias_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16, x = query_7_cast_fp16)[name = tensor("linear_32_cast_fp16")]; - tensor var_902 = const()[name = tensor("op_902"), val = tensor([1, -1, 8, 64])]; - tensor v_7_cast_fp16 = reshape(shape = var_902, x = linear_32_cast_fp16)[name = tensor("v_7_cast_fp16")]; - tensor value_9_perm_0 = const()[name = tensor("value_9_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46136576)))]; - tensor var_914_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = tensor("op_914_cast_fp16")]; - tensor module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46137664)))]; - tensor var_916_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = tensor("op_916_cast_fp16")]; - tensor q_with_bias_v_7_perm_0 = const()[name = tensor("q_with_bias_v_7_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_73_transpose_x_0 = const()[name = tensor("x_73_transpose_x_0"), val = tensor(false)]; - tensor x_73_transpose_y_0 = const()[name = tensor("x_73_transpose_y_0"), val = tensor(false)]; - tensor var_918_to_fp16 = const()[name = tensor("op_918_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46138752)))]; - tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_916_cast_fp16)[name = tensor("transpose_182")]; - tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = var_918_to_fp16)[name = tensor("x_73_cast_fp16")]; - tensor x_75_pad_0 = const()[name = tensor("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_75_mode_0 = const()[name = tensor("x_75_mode_0"), val = tensor("constant")]; - tensor const_100_to_fp16 = const()[name = tensor("const_100_to_fp16"), val = tensor(0x0p+0)]; - tensor x_75_cast_fp16 = pad(constant_val = const_100_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = tensor("x_75_cast_fp16")]; - tensor var_926 = const()[name = tensor("op_926"), val = tensor([1, 8, -1, 188])]; - tensor x_77_cast_fp16 = reshape(shape = var_926, x = x_75_cast_fp16)[name = tensor("x_77_cast_fp16")]; - tensor var_930_begin_0 = const()[name = tensor("op_930_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_930_end_0 = const()[name = tensor("op_930_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_930_end_mask_0 = const()[name = tensor("op_930_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_930_cast_fp16 = slice_by_index(begin = var_930_begin_0, end = var_930_end_0, end_mask = var_930_end_mask_0, x = x_77_cast_fp16)[name = tensor("op_930_cast_fp16")]; - tensor var_931 = const()[name = tensor("op_931"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_13_cast_fp16 = reshape(shape = var_931, x = var_930_cast_fp16)[name = tensor("matrix_bd_13_cast_fp16")]; - tensor matrix_ac_7_transpose_x_0 = const()[name = tensor("matrix_ac_7_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_7_transpose_y_0 = const()[name = tensor("matrix_ac_7_transpose_y_0"), val = tensor(false)]; - tensor transpose_57_perm_0 = const()[name = tensor("transpose_57_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_58_perm_0 = const()[name = tensor("transpose_58_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = k_13_cast_fp16)[name = tensor("transpose_180")]; - tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_914_cast_fp16)[name = tensor("transpose_181")]; - tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_57, y = transpose_58)[name = tensor("matrix_ac_7_cast_fp16")]; - tensor matrix_bd_15_begin_0 = const()[name = tensor("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_15_end_0 = const()[name = tensor("matrix_bd_15_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_15_end_mask_0 = const()[name = tensor("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = tensor("matrix_bd_15_cast_fp16")]; - tensor var_940_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = tensor("op_940_cast_fp16")]; - tensor _inversed_scores_13_y_0_to_fp16 = const()[name = tensor("_inversed_scores_13_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_13_cast_fp16 = mul(x = var_940_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = tensor("_inversed_scores_13_cast_fp16")]; - tensor scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_11)[name = tensor("scores_15_cast_fp16")]; - tensor var_946_cast_fp16 = softmax(axis = var_23, x = scores_15_cast_fp16)[name = tensor("op_946_cast_fp16")]; - tensor input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_946_cast_fp16, cond = mask_11)[name = tensor("input_189_cast_fp16")]; - tensor x_79_transpose_x_0 = const()[name = tensor("x_79_transpose_x_0"), val = tensor(false)]; - tensor x_79_transpose_y_0 = const()[name = tensor("x_79_transpose_y_0"), val = tensor(false)]; - tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = tensor("transpose_183")]; - tensor x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_9_cast_fp16)[name = tensor("x_79_cast_fp16")]; - tensor var_950_perm_0 = const()[name = tensor("op_950_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_951 = const()[name = tensor("op_951"), val = tensor([1, -1, 512])]; - tensor var_950_cast_fp16 = transpose(perm = var_950_perm_0, x = x_79_cast_fp16)[name = tensor("transpose_179")]; - tensor input_191_cast_fp16 = reshape(shape = var_951, x = var_950_cast_fp16)[name = tensor("input_191_cast_fp16")]; - tensor module_layers_3_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46522816)))]; - tensor module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47047168)))]; - tensor linear_34_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_out_bias_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16, x = input_191_cast_fp16)[name = tensor("linear_34_cast_fp16")]; - tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = tensor("input_195_cast_fp16")]; - tensor x_83_axes_0 = const()[name = tensor("x_83_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47048256)))]; - tensor module_layers_3_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47049344)))]; - tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = tensor("x_83_cast_fp16")]; - tensor input_197_perm_0 = const()[name = tensor("input_197_perm_0"), val = tensor([0, 2, 1])]; - tensor input_199_pad_type_0 = const()[name = tensor("input_199_pad_type_0"), val = tensor("valid")]; - tensor input_199_strides_0 = const()[name = tensor("input_199_strides_0"), val = tensor([1])]; - tensor input_199_pad_0 = const()[name = tensor("input_199_pad_0"), val = tensor([0, 0])]; - tensor input_199_dilations_0 = const()[name = tensor("input_199_dilations_0"), val = tensor([1])]; - tensor input_199_groups_0 = const()[name = tensor("input_199_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47050432)))]; - tensor module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48099072)))]; - tensor input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = tensor("transpose_178")]; - tensor input_199_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv1_bias_to_fp16, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16, x = input_197_cast_fp16)[name = tensor("input_199_cast_fp16")]; - tensor x_85_split_num_splits_0 = const()[name = tensor("x_85_split_num_splits_0"), val = tensor(2)]; - tensor x_85_split_axis_0 = const()[name = tensor("x_85_split_axis_0"), val = tensor(1)]; - tensor x_85_split_cast_fp16_0, tensor x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = tensor("x_85_split_cast_fp16")]; - tensor x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = tensor("x_85_split_1_sigmoid_cast_fp16")]; - tensor x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = tensor("x_85_cast_fp16")]; - tensor input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_453)[name = tensor("input_201_cast_fp16")]; - tensor input_203_pad_0 = const()[name = tensor("input_203_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_203_mode_0 = const()[name = tensor("input_203_mode_0"), val = tensor("constant")]; - tensor const_103_to_fp16 = const()[name = tensor("const_103_to_fp16"), val = tensor(0x0p+0)]; - tensor input_203_cast_fp16 = pad(constant_val = const_103_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = tensor("input_203_cast_fp16")]; - tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("valid")]; - tensor input_205_groups_0 = const()[name = tensor("input_205_groups_0"), val = tensor(512)]; - tensor input_205_strides_0 = const()[name = tensor("input_205_strides_0"), val = tensor([1])]; - tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([0, 0])]; - tensor input_205_dilations_0 = const()[name = tensor("input_205_dilations_0"), val = tensor([1])]; - tensor const_240_to_fp16 = const()[name = tensor("const_240_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48101184)))]; - tensor const_241_to_fp16 = const()[name = tensor("const_241_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48110464)))]; - tensor input_207_cast_fp16 = conv(bias = const_241_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_240_to_fp16, x = input_203_cast_fp16)[name = tensor("input_207_cast_fp16")]; - tensor input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = tensor("input_209_cast_fp16")]; - tensor x_87_pad_type_0 = const()[name = tensor("x_87_pad_type_0"), val = tensor("valid")]; - tensor x_87_strides_0 = const()[name = tensor("x_87_strides_0"), val = tensor([1])]; - tensor x_87_pad_0 = const()[name = tensor("x_87_pad_0"), val = tensor([0, 0])]; - tensor x_87_dilations_0 = const()[name = tensor("x_87_dilations_0"), val = tensor([1])]; - tensor x_87_groups_0 = const()[name = tensor("x_87_groups_0"), val = tensor(1)]; - tensor module_layers_3_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48111552)))]; - tensor module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48635904)))]; - tensor x_87_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv2_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16, x = input_209_cast_fp16)[name = tensor("x_87_cast_fp16")]; - tensor input_211_perm_0 = const()[name = tensor("input_211_perm_0"), val = tensor([0, 2, 1])]; - tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = tensor("transpose_177")]; - tensor input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = tensor("input_213_cast_fp16")]; - tensor input_215_axes_0 = const()[name = tensor("input_215_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48636992)))]; - tensor module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48638080)))]; - tensor input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = tensor("input_215_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48639168)))]; - tensor module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50736384)))]; - tensor linear_35_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16, x = input_215_cast_fp16)[name = tensor("linear_35_cast_fp16")]; - tensor input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = tensor("input_219_cast_fp16")]; - tensor module_layers_3_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50740544)))]; - tensor module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52837760)))]; - tensor linear_36_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16, x = input_219_cast_fp16)[name = tensor("linear_36_cast_fp16")]; - tensor var_1017_to_fp16 = const()[name = tensor("op_1017_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1018_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1017_to_fp16)[name = tensor("op_1018_cast_fp16")]; - tensor input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_1018_cast_fp16)[name = tensor("input_225_cast_fp16")]; - tensor input_227_axes_0 = const()[name = tensor("input_227_axes_0"), val = tensor([-1])]; - tensor module_layers_3_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52838848)))]; - tensor module_layers_3_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52839936)))]; - tensor input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = tensor("input_227_cast_fp16")]; - tensor input_229_axes_0 = const()[name = tensor("input_229_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52841024)))]; - tensor module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52842112)))]; - tensor input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = tensor("input_229_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52843200)))]; - tensor module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54940416)))]; - tensor linear_37_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16, x = input_229_cast_fp16)[name = tensor("linear_37_cast_fp16")]; - tensor input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = tensor("input_233_cast_fp16")]; - tensor module_layers_4_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54944576)))]; - tensor module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57041792)))]; - tensor linear_38_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16, x = input_233_cast_fp16)[name = tensor("linear_38_cast_fp16")]; - tensor var_1048_to_fp16 = const()[name = tensor("op_1048_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1049_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1048_to_fp16)[name = tensor("op_1049_cast_fp16")]; - tensor input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_1049_cast_fp16)[name = tensor("input_239_cast_fp16")]; - tensor query_9_axes_0 = const()[name = tensor("query_9_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57042880)))]; - tensor module_layers_4_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57043968)))]; - tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = tensor("query_9_cast_fp16")]; - tensor module_layers_4_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57045056)))]; - tensor module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57569408)))]; - tensor linear_39_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_q_bias_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_39_cast_fp16")]; - tensor var_1066 = const()[name = tensor("op_1066"), val = tensor([1, -1, 8, 64])]; - tensor q_25_cast_fp16 = reshape(shape = var_1066, x = linear_39_cast_fp16)[name = tensor("q_25_cast_fp16")]; - tensor module_layers_4_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57570496)))]; - tensor module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58094848)))]; - tensor linear_40_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_k_bias_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_40_cast_fp16")]; - tensor var_1071 = const()[name = tensor("op_1071"), val = tensor([1, -1, 8, 64])]; - tensor k_17_cast_fp16 = reshape(shape = var_1071, x = linear_40_cast_fp16)[name = tensor("k_17_cast_fp16")]; - tensor module_layers_4_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58095936)))]; - tensor module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58620288)))]; - tensor linear_41_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_v_bias_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16, x = query_9_cast_fp16)[name = tensor("linear_41_cast_fp16")]; - tensor var_1076 = const()[name = tensor("op_1076"), val = tensor([1, -1, 8, 64])]; - tensor v_9_cast_fp16 = reshape(shape = var_1076, x = linear_41_cast_fp16)[name = tensor("v_9_cast_fp16")]; - tensor value_11_perm_0 = const()[name = tensor("value_11_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58621376)))]; - tensor var_1088_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1088_cast_fp16")]; - tensor module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58622464)))]; - tensor var_1090_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1090_cast_fp16")]; - tensor q_with_bias_v_9_perm_0 = const()[name = tensor("q_with_bias_v_9_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_95_transpose_x_0 = const()[name = tensor("x_95_transpose_x_0"), val = tensor(false)]; - tensor x_95_transpose_y_0 = const()[name = tensor("x_95_transpose_y_0"), val = tensor(false)]; - tensor var_1092_to_fp16 = const()[name = tensor("op_1092_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58623552)))]; - tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1090_cast_fp16)[name = tensor("transpose_175")]; - tensor x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = var_1092_to_fp16)[name = tensor("x_95_cast_fp16")]; - tensor x_97_pad_0 = const()[name = tensor("x_97_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_97_mode_0 = const()[name = tensor("x_97_mode_0"), val = tensor("constant")]; - tensor const_110_to_fp16 = const()[name = tensor("const_110_to_fp16"), val = tensor(0x0p+0)]; - tensor x_97_cast_fp16 = pad(constant_val = const_110_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = tensor("x_97_cast_fp16")]; - tensor var_1100 = const()[name = tensor("op_1100"), val = tensor([1, 8, -1, 188])]; - tensor x_99_cast_fp16 = reshape(shape = var_1100, x = x_97_cast_fp16)[name = tensor("x_99_cast_fp16")]; - tensor var_1104_begin_0 = const()[name = tensor("op_1104_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1104_end_0 = const()[name = tensor("op_1104_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1104_end_mask_0 = const()[name = tensor("op_1104_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1104_cast_fp16 = slice_by_index(begin = var_1104_begin_0, end = var_1104_end_0, end_mask = var_1104_end_mask_0, x = x_99_cast_fp16)[name = tensor("op_1104_cast_fp16")]; - tensor var_1105 = const()[name = tensor("op_1105"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_17_cast_fp16 = reshape(shape = var_1105, x = var_1104_cast_fp16)[name = tensor("matrix_bd_17_cast_fp16")]; - tensor matrix_ac_9_transpose_x_0 = const()[name = tensor("matrix_ac_9_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_9_transpose_y_0 = const()[name = tensor("matrix_ac_9_transpose_y_0"), val = tensor(false)]; - tensor transpose_59_perm_0 = const()[name = tensor("transpose_59_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_60_perm_0 = const()[name = tensor("transpose_60_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = k_17_cast_fp16)[name = tensor("transpose_173")]; - tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = var_1088_cast_fp16)[name = tensor("transpose_174")]; - tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_59, y = transpose_60)[name = tensor("matrix_ac_9_cast_fp16")]; - tensor matrix_bd_19_begin_0 = const()[name = tensor("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_19_end_0 = const()[name = tensor("matrix_bd_19_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_19_end_mask_0 = const()[name = tensor("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = tensor("matrix_bd_19_cast_fp16")]; - tensor var_1114_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = tensor("op_1114_cast_fp16")]; - tensor _inversed_scores_17_y_0_to_fp16 = const()[name = tensor("_inversed_scores_17_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_17_cast_fp16 = mul(x = var_1114_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = tensor("_inversed_scores_17_cast_fp16")]; - tensor scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_11)[name = tensor("scores_19_cast_fp16")]; - tensor var_1120_cast_fp16 = softmax(axis = var_23, x = scores_19_cast_fp16)[name = tensor("op_1120_cast_fp16")]; - tensor input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_1120_cast_fp16, cond = mask_11)[name = tensor("input_241_cast_fp16")]; - tensor x_101_transpose_x_0 = const()[name = tensor("x_101_transpose_x_0"), val = tensor(false)]; - tensor x_101_transpose_y_0 = const()[name = tensor("x_101_transpose_y_0"), val = tensor(false)]; - tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = tensor("transpose_176")]; - tensor x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_11_cast_fp16)[name = tensor("x_101_cast_fp16")]; - tensor var_1124_perm_0 = const()[name = tensor("op_1124_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1125 = const()[name = tensor("op_1125"), val = tensor([1, -1, 512])]; - tensor var_1124_cast_fp16 = transpose(perm = var_1124_perm_0, x = x_101_cast_fp16)[name = tensor("transpose_172")]; - tensor input_243_cast_fp16 = reshape(shape = var_1125, x = var_1124_cast_fp16)[name = tensor("input_243_cast_fp16")]; - tensor module_layers_4_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59007616)))]; - tensor module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59531968)))]; - tensor linear_43_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_out_bias_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16, x = input_243_cast_fp16)[name = tensor("linear_43_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = tensor("input_247_cast_fp16")]; - tensor x_105_axes_0 = const()[name = tensor("x_105_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59533056)))]; - tensor module_layers_4_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59534144)))]; - tensor x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = tensor("x_105_cast_fp16")]; - tensor input_249_perm_0 = const()[name = tensor("input_249_perm_0"), val = tensor([0, 2, 1])]; - tensor input_251_pad_type_0 = const()[name = tensor("input_251_pad_type_0"), val = tensor("valid")]; - tensor input_251_strides_0 = const()[name = tensor("input_251_strides_0"), val = tensor([1])]; - tensor input_251_pad_0 = const()[name = tensor("input_251_pad_0"), val = tensor([0, 0])]; - tensor input_251_dilations_0 = const()[name = tensor("input_251_dilations_0"), val = tensor([1])]; - tensor input_251_groups_0 = const()[name = tensor("input_251_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59535232)))]; - tensor module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60583872)))]; - tensor input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = tensor("transpose_171")]; - tensor input_251_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv1_bias_to_fp16, dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16, x = input_249_cast_fp16)[name = tensor("input_251_cast_fp16")]; - tensor x_107_split_num_splits_0 = const()[name = tensor("x_107_split_num_splits_0"), val = tensor(2)]; - tensor x_107_split_axis_0 = const()[name = tensor("x_107_split_axis_0"), val = tensor(1)]; - tensor x_107_split_cast_fp16_0, tensor x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = tensor("x_107_split_cast_fp16")]; - tensor x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = tensor("x_107_split_1_sigmoid_cast_fp16")]; - tensor x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = tensor("x_107_cast_fp16")]; - tensor input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_453)[name = tensor("input_253_cast_fp16")]; - tensor input_255_pad_0 = const()[name = tensor("input_255_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_255_mode_0 = const()[name = tensor("input_255_mode_0"), val = tensor("constant")]; - tensor const_113_to_fp16 = const()[name = tensor("const_113_to_fp16"), val = tensor(0x0p+0)]; - tensor input_255_cast_fp16 = pad(constant_val = const_113_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = tensor("input_255_cast_fp16")]; - tensor input_257_pad_type_0 = const()[name = tensor("input_257_pad_type_0"), val = tensor("valid")]; - tensor input_257_groups_0 = const()[name = tensor("input_257_groups_0"), val = tensor(512)]; - tensor input_257_strides_0 = const()[name = tensor("input_257_strides_0"), val = tensor([1])]; - tensor input_257_pad_0 = const()[name = tensor("input_257_pad_0"), val = tensor([0, 0])]; - tensor input_257_dilations_0 = const()[name = tensor("input_257_dilations_0"), val = tensor([1])]; - tensor const_242_to_fp16 = const()[name = tensor("const_242_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60585984)))]; - tensor const_243_to_fp16 = const()[name = tensor("const_243_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60595264)))]; - tensor input_259_cast_fp16 = conv(bias = const_243_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_242_to_fp16, x = input_255_cast_fp16)[name = tensor("input_259_cast_fp16")]; - tensor input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = tensor("input_261_cast_fp16")]; - tensor x_109_pad_type_0 = const()[name = tensor("x_109_pad_type_0"), val = tensor("valid")]; - tensor x_109_strides_0 = const()[name = tensor("x_109_strides_0"), val = tensor([1])]; - tensor x_109_pad_0 = const()[name = tensor("x_109_pad_0"), val = tensor([0, 0])]; - tensor x_109_dilations_0 = const()[name = tensor("x_109_dilations_0"), val = tensor([1])]; - tensor x_109_groups_0 = const()[name = tensor("x_109_groups_0"), val = tensor(1)]; - tensor module_layers_4_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60596352)))]; - tensor module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61120704)))]; - tensor x_109_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv2_bias_to_fp16, dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16, x = input_261_cast_fp16)[name = tensor("x_109_cast_fp16")]; - tensor input_263_perm_0 = const()[name = tensor("input_263_perm_0"), val = tensor([0, 2, 1])]; - tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = tensor("transpose_170")]; - tensor input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = tensor("input_265_cast_fp16")]; - tensor input_267_axes_0 = const()[name = tensor("input_267_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61121792)))]; - tensor module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61122880)))]; - tensor input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor("input_267_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61123968)))]; - tensor module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63221184)))]; - tensor linear_44_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16, x = input_267_cast_fp16)[name = tensor("linear_44_cast_fp16")]; - tensor input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = tensor("input_271_cast_fp16")]; - tensor module_layers_4_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63225344)))]; - tensor module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65322560)))]; - tensor linear_45_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16, x = input_271_cast_fp16)[name = tensor("linear_45_cast_fp16")]; - tensor var_1191_to_fp16 = const()[name = tensor("op_1191_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1192_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1191_to_fp16)[name = tensor("op_1192_cast_fp16")]; - tensor input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1192_cast_fp16)[name = tensor("input_277_cast_fp16")]; - tensor input_279_axes_0 = const()[name = tensor("input_279_axes_0"), val = tensor([-1])]; - tensor module_layers_4_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65323648)))]; - tensor module_layers_4_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65324736)))]; - tensor input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = tensor("input_279_cast_fp16")]; - tensor input_281_axes_0 = const()[name = tensor("input_281_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65325824)))]; - tensor module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65326912)))]; - tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = tensor("input_281_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65328000)))]; - tensor module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67425216)))]; - tensor linear_46_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16, x = input_281_cast_fp16)[name = tensor("linear_46_cast_fp16")]; - tensor input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = tensor("input_285_cast_fp16")]; - tensor module_layers_5_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67429376)))]; - tensor module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69526592)))]; - tensor linear_47_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16, x = input_285_cast_fp16)[name = tensor("linear_47_cast_fp16")]; - tensor var_1222_to_fp16 = const()[name = tensor("op_1222_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1223_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1222_to_fp16)[name = tensor("op_1223_cast_fp16")]; - tensor input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1223_cast_fp16)[name = tensor("input_291_cast_fp16")]; - tensor query_11_axes_0 = const()[name = tensor("query_11_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69527680)))]; - tensor module_layers_5_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69528768)))]; - tensor query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = tensor("query_11_cast_fp16")]; - tensor module_layers_5_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69529856)))]; - tensor module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70054208)))]; - tensor linear_48_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_q_bias_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_48_cast_fp16")]; - tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1, -1, 8, 64])]; - tensor q_31_cast_fp16 = reshape(shape = var_1240, x = linear_48_cast_fp16)[name = tensor("q_31_cast_fp16")]; - tensor module_layers_5_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70055296)))]; - tensor module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70579648)))]; - tensor linear_49_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_k_bias_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_49_cast_fp16")]; - tensor var_1245 = const()[name = tensor("op_1245"), val = tensor([1, -1, 8, 64])]; - tensor k_21_cast_fp16 = reshape(shape = var_1245, x = linear_49_cast_fp16)[name = tensor("k_21_cast_fp16")]; - tensor module_layers_5_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70580736)))]; - tensor module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71105088)))]; - tensor linear_50_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_v_bias_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16, x = query_11_cast_fp16)[name = tensor("linear_50_cast_fp16")]; - tensor var_1250 = const()[name = tensor("op_1250"), val = tensor([1, -1, 8, 64])]; - tensor v_11_cast_fp16 = reshape(shape = var_1250, x = linear_50_cast_fp16)[name = tensor("v_11_cast_fp16")]; - tensor value_13_perm_0 = const()[name = tensor("value_13_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71106176)))]; - tensor var_1262_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1262_cast_fp16")]; - tensor module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71107264)))]; - tensor var_1264_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1264_cast_fp16")]; - tensor q_with_bias_v_11_perm_0 = const()[name = tensor("q_with_bias_v_11_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_117_transpose_x_0 = const()[name = tensor("x_117_transpose_x_0"), val = tensor(false)]; - tensor x_117_transpose_y_0 = const()[name = tensor("x_117_transpose_y_0"), val = tensor(false)]; - tensor var_1266_to_fp16 = const()[name = tensor("op_1266_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71108352)))]; - tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1264_cast_fp16)[name = tensor("transpose_168")]; - tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = var_1266_to_fp16)[name = tensor("x_117_cast_fp16")]; - tensor x_119_pad_0 = const()[name = tensor("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_119_mode_0 = const()[name = tensor("x_119_mode_0"), val = tensor("constant")]; - tensor const_120_to_fp16 = const()[name = tensor("const_120_to_fp16"), val = tensor(0x0p+0)]; - tensor x_119_cast_fp16 = pad(constant_val = const_120_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = tensor("x_119_cast_fp16")]; - tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([1, 8, -1, 188])]; - tensor x_121_cast_fp16 = reshape(shape = var_1274, x = x_119_cast_fp16)[name = tensor("x_121_cast_fp16")]; - tensor var_1278_begin_0 = const()[name = tensor("op_1278_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1278_end_0 = const()[name = tensor("op_1278_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1278_end_mask_0 = const()[name = tensor("op_1278_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1278_cast_fp16 = slice_by_index(begin = var_1278_begin_0, end = var_1278_end_0, end_mask = var_1278_end_mask_0, x = x_121_cast_fp16)[name = tensor("op_1278_cast_fp16")]; - tensor var_1279 = const()[name = tensor("op_1279"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1279, x = var_1278_cast_fp16)[name = tensor("matrix_bd_21_cast_fp16")]; - tensor matrix_ac_11_transpose_x_0 = const()[name = tensor("matrix_ac_11_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_11_transpose_y_0 = const()[name = tensor("matrix_ac_11_transpose_y_0"), val = tensor(false)]; - tensor transpose_61_perm_0 = const()[name = tensor("transpose_61_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_62_perm_0 = const()[name = tensor("transpose_62_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = k_21_cast_fp16)[name = tensor("transpose_166")]; - tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_1262_cast_fp16)[name = tensor("transpose_167")]; - tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_61, y = transpose_62)[name = tensor("matrix_ac_11_cast_fp16")]; - tensor matrix_bd_23_begin_0 = const()[name = tensor("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_23_end_0 = const()[name = tensor("matrix_bd_23_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_23_end_mask_0 = const()[name = tensor("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = tensor("matrix_bd_23_cast_fp16")]; - tensor var_1288_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = tensor("op_1288_cast_fp16")]; - tensor _inversed_scores_21_y_0_to_fp16 = const()[name = tensor("_inversed_scores_21_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_21_cast_fp16 = mul(x = var_1288_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = tensor("_inversed_scores_21_cast_fp16")]; - tensor scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_11)[name = tensor("scores_23_cast_fp16")]; - tensor var_1294_cast_fp16 = softmax(axis = var_23, x = scores_23_cast_fp16)[name = tensor("op_1294_cast_fp16")]; - tensor input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1294_cast_fp16, cond = mask_11)[name = tensor("input_293_cast_fp16")]; - tensor x_123_transpose_x_0 = const()[name = tensor("x_123_transpose_x_0"), val = tensor(false)]; - tensor x_123_transpose_y_0 = const()[name = tensor("x_123_transpose_y_0"), val = tensor(false)]; - tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = tensor("transpose_169")]; - tensor x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_13_cast_fp16)[name = tensor("x_123_cast_fp16")]; - tensor var_1298_perm_0 = const()[name = tensor("op_1298_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1299 = const()[name = tensor("op_1299"), val = tensor([1, -1, 512])]; - tensor var_1298_cast_fp16 = transpose(perm = var_1298_perm_0, x = x_123_cast_fp16)[name = tensor("transpose_165")]; - tensor input_295_cast_fp16 = reshape(shape = var_1299, x = var_1298_cast_fp16)[name = tensor("input_295_cast_fp16")]; - tensor module_layers_5_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71492416)))]; - tensor module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72016768)))]; - tensor linear_52_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_out_bias_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16, x = input_295_cast_fp16)[name = tensor("linear_52_cast_fp16")]; - tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = tensor("input_299_cast_fp16")]; - tensor x_127_axes_0 = const()[name = tensor("x_127_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72017856)))]; - tensor module_layers_5_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72018944)))]; - tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = tensor("x_127_cast_fp16")]; - tensor input_301_perm_0 = const()[name = tensor("input_301_perm_0"), val = tensor([0, 2, 1])]; - tensor input_303_pad_type_0 = const()[name = tensor("input_303_pad_type_0"), val = tensor("valid")]; - tensor input_303_strides_0 = const()[name = tensor("input_303_strides_0"), val = tensor([1])]; - tensor input_303_pad_0 = const()[name = tensor("input_303_pad_0"), val = tensor([0, 0])]; - tensor input_303_dilations_0 = const()[name = tensor("input_303_dilations_0"), val = tensor([1])]; - tensor input_303_groups_0 = const()[name = tensor("input_303_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72020032)))]; - tensor module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73068672)))]; - tensor input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = tensor("transpose_164")]; - tensor input_303_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv1_bias_to_fp16, dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16, x = input_301_cast_fp16)[name = tensor("input_303_cast_fp16")]; - tensor x_129_split_num_splits_0 = const()[name = tensor("x_129_split_num_splits_0"), val = tensor(2)]; - tensor x_129_split_axis_0 = const()[name = tensor("x_129_split_axis_0"), val = tensor(1)]; - tensor x_129_split_cast_fp16_0, tensor x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = tensor("x_129_split_cast_fp16")]; - tensor x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = tensor("x_129_split_1_sigmoid_cast_fp16")]; - tensor x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = tensor("x_129_cast_fp16")]; - tensor input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_453)[name = tensor("input_305_cast_fp16")]; - tensor input_307_pad_0 = const()[name = tensor("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_307_mode_0 = const()[name = tensor("input_307_mode_0"), val = tensor("constant")]; - tensor const_123_to_fp16 = const()[name = tensor("const_123_to_fp16"), val = tensor(0x0p+0)]; - tensor input_307_cast_fp16 = pad(constant_val = const_123_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = tensor("input_307_cast_fp16")]; - tensor input_309_pad_type_0 = const()[name = tensor("input_309_pad_type_0"), val = tensor("valid")]; - tensor input_309_groups_0 = const()[name = tensor("input_309_groups_0"), val = tensor(512)]; - tensor input_309_strides_0 = const()[name = tensor("input_309_strides_0"), val = tensor([1])]; - tensor input_309_pad_0 = const()[name = tensor("input_309_pad_0"), val = tensor([0, 0])]; - tensor input_309_dilations_0 = const()[name = tensor("input_309_dilations_0"), val = tensor([1])]; - tensor const_244_to_fp16 = const()[name = tensor("const_244_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73070784)))]; - tensor const_245_to_fp16 = const()[name = tensor("const_245_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73080064)))]; - tensor input_311_cast_fp16 = conv(bias = const_245_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_244_to_fp16, x = input_307_cast_fp16)[name = tensor("input_311_cast_fp16")]; - tensor input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = tensor("input_313_cast_fp16")]; - tensor x_131_pad_type_0 = const()[name = tensor("x_131_pad_type_0"), val = tensor("valid")]; - tensor x_131_strides_0 = const()[name = tensor("x_131_strides_0"), val = tensor([1])]; - tensor x_131_pad_0 = const()[name = tensor("x_131_pad_0"), val = tensor([0, 0])]; - tensor x_131_dilations_0 = const()[name = tensor("x_131_dilations_0"), val = tensor([1])]; - tensor x_131_groups_0 = const()[name = tensor("x_131_groups_0"), val = tensor(1)]; - tensor module_layers_5_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73081152)))]; - tensor module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73605504)))]; - tensor x_131_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv2_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_layers_5_conv_pointwise_conv2_weight_to_fp16, x = input_313_cast_fp16)[name = tensor("x_131_cast_fp16")]; - tensor input_315_perm_0 = const()[name = tensor("input_315_perm_0"), val = tensor([0, 2, 1])]; - tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = tensor("transpose_163")]; - tensor input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = tensor("input_317_cast_fp16")]; - tensor input_319_axes_0 = const()[name = tensor("input_319_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73606592)))]; - tensor module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73607680)))]; - tensor input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor("input_319_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73608768)))]; - tensor module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75705984)))]; - tensor linear_53_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16, x = input_319_cast_fp16)[name = tensor("linear_53_cast_fp16")]; - tensor input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = tensor("input_323_cast_fp16")]; - tensor module_layers_5_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75710144)))]; - tensor module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77807360)))]; - tensor linear_54_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16, x = input_323_cast_fp16)[name = tensor("linear_54_cast_fp16")]; - tensor var_1365_to_fp16 = const()[name = tensor("op_1365_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1366_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1365_to_fp16)[name = tensor("op_1366_cast_fp16")]; - tensor input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1366_cast_fp16)[name = tensor("input_329_cast_fp16")]; - tensor input_331_axes_0 = const()[name = tensor("input_331_axes_0"), val = tensor([-1])]; - tensor module_layers_5_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77808448)))]; - tensor module_layers_5_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77809536)))]; - tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("input_331_cast_fp16")]; - tensor input_333_axes_0 = const()[name = tensor("input_333_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77810624)))]; - tensor module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77811712)))]; - tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("input_333_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77812800)))]; - tensor module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79910016)))]; - tensor linear_55_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16, x = input_333_cast_fp16)[name = tensor("linear_55_cast_fp16")]; - tensor input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = tensor("input_337_cast_fp16")]; - tensor module_layers_6_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79914176)))]; - tensor module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82011392)))]; - tensor linear_56_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16, x = input_337_cast_fp16)[name = tensor("linear_56_cast_fp16")]; - tensor var_1396_to_fp16 = const()[name = tensor("op_1396_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1397_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1396_to_fp16)[name = tensor("op_1397_cast_fp16")]; - tensor input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1397_cast_fp16)[name = tensor("input_343_cast_fp16")]; - tensor query_13_axes_0 = const()[name = tensor("query_13_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82012480)))]; - tensor module_layers_6_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82013568)))]; - tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = tensor("query_13_cast_fp16")]; - tensor module_layers_6_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82014656)))]; - tensor module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82539008)))]; - tensor linear_57_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_q_bias_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_57_cast_fp16")]; - tensor var_1414 = const()[name = tensor("op_1414"), val = tensor([1, -1, 8, 64])]; - tensor q_37_cast_fp16 = reshape(shape = var_1414, x = linear_57_cast_fp16)[name = tensor("q_37_cast_fp16")]; - tensor module_layers_6_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82540096)))]; - tensor module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83064448)))]; - tensor linear_58_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_k_bias_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_58_cast_fp16")]; - tensor var_1419 = const()[name = tensor("op_1419"), val = tensor([1, -1, 8, 64])]; - tensor k_25_cast_fp16 = reshape(shape = var_1419, x = linear_58_cast_fp16)[name = tensor("k_25_cast_fp16")]; - tensor module_layers_6_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83065536)))]; - tensor module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83589888)))]; - tensor linear_59_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_v_bias_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16, x = query_13_cast_fp16)[name = tensor("linear_59_cast_fp16")]; - tensor var_1424 = const()[name = tensor("op_1424"), val = tensor([1, -1, 8, 64])]; - tensor v_13_cast_fp16 = reshape(shape = var_1424, x = linear_59_cast_fp16)[name = tensor("v_13_cast_fp16")]; - tensor value_15_perm_0 = const()[name = tensor("value_15_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83590976)))]; - tensor var_1436_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1436_cast_fp16")]; - tensor module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83592064)))]; - tensor var_1438_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1438_cast_fp16")]; - tensor q_with_bias_v_13_perm_0 = const()[name = tensor("q_with_bias_v_13_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_139_transpose_x_0 = const()[name = tensor("x_139_transpose_x_0"), val = tensor(false)]; - tensor x_139_transpose_y_0 = const()[name = tensor("x_139_transpose_y_0"), val = tensor(false)]; - tensor var_1440_to_fp16 = const()[name = tensor("op_1440_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83593152)))]; - tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1438_cast_fp16)[name = tensor("transpose_161")]; - tensor x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = var_1440_to_fp16)[name = tensor("x_139_cast_fp16")]; - tensor x_141_pad_0 = const()[name = tensor("x_141_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_141_mode_0 = const()[name = tensor("x_141_mode_0"), val = tensor("constant")]; - tensor const_130_to_fp16 = const()[name = tensor("const_130_to_fp16"), val = tensor(0x0p+0)]; - tensor x_141_cast_fp16 = pad(constant_val = const_130_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = tensor("x_141_cast_fp16")]; - tensor var_1448 = const()[name = tensor("op_1448"), val = tensor([1, 8, -1, 188])]; - tensor x_143_cast_fp16 = reshape(shape = var_1448, x = x_141_cast_fp16)[name = tensor("x_143_cast_fp16")]; - tensor var_1452_begin_0 = const()[name = tensor("op_1452_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1452_end_0 = const()[name = tensor("op_1452_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1452_end_mask_0 = const()[name = tensor("op_1452_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1452_cast_fp16 = slice_by_index(begin = var_1452_begin_0, end = var_1452_end_0, end_mask = var_1452_end_mask_0, x = x_143_cast_fp16)[name = tensor("op_1452_cast_fp16")]; - tensor var_1453 = const()[name = tensor("op_1453"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1453, x = var_1452_cast_fp16)[name = tensor("matrix_bd_25_cast_fp16")]; - tensor matrix_ac_13_transpose_x_0 = const()[name = tensor("matrix_ac_13_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_13_transpose_y_0 = const()[name = tensor("matrix_ac_13_transpose_y_0"), val = tensor(false)]; - tensor transpose_63_perm_0 = const()[name = tensor("transpose_63_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_64_perm_0 = const()[name = tensor("transpose_64_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = k_25_cast_fp16)[name = tensor("transpose_159")]; - tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_1436_cast_fp16)[name = tensor("transpose_160")]; - tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_63, y = transpose_64)[name = tensor("matrix_ac_13_cast_fp16")]; - tensor matrix_bd_27_begin_0 = const()[name = tensor("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_27_end_0 = const()[name = tensor("matrix_bd_27_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_27_end_mask_0 = const()[name = tensor("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = tensor("matrix_bd_27_cast_fp16")]; - tensor var_1462_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = tensor("op_1462_cast_fp16")]; - tensor _inversed_scores_25_y_0_to_fp16 = const()[name = tensor("_inversed_scores_25_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_25_cast_fp16 = mul(x = var_1462_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = tensor("_inversed_scores_25_cast_fp16")]; - tensor scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_11)[name = tensor("scores_27_cast_fp16")]; - tensor var_1468_cast_fp16 = softmax(axis = var_23, x = scores_27_cast_fp16)[name = tensor("op_1468_cast_fp16")]; - tensor input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1468_cast_fp16, cond = mask_11)[name = tensor("input_345_cast_fp16")]; - tensor x_145_transpose_x_0 = const()[name = tensor("x_145_transpose_x_0"), val = tensor(false)]; - tensor x_145_transpose_y_0 = const()[name = tensor("x_145_transpose_y_0"), val = tensor(false)]; - tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = tensor("transpose_162")]; - tensor x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_15_cast_fp16)[name = tensor("x_145_cast_fp16")]; - tensor var_1472_perm_0 = const()[name = tensor("op_1472_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1473 = const()[name = tensor("op_1473"), val = tensor([1, -1, 512])]; - tensor var_1472_cast_fp16 = transpose(perm = var_1472_perm_0, x = x_145_cast_fp16)[name = tensor("transpose_158")]; - tensor input_347_cast_fp16 = reshape(shape = var_1473, x = var_1472_cast_fp16)[name = tensor("input_347_cast_fp16")]; - tensor module_layers_6_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83977216)))]; - tensor module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84501568)))]; - tensor linear_61_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_out_bias_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16, x = input_347_cast_fp16)[name = tensor("linear_61_cast_fp16")]; - tensor input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = tensor("input_351_cast_fp16")]; - tensor x_149_axes_0 = const()[name = tensor("x_149_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84502656)))]; - tensor module_layers_6_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84503744)))]; - tensor x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = tensor("x_149_cast_fp16")]; - tensor input_353_perm_0 = const()[name = tensor("input_353_perm_0"), val = tensor([0, 2, 1])]; - tensor input_355_pad_type_0 = const()[name = tensor("input_355_pad_type_0"), val = tensor("valid")]; - tensor input_355_strides_0 = const()[name = tensor("input_355_strides_0"), val = tensor([1])]; - tensor input_355_pad_0 = const()[name = tensor("input_355_pad_0"), val = tensor([0, 0])]; - tensor input_355_dilations_0 = const()[name = tensor("input_355_dilations_0"), val = tensor([1])]; - tensor input_355_groups_0 = const()[name = tensor("input_355_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84504832)))]; - tensor module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85553472)))]; - tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = tensor("transpose_157")]; - tensor input_355_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv1_bias_to_fp16, dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16, x = input_353_cast_fp16)[name = tensor("input_355_cast_fp16")]; - tensor x_151_split_num_splits_0 = const()[name = tensor("x_151_split_num_splits_0"), val = tensor(2)]; - tensor x_151_split_axis_0 = const()[name = tensor("x_151_split_axis_0"), val = tensor(1)]; - tensor x_151_split_cast_fp16_0, tensor x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = tensor("x_151_split_cast_fp16")]; - tensor x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = tensor("x_151_split_1_sigmoid_cast_fp16")]; - tensor x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = tensor("x_151_cast_fp16")]; - tensor input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_453)[name = tensor("input_357_cast_fp16")]; - tensor input_359_pad_0 = const()[name = tensor("input_359_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_359_mode_0 = const()[name = tensor("input_359_mode_0"), val = tensor("constant")]; - tensor const_133_to_fp16 = const()[name = tensor("const_133_to_fp16"), val = tensor(0x0p+0)]; - tensor input_359_cast_fp16 = pad(constant_val = const_133_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = tensor("input_359_cast_fp16")]; - tensor input_361_pad_type_0 = const()[name = tensor("input_361_pad_type_0"), val = tensor("valid")]; - tensor input_361_groups_0 = const()[name = tensor("input_361_groups_0"), val = tensor(512)]; - tensor input_361_strides_0 = const()[name = tensor("input_361_strides_0"), val = tensor([1])]; - tensor input_361_pad_0 = const()[name = tensor("input_361_pad_0"), val = tensor([0, 0])]; - tensor input_361_dilations_0 = const()[name = tensor("input_361_dilations_0"), val = tensor([1])]; - tensor const_246_to_fp16 = const()[name = tensor("const_246_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85555584)))]; - tensor const_247_to_fp16 = const()[name = tensor("const_247_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85564864)))]; - tensor input_363_cast_fp16 = conv(bias = const_247_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_246_to_fp16, x = input_359_cast_fp16)[name = tensor("input_363_cast_fp16")]; - tensor input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; - tensor x_153_pad_type_0 = const()[name = tensor("x_153_pad_type_0"), val = tensor("valid")]; - tensor x_153_strides_0 = const()[name = tensor("x_153_strides_0"), val = tensor([1])]; - tensor x_153_pad_0 = const()[name = tensor("x_153_pad_0"), val = tensor([0, 0])]; - tensor x_153_dilations_0 = const()[name = tensor("x_153_dilations_0"), val = tensor([1])]; - tensor x_153_groups_0 = const()[name = tensor("x_153_groups_0"), val = tensor(1)]; - tensor module_layers_6_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85565952)))]; - tensor module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86090304)))]; - tensor x_153_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv2_bias_to_fp16, dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = module_layers_6_conv_pointwise_conv2_weight_to_fp16, x = input_365_cast_fp16)[name = tensor("x_153_cast_fp16")]; - tensor input_367_perm_0 = const()[name = tensor("input_367_perm_0"), val = tensor([0, 2, 1])]; - tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = tensor("transpose_156")]; - tensor input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = tensor("input_369_cast_fp16")]; - tensor input_371_axes_0 = const()[name = tensor("input_371_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86091392)))]; - tensor module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86092480)))]; - tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor("input_371_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86093568)))]; - tensor module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88190784)))]; - tensor linear_62_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16, x = input_371_cast_fp16)[name = tensor("linear_62_cast_fp16")]; - tensor input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = tensor("input_375_cast_fp16")]; - tensor module_layers_6_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88194944)))]; - tensor module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90292160)))]; - tensor linear_63_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16, x = input_375_cast_fp16)[name = tensor("linear_63_cast_fp16")]; - tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1540_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1539_to_fp16)[name = tensor("op_1540_cast_fp16")]; - tensor input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1540_cast_fp16)[name = tensor("input_381_cast_fp16")]; - tensor input_383_axes_0 = const()[name = tensor("input_383_axes_0"), val = tensor([-1])]; - tensor module_layers_6_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90293248)))]; - tensor module_layers_6_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90294336)))]; - tensor input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = tensor("input_383_cast_fp16")]; - tensor input_385_axes_0 = const()[name = tensor("input_385_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90295424)))]; - tensor module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90296512)))]; - tensor input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = tensor("input_385_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90297600)))]; - tensor module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92394816)))]; - tensor linear_64_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16, x = input_385_cast_fp16)[name = tensor("linear_64_cast_fp16")]; - tensor input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = tensor("input_389_cast_fp16")]; - tensor module_layers_7_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92398976)))]; - tensor module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94496192)))]; - tensor linear_65_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16, x = input_389_cast_fp16)[name = tensor("linear_65_cast_fp16")]; - tensor var_1570_to_fp16 = const()[name = tensor("op_1570_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1571_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1570_to_fp16)[name = tensor("op_1571_cast_fp16")]; - tensor input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1571_cast_fp16)[name = tensor("input_395_cast_fp16")]; - tensor query_15_axes_0 = const()[name = tensor("query_15_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94497280)))]; - tensor module_layers_7_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94498368)))]; - tensor query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = tensor("query_15_cast_fp16")]; - tensor module_layers_7_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94499456)))]; - tensor module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95023808)))]; - tensor linear_66_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_q_bias_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_66_cast_fp16")]; - tensor var_1588 = const()[name = tensor("op_1588"), val = tensor([1, -1, 8, 64])]; - tensor q_43_cast_fp16 = reshape(shape = var_1588, x = linear_66_cast_fp16)[name = tensor("q_43_cast_fp16")]; - tensor module_layers_7_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95024896)))]; - tensor module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95549248)))]; - tensor linear_67_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_k_bias_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_67_cast_fp16")]; - tensor var_1593 = const()[name = tensor("op_1593"), val = tensor([1, -1, 8, 64])]; - tensor k_29_cast_fp16 = reshape(shape = var_1593, x = linear_67_cast_fp16)[name = tensor("k_29_cast_fp16")]; - tensor module_layers_7_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95550336)))]; - tensor module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96074688)))]; - tensor linear_68_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_v_bias_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16, x = query_15_cast_fp16)[name = tensor("linear_68_cast_fp16")]; - tensor var_1598 = const()[name = tensor("op_1598"), val = tensor([1, -1, 8, 64])]; - tensor v_15_cast_fp16 = reshape(shape = var_1598, x = linear_68_cast_fp16)[name = tensor("v_15_cast_fp16")]; - tensor value_17_perm_0 = const()[name = tensor("value_17_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96075776)))]; - tensor var_1610_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1610_cast_fp16")]; - tensor module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96076864)))]; - tensor var_1612_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1612_cast_fp16")]; - tensor q_with_bias_v_15_perm_0 = const()[name = tensor("q_with_bias_v_15_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_161_transpose_x_0 = const()[name = tensor("x_161_transpose_x_0"), val = tensor(false)]; - tensor x_161_transpose_y_0 = const()[name = tensor("x_161_transpose_y_0"), val = tensor(false)]; - tensor var_1614_to_fp16 = const()[name = tensor("op_1614_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96077952)))]; - tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1612_cast_fp16)[name = tensor("transpose_154")]; - tensor x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = var_1614_to_fp16)[name = tensor("x_161_cast_fp16")]; - tensor x_163_pad_0 = const()[name = tensor("x_163_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_163_mode_0 = const()[name = tensor("x_163_mode_0"), val = tensor("constant")]; - tensor const_140_to_fp16 = const()[name = tensor("const_140_to_fp16"), val = tensor(0x0p+0)]; - tensor x_163_cast_fp16 = pad(constant_val = const_140_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = tensor("x_163_cast_fp16")]; - tensor var_1622 = const()[name = tensor("op_1622"), val = tensor([1, 8, -1, 188])]; - tensor x_165_cast_fp16 = reshape(shape = var_1622, x = x_163_cast_fp16)[name = tensor("x_165_cast_fp16")]; - tensor var_1626_begin_0 = const()[name = tensor("op_1626_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1626_end_0 = const()[name = tensor("op_1626_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1626_end_mask_0 = const()[name = tensor("op_1626_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1626_cast_fp16 = slice_by_index(begin = var_1626_begin_0, end = var_1626_end_0, end_mask = var_1626_end_mask_0, x = x_165_cast_fp16)[name = tensor("op_1626_cast_fp16")]; - tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_29_cast_fp16 = reshape(shape = var_1627, x = var_1626_cast_fp16)[name = tensor("matrix_bd_29_cast_fp16")]; - tensor matrix_ac_15_transpose_x_0 = const()[name = tensor("matrix_ac_15_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_15_transpose_y_0 = const()[name = tensor("matrix_ac_15_transpose_y_0"), val = tensor(false)]; - tensor transpose_65_perm_0 = const()[name = tensor("transpose_65_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_66_perm_0 = const()[name = tensor("transpose_66_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = k_29_cast_fp16)[name = tensor("transpose_152")]; - tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = var_1610_cast_fp16)[name = tensor("transpose_153")]; - tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_65, y = transpose_66)[name = tensor("matrix_ac_15_cast_fp16")]; - tensor matrix_bd_31_begin_0 = const()[name = tensor("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_31_end_0 = const()[name = tensor("matrix_bd_31_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_31_end_mask_0 = const()[name = tensor("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = tensor("matrix_bd_31_cast_fp16")]; - tensor var_1636_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = tensor("op_1636_cast_fp16")]; - tensor _inversed_scores_29_y_0_to_fp16 = const()[name = tensor("_inversed_scores_29_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_29_cast_fp16 = mul(x = var_1636_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = tensor("_inversed_scores_29_cast_fp16")]; - tensor scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_11)[name = tensor("scores_31_cast_fp16")]; - tensor var_1642_cast_fp16 = softmax(axis = var_23, x = scores_31_cast_fp16)[name = tensor("op_1642_cast_fp16")]; - tensor input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1642_cast_fp16, cond = mask_11)[name = tensor("input_397_cast_fp16")]; - tensor x_167_transpose_x_0 = const()[name = tensor("x_167_transpose_x_0"), val = tensor(false)]; - tensor x_167_transpose_y_0 = const()[name = tensor("x_167_transpose_y_0"), val = tensor(false)]; - tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = tensor("transpose_155")]; - tensor x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_17_cast_fp16)[name = tensor("x_167_cast_fp16")]; - tensor var_1646_perm_0 = const()[name = tensor("op_1646_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1647 = const()[name = tensor("op_1647"), val = tensor([1, -1, 512])]; - tensor var_1646_cast_fp16 = transpose(perm = var_1646_perm_0, x = x_167_cast_fp16)[name = tensor("transpose_151")]; - tensor input_399_cast_fp16 = reshape(shape = var_1647, x = var_1646_cast_fp16)[name = tensor("input_399_cast_fp16")]; - tensor module_layers_7_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96462016)))]; - tensor module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96986368)))]; - tensor linear_70_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_out_bias_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16, x = input_399_cast_fp16)[name = tensor("linear_70_cast_fp16")]; - tensor input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = tensor("input_403_cast_fp16")]; - tensor x_171_axes_0 = const()[name = tensor("x_171_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96987456)))]; - tensor module_layers_7_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96988544)))]; - tensor x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = tensor("x_171_cast_fp16")]; - tensor input_405_perm_0 = const()[name = tensor("input_405_perm_0"), val = tensor([0, 2, 1])]; - tensor input_407_pad_type_0 = const()[name = tensor("input_407_pad_type_0"), val = tensor("valid")]; - tensor input_407_strides_0 = const()[name = tensor("input_407_strides_0"), val = tensor([1])]; - tensor input_407_pad_0 = const()[name = tensor("input_407_pad_0"), val = tensor([0, 0])]; - tensor input_407_dilations_0 = const()[name = tensor("input_407_dilations_0"), val = tensor([1])]; - tensor input_407_groups_0 = const()[name = tensor("input_407_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96989632)))]; - tensor module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98038272)))]; - tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = tensor("transpose_150")]; - tensor input_407_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv1_bias_to_fp16, dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16, x = input_405_cast_fp16)[name = tensor("input_407_cast_fp16")]; - tensor x_173_split_num_splits_0 = const()[name = tensor("x_173_split_num_splits_0"), val = tensor(2)]; - tensor x_173_split_axis_0 = const()[name = tensor("x_173_split_axis_0"), val = tensor(1)]; - tensor x_173_split_cast_fp16_0, tensor x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = tensor("x_173_split_cast_fp16")]; - tensor x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = tensor("x_173_split_1_sigmoid_cast_fp16")]; - tensor x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = tensor("x_173_cast_fp16")]; - tensor input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_453)[name = tensor("input_409_cast_fp16")]; - tensor input_411_pad_0 = const()[name = tensor("input_411_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_411_mode_0 = const()[name = tensor("input_411_mode_0"), val = tensor("constant")]; - tensor const_143_to_fp16 = const()[name = tensor("const_143_to_fp16"), val = tensor(0x0p+0)]; - tensor input_411_cast_fp16 = pad(constant_val = const_143_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = tensor("input_411_cast_fp16")]; - tensor input_413_pad_type_0 = const()[name = tensor("input_413_pad_type_0"), val = tensor("valid")]; - tensor input_413_groups_0 = const()[name = tensor("input_413_groups_0"), val = tensor(512)]; - tensor input_413_strides_0 = const()[name = tensor("input_413_strides_0"), val = tensor([1])]; - tensor input_413_pad_0 = const()[name = tensor("input_413_pad_0"), val = tensor([0, 0])]; - tensor input_413_dilations_0 = const()[name = tensor("input_413_dilations_0"), val = tensor([1])]; - tensor const_248_to_fp16 = const()[name = tensor("const_248_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98040384)))]; - tensor const_249_to_fp16 = const()[name = tensor("const_249_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98049664)))]; - tensor input_415_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_248_to_fp16, x = input_411_cast_fp16)[name = tensor("input_415_cast_fp16")]; - tensor input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = tensor("input_417_cast_fp16")]; - tensor x_175_pad_type_0 = const()[name = tensor("x_175_pad_type_0"), val = tensor("valid")]; - tensor x_175_strides_0 = const()[name = tensor("x_175_strides_0"), val = tensor([1])]; - tensor x_175_pad_0 = const()[name = tensor("x_175_pad_0"), val = tensor([0, 0])]; - tensor x_175_dilations_0 = const()[name = tensor("x_175_dilations_0"), val = tensor([1])]; - tensor x_175_groups_0 = const()[name = tensor("x_175_groups_0"), val = tensor(1)]; - tensor module_layers_7_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98050752)))]; - tensor module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98575104)))]; - tensor x_175_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv2_bias_to_fp16, dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = module_layers_7_conv_pointwise_conv2_weight_to_fp16, x = input_417_cast_fp16)[name = tensor("x_175_cast_fp16")]; - tensor input_419_perm_0 = const()[name = tensor("input_419_perm_0"), val = tensor([0, 2, 1])]; - tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = tensor("transpose_149")]; - tensor input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = tensor("input_421_cast_fp16")]; - tensor input_423_axes_0 = const()[name = tensor("input_423_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98576192)))]; - tensor module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98577280)))]; - tensor input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = tensor("input_423_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98578368)))]; - tensor module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100675584)))]; - tensor linear_71_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor("linear_71_cast_fp16")]; - tensor input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = tensor("input_427_cast_fp16")]; - tensor module_layers_7_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100679744)))]; - tensor module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102776960)))]; - tensor linear_72_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16, x = input_427_cast_fp16)[name = tensor("linear_72_cast_fp16")]; - tensor var_1713_to_fp16 = const()[name = tensor("op_1713_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1714_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1713_to_fp16)[name = tensor("op_1714_cast_fp16")]; - tensor input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1714_cast_fp16)[name = tensor("input_433_cast_fp16")]; - tensor input_435_axes_0 = const()[name = tensor("input_435_axes_0"), val = tensor([-1])]; - tensor module_layers_7_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102778048)))]; - tensor module_layers_7_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102779136)))]; - tensor input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = tensor("input_435_cast_fp16")]; - tensor input_437_axes_0 = const()[name = tensor("input_437_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102780224)))]; - tensor module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102781312)))]; - tensor input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = tensor("input_437_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102782400)))]; - tensor module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104879616)))]; - tensor linear_73_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16, x = input_437_cast_fp16)[name = tensor("linear_73_cast_fp16")]; - tensor input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = tensor("input_441_cast_fp16")]; - tensor module_layers_8_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104883776)))]; - tensor module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106980992)))]; - tensor linear_74_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16, x = input_441_cast_fp16)[name = tensor("linear_74_cast_fp16")]; - tensor var_1744_to_fp16 = const()[name = tensor("op_1744_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1745_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1744_to_fp16)[name = tensor("op_1745_cast_fp16")]; - tensor input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1745_cast_fp16)[name = tensor("input_447_cast_fp16")]; - tensor query_17_axes_0 = const()[name = tensor("query_17_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106982080)))]; - tensor module_layers_8_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106983168)))]; - tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = tensor("query_17_cast_fp16")]; - tensor module_layers_8_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106984256)))]; - tensor module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107508608)))]; - tensor linear_75_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_q_bias_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_75_cast_fp16")]; - tensor var_1762 = const()[name = tensor("op_1762"), val = tensor([1, -1, 8, 64])]; - tensor q_49_cast_fp16 = reshape(shape = var_1762, x = linear_75_cast_fp16)[name = tensor("q_49_cast_fp16")]; - tensor module_layers_8_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107509696)))]; - tensor module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108034048)))]; - tensor linear_76_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_k_bias_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_76_cast_fp16")]; - tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, -1, 8, 64])]; - tensor k_33_cast_fp16 = reshape(shape = var_1767, x = linear_76_cast_fp16)[name = tensor("k_33_cast_fp16")]; - tensor module_layers_8_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108035136)))]; - tensor module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108559488)))]; - tensor linear_77_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_v_bias_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16, x = query_17_cast_fp16)[name = tensor("linear_77_cast_fp16")]; - tensor var_1772 = const()[name = tensor("op_1772"), val = tensor([1, -1, 8, 64])]; - tensor v_17_cast_fp16 = reshape(shape = var_1772, x = linear_77_cast_fp16)[name = tensor("v_17_cast_fp16")]; - tensor value_19_perm_0 = const()[name = tensor("value_19_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108560576)))]; - tensor var_1784_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1784_cast_fp16")]; - tensor module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108561664)))]; - tensor var_1786_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1786_cast_fp16")]; - tensor q_with_bias_v_17_perm_0 = const()[name = tensor("q_with_bias_v_17_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_183_transpose_x_0 = const()[name = tensor("x_183_transpose_x_0"), val = tensor(false)]; - tensor x_183_transpose_y_0 = const()[name = tensor("x_183_transpose_y_0"), val = tensor(false)]; - tensor var_1788_to_fp16 = const()[name = tensor("op_1788_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108562752)))]; - tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1786_cast_fp16)[name = tensor("transpose_147")]; - tensor x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = var_1788_to_fp16)[name = tensor("x_183_cast_fp16")]; - tensor x_185_pad_0 = const()[name = tensor("x_185_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_185_mode_0 = const()[name = tensor("x_185_mode_0"), val = tensor("constant")]; - tensor const_150_to_fp16 = const()[name = tensor("const_150_to_fp16"), val = tensor(0x0p+0)]; - tensor x_185_cast_fp16 = pad(constant_val = const_150_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = tensor("x_185_cast_fp16")]; - tensor var_1796 = const()[name = tensor("op_1796"), val = tensor([1, 8, -1, 188])]; - tensor x_187_cast_fp16 = reshape(shape = var_1796, x = x_185_cast_fp16)[name = tensor("x_187_cast_fp16")]; - tensor var_1800_begin_0 = const()[name = tensor("op_1800_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1800_end_0 = const()[name = tensor("op_1800_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1800_end_mask_0 = const()[name = tensor("op_1800_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1800_cast_fp16 = slice_by_index(begin = var_1800_begin_0, end = var_1800_end_0, end_mask = var_1800_end_mask_0, x = x_187_cast_fp16)[name = tensor("op_1800_cast_fp16")]; - tensor var_1801 = const()[name = tensor("op_1801"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_33_cast_fp16 = reshape(shape = var_1801, x = var_1800_cast_fp16)[name = tensor("matrix_bd_33_cast_fp16")]; - tensor matrix_ac_17_transpose_x_0 = const()[name = tensor("matrix_ac_17_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_17_transpose_y_0 = const()[name = tensor("matrix_ac_17_transpose_y_0"), val = tensor(false)]; - tensor transpose_67_perm_0 = const()[name = tensor("transpose_67_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_68_perm_0 = const()[name = tensor("transpose_68_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = k_33_cast_fp16)[name = tensor("transpose_145")]; - tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_1784_cast_fp16)[name = tensor("transpose_146")]; - tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_67, y = transpose_68)[name = tensor("matrix_ac_17_cast_fp16")]; - tensor matrix_bd_35_begin_0 = const()[name = tensor("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_35_end_0 = const()[name = tensor("matrix_bd_35_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_35_end_mask_0 = const()[name = tensor("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = tensor("matrix_bd_35_cast_fp16")]; - tensor var_1810_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = tensor("op_1810_cast_fp16")]; - tensor _inversed_scores_33_y_0_to_fp16 = const()[name = tensor("_inversed_scores_33_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_33_cast_fp16 = mul(x = var_1810_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = tensor("_inversed_scores_33_cast_fp16")]; - tensor scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_11)[name = tensor("scores_35_cast_fp16")]; - tensor var_1816_cast_fp16 = softmax(axis = var_23, x = scores_35_cast_fp16)[name = tensor("op_1816_cast_fp16")]; - tensor input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1816_cast_fp16, cond = mask_11)[name = tensor("input_449_cast_fp16")]; - tensor x_189_transpose_x_0 = const()[name = tensor("x_189_transpose_x_0"), val = tensor(false)]; - tensor x_189_transpose_y_0 = const()[name = tensor("x_189_transpose_y_0"), val = tensor(false)]; - tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = tensor("transpose_148")]; - tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_19_cast_fp16)[name = tensor("x_189_cast_fp16")]; - tensor var_1820_perm_0 = const()[name = tensor("op_1820_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1821 = const()[name = tensor("op_1821"), val = tensor([1, -1, 512])]; - tensor var_1820_cast_fp16 = transpose(perm = var_1820_perm_0, x = x_189_cast_fp16)[name = tensor("transpose_144")]; - tensor input_451_cast_fp16 = reshape(shape = var_1821, x = var_1820_cast_fp16)[name = tensor("input_451_cast_fp16")]; - tensor module_layers_8_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108946816)))]; - tensor module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109471168)))]; - tensor linear_79_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_out_bias_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16, x = input_451_cast_fp16)[name = tensor("linear_79_cast_fp16")]; - tensor input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = tensor("input_455_cast_fp16")]; - tensor x_193_axes_0 = const()[name = tensor("x_193_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109472256)))]; - tensor module_layers_8_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109473344)))]; - tensor x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = tensor("x_193_cast_fp16")]; - tensor input_457_perm_0 = const()[name = tensor("input_457_perm_0"), val = tensor([0, 2, 1])]; - tensor input_459_pad_type_0 = const()[name = tensor("input_459_pad_type_0"), val = tensor("valid")]; - tensor input_459_strides_0 = const()[name = tensor("input_459_strides_0"), val = tensor([1])]; - tensor input_459_pad_0 = const()[name = tensor("input_459_pad_0"), val = tensor([0, 0])]; - tensor input_459_dilations_0 = const()[name = tensor("input_459_dilations_0"), val = tensor([1])]; - tensor input_459_groups_0 = const()[name = tensor("input_459_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109474432)))]; - tensor module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110523072)))]; - tensor input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = tensor("transpose_143")]; - tensor input_459_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv1_bias_to_fp16, dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16, x = input_457_cast_fp16)[name = tensor("input_459_cast_fp16")]; - tensor x_195_split_num_splits_0 = const()[name = tensor("x_195_split_num_splits_0"), val = tensor(2)]; - tensor x_195_split_axis_0 = const()[name = tensor("x_195_split_axis_0"), val = tensor(1)]; - tensor x_195_split_cast_fp16_0, tensor x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = tensor("x_195_split_cast_fp16")]; - tensor x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = tensor("x_195_split_1_sigmoid_cast_fp16")]; - tensor x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = tensor("x_195_cast_fp16")]; - tensor input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_453)[name = tensor("input_461_cast_fp16")]; - tensor input_463_pad_0 = const()[name = tensor("input_463_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_463_mode_0 = const()[name = tensor("input_463_mode_0"), val = tensor("constant")]; - tensor const_153_to_fp16 = const()[name = tensor("const_153_to_fp16"), val = tensor(0x0p+0)]; - tensor input_463_cast_fp16 = pad(constant_val = const_153_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = tensor("input_463_cast_fp16")]; - tensor input_465_pad_type_0 = const()[name = tensor("input_465_pad_type_0"), val = tensor("valid")]; - tensor input_465_groups_0 = const()[name = tensor("input_465_groups_0"), val = tensor(512)]; - tensor input_465_strides_0 = const()[name = tensor("input_465_strides_0"), val = tensor([1])]; - tensor input_465_pad_0 = const()[name = tensor("input_465_pad_0"), val = tensor([0, 0])]; - tensor input_465_dilations_0 = const()[name = tensor("input_465_dilations_0"), val = tensor([1])]; - tensor const_250_to_fp16 = const()[name = tensor("const_250_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110525184)))]; - tensor const_251_to_fp16 = const()[name = tensor("const_251_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110534464)))]; - tensor input_467_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_250_to_fp16, x = input_463_cast_fp16)[name = tensor("input_467_cast_fp16")]; - tensor input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = tensor("input_469_cast_fp16")]; - tensor x_197_pad_type_0 = const()[name = tensor("x_197_pad_type_0"), val = tensor("valid")]; - tensor x_197_strides_0 = const()[name = tensor("x_197_strides_0"), val = tensor([1])]; - tensor x_197_pad_0 = const()[name = tensor("x_197_pad_0"), val = tensor([0, 0])]; - tensor x_197_dilations_0 = const()[name = tensor("x_197_dilations_0"), val = tensor([1])]; - tensor x_197_groups_0 = const()[name = tensor("x_197_groups_0"), val = tensor(1)]; - tensor module_layers_8_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110535552)))]; - tensor module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111059904)))]; - tensor x_197_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv2_bias_to_fp16, dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = module_layers_8_conv_pointwise_conv2_weight_to_fp16, x = input_469_cast_fp16)[name = tensor("x_197_cast_fp16")]; - tensor input_471_perm_0 = const()[name = tensor("input_471_perm_0"), val = tensor([0, 2, 1])]; - tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = tensor("transpose_142")]; - tensor input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = tensor("input_473_cast_fp16")]; - tensor input_475_axes_0 = const()[name = tensor("input_475_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111060992)))]; - tensor module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111062080)))]; - tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = tensor("input_475_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111063168)))]; - tensor module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113160384)))]; - tensor linear_80_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16, x = input_475_cast_fp16)[name = tensor("linear_80_cast_fp16")]; - tensor input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = tensor("input_479_cast_fp16")]; - tensor module_layers_8_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113164544)))]; - tensor module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115261760)))]; - tensor linear_81_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16, x = input_479_cast_fp16)[name = tensor("linear_81_cast_fp16")]; - tensor var_1887_to_fp16 = const()[name = tensor("op_1887_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1888_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1887_to_fp16)[name = tensor("op_1888_cast_fp16")]; - tensor input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1888_cast_fp16)[name = tensor("input_485_cast_fp16")]; - tensor input_487_axes_0 = const()[name = tensor("input_487_axes_0"), val = tensor([-1])]; - tensor module_layers_8_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115262848)))]; - tensor module_layers_8_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115263936)))]; - tensor input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = tensor("input_487_cast_fp16")]; - tensor input_489_axes_0 = const()[name = tensor("input_489_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115265024)))]; - tensor module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115266112)))]; - tensor input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = tensor("input_489_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115267200)))]; - tensor module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117364416)))]; - tensor linear_82_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16, x = input_489_cast_fp16)[name = tensor("linear_82_cast_fp16")]; - tensor input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = tensor("input_493_cast_fp16")]; - tensor module_layers_9_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117368576)))]; - tensor module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119465792)))]; - tensor linear_83_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16, x = input_493_cast_fp16)[name = tensor("linear_83_cast_fp16")]; - tensor var_1918_to_fp16 = const()[name = tensor("op_1918_to_fp16"), val = tensor(0x1p-1)]; - tensor var_1919_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1918_to_fp16)[name = tensor("op_1919_cast_fp16")]; - tensor input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1919_cast_fp16)[name = tensor("input_499_cast_fp16")]; - tensor query_19_axes_0 = const()[name = tensor("query_19_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119466880)))]; - tensor module_layers_9_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119467968)))]; - tensor query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = tensor("query_19_cast_fp16")]; - tensor module_layers_9_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119469056)))]; - tensor module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119993408)))]; - tensor linear_84_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_q_bias_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_84_cast_fp16")]; - tensor var_1936 = const()[name = tensor("op_1936"), val = tensor([1, -1, 8, 64])]; - tensor q_55_cast_fp16 = reshape(shape = var_1936, x = linear_84_cast_fp16)[name = tensor("q_55_cast_fp16")]; - tensor module_layers_9_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119994496)))]; - tensor module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120518848)))]; - tensor linear_85_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_k_bias_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_85_cast_fp16")]; - tensor var_1941 = const()[name = tensor("op_1941"), val = tensor([1, -1, 8, 64])]; - tensor k_37_cast_fp16 = reshape(shape = var_1941, x = linear_85_cast_fp16)[name = tensor("k_37_cast_fp16")]; - tensor module_layers_9_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120519936)))]; - tensor module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121044288)))]; - tensor linear_86_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_v_bias_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16, x = query_19_cast_fp16)[name = tensor("linear_86_cast_fp16")]; - tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([1, -1, 8, 64])]; - tensor v_19_cast_fp16 = reshape(shape = var_1946, x = linear_86_cast_fp16)[name = tensor("v_19_cast_fp16")]; - tensor value_21_perm_0 = const()[name = tensor("value_21_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121045376)))]; - tensor var_1958_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = tensor("op_1958_cast_fp16")]; - tensor module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121046464)))]; - tensor var_1960_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = tensor("op_1960_cast_fp16")]; - tensor q_with_bias_v_19_perm_0 = const()[name = tensor("q_with_bias_v_19_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_205_transpose_x_0 = const()[name = tensor("x_205_transpose_x_0"), val = tensor(false)]; - tensor x_205_transpose_y_0 = const()[name = tensor("x_205_transpose_y_0"), val = tensor(false)]; - tensor var_1962_to_fp16 = const()[name = tensor("op_1962_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121047552)))]; - tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1960_cast_fp16)[name = tensor("transpose_140")]; - tensor x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = var_1962_to_fp16)[name = tensor("x_205_cast_fp16")]; - tensor x_207_pad_0 = const()[name = tensor("x_207_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_207_mode_0 = const()[name = tensor("x_207_mode_0"), val = tensor("constant")]; - tensor const_160_to_fp16 = const()[name = tensor("const_160_to_fp16"), val = tensor(0x0p+0)]; - tensor x_207_cast_fp16 = pad(constant_val = const_160_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = tensor("x_207_cast_fp16")]; - tensor var_1970 = const()[name = tensor("op_1970"), val = tensor([1, 8, -1, 188])]; - tensor x_209_cast_fp16 = reshape(shape = var_1970, x = x_207_cast_fp16)[name = tensor("x_209_cast_fp16")]; - tensor var_1974_begin_0 = const()[name = tensor("op_1974_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_1974_end_0 = const()[name = tensor("op_1974_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_1974_end_mask_0 = const()[name = tensor("op_1974_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_1974_cast_fp16 = slice_by_index(begin = var_1974_begin_0, end = var_1974_end_0, end_mask = var_1974_end_mask_0, x = x_209_cast_fp16)[name = tensor("op_1974_cast_fp16")]; - tensor var_1975 = const()[name = tensor("op_1975"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_37_cast_fp16 = reshape(shape = var_1975, x = var_1974_cast_fp16)[name = tensor("matrix_bd_37_cast_fp16")]; - tensor matrix_ac_19_transpose_x_0 = const()[name = tensor("matrix_ac_19_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_19_transpose_y_0 = const()[name = tensor("matrix_ac_19_transpose_y_0"), val = tensor(false)]; - tensor transpose_69_perm_0 = const()[name = tensor("transpose_69_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_70_perm_0 = const()[name = tensor("transpose_70_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = k_37_cast_fp16)[name = tensor("transpose_138")]; - tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1958_cast_fp16)[name = tensor("transpose_139")]; - tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_69, y = transpose_70)[name = tensor("matrix_ac_19_cast_fp16")]; - tensor matrix_bd_39_begin_0 = const()[name = tensor("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_39_end_0 = const()[name = tensor("matrix_bd_39_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_39_end_mask_0 = const()[name = tensor("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = tensor("matrix_bd_39_cast_fp16")]; - tensor var_1984_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = tensor("op_1984_cast_fp16")]; - tensor _inversed_scores_37_y_0_to_fp16 = const()[name = tensor("_inversed_scores_37_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_37_cast_fp16 = mul(x = var_1984_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = tensor("_inversed_scores_37_cast_fp16")]; - tensor scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_11)[name = tensor("scores_39_cast_fp16")]; - tensor var_1990_cast_fp16 = softmax(axis = var_23, x = scores_39_cast_fp16)[name = tensor("op_1990_cast_fp16")]; - tensor input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1990_cast_fp16, cond = mask_11)[name = tensor("input_501_cast_fp16")]; - tensor x_211_transpose_x_0 = const()[name = tensor("x_211_transpose_x_0"), val = tensor(false)]; - tensor x_211_transpose_y_0 = const()[name = tensor("x_211_transpose_y_0"), val = tensor(false)]; - tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = tensor("transpose_141")]; - tensor x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_21_cast_fp16)[name = tensor("x_211_cast_fp16")]; - tensor var_1994_perm_0 = const()[name = tensor("op_1994_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1995 = const()[name = tensor("op_1995"), val = tensor([1, -1, 512])]; - tensor var_1994_cast_fp16 = transpose(perm = var_1994_perm_0, x = x_211_cast_fp16)[name = tensor("transpose_137")]; - tensor input_503_cast_fp16 = reshape(shape = var_1995, x = var_1994_cast_fp16)[name = tensor("input_503_cast_fp16")]; - tensor module_layers_9_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121431616)))]; - tensor module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121955968)))]; - tensor linear_88_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_out_bias_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16, x = input_503_cast_fp16)[name = tensor("linear_88_cast_fp16")]; - tensor input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = tensor("input_507_cast_fp16")]; - tensor x_215_axes_0 = const()[name = tensor("x_215_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121957056)))]; - tensor module_layers_9_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121958144)))]; - tensor x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = tensor("x_215_cast_fp16")]; - tensor input_509_perm_0 = const()[name = tensor("input_509_perm_0"), val = tensor([0, 2, 1])]; - tensor input_511_pad_type_0 = const()[name = tensor("input_511_pad_type_0"), val = tensor("valid")]; - tensor input_511_strides_0 = const()[name = tensor("input_511_strides_0"), val = tensor([1])]; - tensor input_511_pad_0 = const()[name = tensor("input_511_pad_0"), val = tensor([0, 0])]; - tensor input_511_dilations_0 = const()[name = tensor("input_511_dilations_0"), val = tensor([1])]; - tensor input_511_groups_0 = const()[name = tensor("input_511_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121959232)))]; - tensor module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123007872)))]; - tensor input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = tensor("transpose_136")]; - tensor input_511_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv1_bias_to_fp16, dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16, x = input_509_cast_fp16)[name = tensor("input_511_cast_fp16")]; - tensor x_217_split_num_splits_0 = const()[name = tensor("x_217_split_num_splits_0"), val = tensor(2)]; - tensor x_217_split_axis_0 = const()[name = tensor("x_217_split_axis_0"), val = tensor(1)]; - tensor x_217_split_cast_fp16_0, tensor x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = tensor("x_217_split_cast_fp16")]; - tensor x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = tensor("x_217_split_1_sigmoid_cast_fp16")]; - tensor x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = tensor("x_217_cast_fp16")]; - tensor input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_453)[name = tensor("input_513_cast_fp16")]; - tensor input_515_pad_0 = const()[name = tensor("input_515_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_515_mode_0 = const()[name = tensor("input_515_mode_0"), val = tensor("constant")]; - tensor const_163_to_fp16 = const()[name = tensor("const_163_to_fp16"), val = tensor(0x0p+0)]; - tensor input_515_cast_fp16 = pad(constant_val = const_163_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = tensor("input_515_cast_fp16")]; - tensor input_517_pad_type_0 = const()[name = tensor("input_517_pad_type_0"), val = tensor("valid")]; - tensor input_517_groups_0 = const()[name = tensor("input_517_groups_0"), val = tensor(512)]; - tensor input_517_strides_0 = const()[name = tensor("input_517_strides_0"), val = tensor([1])]; - tensor input_517_pad_0 = const()[name = tensor("input_517_pad_0"), val = tensor([0, 0])]; - tensor input_517_dilations_0 = const()[name = tensor("input_517_dilations_0"), val = tensor([1])]; - tensor const_252_to_fp16 = const()[name = tensor("const_252_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123009984)))]; - tensor const_253_to_fp16 = const()[name = tensor("const_253_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123019264)))]; - tensor input_519_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_252_to_fp16, x = input_515_cast_fp16)[name = tensor("input_519_cast_fp16")]; - tensor input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = tensor("input_521_cast_fp16")]; - tensor x_219_pad_type_0 = const()[name = tensor("x_219_pad_type_0"), val = tensor("valid")]; - tensor x_219_strides_0 = const()[name = tensor("x_219_strides_0"), val = tensor([1])]; - tensor x_219_pad_0 = const()[name = tensor("x_219_pad_0"), val = tensor([0, 0])]; - tensor x_219_dilations_0 = const()[name = tensor("x_219_dilations_0"), val = tensor([1])]; - tensor x_219_groups_0 = const()[name = tensor("x_219_groups_0"), val = tensor(1)]; - tensor module_layers_9_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123020352)))]; - tensor module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123544704)))]; - tensor x_219_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv2_bias_to_fp16, dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = module_layers_9_conv_pointwise_conv2_weight_to_fp16, x = input_521_cast_fp16)[name = tensor("x_219_cast_fp16")]; - tensor input_523_perm_0 = const()[name = tensor("input_523_perm_0"), val = tensor([0, 2, 1])]; - tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = tensor("transpose_135")]; - tensor input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = tensor("input_525_cast_fp16")]; - tensor input_527_axes_0 = const()[name = tensor("input_527_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123545792)))]; - tensor module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123546880)))]; - tensor input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = tensor("input_527_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123547968)))]; - tensor module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125645184)))]; - tensor linear_89_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16, x = input_527_cast_fp16)[name = tensor("linear_89_cast_fp16")]; - tensor input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = tensor("input_531_cast_fp16")]; - tensor module_layers_9_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125649344)))]; - tensor module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127746560)))]; - tensor linear_90_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16, x = input_531_cast_fp16)[name = tensor("linear_90_cast_fp16")]; - tensor var_2061_to_fp16 = const()[name = tensor("op_2061_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2062_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2061_to_fp16)[name = tensor("op_2062_cast_fp16")]; - tensor input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_2062_cast_fp16)[name = tensor("input_537_cast_fp16")]; - tensor input_539_axes_0 = const()[name = tensor("input_539_axes_0"), val = tensor([-1])]; - tensor module_layers_9_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127747648)))]; - tensor module_layers_9_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127748736)))]; - tensor input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = tensor("input_539_cast_fp16")]; - tensor input_541_axes_0 = const()[name = tensor("input_541_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127749824)))]; - tensor module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127750912)))]; - tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = tensor("input_541_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127752000)))]; - tensor module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129849216)))]; - tensor linear_91_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16, x = input_541_cast_fp16)[name = tensor("linear_91_cast_fp16")]; - tensor input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = tensor("input_545_cast_fp16")]; - tensor module_layers_10_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129853376)))]; - tensor module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131950592)))]; - tensor linear_92_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16, x = input_545_cast_fp16)[name = tensor("linear_92_cast_fp16")]; - tensor var_2092_to_fp16 = const()[name = tensor("op_2092_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2093_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2092_to_fp16)[name = tensor("op_2093_cast_fp16")]; - tensor input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_2093_cast_fp16)[name = tensor("input_551_cast_fp16")]; - tensor query_21_axes_0 = const()[name = tensor("query_21_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131951680)))]; - tensor module_layers_10_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131952768)))]; - tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = tensor("query_21_cast_fp16")]; - tensor module_layers_10_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131953856)))]; - tensor module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132478208)))]; - tensor linear_93_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_q_bias_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_93_cast_fp16")]; - tensor var_2110 = const()[name = tensor("op_2110"), val = tensor([1, -1, 8, 64])]; - tensor q_61_cast_fp16 = reshape(shape = var_2110, x = linear_93_cast_fp16)[name = tensor("q_61_cast_fp16")]; - tensor module_layers_10_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132479296)))]; - tensor module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133003648)))]; - tensor linear_94_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_k_bias_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_94_cast_fp16")]; - tensor var_2115 = const()[name = tensor("op_2115"), val = tensor([1, -1, 8, 64])]; - tensor k_41_cast_fp16 = reshape(shape = var_2115, x = linear_94_cast_fp16)[name = tensor("k_41_cast_fp16")]; - tensor module_layers_10_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133004736)))]; - tensor module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133529088)))]; - tensor linear_95_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_v_bias_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16, x = query_21_cast_fp16)[name = tensor("linear_95_cast_fp16")]; - tensor var_2120 = const()[name = tensor("op_2120"), val = tensor([1, -1, 8, 64])]; - tensor v_21_cast_fp16 = reshape(shape = var_2120, x = linear_95_cast_fp16)[name = tensor("v_21_cast_fp16")]; - tensor value_23_perm_0 = const()[name = tensor("value_23_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133530176)))]; - tensor var_2132_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2132_cast_fp16")]; - tensor module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133531264)))]; - tensor var_2134_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2134_cast_fp16")]; - tensor q_with_bias_v_21_perm_0 = const()[name = tensor("q_with_bias_v_21_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_227_transpose_x_0 = const()[name = tensor("x_227_transpose_x_0"), val = tensor(false)]; - tensor x_227_transpose_y_0 = const()[name = tensor("x_227_transpose_y_0"), val = tensor(false)]; - tensor var_2136_to_fp16 = const()[name = tensor("op_2136_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133532352)))]; - tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2134_cast_fp16)[name = tensor("transpose_133")]; - tensor x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = var_2136_to_fp16)[name = tensor("x_227_cast_fp16")]; - tensor x_229_pad_0 = const()[name = tensor("x_229_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_229_mode_0 = const()[name = tensor("x_229_mode_0"), val = tensor("constant")]; - tensor const_170_to_fp16 = const()[name = tensor("const_170_to_fp16"), val = tensor(0x0p+0)]; - tensor x_229_cast_fp16 = pad(constant_val = const_170_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = tensor("x_229_cast_fp16")]; - tensor var_2144 = const()[name = tensor("op_2144"), val = tensor([1, 8, -1, 188])]; - tensor x_231_cast_fp16 = reshape(shape = var_2144, x = x_229_cast_fp16)[name = tensor("x_231_cast_fp16")]; - tensor var_2148_begin_0 = const()[name = tensor("op_2148_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2148_end_0 = const()[name = tensor("op_2148_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2148_end_mask_0 = const()[name = tensor("op_2148_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2148_cast_fp16 = slice_by_index(begin = var_2148_begin_0, end = var_2148_end_0, end_mask = var_2148_end_mask_0, x = x_231_cast_fp16)[name = tensor("op_2148_cast_fp16")]; - tensor var_2149 = const()[name = tensor("op_2149"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_41_cast_fp16 = reshape(shape = var_2149, x = var_2148_cast_fp16)[name = tensor("matrix_bd_41_cast_fp16")]; - tensor matrix_ac_21_transpose_x_0 = const()[name = tensor("matrix_ac_21_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_21_transpose_y_0 = const()[name = tensor("matrix_ac_21_transpose_y_0"), val = tensor(false)]; - tensor transpose_71_perm_0 = const()[name = tensor("transpose_71_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_72_perm_0 = const()[name = tensor("transpose_72_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = k_41_cast_fp16)[name = tensor("transpose_131")]; - tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = var_2132_cast_fp16)[name = tensor("transpose_132")]; - tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_71, y = transpose_72)[name = tensor("matrix_ac_21_cast_fp16")]; - tensor matrix_bd_43_begin_0 = const()[name = tensor("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_43_end_0 = const()[name = tensor("matrix_bd_43_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_43_end_mask_0 = const()[name = tensor("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = tensor("matrix_bd_43_cast_fp16")]; - tensor var_2158_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = tensor("op_2158_cast_fp16")]; - tensor _inversed_scores_41_y_0_to_fp16 = const()[name = tensor("_inversed_scores_41_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_41_cast_fp16 = mul(x = var_2158_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = tensor("_inversed_scores_41_cast_fp16")]; - tensor scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_11)[name = tensor("scores_43_cast_fp16")]; - tensor var_2164_cast_fp16 = softmax(axis = var_23, x = scores_43_cast_fp16)[name = tensor("op_2164_cast_fp16")]; - tensor input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_2164_cast_fp16, cond = mask_11)[name = tensor("input_553_cast_fp16")]; - tensor x_233_transpose_x_0 = const()[name = tensor("x_233_transpose_x_0"), val = tensor(false)]; - tensor x_233_transpose_y_0 = const()[name = tensor("x_233_transpose_y_0"), val = tensor(false)]; - tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = tensor("transpose_134")]; - tensor x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_23_cast_fp16)[name = tensor("x_233_cast_fp16")]; - tensor var_2168_perm_0 = const()[name = tensor("op_2168_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2169 = const()[name = tensor("op_2169"), val = tensor([1, -1, 512])]; - tensor var_2168_cast_fp16 = transpose(perm = var_2168_perm_0, x = x_233_cast_fp16)[name = tensor("transpose_130")]; - tensor input_555_cast_fp16 = reshape(shape = var_2169, x = var_2168_cast_fp16)[name = tensor("input_555_cast_fp16")]; - tensor module_layers_10_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133916416)))]; - tensor module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134440768)))]; - tensor linear_97_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_out_bias_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16, x = input_555_cast_fp16)[name = tensor("linear_97_cast_fp16")]; - tensor input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = tensor("input_559_cast_fp16")]; - tensor x_237_axes_0 = const()[name = tensor("x_237_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134441856)))]; - tensor module_layers_10_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134442944)))]; - tensor x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = tensor("x_237_cast_fp16")]; - tensor input_561_perm_0 = const()[name = tensor("input_561_perm_0"), val = tensor([0, 2, 1])]; - tensor input_563_pad_type_0 = const()[name = tensor("input_563_pad_type_0"), val = tensor("valid")]; - tensor input_563_strides_0 = const()[name = tensor("input_563_strides_0"), val = tensor([1])]; - tensor input_563_pad_0 = const()[name = tensor("input_563_pad_0"), val = tensor([0, 0])]; - tensor input_563_dilations_0 = const()[name = tensor("input_563_dilations_0"), val = tensor([1])]; - tensor input_563_groups_0 = const()[name = tensor("input_563_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134444032)))]; - tensor module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135492672)))]; - tensor input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = tensor("transpose_129")]; - tensor input_563_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv1_bias_to_fp16, dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16, x = input_561_cast_fp16)[name = tensor("input_563_cast_fp16")]; - tensor x_239_split_num_splits_0 = const()[name = tensor("x_239_split_num_splits_0"), val = tensor(2)]; - tensor x_239_split_axis_0 = const()[name = tensor("x_239_split_axis_0"), val = tensor(1)]; - tensor x_239_split_cast_fp16_0, tensor x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = tensor("x_239_split_cast_fp16")]; - tensor x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = tensor("x_239_split_1_sigmoid_cast_fp16")]; - tensor x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = tensor("x_239_cast_fp16")]; - tensor input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_453)[name = tensor("input_565_cast_fp16")]; - tensor input_567_pad_0 = const()[name = tensor("input_567_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_567_mode_0 = const()[name = tensor("input_567_mode_0"), val = tensor("constant")]; - tensor const_173_to_fp16 = const()[name = tensor("const_173_to_fp16"), val = tensor(0x0p+0)]; - tensor input_567_cast_fp16 = pad(constant_val = const_173_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = tensor("input_567_cast_fp16")]; - tensor input_569_pad_type_0 = const()[name = tensor("input_569_pad_type_0"), val = tensor("valid")]; - tensor input_569_groups_0 = const()[name = tensor("input_569_groups_0"), val = tensor(512)]; - tensor input_569_strides_0 = const()[name = tensor("input_569_strides_0"), val = tensor([1])]; - tensor input_569_pad_0 = const()[name = tensor("input_569_pad_0"), val = tensor([0, 0])]; - tensor input_569_dilations_0 = const()[name = tensor("input_569_dilations_0"), val = tensor([1])]; - tensor const_254_to_fp16 = const()[name = tensor("const_254_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135494784)))]; - tensor const_255_to_fp16 = const()[name = tensor("const_255_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135504064)))]; - tensor input_571_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_254_to_fp16, x = input_567_cast_fp16)[name = tensor("input_571_cast_fp16")]; - tensor input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = tensor("input_573_cast_fp16")]; - tensor x_241_pad_type_0 = const()[name = tensor("x_241_pad_type_0"), val = tensor("valid")]; - tensor x_241_strides_0 = const()[name = tensor("x_241_strides_0"), val = tensor([1])]; - tensor x_241_pad_0 = const()[name = tensor("x_241_pad_0"), val = tensor([0, 0])]; - tensor x_241_dilations_0 = const()[name = tensor("x_241_dilations_0"), val = tensor([1])]; - tensor x_241_groups_0 = const()[name = tensor("x_241_groups_0"), val = tensor(1)]; - tensor module_layers_10_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135505152)))]; - tensor module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136029504)))]; - tensor x_241_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv2_bias_to_fp16, dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = module_layers_10_conv_pointwise_conv2_weight_to_fp16, x = input_573_cast_fp16)[name = tensor("x_241_cast_fp16")]; - tensor input_575_perm_0 = const()[name = tensor("input_575_perm_0"), val = tensor([0, 2, 1])]; - tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = tensor("transpose_128")]; - tensor input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = tensor("input_577_cast_fp16")]; - tensor input_579_axes_0 = const()[name = tensor("input_579_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136030592)))]; - tensor module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136031680)))]; - tensor input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = tensor("input_579_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136032768)))]; - tensor module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138129984)))]; - tensor linear_98_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16, x = input_579_cast_fp16)[name = tensor("linear_98_cast_fp16")]; - tensor input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = tensor("input_583_cast_fp16")]; - tensor module_layers_10_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138134144)))]; - tensor module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140231360)))]; - tensor linear_99_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16, x = input_583_cast_fp16)[name = tensor("linear_99_cast_fp16")]; - tensor var_2235_to_fp16 = const()[name = tensor("op_2235_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2236_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2235_to_fp16)[name = tensor("op_2236_cast_fp16")]; - tensor input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_2236_cast_fp16)[name = tensor("input_589_cast_fp16")]; - tensor input_591_axes_0 = const()[name = tensor("input_591_axes_0"), val = tensor([-1])]; - tensor module_layers_10_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140232448)))]; - tensor module_layers_10_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140233536)))]; - tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = tensor("input_591_cast_fp16")]; - tensor input_593_axes_0 = const()[name = tensor("input_593_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140234624)))]; - tensor module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140235712)))]; - tensor input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = tensor("input_593_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140236800)))]; - tensor module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142334016)))]; - tensor linear_100_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16, x = input_593_cast_fp16)[name = tensor("linear_100_cast_fp16")]; - tensor input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = tensor("input_597_cast_fp16")]; - tensor module_layers_11_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142338176)))]; - tensor module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144435392)))]; - tensor linear_101_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16, x = input_597_cast_fp16)[name = tensor("linear_101_cast_fp16")]; - tensor var_2266_to_fp16 = const()[name = tensor("op_2266_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2267_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2266_to_fp16)[name = tensor("op_2267_cast_fp16")]; - tensor input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2267_cast_fp16)[name = tensor("input_603_cast_fp16")]; - tensor query_23_axes_0 = const()[name = tensor("query_23_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144436480)))]; - tensor module_layers_11_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144437568)))]; - tensor query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = tensor("query_23_cast_fp16")]; - tensor module_layers_11_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144438656)))]; - tensor module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144963008)))]; - tensor linear_102_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_q_bias_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_102_cast_fp16")]; - tensor var_2284 = const()[name = tensor("op_2284"), val = tensor([1, -1, 8, 64])]; - tensor q_67_cast_fp16 = reshape(shape = var_2284, x = linear_102_cast_fp16)[name = tensor("q_67_cast_fp16")]; - tensor module_layers_11_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144964096)))]; - tensor module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145488448)))]; - tensor linear_103_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_k_bias_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_103_cast_fp16")]; - tensor var_2289 = const()[name = tensor("op_2289"), val = tensor([1, -1, 8, 64])]; - tensor k_45_cast_fp16 = reshape(shape = var_2289, x = linear_103_cast_fp16)[name = tensor("k_45_cast_fp16")]; - tensor module_layers_11_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145489536)))]; - tensor module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146013888)))]; - tensor linear_104_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_v_bias_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16, x = query_23_cast_fp16)[name = tensor("linear_104_cast_fp16")]; - tensor var_2294 = const()[name = tensor("op_2294"), val = tensor([1, -1, 8, 64])]; - tensor v_23_cast_fp16 = reshape(shape = var_2294, x = linear_104_cast_fp16)[name = tensor("v_23_cast_fp16")]; - tensor value_25_perm_0 = const()[name = tensor("value_25_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146014976)))]; - tensor var_2306_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2306_cast_fp16")]; - tensor module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146016064)))]; - tensor var_2308_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2308_cast_fp16")]; - tensor q_with_bias_v_23_perm_0 = const()[name = tensor("q_with_bias_v_23_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_249_transpose_x_0 = const()[name = tensor("x_249_transpose_x_0"), val = tensor(false)]; - tensor x_249_transpose_y_0 = const()[name = tensor("x_249_transpose_y_0"), val = tensor(false)]; - tensor var_2310_to_fp16 = const()[name = tensor("op_2310_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146017152)))]; - tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2308_cast_fp16)[name = tensor("transpose_126")]; - tensor x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = var_2310_to_fp16)[name = tensor("x_249_cast_fp16")]; - tensor x_251_pad_0 = const()[name = tensor("x_251_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_251_mode_0 = const()[name = tensor("x_251_mode_0"), val = tensor("constant")]; - tensor const_180_to_fp16 = const()[name = tensor("const_180_to_fp16"), val = tensor(0x0p+0)]; - tensor x_251_cast_fp16 = pad(constant_val = const_180_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = tensor("x_251_cast_fp16")]; - tensor var_2318 = const()[name = tensor("op_2318"), val = tensor([1, 8, -1, 188])]; - tensor x_253_cast_fp16 = reshape(shape = var_2318, x = x_251_cast_fp16)[name = tensor("x_253_cast_fp16")]; - tensor var_2322_begin_0 = const()[name = tensor("op_2322_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2322_end_0 = const()[name = tensor("op_2322_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2322_end_mask_0 = const()[name = tensor("op_2322_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2322_cast_fp16 = slice_by_index(begin = var_2322_begin_0, end = var_2322_end_0, end_mask = var_2322_end_mask_0, x = x_253_cast_fp16)[name = tensor("op_2322_cast_fp16")]; - tensor var_2323 = const()[name = tensor("op_2323"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2323, x = var_2322_cast_fp16)[name = tensor("matrix_bd_45_cast_fp16")]; - tensor matrix_ac_23_transpose_x_0 = const()[name = tensor("matrix_ac_23_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_23_transpose_y_0 = const()[name = tensor("matrix_ac_23_transpose_y_0"), val = tensor(false)]; - tensor transpose_73_perm_0 = const()[name = tensor("transpose_73_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_74_perm_0 = const()[name = tensor("transpose_74_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = k_45_cast_fp16)[name = tensor("transpose_124")]; - tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_2306_cast_fp16)[name = tensor("transpose_125")]; - tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_73, y = transpose_74)[name = tensor("matrix_ac_23_cast_fp16")]; - tensor matrix_bd_47_begin_0 = const()[name = tensor("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_47_end_0 = const()[name = tensor("matrix_bd_47_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_47_end_mask_0 = const()[name = tensor("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = tensor("matrix_bd_47_cast_fp16")]; - tensor var_2332_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = tensor("op_2332_cast_fp16")]; - tensor _inversed_scores_45_y_0_to_fp16 = const()[name = tensor("_inversed_scores_45_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_45_cast_fp16 = mul(x = var_2332_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = tensor("_inversed_scores_45_cast_fp16")]; - tensor scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_11)[name = tensor("scores_47_cast_fp16")]; - tensor var_2338_cast_fp16 = softmax(axis = var_23, x = scores_47_cast_fp16)[name = tensor("op_2338_cast_fp16")]; - tensor input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2338_cast_fp16, cond = mask_11)[name = tensor("input_605_cast_fp16")]; - tensor x_255_transpose_x_0 = const()[name = tensor("x_255_transpose_x_0"), val = tensor(false)]; - tensor x_255_transpose_y_0 = const()[name = tensor("x_255_transpose_y_0"), val = tensor(false)]; - tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = tensor("transpose_127")]; - tensor x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_25_cast_fp16)[name = tensor("x_255_cast_fp16")]; - tensor var_2342_perm_0 = const()[name = tensor("op_2342_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2343 = const()[name = tensor("op_2343"), val = tensor([1, -1, 512])]; - tensor var_2342_cast_fp16 = transpose(perm = var_2342_perm_0, x = x_255_cast_fp16)[name = tensor("transpose_123")]; - tensor input_607_cast_fp16 = reshape(shape = var_2343, x = var_2342_cast_fp16)[name = tensor("input_607_cast_fp16")]; - tensor module_layers_11_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146401216)))]; - tensor module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146925568)))]; - tensor linear_106_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_out_bias_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16, x = input_607_cast_fp16)[name = tensor("linear_106_cast_fp16")]; - tensor input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = tensor("input_611_cast_fp16")]; - tensor x_259_axes_0 = const()[name = tensor("x_259_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146926656)))]; - tensor module_layers_11_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146927744)))]; - tensor x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = tensor("x_259_cast_fp16")]; - tensor input_613_perm_0 = const()[name = tensor("input_613_perm_0"), val = tensor([0, 2, 1])]; - tensor input_615_pad_type_0 = const()[name = tensor("input_615_pad_type_0"), val = tensor("valid")]; - tensor input_615_strides_0 = const()[name = tensor("input_615_strides_0"), val = tensor([1])]; - tensor input_615_pad_0 = const()[name = tensor("input_615_pad_0"), val = tensor([0, 0])]; - tensor input_615_dilations_0 = const()[name = tensor("input_615_dilations_0"), val = tensor([1])]; - tensor input_615_groups_0 = const()[name = tensor("input_615_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146928832)))]; - tensor module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147977472)))]; - tensor input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = tensor("transpose_122")]; - tensor input_615_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv1_bias_to_fp16, dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16, x = input_613_cast_fp16)[name = tensor("input_615_cast_fp16")]; - tensor x_261_split_num_splits_0 = const()[name = tensor("x_261_split_num_splits_0"), val = tensor(2)]; - tensor x_261_split_axis_0 = const()[name = tensor("x_261_split_axis_0"), val = tensor(1)]; - tensor x_261_split_cast_fp16_0, tensor x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = tensor("x_261_split_cast_fp16")]; - tensor x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = tensor("x_261_split_1_sigmoid_cast_fp16")]; - tensor x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = tensor("x_261_cast_fp16")]; - tensor input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_453)[name = tensor("input_617_cast_fp16")]; - tensor input_619_pad_0 = const()[name = tensor("input_619_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_619_mode_0 = const()[name = tensor("input_619_mode_0"), val = tensor("constant")]; - tensor const_183_to_fp16 = const()[name = tensor("const_183_to_fp16"), val = tensor(0x0p+0)]; - tensor input_619_cast_fp16 = pad(constant_val = const_183_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = tensor("input_619_cast_fp16")]; - tensor input_621_pad_type_0 = const()[name = tensor("input_621_pad_type_0"), val = tensor("valid")]; - tensor input_621_groups_0 = const()[name = tensor("input_621_groups_0"), val = tensor(512)]; - tensor input_621_strides_0 = const()[name = tensor("input_621_strides_0"), val = tensor([1])]; - tensor input_621_pad_0 = const()[name = tensor("input_621_pad_0"), val = tensor([0, 0])]; - tensor input_621_dilations_0 = const()[name = tensor("input_621_dilations_0"), val = tensor([1])]; - tensor const_256_to_fp16 = const()[name = tensor("const_256_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147979584)))]; - tensor const_257_to_fp16 = const()[name = tensor("const_257_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147988864)))]; - tensor input_623_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_256_to_fp16, x = input_619_cast_fp16)[name = tensor("input_623_cast_fp16")]; - tensor input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = tensor("input_625_cast_fp16")]; - tensor x_263_pad_type_0 = const()[name = tensor("x_263_pad_type_0"), val = tensor("valid")]; - tensor x_263_strides_0 = const()[name = tensor("x_263_strides_0"), val = tensor([1])]; - tensor x_263_pad_0 = const()[name = tensor("x_263_pad_0"), val = tensor([0, 0])]; - tensor x_263_dilations_0 = const()[name = tensor("x_263_dilations_0"), val = tensor([1])]; - tensor x_263_groups_0 = const()[name = tensor("x_263_groups_0"), val = tensor(1)]; - tensor module_layers_11_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147989952)))]; - tensor module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148514304)))]; - tensor x_263_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv2_bias_to_fp16, dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = module_layers_11_conv_pointwise_conv2_weight_to_fp16, x = input_625_cast_fp16)[name = tensor("x_263_cast_fp16")]; - tensor input_627_perm_0 = const()[name = tensor("input_627_perm_0"), val = tensor([0, 2, 1])]; - tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = tensor("transpose_121")]; - tensor input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = tensor("input_629_cast_fp16")]; - tensor input_631_axes_0 = const()[name = tensor("input_631_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148515392)))]; - tensor module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148516480)))]; - tensor input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = tensor("input_631_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148517568)))]; - tensor module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150614784)))]; - tensor linear_107_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16, x = input_631_cast_fp16)[name = tensor("linear_107_cast_fp16")]; - tensor input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = tensor("input_635_cast_fp16")]; - tensor module_layers_11_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150618944)))]; - tensor module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152716160)))]; - tensor linear_108_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16, x = input_635_cast_fp16)[name = tensor("linear_108_cast_fp16")]; - tensor var_2409_to_fp16 = const()[name = tensor("op_2409_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2410_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2409_to_fp16)[name = tensor("op_2410_cast_fp16")]; - tensor input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2410_cast_fp16)[name = tensor("input_641_cast_fp16")]; - tensor input_643_axes_0 = const()[name = tensor("input_643_axes_0"), val = tensor([-1])]; - tensor module_layers_11_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152717248)))]; - tensor module_layers_11_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152718336)))]; - tensor input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = tensor("input_643_cast_fp16")]; - tensor input_645_axes_0 = const()[name = tensor("input_645_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152719424)))]; - tensor module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152720512)))]; - tensor input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = tensor("input_645_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152721600)))]; - tensor module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154818816)))]; - tensor linear_109_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16, x = input_645_cast_fp16)[name = tensor("linear_109_cast_fp16")]; - tensor input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = tensor("input_649_cast_fp16")]; - tensor module_layers_12_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154822976)))]; - tensor module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156920192)))]; - tensor linear_110_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16, x = input_649_cast_fp16)[name = tensor("linear_110_cast_fp16")]; - tensor var_2440_to_fp16 = const()[name = tensor("op_2440_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2441_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2440_to_fp16)[name = tensor("op_2441_cast_fp16")]; - tensor input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2441_cast_fp16)[name = tensor("input_655_cast_fp16")]; - tensor query_25_axes_0 = const()[name = tensor("query_25_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156921280)))]; - tensor module_layers_12_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156922368)))]; - tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = tensor("query_25_cast_fp16")]; - tensor module_layers_12_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156923456)))]; - tensor module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157447808)))]; - tensor linear_111_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_q_bias_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_111_cast_fp16")]; - tensor var_2458 = const()[name = tensor("op_2458"), val = tensor([1, -1, 8, 64])]; - tensor q_73_cast_fp16 = reshape(shape = var_2458, x = linear_111_cast_fp16)[name = tensor("q_73_cast_fp16")]; - tensor module_layers_12_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157448896)))]; - tensor module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157973248)))]; - tensor linear_112_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_k_bias_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_112_cast_fp16")]; - tensor var_2463 = const()[name = tensor("op_2463"), val = tensor([1, -1, 8, 64])]; - tensor k_49_cast_fp16 = reshape(shape = var_2463, x = linear_112_cast_fp16)[name = tensor("k_49_cast_fp16")]; - tensor module_layers_12_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157974336)))]; - tensor module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158498688)))]; - tensor linear_113_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_v_bias_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16, x = query_25_cast_fp16)[name = tensor("linear_113_cast_fp16")]; - tensor var_2468 = const()[name = tensor("op_2468"), val = tensor([1, -1, 8, 64])]; - tensor v_25_cast_fp16 = reshape(shape = var_2468, x = linear_113_cast_fp16)[name = tensor("v_25_cast_fp16")]; - tensor value_27_perm_0 = const()[name = tensor("value_27_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158499776)))]; - tensor var_2480_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2480_cast_fp16")]; - tensor module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158500864)))]; - tensor var_2482_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2482_cast_fp16")]; - tensor q_with_bias_v_25_perm_0 = const()[name = tensor("q_with_bias_v_25_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_271_transpose_x_0 = const()[name = tensor("x_271_transpose_x_0"), val = tensor(false)]; - tensor x_271_transpose_y_0 = const()[name = tensor("x_271_transpose_y_0"), val = tensor(false)]; - tensor var_2484_to_fp16 = const()[name = tensor("op_2484_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158501952)))]; - tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2482_cast_fp16)[name = tensor("transpose_119")]; - tensor x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = var_2484_to_fp16)[name = tensor("x_271_cast_fp16")]; - tensor x_273_pad_0 = const()[name = tensor("x_273_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_273_mode_0 = const()[name = tensor("x_273_mode_0"), val = tensor("constant")]; - tensor const_190_to_fp16 = const()[name = tensor("const_190_to_fp16"), val = tensor(0x0p+0)]; - tensor x_273_cast_fp16 = pad(constant_val = const_190_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = tensor("x_273_cast_fp16")]; - tensor var_2492 = const()[name = tensor("op_2492"), val = tensor([1, 8, -1, 188])]; - tensor x_275_cast_fp16 = reshape(shape = var_2492, x = x_273_cast_fp16)[name = tensor("x_275_cast_fp16")]; - tensor var_2496_begin_0 = const()[name = tensor("op_2496_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2496_end_0 = const()[name = tensor("op_2496_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2496_end_mask_0 = const()[name = tensor("op_2496_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2496_cast_fp16 = slice_by_index(begin = var_2496_begin_0, end = var_2496_end_0, end_mask = var_2496_end_mask_0, x = x_275_cast_fp16)[name = tensor("op_2496_cast_fp16")]; - tensor var_2497 = const()[name = tensor("op_2497"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_49_cast_fp16 = reshape(shape = var_2497, x = var_2496_cast_fp16)[name = tensor("matrix_bd_49_cast_fp16")]; - tensor matrix_ac_25_transpose_x_0 = const()[name = tensor("matrix_ac_25_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_25_transpose_y_0 = const()[name = tensor("matrix_ac_25_transpose_y_0"), val = tensor(false)]; - tensor transpose_75_perm_0 = const()[name = tensor("transpose_75_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_76_perm_0 = const()[name = tensor("transpose_76_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = k_49_cast_fp16)[name = tensor("transpose_117")]; - tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_2480_cast_fp16)[name = tensor("transpose_118")]; - tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_75, y = transpose_76)[name = tensor("matrix_ac_25_cast_fp16")]; - tensor matrix_bd_51_begin_0 = const()[name = tensor("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_51_end_0 = const()[name = tensor("matrix_bd_51_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_51_end_mask_0 = const()[name = tensor("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = tensor("matrix_bd_51_cast_fp16")]; - tensor var_2506_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = tensor("op_2506_cast_fp16")]; - tensor _inversed_scores_49_y_0_to_fp16 = const()[name = tensor("_inversed_scores_49_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_49_cast_fp16 = mul(x = var_2506_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = tensor("_inversed_scores_49_cast_fp16")]; - tensor scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_11)[name = tensor("scores_51_cast_fp16")]; - tensor var_2512_cast_fp16 = softmax(axis = var_23, x = scores_51_cast_fp16)[name = tensor("op_2512_cast_fp16")]; - tensor input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2512_cast_fp16, cond = mask_11)[name = tensor("input_657_cast_fp16")]; - tensor x_277_transpose_x_0 = const()[name = tensor("x_277_transpose_x_0"), val = tensor(false)]; - tensor x_277_transpose_y_0 = const()[name = tensor("x_277_transpose_y_0"), val = tensor(false)]; - tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = tensor("transpose_120")]; - tensor x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_27_cast_fp16)[name = tensor("x_277_cast_fp16")]; - tensor var_2516_perm_0 = const()[name = tensor("op_2516_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2517 = const()[name = tensor("op_2517"), val = tensor([1, -1, 512])]; - tensor var_2516_cast_fp16 = transpose(perm = var_2516_perm_0, x = x_277_cast_fp16)[name = tensor("transpose_116")]; - tensor input_659_cast_fp16 = reshape(shape = var_2517, x = var_2516_cast_fp16)[name = tensor("input_659_cast_fp16")]; - tensor module_layers_12_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158886016)))]; - tensor module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159410368)))]; - tensor linear_115_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_out_bias_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16, x = input_659_cast_fp16)[name = tensor("linear_115_cast_fp16")]; - tensor input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = tensor("input_663_cast_fp16")]; - tensor x_281_axes_0 = const()[name = tensor("x_281_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159411456)))]; - tensor module_layers_12_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159412544)))]; - tensor x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = tensor("x_281_cast_fp16")]; - tensor input_665_perm_0 = const()[name = tensor("input_665_perm_0"), val = tensor([0, 2, 1])]; - tensor input_667_pad_type_0 = const()[name = tensor("input_667_pad_type_0"), val = tensor("valid")]; - tensor input_667_strides_0 = const()[name = tensor("input_667_strides_0"), val = tensor([1])]; - tensor input_667_pad_0 = const()[name = tensor("input_667_pad_0"), val = tensor([0, 0])]; - tensor input_667_dilations_0 = const()[name = tensor("input_667_dilations_0"), val = tensor([1])]; - tensor input_667_groups_0 = const()[name = tensor("input_667_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159413632)))]; - tensor module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160462272)))]; - tensor input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = tensor("transpose_115")]; - tensor input_667_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv1_bias_to_fp16, dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16, x = input_665_cast_fp16)[name = tensor("input_667_cast_fp16")]; - tensor x_283_split_num_splits_0 = const()[name = tensor("x_283_split_num_splits_0"), val = tensor(2)]; - tensor x_283_split_axis_0 = const()[name = tensor("x_283_split_axis_0"), val = tensor(1)]; - tensor x_283_split_cast_fp16_0, tensor x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = tensor("x_283_split_cast_fp16")]; - tensor x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = tensor("x_283_split_1_sigmoid_cast_fp16")]; - tensor x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = tensor("x_283_cast_fp16")]; - tensor input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_453)[name = tensor("input_669_cast_fp16")]; - tensor input_671_pad_0 = const()[name = tensor("input_671_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_671_mode_0 = const()[name = tensor("input_671_mode_0"), val = tensor("constant")]; - tensor const_193_to_fp16 = const()[name = tensor("const_193_to_fp16"), val = tensor(0x0p+0)]; - tensor input_671_cast_fp16 = pad(constant_val = const_193_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = tensor("input_671_cast_fp16")]; - tensor input_673_pad_type_0 = const()[name = tensor("input_673_pad_type_0"), val = tensor("valid")]; - tensor input_673_groups_0 = const()[name = tensor("input_673_groups_0"), val = tensor(512)]; - tensor input_673_strides_0 = const()[name = tensor("input_673_strides_0"), val = tensor([1])]; - tensor input_673_pad_0 = const()[name = tensor("input_673_pad_0"), val = tensor([0, 0])]; - tensor input_673_dilations_0 = const()[name = tensor("input_673_dilations_0"), val = tensor([1])]; - tensor const_258_to_fp16 = const()[name = tensor("const_258_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160464384)))]; - tensor const_259_to_fp16 = const()[name = tensor("const_259_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160473664)))]; - tensor input_675_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_258_to_fp16, x = input_671_cast_fp16)[name = tensor("input_675_cast_fp16")]; - tensor input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = tensor("input_677_cast_fp16")]; - tensor x_285_pad_type_0 = const()[name = tensor("x_285_pad_type_0"), val = tensor("valid")]; - tensor x_285_strides_0 = const()[name = tensor("x_285_strides_0"), val = tensor([1])]; - tensor x_285_pad_0 = const()[name = tensor("x_285_pad_0"), val = tensor([0, 0])]; - tensor x_285_dilations_0 = const()[name = tensor("x_285_dilations_0"), val = tensor([1])]; - tensor x_285_groups_0 = const()[name = tensor("x_285_groups_0"), val = tensor(1)]; - tensor module_layers_12_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160474752)))]; - tensor module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160999104)))]; - tensor x_285_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv2_bias_to_fp16, dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = module_layers_12_conv_pointwise_conv2_weight_to_fp16, x = input_677_cast_fp16)[name = tensor("x_285_cast_fp16")]; - tensor input_679_perm_0 = const()[name = tensor("input_679_perm_0"), val = tensor([0, 2, 1])]; - tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = tensor("transpose_114")]; - tensor input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = tensor("input_681_cast_fp16")]; - tensor input_683_axes_0 = const()[name = tensor("input_683_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161000192)))]; - tensor module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161001280)))]; - tensor input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = tensor("input_683_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161002368)))]; - tensor module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163099584)))]; - tensor linear_116_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16, x = input_683_cast_fp16)[name = tensor("linear_116_cast_fp16")]; - tensor input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = tensor("input_687_cast_fp16")]; - tensor module_layers_12_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163103744)))]; - tensor module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165200960)))]; - tensor linear_117_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16, x = input_687_cast_fp16)[name = tensor("linear_117_cast_fp16")]; - tensor var_2583_to_fp16 = const()[name = tensor("op_2583_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2584_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2583_to_fp16)[name = tensor("op_2584_cast_fp16")]; - tensor input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2584_cast_fp16)[name = tensor("input_693_cast_fp16")]; - tensor input_695_axes_0 = const()[name = tensor("input_695_axes_0"), val = tensor([-1])]; - tensor module_layers_12_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165202048)))]; - tensor module_layers_12_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165203136)))]; - tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = tensor("input_695_cast_fp16")]; - tensor input_697_axes_0 = const()[name = tensor("input_697_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165204224)))]; - tensor module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165205312)))]; - tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = tensor("input_697_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165206400)))]; - tensor module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167303616)))]; - tensor linear_118_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16, x = input_697_cast_fp16)[name = tensor("linear_118_cast_fp16")]; - tensor input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = tensor("input_701_cast_fp16")]; - tensor module_layers_13_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167307776)))]; - tensor module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169404992)))]; - tensor linear_119_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16, x = input_701_cast_fp16)[name = tensor("linear_119_cast_fp16")]; - tensor var_2614_to_fp16 = const()[name = tensor("op_2614_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2615_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2614_to_fp16)[name = tensor("op_2615_cast_fp16")]; - tensor input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2615_cast_fp16)[name = tensor("input_707_cast_fp16")]; - tensor query_27_axes_0 = const()[name = tensor("query_27_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169406080)))]; - tensor module_layers_13_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169407168)))]; - tensor query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = tensor("query_27_cast_fp16")]; - tensor module_layers_13_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169408256)))]; - tensor module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169932608)))]; - tensor linear_120_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_q_bias_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_120_cast_fp16")]; - tensor var_2632 = const()[name = tensor("op_2632"), val = tensor([1, -1, 8, 64])]; - tensor q_79_cast_fp16 = reshape(shape = var_2632, x = linear_120_cast_fp16)[name = tensor("q_79_cast_fp16")]; - tensor module_layers_13_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169933696)))]; - tensor module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170458048)))]; - tensor linear_121_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_k_bias_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_121_cast_fp16")]; - tensor var_2637 = const()[name = tensor("op_2637"), val = tensor([1, -1, 8, 64])]; - tensor k_53_cast_fp16 = reshape(shape = var_2637, x = linear_121_cast_fp16)[name = tensor("k_53_cast_fp16")]; - tensor module_layers_13_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170459136)))]; - tensor module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170983488)))]; - tensor linear_122_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_v_bias_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16, x = query_27_cast_fp16)[name = tensor("linear_122_cast_fp16")]; - tensor var_2642 = const()[name = tensor("op_2642"), val = tensor([1, -1, 8, 64])]; - tensor v_27_cast_fp16 = reshape(shape = var_2642, x = linear_122_cast_fp16)[name = tensor("v_27_cast_fp16")]; - tensor value_29_perm_0 = const()[name = tensor("value_29_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170984576)))]; - tensor var_2654_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2654_cast_fp16")]; - tensor module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170985664)))]; - tensor var_2656_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2656_cast_fp16")]; - tensor q_with_bias_v_27_perm_0 = const()[name = tensor("q_with_bias_v_27_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_293_transpose_x_0 = const()[name = tensor("x_293_transpose_x_0"), val = tensor(false)]; - tensor x_293_transpose_y_0 = const()[name = tensor("x_293_transpose_y_0"), val = tensor(false)]; - tensor var_2658_to_fp16 = const()[name = tensor("op_2658_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170986752)))]; - tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2656_cast_fp16)[name = tensor("transpose_112")]; - tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = var_2658_to_fp16)[name = tensor("x_293_cast_fp16")]; - tensor x_295_pad_0 = const()[name = tensor("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_295_mode_0 = const()[name = tensor("x_295_mode_0"), val = tensor("constant")]; - tensor const_200_to_fp16 = const()[name = tensor("const_200_to_fp16"), val = tensor(0x0p+0)]; - tensor x_295_cast_fp16 = pad(constant_val = const_200_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = tensor("x_295_cast_fp16")]; - tensor var_2666 = const()[name = tensor("op_2666"), val = tensor([1, 8, -1, 188])]; - tensor x_297_cast_fp16 = reshape(shape = var_2666, x = x_295_cast_fp16)[name = tensor("x_297_cast_fp16")]; - tensor var_2670_begin_0 = const()[name = tensor("op_2670_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2670_end_0 = const()[name = tensor("op_2670_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2670_end_mask_0 = const()[name = tensor("op_2670_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2670_cast_fp16 = slice_by_index(begin = var_2670_begin_0, end = var_2670_end_0, end_mask = var_2670_end_mask_0, x = x_297_cast_fp16)[name = tensor("op_2670_cast_fp16")]; - tensor var_2671 = const()[name = tensor("op_2671"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_53_cast_fp16 = reshape(shape = var_2671, x = var_2670_cast_fp16)[name = tensor("matrix_bd_53_cast_fp16")]; - tensor matrix_ac_27_transpose_x_0 = const()[name = tensor("matrix_ac_27_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_27_transpose_y_0 = const()[name = tensor("matrix_ac_27_transpose_y_0"), val = tensor(false)]; - tensor transpose_77_perm_0 = const()[name = tensor("transpose_77_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_78_perm_0 = const()[name = tensor("transpose_78_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = k_53_cast_fp16)[name = tensor("transpose_110")]; - tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = var_2654_cast_fp16)[name = tensor("transpose_111")]; - tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_77, y = transpose_78)[name = tensor("matrix_ac_27_cast_fp16")]; - tensor matrix_bd_55_begin_0 = const()[name = tensor("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_55_end_0 = const()[name = tensor("matrix_bd_55_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_55_end_mask_0 = const()[name = tensor("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = tensor("matrix_bd_55_cast_fp16")]; - tensor var_2680_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = tensor("op_2680_cast_fp16")]; - tensor _inversed_scores_53_y_0_to_fp16 = const()[name = tensor("_inversed_scores_53_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_53_cast_fp16 = mul(x = var_2680_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = tensor("_inversed_scores_53_cast_fp16")]; - tensor scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_11)[name = tensor("scores_55_cast_fp16")]; - tensor var_2686_cast_fp16 = softmax(axis = var_23, x = scores_55_cast_fp16)[name = tensor("op_2686_cast_fp16")]; - tensor input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2686_cast_fp16, cond = mask_11)[name = tensor("input_709_cast_fp16")]; - tensor x_299_transpose_x_0 = const()[name = tensor("x_299_transpose_x_0"), val = tensor(false)]; - tensor x_299_transpose_y_0 = const()[name = tensor("x_299_transpose_y_0"), val = tensor(false)]; - tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = tensor("transpose_113")]; - tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_29_cast_fp16)[name = tensor("x_299_cast_fp16")]; - tensor var_2690_perm_0 = const()[name = tensor("op_2690_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2691 = const()[name = tensor("op_2691"), val = tensor([1, -1, 512])]; - tensor var_2690_cast_fp16 = transpose(perm = var_2690_perm_0, x = x_299_cast_fp16)[name = tensor("transpose_109")]; - tensor input_711_cast_fp16 = reshape(shape = var_2691, x = var_2690_cast_fp16)[name = tensor("input_711_cast_fp16")]; - tensor module_layers_13_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171370816)))]; - tensor module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171895168)))]; - tensor linear_124_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_out_bias_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16, x = input_711_cast_fp16)[name = tensor("linear_124_cast_fp16")]; - tensor input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = tensor("input_715_cast_fp16")]; - tensor x_303_axes_0 = const()[name = tensor("x_303_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171896256)))]; - tensor module_layers_13_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171897344)))]; - tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = tensor("x_303_cast_fp16")]; - tensor input_717_perm_0 = const()[name = tensor("input_717_perm_0"), val = tensor([0, 2, 1])]; - tensor input_719_pad_type_0 = const()[name = tensor("input_719_pad_type_0"), val = tensor("valid")]; - tensor input_719_strides_0 = const()[name = tensor("input_719_strides_0"), val = tensor([1])]; - tensor input_719_pad_0 = const()[name = tensor("input_719_pad_0"), val = tensor([0, 0])]; - tensor input_719_dilations_0 = const()[name = tensor("input_719_dilations_0"), val = tensor([1])]; - tensor input_719_groups_0 = const()[name = tensor("input_719_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171898432)))]; - tensor module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172947072)))]; - tensor input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = tensor("transpose_108")]; - tensor input_719_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv1_bias_to_fp16, dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16, x = input_717_cast_fp16)[name = tensor("input_719_cast_fp16")]; - tensor x_305_split_num_splits_0 = const()[name = tensor("x_305_split_num_splits_0"), val = tensor(2)]; - tensor x_305_split_axis_0 = const()[name = tensor("x_305_split_axis_0"), val = tensor(1)]; - tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = tensor("x_305_split_cast_fp16")]; - tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = tensor("x_305_split_1_sigmoid_cast_fp16")]; - tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = tensor("x_305_cast_fp16")]; - tensor input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_453)[name = tensor("input_721_cast_fp16")]; - tensor input_723_pad_0 = const()[name = tensor("input_723_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_723_mode_0 = const()[name = tensor("input_723_mode_0"), val = tensor("constant")]; - tensor const_203_to_fp16 = const()[name = tensor("const_203_to_fp16"), val = tensor(0x0p+0)]; - tensor input_723_cast_fp16 = pad(constant_val = const_203_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = tensor("input_723_cast_fp16")]; - tensor input_725_pad_type_0 = const()[name = tensor("input_725_pad_type_0"), val = tensor("valid")]; - tensor input_725_groups_0 = const()[name = tensor("input_725_groups_0"), val = tensor(512)]; - tensor input_725_strides_0 = const()[name = tensor("input_725_strides_0"), val = tensor([1])]; - tensor input_725_pad_0 = const()[name = tensor("input_725_pad_0"), val = tensor([0, 0])]; - tensor input_725_dilations_0 = const()[name = tensor("input_725_dilations_0"), val = tensor([1])]; - tensor const_260_to_fp16 = const()[name = tensor("const_260_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172949184)))]; - tensor const_261_to_fp16 = const()[name = tensor("const_261_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172958464)))]; - tensor input_727_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_260_to_fp16, x = input_723_cast_fp16)[name = tensor("input_727_cast_fp16")]; - tensor input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = tensor("input_729_cast_fp16")]; - tensor x_307_pad_type_0 = const()[name = tensor("x_307_pad_type_0"), val = tensor("valid")]; - tensor x_307_strides_0 = const()[name = tensor("x_307_strides_0"), val = tensor([1])]; - tensor x_307_pad_0 = const()[name = tensor("x_307_pad_0"), val = tensor([0, 0])]; - tensor x_307_dilations_0 = const()[name = tensor("x_307_dilations_0"), val = tensor([1])]; - tensor x_307_groups_0 = const()[name = tensor("x_307_groups_0"), val = tensor(1)]; - tensor module_layers_13_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172959552)))]; - tensor module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173483904)))]; - tensor x_307_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv2_bias_to_fp16, dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = module_layers_13_conv_pointwise_conv2_weight_to_fp16, x = input_729_cast_fp16)[name = tensor("x_307_cast_fp16")]; - tensor input_731_perm_0 = const()[name = tensor("input_731_perm_0"), val = tensor([0, 2, 1])]; - tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = tensor("transpose_107")]; - tensor input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = tensor("input_733_cast_fp16")]; - tensor input_735_axes_0 = const()[name = tensor("input_735_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173484992)))]; - tensor module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173486080)))]; - tensor input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = tensor("input_735_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173487168)))]; - tensor module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175584384)))]; - tensor linear_125_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16, x = input_735_cast_fp16)[name = tensor("linear_125_cast_fp16")]; - tensor input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = tensor("input_739_cast_fp16")]; - tensor module_layers_13_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175588544)))]; - tensor module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177685760)))]; - tensor linear_126_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16, x = input_739_cast_fp16)[name = tensor("linear_126_cast_fp16")]; - tensor var_2757_to_fp16 = const()[name = tensor("op_2757_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2758_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2757_to_fp16)[name = tensor("op_2758_cast_fp16")]; - tensor input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2758_cast_fp16)[name = tensor("input_745_cast_fp16")]; - tensor input_747_axes_0 = const()[name = tensor("input_747_axes_0"), val = tensor([-1])]; - tensor module_layers_13_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177686848)))]; - tensor module_layers_13_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177687936)))]; - tensor input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = tensor("input_747_cast_fp16")]; - tensor input_749_axes_0 = const()[name = tensor("input_749_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177689024)))]; - tensor module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177690112)))]; - tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = tensor("input_749_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177691200)))]; - tensor module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179788416)))]; - tensor linear_127_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16, x = input_749_cast_fp16)[name = tensor("linear_127_cast_fp16")]; - tensor input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = tensor("input_753_cast_fp16")]; - tensor module_layers_14_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179792576)))]; - tensor module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181889792)))]; - tensor linear_128_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16, x = input_753_cast_fp16)[name = tensor("linear_128_cast_fp16")]; - tensor var_2788_to_fp16 = const()[name = tensor("op_2788_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2789_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2788_to_fp16)[name = tensor("op_2789_cast_fp16")]; - tensor input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2789_cast_fp16)[name = tensor("input_759_cast_fp16")]; - tensor query_29_axes_0 = const()[name = tensor("query_29_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181890880)))]; - tensor module_layers_14_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181891968)))]; - tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = tensor("query_29_cast_fp16")]; - tensor module_layers_14_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181893056)))]; - tensor module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182417408)))]; - tensor linear_129_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_q_bias_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_129_cast_fp16")]; - tensor var_2806 = const()[name = tensor("op_2806"), val = tensor([1, -1, 8, 64])]; - tensor q_85_cast_fp16 = reshape(shape = var_2806, x = linear_129_cast_fp16)[name = tensor("q_85_cast_fp16")]; - tensor module_layers_14_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182418496)))]; - tensor module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182942848)))]; - tensor linear_130_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_k_bias_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_130_cast_fp16")]; - tensor var_2811 = const()[name = tensor("op_2811"), val = tensor([1, -1, 8, 64])]; - tensor k_57_cast_fp16 = reshape(shape = var_2811, x = linear_130_cast_fp16)[name = tensor("k_57_cast_fp16")]; - tensor module_layers_14_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182943936)))]; - tensor module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183468288)))]; - tensor linear_131_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_v_bias_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16, x = query_29_cast_fp16)[name = tensor("linear_131_cast_fp16")]; - tensor var_2816 = const()[name = tensor("op_2816"), val = tensor([1, -1, 8, 64])]; - tensor v_29_cast_fp16 = reshape(shape = var_2816, x = linear_131_cast_fp16)[name = tensor("v_29_cast_fp16")]; - tensor value_31_perm_0 = const()[name = tensor("value_31_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183469376)))]; - tensor var_2828_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = tensor("op_2828_cast_fp16")]; - tensor module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183470464)))]; - tensor var_2830_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = tensor("op_2830_cast_fp16")]; - tensor q_with_bias_v_29_perm_0 = const()[name = tensor("q_with_bias_v_29_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_315_transpose_x_0 = const()[name = tensor("x_315_transpose_x_0"), val = tensor(false)]; - tensor x_315_transpose_y_0 = const()[name = tensor("x_315_transpose_y_0"), val = tensor(false)]; - tensor var_2832_to_fp16 = const()[name = tensor("op_2832_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183471552)))]; - tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2830_cast_fp16)[name = tensor("transpose_105")]; - tensor x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = var_2832_to_fp16)[name = tensor("x_315_cast_fp16")]; - tensor x_317_pad_0 = const()[name = tensor("x_317_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_317_mode_0 = const()[name = tensor("x_317_mode_0"), val = tensor("constant")]; - tensor const_210_to_fp16 = const()[name = tensor("const_210_to_fp16"), val = tensor(0x0p+0)]; - tensor x_317_cast_fp16 = pad(constant_val = const_210_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = tensor("x_317_cast_fp16")]; - tensor var_2840 = const()[name = tensor("op_2840"), val = tensor([1, 8, -1, 188])]; - tensor x_319_cast_fp16 = reshape(shape = var_2840, x = x_317_cast_fp16)[name = tensor("x_319_cast_fp16")]; - tensor var_2844_begin_0 = const()[name = tensor("op_2844_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_2844_end_0 = const()[name = tensor("op_2844_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_2844_end_mask_0 = const()[name = tensor("op_2844_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_2844_cast_fp16 = slice_by_index(begin = var_2844_begin_0, end = var_2844_end_0, end_mask = var_2844_end_mask_0, x = x_319_cast_fp16)[name = tensor("op_2844_cast_fp16")]; - tensor var_2845 = const()[name = tensor("op_2845"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_57_cast_fp16 = reshape(shape = var_2845, x = var_2844_cast_fp16)[name = tensor("matrix_bd_57_cast_fp16")]; - tensor matrix_ac_29_transpose_x_0 = const()[name = tensor("matrix_ac_29_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_29_transpose_y_0 = const()[name = tensor("matrix_ac_29_transpose_y_0"), val = tensor(false)]; - tensor transpose_79_perm_0 = const()[name = tensor("transpose_79_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_80_perm_0 = const()[name = tensor("transpose_80_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = k_57_cast_fp16)[name = tensor("transpose_103")]; - tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_2828_cast_fp16)[name = tensor("transpose_104")]; - tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_79, y = transpose_80)[name = tensor("matrix_ac_29_cast_fp16")]; - tensor matrix_bd_59_begin_0 = const()[name = tensor("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_59_end_0 = const()[name = tensor("matrix_bd_59_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_59_end_mask_0 = const()[name = tensor("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = tensor("matrix_bd_59_cast_fp16")]; - tensor var_2854_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = tensor("op_2854_cast_fp16")]; - tensor _inversed_scores_57_y_0_to_fp16 = const()[name = tensor("_inversed_scores_57_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_57_cast_fp16 = mul(x = var_2854_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = tensor("_inversed_scores_57_cast_fp16")]; - tensor scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_11)[name = tensor("scores_59_cast_fp16")]; - tensor var_2860_cast_fp16 = softmax(axis = var_23, x = scores_59_cast_fp16)[name = tensor("op_2860_cast_fp16")]; - tensor input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2860_cast_fp16, cond = mask_11)[name = tensor("input_761_cast_fp16")]; - tensor x_321_transpose_x_0 = const()[name = tensor("x_321_transpose_x_0"), val = tensor(false)]; - tensor x_321_transpose_y_0 = const()[name = tensor("x_321_transpose_y_0"), val = tensor(false)]; - tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = tensor("transpose_106")]; - tensor x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_31_cast_fp16)[name = tensor("x_321_cast_fp16")]; - tensor var_2864_perm_0 = const()[name = tensor("op_2864_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_2865 = const()[name = tensor("op_2865"), val = tensor([1, -1, 512])]; - tensor var_2864_cast_fp16 = transpose(perm = var_2864_perm_0, x = x_321_cast_fp16)[name = tensor("transpose_102")]; - tensor input_763_cast_fp16 = reshape(shape = var_2865, x = var_2864_cast_fp16)[name = tensor("input_763_cast_fp16")]; - tensor module_layers_14_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183855616)))]; - tensor module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184379968)))]; - tensor linear_133_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_out_bias_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16, x = input_763_cast_fp16)[name = tensor("linear_133_cast_fp16")]; - tensor input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = tensor("input_767_cast_fp16")]; - tensor x_325_axes_0 = const()[name = tensor("x_325_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184381056)))]; - tensor module_layers_14_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184382144)))]; - tensor x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = tensor("x_325_cast_fp16")]; - tensor input_769_perm_0 = const()[name = tensor("input_769_perm_0"), val = tensor([0, 2, 1])]; - tensor input_771_pad_type_0 = const()[name = tensor("input_771_pad_type_0"), val = tensor("valid")]; - tensor input_771_strides_0 = const()[name = tensor("input_771_strides_0"), val = tensor([1])]; - tensor input_771_pad_0 = const()[name = tensor("input_771_pad_0"), val = tensor([0, 0])]; - tensor input_771_dilations_0 = const()[name = tensor("input_771_dilations_0"), val = tensor([1])]; - tensor input_771_groups_0 = const()[name = tensor("input_771_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184383232)))]; - tensor module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185431872)))]; - tensor input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = tensor("transpose_101")]; - tensor input_771_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv1_bias_to_fp16, dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16, x = input_769_cast_fp16)[name = tensor("input_771_cast_fp16")]; - tensor x_327_split_num_splits_0 = const()[name = tensor("x_327_split_num_splits_0"), val = tensor(2)]; - tensor x_327_split_axis_0 = const()[name = tensor("x_327_split_axis_0"), val = tensor(1)]; - tensor x_327_split_cast_fp16_0, tensor x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = tensor("x_327_split_cast_fp16")]; - tensor x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = tensor("x_327_split_1_sigmoid_cast_fp16")]; - tensor x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = tensor("x_327_cast_fp16")]; - tensor input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_453)[name = tensor("input_773_cast_fp16")]; - tensor input_775_pad_0 = const()[name = tensor("input_775_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_775_mode_0 = const()[name = tensor("input_775_mode_0"), val = tensor("constant")]; - tensor const_213_to_fp16 = const()[name = tensor("const_213_to_fp16"), val = tensor(0x0p+0)]; - tensor input_775_cast_fp16 = pad(constant_val = const_213_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = tensor("input_775_cast_fp16")]; - tensor input_777_pad_type_0 = const()[name = tensor("input_777_pad_type_0"), val = tensor("valid")]; - tensor input_777_groups_0 = const()[name = tensor("input_777_groups_0"), val = tensor(512)]; - tensor input_777_strides_0 = const()[name = tensor("input_777_strides_0"), val = tensor([1])]; - tensor input_777_pad_0 = const()[name = tensor("input_777_pad_0"), val = tensor([0, 0])]; - tensor input_777_dilations_0 = const()[name = tensor("input_777_dilations_0"), val = tensor([1])]; - tensor const_262_to_fp16 = const()[name = tensor("const_262_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185433984)))]; - tensor const_263_to_fp16 = const()[name = tensor("const_263_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185443264)))]; - tensor input_779_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_262_to_fp16, x = input_775_cast_fp16)[name = tensor("input_779_cast_fp16")]; - tensor input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = tensor("input_781_cast_fp16")]; - tensor x_329_pad_type_0 = const()[name = tensor("x_329_pad_type_0"), val = tensor("valid")]; - tensor x_329_strides_0 = const()[name = tensor("x_329_strides_0"), val = tensor([1])]; - tensor x_329_pad_0 = const()[name = tensor("x_329_pad_0"), val = tensor([0, 0])]; - tensor x_329_dilations_0 = const()[name = tensor("x_329_dilations_0"), val = tensor([1])]; - tensor x_329_groups_0 = const()[name = tensor("x_329_groups_0"), val = tensor(1)]; - tensor module_layers_14_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185444352)))]; - tensor module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185968704)))]; - tensor x_329_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv2_bias_to_fp16, dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = module_layers_14_conv_pointwise_conv2_weight_to_fp16, x = input_781_cast_fp16)[name = tensor("x_329_cast_fp16")]; - tensor input_783_perm_0 = const()[name = tensor("input_783_perm_0"), val = tensor([0, 2, 1])]; - tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = tensor("transpose_100")]; - tensor input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = tensor("input_785_cast_fp16")]; - tensor input_787_axes_0 = const()[name = tensor("input_787_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185969792)))]; - tensor module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185970880)))]; - tensor input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = tensor("input_787_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185971968)))]; - tensor module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188069184)))]; - tensor linear_134_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16, x = input_787_cast_fp16)[name = tensor("linear_134_cast_fp16")]; - tensor input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = tensor("input_791_cast_fp16")]; - tensor module_layers_14_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188073344)))]; - tensor module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190170560)))]; - tensor linear_135_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16, x = input_791_cast_fp16)[name = tensor("linear_135_cast_fp16")]; - tensor var_2931_to_fp16 = const()[name = tensor("op_2931_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2932_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2931_to_fp16)[name = tensor("op_2932_cast_fp16")]; - tensor input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2932_cast_fp16)[name = tensor("input_797_cast_fp16")]; - tensor input_799_axes_0 = const()[name = tensor("input_799_axes_0"), val = tensor([-1])]; - tensor module_layers_14_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190171648)))]; - tensor module_layers_14_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190172736)))]; - tensor input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = tensor("input_799_cast_fp16")]; - tensor input_801_axes_0 = const()[name = tensor("input_801_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190173824)))]; - tensor module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190174912)))]; - tensor input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = tensor("input_801_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190176000)))]; - tensor module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192273216)))]; - tensor linear_136_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16, x = input_801_cast_fp16)[name = tensor("linear_136_cast_fp16")]; - tensor input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = tensor("input_805_cast_fp16")]; - tensor module_layers_15_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192277376)))]; - tensor module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194374592)))]; - tensor linear_137_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16, x = input_805_cast_fp16)[name = tensor("linear_137_cast_fp16")]; - tensor var_2962_to_fp16 = const()[name = tensor("op_2962_to_fp16"), val = tensor(0x1p-1)]; - tensor var_2963_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2962_to_fp16)[name = tensor("op_2963_cast_fp16")]; - tensor input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2963_cast_fp16)[name = tensor("input_811_cast_fp16")]; - tensor query_31_axes_0 = const()[name = tensor("query_31_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194375680)))]; - tensor module_layers_15_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194376768)))]; - tensor query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = tensor("query_31_cast_fp16")]; - tensor module_layers_15_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194377856)))]; - tensor module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194902208)))]; - tensor linear_138_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_q_bias_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_138_cast_fp16")]; - tensor var_2980 = const()[name = tensor("op_2980"), val = tensor([1, -1, 8, 64])]; - tensor q_91_cast_fp16 = reshape(shape = var_2980, x = linear_138_cast_fp16)[name = tensor("q_91_cast_fp16")]; - tensor module_layers_15_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194903296)))]; - tensor module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195427648)))]; - tensor linear_139_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_k_bias_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_139_cast_fp16")]; - tensor var_2985 = const()[name = tensor("op_2985"), val = tensor([1, -1, 8, 64])]; - tensor k_61_cast_fp16 = reshape(shape = var_2985, x = linear_139_cast_fp16)[name = tensor("k_61_cast_fp16")]; - tensor module_layers_15_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195428736)))]; - tensor module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195953088)))]; - tensor linear_140_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_v_bias_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16, x = query_31_cast_fp16)[name = tensor("linear_140_cast_fp16")]; - tensor var_2990 = const()[name = tensor("op_2990"), val = tensor([1, -1, 8, 64])]; - tensor v_31_cast_fp16 = reshape(shape = var_2990, x = linear_140_cast_fp16)[name = tensor("v_31_cast_fp16")]; - tensor value_33_perm_0 = const()[name = tensor("value_33_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195954176)))]; - tensor var_3002_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3002_cast_fp16")]; - tensor module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195955264)))]; - tensor var_3004_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3004_cast_fp16")]; - tensor q_with_bias_v_31_perm_0 = const()[name = tensor("q_with_bias_v_31_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_337_transpose_x_0 = const()[name = tensor("x_337_transpose_x_0"), val = tensor(false)]; - tensor x_337_transpose_y_0 = const()[name = tensor("x_337_transpose_y_0"), val = tensor(false)]; - tensor var_3006_to_fp16 = const()[name = tensor("op_3006_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195956352)))]; - tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3004_cast_fp16)[name = tensor("transpose_98")]; - tensor x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = var_3006_to_fp16)[name = tensor("x_337_cast_fp16")]; - tensor x_339_pad_0 = const()[name = tensor("x_339_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_339_mode_0 = const()[name = tensor("x_339_mode_0"), val = tensor("constant")]; - tensor const_220_to_fp16 = const()[name = tensor("const_220_to_fp16"), val = tensor(0x0p+0)]; - tensor x_339_cast_fp16 = pad(constant_val = const_220_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = tensor("x_339_cast_fp16")]; - tensor var_3014 = const()[name = tensor("op_3014"), val = tensor([1, 8, -1, 188])]; - tensor x_341_cast_fp16 = reshape(shape = var_3014, x = x_339_cast_fp16)[name = tensor("x_341_cast_fp16")]; - tensor var_3018_begin_0 = const()[name = tensor("op_3018_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3018_end_0 = const()[name = tensor("op_3018_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3018_end_mask_0 = const()[name = tensor("op_3018_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3018_cast_fp16 = slice_by_index(begin = var_3018_begin_0, end = var_3018_end_0, end_mask = var_3018_end_mask_0, x = x_341_cast_fp16)[name = tensor("op_3018_cast_fp16")]; - tensor var_3019 = const()[name = tensor("op_3019"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_61_cast_fp16 = reshape(shape = var_3019, x = var_3018_cast_fp16)[name = tensor("matrix_bd_61_cast_fp16")]; - tensor matrix_ac_31_transpose_x_0 = const()[name = tensor("matrix_ac_31_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_31_transpose_y_0 = const()[name = tensor("matrix_ac_31_transpose_y_0"), val = tensor(false)]; - tensor transpose_81_perm_0 = const()[name = tensor("transpose_81_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_82_perm_0 = const()[name = tensor("transpose_82_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = k_61_cast_fp16)[name = tensor("transpose_96")]; - tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_3002_cast_fp16)[name = tensor("transpose_97")]; - tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_81, y = transpose_82)[name = tensor("matrix_ac_31_cast_fp16")]; - tensor matrix_bd_63_begin_0 = const()[name = tensor("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_63_end_0 = const()[name = tensor("matrix_bd_63_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_63_end_mask_0 = const()[name = tensor("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = tensor("matrix_bd_63_cast_fp16")]; - tensor var_3028_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = tensor("op_3028_cast_fp16")]; - tensor _inversed_scores_61_y_0_to_fp16 = const()[name = tensor("_inversed_scores_61_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_61_cast_fp16 = mul(x = var_3028_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = tensor("_inversed_scores_61_cast_fp16")]; - tensor scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_11)[name = tensor("scores_63_cast_fp16")]; - tensor var_3034_cast_fp16 = softmax(axis = var_23, x = scores_63_cast_fp16)[name = tensor("op_3034_cast_fp16")]; - tensor input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_3034_cast_fp16, cond = mask_11)[name = tensor("input_813_cast_fp16")]; - tensor x_343_transpose_x_0 = const()[name = tensor("x_343_transpose_x_0"), val = tensor(false)]; - tensor x_343_transpose_y_0 = const()[name = tensor("x_343_transpose_y_0"), val = tensor(false)]; - tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = tensor("transpose_99")]; - tensor x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_33_cast_fp16)[name = tensor("x_343_cast_fp16")]; - tensor var_3038_perm_0 = const()[name = tensor("op_3038_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3039 = const()[name = tensor("op_3039"), val = tensor([1, -1, 512])]; - tensor var_3038_cast_fp16 = transpose(perm = var_3038_perm_0, x = x_343_cast_fp16)[name = tensor("transpose_95")]; - tensor input_815_cast_fp16 = reshape(shape = var_3039, x = var_3038_cast_fp16)[name = tensor("input_815_cast_fp16")]; - tensor module_layers_15_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196340416)))]; - tensor module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196864768)))]; - tensor linear_142_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_out_bias_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16, x = input_815_cast_fp16)[name = tensor("linear_142_cast_fp16")]; - tensor input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = tensor("input_819_cast_fp16")]; - tensor x_347_axes_0 = const()[name = tensor("x_347_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196865856)))]; - tensor module_layers_15_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196866944)))]; - tensor x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = tensor("x_347_cast_fp16")]; - tensor input_821_perm_0 = const()[name = tensor("input_821_perm_0"), val = tensor([0, 2, 1])]; - tensor input_823_pad_type_0 = const()[name = tensor("input_823_pad_type_0"), val = tensor("valid")]; - tensor input_823_strides_0 = const()[name = tensor("input_823_strides_0"), val = tensor([1])]; - tensor input_823_pad_0 = const()[name = tensor("input_823_pad_0"), val = tensor([0, 0])]; - tensor input_823_dilations_0 = const()[name = tensor("input_823_dilations_0"), val = tensor([1])]; - tensor input_823_groups_0 = const()[name = tensor("input_823_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196868032)))]; - tensor module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197916672)))]; - tensor input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = tensor("transpose_94")]; - tensor input_823_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv1_bias_to_fp16, dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16, x = input_821_cast_fp16)[name = tensor("input_823_cast_fp16")]; - tensor x_349_split_num_splits_0 = const()[name = tensor("x_349_split_num_splits_0"), val = tensor(2)]; - tensor x_349_split_axis_0 = const()[name = tensor("x_349_split_axis_0"), val = tensor(1)]; - tensor x_349_split_cast_fp16_0, tensor x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = tensor("x_349_split_cast_fp16")]; - tensor x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = tensor("x_349_split_1_sigmoid_cast_fp16")]; - tensor x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = tensor("x_349_cast_fp16")]; - tensor input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_453)[name = tensor("input_825_cast_fp16")]; - tensor input_827_pad_0 = const()[name = tensor("input_827_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_827_mode_0 = const()[name = tensor("input_827_mode_0"), val = tensor("constant")]; - tensor const_223_to_fp16 = const()[name = tensor("const_223_to_fp16"), val = tensor(0x0p+0)]; - tensor input_827_cast_fp16 = pad(constant_val = const_223_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = tensor("input_827_cast_fp16")]; - tensor input_829_pad_type_0 = const()[name = tensor("input_829_pad_type_0"), val = tensor("valid")]; - tensor input_829_groups_0 = const()[name = tensor("input_829_groups_0"), val = tensor(512)]; - tensor input_829_strides_0 = const()[name = tensor("input_829_strides_0"), val = tensor([1])]; - tensor input_829_pad_0 = const()[name = tensor("input_829_pad_0"), val = tensor([0, 0])]; - tensor input_829_dilations_0 = const()[name = tensor("input_829_dilations_0"), val = tensor([1])]; - tensor const_264_to_fp16 = const()[name = tensor("const_264_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197918784)))]; - tensor const_265_to_fp16 = const()[name = tensor("const_265_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197928064)))]; - tensor input_831_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_264_to_fp16, x = input_827_cast_fp16)[name = tensor("input_831_cast_fp16")]; - tensor input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = tensor("input_833_cast_fp16")]; - tensor x_351_pad_type_0 = const()[name = tensor("x_351_pad_type_0"), val = tensor("valid")]; - tensor x_351_strides_0 = const()[name = tensor("x_351_strides_0"), val = tensor([1])]; - tensor x_351_pad_0 = const()[name = tensor("x_351_pad_0"), val = tensor([0, 0])]; - tensor x_351_dilations_0 = const()[name = tensor("x_351_dilations_0"), val = tensor([1])]; - tensor x_351_groups_0 = const()[name = tensor("x_351_groups_0"), val = tensor(1)]; - tensor module_layers_15_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197929152)))]; - tensor module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198453504)))]; - tensor x_351_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv2_bias_to_fp16, dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = module_layers_15_conv_pointwise_conv2_weight_to_fp16, x = input_833_cast_fp16)[name = tensor("x_351_cast_fp16")]; - tensor input_835_perm_0 = const()[name = tensor("input_835_perm_0"), val = tensor([0, 2, 1])]; - tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = tensor("transpose_93")]; - tensor input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = tensor("input_837_cast_fp16")]; - tensor input_839_axes_0 = const()[name = tensor("input_839_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198454592)))]; - tensor module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198455680)))]; - tensor input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = tensor("input_839_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198456768)))]; - tensor module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200553984)))]; - tensor linear_143_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16, x = input_839_cast_fp16)[name = tensor("linear_143_cast_fp16")]; - tensor input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = tensor("input_843_cast_fp16")]; - tensor module_layers_15_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200558144)))]; - tensor module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202655360)))]; - tensor linear_144_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16, x = input_843_cast_fp16)[name = tensor("linear_144_cast_fp16")]; - tensor var_3105_to_fp16 = const()[name = tensor("op_3105_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3106_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3105_to_fp16)[name = tensor("op_3106_cast_fp16")]; - tensor input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_3106_cast_fp16)[name = tensor("input_849_cast_fp16")]; - tensor input_851_axes_0 = const()[name = tensor("input_851_axes_0"), val = tensor([-1])]; - tensor module_layers_15_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202656448)))]; - tensor module_layers_15_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202657536)))]; - tensor input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = tensor("input_851_cast_fp16")]; - tensor input_853_axes_0 = const()[name = tensor("input_853_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202658624)))]; - tensor module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202659712)))]; - tensor input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = tensor("input_853_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202660800)))]; - tensor module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204758016)))]; - tensor linear_145_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16, x = input_853_cast_fp16)[name = tensor("linear_145_cast_fp16")]; - tensor input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = tensor("input_857_cast_fp16")]; - tensor module_layers_16_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204762176)))]; - tensor module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206859392)))]; - tensor linear_146_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16, x = input_857_cast_fp16)[name = tensor("linear_146_cast_fp16")]; - tensor var_3136_to_fp16 = const()[name = tensor("op_3136_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3137_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3136_to_fp16)[name = tensor("op_3137_cast_fp16")]; - tensor input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_3137_cast_fp16)[name = tensor("input_863_cast_fp16")]; - tensor query_axes_0 = const()[name = tensor("query_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_self_att_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206860480)))]; - tensor module_layers_16_norm_self_att_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206861568)))]; - tensor query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = tensor("query_cast_fp16")]; - tensor module_layers_16_self_attn_linear_q_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206862656)))]; - tensor module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207387008)))]; - tensor linear_147_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_q_bias_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_147_cast_fp16")]; - tensor var_3154 = const()[name = tensor("op_3154"), val = tensor([1, -1, 8, 64])]; - tensor q_97_cast_fp16 = reshape(shape = var_3154, x = linear_147_cast_fp16)[name = tensor("q_97_cast_fp16")]; - tensor module_layers_16_self_attn_linear_k_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207388096)))]; - tensor module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207912448)))]; - tensor linear_148_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_k_bias_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_148_cast_fp16")]; - tensor var_3159 = const()[name = tensor("op_3159"), val = tensor([1, -1, 8, 64])]; - tensor k_65_cast_fp16 = reshape(shape = var_3159, x = linear_148_cast_fp16)[name = tensor("k_65_cast_fp16")]; - tensor module_layers_16_self_attn_linear_v_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207913536)))]; - tensor module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208437888)))]; - tensor linear_149_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_v_bias_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16, x = query_cast_fp16)[name = tensor("linear_149_cast_fp16")]; - tensor var_3164 = const()[name = tensor("op_3164"), val = tensor([1, -1, 8, 64])]; - tensor v_cast_fp16 = reshape(shape = var_3164, x = linear_149_cast_fp16)[name = tensor("v_cast_fp16")]; - tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208438976)))]; - tensor var_3176_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = tensor("op_3176_cast_fp16")]; - tensor module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = tensor("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208440064)))]; - tensor var_3178_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = tensor("op_3178_cast_fp16")]; - tensor q_with_bias_v_perm_0 = const()[name = tensor("q_with_bias_v_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor x_359_transpose_x_0 = const()[name = tensor("x_359_transpose_x_0"), val = tensor(false)]; - tensor x_359_transpose_y_0 = const()[name = tensor("x_359_transpose_y_0"), val = tensor(false)]; - tensor var_3180_to_fp16 = const()[name = tensor("op_3180_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208441152)))]; - tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_3178_cast_fp16)[name = tensor("transpose_91")]; - tensor x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_cast_fp16, y = var_3180_to_fp16)[name = tensor("x_359_cast_fp16")]; - tensor x_361_pad_0 = const()[name = tensor("x_361_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; - tensor x_361_mode_0 = const()[name = tensor("x_361_mode_0"), val = tensor("constant")]; - tensor const_230_to_fp16 = const()[name = tensor("const_230_to_fp16"), val = tensor(0x0p+0)]; - tensor x_361_cast_fp16 = pad(constant_val = const_230_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = tensor("x_361_cast_fp16")]; - tensor var_3188 = const()[name = tensor("op_3188"), val = tensor([1, 8, -1, 188])]; - tensor x_363_cast_fp16 = reshape(shape = var_3188, x = x_361_cast_fp16)[name = tensor("x_363_cast_fp16")]; - tensor var_3192_begin_0 = const()[name = tensor("op_3192_begin_0"), val = tensor([0, 0, 1, 0])]; - tensor var_3192_end_0 = const()[name = tensor("op_3192_end_0"), val = tensor([1, 8, 376, 188])]; - tensor var_3192_end_mask_0 = const()[name = tensor("op_3192_end_mask_0"), val = tensor([true, true, true, true])]; - tensor var_3192_cast_fp16 = slice_by_index(begin = var_3192_begin_0, end = var_3192_end_0, end_mask = var_3192_end_mask_0, x = x_363_cast_fp16)[name = tensor("op_3192_cast_fp16")]; - tensor var_3193 = const()[name = tensor("op_3193"), val = tensor([1, 8, 188, 375])]; - tensor matrix_bd_65_cast_fp16 = reshape(shape = var_3193, x = var_3192_cast_fp16)[name = tensor("matrix_bd_65_cast_fp16")]; - tensor matrix_ac_transpose_x_0 = const()[name = tensor("matrix_ac_transpose_x_0"), val = tensor(false)]; - tensor matrix_ac_transpose_y_0 = const()[name = tensor("matrix_ac_transpose_y_0"), val = tensor(false)]; - tensor transpose_83_perm_0 = const()[name = tensor("transpose_83_perm_0"), val = tensor([0, 2, -3, -1])]; - tensor transpose_84_perm_0 = const()[name = tensor("transpose_84_perm_0"), val = tensor([0, 2, -1, -3])]; - tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = k_65_cast_fp16)[name = tensor("transpose_89")]; - tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = var_3176_cast_fp16)[name = tensor("transpose_90")]; - tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_83, y = transpose_84)[name = tensor("matrix_ac_cast_fp16")]; - tensor matrix_bd_begin_0 = const()[name = tensor("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor matrix_bd_end_0 = const()[name = tensor("matrix_bd_end_0"), val = tensor([1, 8, 188, 188])]; - tensor matrix_bd_end_mask_0 = const()[name = tensor("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; - tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_65_cast_fp16)[name = tensor("matrix_bd_cast_fp16")]; - tensor var_3202_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = tensor("op_3202_cast_fp16")]; - tensor _inversed_scores_65_y_0_to_fp16 = const()[name = tensor("_inversed_scores_65_y_0_to_fp16"), val = tensor(0x1p-3)]; - tensor _inversed_scores_65_cast_fp16 = mul(x = var_3202_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = tensor("_inversed_scores_65_cast_fp16")]; - tensor scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_11)[name = tensor("scores_cast_fp16")]; - tensor var_3208_cast_fp16 = softmax(axis = var_23, x = scores_cast_fp16)[name = tensor("op_3208_cast_fp16")]; - tensor input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_3208_cast_fp16, cond = mask_11)[name = tensor("input_865_cast_fp16")]; - tensor x_365_transpose_x_0 = const()[name = tensor("x_365_transpose_x_0"), val = tensor(false)]; - tensor x_365_transpose_y_0 = const()[name = tensor("x_365_transpose_y_0"), val = tensor(false)]; - tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = tensor("transpose_92")]; - tensor x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_cast_fp16)[name = tensor("x_365_cast_fp16")]; - tensor var_3212_perm_0 = const()[name = tensor("op_3212_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_3213 = const()[name = tensor("op_3213"), val = tensor([1, -1, 512])]; - tensor var_3212_cast_fp16 = transpose(perm = var_3212_perm_0, x = x_365_cast_fp16)[name = tensor("transpose_88")]; - tensor input_867_cast_fp16 = reshape(shape = var_3213, x = var_3212_cast_fp16)[name = tensor("input_867_cast_fp16")]; - tensor module_layers_16_self_attn_linear_out_weight_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208825216)))]; - tensor module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = tensor("module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209349568)))]; - tensor linear_151_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_out_bias_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16, x = input_867_cast_fp16)[name = tensor("linear_151_cast_fp16")]; - tensor input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = tensor("input_871_cast_fp16")]; - tensor x_369_axes_0 = const()[name = tensor("x_369_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_conv_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209350656)))]; - tensor module_layers_16_norm_conv_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209351744)))]; - tensor x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = tensor("x_369_cast_fp16")]; - tensor input_873_perm_0 = const()[name = tensor("input_873_perm_0"), val = tensor([0, 2, 1])]; - tensor input_875_pad_type_0 = const()[name = tensor("input_875_pad_type_0"), val = tensor("valid")]; - tensor input_875_strides_0 = const()[name = tensor("input_875_strides_0"), val = tensor([1])]; - tensor input_875_pad_0 = const()[name = tensor("input_875_pad_0"), val = tensor([0, 0])]; - tensor input_875_dilations_0 = const()[name = tensor("input_875_dilations_0"), val = tensor([1])]; - tensor input_875_groups_0 = const()[name = tensor("input_875_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209352832)))]; - tensor module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210401472)))]; - tensor input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = tensor("transpose_87")]; - tensor input_875_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv1_bias_to_fp16, dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16, x = input_873_cast_fp16)[name = tensor("input_875_cast_fp16")]; - tensor x_371_split_num_splits_0 = const()[name = tensor("x_371_split_num_splits_0"), val = tensor(2)]; - tensor x_371_split_axis_0 = const()[name = tensor("x_371_split_axis_0"), val = tensor(1)]; - tensor x_371_split_cast_fp16_0, tensor x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = tensor("x_371_split_cast_fp16")]; - tensor x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = tensor("x_371_split_1_sigmoid_cast_fp16")]; - tensor x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = tensor("x_371_cast_fp16")]; - tensor input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_453)[name = tensor("input_877_cast_fp16")]; - tensor input_879_pad_0 = const()[name = tensor("input_879_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; - tensor input_879_mode_0 = const()[name = tensor("input_879_mode_0"), val = tensor("constant")]; - tensor const_233_to_fp16 = const()[name = tensor("const_233_to_fp16"), val = tensor(0x0p+0)]; - tensor input_879_cast_fp16 = pad(constant_val = const_233_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = tensor("input_879_cast_fp16")]; - tensor input_881_pad_type_0 = const()[name = tensor("input_881_pad_type_0"), val = tensor("valid")]; - tensor input_881_groups_0 = const()[name = tensor("input_881_groups_0"), val = tensor(512)]; - tensor input_881_strides_0 = const()[name = tensor("input_881_strides_0"), val = tensor([1])]; - tensor input_881_pad_0 = const()[name = tensor("input_881_pad_0"), val = tensor([0, 0])]; - tensor input_881_dilations_0 = const()[name = tensor("input_881_dilations_0"), val = tensor([1])]; - tensor const_266_to_fp16 = const()[name = tensor("const_266_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210403584)))]; - tensor const_267_to_fp16 = const()[name = tensor("const_267_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210412864)))]; - tensor input_883_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_266_to_fp16, x = input_879_cast_fp16)[name = tensor("input_883_cast_fp16")]; - tensor input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = tensor("input_885_cast_fp16")]; - tensor x_373_pad_type_0 = const()[name = tensor("x_373_pad_type_0"), val = tensor("valid")]; - tensor x_373_strides_0 = const()[name = tensor("x_373_strides_0"), val = tensor([1])]; - tensor x_373_pad_0 = const()[name = tensor("x_373_pad_0"), val = tensor([0, 0])]; - tensor x_373_dilations_0 = const()[name = tensor("x_373_dilations_0"), val = tensor([1])]; - tensor x_373_groups_0 = const()[name = tensor("x_373_groups_0"), val = tensor(1)]; - tensor module_layers_16_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210413952)))]; - tensor module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor("module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210938304)))]; - tensor x_373_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv2_bias_to_fp16, dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = module_layers_16_conv_pointwise_conv2_weight_to_fp16, x = input_885_cast_fp16)[name = tensor("x_373_cast_fp16")]; - tensor input_887_perm_0 = const()[name = tensor("input_887_perm_0"), val = tensor([0, 2, 1])]; - tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = tensor("transpose_86")]; - tensor input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = tensor("input_889_cast_fp16")]; - tensor input_891_axes_0 = const()[name = tensor("input_891_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210939392)))]; - tensor module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210940480)))]; - tensor input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = tensor("input_891_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210941568)))]; - tensor module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213038784)))]; - tensor linear_152_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16, x = input_891_cast_fp16)[name = tensor("linear_152_cast_fp16")]; - tensor input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = tensor("input_895_cast_fp16")]; - tensor module_layers_16_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213042944)))]; - tensor module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor("module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215140160)))]; - tensor linear_153_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16, x = input_895_cast_fp16)[name = tensor("linear_153_cast_fp16")]; - tensor var_3279_to_fp16 = const()[name = tensor("op_3279_to_fp16"), val = tensor(0x1p-1)]; - tensor var_3280_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3279_to_fp16)[name = tensor("op_3280_cast_fp16")]; - tensor input_cast_fp16 = add(x = input_889_cast_fp16, y = var_3280_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor audio_signal_axes_0 = const()[name = tensor("audio_signal_axes_0"), val = tensor([-1])]; - tensor module_layers_16_norm_out_weight_to_fp16 = const()[name = tensor("module_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215141248)))]; - tensor module_layers_16_norm_out_bias_to_fp16 = const()[name = tensor("module_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215142336)))]; - tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("audio_signal_cast_fp16")]; - tensor obj_1_perm_0 = const()[name = tensor("obj_1_perm_0"), val = tensor([0, 2, 1])]; - tensor obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("obj_1_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = tensor("transpose_85")]; - tensor encoder_output = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = tensor("cast_172")]; - } -> (encoder_output, encoder_length); -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/weights/weight.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/Encoder.mlmodelc/weights/weight.bin deleted file mode 100644 index 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: "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.version" : "8.3.0", - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0" - }, - "generatedClassName" : "parakeet_joint_decision", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/model.mil b/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/model.mil deleted file mode 100644 index edfc5050f47ffbe1f5344799cef5dc0b21c392e0..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/model.mil +++ /dev/null @@ -1,58 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor decoder, tensor encoder) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_to_fp16_dtype_0 = const()[name = tensor("encoder_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_to_fp16_dtype_0 = const()[name = tensor("decoder_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488)))]; - tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = tensor("cast_6")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656832)))]; - tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476096)))]; - tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = tensor("cast_5")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; - tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; - tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; - tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1477440)))]; - tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2795904)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 188, 1, 1025])]; - tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; - tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 188, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; - tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; - tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; - tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; - tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; - tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; - tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; - tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; - tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_4")]; - tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; - tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; - tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; - tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; - tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; - tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; - tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_3")]; - } -> (token_id, token_prob, duration); -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/weights/weight.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecision.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b3f771cb65b190f1873e39629676ed79b65a8361522f451b37bdba8b1106e6ff -size 2798028 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/analytics/coremldata.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index ab3e67b973cf846c0af5e1f075f1b4b7ffb77a00..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c7c11c6bb985fab7f835ba687a575f1eb04f4c93b0783155d634adbc49f0e797 -size 243 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/coremldata.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/coremldata.bin deleted file mode 100644 index bc71cc6aa73de6959f4c6718500197ebbf364633..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1af2cb9bcc13eec83ce006e4f1c2cf158393745cd9187428333fbcb6917da244 -size 535 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/metadata.json b/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/metadata.json deleted file mode 100644 index b96db3615aa1d5e110c61c1f4694def827cb0a1f..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/metadata.json +++ /dev/null @@ -1,123 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M single-step joint decision (current frame)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_id", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_prob", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "duration", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1 × 64)", - "shortDescription" : "", - "shape" : "[1, 1, 1, 64]", - "name" : "top_k_ids", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 1 × 64)", - "shortDescription" : "", - "shape" : "[1, 1, 1, 64]", - "name" : "top_k_logits", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.reduceArgmax" : 2, - "Ios17.linear" : 3, - "Ios17.transpose" : 2, - "Ios17.sliceByIndex" : 2, - "Ios17.add" : 1, - "Ios17.topk" : 1, - "Ios16.relu" : 1, - "Ios16.softmax" : 1, - "Ios17.expandDims" : 3, - "Ios17.squeeze" : 1, - "Ios17.cast" : 6, - "Ios17.gatherAlongAxis" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32, UInt16)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 512 × 1)", - "shortDescription" : "", - "shape" : "[1, 512, 1]", - "name" : "encoder_step", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder_step", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "generatedClassName" : "parakeet_joint_decision_single_step", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/model.mil b/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/model.mil deleted file mode 100644 index 42bd4ac5def601f37ece871ff09bd6242de025aa..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/model.mil +++ /dev/null @@ -1,69 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor decoder_step, tensor encoder_step) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_step_to_fp16_dtype_0 = const()[name = tensor("encoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_step_to_fp16_dtype_0 = const()[name = tensor("decoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_module_enc_weight_to_fp16 = const()[name = tensor("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_module_enc_bias_to_fp16 = const()[name = tensor("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488)))]; - tensor encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor("cast_9")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor joint_module_pred_weight_to_fp16 = const()[name = tensor("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656832)))]; - tensor joint_module_pred_bias_to_fp16 = const()[name = tensor("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476096)))]; - tensor decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor("cast_8")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor var_23_axes_0 = const()[name = tensor("op_23_axes_0"), val = tensor([2])]; - tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor("op_23_cast_fp16")]; - tensor var_24_axes_0 = const()[name = tensor("op_24_axes_0"), val = tensor([1])]; - tensor var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor("op_24_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1477440)))]; - tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2795904)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor token_logits_begin_0 = const()[name = tensor("token_logits_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor token_logits_end_0 = const()[name = tensor("token_logits_end_0"), val = tensor([1, 1, 1, 1025])]; - tensor token_logits_end_mask_0 = const()[name = tensor("token_logits_end_mask_0"), val = tensor([true, true, true, false])]; - tensor token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("token_logits_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 1, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_43_axis_0 = const()[name = tensor("op_43_axis_0"), val = tensor(-1)]; - tensor var_43_keep_dims_0 = const()[name = tensor("op_43_keep_dims_0"), val = tensor(false)]; - tensor var_43_output_dtype_0 = const()[name = tensor("op_43_output_dtype_0"), val = tensor("int32")]; - tensor token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor("op_43_cast_fp16")]; - tensor var_49 = const()[name = tensor("op_49"), val = tensor(-1)]; - tensor token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor("token_probs_all_cast_fp16")]; - tensor var_58_axes_0 = const()[name = tensor("op_58_axes_0"), val = tensor([-1])]; - tensor var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor("op_58")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(-1)]; - tensor var_61_validate_indices_0 = const()[name = tensor("op_61_validate_indices_0"), val = tensor(false)]; - tensor var_58_to_int16_dtype_0 = const()[name = tensor("op_58_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor("cast_7")]; - tensor var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor("op_61_cast_fp16_cast_int16")]; - tensor var_63_axes_0 = const()[name = tensor("op_63_axes_0"), val = tensor([-1])]; - tensor var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor("op_63_cast_fp16")]; - tensor var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor var_66_axis_0 = const()[name = tensor("op_66_axis_0"), val = tensor(-1)]; - tensor var_66_keep_dims_0 = const()[name = tensor("op_66_keep_dims_0"), val = tensor(false)]; - tensor var_66_output_dtype_0 = const()[name = tensor("op_66_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("op_66_cast_fp16")]; - tensor var_72 = const()[name = tensor("op_72"), val = tensor(64)]; - tensor var_76_axis_0 = const()[name = tensor("op_76_axis_0"), val = tensor(-1)]; - tensor var_76_ascending_0 = const()[name = tensor("op_76_ascending_0"), val = tensor(false)]; - tensor var_76_sort_0 = const()[name = tensor("op_76_sort_0"), val = tensor(true)]; - tensor var_76_return_indices_0 = const()[name = tensor("op_76_return_indices_0"), val = tensor(true)]; - tensor var_76_cast_fp16_cast_int16_output_indices_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_output_indices_dtype_0"), val = tensor("uint16")]; - tensor var_76_cast_fp16_cast_int16_0, tensor var_76_cast_fp16_cast_int16_1 = topk(ascending = var_76_ascending_0, axis = var_76_axis_0, k = var_72, output_indices_dtype = var_76_cast_fp16_cast_int16_output_indices_dtype_0, return_indices = var_76_return_indices_0, sort = var_76_sort_0, x = token_logits_cast_fp16)[name = tensor("op_76_cast_fp16_cast_int16")]; - tensor var_76_cast_fp16_cast_int16_1_to_int32_dtype_0 = const()[name = tensor("op_76_cast_fp16_cast_int16_1_to_int32_dtype_0"), val = tensor("int32")]; - tensor var_76_cast_fp16_0_to_fp32_dtype_0 = const()[name = tensor("op_76_cast_fp16_0_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor top_k_logits = cast(dtype = var_76_cast_fp16_0_to_fp32_dtype_0, x = var_76_cast_fp16_cast_int16_0)[name = tensor("cast_4")]; - tensor top_k_ids = cast(dtype = var_76_cast_fp16_cast_int16_1_to_int32_dtype_0, x = var_76_cast_fp16_cast_int16_1)[name = tensor("cast_5")]; - tensor token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor("cast_6")]; - } -> (token_id, token_prob, duration, top_k_ids, top_k_logits); -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/weights/weight.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/JointDecisionSingleStep.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b3f771cb65b190f1873e39629676ed79b65a8361522f451b37bdba8b1106e6ff -size 2798028 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/analytics/coremldata.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index d34457003aea6648633a14e7cd6134edc9c30f17..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a1ac15543fbb9301fba5f018b147e44d767479dec352aaa91dfe7bcf65949693 -size 243 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/coremldata.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/coremldata.bin deleted file mode 100644 index cd81f47cc6672a441cbe9556757a30ffc0ff0bc7..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4940877938cc1b6d8830bbdd68ac8a49377cc57d75b61308883da5235b6a1914 -size 439 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/metadata.json b/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/metadata.json deleted file mode 100644 index 6087d5e76f7dd060cf4f135f1f3ae0bbbeb2f30a..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/metadata.json +++ /dev/null @@ -1,112 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Parakeet 110M preprocessor (15 s window)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32)", - "shortDescription" : "", - "shape" : "[]", - "name" : "mel_features", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "mel_length", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Range1d" : 3, - "Ios17.equal" : 1, - "Ios17.notEqual" : 1, - "Ios17.reshape" : 2, - "Identity" : 1, - "Ios17.matmul" : 1, - "Select" : 6, - "Ios17.expandDims" : 12, - "Ios17.add" : 3, - "Tile" : 2, - "Ios17.sliceByIndex" : 3, - "Ios16.reduceSum" : 4, - "Shape" : 4, - "Ios17.gather" : 4, - "Ios17.logicalNot" : 1, - "Pad" : 1, - "Ios17.log" : 1, - "Ios17.less" : 2, - "Ios17.sub" : 4, - "Ios17.conv" : 2, - "Ios17.pow" : 2, - "Ios17.cast" : 10, - "Ios17.concat" : 3, - "Stack" : 1, - "Ios17.floorDiv" : 1, - "Ios17.realDiv" : 4, - "Ios17.sqrt" : 1, - "Ios17.greaterEqual" : 1, - "Ios17.mul" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32, UInt16)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "dataType" : "Float32", - "hasShapeFlexibility" : "1", - "isOptional" : "0", - "shapeFlexibility" : "1 × 1...240000", - "shapeRange" : "[[1, 1], [1, 240000]]", - "formattedType" : "MultiArray (Float32 1 × 1)", - "type" : "MultiArray", - "shape" : "[1, 1]", - "name" : "audio", - "shortDescription" : "" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "audio_length", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.source_dialect" : "TorchScript", - "com.github.apple.coremltools.source" : "torch==2.9.0", - "com.github.apple.coremltools.version" : "8.3.0" - }, - "generatedClassName" : "parakeet_preprocessor", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/model.mil b/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/model.mil deleted file mode 100644 index ae325b6071a8ee5dad74a2e516e60889d89771d8..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/model.mil +++ /dev/null @@ -1,191 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] -{ - func main(tensor audio, tensor audio_length) [FlexibleShapeInformation = tuple, dict, tensor>>, tuple, dict, list, ?>>>>((("DefaultShapes", {{"audio", [1, 1]}}), ("RangeDims", {{"audio", [[1, 1], [1, 240000]]}})))] { - tensor var_9 = const()[name = tensor("op_9"), val = tensor(1)]; - tensor var_10 = const()[name = tensor("op_10"), val = tensor(160)]; - tensor var_12 = const()[name = tensor("op_12"), val = tensor(0)]; - tensor var_34 = const()[name = tensor("op_34"), val = tensor(512)]; - tensor var_35 = add(x = audio_length, y = var_34)[name = tensor("op_35")]; - tensor var_36 = const()[name = tensor("op_36"), val = tensor(512)]; - tensor var_37 = sub(x = var_35, y = var_36)[name = tensor("op_37")]; - tensor floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor("floor_div_0")]; - tensor var_40 = equal(x = audio_length, y = var_12)[name = tensor("op_40")]; - tensor var_41 = const()[name = tensor("op_41"), val = tensor([0])]; - tensor mel_length = select(a = var_41, b = floor_div_0, cond = var_40)[name = tensor("seq_len")]; - tensor audio_to_fp16_dtype_0 = const()[name = tensor("audio_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = tensor("cast_27")]; - tensor var_43_shape_cast_fp16 = shape(x = audio_to_fp16)[name = tensor("op_43_shape_cast_fp16")]; - tensor gather_0_axis_0 = const()[name = tensor("gather_0_axis_0"), val = tensor(0)]; - tensor gather_0_batch_dims_0 = const()[name = tensor("gather_0_batch_dims_0"), val = tensor(0)]; - tensor gather_0_validate_indices_0 = const()[name = tensor("gather_0_validate_indices_0"), val = tensor(false)]; - tensor var_43_shape_cast_fp16_to_int16_dtype_0 = const()[name = tensor("op_43_shape_cast_fp16_to_int16_dtype_0"), val = tensor("int16")]; - tensor select_0_to_uint16 = const()[name = tensor("select_0_to_uint16"), val = tensor(1)]; - tensor var_43_shape_cast_fp16_to_int16 = cast(dtype = var_43_shape_cast_fp16_to_int16_dtype_0, x = var_43_shape_cast_fp16)[name = tensor("cast_26")]; - tensor gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_43_shape_cast_fp16_to_int16)[name = tensor("gather_0_cast_uint16")]; - tensor gather_0_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_0_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_0 = const()[name = tensor("const_0"), val = tensor(0)]; - tensor const_1 = const()[name = tensor("const_1"), val = tensor(1)]; - tensor gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = tensor("cast_25")]; - tensor var_44 = range_1d(end = gather_0_cast_uint16_to_int32, start = const_0, step = const_1)[name = tensor("op_44")]; - tensor var_45_axes_0 = const()[name = tensor("op_45_axes_0"), val = tensor([0])]; - tensor var_45 = expand_dims(axes = var_45_axes_0, x = var_44)[name = tensor("op_45")]; - tensor var_46_axes_0 = const()[name = tensor("op_46_axes_0"), val = tensor([1])]; - tensor var_46 = expand_dims(axes = var_46_axes_0, x = audio_length)[name = tensor("op_46")]; - tensor timemask = less(x = var_45, y = var_46)[name = tensor("timemask")]; - tensor var_49_begin_0 = const()[name = tensor("op_49_begin_0"), val = tensor([0, 0])]; - tensor var_49_end_0 = const()[name = tensor("op_49_end_0"), val = tensor([1, 1])]; - tensor var_49_end_mask_0 = const()[name = tensor("op_49_end_mask_0"), val = tensor([true, false])]; - tensor var_49_squeeze_mask_0 = const()[name = tensor("op_49_squeeze_mask_0"), val = tensor([false, true])]; - tensor var_49_cast_fp16 = slice_by_index(begin = var_49_begin_0, end = var_49_end_0, end_mask = var_49_end_mask_0, squeeze_mask = var_49_squeeze_mask_0, x = audio_to_fp16)[name = tensor("op_49_cast_fp16")]; - tensor var_50_axes_0 = const()[name = tensor("op_50_axes_0"), val = tensor([1])]; - tensor var_50_cast_fp16 = expand_dims(axes = var_50_axes_0, x = var_49_cast_fp16)[name = tensor("op_50_cast_fp16")]; - tensor var_52_begin_0 = const()[name = tensor("op_52_begin_0"), val = tensor([0, 1])]; - tensor var_52_end_0 = const()[name = tensor("op_52_end_0"), val = tensor([1, 0])]; - tensor var_52_end_mask_0 = const()[name = tensor("op_52_end_mask_0"), val = tensor([true, true])]; - tensor var_52_cast_fp16 = slice_by_index(begin = var_52_begin_0, end = var_52_end_0, end_mask = var_52_end_mask_0, x = audio_to_fp16)[name = tensor("op_52_cast_fp16")]; - tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 0])]; - tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([1, -1])]; - tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, false])]; - tensor var_54_cast_fp16 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = audio_to_fp16)[name = tensor("op_54_cast_fp16")]; - tensor var_55_to_fp16 = const()[name = tensor("op_55_to_fp16"), val = tensor(0x1.f0cp-1)]; - tensor var_56_cast_fp16 = mul(x = var_54_cast_fp16, y = var_55_to_fp16)[name = tensor("op_56_cast_fp16")]; - tensor var_57_cast_fp16 = sub(x = var_52_cast_fp16, y = var_56_cast_fp16)[name = tensor("op_57_cast_fp16")]; - tensor x_3_interleave_0 = const()[name = tensor("x_3_interleave_0"), val = tensor(false)]; - tensor x_3_cast_fp16 = concat(axis = var_9, interleave = x_3_interleave_0, values = (var_50_cast_fp16, var_57_cast_fp16))[name = tensor("x_3_cast_fp16")]; - tensor var_60 = logical_not(x = timemask)[name = tensor("op_60")]; - tensor var_16_to_fp16 = const()[name = tensor("op_16_to_fp16"), val = tensor(0x0p+0)]; - tensor input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_60)[name = tensor("input_1_cast_fp16")]; - tensor concat_1x = const()[name = tensor("concat_1x"), val = tensor([1, 1, -1])]; - tensor input_3_cast_fp16 = reshape(shape = concat_1x, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; - tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; - tensor input_5_mode_0 = const()[name = tensor("input_5_mode_0"), val = tensor("constant")]; - tensor const_3_to_fp16 = const()[name = tensor("const_3_to_fp16"), val = tensor(0x0p+0)]; - tensor input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor concat_2x = const()[name = tensor("concat_2x"), val = tensor([1, -1])]; - tensor input_cast_fp16 = reshape(shape = concat_2x, x = input_5_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor expand_dims_3 = const()[name = tensor("expand_dims_3"), val = tensor([160])]; - tensor expand_dims_4_axes_0 = const()[name = tensor("expand_dims_4_axes_0"), val = tensor([1])]; - tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = tensor("expand_dims_4_cast_fp16")]; - tensor conv_0_pad_type_0 = const()[name = tensor("conv_0_pad_type_0"), val = tensor("valid")]; - tensor conv_0_pad_0 = const()[name = tensor("conv_0_pad_0"), val = tensor([0, 0])]; - tensor conv_0_dilations_0 = const()[name = tensor("conv_0_dilations_0"), val = tensor([1])]; - tensor conv_0_groups_0 = const()[name = tensor("conv_0_groups_0"), val = tensor(1)]; - tensor expand_dims_1_to_fp16 = const()[name = tensor("expand_dims_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_0_cast_fp16")]; - tensor conv_1_pad_type_0 = const()[name = tensor("conv_1_pad_type_0"), val = tensor("valid")]; - tensor conv_1_pad_0 = const()[name = tensor("conv_1_pad_0"), val = tensor([0, 0])]; - tensor conv_1_dilations_0 = const()[name = tensor("conv_1_dilations_0"), val = tensor([1])]; - tensor conv_1_groups_0 = const()[name = tensor("conv_1_groups_0"), val = tensor(1)]; - tensor expand_dims_2_to_fp16 = const()[name = tensor("expand_dims_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263296)))]; - tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = tensor("conv_1_cast_fp16")]; - tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(-1)]; - tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor("stack_0_cast_fp16")]; - tensor var_19_promoted_to_fp16 = const()[name = tensor("op_19_promoted_to_fp16"), val = tensor(0x1p+1)]; - tensor var_75_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_19_promoted_to_fp16)[name = tensor("op_75_cast_fp16")]; - tensor var_77_axes_0 = const()[name = tensor("op_77_axes_0"), val = tensor([-1])]; - tensor var_77_keep_dims_0 = const()[name = tensor("op_77_keep_dims_0"), val = tensor(false)]; - tensor var_77_cast_fp16 = reduce_sum(axes = var_77_axes_0, keep_dims = var_77_keep_dims_0, x = var_75_cast_fp16)[name = tensor("op_77_cast_fp16")]; - tensor x_11_cast_fp16 = identity(x = var_77_cast_fp16)[name = tensor("x_11_cast_fp16")]; - tensor x_13_transpose_x_0 = const()[name = tensor("x_13_transpose_x_0"), val = tensor(false)]; - tensor x_13_transpose_y_0 = const()[name = tensor("x_13_transpose_y_0"), val = tensor(false)]; - tensor const_4_to_fp16 = const()[name = tensor("const_4_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526528)))]; - tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_4_to_fp16, y = x_11_cast_fp16)[name = tensor("x_13_cast_fp16")]; - tensor var_84_to_fp16 = const()[name = tensor("op_84_to_fp16"), val = tensor(0x1p-24)]; - tensor var_85_cast_fp16 = add(x = x_13_cast_fp16, y = var_84_to_fp16)[name = tensor("op_85_cast_fp16")]; - tensor x_15_epsilon_0 = const()[name = tensor("x_15_epsilon_0"), val = tensor(0x1p-149)]; - tensor x_15_cast_fp16 = log(epsilon = x_15_epsilon_0, x = var_85_cast_fp16)[name = tensor("x_15_cast_fp16")]; - tensor var_87_shape_cast_fp16 = shape(x = x_15_cast_fp16)[name = tensor("op_87_shape_cast_fp16")]; - tensor gather_5 = const()[name = tensor("gather_5"), val = tensor(1)]; - tensor gather_6_axis_0 = const()[name = tensor("gather_6_axis_0"), val = tensor(0)]; - tensor gather_6_batch_dims_0 = const()[name = tensor("gather_6_batch_dims_0"), val = tensor(0)]; - tensor gather_6_validate_indices_0 = const()[name = tensor("gather_6_validate_indices_0"), val = tensor(false)]; - tensor var_87_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_87_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor select_6_to_uint16 = const()[name = tensor("select_6_to_uint16"), val = tensor(2)]; - tensor var_87_shape_cast_fp16_to_uint16 = cast(dtype = var_87_shape_cast_fp16_to_uint16_dtype_0, x = var_87_shape_cast_fp16)[name = tensor("cast_24")]; - tensor gather_6_cast_uint16 = gather(axis = gather_6_axis_0, batch_dims = gather_6_batch_dims_0, indices = select_6_to_uint16, validate_indices = gather_6_validate_indices_0, x = var_87_shape_cast_fp16_to_uint16)[name = tensor("gather_6_cast_uint16")]; - tensor gather_6_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_6_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_5 = const()[name = tensor("const_5"), val = tensor(0)]; - tensor const_6 = const()[name = tensor("const_6"), val = tensor(1)]; - tensor gather_6_cast_uint16_to_int32 = cast(dtype = gather_6_cast_uint16_to_int32_dtype_0, x = gather_6_cast_uint16)[name = tensor("cast_23")]; - tensor var_89 = range_1d(end = gather_6_cast_uint16_to_int32, start = const_5, step = const_6)[name = tensor("op_89")]; - tensor var_90_axes_0 = const()[name = tensor("op_90_axes_0"), val = tensor([0])]; - tensor var_90 = expand_dims(axes = var_90_axes_0, x = var_89)[name = tensor("op_90")]; - tensor concat_3_axis_0 = const()[name = tensor("concat_3_axis_0"), val = tensor(0)]; - tensor concat_3_interleave_0 = const()[name = tensor("concat_3_interleave_0"), val = tensor(false)]; - tensor concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (gather_5, gather_6_cast_uint16_to_int32))[name = tensor("concat_3")]; - tensor shape_8 = shape(x = var_90)[name = tensor("shape_8")]; - tensor real_div_0 = real_div(x = concat_3, y = shape_8)[name = tensor("real_div_0")]; - tensor time_steps = tile(reps = real_div_0, x = var_90)[name = tensor("time_steps")]; - tensor var_93_axes_0 = const()[name = tensor("op_93_axes_0"), val = tensor([1])]; - tensor var_93 = expand_dims(axes = var_93_axes_0, x = mel_length)[name = tensor("op_93")]; - tensor valid_mask = less(x = time_steps, y = var_93)[name = tensor("valid_mask")]; - tensor var_95_axes_0 = const()[name = tensor("op_95_axes_0"), val = tensor([1])]; - tensor var_95 = expand_dims(axes = var_95_axes_0, x = valid_mask)[name = tensor("op_95")]; - tensor var_96_cast_fp16 = select(a = x_15_cast_fp16, b = var_16_to_fp16, cond = var_95)[name = tensor("op_96_cast_fp16")]; - tensor x_mean_numerator_axes_0 = const()[name = tensor("x_mean_numerator_axes_0"), val = tensor([2])]; - tensor x_mean_numerator_keep_dims_0 = const()[name = tensor("x_mean_numerator_keep_dims_0"), val = tensor(false)]; - tensor x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_96_cast_fp16)[name = tensor("x_mean_numerator_cast_fp16")]; - tensor x_mean_denominator_axes_0 = const()[name = tensor("x_mean_denominator_axes_0"), val = tensor([1])]; - tensor x_mean_denominator_keep_dims_0 = const()[name = tensor("x_mean_denominator_keep_dims_0"), val = tensor(false)]; - tensor cast_6_to_fp16_dtype_0 = const()[name = tensor("cast_6_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor valid_mask_to_fp16 = cast(dtype = cast_6_to_fp16_dtype_0, x = valid_mask)[name = tensor("cast_22")]; - tensor x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor("x_mean_denominator_cast_fp16")]; - tensor var_101_axes_0 = const()[name = tensor("op_101_axes_0"), val = tensor([1])]; - tensor var_101_cast_fp16 = expand_dims(axes = var_101_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor("op_101_cast_fp16")]; - tensor x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_101_cast_fp16)[name = tensor("x_mean_cast_fp16")]; - tensor var_104_axes_0 = const()[name = tensor("op_104_axes_0"), val = tensor([2])]; - tensor var_104_cast_fp16 = expand_dims(axes = var_104_axes_0, x = x_mean_cast_fp16)[name = tensor("op_104_cast_fp16")]; - tensor var_105_cast_fp16 = sub(x = x_15_cast_fp16, y = var_104_cast_fp16)[name = tensor("op_105_cast_fp16")]; - tensor var_106_cast_fp16 = select(a = var_105_cast_fp16, b = var_16_to_fp16, cond = var_95)[name = tensor("op_106_cast_fp16")]; - tensor var_19_promoted_1_to_fp16 = const()[name = tensor("op_19_promoted_1_to_fp16"), val = tensor(0x1p+1)]; - tensor var_107_cast_fp16 = pow(x = var_106_cast_fp16, y = var_19_promoted_1_to_fp16)[name = tensor("op_107_cast_fp16")]; - tensor var_109_axes_0 = const()[name = tensor("op_109_axes_0"), val = tensor([2])]; - tensor var_109_keep_dims_0 = const()[name = tensor("op_109_keep_dims_0"), val = tensor(false)]; - tensor var_109_cast_fp16 = reduce_sum(axes = var_109_axes_0, keep_dims = var_109_keep_dims_0, x = var_107_cast_fp16)[name = tensor("op_109_cast_fp16")]; - tensor var_111_to_fp16 = const()[name = tensor("op_111_to_fp16"), val = tensor(0x1p+0)]; - tensor var_112_cast_fp16 = sub(x = var_101_cast_fp16, y = var_111_to_fp16)[name = tensor("op_112_cast_fp16")]; - tensor var_113_cast_fp16 = real_div(x = var_109_cast_fp16, y = var_112_cast_fp16)[name = tensor("op_113_cast_fp16")]; - tensor x_std_1_cast_fp16 = sqrt(x = var_113_cast_fp16)[name = tensor("x_std_1_cast_fp16")]; - tensor var_115_cast_fp16 = not_equal(x = x_std_1_cast_fp16, y = x_std_1_cast_fp16)[name = tensor("op_115_cast_fp16")]; - tensor x_std_3_cast_fp16 = select(a = var_16_to_fp16, b = x_std_1_cast_fp16, cond = var_115_cast_fp16)[name = tensor("x_std_3_cast_fp16")]; - tensor var_25_to_fp16 = const()[name = tensor("op_25_to_fp16"), val = tensor(0x1.5p-17)]; - tensor x_std_cast_fp16 = add(x = x_std_3_cast_fp16, y = var_25_to_fp16)[name = tensor("x_std_cast_fp16")]; - tensor var_120_axes_0 = const()[name = tensor("op_120_axes_0"), val = tensor([2])]; - tensor var_120_cast_fp16 = expand_dims(axes = var_120_axes_0, x = x_std_cast_fp16)[name = tensor("op_120_cast_fp16")]; - tensor x_cast_fp16 = real_div(x = var_105_cast_fp16, y = var_120_cast_fp16)[name = tensor("x_cast_fp16")]; - tensor var_122_shape_cast_fp16 = shape(x = x_cast_fp16)[name = tensor("op_122_shape_cast_fp16")]; - tensor gather_7_axis_0 = const()[name = tensor("gather_7_axis_0"), val = tensor(0)]; - tensor gather_7_batch_dims_0 = const()[name = tensor("gather_7_batch_dims_0"), val = tensor(0)]; - tensor gather_7_validate_indices_0 = const()[name = tensor("gather_7_validate_indices_0"), val = tensor(false)]; - tensor var_122_shape_cast_fp16_to_uint16_dtype_0 = const()[name = tensor("op_122_shape_cast_fp16_to_uint16_dtype_0"), val = tensor("uint16")]; - tensor select_7_to_uint16 = const()[name = tensor("select_7_to_uint16"), val = tensor(2)]; - tensor var_122_shape_cast_fp16_to_uint16 = cast(dtype = var_122_shape_cast_fp16_to_uint16_dtype_0, x = var_122_shape_cast_fp16)[name = tensor("cast_21")]; - tensor gather_7_cast_uint16 = gather(axis = gather_7_axis_0, batch_dims = gather_7_batch_dims_0, indices = select_7_to_uint16, validate_indices = gather_7_validate_indices_0, x = var_122_shape_cast_fp16_to_uint16)[name = tensor("gather_7_cast_uint16")]; - tensor gather_7_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_7_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor const_7 = const()[name = tensor("const_7"), val = tensor(0)]; - tensor const_8 = const()[name = tensor("const_8"), val = tensor(1)]; - tensor gather_7_cast_uint16_to_int32 = cast(dtype = gather_7_cast_uint16_to_int32_dtype_0, x = gather_7_cast_uint16)[name = tensor("cast_20")]; - tensor mask_1 = range_1d(end = gather_7_cast_uint16_to_int32, start = const_7, step = const_8)[name = tensor("mask_1")]; - tensor gather_8_axis_0 = const()[name = tensor("gather_8_axis_0"), val = tensor(0)]; - tensor gather_8_batch_dims_0 = const()[name = tensor("gather_8_batch_dims_0"), val = tensor(0)]; - tensor gather_8_validate_indices_0 = const()[name = tensor("gather_8_validate_indices_0"), val = tensor(false)]; - tensor select_8_to_uint16 = const()[name = tensor("select_8_to_uint16"), val = tensor(0)]; - tensor gather_8_cast_uint16 = gather(axis = gather_8_axis_0, batch_dims = gather_8_batch_dims_0, indices = select_8_to_uint16, validate_indices = gather_8_validate_indices_0, x = var_122_shape_cast_fp16_to_uint16)[name = tensor("gather_8_cast_uint16")]; - tensor gather_8_cast_uint16_to_int32_dtype_0 = const()[name = tensor("gather_8_cast_uint16_to_int32_dtype_0"), val = tensor("int32")]; - tensor concat_4_axis_0 = const()[name = tensor("concat_4_axis_0"), val = tensor(0)]; - tensor concat_4_interleave_0 = const()[name = tensor("concat_4_interleave_0"), val = tensor(false)]; - tensor gather_8_cast_uint16_to_int32 = cast(dtype = gather_8_cast_uint16_to_int32_dtype_0, x = gather_8_cast_uint16)[name = tensor("cast_19")]; - tensor concat_4 = concat(axis = concat_4_axis_0, interleave = concat_4_interleave_0, values = (gather_8_cast_uint16_to_int32, var_9))[name = tensor("concat_4")]; - tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0])]; - tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = tensor("expand_dims_0")]; - tensor var_126 = tile(reps = concat_4, x = expand_dims_0)[name = tensor("op_126")]; - tensor mask = greater_equal(x = var_126, y = var_93)[name = tensor("mask")]; - tensor var_129_axes_0 = const()[name = tensor("op_129_axes_0"), val = tensor([1])]; - tensor var_129 = expand_dims(axes = var_129_axes_0, x = mask)[name = tensor("op_129")]; - tensor cast_15_to_fp16 = const()[name = tensor("cast_15_to_fp16"), val = tensor(0x0p+0)]; - tensor processed_signal_cast_fp16 = select(a = cast_15_to_fp16, b = x_cast_fp16, cond = var_129)[name = tensor("processed_signal_cast_fp16")]; - tensor processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("processed_signal_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; - tensor mel_features = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = tensor("cast_18")]; - } -> (mel_features, mel_length); -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/weights/weight.bin b/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/weights/weight.bin deleted file mode 100644 index d0c6ac87e5f78315662149af9ab4ea3658497dbe..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/Preprocessor.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c062338de852a26607ce4101f74e6895de3a4134a57b07232bd72bfc6f1d7f1a -size 567712 diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/metadata.json b/parakeet-tdt-ctc-110m/coreml/compiled_models/metadata.json deleted file mode 100644 index 6db872ae8a7d463c95dc180210ace0fce019f4a6..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/metadata.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "model_id": "nvidia/parakeet-tdt_ctc-110m", - "model_type": "hybrid_rnnt_ctc", - "sample_rate": 16000, - "max_audio_seconds": 15.0, - "max_audio_samples": 240000, - "max_symbol_steps": 1, - "vocab_size": 1024, - "joint_extra_outputs": 5, - "encoder_dim": 512, - "decoder_dim": 640, - "decoder_hidden": 640, - "decoder_layers": 1, - "blank_id": 1024, - "checkpoint": { - "type": "pretrained", - "model_id": "nvidia/parakeet-tdt_ctc-110m" - }, - "coreml": { - "compute_units": "CPU_ONLY", - "compute_precision": "FLOAT32" - }, - "components": { - "preprocessor": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "path": "parakeet_preprocessor.mlpackage" - }, - "encoder": { - "inputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_encoder.mlpackage" - }, - "ctc_head": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ] - }, - "outputs": { - "log_probs": [ - 1, - 188, - 1025 - ] - }, - "path": "parakeet_ctc_head.mlpackage" - }, - "mel_encoder": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_mel_encoder.mlpackage" - }, - "decoder": { - "inputs": { - "targets": [ - 1, - 1 - ], - "target_length": [ - 1 - ], - "h_in": [ - 1, - 1, - 640 - ], - "c_in": [ - 1, - 1, - 640 - ] - }, - "outputs": { - "decoder": [ - 1, - 640, - 1 - ], - "h_out": [ - 1, - 1, - 640 - ], - "c_out": [ - 1, - 1, - 640 - ] - }, - "path": "parakeet_decoder.mlpackage" - }, - "joint": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "logits": [ - 1, - 188, - 1, - 1030 - ] - }, - "path": "parakeet_joint.mlpackage" - }, - "joint_decision": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 188, - 1 - ], - "token_prob": [ - 1, - 188, - 1 - ], - "duration": [ - 1, - 188, - 1 - ] - }, - "path": "parakeet_joint_decision.mlpackage" - }, - "joint_decision_single_step": { - "inputs": { - "encoder_step": [ - 1, - 512, - 1 - ], - "decoder_step": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 1, - 1 - ], - "token_prob": [ - 1, - 1, - 1 - ], - "duration": [ - 1, - 1, - 1 - ], - "top_k_ids": [ - 1, - 1, - 1, - 64 - ], - "top_k_logits": [ - 1, - 1, - 1, - 64 - ] - }, - "path": "parakeet_joint_decision_single_step.mlpackage" - } - } -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/compiled_models/vocab.json b/parakeet-tdt-ctc-110m/coreml/compiled_models/vocab.json deleted file mode 100644 index 33b95831722563db5362641673b37885e25fb47a..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/compiled_models/vocab.json +++ /dev/null @@ -1 +0,0 @@ -{"0": "", "1": "▁t", "2": "▁th", "3": "▁a", "4": "in", "5": "re", "6": "▁the", "7": "▁w", "8": "▁s", "9": "▁o", "10": "er", "11": "ou", "12": "at", "13": "nd", "14": "it", "15": "▁h", "16": "▁c", "17": "▁b", "18": "is", "19": "en", "20": "on", "21": "ing", "22": "▁f", "23": "▁to", "24": "▁m", "25": "es", "26": "▁p", "27": "or", "28": "an", "29": "▁d", "30": "ll", "31": "▁I", "32": "ed", "33": "▁and", "34": "▁l", "35": "▁of", "36": "▁in", "37": "▁y", "38": "ar", "39": "▁g", "40": "▁you", "41": "as", "42": "om", "43": "▁n", "44": "ve", "45": "▁that", "46": "le", "47": "ic", "48": "us", "49": "ow", "50": "et", "51": "al", "52": "▁e", "53": "ut", "54": "▁it", "55": "ot", "56": "▁be", "57": "▁T", "58": "ion", "59": "▁is", "60": "▁wh", "61": "▁re", "62": "▁on", "63": "▁we", "64": "ent", "65": "▁A", "66": "ay", "67": "▁ha", "68": "▁Th", "69": "id", "70": "▁S", "71": "ac", "72": "gh", "73": "ver", "74": "ke", "75": "▁for", "76": "im", "77": "ly", "78": "ur", "79": "ld", "80": "▁he", "81": "▁st", "82": "all", "83": "ro", "84": "st", "85": "se", "86": "ct", "87": "ith", "88": "ir", "89": "am", "90": "▁this", "91": "if", "92": "▁W", "93": "oo", "94": "ri", "95": "▁was", "96": "ght", "97": "▁u", "98": "▁with", "99": "ad", "100": "ch", "101": "▁se", "102": "▁k", "103": "▁an", "104": "▁The", "105": "▁li", "106": "▁do", "107": "▁B", "108": "▁have", "109": "▁as", "110": "th", "111": "▁are", "112": "▁sh", "113": "ust", "114": "ce", "115": "ally", "116": "ill", "117": "▁H", "118": "▁j", "119": "ter", "120": "▁go", "121": "▁And", "122": "ation", "123": "▁C", "124": "▁so", "125": "ome", "126": "▁not", "127": "op", "128": "il", "129": "ore", "130": "▁ne", "131": "▁can", "132": "▁me", "133": "▁at", "134": "ould", "135": "ant", "136": "▁M", "137": "▁like", "138": "ere", "139": "▁they", "140": "ra", "141": "ers", "142": "▁ab", "143": "▁de", "144": "▁kn", "145": "ge", "146": "▁Y", "147": "▁ch", "148": "ul", "149": "pp", "150": "▁or", "151": "▁al", "152": "▁con", "153": "▁com", "154": "ess", "155": "▁su", "156": "out", "157": "▁your", "158": "▁So", "159": "ate", "160": "▁one", "161": "▁all", "162": "▁ex", "163": "est", "164": "▁fr", "165": "▁just", "166": "▁pro", "167": "▁know", "168": "▁O", "169": "ain", "170": "▁but", "171": "ol", "172": "ive", "173": "▁v", "174": "use", "175": "very", "176": "art", "177": "qu", "178": "▁my", "179": "el", "180": "▁N", "181": "nt", "182": "▁It", "183": "▁what", "184": "ab", "185": "▁P", "186": "▁wor", "187": "▁out", "188": "▁there", "189": "▁up", "190": "um", "191": "▁from", "192": "pe", "193": "▁tw", "194": "▁r", "195": "and", "196": "ight", "197": "ort", "198": "un", "199": "▁L", "200": "ist", "201": "▁about", "202": "ide", "203": "ig", "204": "ake", "205": "▁D", "206": "em", "207": "os", "208": "king", "209": "rou", "210": "ind", "211": "our", "212": "res", "213": "▁We", "214": "▁get", "215": "▁E", "216": "▁G", "217": "ack", "218": "▁le", "219": "ity", "220": "od", "221": "▁F", "222": "ard", "223": "▁pl", "224": "▁our", "225": "▁int", "226": "ment", "227": "▁will", "228": "ies", "229": "▁by", "230": "ink", "231": "ca", "232": "▁if", "233": "red", "234": "her", "235": "ie", "236": "▁us", "237": "▁some", "238": "▁don", "239": "ven", "240": "ood", "241": "ast", "242": "▁R", "243": "▁his", "244": "▁tim", "245": "▁tr", "246": "▁more", "247": "ich", "248": "ous", "249": "ame", "250": "▁going", "251": "▁had", "252": "▁them", "253": "ook", "254": "▁pe", "255": "▁Wh", "256": "▁You", "257": "▁But", "258": "ine", "259": "▁here", "260": "▁would", "261": "cause", "262": "right", "263": "so", "264": "ost", "265": "ure", "266": "▁has", "267": "ect", "268": "▁think", "269": "▁fe", "270": "ong", "271": "▁see", "272": "▁when", "273": "▁who", "274": "▁were", "275": "▁really", "276": "▁their", "277": "▁want", "278": "one", "279": "ople", "280": "▁then", "281": "▁time", "282": "▁sa", "283": "ap", "284": "▁te", "285": "▁He", "286": "▁ye", "287": "ck", "288": "▁her", "289": "▁thing", "290": "▁right", "291": "▁which", "292": "itt", "293": "ice", "294": "act", "295": "▁people", "296": "ty", "297": "▁two", "298": "▁J", "299": "▁im", "300": "ther", "301": "ci", "302": "ose", "303": "▁cl", "304": "▁qu", "305": "▁man", "306": "▁also", "307": "ree", "308": "▁en", "309": "ud", "310": "▁how", "311": "reat", "312": "ak", "313": "hing", "314": "ag", "315": "▁any", "316": "ff", "317": "ace", "318": "per", "319": "▁because", "320": "▁very", "321": "own", "322": "▁ad", "323": "▁act", "324": "▁been", "325": "▁now", "326": "▁ag", "327": "▁into", "328": "▁comp", "329": "ars", "330": "ions", "331": "are", "332": "ite", "333": "iv", "334": "▁these", "335": "ays", "336": "ep", "337": "▁This", "338": "▁she", "339": "ans", "340": "ah", "341": "een", "342": "▁over", "343": "ry", "344": "▁lo", "345": "age", "346": "▁pr", "347": "▁sp", "348": "ue", "349": "▁co", "350": "ick", "351": "ber", "352": "▁did", "353": "ip", "354": "ach", "355": "▁back", "356": "▁no", "357": "▁cont", "358": "▁other", "359": "▁every", "360": "pt", "361": "▁need", "362": "▁him", "363": "▁U", "364": "▁In", "365": "▁work", "366": "irst", "367": "▁part", "368": "▁look", "369": "ittle", "370": "ble", "371": "iz", "372": "▁un", "373": "▁make", "374": "omet", "375": "nder", "376": "ish", "377": "na", "378": "▁little", "379": "▁off", "380": "▁than", "381": "▁got", "382": "ually", "383": "▁per", "384": "▁good", "385": "▁way", "386": "▁could", "387": "▁ac", "388": "▁imp", "389": "able", "390": "▁where", "391": "iff", "392": "▁That", "393": "▁res", "394": "ount", "395": "pl", "396": "ance", "397": "▁first", "398": "▁ro", "399": "▁pre", "400": "ass", "401": "▁say", "402": "int", "403": "ated", "404": "ire", "405": "uch", "406": "ase", "407": "▁somet", "408": "ound", "409": "▁down", "410": "▁diff", "411": "sel", "412": "▁gu", "413": "▁am", "414": "ress", "415": "▁lot", "416": "ence", "417": "▁dis", "418": "orm", "419": "ix", "420": "▁po", "421": "ving", "422": "enty", "423": "▁K", "424": "▁spe", "425": "und", "426": "he", "427": "▁much", "428": "▁ar", "429": "round", "430": "▁app", "431": "co", "432": "ark", "433": "▁new", "434": "ater", "435": "ult", "436": "end", "437": "▁even", "438": "▁start", "439": "ations", "440": "rough", "441": "ile", "442": "fter", "443": "▁well", "444": "be", "445": "▁They", "446": "▁three", "447": "ign", "448": "ild", "449": "▁said", "450": "ough", "451": "ang", "452": "▁too", "453": "ade", "454": "▁bl", "455": "ens", "456": "▁inc", "457": "ia", "458": "▁those", "459": "▁mo", "460": "▁take", "461": "▁through", "462": "▁fl", "463": "▁kind", "464": "▁things", "465": "▁bet", "466": "▁only", "467": "▁St", "468": "▁let", "469": "cess", "470": "▁Ch", "471": "ary", "472": "vel", "473": "▁If", "474": "xt", "475": "other", "476": "av", "477": "ical", "478": "ord", "479": "▁again", "480": "▁something", "481": "onna", "482": "fore", "483": "▁may", "484": "ting", "485": "▁bu", "486": "▁differe", "487": "urn", "488": "▁gonna", "489": "▁does", "490": "uct", "491": "og", "492": "▁twenty", "493": "▁gr", "494": "▁Ye", "495": "wn", "496": "▁should", "497": "▁comm", "498": "ition", "499": "▁under", "500": "▁hel", "501": "ory", "502": "▁fo", "503": "▁use", "504": "igh", "505": "ife", "506": "▁actually", "507": "▁tal", "508": "▁call", "509": "ents", "510": "ious", "511": "ull", "512": "▁There", "513": "▁Yeah", "514": "▁most", "515": "▁ke", "516": "ors", "517": "ved", "518": "ys", "519": "▁sc", "520": "▁happ", "521": "ope", "522": "▁help", "523": "atch", "524": "▁What", "525": "▁rem", "526": "ple", "527": "▁Now", "528": "▁br", "529": "ool", "530": "oth", "531": "▁four", "532": "self", "533": "▁str", "534": "ne", "535": "thing", "536": "▁put", "537": "ial", "538": "▁great", "539": "ail", "540": "ub", "541": "ning", "542": "▁sm", "543": "▁feel", "544": "▁five", "545": "ody", "546": "undred", "547": "iss", "548": "ank", "549": "get", "550": "aking", "551": "▁many", "552": "▁hundred", "553": "▁years", "554": "▁being", "555": "▁come", "556": "▁mean", "557": "ily", "558": "▁different", "559": "▁after", "560": "▁ser", "561": "▁show", "562": "form", "563": "ful", "564": "oy", "565": "▁six", "566": "▁vide", "567": "▁V", "568": "▁its", "569": "▁point", "570": "▁day", "571": "▁des", "572": "ons", "573": "▁bit", "574": "▁bel", "575": "▁before", "576": "▁aw", "577": "▁end", "578": "▁Oh", "579": "▁still", "580": "ath", "581": "▁long", "582": "▁'", "583": "ise", "584": "ob", "585": "day", "586": "▁add", "587": "ft", "588": "ves", "589": "ces", "590": "ady", "591": "▁cr", "592": "▁around", "593": "▁try", "594": "les", "595": "vers", "596": "kay", "597": "ian", "598": "ates", "599": "▁find", "600": "ward", "601": "▁As", "602": "▁eight", "603": "lic", "604": "▁same", "605": "▁pos", "606": "▁em", "607": "▁made", "608": "▁supp", "609": "▁life", "610": "▁Be", "611": "pect", "612": "▁dec", "613": "▁play", "614": "ange", "615": "▁att", "616": "▁pers", "617": "ways", "618": "▁high", "619": "▁hand", "620": "▁next", "621": "▁cons", "622": "▁own", "623": "▁inv", "624": "ower", "625": "▁ind", "626": "ert", "627": "ng", "628": "ave", "629": "▁year", "630": "▁big", "631": "ating", "632": "▁world", "633": "▁rel", "634": "▁sure", "635": "▁tra", "636": "ew", "637": "ered", "638": "▁fin", "639": "▁Well", "640": "▁sl", "641": "▁doing", "642": "bs", "643": "▁set", "644": "▁rec", "645": "ual", "646": "cial", "647": "▁ph", "648": "erm", "649": "▁love", "650": "ph", "651": "▁real", "652": "▁last", "653": "ict", "654": "▁bo", "655": "▁ra", "656": "ible", "657": "▁wr", "658": "mer", "659": "▁count", "660": "ities", "661": "▁always", "662": "inet", "663": "ments", "664": "uc", "665": "▁might", "666": "▁inter", "667": "▁video", "668": "gin", "669": "▁tell", "670": "▁never", "671": "vent", "672": "▁import", "673": "ied", "674": "▁sy", "675": "▁How", "676": "ically", "677": "ought", "678": "▁thir", "679": "▁rep", "680": "ks", "681": "ib", "682": "▁fam", "683": "ject", "684": "▁bas", "685": "▁She", "686": "▁give", "687": "akes", "688": "▁ninet", "689": "▁reg", "690": "▁min", "691": "▁op", "692": "▁def", "693": "▁didn", "694": "te", "695": "▁cour", "696": "▁why", "697": "▁ent", "698": "▁place", "699": "▁ins", "700": "▁car", "701": "ather", "702": "▁person", "703": "ular", "704": "▁inst", "705": "▁prod", "706": "lect", "707": "▁Al", "708": "▁today", "709": "▁bec", "710": "▁sur", "711": "▁All", "712": "▁another", "713": "▁bus", "714": "▁keep", "715": "ell", "716": "ese", "717": "riend", "718": "▁quest", "719": "▁talk", "720": "als", "721": "ings", "722": "▁mon", "723": "cond", "724": "old", "725": "▁acc", "726": "▁la", "727": "▁num", "728": "ident", "729": "▁che", "730": "iness", "731": "▁turn", "732": "▁ear", "733": "▁No", "734": "ousand", "735": "▁better", "736": "ific", "737": "▁loo", "738": "▁gl", "739": "oc", "740": "▁important", "741": "ited", "742": "▁An", "743": "▁thousand", "744": "ility", "745": "llow", "746": "▁used", "747": "▁gen", "748": "▁sim", "749": "li", "750": "▁happen", "751": "▁Un", "752": "▁Let", "753": "air", "754": "ock", "755": "ably", "756": "gg", "757": "▁watch", "758": "▁For", "759": "▁sw", "760": "ren", "761": "ute", "762": "ever", "763": "▁pol", "764": "▁sch", "765": "▁When", "766": "▁such", "767": "▁fif", "768": "▁home", "769": "▁cle", "770": "▁contin", "771": "ouse", "772": "▁friend", "773": "uring", "774": "▁Okay", "775": "gr", "776": "▁able", "777": "▁stud", "778": "▁eff", "779": "hip", "780": "body", "781": "▁top", "782": "ness", "783": "▁exper", "784": "▁pret", "785": "▁both", "786": "▁done", "787": "cri", "788": "▁mark", "789": "▁while", "790": "▁old", "791": "ros", "792": "ont", "793": "▁second", "794": "ative", "795": "▁thought", "796": "▁best", "797": "▁found", "798": "iew", "799": "▁belie", "800": "▁each", "801": "erest", "802": "▁tri", "803": "▁eas", "804": "▁ca", "805": "▁fact", "806": "▁care", "807": "▁fun", "808": "atter", "809": "ures", "810": "▁head", "811": "▁lear", "812": "▁water", "813": "▁hard", "814": "▁few", "815": "▁side", "816": "ween", "817": "▁exp", "818": "▁away", "819": "its", "820": "▁ext", "821": "lud", "822": "▁run", "823": "▁trans", "824": "ince", "825": "▁sk", "826": "▁open", "827": "cus", "828": "▁between", "829": "▁called", "830": "▁wee", "831": "▁pretty", "832": "ason", "833": "▁far", "834": "ember", "835": "omm", "836": "▁interest", "837": "any", "838": "ner", "839": "uff", "840": "▁pres", "841": "▁cur", "842": "▁child", "843": "ee", "844": "▁toget", "845": "▁together", "846": "olog", "847": "▁God", "848": "ond", "849": "▁char", "850": "▁looking", "851": "stem", "852": "az", "853": "cent", "854": "▁ob", "855": "▁ass", "856": "land", "857": "▁doesn", "858": "▁business", "859": "▁course", "860": "▁ten", "861": "ps", "862": "arch", "863": "ced", "864": 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"▁probably", "929": "▁dri", "930": "▁Do", "931": "▁gener", "932": "▁anything", "933": "▁lar", "934": "▁My", "935": "▁school", "936": "▁lead", "937": "▁sub", "938": "▁ty", "939": "▁plan", "940": "▁seem", "941": "▁whole", "942": "irect", "943": "▁light", "944": "▁must", "945": "▁mom", "946": "▁opp", "947": "▁support", "948": "▁family", "949": "ices", "950": "amp", "951": "▁proble", "952": "▁dr", "953": "ready", "954": "▁using", "955": "ense", "956": "▁prov", "957": "ush", "958": "ax", "959": "▁power", "960": "▁Re", "961": "alth", "962": "▁ev", "963": "▁stand", "964": "▁war", "965": "ts", "966": "▁", "967": "e", "968": "t", "969": "o", "970": "a", "971": "n", "972": "i", "973": "s", "974": "r", "975": "h", "976": "l", "977": "d", "978": "u", "979": "c", "980": "m", "981": "y", "982": "g", "983": "w", "984": "f", "985": "p", "986": ".", "987": "b", "988": ",", "989": "v", "990": "k", "991": "'", "992": "I", "993": "T", "994": "A", "995": "S", "996": "x", "997": "W", "998": "j", "999": "B", "1000": "C", "1001": "H", "1002": "?", "1003": "M", "1004": "O", "1005": "Y", "1006": "N", "1007": "P", "1008": "E", "1009": "q", "1010": "L", "1011": "D", "1012": "z", "1013": "G", "1014": "F", "1015": "R", "1016": "!", "1017": "J", "1018": "U", "1019": "K", "1020": "V", "1021": "Q", "1022": "Z", "1023": "X"} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/convert-parakeet.py b/parakeet-tdt-ctc-110m/coreml/convert-parakeet.py deleted file mode 100644 index 6cd1c871c81f26b9449bf6313077afe8e27527d9..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/convert-parakeet.py +++ /dev/null @@ -1,697 +0,0 @@ -#!/usr/bin/env python3 -"""CLI for exporting Parakeet TDT-CTC 110M Hybrid components to CoreML.""" -from __future__ import annotations - -import json -from dataclasses import asdict -from pathlib import Path -from typing import Dict, Optional, Tuple - -import coremltools as ct -import numpy as np -import soundfile as sf -import torch -import typer - -import nemo.collections.asr as nemo_asr - -from individual_components import ( - CTCHeadWrapper, - DecoderWrapper, - EncoderWrapper, - ExportSettings, - JointWrapper, - JointDecisionWrapper, - JointDecisionSingleStep, - PreprocessorWrapper, - MelEncoderWrapper, - _coreml_convert, -) - -DEFAULT_MODEL_ID = "nvidia/parakeet-tdt_ctc-110m" -AUTHOR = "Fluid Inference" - - -def _compute_length(seconds: float, sample_rate: int) -> int: - return int(round(seconds * sample_rate)) - - -def _prepare_audio( - validation_audio: Optional[Path], - sample_rate: int, - max_samples: int, - seed: Optional[int], -) -> torch.Tensor: - if validation_audio is None: - if seed is not None: - torch.manual_seed(seed) - audio = torch.randn(1, max_samples, dtype=torch.float32) - return audio - - data, sr = sf.read(str(validation_audio), dtype="float32") - if sr != sample_rate: - raise typer.BadParameter( - f"Validation audio sample rate {sr} does not match model rate {sample_rate}" - ) - - if data.ndim > 1: - data = data[:, 0] - - if data.size == 0: - raise typer.BadParameter("Validation audio is empty") - - if data.size < max_samples: - pad_width = max_samples - data.size - data = np.pad(data, (0, pad_width)) - elif data.size > max_samples: - data = data[:max_samples] - - audio = torch.from_numpy(data).unsqueeze(0).to(dtype=torch.float32) - return audio - - -def _save_mlpackage(model: ct.models.MLModel, path: Path, description: str) -> None: - # Ensure iOS 17+ target for MLProgram ops and ANE readiness - try: - model.minimum_deployment_target = ct.target.iOS17 - except Exception: - pass - model.short_description = description - model.author = AUTHOR - path.parent.mkdir(parents=True, exist_ok=True) - model.save(str(path)) - - -def _tensor_shape(tensor: torch.Tensor) -> Tuple[int, ...]: - return tuple(int(dim) for dim in tensor.shape) - - -def _parse_compute_units(name: str) -> ct.ComputeUnit: - """Parse a human-friendly compute units string into ct.ComputeUnit. - - Accepted (case-insensitive): ALL, CPU_ONLY, CPU_AND_GPU, CPU_AND_NE. - """ - normalized = str(name).strip().upper() - mapping = { - "ALL": ct.ComputeUnit.ALL, - "CPU_ONLY": ct.ComputeUnit.CPU_ONLY, - "CPU_AND_GPU": ct.ComputeUnit.CPU_AND_GPU, - "CPU_AND_NE": ct.ComputeUnit.CPU_AND_NE, - "CPU_AND_NEURALENGINE": ct.ComputeUnit.CPU_AND_NE, - } - if normalized not in mapping: - raise typer.BadParameter( - f"Unknown compute units '{name}'. Choose from: " + ", ".join(mapping.keys()) - ) - return mapping[normalized] - - -def _parse_compute_precision(name: Optional[str]) -> Optional[ct.precision]: - """Parse compute precision string into ct.precision or None. - - Accepted (case-insensitive): FLOAT32, FLOAT16. If None/empty, returns None (tool default). - """ - if name is None: - return None - normalized = str(name).strip().upper() - if normalized == "": - return None - mapping = { - "FLOAT32": ct.precision.FLOAT32, - "FLOAT16": ct.precision.FLOAT16, - } - if normalized not in mapping: - raise typer.BadParameter( - f"Unknown compute precision '{name}'. Choose from: " + ", ".join(mapping.keys()) - ) - return mapping[normalized] - - -# Fixed export choices: CPU_ONLY + FP32, min target iOS17 - - -app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) - - -@app.command() -def convert( - nemo_path: Optional[Path] = typer.Option( - None, - "--nemo-path", - exists=True, - resolve_path=True, - help="Path to parakeet-tdt_ctc-110m .nemo checkpoint (skip to auto-download)", - ), - model_id: str = typer.Option( - DEFAULT_MODEL_ID, - "--model-id", - help="Model identifier to download when --nemo-path is omitted", - ), - output_dir: Path = typer.Option(Path("parakeet_110m_coreml"), help="Directory where mlpackages and metadata will be written"), - preprocessor_cu: str = typer.Option( - "CPU_ONLY", - "--preprocessor-cu", - help="Compute units for preprocessor (default CPU_ONLY)", - ), - mel_encoder_cu: str = typer.Option( - "CPU_ONLY", - "--mel-encoder-cu", - help="Compute units for fused mel+encoder (default CPU_ONLY)", - ), - compute_precision: Optional[str] = typer.Option( - None, - "--compute-precision", - help="Export precision: FLOAT32 (default) or FLOAT16 to shrink non-quantized weights.", - ), -) -> None: - """Export all Parakeet TDT-CTC 110M Hybrid sub-modules to CoreML with a fixed 15-second window. - - This exports both CTC and TDT components from the hybrid model. - """ - # Runtime CoreML contract keeps U=1 so the prediction net matches the streaming decoder. - export_settings = ExportSettings( - output_dir=output_dir, - compute_units=ct.ComputeUnit.CPU_ONLY, # Default: CPU-only for all components - deployment_target=ct.target.iOS17, # iOS 17+ features and kernels - compute_precision=_parse_compute_precision(compute_precision), - max_audio_seconds=15.0, - max_symbol_steps=1, - ) - - typer.echo("Export configuration:") - typer.echo(asdict(export_settings)) - - output_dir.mkdir(parents=True, exist_ok=True) - pre_cu = _parse_compute_units(preprocessor_cu) - melenc_cu = _parse_compute_units(mel_encoder_cu) - - if nemo_path is not None: - typer.echo(f"Loading NeMo model from {nemo_path}
") - # 110M is a hybrid model: EncDecHybridRNNTCTCBPEModel - asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.restore_from( - str(nemo_path), map_location="cpu" - ) - checkpoint_meta = { - "type": "file", - "path": str(nemo_path), - } - else: - typer.echo(f"Downloading NeMo model via {model_id}
") - # 110M is a hybrid model: EncDecHybridRNNTCTCBPEModel - asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained( - model_id, map_location="cpu" - ) - checkpoint_meta = { - "type": "pretrained", - "model_id": model_id, - } - asr_model.eval() - - sample_rate = int(asr_model.cfg.preprocessor.sample_rate) - max_samples = _compute_length(export_settings.max_audio_seconds, sample_rate) - - # Look for a bundled 15s 16kHz audio file - default_audio = (Path(__file__).parent / "audio" / "yc_first_minute_16k_15s.wav").resolve() - if default_audio.exists(): - typer.echo(f"Using trace audio: {default_audio}") - audio_tensor = _prepare_audio(default_audio, sample_rate, max_samples, seed=None) - else: - typer.echo("No trace audio found, using random noise for tracing") - audio_tensor = _prepare_audio(None, sample_rate, max_samples, seed=42) - audio_length = torch.tensor([max_samples], dtype=torch.int32) - - preprocessor = PreprocessorWrapper(asr_model.preprocessor.eval()) - encoder = EncoderWrapper(asr_model.encoder.eval()) - decoder = DecoderWrapper(asr_model.decoder.eval()) - joint = JointWrapper(asr_model.joint.eval()) - # CTC head for hybrid model - ctc_head = CTCHeadWrapper(asr_model.ctc_decoder.eval()) - - decoder_export_flag = getattr(asr_model.decoder, "_rnnt_export", False) - asr_model.decoder._rnnt_export = True - - try: - with torch.inference_mode(): - mel_ref, mel_length_ref = preprocessor(audio_tensor, audio_length) - mel_length_ref = mel_length_ref.to(dtype=torch.int32) - encoder_ref, encoder_length_ref = encoder(mel_ref, mel_length_ref) - encoder_length_ref = encoder_length_ref.to(dtype=torch.int32) - # CTC log probs - ctc_log_probs_ref = ctc_head(encoder_ref) - - # Clone Tensors to drop the inference tensor flag before tracing - mel_ref = mel_ref.clone() - mel_length_ref = mel_length_ref.clone() - encoder_ref = encoder_ref.clone() - encoder_length_ref = encoder_length_ref.clone() - ctc_log_probs_ref = ctc_log_probs_ref.clone() - - vocab_size = int(asr_model.tokenizer.vocab_size) - num_extra = int(asr_model.joint.num_extra_outputs) - decoder_hidden = int(asr_model.decoder.pred_hidden) - decoder_layers = int(asr_model.decoder.pred_rnn_layers) - - typer.echo(f"Model info:") - typer.echo(f" Vocab size: {vocab_size}") - typer.echo(f" Num extra (duration bins): {num_extra}") - typer.echo(f" Decoder hidden: {decoder_hidden}") - typer.echo(f" Decoder layers: {decoder_layers}") - typer.echo(f" Encoder output shape: {_tensor_shape(encoder_ref)}") - - targets = torch.full( - (1, export_settings.max_symbol_steps), - fill_value=asr_model.decoder.blank_idx, - dtype=torch.int32, - ) - target_lengths = torch.tensor( - [export_settings.max_symbol_steps], dtype=torch.int32 - ) - zero_state = torch.zeros( - decoder_layers, - 1, - decoder_hidden, - dtype=torch.float32, - ) - - with torch.inference_mode(): - decoder_ref, h_ref, c_ref = decoder(targets, target_lengths, zero_state, zero_state) - joint_ref = joint(encoder_ref, decoder_ref) - - decoder_ref = decoder_ref.clone() - h_ref = h_ref.clone() - c_ref = c_ref.clone() - joint_ref = joint_ref.clone() - - typer.echo(f" Decoder output shape: {_tensor_shape(decoder_ref)}") - typer.echo(f" Joint output shape: {_tensor_shape(joint_ref)}") - typer.echo(f" CTC log probs shape: {_tensor_shape(ctc_log_probs_ref)}") - - typer.echo("Tracing and converting preprocessor
") - # Ensure tracing happens on CPU explicitly - preprocessor = preprocessor.cpu() - audio_tensor = audio_tensor.cpu() - audio_length = audio_length.cpu() - traced_preprocessor = torch.jit.trace( - preprocessor, (audio_tensor, audio_length), strict=False - ) - traced_preprocessor.eval() - preprocessor_inputs = [ - # Allow variable-length audio up to the fixed 15s window using RangeDim - ct.TensorType( - name="audio", - shape=(1, ct.RangeDim(1, max_samples)), - dtype=np.float32, - ), - ct.TensorType(name="audio_length", shape=(1,), dtype=np.int32), - ] - preprocessor_outputs = [ - ct.TensorType(name="mel_features", dtype=np.float32), - ct.TensorType(name="mel_length", dtype=np.int32), - ] - # Preprocessor compute units (parametrized; default CPU_ONLY) - preprocessor_model = _coreml_convert( - traced_preprocessor, - preprocessor_inputs, - preprocessor_outputs, - export_settings, - compute_units_override=pre_cu, - ) - preprocessor_path = output_dir / "parakeet_preprocessor.mlpackage" - _save_mlpackage( - preprocessor_model, - preprocessor_path, - "Parakeet 110M preprocessor (15 s window)", - ) - - typer.echo("Tracing and converting encoder
") - traced_encoder = torch.jit.trace( - encoder, (mel_ref, mel_length_ref), strict=False - ) - traced_encoder.eval() - encoder_inputs = [ - ct.TensorType(name="mel_features", shape=_tensor_shape(mel_ref), dtype=np.float32), - ct.TensorType(name="mel_length", shape=(1,), dtype=np.int32), - ] - encoder_outputs = [ - ct.TensorType(name="encoder_output", dtype=np.float32), - ct.TensorType(name="encoder_length", dtype=np.int32), - ] - # Encoder: CPU only - encoder_model = _coreml_convert( - traced_encoder, - encoder_inputs, - encoder_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - encoder_path = output_dir / "parakeet_encoder.mlpackage" - _save_mlpackage( - encoder_model, - encoder_path, - "Parakeet 110M encoder (15 s window)", - ) - - # CTC Head for hybrid model - typer.echo("Tracing and converting CTC head
") - traced_ctc_head = torch.jit.trace( - ctc_head, (encoder_ref,), strict=False - ) - traced_ctc_head.eval() - ctc_head_inputs = [ - ct.TensorType(name="encoder_output", shape=_tensor_shape(encoder_ref), dtype=np.float32), - ] - ctc_head_outputs = [ - ct.TensorType(name="ctc_logits", dtype=np.float32), - ] - ctc_head_model = _coreml_convert( - traced_ctc_head, - ctc_head_inputs, - ctc_head_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - ctc_head_path = output_dir / "parakeet_ctc_head.mlpackage" - _save_mlpackage( - ctc_head_model, - ctc_head_path, - "Parakeet 110M CTC decoder head", - ) - - # Optional fused export: Preprocessor + Encoder - typer.echo("Tracing and converting fused mel+encoder
") - mel_encoder = MelEncoderWrapper(preprocessor, encoder) - traced_mel_encoder = torch.jit.trace( - mel_encoder, (audio_tensor, audio_length), strict=False - ) - traced_mel_encoder.eval() - mel_encoder_inputs = [ - # Keep fixed 15s window for fused Mel+Encoder - ct.TensorType(name="audio", shape=(1, max_samples), dtype=np.float32), - ct.TensorType(name="audio_length", shape=(1,), dtype=np.int32), - ] - mel_encoder_outputs = [ - ct.TensorType(name="encoder_output", dtype=np.float32), - ct.TensorType(name="encoder_length", dtype=np.int32), - ] - # Fused mel+encoder compute units (parametrized; default CPU_ONLY) - mel_encoder_model = _coreml_convert( - traced_mel_encoder, - mel_encoder_inputs, - mel_encoder_outputs, - export_settings, - compute_units_override=melenc_cu, - ) - mel_encoder_path = output_dir / "parakeet_mel_encoder.mlpackage" - _save_mlpackage( - mel_encoder_model, - mel_encoder_path, - "Parakeet 110M fused Mel+Encoder (15 s window)", - ) - - typer.echo("Tracing and converting decoder
") - traced_decoder = torch.jit.trace( - decoder, - (targets, target_lengths, zero_state, zero_state), - strict=False, - ) - traced_decoder.eval() - decoder_inputs = [ - ct.TensorType(name="targets", shape=_tensor_shape(targets), dtype=np.int32), - ct.TensorType(name="target_length", shape=(1,), dtype=np.int32), - ct.TensorType(name="h_in", shape=_tensor_shape(zero_state), dtype=np.float32), - ct.TensorType(name="c_in", shape=_tensor_shape(zero_state), dtype=np.float32), - ] - decoder_outputs = [ - ct.TensorType(name="decoder", dtype=np.float32), - ct.TensorType(name="h_out", dtype=np.float32), - ct.TensorType(name="c_out", dtype=np.float32), - ] - # Decoder: CPU only - decoder_model = _coreml_convert( - traced_decoder, - decoder_inputs, - decoder_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - decoder_path = output_dir / "parakeet_decoder.mlpackage" - _save_mlpackage( - decoder_model, - decoder_path, - "Parakeet 110M decoder (RNNT prediction network)", - ) - - typer.echo("Tracing and converting joint
") - traced_joint = torch.jit.trace( - joint, - (encoder_ref, decoder_ref), - strict=False, - ) - traced_joint.eval() - joint_inputs = [ - ct.TensorType(name="encoder", shape=_tensor_shape(encoder_ref), dtype=np.float32), - ct.TensorType(name="decoder", shape=_tensor_shape(decoder_ref), dtype=np.float32), - ] - joint_outputs = [ - ct.TensorType(name="logits", dtype=np.float32), - ] - # Joint: CPU only - joint_model = _coreml_convert( - traced_joint, - joint_inputs, - joint_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - joint_path = output_dir / "parakeet_joint.mlpackage" - _save_mlpackage( - joint_model, - joint_path, - "Parakeet 110M joint network (RNNT)", - ) - - # Joint + decision head (split logits, softmax, argmax) - typer.echo("Tracing and converting joint decision head
") - vocab_size = int(asr_model.tokenizer.vocab_size) - num_extra = int(asr_model.joint.num_extra_outputs) - joint_decision = JointDecisionWrapper(joint, vocab_size=vocab_size, num_extra=num_extra) - traced_joint_decision = torch.jit.trace( - joint_decision, - (encoder_ref, decoder_ref), - strict=False, - ) - traced_joint_decision.eval() - joint_decision_inputs = [ - ct.TensorType(name="encoder", shape=_tensor_shape(encoder_ref), dtype=np.float32), - ct.TensorType(name="decoder", shape=_tensor_shape(decoder_ref), dtype=np.float32), - ] - joint_decision_outputs = [ - ct.TensorType(name="token_id", dtype=np.int32), - ct.TensorType(name="token_prob", dtype=np.float32), - ct.TensorType(name="duration", dtype=np.int32), - ] - # JointDecision: CPU only - joint_decision_model = _coreml_convert( - traced_joint_decision, - joint_decision_inputs, - joint_decision_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - joint_decision_path = output_dir / "parakeet_joint_decision.mlpackage" - _save_mlpackage( - joint_decision_model, - joint_decision_path, - "Parakeet 110M joint + decision head (split, softmax, argmax)", - ) - - # Single-step JointDecision for [1,512,1] x [1,640,1] -> [1,1,1] - # Note: 110M encoder dim is 512 (not 1024 like 0.6B) - typer.echo("Tracing and converting single-step joint decision
") - jd_single = JointDecisionSingleStep(joint, vocab_size=vocab_size, num_extra=num_extra) - # Create single-step slices from refs - enc_step = encoder_ref[:, :, :1].contiguous() - dec_step = decoder_ref[:, :, :1].contiguous() - traced_jd_single = torch.jit.trace( - jd_single, - (enc_step, dec_step), - strict=False, - ) - traced_jd_single.eval() - jd_single_inputs = [ - ct.TensorType(name="encoder_step", shape=(1, enc_step.shape[1], 1), dtype=np.float32), - ct.TensorType(name="decoder_step", shape=(1, dec_step.shape[1], 1), dtype=np.float32), - ] - jd_single_outputs = [ - ct.TensorType(name="token_id", dtype=np.int32), - ct.TensorType(name="token_prob", dtype=np.float32), - ct.TensorType(name="duration", dtype=np.int32), - ct.TensorType(name="top_k_ids", dtype=np.int32), - ct.TensorType(name="top_k_logits", dtype=np.float32), - ] - # Single-step JointDecision: CPU only - jd_single_model = _coreml_convert( - traced_jd_single, - jd_single_inputs, - jd_single_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - jd_single_path = output_dir / "parakeet_joint_decision_single_step.mlpackage" - _save_mlpackage( - jd_single_model, - jd_single_path, - "Parakeet 110M single-step joint decision (current frame)", - ) - - # Export vocabulary - typer.echo("Exporting vocabulary
") - vocab_path = output_dir / "vocab.json" - vocab_dict = { - "vocab_size": vocab_size, - "blank_id": int(asr_model.decoder.blank_idx), - "tokens": asr_model.tokenizer.vocab, - } - vocab_path.write_text(json.dumps(vocab_dict, indent=2, ensure_ascii=False)) - - metadata: Dict[str, object] = { - "model_id": model_id, - "model_type": "hybrid_rnnt_ctc", - "sample_rate": sample_rate, - "max_audio_seconds": export_settings.max_audio_seconds, - "max_audio_samples": max_samples, - "max_symbol_steps": export_settings.max_symbol_steps, - "vocab_size": vocab_size, - "joint_extra_outputs": num_extra, - "encoder_dim": int(encoder_ref.shape[1]), # 512 for 110M - "decoder_dim": int(decoder_ref.shape[1]), # 640 - "decoder_hidden": decoder_hidden, - "decoder_layers": decoder_layers, - "blank_id": int(asr_model.decoder.blank_idx), - "checkpoint": checkpoint_meta, - "coreml": { - "compute_units": export_settings.compute_units.name, - "compute_precision": ( - export_settings.compute_precision.name - if export_settings.compute_precision is not None - else "FLOAT32" - ), - }, - "components": { - "preprocessor": { - "inputs": { - "audio_signal": list(_tensor_shape(audio_tensor)), - "audio_length": [1], - }, - "outputs": { - "mel": list(_tensor_shape(mel_ref)), - "mel_length": [1], - }, - "path": preprocessor_path.name, - }, - "encoder": { - "inputs": { - "mel": list(_tensor_shape(mel_ref)), - "mel_length": [1], - }, - "outputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "encoder_length": [1], - }, - "path": encoder_path.name, - }, - "ctc_head": { - "inputs": { - "encoder": list(_tensor_shape(encoder_ref)), - }, - "outputs": { - "log_probs": list(_tensor_shape(ctc_log_probs_ref)), - }, - "path": ctc_head_path.name, - }, - "mel_encoder": { - "inputs": { - "audio_signal": [1, max_samples], - "audio_length": [1], - }, - "outputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "encoder_length": [1], - }, - "path": mel_encoder_path.name, - }, - "decoder": { - "inputs": { - "targets": list(_tensor_shape(targets)), - "target_length": [1], - "h_in": list(_tensor_shape(zero_state)), - "c_in": list(_tensor_shape(zero_state)), - }, - "outputs": { - "decoder": list(_tensor_shape(decoder_ref)), - "h_out": list(_tensor_shape(h_ref)), - "c_out": list(_tensor_shape(c_ref)), - }, - "path": decoder_path.name, - }, - "joint": { - "inputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "decoder": list(_tensor_shape(decoder_ref)), - }, - "outputs": { - "logits": list(_tensor_shape(joint_ref)), - }, - "path": joint_path.name, - }, - "joint_decision": { - "inputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "decoder": list(_tensor_shape(decoder_ref)), - }, - "outputs": { - "token_id": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[2], - _tensor_shape(decoder_ref)[2], - ], - "token_prob": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[2], - _tensor_shape(decoder_ref)[2], - ], - "duration": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[2], - _tensor_shape(decoder_ref)[2], - ], - }, - "path": joint_decision_path.name, - }, - "joint_decision_single_step": { - "inputs": { - "encoder_step": [1, int(encoder_ref.shape[1]), 1], - "decoder_step": [1, int(decoder_ref.shape[1]), 1], - }, - "outputs": { - "token_id": [1, 1, 1], - "token_prob": [1, 1, 1], - "duration": [1, 1, 1], - "top_k_ids": [1, 1, 1, 64], - "top_k_logits": [1, 1, 1, 64], - }, - "path": jd_single_path.name, - }, - }, - } - - metadata_path = output_dir / "metadata.json" - metadata_path.write_text(json.dumps(metadata, indent=2)) - typer.echo(f"Export complete. Metadata written to {metadata_path}") - - finally: - asr_model.decoder._rnnt_export = decoder_export_flag - - -if __name__ == "__main__": - app() diff --git a/parakeet-tdt-ctc-110m/coreml/hybrid_earnings_benchmark.json b/parakeet-tdt-ctc-110m/coreml/hybrid_earnings_benchmark.json deleted file mode 100644 index 3dd6bc7551d07ef8a966ba06590d2454e4324983..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/hybrid_earnings_benchmark.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "approach" : "single-encoder", - "model" : "parakeet-tdt-ctc-110m-hybrid", - "results" : [ - { - "audioLength" : 15, - "ctcDetections" : [ - { - "endTime" : 6.0800000000000001, - "inReference" : true, - "score" : -8.3699999999999992, - "source" : "ctc", - "startTime" : 4.96, - "word" : "LATAM Airlines" - } - ], - "dictFound" : 1, - "dictTotal" : 1, - "fileId" : "4329526_chunk0", - "hypothesis" : "goodday everyone and welcome to latam airlines group earnings release confonference call just as a reminder this conference is being recorded lat tam airlines group eararnings releaseed for the", - "processingTime" : 0.070000000000000007, - "reference" : "good day everyone and welcome to latam airlines group earnings release conference call just as a reminder this conference is being recorded latam airlines group earnings released for the", - "wer" : 24.140000000000001 - } - ], - "summary" : { - "avgWer" : 24.140000000000001, - "dictPass" : 1, - "dictRate" : 100, - "dictTotal" : 1, - "totalAudioDuration" : 15, - "totalProcessingTime" : 0.070000000000000007, - "totalTests" : 1 - } -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/individual_components.py b/parakeet-tdt-ctc-110m/coreml/individual_components.py deleted file mode 100644 index 0f6ae75f46ab359edc5c6dd6799dccf0b04a7ed7..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/individual_components.py +++ /dev/null @@ -1,265 +0,0 @@ -#!/usr/bin/env python3 -"""Export Parakeet TDT-CTC 110M Hybrid RNNT components into CoreML and validate outputs.""" -from __future__ import annotations - -from dataclasses import dataclass -from pathlib import Path -from typing import Optional, Tuple - -import coremltools as ct -import torch - - -@dataclass -class ExportSettings: - output_dir: Path - compute_units: ct.ComputeUnit - deployment_target: Optional[ct.target.iOS17] - compute_precision: Optional[ct.precision] - max_audio_seconds: float - max_symbol_steps: int - - -@dataclass -class ValidationSettings: - audio_path: Optional[Path] - seconds: float - seed: Optional[int] - rtol: float - atol: float - skip: bool - - -@dataclass -class ValidationDiff: - name: str - max_abs_diff: float - max_rel_diff: float - - -@dataclass -class ValidationResult: - source: str - audio_num_samples: int - audio_seconds: float - token_length: int - atol: float - rtol: float - diffs: Tuple[ValidationDiff, ...] - - -class PreprocessorWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, audio_signal: torch.Tensor, length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - mel, mel_length = self.module(input_signal=audio_signal, length=length.to(dtype=torch.long)) - return mel, mel_length - - -class EncoderWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, features: torch.Tensor, length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - encoded, encoded_lengths = self.module(audio_signal=features, length=length.to(dtype=torch.long)) - return encoded, encoded_lengths - - -class DecoderWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward( - self, - targets: torch.Tensor, - target_lengths: torch.Tensor, - h_in: torch.Tensor, - c_in: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - state = [h_in, c_in] - decoder_output, _, new_state = self.module( - targets=targets.to(dtype=torch.long), - target_length=target_lengths.to(dtype=torch.long), - states=state, - ) - return decoder_output, new_state[0], new_state[1] - - -class JointWrapper(torch.nn.Module): - """Joint network for 110M hybrid model. - - Note: The 110M model has encoder_dim=512 and decoder_dim=640. - The joint network projects both to 640, then combines them. - """ - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor) -> torch.Tensor: - # Input: encoder_outputs [B, D_enc, T], decoder_outputs [B, D_dec, U] - # For 110M: D_enc=512, D_dec=640 - # Transpose to match what projection layers expect - encoder_outputs = encoder_outputs.transpose(1, 2) # [B, T, D_enc] - decoder_outputs = decoder_outputs.transpose(1, 2) # [B, U, D_dec] - - # Apply projections - enc_proj = self.module.enc(encoder_outputs) # [B, T, 640] - dec_proj = self.module.pred(decoder_outputs) # [B, U, 640] - - # Explicit broadcasting along T and U to avoid converter ambiguity - x = enc_proj.unsqueeze(2) + dec_proj.unsqueeze(1) # [B, T, U, 640] - x = self.module.joint_net[0](x) # ReLU - x = self.module.joint_net[1](x) # Dropout (no-op in eval) - out = self.module.joint_net[2](x) # Linear -> logits [B, T, U, vocab+1+durations] - return out - - -class CTCHeadWrapper(torch.nn.Module): - """CTC decoder head for 110M hybrid model. - - Takes encoder output and produces log probabilities over vocabulary. - The NeMo CTC decoder (ConvASRDecoder) uses Conv1d so it expects [B, D, T] format. - """ - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, encoder_outputs: torch.Tensor) -> torch.Tensor: - # Input: encoder_outputs [B, D_enc, T] - already in the format CTC decoder expects - # The NeMo CTC decoder uses Conv1d internally, so it expects [B, D, T] - # Output: log probabilities [B, T, vocab+1] - log_probs = self.module(encoder_output=encoder_outputs) - return log_probs - - -class MelEncoderWrapper(torch.nn.Module): - """Fused wrapper: waveform -> mel -> encoder. - - Inputs: - - audio_signal: [B, S] - - audio_length: [B] - - Outputs: - - encoder: [B, D, T_enc] - - encoder_length: [B] - """ - def __init__(self, preprocessor: PreprocessorWrapper, encoder: EncoderWrapper) -> None: - super().__init__() - self.preprocessor = preprocessor - self.encoder = encoder - - def forward(self, audio_signal: torch.Tensor, audio_length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - mel, mel_length = self.preprocessor(audio_signal, audio_length) - encoded, enc_len = self.encoder(mel, mel_length.to(dtype=torch.int32)) - return encoded, enc_len - - -class JointDecisionWrapper(torch.nn.Module): - """Joint + decision head: outputs label id, label prob, duration frames. - - Splits joint logits into token logits and duration logits, applies softmax - over tokens, argmax for both heads, and gathers probability of the chosen token. - - Inputs: - - encoder_outputs: [B, D, T] - - decoder_outputs: [B, D, U] - - Returns: - - token_id: [B, T, U] int32 - - token_prob: [B, T, U] float32 - - duration: [B, T, U] int32 (frames; for v3 bins=[0,1,2,3,4]) - """ - def __init__(self, joint: JointWrapper, vocab_size: int, num_extra: int) -> None: - super().__init__() - self.joint = joint - self.vocab_with_blank = int(vocab_size) + 1 - self.num_extra = int(num_extra) - - def forward(self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor): - logits = self.joint(encoder_outputs, decoder_outputs) - token_logits = logits[..., : self.vocab_with_blank] - duration_logits = logits[..., -self.num_extra :] - - # Token selection - token_ids = torch.argmax(token_logits, dim=-1).to(dtype=torch.int32) - token_probs_all = torch.softmax(token_logits, dim=-1) - # gather expects int64 (long) indices; cast only for gather - token_prob = torch.gather( - token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) - ).squeeze(-1) - - # Duration prediction (bins are identity mapping to frames for v3) - duration = torch.argmax(duration_logits, dim=-1).to(dtype=torch.int32) - return token_ids, token_prob, duration - - -class JointDecisionSingleStep(torch.nn.Module): - """Single-step variant for streaming: encoder_step [1, 512, 1] -> [1,1,1]. - - Note: For 110M model, encoder_dim is 512 (not 1024 like 0.6B). - - Inputs: - - encoder_step: [B=1, D=512, T=1] - - decoder_step: [B=1, D=640, U=1] - - Returns: - - token_id: [1, 1, 1] int32 - - token_prob: [1, 1, 1] float32 - - duration: [1, 1, 1] int32 - - top_k_ids: [1, 1, 1, K] int32 - - top_k_logits: [1, 1, 1, K] float32 - """ - def __init__(self, joint: JointWrapper, vocab_size: int, num_extra: int, top_k: int = 64) -> None: - super().__init__() - self.joint = joint - self.vocab_with_blank = int(vocab_size) + 1 - self.num_extra = int(num_extra) - # Emit top-K candidates to enable host-side re-ranking with contextual biasing - self.top_k = int(top_k) - - def forward(self, encoder_step: torch.Tensor, decoder_step: torch.Tensor): - # Reuse JointWrapper which expects [B, D, T] and [B, D, U] - logits = self.joint(encoder_step, decoder_step) # [1, 1, 1, V+extra] - token_logits = logits[..., : self.vocab_with_blank] - duration_logits = logits[..., -self.num_extra :] - - token_ids = torch.argmax(token_logits, dim=-1, keepdim=False).to(dtype=torch.int32) - token_probs_all = torch.softmax(token_logits, dim=-1) - token_prob = torch.gather( - token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) - ).squeeze(-1) - duration = torch.argmax(duration_logits, dim=-1, keepdim=False).to(dtype=torch.int32) - - # Also expose top-K candidates for host-side re-ranking. - # Shapes preserved as [1, 1, 1, K] to match CoreML broadcasting expectations. - # Note: topk expects last dimension; original shape is [1, 1, 1, V]. - topk_logits, topk_ids_long = torch.topk(token_logits, k=min(self.top_k, token_logits.shape[-1]), dim=-1) - topk_ids = topk_ids_long.to(dtype=torch.int32) - return token_ids, token_prob, duration, topk_ids, topk_logits - - -def _coreml_convert( - traced: torch.jit.ScriptModule, - inputs, - outputs, - settings: ExportSettings, - compute_units_override: Optional[ct.ComputeUnit] = None, -) -> ct.models.MLModel: - cu = compute_units_override if compute_units_override is not None else settings.compute_units - kwargs = { - "convert_to": "mlprogram", - "inputs": inputs, - "outputs": outputs, - "compute_units": cu, - } - print("Converting:", traced.__class__.__name__) - print("Conversion kwargs:", kwargs) - if settings.deployment_target is not None: - kwargs["minimum_deployment_target"] = settings.deployment_target - if settings.compute_precision is not None: - kwargs["compute_precision"] = settings.compute_precision - return ct.convert(traced, **kwargs) diff --git a/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/metadata.json b/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/metadata.json deleted file mode 100644 index 6db872ae8a7d463c95dc180210ace0fce019f4a6..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/metadata.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "model_id": "nvidia/parakeet-tdt_ctc-110m", - "model_type": "hybrid_rnnt_ctc", - "sample_rate": 16000, - "max_audio_seconds": 15.0, - "max_audio_samples": 240000, - "max_symbol_steps": 1, - "vocab_size": 1024, - "joint_extra_outputs": 5, - "encoder_dim": 512, - "decoder_dim": 640, - "decoder_hidden": 640, - "decoder_layers": 1, - "blank_id": 1024, - "checkpoint": { - "type": "pretrained", - "model_id": "nvidia/parakeet-tdt_ctc-110m" - }, - "coreml": { - "compute_units": "CPU_ONLY", - "compute_precision": "FLOAT32" - }, - "components": { - "preprocessor": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "path": "parakeet_preprocessor.mlpackage" - }, - "encoder": { - "inputs": { - "mel": [ - 1, - 80, - 1501 - ], - "mel_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_encoder.mlpackage" - }, - "ctc_head": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ] - }, - "outputs": { - "log_probs": [ - 1, - 188, - 1025 - ] - }, - "path": "parakeet_ctc_head.mlpackage" - }, - "mel_encoder": { - "inputs": { - "audio_signal": [ - 1, - 240000 - ], - "audio_length": [ - 1 - ] - }, - "outputs": { - "encoder": [ - 1, - 512, - 188 - ], - "encoder_length": [ - 1 - ] - }, - "path": "parakeet_mel_encoder.mlpackage" - }, - "decoder": { - "inputs": { - "targets": [ - 1, - 1 - ], - "target_length": [ - 1 - ], - "h_in": [ - 1, - 1, - 640 - ], - "c_in": [ - 1, - 1, - 640 - ] - }, - "outputs": { - "decoder": [ - 1, - 640, - 1 - ], - "h_out": [ - 1, - 1, - 640 - ], - "c_out": [ - 1, - 1, - 640 - ] - }, - "path": "parakeet_decoder.mlpackage" - }, - "joint": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "logits": [ - 1, - 188, - 1, - 1030 - ] - }, - "path": "parakeet_joint.mlpackage" - }, - "joint_decision": { - "inputs": { - "encoder": [ - 1, - 512, - 188 - ], - "decoder": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 188, - 1 - ], - "token_prob": [ - 1, - 188, - 1 - ], - "duration": [ - 1, - 188, - 1 - ] - }, - "path": "parakeet_joint_decision.mlpackage" - }, - "joint_decision_single_step": { - "inputs": { - "encoder_step": [ - 1, - 512, - 1 - ], - "decoder_step": [ - 1, - 640, - 1 - ] - }, - "outputs": { - "token_id": [ - 1, - 1, - 1 - ], - "token_prob": [ - 1, - 1, - 1 - ], - "duration": [ - 1, - 1, - 1 - ], - "top_k_ids": [ - 1, - 1, - 1, - 64 - ], - "top_k_logits": [ - 1, - 1, - 1, - 64 - ] - }, - "path": "parakeet_joint_decision_single_step.mlpackage" - } - } -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/model.mlmodel b/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/model.mlmodel deleted file mode 100644 index b807d0d0234624c649b82a7bd3e36bd9095fa961..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/model.mlmodel +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:6459b9564e0630f2eec300eb732fceccbc1d2d16f12cb0694ce310d84bfbecf2 -size 3366 diff --git a/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/weights/weight.bin deleted file mode 100644 index 23cedd570125191064c6111997f08c48898245f7..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Data/com.apple.CoreML/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fb9bead064427ffcb7529c0e3f378e421b4dde8e6d81447b6d1ca3352ca850e1 -size 1051842 diff --git a/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Manifest.json b/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Manifest.json deleted file mode 100644 index 6fa9ee67551a75a4b78fabbd02c58c0b39b247ed..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/coreml/parakeet_110m_coreml/parakeet_ctc_head.mlpackage/Manifest.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - 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"▁feel", - "▁five", - "ody", - "undred", - "iss", - "ank", - "get", - "aking", - "▁many", - "▁hundred", - "▁years", - "▁being", - "▁come", - "▁mean", - "ily", - "▁different", - "▁after", - "▁ser", - "▁show", - "form", - "ful", - "oy", - "▁six", - "▁vide", - "▁V", - "▁its", - "▁point", - "▁day", - "▁des", - "ons", - "▁bit", - "▁bel", - "▁before", - "▁aw", - "▁end", - "▁Oh", - "▁still", - "ath", - "▁long", - "▁'", - "ise", - "ob", - "day", - "▁add", - "ft", - "ves", - "ces", - "ady", - "▁cr", - "▁around", - "▁try", - "les", - "vers", - "kay", - "ian", - "ates", - "▁find", - "ward", - "▁As", - "▁eight", - "lic", - "▁same", - "▁pos", - "▁em", - "▁made", - "▁supp", - "▁life", - "▁Be", - "pect", - "▁dec", - "▁play", - "ange", - "▁att", - "▁pers", - "ways", - "▁high", - "▁hand", - "▁next", - "▁cons", - "▁own", - "▁inv", - "ower", - "▁ind", - "ert", - "ng", - "ave", - "▁year", - "▁big", - "ating", - "▁world", - "▁rel", - "▁sure", - "▁tra", - "ew", - "ered", - "▁fin", - "▁Well", - "▁sl", - "▁doing", - "bs", - "▁set", - "▁rec", - "ual", - "cial", - "▁ph", - "erm", - "▁love", - "ph", - "▁real", - "▁last", - "ict", - "▁bo", - "▁ra", - "ible", - "▁wr", - "mer", - "▁count", - "ities", - "▁always", - "inet", - "ments", - "uc", - "▁might", - "▁inter", - "▁video", - "gin", - "▁tell", - "▁never", - "vent", - "▁import", - "ied", - "▁sy", - "▁How", - "ically", - "ought", - "▁thir", - "▁rep", - "ks", - "ib", - "▁fam", - "ject", - "▁bas", - "▁She", - "▁give", - "akes", - "▁ninet", - "▁reg", - "▁min", - "▁op", - "▁def", - "▁didn", - "te", - "▁cour", - "▁why", - "▁ent", - "▁place", - "▁ins", - "▁car", - "ather", - "▁person", - "ular", - "▁inst", - "▁prod", - "lect", - "▁Al", - "▁today", - "▁bec", - "▁sur", - "▁All", - "▁another", - "▁bus", - "▁keep", - "ell", - "ese", - "riend", - "▁quest", - "▁talk", - "als", - "ings", - "▁mon", - "cond", - "old", - "▁acc", - "▁la", - "▁num", - "ident", - "▁che", - "iness", - "▁turn", - "▁ear", - "▁No", - "ousand", - "▁better", - "ific", - "▁loo", - "▁gl", - "oc", - "▁important", - "ited", - "▁An", - "▁thousand", - "ility", - "llow", - "▁used", - "▁gen", - "▁sim", - "li", - "▁happen", - "▁Un", - "▁Let", - "air", - "ock", - "ably", - "gg", - "▁watch", - "▁For", - "▁sw", - "ren", - "ute", - "ever", - "▁pol", - "▁sch", - "▁When", - "▁such", - "▁fif", - "▁home", - "▁cle", - "▁contin", - "ouse", - "▁friend", - "uring", - "▁Okay", - "gr", - "▁able", - "▁stud", - "▁eff", - "hip", - "body", - "▁top", - "ness", - "▁exper", - "▁pret", - "▁both", - "▁done", - "cri", - "▁mark", - "▁while", - "▁old", - "ros", - "ont", - "▁second", - "ative", - "▁thought", - "▁best", - "▁found", - "iew", - "▁belie", - "▁each", - "erest", - "▁tri", - "▁eas", - "▁ca", - "▁fact", - "▁care", - "▁fun", - "atter", - "ures", - "▁head", - "▁lear", - "▁water", - "▁hard", - "▁few", - "▁side", - "ween", - "▁exp", - "▁away", - "its", - "▁ext", - "lud", - "▁run", - "▁trans", - "ince", - "▁sk", - "▁open", - "cus", - "▁between", - "▁called", - "▁wee", - "▁pretty", - "ason", - "▁far", - "ember", - "omm", - "▁interest", - "any", - "ner", - "uff", - "▁pres", - "▁cur", - "▁child", - "ee", - "▁toget", - "▁together", - "olog", - "▁God", - "ond", - "▁char", - "▁looking", - "stem", - "az", - "cent", - "▁ob", - "▁ass", - "land", - "▁doesn", - "▁business", - "▁course", - "▁ten", - "ps", - "arch", - "ced", - "ms", - "ize", - "nce", - "▁ref", - "▁name", - "ross", - "▁grow", - "oney", - "▁went", - "ics", - "teen", - "▁cou", - "▁prob", - "▁ret", - "▁guys", - "▁came", - "ash", - "led", - "▁Eur", - "ues", - "▁ide", - "gan", - "▁everything", - "▁getting", - "▁ask", - "▁cor", - "▁build", - "▁sign", - "▁small", - "uck", - "▁el", - "▁col", - "▁Is", - "ational", - "stand", - "cy", - "▁conf", - "der", - "▁bre", - "▁cap", - "▁mod", - "ets", - "ike", - "▁number", - "▁comple", - "ertain", - "▁ever", - "▁coll", - "▁hum", - "▁Europe", - "▁cre", - "▁met", - "▁exam", - "▁move", - "▁pass", - "▁left", - "▁system", - "▁includ", - "▁Thank", - "cept", - "▁wom", - "▁product", - "ten", - "▁rest", - "▁probably", - "▁dri", - "▁Do", - "▁gener", - "▁anything", - "▁lar", - "▁My", - "▁school", - "▁lead", - "▁sub", - "▁ty", - "▁plan", - "▁seem", - "▁whole", - "irect", - "▁light", - "▁must", - "▁mom", - "▁opp", - "▁support", - "▁family", - "ices", - "amp", - "▁proble", - "▁dr", - "ready", - "▁using", - "ense", - "▁prov", - "ush", - "ax", - "▁power", - "▁Re", - "alth", - "▁ev", - "▁stand", - "▁war", - "ts", - "▁", - "e", - "t", - "o", - "a", - "n", - "i", - "s", - "r", - "h", - "l", - "d", - "u", - "c", - "m", - "y", - "g", - "w", - "f", - "p", - ".", - "b", - ",", - "v", - "k", - "'", - "I", - "T", - "A", - "S", - "x", - "W", - "j", - "B", - "C", - "H", - "?", - "M", - "O", - "Y", - "N", - "P", - "E", - "q", - "L", - "D", - "z", - "G", - "F", - "R", - "!", - "J", - "U", - "K", - "V", - "Q", - "Z", - "X" - ] -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/coreml/parakeet_ctc_coreml/.DS_Store b/parakeet-tdt-ctc-110m/coreml/parakeet_ctc_coreml/.DS_Store deleted file mode 100644 index 7b37950bb8ce3a439220b30b68fcd5a01d75cb8d..0000000000000000000000000000000000000000 Binary files a/parakeet-tdt-ctc-110m/coreml/parakeet_ctc_coreml/.DS_Store and /dev/null differ diff --git a/parakeet-tdt-ctc-110m/get-pip.py b/parakeet-tdt-ctc-110m/get-pip.py deleted file mode 100644 index 22f29f881487fe04461440ad6bc7b6a2e5e781de..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/get-pip.py +++ /dev/null @@ -1,27368 +0,0 @@ -#!/usr/bin/env python -# -# Hi There! -# -# You may be wondering what this giant blob of binary data here is, you might -# even be worried that we're up to something nefarious (good for you for being -# paranoid!). This is a base85 encoding of a zip file, this zip file contains -# an entire copy of pip (version 25.3). -# -# Pip is a thing that installs packages, pip itself is a package that someone -# might want to install, especially if they're looking to run this get-pip.py -# script. Pip has a lot of code to deal with the security of installing -# packages, various edge cases on various platforms, and other such sort of -# "tribal knowledge" that has been encoded in its code base. Because of this -# we basically include an entire copy of pip inside this blob. We do this -# because the alternatives are attempt to implement a "minipip" that probably -# doesn't do things correctly and has weird edge cases, or compress pip itself -# down into a single file. -# -# If you're wondering how this is created, it is generated using -# `scripts/generate.py` in https://github.com/pypa/get-pip. - -import sys - -this_python = sys.version_info[:2] -min_version = (3, 9) -if this_python < min_version: - message_parts = [ - "This script does not work on Python {}.{}.".format(*this_python), - "The minimum supported Python version is {}.{}.".format(*min_version), - "Please use https://bootstrap.pypa.io/pip/{}.{}/get-pip.py instead.".format(*this_python), - ] - print("ERROR: " + " ".join(message_parts)) - sys.exit(1) - - -import os.path -import pkgutil -import shutil -import tempfile -import argparse -import importlib -from base64 import b85decode - - -def include_setuptools(args): - """ - Install setuptools only if absent, not excluded and when using Python <3.12. - """ - cli = not args.no_setuptools - env = not os.environ.get("PIP_NO_SETUPTOOLS") - absent = not importlib.util.find_spec("setuptools") - python_lt_3_12 = this_python < (3, 12) - return cli and env and absent and python_lt_3_12 - - -def include_wheel(args): - """ - Install wheel only if absent, not excluded and when using Python <3.12. - """ - cli = not args.no_wheel - env = not os.environ.get("PIP_NO_WHEEL") - absent = not importlib.util.find_spec("wheel") - python_lt_3_12 = this_python < (3, 12) - return cli and env and absent and python_lt_3_12 - - -def determine_pip_install_arguments(): - pre_parser = argparse.ArgumentParser() - pre_parser.add_argument("--no-setuptools", action="store_true") - pre_parser.add_argument("--no-wheel", action="store_true") - pre, args = pre_parser.parse_known_args() - - args.append("pip") - - if include_setuptools(pre): - args.append("setuptools") - - if include_wheel(pre): - args.append("wheel") - - return ["install", "--upgrade", "--force-reinstall"] + args - - -def monkeypatch_for_cert(tmpdir): - """Patches `pip install` to provide default certificate with the lowest priority. - - This ensures that the bundled certificates are used unless the user specifies a - custom cert via any of pip's option passing mechanisms (config, env-var, CLI). - - A monkeypatch is the easiest way to achieve this, without messing too much with - the rest of pip's internals. - """ - from pip._internal.commands.install import InstallCommand - - # We want to be using the internal certificates. - cert_path = os.path.join(tmpdir, "cacert.pem") - with open(cert_path, "wb") as cert: - cert.write(pkgutil.get_data("pip._vendor.certifi", "cacert.pem")) - - install_parse_args = InstallCommand.parse_args - - def cert_parse_args(self, args): - if not self.parser.get_default_values().cert: - # There are no user provided cert -- force use of bundled cert - self.parser.defaults["cert"] = cert_path # calculated above - return install_parse_args(self, args) - - InstallCommand.parse_args = cert_parse_args - - -def bootstrap(tmpdir): - monkeypatch_for_cert(tmpdir) - - # Execute the included pip and use it to install the latest pip and - # any user-requested packages from PyPI. - from pip._internal.cli.main import main as pip_entry_point - args = determine_pip_install_arguments() - sys.exit(pip_entry_point(args)) - - -def main(): - tmpdir = None - try: - # Create a temporary working directory - tmpdir = tempfile.mkdtemp() - - # Unpack the zipfile into the temporary directory - pip_zip = os.path.join(tmpdir, "pip.zip") - with open(pip_zip, "wb") as fp: - fp.write(b85decode(DATA.replace(b"\n", b""))) - - # Add the zipfile to sys.path so that we can import it - sys.path.insert(0, pip_zip) - - # Run the bootstrap - bootstrap(tmpdir=tmpdir) - finally: - # Clean up our temporary working directory - if tmpdir: - shutil.rmtree(tmpdir, ignore_errors=True) - - -DATA = b""" 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-BTyLtaA|NaUv_0~WN&gWb#iQMX<{=kb#!TLFLGsca(OOrc~DCM0u%!j000080LuVbTR1R}=I#~%05n7 -Z03ZMW00000000000HlFzXBhx+X>c!Jc4cm4Z*nhna%^mAVlyvwbZKlab8~E8E^v8JO928D0~7!N00; -of09jic!Jc4cm4Z*nhna%^mAVlyvwbZKlabZKp6Z*_DoaCuNm0Rj{ -Q6aWAK2ms3fSz9aB=w25S007uG0018V0000000000005+cIh+{)aA|NaUv_0~WN&gWb#iQMX<{=kb#! -TLFLiQkE^v8JO928D0~7!N00;of09jjB)K=BM2LJ#Y6#xJr00000000000001_fsL;j0B~t=FJE?LZe -(wAFLiQkY-wUMFLiWjY%h0VX>=}dc~DCQ1^@s60J;IX0sn^p0E)L60000 -""" - - -if __name__ == "__main__": - main() diff --git a/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/analytics/coremldata.bin b/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 18de02b6523b282aa52b4e816db388a961557778..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fefed2be21166b9afcc5913192d8b86b4a621ee236b459a2cc754584020d4878 -size 243 diff --git a/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/coremldata.bin b/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/coremldata.bin deleted file mode 100644 index f6cebbf78c8aa9626ae27c1a9d8b681c3bf0f441..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:66953a7662cfdc8247e5f3da0ec38847d1e198a5b5dd94b51fa1c711f7b777ac -size 543 diff --git a/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/metadata.json b/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/metadata.json deleted file mode 100644 index 84ebb9b5a894782e6def44d0b3707784e36a1757..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/metadata.json +++ /dev/null @@ -1,123 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Hybrid TDT Decoder (110M)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 640 × 2)", - "shortDescription" : "", - "shape" : "[1, 640, 2]", - "name" : "decoder_output", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "h_out", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "c_out", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Select" : 1, - "Ios17.squeeze" : 2, - "Ios17.gather" : 1, - "Ios17.cast" : 5, - "Ios17.lstm" : 1, - "Ios17.transpose" : 2, - "Ios17.add" : 1, - "Ios17.concat" : 1, - "Ios17.greaterEqual" : 1, - "Identity" : 1, - "Ios17.expandDims" : 2 - }, - "computePrecision" : "Mixed (Float16, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1]", - "name" : "targets", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1)", - "shortDescription" : "", - "shape" : "[1]", - "name" : "target_lengths", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "h_in", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 1 × 640)", - "shortDescription" : "", - "shape" : "[1, 1, 640]", - "name" : "c_in", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2026-01-01", - "com.github.apple.coremltools.source" : "torch==2.8.0", - "com.github.apple.coremltools.version" : "9.0", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "Decoder", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/model.mil b/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/model.mil deleted file mode 100644 index 3d8abb8f2d6b323d3d0fd6e3d34bc1f8145e5ca4..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/model.mil +++ /dev/null @@ -1,51 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] -{ - func main(tensor c_in, tensor h_in, tensor target_lengths, tensor targets) { - tensor y_batch_dims_0 = const()[name = tensor("y_batch_dims_0"), val = tensor(0)]; - tensor y_validate_indices_0 = const()[name = tensor("y_validate_indices_0"), val = tensor(false)]; - tensor decoder_prediction_embed_weight_to_fp16 = const()[name = tensor("decoder_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor targets_to_int16_dtype_0 = const()[name = tensor("targets_to_int16_dtype_0"), val = tensor("int16")]; - tensor cast_4_dtype_0 = const()[name = tensor("cast_4_dtype_0"), val = tensor("int32")]; - tensor greater_equal_0_y_0 = const()[name = tensor("greater_equal_0_y_0"), val = tensor(0)]; - tensor targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor("cast_9")]; - tensor cast_4 = cast(dtype = cast_4_dtype_0, x = targets_to_int16)[name = tensor("cast_8")]; - tensor greater_equal_0 = greater_equal(x = cast_4, y = greater_equal_0_y_0)[name = tensor("greater_equal_0")]; - tensor slice_by_index_0 = const()[name = tensor("slice_by_index_0"), val = tensor(1025)]; - tensor add_1 = add(x = cast_4, y = slice_by_index_0)[name = tensor("add_1")]; - tensor select_0 = select(a = cast_4, b = add_1, cond = greater_equal_0)[name = tensor("select_0")]; - tensor y_cast_fp16_cast_uint16_axis_0 = const()[name = tensor("y_cast_fp16_cast_uint16_axis_0"), val = tensor(0)]; - tensor select_0_to_int16_dtype_0 = const()[name = tensor("select_0_to_int16_dtype_0"), val = tensor("int16")]; - tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor("cast_7")]; - tensor y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = decoder_prediction_embed_weight_to_fp16)[name = tensor("y_cast_fp16_cast_uint16_cast_uint16")]; - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([1, 0, 2])]; - tensor input_lstm_h0_squeeze_axes_0 = const()[name = tensor("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; - tensor h_in_to_fp16_dtype_0 = const()[name = tensor("h_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor("cast_6")]; - tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = h_in_to_fp16)[name = tensor("input_lstm_h0_squeeze_cast_fp16")]; - tensor input_lstm_c0_squeeze_axes_0 = const()[name = tensor("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; - tensor c_in_to_fp16_dtype_0 = const()[name = tensor("c_in_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor("cast_5")]; - tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = c_in_to_fp16)[name = tensor("input_lstm_c0_squeeze_cast_fp16")]; - tensor input_direction_0 = const()[name = tensor("input_direction_0"), val = tensor("forward")]; - tensor input_output_sequence_0 = const()[name = tensor("input_output_sequence_0"), val = tensor(true)]; - tensor input_recurrent_activation_0 = const()[name = tensor("input_recurrent_activation_0"), val = tensor("sigmoid")]; - tensor input_cell_activation_0 = const()[name = tensor("input_cell_activation_0"), val = tensor("tanh")]; - tensor input_activation_0 = const()[name = tensor("input_activation_0"), val = tensor("tanh")]; - tensor concat_1_to_fp16 = const()[name = tensor("concat_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312128)))]; - tensor concat_2_to_fp16 = const()[name = tensor("concat_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4588992)))]; - tensor concat_0_to_fp16 = const()[name = tensor("concat_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7865856)))]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = tensor("transpose_2")]; - tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_0_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_1_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor var_30_axes_0 = const()[name = tensor("op_30_axes_0"), val = tensor([0])]; - tensor h_out = expand_dims(axes = var_30_axes_0, x = input_cast_fp16_1)[name = tensor("op_30_cast_fp16")]; - tensor var_31_axes_0 = const()[name = tensor("op_31_axes_0"), val = tensor([0])]; - tensor c_out = expand_dims(axes = var_31_axes_0, x = input_cast_fp16_2)[name = tensor("op_31_cast_fp16")]; - tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([1, 2, 0])]; - tensor var_48 = const()[name = tensor("op_48"), val = tensor(2)]; - tensor var_49_interleave_0 = const()[name = tensor("op_49_interleave_0"), val = tensor(false)]; - tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor("transpose_1")]; - tensor decoder_output = concat(axis = var_48, interleave = var_49_interleave_0, values = (transpose_0_cast_fp16, transpose_0_cast_fp16))[name = tensor("op_49_cast_fp16")]; - tensor target_lengths_tmp = identity(x = target_lengths)[name = tensor("target_lengths_tmp")]; - } -> (decoder_output, h_out, c_out); -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/weights/weight.bin b/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/weights/weight.bin deleted file mode 100644 index 4543ad4ea89b21ebed0de0d5ec3975d07eace7fb..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/Decoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:dd90b58597ee2c172c672dffe13b1110898ba07394c1a15efc96cc8c6b18411b -size 7871040 diff --git a/parakeet-tdt-ctc-110m/output/Decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/parakeet-tdt-ctc-110m/output/Decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel deleted file mode 100644 index 70eaf8653921b3a9c2809f32857e034913cc2e86..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/Decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:75a807f2b8ec1cde294f77fe8a1faf50229f011a8e159d7d6c6beaab778ce8c1 -size 7501 diff --git a/parakeet-tdt-ctc-110m/output/Decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/parakeet-tdt-ctc-110m/output/Decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin deleted file mode 100644 index 4543ad4ea89b21ebed0de0d5ec3975d07eace7fb..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/Decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:dd90b58597ee2c172c672dffe13b1110898ba07394c1a15efc96cc8c6b18411b -size 7871040 diff --git a/parakeet-tdt-ctc-110m/output/Decoder.mlpackage/Manifest.json b/parakeet-tdt-ctc-110m/output/Decoder.mlpackage/Manifest.json deleted file mode 100644 index b24fa578e7986b6fc0ac0dba9f4481b1a819b0fe..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/Decoder.mlpackage/Manifest.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "fileFormatVersion": "1.0.0", - "itemInfoEntries": { - "6BDFA15B-7DF3-44B6-A308-B95282CBC63A": { - "author": "com.apple.CoreML", - "description": "CoreML Model Specification", - "name": "model.mlmodel", - "path": "com.apple.CoreML/model.mlmodel" - }, - "A04A1CE0-C74A-460D-B289-34CAF7B6F6C9": { - "author": "com.apple.CoreML", - "description": "CoreML Model Weights", - "name": "weights", - "path": "com.apple.CoreML/weights" - } - }, - "rootModelIdentifier": "6BDFA15B-7DF3-44B6-A308-B95282CBC63A" -} diff --git a/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/analytics/coremldata.bin b/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index f7d619494d2f2061ae52418edbb5c5fc2ec1f11e..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e51c4ee19e8d7e8392896e5d3b5464892f9e16ba271335ff7eb3d2eb4a015f27 -size 243 diff --git a/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/coremldata.bin b/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/coremldata.bin deleted file mode 100644 index cc76621d9ad55dc03a554246d198a4851607172f..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:5c45adaff9e0e6d8c49949666f07a6fcde0affc7a631a8f93d12a0e51c1393d3 -size 509 diff --git a/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/metadata.json b/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/metadata.json deleted file mode 100644 index 4d8cd2c6a2393dfafad11338ff96f388b2d59295..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/metadata.json +++ /dev/null @@ -1,104 +0,0 @@ -[ - { - "metadataOutputVersion" : "3.0", - "shortDescription" : "Hybrid Joint Decision (110M)", - "outputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_id", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float16", - "formattedType" : "MultiArray (Float16 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "token_prob", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Int32", - "formattedType" : "MultiArray (Int32 1 × 1 × 1)", - "shortDescription" : "", - "shape" : "[1, 1, 1]", - "name" : "duration", - "type" : "MultiArray" - } - ], - "storagePrecision" : "Float16", - "modelParameters" : [ - - ], - "author" : "Fluid Inference", - "specificationVersion" : 8, - "mlProgramOperationTypeHistogram" : { - "Ios17.reduceArgmax" : 2, - "Ios17.linear" : 3, - "Ios17.transpose" : 2, - "Ios17.log" : 1, - "Ios17.add" : 1, - "Ios17.sliceByIndex" : 2, - "Ios16.relu" : 1, - "Ios16.softmax" : 2, - "Ios17.expandDims" : 3, - "Ios17.squeeze" : 2, - "Ios17.cast" : 5, - "Ios17.gatherAlongAxis" : 1 - }, - "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", - "isUpdatable" : "0", - "stateSchema" : [ - - ], - "availability" : { - "macOS" : "14.0", - "tvOS" : "17.0", - "visionOS" : "1.0", - "watchOS" : "10.0", - "iOS" : "17.0", - "macCatalyst" : "17.0" - }, - "modelType" : { - "name" : "MLModelType_mlProgram" - }, - "inputSchema" : [ - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 512 × 1)", - "shortDescription" : "", - "shape" : "[1, 512, 1]", - "name" : "encoder_step", - "type" : "MultiArray" - }, - { - "hasShapeFlexibility" : "0", - "isOptional" : "0", - "dataType" : "Float32", - "formattedType" : "MultiArray (Float32 1 × 640 × 1)", - "shortDescription" : "", - "shape" : "[1, 640, 1]", - "name" : "decoder_step", - "type" : "MultiArray" - } - ], - "userDefinedMetadata" : { - "com.github.apple.coremltools.conversion_date" : "2026-01-01", - "com.github.apple.coremltools.source" : "torch==2.8.0", - "com.github.apple.coremltools.version" : "9.0", - "com.github.apple.coremltools.source_dialect" : "TorchScript" - }, - "generatedClassName" : "JointDecision", - "method" : "predict" - } -] \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/model.mil b/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/model.mil deleted file mode 100644 index 7057845c459a15cef5cb1a4baf0695b949c7f703..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/model.mil +++ /dev/null @@ -1,66 +0,0 @@ -program(1.0) -[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] -{ - func main(tensor decoder_step, tensor encoder_step) { - tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; - tensor encoder_step_to_fp16_dtype_0 = const()[name = tensor("encoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; - tensor decoder_step_to_fp16_dtype_0 = const()[name = tensor("decoder_step_to_fp16_dtype_0"), val = tensor("fp16")]; - tensor joint_enc_weight_to_fp16 = const()[name = tensor("joint_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor joint_enc_bias_to_fp16 = const()[name = tensor("joint_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488)))]; - tensor encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor("cast_5")]; - tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor("transpose_1")]; - tensor linear_0_cast_fp16 = linear(bias = joint_enc_bias_to_fp16, weight = joint_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; - tensor f_axes_0 = const()[name = tensor("f_axes_0"), val = tensor([2])]; - tensor f_cast_fp16 = expand_dims(axes = f_axes_0, x = linear_0_cast_fp16)[name = tensor("f_cast_fp16")]; - tensor joint_pred_weight_to_fp16 = const()[name = tensor("joint_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656832)))]; - tensor joint_pred_bias_to_fp16 = const()[name = tensor("joint_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476096)))]; - tensor decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor("cast_4")]; - tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor("transpose_0")]; - tensor linear_1_cast_fp16 = linear(bias = joint_pred_bias_to_fp16, weight = joint_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("linear_1_cast_fp16")]; - tensor g_axes_0 = const()[name = tensor("g_axes_0"), val = tensor([1])]; - tensor g_cast_fp16 = expand_dims(axes = g_axes_0, x = linear_1_cast_fp16)[name = tensor("g_cast_fp16")]; - tensor input_5_cast_fp16 = add(x = f_cast_fp16, y = g_cast_fp16)[name = tensor("input_5_cast_fp16")]; - tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; - tensor joint_joint_net_2_weight_to_fp16 = const()[name = tensor("joint_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1477440)))]; - tensor joint_joint_net_2_bias_to_fp16 = const()[name = tensor("joint_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2795904)))]; - tensor linear_2_cast_fp16 = linear(bias = joint_joint_net_2_bias_to_fp16, weight = joint_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_2_cast_fp16")]; - tensor var_35 = const()[name = tensor("op_35"), val = tensor(-1)]; - tensor joint_out_softmax_cast_fp16 = softmax(axis = var_35, x = linear_2_cast_fp16)[name = tensor("joint_out_softmax_cast_fp16")]; - tensor joint_out_epsilon_0 = const()[name = tensor("joint_out_epsilon_0"), val = tensor(0x1p-149)]; - tensor joint_out_cast_fp16 = log(epsilon = joint_out_epsilon_0, x = joint_out_softmax_cast_fp16)[name = tensor("joint_out_cast_fp16")]; - tensor input_begin_0 = const()[name = tensor("input_begin_0"), val = tensor([0, 0, 0, 0])]; - tensor input_end_0 = const()[name = tensor("input_end_0"), val = tensor([1, 1, 1, 1025])]; - tensor input_end_mask_0 = const()[name = tensor("input_end_mask_0"), val = tensor([true, true, true, false])]; - tensor input_cast_fp16 = slice_by_index(begin = input_begin_0, end = input_end_0, end_mask = input_end_mask_0, x = joint_out_cast_fp16)[name = tensor("input_cast_fp16")]; - tensor duration_logits_begin_0 = const()[name = tensor("duration_logits_begin_0"), val = tensor([0, 0, 0, 1025])]; - tensor duration_logits_end_0 = const()[name = tensor("duration_logits_end_0"), val = tensor([1, 1, 1, 1030])]; - tensor duration_logits_end_mask_0 = const()[name = tensor("duration_logits_end_mask_0"), val = tensor([true, true, true, true])]; - tensor duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = joint_out_cast_fp16)[name = tensor("duration_logits_cast_fp16")]; - tensor var_48 = const()[name = tensor("op_48"), val = tensor(-1)]; - tensor probs_cast_fp16 = softmax(axis = var_48, x = input_cast_fp16)[name = tensor("probs_cast_fp16")]; - tensor token_id_1_axis_0 = const()[name = tensor("token_id_1_axis_0"), val = tensor(-1)]; - tensor token_id_1_keep_dims_0 = const()[name = tensor("token_id_1_keep_dims_0"), val = tensor(true)]; - tensor token_id_1_output_dtype_0 = const()[name = tensor("token_id_1_output_dtype_0"), val = tensor("int32")]; - tensor token_id_1_cast_fp16 = reduce_argmax(axis = token_id_1_axis_0, keep_dims = token_id_1_keep_dims_0, output_dtype = token_id_1_output_dtype_0, x = input_cast_fp16)[name = tensor("token_id_1_cast_fp16")]; - tensor token_id_axes_0 = const()[name = tensor("token_id_axes_0"), val = tensor([-1])]; - tensor token_id_1_cast_fp16_to_int16_dtype_0 = const()[name = tensor("token_id_1_cast_fp16_to_int16_dtype_0"), val = tensor("int16")]; - tensor token_id_1_cast_fp16_to_int16 = cast(dtype = token_id_1_cast_fp16_to_int16_dtype_0, x = token_id_1_cast_fp16)[name = tensor("cast_3")]; - tensor token_id_cast_int16 = squeeze(axes = token_id_axes_0, x = token_id_1_cast_fp16_to_int16)[name = tensor("token_id_cast_int16")]; - tensor token_id_cast_int16_to_int32_dtype_0 = const()[name = tensor("token_id_cast_int16_to_int32_dtype_0"), val = tensor("int32")]; - tensor var_57_axes_0 = const()[name = tensor("op_57_axes_0"), val = tensor([-1])]; - tensor token_id = cast(dtype = token_id_cast_int16_to_int32_dtype_0, x = token_id_cast_int16)[name = tensor("cast_2")]; - tensor var_57 = expand_dims(axes = var_57_axes_0, x = token_id)[name = tensor("op_57")]; - tensor var_58 = const()[name = tensor("op_58"), val = tensor(-1)]; - tensor token_prob_validate_indices_0 = const()[name = tensor("token_prob_validate_indices_0"), val = tensor(false)]; - tensor var_57_to_int16_dtype_0 = const()[name = tensor("op_57_to_int16_dtype_0"), val = tensor("int16")]; - tensor var_57_to_int16 = cast(dtype = var_57_to_int16_dtype_0, x = var_57)[name = tensor("cast_1")]; - tensor token_prob_cast_fp16_cast_int16 = gather_along_axis(axis = var_58, indices = var_57_to_int16, validate_indices = token_prob_validate_indices_0, x = probs_cast_fp16)[name = tensor("token_prob_cast_fp16_cast_int16")]; - tensor var_62_axes_0 = const()[name = tensor("op_62_axes_0"), val = tensor([-1])]; - tensor token_prob = squeeze(axes = var_62_axes_0, x = token_prob_cast_fp16_cast_int16)[name = tensor("op_62_cast_fp16")]; - tensor duration_axis_0 = const()[name = tensor("duration_axis_0"), val = tensor(-1)]; - tensor duration_keep_dims_0 = const()[name = tensor("duration_keep_dims_0"), val = tensor(false)]; - tensor duration_output_dtype_0 = const()[name = tensor("duration_output_dtype_0"), val = tensor("int32")]; - tensor duration = reduce_argmax(axis = duration_axis_0, keep_dims = duration_keep_dims_0, output_dtype = duration_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor("duration_cast_fp16")]; - } -> (token_id, token_prob, duration); -} \ No newline at end of file diff --git a/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/weights/weight.bin b/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/JointDecision.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b3f771cb65b190f1873e39629676ed79b65a8361522f451b37bdba8b1106e6ff -size 2798028 diff --git a/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/model.mlmodel b/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/model.mlmodel deleted file mode 100644 index 00748a425eda1d051f13fc524fcfc160d1cb7ca1..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/model.mlmodel +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:73d76247715622313f066e93874f5eff0706410e8572e1b3c51f6f7f2b14a78b -size 9790 diff --git a/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/weights/weight.bin deleted file mode 100644 index 747ec9d24e2d3e008bc869d2c404bb3d2653325c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Data/com.apple.CoreML/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b3f771cb65b190f1873e39629676ed79b65a8361522f451b37bdba8b1106e6ff -size 2798028 diff --git a/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Manifest.json b/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Manifest.json deleted file mode 100644 index 51edbc5622d98d269493af788ab212f926544b0c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-ctc-110m/output/JointDecision.mlpackage/Manifest.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "fileFormatVersion": "1.0.0", - "itemInfoEntries": { - "601AC192-A217-4675-8F63-01F426D7BC52": { - "author": "com.apple.CoreML", - "description": "CoreML Model Weights", - "name": "weights", - "path": "com.apple.CoreML/weights" - }, - "76B2BF33-9547-4031-8668-01C0E473C306": { - "author": "com.apple.CoreML", - "description": "CoreML Model Specification", - "name": "model.mlmodel", - "path": "com.apple.CoreML/model.mlmodel" - } - }, - "rootModelIdentifier": "76B2BF33-9547-4031-8668-01C0E473C306" -} diff --git a/parakeet-tdt-v2-0.6b/.DS_Store b/parakeet-tdt-v2-0.6b/.DS_Store deleted file mode 100644 index 4a654c3e785ab1ee5f435f3eda3c58ea35b780d4..0000000000000000000000000000000000000000 Binary files a/parakeet-tdt-v2-0.6b/.DS_Store and /dev/null differ diff --git a/parakeet-tdt-v2-0.6b/coreml/.gitignore b/parakeet-tdt-v2-0.6b/coreml/.gitignore deleted file mode 100644 index e2e12b1c4fd2deeef4b38477dccac90f560a627c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -mlpackages/ -parakeet_coreml/ -parakeet_coreml_quantized/ -compiled/ diff --git a/parakeet-tdt-v2-0.6b/coreml/README.md b/parakeet-tdt-v2-0.6b/coreml/README.md deleted file mode 100644 index 8ef855d29d09b61830ca71b880c99e953f44f667..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/README.md +++ /dev/null @@ -1,136 +0,0 @@ -# Parakeet‑TDT v2 (0.6B) — CoreML Export, Parity, and Quantization - -Tools to export NVIDIA Parakeet‑TDT v2 (0.6B) RNNT ASR to CoreML, validate numerical parity with the NeMo reference, measure latency, and explore quantization trade‑offs. All CoreML components use a fixed 15‑second audio window for export and validation. - -## Environment - -1. Create or reuse the local environment with `uv venv`. -2. Activate the repo `.venv` and install deps via `uv pip sync`. -3. Run everything through `uv run` to keep resolutions reproducible. - -## Test Environment - -All tests and measurements referenced here were run on an Apple M4 Pro with 48 GB of RAM. - -## Export CoreML packages - -Exports preprocessor, encoder, decoder, joint, and two fused variants (mel+encoder, joint+decision). Shapes and I/O match the fixed 15‑second window contract. - -``` -uv run python convert-parakeet.py convert \ - --nemo-path /path/to/parakeet-tdt-0.6b-v2.nemo \ - --output-dir parakeet_coreml -``` - -Notes -- Minimum deployment target: iOS 17. Export uses CPU_ONLY by default; runtime compute units can be set when loading the model (Python or Swift). -- Audio is 16 kHz, single‑channel. The 15 s window is enforced during export and validation. - -## Validate parity and speed (Torch vs CoreML) - -Runs Torch and CoreML side‑by‑side on the same 15 s input, records diffs and latency, and saves plots under `plots/compare-components/`. The tool updates `parakeet_coreml/metadata.json` with all measurements. - -``` -uv run python compare-components.py compare \ - --output-dir parakeet_coreml \ - --model-id nvidia/parakeet-tdt-0.6b-v2 \ - --runs 10 --warmup 3 -``` - -Output comparison: - -![./plots/compare-components/mel_encoder_time_l2.png](./plots/compare-components/mel_encoder_time_l2.png) -![./plots/compare-components/joint_decision_prob_u0.png](./plots/compare-components/joint_decision_prob_u0.png) -![./plots/compare-components/decoder_steps_l2.png](./plots/compare-components/decoder_steps_l2.png) - -Latency: - -![./plots/quantize/all/all_components_compile.png](./plots/quantize/all/all_components_compile.png) -![./plots/quantize/all/all_components_compression.png](./plots/quantize/all/all_components_compression.png) -![./plots/quantize/all/all_components_quality.png](./plots/quantize/all/all_components_quality.png) -![./plots/quantize/all/all_components_latency.png](./plots/quantize/all/all_components_latency.png) - - -Quants: - - - -### Key results (quality first) - -Numerical parity is strong across components on the fixed window: -- Preprocessor mel: match=true; max_abs≈0.484, max_rel≈2.00 (near‑zero bins inflate relative error). -- Encoder: match=true; max_abs≈0.0054, strong agreement over time (see plot). -- Decoder h/c state: match=true; value deltas within tolerance. -- Joint logits: match=true; max_abs≈0.099, distributions align (see top‑k plot). -- Joint+Decision: Fused CoreML head exactly matches decisions computed on CoreML logits (token_id/prob/duration). PyTorch logits produce slightly different argmax paths (expected from small logit differences). - -### Speed (latency and RTF) - -Component latency on a 15 s clip, Torch CPU vs CoreML (CPU+NE) from `parakeet_coreml/metadata.json`: -- Encoder: Torch 1030.48 ms → CoreML 25.44 ms (≈40.5× faster, RTF 0.00170) -- Preprocessor: 1.99 ms → 1.19 ms (≈1.68×) -- Joint: 28.34 ms → 22.66 ms (≈1.25×) -- Decoder (U=1): 7.51 ms → 4.32 ms (≈1.73×) - -Fused paths: -- Mel+Encoder (Torch separate vs CoreML fused): 1032.48 ms → 27.10 ms (≈38.1× faster) -- Joint+Decision (CoreML joint + CPU post vs fused CoreML head): 50.05 ms → 64.09 ms (fused is slower here; prefer CoreML joint + lightweight CPU decision on host). - -Plots -- Latency bars and speedups: `plots/compare-components/latency_summary.png`, `plots/compare-components/latency_speedup.png` -- Fused vs separate: `plots/compare-components/latency_fused_vs_separate.png`, `plots/compare-components/latency_fused_speedup.png` -- Quality visuals: mel composite (`mel_composite.png`), encoder L2 over time (`encoder_time_l2.png`), decoder step L2 (`decoder_steps_l2.png`), joint top‑k/time L2 (`joint_top50.png`, `joint_time_l2.png`), joint‑decision agreement (`joint_decision_token_agree.png`, `joint_decision_prob_u0.png`). - -## Quantization (size ‱ quality ‱ speed) - -`uv run python quantize_coreml.py` evaluates several variants and writes a roll‑up to `parakeet_coreml_quantized/quantization_summary.json`. Plots are mirrored to `plots/quantize//` (we include `plots/quantize/all/`). Quality here is reported as 1 − normalized L2 error (1.0 = identical). For JointDecision we report token‑id match rate, duration match, and token‑prob MAE. - -Quick highlights (ComputeUnits=ALL): -- int8 linear (per‑channel): ~2.0× smaller across components with minimal quality loss - - MelEncoder quality≈0.963; latency≈31.13 ms (baseline≈29.34 ms) - - JointDecision acc≈0.995; latency≈1.96 ms (baseline≈2.15 ms) -- int8 linear (per‑tensor symmetric): large encoder quality drop (≈0.50) — not recommended - -Quantization plots (ALL) -- Fused: `plots/quantize/all/fused_quality.png`, `fused_latency.png`, `fused_compression.png`, `fused_size.png` -- Component breakdown: `plots/quantize/all/all_components_quality.png`, `all_components_latency.png`, `all_components_compression.png`, `all_components_size.png`, `all_components_compile.png` - -## Reproduce the figures - -1) Export baseline CoreML packages -``` -uv run python convert-parakeet.py convert --model-id nvidia/parakeet-tdt-0.6b-v2 --output-dir parakeet_coreml -``` - -2) Compare Torch vs CoreML and generate parity/latency plots -``` -uv run python compare-components.py compare --output-dir parakeet_coreml --runs 10 --warmup 3 -``` - -3) Run quantization sweeps (mirrors plots into `plots/quantize//`) -``` -uv run python quantize_coreml.py \ - --input-dir parakeet_coreml \ - --output-root parakeet_coreml_quantized \ - --compute-units ALL --runs 10 -``` - -Examples -- Encoder 6‑bit palette only: - `uv run python quantize_coreml.py -c encoder-palettize` -- MelEncoder 6‑bit palette only: - `uv run python quantize_coreml.py -c mel-palettize` - (By default, the script derives the component whitelist from the selected - variants. Use `-m/--component` to explicitly restrict or `-m all` to force all.) - -## Notes & limits - -- Fixed 15‑second window shapes are required for all CoreML exports and validations. -- Latency measurements are host‑side CoreML predictions (CPU+NE or ALL); on‑device results can differ by chip/OS. -- For streaming decode, the exported decoder uses U=1 inputs with explicit LSTM state I/O. -- Minimum deployment target is iOS 17; models are saved as MLProgram and eligible for ANE when loaded with `ComputeUnits=ALL`. - -## Acknowledgements - -- Parakeet‑TDT v2 model from NVIDIA NeMo (`nvidia/parakeet-tdt-0.6b-v2`). -- This directory provides export/validation utilities and plots to help the community reproduce quality and performance on Apple devices. diff --git a/parakeet-tdt-v2-0.6b/coreml/agents.md b/parakeet-tdt-v2-0.6b/coreml/agents.md deleted file mode 100644 index d6765848d59f76846549712d8c31a4fce733a98a..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/agents.md +++ /dev/null @@ -1,7 +0,0 @@ -# Agent Notes - -- Preferred Python workflow uses `uv` (https://github.com/astral-sh/uv). - - Create and manage environments with `uv venv`. - - Install dependencies with `uv pip install` or `uv pip sync` as needed. -- When working in this repo, activate the local `.venv` and run tooling through `uv run` to keep resolutions reproducible. -- Keep CoreML conversions constrained to the fixed 15-second audio window when exporting or validating Parakeet components. diff --git a/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute.wav b/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute.wav deleted file mode 100644 index 8682a32b66200c635a60706e722c2b03b5701db0..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:004afd9a8b6265d321b1faeb3b22ca5efcc78b570566fca633327d73f545f3fa -size 10584142 diff --git a/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k.wav b/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k.wav deleted file mode 100644 index de71da9ab403797061b7051b820337a754336481..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:555404115420c8b4a77fcfacc450ecfc767211ca7b449a9dcb292b1734256103 -size 1920090 diff --git a/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k_15s.wav b/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k_15s.wav deleted file mode 100644 index 0b8040f5f124d20a1d7e812c576c831a7573eddf..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/audio/yc_first_minute_16k_15s.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c79c8bc763b4efccb3e12f199ec0a59aa2edc5e9e4d21ca70fde8f36762d4147 -size 480078 diff --git a/parakeet-tdt-v2-0.6b/coreml/compare-components.py b/parakeet-tdt-v2-0.6b/coreml/compare-components.py deleted file mode 100644 index 114b74c388a2562cc2576f1ac331b8cd61dc518e..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/compare-components.py +++ /dev/null @@ -1,958 +0,0 @@ -#!/usr/bin/env python3 -"""Compare Parakeet TDT v2 Torch vs CoreML components on a fixed 15s window. - -Writes numeric diffs to the specified output directory (metadata.json) and -saves plots under a repo-tracked directory: plots//. -""" -from __future__ import annotations - -import json -import time -from dataclasses import dataclass -from pathlib import Path -from typing import Dict, Optional, Tuple - -import coremltools as ct -import numpy as np -import soundfile as sf -import torch -import typer - -import nemo.collections.asr as nemo_asr - -# Optional plotting -try: - import matplotlib - matplotlib.use("Agg") - import matplotlib.pyplot as plt - HAS_MPL = True -except Exception: - HAS_MPL = False - - -@dataclass -class ValidationSettings: - audio_path: Optional[Path] - seconds: float - seed: Optional[int] - rtol: float - atol: float - - -def _compute_length(seconds: float, sample_rate: int) -> int: - return int(round(seconds * sample_rate)) - - -def _prepare_audio( - validation_audio: Optional[Path], - sample_rate: int, - max_samples: int, - seed: Optional[int], -) -> torch.Tensor: - if validation_audio is None: - if seed is not None: - torch.manual_seed(seed) - return torch.randn(1, max_samples, dtype=torch.float32) - - data, sr = sf.read(str(validation_audio), dtype="float32") - if sr != sample_rate: - raise typer.BadParameter( - f"Validation audio sample rate {sr} does not match model rate {sample_rate}" - ) - if data.ndim > 1: - data = data[:, 0] - if data.size == 0: - raise typer.BadParameter("Validation audio is empty") - if data.size < max_samples: - data = np.pad(data, (0, max_samples - data.size)) - elif data.size > max_samples: - data = data[:max_samples] - return torch.from_numpy(data).unsqueeze(0).to(dtype=torch.float32) - - -def _np(x: torch.Tensor, dtype=None) -> np.ndarray: - arr = x.detach().cpu().numpy() - if dtype is not None: - return arr.astype(dtype, copy=False) - return arr - - -def _to_t(x) -> torch.Tensor: - if isinstance(x, torch.Tensor): - return x.detach().cpu() - elif isinstance(x, np.ndarray): - # Ensure a separate tensor (avoid shared memory weirdness) - return torch.from_numpy(np.array(x, copy=True)) - else: - return torch.tensor(x) - - -def _max_diffs(a, b, rtol: float, atol: float) -> Tuple[float, float, bool]: - # Use NumPy for comparisons to avoid invoking the PyTorch C-API in contexts - # where the GIL may not be held (which can trigger PyEval_SaveThread errors). - na = np.array(a, dtype=np.float32, copy=True) - nb = np.array(b, dtype=np.float32, copy=True) - if na.size == 0: - return 0.0, 0.0, True - diff = np.abs(na - nb) - max_abs = float(diff.max()) - denom = np.maximum(np.abs(na), np.abs(nb)) - with np.errstate(divide="ignore", invalid="ignore"): - rel = np.where(denom == 0.0, 0.0, diff / denom) - max_rel = float(rel.max()) - ok = bool(np.allclose(na, nb, rtol=rtol, atol=atol)) - return max_abs, max_rel, ok - - -def _plot_line(x_ref: np.ndarray, x_ml: np.ndarray, title: str, path: Path, also_delta: bool = False): - if not HAS_MPL: - return None - if also_delta: - fig, axes = plt.subplots(2, 1, figsize=(8, 5), sharex=True) - axes[0].plot(x_ref, label="torch", linewidth=1) - axes[0].plot(x_ml, label="coreml", linewidth=1, alpha=0.8) - axes[0].set_title(title) - axes[0].legend() - delta = np.asarray(x_ref) - np.asarray(x_ml) - axes[1].plot(delta, color="C3", linewidth=1) - axes[1].set_title("Delta (torch - coreml)") - axes[1].set_xlabel("time/step") - plt.tight_layout() - plt.savefig(path) - plt.close(fig) - else: - plt.figure(figsize=(8, 3)) - plt.plot(x_ref, label="torch", linewidth=1) - plt.plot(x_ml, label="coreml", linewidth=1, alpha=0.8) - plt.title(title) - plt.legend() - plt.tight_layout() - plt.savefig(path) - plt.close() - return str(path.name) - - -def _plot_image(img: np.ndarray, title: str, path: Path, vmin=None, vmax=None): - if not HAS_MPL: - return None - plt.figure(figsize=(6, 4)) - plt.imshow(img, aspect='auto', origin='lower', interpolation='nearest', vmin=vmin, vmax=vmax) - plt.title(title) - plt.colorbar(shrink=0.8) - plt.tight_layout() - plt.savefig(path) - plt.close() - return str(path.name) - - -def _plot_mel_composite( - mel_torch: np.ndarray, - mel_coreml: np.ndarray, - path: Path, - vmin=None, - vmax=None, -): - """Create a single PNG with mel torch, mel coreml, abs diff heatmap, and mean-over-time curves with delta.""" - if not HAS_MPL: - return None - mel_torch = np.asarray(mel_torch) - mel_coreml = np.asarray(mel_coreml) - absdiff = np.abs(mel_torch - mel_coreml) - mean_t = mel_torch.mean(axis=0) - mean_c = mel_coreml.mean(axis=0) - delta = mean_t - mean_c - - fig = plt.figure(figsize=(12, 8)) - gs = fig.add_gridspec(2, 2, height_ratios=[1, 1]) - ax1 = fig.add_subplot(gs[0, 0]) - im1 = ax1.imshow(mel_torch, aspect='auto', origin='lower', interpolation='nearest', vmin=vmin, vmax=vmax) - ax1.set_title("Mel (Torch)") - fig.colorbar(im1, ax=ax1, shrink=0.8) - - ax2 = fig.add_subplot(gs[0, 1]) - im2 = ax2.imshow(mel_coreml, aspect='auto', origin='lower', interpolation='nearest', vmin=vmin, vmax=vmax) - ax2.set_title("Mel (CoreML)") - fig.colorbar(im2, ax=ax2, shrink=0.8) - - ax3 = fig.add_subplot(gs[1, 0]) - im3 = ax3.imshow(absdiff, aspect='auto', origin='lower', interpolation='nearest') - ax3.set_title("Mel |diff|") - fig.colorbar(im3, ax=ax3, shrink=0.8) - - ax4 = fig.add_subplot(gs[1, 1]) - ax4.plot(mean_t, label="torch", linewidth=1) - ax4.plot(mean_c, label="coreml", linewidth=1, alpha=0.8) - ax4.plot(delta, label="delta", linewidth=1, color="C3") - ax4.set_title("Mel mean over time + delta") - ax4.legend() - - plt.tight_layout() - plt.savefig(path) - plt.close(fig) - return str(path.name) - - -def _plot_latency_bars( - labels, - torch_means, - torch_stds, - coreml_means, - coreml_stds, - path: Path, -): - if not HAS_MPL: - return None - x = np.arange(len(labels)) - width = 0.35 - fig, ax = plt.subplots(figsize=(8, 4)) - b1 = ax.bar(x - width/2, torch_means, width, yerr=torch_stds, label="torch", color="C0", alpha=0.9) - b2 = ax.bar(x + width/2, coreml_means, width, yerr=coreml_stds, label="coreml", color="C1", alpha=0.9) - ax.set_xticks(x, labels, rotation=15) - ax.set_ylabel("latency (ms)") - ax.set_title("Component latency (15s window inputs)") - ax.legend() - # Add value labels on bars - def _annotate(bars): - for bar in bars: - h = bar.get_height() - if np.isnan(h): - continue - ax.annotate(f"{h:.0f}", - xy=(bar.get_x() + bar.get_width()/2, h), - xytext=(0, 3), textcoords="offset points", - ha='center', va='bottom', fontsize=8) - _annotate(b1) - _annotate(b2) - plt.tight_layout() - plt.savefig(path) - plt.close(fig) - return str(path.name) - - -def _plot_speedup_bars(labels, torch_means, coreml_means, path: Path): - if not HAS_MPL: - return None - speedup = [] - for t, c in zip(torch_means, coreml_means): - if c and c > 0: - speedup.append(float(t) / float(c)) - else: - speedup.append(np.nan) - x = np.arange(len(labels)) - fig, ax = plt.subplots(figsize=(8, 4)) - bars = ax.bar(x, speedup, color="C2") - ax.set_xticks(x, labels, rotation=15) - ax.set_ylabel("torch/coreml speedup") - ax.set_title("CoreML speedup vs Torch (higher is better)") - ax.axhline(1.0, color="gray", linestyle="--", linewidth=1) - # Add value labels - for bar in bars: - h = bar.get_height() - if np.isnan(h): - continue - ax.annotate(f"{h:.2f}", - xy=(bar.get_x() + bar.get_width()/2, h), - xytext=(0, 3), textcoords="offset points", - ha='center', va='bottom', fontsize=8) - plt.tight_layout() - plt.savefig(path) - plt.close(fig) - return str(path.name) - - - -app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) - - -@app.command() -def compare( - output_dir: Path = typer.Option(Path("parakeet_coreml"), help="Directory containing mlpackages + metadata.json"), - nemo_path: Optional[Path] = typer.Option(None, "--nemo-path", exists=True, resolve_path=True, help="Path to .nemo checkpoint"), - model_id: str = typer.Option("nvidia/parakeet-tdt-0.6b-v2", "--model-id", help="HF model id if --nemo-path omitted"), - validation_audio: Optional[Path] = typer.Option(None, exists=True, resolve_path=True, help="15s, 16kHz wav for validation (defaults to audio/yc_first_minute_16k_15s.wav if present)"), - seed: Optional[int] = typer.Option(None, help="Random seed for synthetic input when audio is not provided"), - rtol: float = typer.Option(1e-3, help="Relative tolerance for comparisons"), - atol: float = typer.Option(1e-4, help="Absolute tolerance for comparisons"), - runs: int = typer.Option(10, help="Timed runs per model for latency measurement"), - warmup: int = typer.Option(3, help="Warmup runs before timing (compilation, caches)"), - symbol_steps: int = typer.Option( - 32, - help="Number of sequential decoder steps to validate with streaming U=1 inputs", - ), -) -> None: - """Run Torch vs CoreML comparisons and update metadata.json with plots and diffs.""" - output_dir.mkdir(parents=True, exist_ok=True) - if symbol_steps < 1: - raise typer.BadParameter("symbol_steps must be >= 1") - - meta_path = output_dir / "metadata.json" - exported_meta: Dict[str, object] = {} - if meta_path.exists(): - try: - exported_meta = json.loads(meta_path.read_text()) - except Exception: - exported_meta = {} - exported_max_u = int(exported_meta.get("max_symbol_steps", 1)) - if exported_max_u != 1: - typer.echo( - f"Note: CoreML export reports max_symbol_steps={exported_max_u}; " - "comparison still drives decoder step-wise with U=1 inputs." - ) - if nemo_path is not None: - typer.echo(f"Loading NeMo model from {nemo_path}
") - asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(str(nemo_path), map_location="cpu") - else: - typer.echo(f"Downloading NeMo model via {model_id}
") - asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_id, map_location="cpu") - asr_model.eval() - - sample_rate = int(asr_model.cfg.preprocessor.sample_rate) - max_samples = _compute_length(15.0, sample_rate) - default_audio = (Path(__file__).parent / "audio" / "yc_first_minute_16k_15s.wav").resolve() - chosen_audio = validation_audio if validation_audio is not None else (default_audio if default_audio.exists() else None) - if chosen_audio is not None and validation_audio is None: - typer.echo(f"Using default validation audio: {chosen_audio}") - - audio_tensor = _prepare_audio(chosen_audio, sample_rate, max_samples, seed) - audio_length = torch.tensor([max_samples], dtype=torch.int32) - - asr_model.decoder._rnnt_export = True - # Disable fused loss/WER computation for simpler joint inference - asr_model.joint.set_fuse_loss_wer(False) - # Important: ensure the joint returns raw logits (not log-softmax) - # RNNTJoint applies log_softmax on CPU by default when `log_softmax is None`. - # Our exported CoreML joint emits pre-softmax logits, so make the Torch - # reference do the same to avoid systematic offsets in comparisons/plots. - try: - # Some versions expose this as a plain attribute - asr_model.joint.log_softmax = False - except Exception: - pass - - # Generate reference outputs directly from NeMo model components - with torch.inference_mode(): - # Preprocessor - direct NeMo call - mel_ref, mel_length_ref = asr_model.preprocessor( - input_signal=audio_tensor, - length=audio_length.to(dtype=torch.long) - ) - mel_length_ref = mel_length_ref.to(dtype=torch.int32) - - # Encoder - direct NeMo call - encoder_ref, encoder_length_ref = asr_model.encoder( - audio_signal=mel_ref, - length=mel_length_ref.to(dtype=torch.long) - ) - encoder_length_ref = encoder_length_ref.to(dtype=torch.int32) - - vocab_size = int(asr_model.tokenizer.vocab_size) - num_extra = int(asr_model.joint.num_extra_outputs) - decoder_hidden = int(asr_model.decoder.pred_hidden) - decoder_layers = int(asr_model.decoder.pred_rnn_layers) - blank_id = int(asr_model.decoder.blank_idx) - - blank_targets = torch.tensor([[blank_id]], dtype=torch.int32) - blank_target_lengths = torch.tensor([1], dtype=torch.int32) - blank_targets_long = blank_targets.to(dtype=torch.long) - blank_target_lengths_long = blank_target_lengths.to(dtype=torch.long) - - def _decoder_rollout_torch(num_steps: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - outputs = [] - h_state = torch.zeros(decoder_layers, 1, decoder_hidden, dtype=torch.float32) - c_state = torch.zeros(decoder_layers, 1, decoder_hidden, dtype=torch.float32) - state = [h_state, c_state] - with torch.inference_mode(): - for _ in range(num_steps): - y, _, new_state = asr_model.decoder( - targets=blank_targets_long, - target_length=blank_target_lengths_long, - states=state, - ) - outputs.append(y.detach()) - state = [new_state[0].detach(), new_state[1].detach()] - if outputs: - decoder_seq = torch.cat(outputs, dim=-1) - else: - decoder_seq = torch.zeros(1, decoder_hidden, 0, dtype=torch.float32) - return decoder_seq, state[0], state[1] - - decoder_ref, h_ref, c_ref = _decoder_rollout_torch(symbol_steps) - - with torch.inference_mode(): - logits_ref = asr_model.joint( - encoder_outputs=encoder_ref, - decoder_outputs=decoder_ref, - ) - - # Convert tensors to numpy for CoreML - def _np32(x): - return np.array(x.detach().cpu().numpy(), dtype=np.float32, copy=True) - - # Prepare plot dir (write to repo-tracked plots//) - plots_root = Path(__file__).parent / "plots" - plots_dir = plots_root / Path(__file__).stem - plots_dir.mkdir(parents=True, exist_ok=True) - - encoder_np = _np32(encoder_ref) - decoder_ref_np = _np32(decoder_ref) - - summary: Dict[str, object] = { - "requested": True, - "status": "ok", - "atol": atol, - "rtol": rtol, - "symbol_steps": int(symbol_steps), - "audio_path": None if validation_audio is None else str(validation_audio), - "components": {}, - } - - # Preprocessor - pre = ct.models.MLModel(str(output_dir / "parakeet_preprocessor.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - t0 = time.perf_counter() - pre_out = pre.predict({"audio_signal": _np32(audio_tensor), "audio_length": _np32(audio_length).astype(np.int32)}) - t1 = time.perf_counter() - pre_first_ms = (t1 - t0) * 1000.0 - mel_ml = np.array(pre_out["mel"], dtype=np.float32, copy=True) - mel_len_ml = np.array(pre_out["mel_length"], dtype=np.int32, copy=True) - pre_atol, pre_rtol = max(atol, 1.0), max(rtol, 1e-2) - a_mel, r_mel, ok_mel = _max_diffs(_np32(mel_ref), mel_ml, pre_rtol, pre_atol) - ok_len = int(_np32(mel_length_ref).astype(np.int32)[0]) == int(np.array(mel_len_ml).astype(np.int32)[0]) - mel_t = _np32(mel_ref)[0] - mel_c = mel_ml[0] - vmin = float(min(mel_t.min(), mel_c.min())) - vmax = float(max(mel_t.max(), mel_c.max())) - pre_plots = { - "mel_composite.png": _plot_mel_composite(mel_t, mel_c, plots_dir / "mel_composite.png", vmin=vmin, vmax=vmax), - } - # Latency measurements: Torch and CoreML - def _time_coreml(model: ct.models.MLModel, inputs: Dict[str, np.ndarray]) -> Tuple[float, float]: - # Warmup - for _ in range(max(0, warmup)): - _ = model.predict(inputs) - times = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _ = model.predict(inputs) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - def _time_torch(fn, *args, **kwargs) -> Tuple[float, float]: - with torch.inference_mode(): - for _ in range(max(0, warmup)): - _ = fn(*args, **kwargs) - times = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _ = fn(*args, **kwargs) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - pre_torch_ms_mean, pre_torch_ms_std = _time_torch( - asr_model.preprocessor, input_signal=audio_tensor, length=audio_length.to(dtype=torch.long) - ) - pre_coreml_ms_mean, pre_coreml_ms_std = _time_coreml( - pre, - {"audio_signal": _np32(audio_tensor), "audio_length": _np32(audio_length).astype(np.int32)}, - ) - seconds = 15.0 - pre_coreml_rtf = float(pre_coreml_ms_mean / (seconds * 1000.0)) if pre_coreml_ms_mean > 0 else None - pre_torch_rtf = float(pre_torch_ms_mean / (seconds * 1000.0)) if pre_torch_ms_mean > 0 else None - - summary["components"]["preprocessor"] = { - "mel": {"max_abs": a_mel, "max_rel": r_mel, "match": bool(ok_mel)}, - "length_match": bool(ok_len), - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "coreml_first_ms": pre_first_ms, - "torch_ms": {"mean": pre_torch_ms_mean, "std": pre_torch_ms_std}, - "coreml_ms": {"mean": pre_coreml_ms_mean, "std": pre_coreml_ms_std}, - "rtf": {"torch": pre_torch_rtf, "coreml": pre_coreml_rtf}, - }, - "plots": {k: v for k, v in pre_plots.items() if v}, - } - - # Encoder - enc = ct.models.MLModel(str(output_dir / "parakeet_encoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - t0 = time.perf_counter() - enc_out = enc.predict({"mel": _np32(mel_ref), "mel_length": _np32(mel_length_ref).astype(np.int32)}) - t1 = time.perf_counter() - enc_first_ms = (t1 - t0) * 1000.0 - enc_ml = np.array(enc_out["encoder"], dtype=np.float32, copy=True) - enc_len_ml = np.array(enc_out["encoder_length"], dtype=np.int32, copy=True) - a_enc, r_enc, ok_enc = _max_diffs(_np32(encoder_ref), enc_ml, max(rtol, 5e-3), max(atol, 5e-2)) - ok_enc_len = int(_np32(encoder_length_ref).astype(np.int32)[0]) == int(np.array(enc_len_ml).astype(np.int32)[0]) - enc_t = _np32(encoder_ref)[0] - enc_c = enc_ml[0] - enc_plots = { - "encoder_time_l2.png": _plot_line( - np.linalg.norm(enc_t, axis=0), # L2 norm over features (D) for each time step - np.linalg.norm(enc_c, axis=0), # enc_t shape is (D, T), so axis=0 is features - "Encoder L2 over time", - plots_dir / "encoder_time_l2.png", - also_delta=True, - ), - } - enc_torch_ms_mean, enc_torch_ms_std = _time_torch( - asr_model.encoder, audio_signal=mel_ref, length=mel_length_ref.to(dtype=torch.long) - ) - enc_coreml_ms_mean, enc_coreml_ms_std = _time_coreml( - enc, {"mel": _np32(mel_ref), "mel_length": _np32(mel_length_ref).astype(np.int32)} - ) - enc_coreml_rtf = float(enc_coreml_ms_mean / (seconds * 1000.0)) if enc_coreml_ms_mean > 0 else None - enc_torch_rtf = float(enc_torch_ms_mean / (seconds * 1000.0)) if enc_torch_ms_mean > 0 else None - - summary["components"]["encoder"] = { - "encoder": {"max_abs": a_enc, "max_rel": r_enc, "match": bool(ok_enc)}, - "length_match": bool(ok_enc_len), - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "coreml_first_ms": enc_first_ms, - "torch_ms": {"mean": enc_torch_ms_mean, "std": enc_torch_ms_std}, - "coreml_ms": {"mean": enc_coreml_ms_mean, "std": enc_coreml_ms_std}, - "rtf": {"torch": enc_torch_rtf, "coreml": enc_coreml_rtf}, - }, - "plots": {k: v for k, v in enc_plots.items() if v}, - } - - # Decoder (sequential U=1 rollout) - dec = ct.models.MLModel(str(output_dir / "parakeet_decoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - - zero_state_np = np.zeros((decoder_layers, 1, decoder_hidden), dtype=np.float32) - blank_targets_np = np.array(blank_targets.detach().cpu().numpy(), dtype=np.int32, copy=True) - blank_target_lengths_np = np.array(blank_target_lengths.detach().cpu().numpy(), dtype=np.int32, copy=True) - - def _decoder_rollout_coreml(num_steps: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray, float]: - outputs = [] - h_np = zero_state_np.copy() - c_np = zero_state_np.copy() - first_ms: Optional[float] = None - for i in range(num_steps): - t0_i = time.perf_counter() if i == 0 else None - res = dec.predict( - { - "targets": blank_targets_np, - "target_length": blank_target_lengths_np, - "h_in": h_np, - "c_in": c_np, - } - ) - if t0_i is not None: - t1_i = time.perf_counter() - first_ms = (t1_i - t0_i) * 1000.0 - outputs.append(np.array(res["decoder"], dtype=np.float32, copy=True)) - h_np = np.array(res["h_out"], dtype=np.float32, copy=True) - c_np = np.array(res["c_out"], dtype=np.float32, copy=True) - if outputs: - decoder_seq = np.concatenate(outputs, axis=-1) - else: - decoder_seq = np.zeros((1, decoder_hidden, 0), dtype=np.float32) - return decoder_seq, h_np, c_np, (0.0 if first_ms is None else float(first_ms)) - - dec_ml, h_ml, c_ml, dec_first_ms = _decoder_rollout_coreml(symbol_steps) - h_ref_np = _np32(h_ref) - c_ref_np = _np32(c_ref) - - a_dec, r_dec, ok_dec = _max_diffs(decoder_ref_np, dec_ml, max(rtol, 1e-2), max(atol, 1e-1)) - a_h, r_h, ok_h = _max_diffs(h_ref_np, h_ml, max(rtol, 1e-2), max(atol, 2.5e-1)) - a_c, r_c, ok_c = _max_diffs(c_ref_np, c_ml, max(rtol, 5e-2), max(atol, 1.5e0)) - - dec_t = decoder_ref_np[0] - dec_c = dec_ml[0] - dec_plots = { - "decoder_steps_l2.png": _plot_line( - np.linalg.norm(dec_t, axis=0), - np.linalg.norm(dec_c, axis=0), - "Decoder L2 over steps", - plots_dir / "decoder_steps_l2.png", - also_delta=True, - ), - } - - def _time_decoder_coreml() -> Tuple[float, float]: - for _ in range(max(0, warmup)): - _decoder_rollout_coreml(symbol_steps) - times = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _decoder_rollout_coreml(symbol_steps) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - dec_torch_ms_mean, dec_torch_ms_std = _time_torch(lambda: _decoder_rollout_torch(symbol_steps)) - dec_coreml_ms_mean, dec_coreml_ms_std = _time_decoder_coreml() - dec_coreml_rtf = float(dec_coreml_ms_mean / (seconds * 1000.0)) if dec_coreml_ms_mean > 0 else None - dec_torch_rtf = float(dec_torch_ms_mean / (seconds * 1000.0)) if dec_torch_ms_mean > 0 else None - - summary["components"]["decoder"] = { - "decoder": {"max_abs": a_dec, "max_rel": r_dec, "match": bool(ok_dec)}, - "h_out": {"max_abs": a_h, "max_rel": r_h, "match": bool(ok_h)}, - "c_out": {"max_abs": a_c, "max_rel": r_c, "match": bool(ok_c)}, - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "coreml_first_ms": dec_first_ms, - "torch_ms": {"mean": dec_torch_ms_mean, "std": dec_torch_ms_std}, - "coreml_ms": {"mean": dec_coreml_ms_mean, "std": dec_coreml_ms_std}, - "rtf": {"torch": dec_torch_rtf, "coreml": dec_coreml_rtf}, - }, - "plots": {k: v for k, v in dec_plots.items() if v}, - } - - # Joint (sequential U=1 rollouts) - j = ct.models.MLModel(str(output_dir / "parakeet_joint.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - - def _joint_rollout_coreml(decoder_seq_np: np.ndarray) -> Tuple[np.ndarray, float]: - logits_steps = [] - first_ms: Optional[float] = None - for u in range(decoder_seq_np.shape[2]): - dec_slice = decoder_seq_np[:, :, u : u + 1] - t0_u = time.perf_counter() if u == 0 else None - res = j.predict({"encoder": encoder_np, "decoder": dec_slice}) - if t0_u is not None: - t1_u = time.perf_counter() - first_ms = (t1_u - t0_u) * 1000.0 - logits_steps.append(np.array(res["logits"], dtype=np.float32, copy=True)) - if not logits_steps: - raise RuntimeError("No decoder steps provided for joint rollout") - return np.concatenate(logits_steps, axis=2), (0.0 if first_ms is None else float(first_ms)) - - logits_ml, joint_first_ms = _joint_rollout_coreml(decoder_ref_np) - logits_ref_np = _np32(logits_ref) - a_j, r_j, ok_j = _max_diffs(logits_ref_np, logits_ml, max(rtol, 1e-2), max(atol, 1e-1)) - joint_plots = {} - if HAS_MPL: - lt = logits_ref_np[0, 0, 0, :] - lc = logits_ml[0, 0, 0, :] - top_idx = np.argsort(-np.abs(lt))[:50] - path = plots_dir / "joint_top50.png" - plt.figure(figsize=(8, 3)) - plt.plot(lt[top_idx], label="torch") - plt.plot(lc[top_idx], label="coreml", alpha=0.8) - plt.title("Joint logits (t=0,u=0) top-50 |torch|") - plt.legend(); plt.tight_layout(); plt.savefig(path); plt.close() - joint_plots["joint_top50.png"] = str(path.name) - - # Delta-over-time visualization (fix u=0; summarize over vocab) - jt = logits_ref_np[0, :, 0, :] - jc = logits_ml[0, :, 0, :] - l2_t = np.linalg.norm(jt, axis=1) - l2_c = np.linalg.norm(jc, axis=1) - path2 = plots_dir / "joint_time_l2.png" - _plot_line(l2_t, l2_c, "Joint L2 over time (u=0)", path2, also_delta=True) - joint_plots["joint_time_l2.png"] = str(path2.name) - - def _time_joint_coreml() -> Tuple[float, float]: - for _ in range(max(0, warmup)): - _joint_rollout_coreml(decoder_ref_np) - times = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _joint_rollout_coreml(decoder_ref_np) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - joint_torch_ms_mean, joint_torch_ms_std = _time_torch( - asr_model.joint, encoder_outputs=encoder_ref, decoder_outputs=decoder_ref - ) - joint_coreml_ms_mean, joint_coreml_ms_std = _time_joint_coreml() - joint_coreml_rtf = float(joint_coreml_ms_mean / (seconds * 1000.0)) if joint_coreml_ms_mean > 0 else None - joint_torch_rtf = float(joint_torch_ms_mean / (seconds * 1000.0)) if joint_torch_ms_mean > 0 else None - - summary["components"]["joint"] = { - "logits": {"max_abs": a_j, "max_rel": r_j, "match": bool(ok_j)}, - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "coreml_first_ms": joint_first_ms, - "torch_ms": {"mean": joint_torch_ms_mean, "std": joint_torch_ms_std}, - "coreml_ms": {"mean": joint_coreml_ms_mean, "std": joint_coreml_ms_std}, - "rtf": {"torch": joint_torch_rtf, "coreml": joint_coreml_rtf}, - }, - "plots": joint_plots, - } - - # Fused components - # 1) Mel+Encoder fused vs separate - mel_enc_plots = {} - try: - mel_enc = ct.models.MLModel(str(output_dir / "parakeet_mel_encoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - t0 = time.perf_counter() - mel_enc_out = mel_enc.predict({ - "audio_signal": _np32(audio_tensor), - "audio_length": _np32(audio_length).astype(np.int32), - }) - t1 = time.perf_counter() - mel_enc_first_ms = (t1 - t0) * 1000.0 - mel_enc_ml = np.array(mel_enc_out["encoder"], dtype=np.float32, copy=True) - mel_enc_len_ml = np.array(mel_enc_out["encoder_length"], dtype=np.int32, copy=True) - # Compare fused output vs Torch reference encoder - a_melenc, r_melenc, ok_melenc = _max_diffs(_np32(encoder_ref), mel_enc_ml, max(rtol, 5e-3), max(atol, 5e-2)) - ok_melenc_len = int(_np32(encoder_length_ref).astype(np.int32)[0]) == int(mel_enc_len_ml.astype(np.int32)[0]) - # Also compare fused vs separate CoreML pipeline (pre -> enc) - a_melenc_vs_sep, r_melenc_vs_sep, ok_melenc_vs_sep = _max_diffs(enc_ml, mel_enc_ml, max(rtol, 5e-3), max(atol, 5e-2)) - - # Plots: L2 over time (fused vs torch) - enc_t_ref = _np32(encoder_ref)[0] - enc_c_fused = mel_enc_ml[0] - mel_enc_plots["mel_encoder_time_l2.png"] = _plot_line( - np.linalg.norm(enc_t_ref, axis=0), - np.linalg.norm(enc_c_fused, axis=0), - "Mel+Encoder (fused) L2 over time", - plots_dir / "mel_encoder_time_l2.png", - also_delta=True, - ) - - # Latency: fused CoreML vs separate (CoreML pre + CoreML enc) - mel_enc_coreml_ms_mean, mel_enc_coreml_ms_std = _time_coreml( - mel_enc, - {"audio_signal": _np32(audio_tensor), "audio_length": _np32(audio_length).astype(np.int32)}, - ) - sep_coreml_ms_mean = float(pre_coreml_ms_mean + enc_coreml_ms_mean) - sep_coreml_ms_std = float((pre_coreml_ms_std ** 2 + enc_coreml_ms_std ** 2) ** 0.5) - # Torch baseline (separate torch pre + enc) - sep_torch_ms_mean = float(pre_torch_ms_mean + enc_torch_ms_mean) - sep_torch_ms_std = float((pre_torch_ms_std ** 2 + enc_torch_ms_std ** 2) ** 0.5) - - mel_enc_coreml_rtf = float(mel_enc_coreml_ms_mean / (seconds * 1000.0)) if mel_enc_coreml_ms_mean > 0 else None - sep_coreml_rtf = float(sep_coreml_ms_mean / (seconds * 1000.0)) if sep_coreml_ms_mean > 0 else None - sep_torch_rtf = float(sep_torch_ms_mean / (seconds * 1000.0)) if sep_torch_ms_mean > 0 else None - - summary["components"]["mel_encoder"] = { - "encoder": {"max_abs": a_melenc, "max_rel": r_melenc, "match": bool(ok_melenc)}, - "length_match": bool(ok_melenc_len), - "vs_separate_coreml": {"max_abs": a_melenc_vs_sep, "max_rel": r_melenc_vs_sep, "match": bool(ok_melenc_vs_sep)}, - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "fused_coreml_first_ms": mel_enc_first_ms, - "fused_coreml_ms": {"mean": mel_enc_coreml_ms_mean, "std": mel_enc_coreml_ms_std}, - "separate_coreml_ms": {"mean": sep_coreml_ms_mean, "std": sep_coreml_ms_std}, - "separate_torch_ms": {"mean": sep_torch_ms_mean, "std": sep_torch_ms_std}, - "rtf": {"fused_coreml": mel_enc_coreml_rtf, "separate_coreml": sep_coreml_rtf, "separate_torch": sep_torch_rtf}, - }, - "plots": {k: v for k, v in mel_enc_plots.items() if v}, - } - except Exception as e: - summary["components"]["mel_encoder_error"] = str(e) - - # 2) JointDecision fused vs CPU PyTorch post-processing - jd_plots = {} - try: - # Fused CoreML joint decision - jd = ct.models.MLModel(str(output_dir / "parakeet_joint_decision.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - def _joint_decision_rollout_coreml(decoder_seq_np: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, float]: - token_ids = [] - token_probs = [] - durations = [] - first_ms: Optional[float] = None - for u in range(decoder_seq_np.shape[2]): - dec_slice = decoder_seq_np[:, :, u : u + 1] - t0_u = time.perf_counter() if u == 0 else None - res = jd.predict({"encoder": encoder_np, "decoder": dec_slice}) - if t0_u is not None: - t1_u = time.perf_counter() - first_ms = (t1_u - t0_u) * 1000.0 - token_ids.append(np.array(res["token_id"], dtype=np.int32, copy=True)) - token_probs.append(np.array(res["token_prob"], dtype=np.float32, copy=True)) - durations.append(np.array(res["duration"], dtype=np.int32, copy=True)) - if not token_ids: - raise RuntimeError("No decoder steps provided for joint decision rollout") - return ( - np.concatenate(token_ids, axis=2), - np.concatenate(token_probs, axis=2), - np.concatenate(durations, axis=2), - (0.0 if first_ms is None else float(first_ms)), - ) - - token_id_ml, token_prob_ml, duration_ml, jd_first_ms = _joint_decision_rollout_coreml(decoder_ref_np) - - # CPU PyTorch decision using Torch logits - vocab_with_blank = int(vocab_size) + 1 - with torch.inference_mode(): - logits_t = logits_ref - token_logits_t = logits_t[..., :vocab_with_blank] - duration_logits_t = logits_t[..., -num_extra:] if num_extra > 0 else None - token_ids_t = torch.argmax(token_logits_t, dim=-1).to(dtype=torch.int32) - token_probs_all_t = torch.softmax(token_logits_t, dim=-1) - token_prob_t = torch.gather( - token_probs_all_t, dim=-1, index=token_ids_t.long().unsqueeze(-1) - ).squeeze(-1) - if duration_logits_t is not None and duration_logits_t.numel() > 0: - duration_t = torch.argmax(duration_logits_t, dim=-1).to(dtype=torch.int32) - else: - duration_t = torch.zeros_like(token_ids_t, dtype=torch.int32) - - # Also derive CPU decision from CoreML joint logits for "separate" path - token_logits_c = _to_t(logits_ml)[..., :vocab_with_blank] - duration_logits_c = _to_t(logits_ml)[..., -num_extra:] if num_extra > 0 else None - token_ids_c = torch.argmax(token_logits_c, dim=-1).to(dtype=torch.int32) - token_probs_all_c = torch.softmax(token_logits_c, dim=-1) - token_prob_c = torch.gather( - token_probs_all_c, dim=-1, index=token_ids_c.long().unsqueeze(-1) - ).squeeze(-1) - if duration_logits_c is not None and duration_logits_c.numel() > 0: - duration_c = torch.argmax(duration_logits_c, dim=-1).to(dtype=torch.int32) - else: - duration_c = torch.zeros_like(token_ids_c, dtype=torch.int32) - - # Compare fused outputs to CPU PyTorch decisions - a_tid_t, r_tid_t, ok_tid_t = _max_diffs(_np(token_ids_t), token_id_ml, 0.0, 0.0) - a_tprob_t, r_tprob_t, ok_tprob_t = _max_diffs(_np(token_prob_t), token_prob_ml, max(rtol, 1e-2), max(atol, 1e-1)) - a_dur_t, r_dur_t, ok_dur_t = _max_diffs(_np(duration_t), duration_ml, 0.0, 0.0) - - a_tid_c, r_tid_c, ok_tid_c = _max_diffs(_np(token_ids_c), token_id_ml, 0.0, 0.0) - a_tprob_c, r_tprob_c, ok_tprob_c = _max_diffs(_np(token_prob_c), token_prob_ml, max(rtol, 1e-2), max(atol, 1e-1)) - a_dur_c, r_dur_c, ok_dur_c = _max_diffs(_np(duration_c), duration_ml, 0.0, 0.0) - - # Plots: token_prob over time for u=0 (fused vs torch CPU) - if HAS_MPL: - prob_t = _np(token_prob_t)[0, :, 0] - prob_ml = token_prob_ml[0, :, 0] - jd_plots["joint_decision_prob_u0.png"] = _plot_line( - prob_t, - prob_ml, - "JointDecision token_prob (u=0)", - plots_dir / "joint_decision_prob_u0.png", - also_delta=True, - ) - - # Agreement heatmap for token_id - agree = (_np(token_ids_t)[0] == token_id_ml[0]).astype(np.float32) - jd_plots["joint_decision_token_agree.png"] = _plot_image( - agree, - "token_id agreement (torch CPU vs fused)", - plots_dir / "joint_decision_token_agree.png", - vmin=0.0, - vmax=1.0, - ) - - # Latency: fused CoreML vs separate (CoreML joint + CPU PyTorch decision) - def _time_joint_decision_coreml() -> Tuple[float, float]: - for _ in range(max(0, warmup)): - _joint_decision_rollout_coreml(decoder_ref_np) - times = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _joint_decision_rollout_coreml(decoder_ref_np) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - jd_coreml_ms_mean, jd_coreml_ms_std = _time_joint_decision_coreml() - - # Time CPU post-processing only (Torch) on top of CoreML or Torch logits. Use Torch logits. - def _decision_torch_call(): - with torch.inference_mode(): - tl = logits_ref - tl_token = tl[..., :vocab_with_blank] - tl_ids = torch.argmax(tl_token, dim=-1) - tl_probs = torch.softmax(tl_token, dim=-1) - _ = torch.gather(tl_probs, -1, tl_ids.long().unsqueeze(-1)).squeeze(-1) - if num_extra > 0: - _ = torch.argmax(tl[..., -num_extra:], dim=-1) - return None - - jd_decision_torch_ms_mean, jd_decision_torch_ms_std = _time_torch(lambda: _decision_torch_call()) - sep_joint_plus_cpu_ms_mean = float(joint_coreml_ms_mean + jd_decision_torch_ms_mean) - sep_joint_plus_cpu_ms_std = float((joint_coreml_ms_std ** 2 + jd_decision_torch_ms_std ** 2) ** 0.5) - jd_coreml_rtf = float(jd_coreml_ms_mean / (seconds * 1000.0)) if jd_coreml_ms_mean > 0 else None - sep_joint_cpu_rtf = float(sep_joint_plus_cpu_ms_mean / (seconds * 1000.0)) if sep_joint_plus_cpu_ms_mean > 0 else None - - summary["components"]["joint_decision"] = { - "vs_torch_cpu": { - "token_id": {"max_abs": a_tid_t, "max_rel": r_tid_t, "match": bool(ok_tid_t)}, - "token_prob": {"max_abs": a_tprob_t, "max_rel": r_tprob_t, "match": bool(ok_tprob_t)}, - "duration": {"max_abs": a_dur_t, "max_rel": r_dur_t, "match": bool(ok_dur_t)}, - }, - "vs_coreml_joint_cpu": { - "token_id": {"max_abs": a_tid_c, "max_rel": r_tid_c, "match": bool(ok_tid_c)}, - "token_prob": {"max_abs": a_tprob_c, "max_rel": r_tprob_c, "match": bool(ok_tprob_c)}, - "duration": {"max_abs": a_dur_c, "max_rel": r_dur_c, "match": bool(ok_dur_c)}, - }, - "latency": { - "runs": int(runs), - "warmup": int(warmup), - "fused_coreml_first_ms": jd_first_ms, - "fused_coreml_ms": {"mean": jd_coreml_ms_mean, "std": jd_coreml_ms_std}, - "separate_joint_coreml_plus_cpu_ms": {"mean": sep_joint_plus_cpu_ms_mean, "std": sep_joint_plus_cpu_ms_std}, - "rtf": {"fused_coreml": jd_coreml_rtf, "separate_joint_coreml_plus_cpu": sep_joint_cpu_rtf}, - }, - "plots": {k: v for k, v in jd_plots.items() if v}, - } - except Exception as e: - summary["components"]["joint_decision_error"] = str(e) - - # Latency overview plots (saved alongside component plots) - latency_plots = {} - labels = ["preprocessor", "encoder", "decoder", "joint"] - torch_means = [pre_torch_ms_mean, enc_torch_ms_mean, dec_torch_ms_mean, joint_torch_ms_mean] - torch_stds = [pre_torch_ms_std, enc_torch_ms_std, dec_torch_ms_std, joint_torch_ms_std] - coreml_means = [pre_coreml_ms_mean, enc_coreml_ms_mean, dec_coreml_ms_mean, joint_coreml_ms_mean] - coreml_stds = [pre_coreml_ms_std, enc_coreml_ms_std, dec_coreml_ms_std, joint_coreml_ms_std] - lat_path = plots_dir / "latency_summary.png" - spd_path = plots_dir / "latency_speedup.png" - latency_plots["latency_summary.png"] = _plot_latency_bars( - labels, torch_means, torch_stds, coreml_means, coreml_stds, lat_path - ) - latency_plots["latency_speedup.png"] = _plot_speedup_bars( - labels, torch_means, coreml_means, spd_path - ) - - # Fused vs separate latency summary - fused_labels = ["mel+encoder", "joint_decision"] - fused_baseline_means = [ - float(pre_torch_ms_mean + enc_torch_ms_mean), - float(joint_coreml_ms_mean + jd_decision_torch_ms_mean if 'jd_coreml_ms_mean' in locals() else joint_coreml_ms_mean), - ] - fused_coreml_means = [ - float(mel_enc_coreml_ms_mean if 'mel_enc_coreml_ms_mean' in locals() else np.nan), - float(jd_coreml_ms_mean if 'jd_coreml_ms_mean' in locals() else np.nan), - ] - fused_latency_path = plots_dir / "latency_fused_vs_separate.png" - fused_speedup_path = plots_dir / "latency_fused_speedup.png" - latency_plots["latency_fused_vs_separate.png"] = _plot_latency_bars( - fused_labels, fused_baseline_means, [0, 0], fused_coreml_means, [0, 0], fused_latency_path - ) - latency_plots["latency_fused_speedup.png"] = _plot_speedup_bars( - fused_labels, fused_baseline_means, fused_coreml_means, fused_speedup_path - ) - - all_ok = ( - summary["components"]["preprocessor"]["mel"]["match"] - and summary["components"]["preprocessor"]["length_match"] - and summary["components"]["encoder"]["encoder"]["match"] - and summary["components"]["encoder"]["length_match"] - and summary["components"]["decoder"]["decoder"]["match"] - and summary["components"]["decoder"]["h_out"]["match"] - and summary["components"]["decoder"]["c_out"]["match"] - and summary["components"]["joint"]["logits"]["match"] - ) - summary["status"] = "ok" if all_ok else "mismatch" - - # Update metadata.json - meta_path = output_dir / "metadata.json" - try: - meta = json.loads(meta_path.read_text()) - except Exception: - meta = {} - meta["validation"] = summary - meta_path.write_text(json.dumps(meta, indent=2)) - - typer.echo(f"Validation {'passed' if all_ok else 'mismatched'}. Updated {meta_path}") - if HAS_MPL: - typer.echo(f"Saved plots to {plots_dir}") - - -if __name__ == "__main__": - app() diff --git a/parakeet-tdt-v2-0.6b/coreml/compile_modelc.py b/parakeet-tdt-v2-0.6b/coreml/compile_modelc.py deleted file mode 100644 index 9695e03b41fce9280f2ca2ee0e9585c26b2393cc..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/compile_modelc.py +++ /dev/null @@ -1,91 +0,0 @@ -#!/usr/bin/env python3 -"""Compile Core ML packages into ``.mlmodelc`` bundles via ``xcrun``. - -This script walks through the default Parakeet CoreML directories, finds -all ``*.mlpackage`` bundles, and compiles each of them with -``xcrun coremlcompiler`` into ``./compiled`` while preserving the -relative directory structure. -""" -from __future__ import annotations - -import shutil -import subprocess -import sys -from pathlib import Path - -BASE_DIR = Path(__file__).resolve().parent -OUTPUT_ROOT = BASE_DIR / "compiled" -SOURCE_DIRS = [BASE_DIR / "parakeet_coreml", BASE_DIR / "parakeet_coreml_quantized"] - - -def ensure_coremlcompiler() -> None: - """Ensure ``xcrun coremlcompiler`` is available for the active Xcode.""" - xcrun_path = shutil.which("xcrun") - if xcrun_path is None: - print("Error: 'xcrun' not found on PATH. Install Xcode command line tools.", file=sys.stderr) - sys.exit(1) - - try: - subprocess.run([ - xcrun_path, - "--find", - "coremlcompiler", - ], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - except subprocess.CalledProcessError: - print("Error: 'coremlcompiler' not found via xcrun. Check your Xcode installation.", file=sys.stderr) - sys.exit(1) - - -def gather_packages() -> list[Path]: - """Return a list of all ``*.mlpackage`` bundles under the source dirs.""" - packages: list[Path] = [] - for source in SOURCE_DIRS: - if not source.exists(): - print(f"Warning: {source.relative_to(BASE_DIR)} does not exist; skipping", file=sys.stderr) - continue - packages.extend(source.rglob("*.mlpackage")) - return packages - - -def compile_package(package: Path) -> None: - """Compile a single ``.mlpackage`` bundle using ``xcrun coremlcompiler``.""" - relative_pkg = package.relative_to(BASE_DIR) - output_dir = OUTPUT_ROOT / relative_pkg.parent - output_dir.mkdir(parents=True, exist_ok=True) - output_path = output_dir / f"{package.stem}.mlmodelc" - - if output_path.exists(): - shutil.rmtree(output_path) - - cmd = [ - "xcrun", - "coremlcompiler", - "compile", - str(package), - str(output_dir), - ] - - print(f"Compiling {relative_pkg} -> {output_path.relative_to(BASE_DIR)}") - subprocess.run(cmd, check=True) - - -def main() -> None: - ensure_coremlcompiler() - packages = gather_packages() - - if not packages: - print("No .mlpackage bundles found to compile.") - return - - for package in packages: - try: - compile_package(package) - except subprocess.CalledProcessError as exc: - print(f"Failed to compile {package}: {exc}", file=sys.stderr) - sys.exit(exc.returncode) - - print(f"Finished compiling {len(packages)} package(s) into {OUTPUT_ROOT.relative_to(BASE_DIR)}.") - - -if __name__ == "__main__": - main() diff --git a/parakeet-tdt-v2-0.6b/coreml/context/Efficient Sequence Transduction by Jointly Predicting Tokens and Durations.pdf b/parakeet-tdt-v2-0.6b/coreml/context/Efficient Sequence Transduction by Jointly Predicting Tokens and Durations.pdf deleted file mode 100644 index b3fdd0192810231ef4b0ac61b608a7e06d9d4f6c..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/context/Efficient Sequence Transduction by Jointly Predicting Tokens and Durations.pdf +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a29e637f2f977a44bece33863aeb01b45b0fc73a9bb723aea2960986e41c5bba -size 3071390 diff --git a/parakeet-tdt-v2-0.6b/coreml/context/FAST CONFORMER WITH LINEARLY SCALABLE ATTENTION.pdf b/parakeet-tdt-v2-0.6b/coreml/context/FAST CONFORMER WITH LINEARLY SCALABLE ATTENTION.pdf deleted file mode 100644 index d0aef3ea5d69a76e18bbd94161a522246ccfd1fc..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/context/FAST CONFORMER WITH LINEARLY SCALABLE ATTENTION.pdf +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:eb8c7706a567ec5795333139f8cd41ff9c127ba99b6dc85d378eb3fa3213bf8d -size 586484 diff --git a/parakeet-tdt-v2-0.6b/coreml/context/coreml_component_io.md b/parakeet-tdt-v2-0.6b/coreml/context/coreml_component_io.md deleted file mode 100644 index 85a34b5927f4e7e98a4c672ebc7cc249992c3100..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/context/coreml_component_io.md +++ /dev/null @@ -1,96 +0,0 @@ -# CoreML Component I/O (Parakeet‑TDT‑0.6B‑v2) - -This file documents the CoreML I/O contracts for each exported sub‑module. All shapes below target the fixed 15‑second window at 16 kHz (240,000 samples). Batch is fixed to 1 for on-device use, and we do not plan to support other window sizes in CoreML (re-export if the requirement changes). - -## Conventions - -- Dtypes: `float32` for real‑valued tensors, `int32` for lengths and token IDs. -- Batch: `B=1` everywhere. -- Vocab: `V=8192` BPE tokens; RNNT adds blank (+1). TDT adds 5 duration logits appended to the class axis. -- Subsampling: overall encoder time reduction ×8. - -## PreprocessorWrapper - -- Inputs - - `audio_signal`: `[1, 240000]` float32, PCM range ~[-1, 1] - - `length`: `[1]` int32, number of valid samples (<= 240000) -- Outputs - - `mel`: `[1, 128, 1501]` float32 (center=True => frames = floor(240000/160)+1 = 1501) - - `mel_length`: `[1]` int32 (=1501 for full 15 s) - -## EncoderWrapper - -- Inputs - - `features`: `[1, 128, 1501]` float32 (mel) - - `length`: `[1]` int32 (=1501) -- Outputs - - `encoded`: `[1, 188, 1024]` float32 (wrapper has time‑major last: it transposes `[B,D,T] -> [B,T,D]`) - - `encoded_length`: `[1]` int32 (`ceil(1501/8)=188`) - -## DecoderWrapper (stateful LSTM, 2 layers) - -- Inputs - - `targets`: `[1, U]` int32 (token IDs in [0, V)) - - `target_lengths`: `[1]` int32 (=U) - - `h_in`: `[2, 1, 640]` float32 (LSTM hidden, L=2 layers) - - `c_in`: `[2, 1, 640]` float32 (LSTM cell, L=2 layers) -- Outputs - - `decoder_output`: `[1, U, 640]` float32 - - `h_out`: `[2, 1, 640]` float32 - - `c_out`: `[2, 1, 640]` float32 - -Notes - -- For step‑wise greedy decoding, set `U=1` and feed back `h_out`, `c_out`. -- For batched token scoring, `U` can be >1 to evaluate multiple symbols per call. - -## JointWrapper - -- Inputs - - `encoder_outputs`: `[1, 188, 1024]` float32 (time‑major) - - `decoder_outputs`: `[1, U, 640]` float32 -- Output - - `logits`: `[1, 188, U, 8192 + 1 + 5]` float32 - -Notes - -- Split the last dimension into `[token_logits: V+1]` and `[duration_logits: 5]` when post‑processing. -- `log_softmax` is disabled for export; apply on CPU if needed. - -## MelEncoderWrapper (fused) - -- Inputs - - `audio_signal`: `[1, 240000]` float32 - - `audio_length`: `[1]` int32 -- Outputs - - `encoder`: `[1, 188, 1024]` float32 - - `encoder_length`: `[1]` int32 - -Notes - -- Fuses preprocessor + encoder to avoid a mel round‑trip. Shapes match chaining `PreprocessorWrapper` then `EncoderWrapper` on the fixed 15 s window. - -## JointDecisionWrapper (fused post‑processing) - -- Inputs - - `encoder`: `[1, 188, 1024]` float32 - - `decoder`: `[1, U, 640]` float32 -- Outputs - - `token_id`: `[1, 188, U]` int32 (argmax over token logits, includes blank) - - `token_prob`: `[1, 188, U]` float32 (softmax probability of chosen token) - - `duration`: `[1, 188, U]` int32 (argmax over 5 duration logits) - -Notes - -- Embeds the common host‑side steps: split joint logits, softmax over token logits, argmax over both heads, and gather chosen token probability. Replaces separate `runJoint` → `splitLogits` → `softmax/argmax` in Swift. - -## Suggested CoreML tensor names - -- Preprocessor: `audio_signal`, `audio_length` → `mel`, `mel_length` -- Encoder: `mel`, `mel_length` → `encoder`, `encoder_length` -- Decoder: `targets`, `target_length`, `h_in`, `c_in` → `decoder`, `h_out`, `c_out` -- Joint: `encoder`, `decoder` → `logits` -- MelEncoder (fused): `audio_signal`, `audio_length` → `encoder`, `encoder_length` -- JointDecision (fused): `encoder`, `decoder` → `token_id`, `token_prob`, `duration` - -These names align with wrappers in `individual_components.py` and simplify downstream wiring. diff --git a/parakeet-tdt-v2-0.6b/coreml/context/coreml_conversion_plan.md b/parakeet-tdt-v2-0.6b/coreml/context/coreml_conversion_plan.md deleted file mode 100644 index cf3cd4c09dbd02b882cca3f37f0802f7bfa569b1..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/context/coreml_conversion_plan.md +++ /dev/null @@ -1,65 +0,0 @@ -# CoreML Conversion Plan (Parakeet‑TDT‑0.6B‑v2) - -This plan describes how we export Parakeet’s sub‑modules to CoreML, validate numerics, and prepare for on‑device decoding. The pipeline keeps a fixed 15‑second audio window. - -## Goals - -- Export preprocessor, encoder, decoder, and joint as separate mlpackages. -- Also export fused variants: `mel_encoder` (preprocessor+encoder) and `joint_decision` (joint + split/softmax/argmax). -- Preserve streaming decoder state I/O for on‑device greedy decoding. -- Validate component outputs against the NeMo reference on a 15‑second clip. - -## Environment - -- Use `uv venv` and run via `uv run` to ensure reproducible resolutions. -- Python 3.10.12, `torch 2.5.0`, `coremltools 8.3.0`, `nemo-toolkit 2.3.1`. - -## Export settings - -- `convert_to = "mlprogram"` -- `minimum_deployment_target` = `ct.target.iOS17` (we only target iOS 17+ for the CoreML deployment) -- `compute_units` = `.CPU_AND_GPU` (or `.ALL` on iOS) -- `compute_precision` = `ct.precision.FLOAT16` or `None` (start with fp32 for validation, then try fp16) -- Fixed window: 15 seconds at 16 kHz → shapes as in `context/coreml_component_io.md`. No variable-length support is required; CoreML graphs can assume the 15 s window. - -## Steps - -- Load the NeMo checkpoint with `ASRModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v2")`. -- Extract modules: preprocessor, encoder, decoder, joint. -- Wrap modules with `PreprocessorWrapper`, `EncoderWrapper`, `DecoderWrapper`, `JointWrapper`; derive `MelEncoderWrapper` and `JointDecisionWrapper` for fused exports. -- Trace each wrapper with static 15‑second inputs (batch=1). Ensure outputs match I/O contracts in `coreml_component_io.md`. -- Define CoreML inputs with explicit names and fixed shapes (15 s audio → mel 1501 → encoder 188). Keeping the window fixed simplifies CoreML deployment; re‑export if window size changes. -- Convert via `ct.convert(...)` with the settings above. -- Save each mlpackage under the chosen `output_dir`, along with a `metadata.json` capturing shapes, sample rate, vocab size, tokenizer path, and export metadata (author, conversion commit, etc.). -- Emit fused graphs (`parakeet_mel_encoder.mlpackage` and `parakeet_joint_decision.mlpackage`) alongside standalone components to reduce host I/O and post‑processing. -- Provide a helper like `compile_modelc.py` to batch `xcrun coremlcompiler` invocations when packaging for release. - -## CLI layout - -- `convert_parakeet_components.py` → conversion-only entry point (no validation), parameterized by compute units, precision, and optional fused exports. Model inspection (encoder strides, etc.) should auto-populate config, inspired by Silero VAD’s converter. -- `compare_parakeet_models.py` → validation/plotting script that loads reference NeMo modules and CoreML packages, runs diffs per component, and reports max/mean errors plus RTF metrics. Use lightweight wrappers similar to Silero’s `PyTorchJITWrapper` / `CoreMLVADWrapper`. -- Future helpers: `compile_modelc.py` (reuse pattern from VAD project) and optional quantization experiments once parity is established. - -## Validation - -- Run inference on a known 16 kHz clip (trim/pad to 15 s): - - Compare preprocessor mel and lengths (max abs/rel diff thresholds: atol=1e-4, rtol=1e-3). - - Compare encoder outputs and lengths. - - Compare decoder outputs for a fixed target sequence and initial zero states. - - Compare joint logits on the same `(T_enc, U)` grid; split `[V+1]` token logits from the last 5 duration logits when computing metrics. -- Record per‑component diffs in `metadata.json` for auditability. - -## Decoding (device) - -- Implement greedy RNNT in app code calling CoreML: - - Either: use modular `joint` and perform split/softmax/argmax on host. - - Or: call fused `joint_decision` to receive `token_id`, `token_prob`, and `duration` directly. - - Preprocess → Encode once per window (or call fused `mel_encoder`). - - Maintain `(h, c)` across symbol steps. - - Blank handling: `token_id == V` indicates blank. - -## Known caveats - -- RNNT joint logits are large: `[188 × U × ~8200]` per window; consider fp16 and/or tiling over `U` to reduce memory. -- Length maths: mel frames use `center=True`, yielding 1501 frames for exactly 240,000 samples; encoder length computed via ceil divide by 8. -- For accurate timestamping, use the original NeMo decoder on server to validate any device‑side greedy implementation. diff --git a/parakeet-tdt-v2-0.6b/coreml/context/hf_model_card_notes.md b/parakeet-tdt-v2-0.6b/coreml/context/hf_model_card_notes.md deleted file mode 100644 index 082919ab08fdfb6067b8537d0af73b3c5001ff5a..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/context/hf_model_card_notes.md +++ /dev/null @@ -1,24 +0,0 @@ -# Hugging Face Model Card Notes: nvidia/parakeet-tdt-0.6b-v2 - -Key details extracted from the public model card for quick reference. - -## Summary -- Task: Multilingual automatic speech recognition (ASR) -- Architecture: FastConformer encoder + RNNT + TDT -- Params: ~0.6B -- License: CC‑BY‑4.0 - -## Languages (25 EU + Russian/Ukrainian) -bg, hr, cs, da, nl, et, fi, fr, de, el, hu, it, lv, lt, mt, pl, pt, ro, sk, sl, sv, es, en, ru, uk - -## Input / Output -- Input: 16 kHz mono audio (`.wav`, `.flac`) -- Output: text with punctuation and capitalization - -## Notes relevant to conversion -- The model card reports accurate word/segment timestamps; Parakeet TDT uses duration buckets which we expose from the joint head (last 5 logits along the class axis). -- Long‑audio inference is reported for GPU; our CoreML export intentionally uses a fixed 15‑second window to fit on‑device constraints. - -References -- HF repo: https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2 -- NeMo FastConformer: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer diff --git a/parakeet-tdt-v2-0.6b/coreml/context/mel_encoder_ane_behavior.md b/parakeet-tdt-v2-0.6b/coreml/context/mel_encoder_ane_behavior.md deleted file mode 100644 index e55cf015d7b17e89d1ce1dedeb54512a056cf5ae..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/context/mel_encoder_ane_behavior.md +++ /dev/null @@ -1,67 +0,0 @@ -# MelEncoder ANE behavior and iPhone 13 issue - -Summary of the failure seen on iPhone 13 (A15 / E5): - -> ANE model load has failed for on-device compiled macho. Must re-compile the E5 bundle. Invalid layer: Tensor dimensions N1D1C1H1W240000 are not within supported range, N[1-65536] D[1-16384] C[1-65536] H[1-16384] W[1-16384]. - -## Root cause - -- The fused MelEncoder model takes raw waveform input shaped `[1, 240000]` for the fixed 15 s window (16 kHz × 15 s). -- In Core ML’s internal layout this maps to `N1 D1 C1 H1 W240000`. -- Apple Neural Engine (ANE) enforces a per-dimension cap of ≀ 16384 for H and W. `W=240000` violates this, so any ANE partition that “sees” the waveform will fail validation on A15 (and other iPhone ANE generations). -- The failure is caused by the preprocessor stage inside the fused MelEncoder (waveform → STFT/mel). The standalone encoder (mel → encoder) uses inputs around `[1, 128, 1501]` and is ANE-safe. - -## Why earlier models “worked” - -- Prior exports either: - - Avoided fusing the waveform preprocessor with the encoder; or - - Exported a “mel→encoder” model only; or - - Relied on Core ML partitioning that kept the preprocessor on CPU/GPU while the encoder ran on ANE. -- Even with `computeUnits = .cpuAndNeuralEngine`, Core ML will fall back to CPU for non-ANE-eligible subgraphs. With split models, the preprocessor silently ran on CPU and the encoder on ANE. -- With the new fused MelEncoder, the compiler tries to start an ANE partition earlier (due to the encoder being ANE-friendly). That exposes the 240000-wide waveform to the ANE plan and triggers the dimension error. - -## Device differences (M‑series Macs vs iPhones) - -- Inputs are identical across devices; what differs is how Core ML partitions the graph. -- On M‑series Macs, the waveform preprocessor commonly stays on CPU/GPU; the ANE (if present) or GPU is used for the encoder. No ANE error is logged. -- On iPhones (A14/A15/A16/A17), the ANE compiler (E-series) may attempt to include earlier elementwise ops, making the NE subgraph input be the waveform itself, which fails validation with `W>16384`. -- Treat this limitation as universal across iPhones with ANE; the precise logging/behavior can vary, but no iPhone ANE can accept a subgraph that sees `W=240000`. - -## Current repo behavior and fix - -- The exporter now sets CPU+GPU compute units for waveform‑in components to avoid ANE attempts while preserving the 15 s window: - - `parakeet_preprocessor.mlpackage` - - `parakeet_mel_encoder.mlpackage` (fused waveform→mel→encoder) -- See the changes in `convert-parakeet.py`: - - `convert-parakeet.py:225` and `convert-parakeet.py:237` — preprocessor exported with `ct.ComputeUnit.CPU_AND_GPU`. - - `convert-parakeet.py:289` and `convert-parakeet.py:301` — fused mel+encoder exported with `ct.ComputeUnit.CPU_AND_GPU`. - -## Recommended runtime settings (iOS) - -- Preprocessor (waveform→mel): set `MLModelConfiguration.computeUnits = .cpuAndGPU` to skip ANE attempts and use CPU/GPU efficiently. -- Encoder / Decoder / Joint: set `computeUnits = .cpuAndNeuralEngine` (or `.all`) to leverage ANE. -- Using `.cpuAndNeuralEngine` for all models also works with the split setup (preprocessor falls back to CPU), but setting `.cpuAndGPU` on the preprocessor avoids ANE compile warnings and extra load-time overhead. - -## Using the split models (recommended) - -- Pipeline: - 1. `parakeet_preprocessor.mlmodelc` (CPU+GPU): input `audio_signal [1, 240000]`, `audio_length [1]` → output `mel [1, 128, 1501]`, `mel_length [1]`. - 2. `parakeet_encoder.mlmodelc` (ANE): input `mel [1, 128, 1501]`, `mel_length [1]` → output `encoder [1, 1024, 188]`, `encoder_length [1]`. - 3. Decoder / Joint / Decision: keep on ANE. -- Shapes are captured in `parakeet_coreml/metadata.json`. - -## If fused‑on‑ANE is required - -- The current fused API (`[1, 240000]` waveform input) cannot compile to ANE on iPhones due to the hard ≀16384 per‑dimension cap. -- Viable approach: introduce a chunked fused variant with input like `[1, 15, 16000]` and implement overlap‑aware preemphasis + STFT + mel inside the model to preserve exact parity with global STFT (center padding, hop=160, win=400). Ensure no op sees a dimension >16384. This changes the model API and requires careful seam handling. -- Alternative: stream ≀1.024 s windows with state and accumulate outputs, but this changes runtime control flow. - -## FAQ - -- Q: Which model causes the iPhone 13 error? - - A: The preprocessor stage inside the fused MelEncoder (not the standalone encoder). -- Q: Is this only iPhone 13? - - A: No. Treat it as affecting all iPhone ANE generations; some may just silently partition or fall back differently. -- Q: Why does M4 work? - - A: Likely because the preprocessor subgraph runs on CPU/GPU; the ANE never “sees” `W=240000` on macOS. - diff --git a/parakeet-tdt-v2-0.6b/coreml/context/nemo-joint-fuse-mode.md b/parakeet-tdt-v2-0.6b/coreml/context/nemo-joint-fuse-mode.md deleted file mode 100644 index 1c5eeae7a0f89608d7a6abddf40a1412c13c642a..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/context/nemo-joint-fuse-mode.md +++ /dev/null @@ -1,87 +0,0 @@ -# NeMo Joint Network Fuse Mode - -## Overview - -The NeMo RNNT Joint network has two operational modes controlled by the `fuse_loss_wer` flag. Understanding this is crucial for proper validation and inference. - -## Fuse Mode Details - -### Normal Mode (`fuse_loss_wer = False`) - -**Purpose**: Standard inference and model export -**Interface**: `joint(encoder_outputs, decoder_outputs)` -**Returns**: Raw logits `[B, T, U, V + 1]` -**Use case**: -- Model inference -- Model export (CoreML, ONNX, etc.) -- Validation and comparison - -```python -# Simple joint computation -logits = model.joint(encoder_outputs=encoder_out, decoder_outputs=decoder_out) -``` - -### Fused Mode (`fuse_loss_wer = True`) - -**Purpose**: Memory-optimized training with integrated loss/WER computation -**Interface**: Requires additional parameters -**Returns**: Loss and WER metrics -**Use case**: Training with memory constraints - -**Required parameters when fused**: -- `encoder_outputs` (mandatory) -- `decoder_outputs` (optional, required for loss) -- `encoder_lengths` (required) -- `transcripts` (optional, required for WER) -- `transcript_lengths` (required) - -**Memory optimization**: Processes batch in sub-batches to reduce memory usage - -```python -# Fused mode - more complex interface -result = model.joint( - encoder_outputs=encoder_out, - decoder_outputs=decoder_out, - encoder_lengths=enc_lengths, - transcripts=transcripts, # Ground truth tokens - transcript_lengths=trans_lengths -) -``` - -## Why We Disable Fused Mode for Validation - -### 1. Interface Simplicity -- Fused mode requires ground truth transcripts that we don't have during validation -- Normal mode only needs encoder/decoder outputs, matching CoreML model interface - -### 2. Export Compatibility -```python -def _prepare_for_export(self, **kwargs): - self._fuse_loss_wer = False # Automatically disabled for export - self.log_softmax = False -``` -NeMo automatically disables fused mode during model export, indicating this is the correct mode for inference. - -### 3. Validation Purpose -- We want raw logits for comparison, not loss/WER calculations -- Fused mode is training-oriented, not inference-oriented - -### 4. CoreML Conversion -CoreML models are converted from the non-fused mode, so validation should use the same mode to ensure fair comparison. - -## Implementation in compare-components.py - -```python -# Disable fused mode for proper inference comparison -asr_model.joint.set_fuse_loss_wer(False) - -# Now we can use the simple interface that matches CoreML -logits_ref = asr_model.joint( - encoder_outputs=encoder_ref, - decoder_outputs=decoder_ref -) -``` - -## Key Takeaway - -For validation and inference, always use `fuse_loss_wer = False` to get the standard joint network behavior that matches exported models and enables fair comparison with CoreML implementations. \ No newline at end of file diff --git a/parakeet-tdt-v2-0.6b/coreml/context/parakeet_tdt_v2_architecture.md b/parakeet-tdt-v2-0.6b/coreml/context/parakeet_tdt_v2_architecture.md deleted file mode 100644 index ca086b16c6a2b05b0bcf49a3d31cd9c98d7844d9..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/context/parakeet_tdt_v2_architecture.md +++ /dev/null @@ -1,61 +0,0 @@ -# Parakeet‑TDT‑0.6B‑v2 Architecture Overview - -## Model topology - -- Streaming‑capable RNN‑Transducer (RNNT) ASR model trained at 16 kHz. -- Time‑Depth Transducer (TDT) extends RNNT to jointly predict token and duration buckets (commonly [0, 1, 2, 3, 4]). -- Four major learnable blocks packaged in the `.nemo` checkpoint: - 1. `AudioToMelSpectrogramPreprocessor` – waveform → 128×T mel‑spectrogram. - 2. `ConformerEncoder` (FastConformer‑style) – 24 conformer blocks, `d_model=1024`, `n_heads=8`, depthwise CNN subsampling with total reduction ×8. - 3. `RNNTDecoder` – 2‑layer LSTM prediction network, hidden size 640, `blank_as_pad=True` for efficient batching and blank handling. - 4. `RNNTJoint` – linear projections from encoder (1024) and decoder (640) into joint space 640 with ReLU, then to class logits. - -## Audio front‑end - -- `sample_rate = 16000`, Hann window, 25 ms window, 10 ms hop (`n_fft=512`, `win_length=400`, `hop_length=160`, `center=True`). -- 128 mel filters (Slaney‑style), log‑magnitude power 2.0 and per‑feature normalization. -- Outputs `(mel, mel_length)` feed the encoder. For CoreML, both tensors must be surfaced to preserve valid‑lengths through subsampling. - -## Encoder (FastConformer) - -- Implemented as `nemo.collections.asr.modules.ConformerEncoder` configured per FastConformer. -- 24 blocks, `d_model=1024`, FFN expansion ×4, 8‑head relative‑position attention, conv kernel 9, batch‑norm. -- Dropouts: 0.1 general, 0.1 pre‑encoder, 0.0 embedding, 0.1 attention; stochastic depth disabled. -- Depthwise CNN subsampling yields overall time reduction ×8. Returns features `[B, D=1024, T_enc]` and lengths. - -## Decoder (`RNNTDecoder`) - -- Stateful LSTM prediction network (2 layers, hidden=640). See `nemo/collections/asr/modules/rnnt.py`. -- `blank_as_pad=True` adds a learnable pad/blank to the embedding; embedding returns zeros for blank, simplifying blank handling. -- Export toggles `_rnnt_export` and uses `.predict()` without prepending SOS so decoder outputs shape `[B, U, 640]`. -- Streaming state is a tuple `(h, c)`, each `[L=2, B, H=640]`. Utilities exist to batch and select states during beam search. - -## Joint network & TDT - -- `nemo.collections.asr.modules.RNNTJoint` with `joint_hidden=640`, activation=ReLU, dropout=0.2. -- Input shapes: encoder `[B, T_enc, 1024]`, decoder `[B, U, 640]` → output logits `[B, T_enc, U, V+1+E]`. -- Vocabulary `V=8192` (SentencePiece BPE). RNNT blank adds `+1`. TDT uses `num_extra_outputs=E=5`, appended in the last dimension of the joint logits. The TDT loss splits the tail 5 entries as duration logits for buckets `[0,1,2,3,4]`. -- Training often enables `fuse_loss_wer=true` with `fused_batch_size=4`; export/inference use just `joint.joint(...)`. - -## Auxiliary heads - -- No separate CTC decoder is attached in the public v2 checkpoint. -- Loss is TDT: `fastemit_lambda=0.0`, `durations=[0,1,2,3,4]`, `sigma=0.02`, `omega=0.1`. - -## Tokenizer - -- SentencePiece BPE (`tokenizer.model`) with 8192 subword units plus control symbols (e.g., punctuation/timestamp markers). RNNT adds blank internally. - -## Notes for CoreML export - -- Exportable subgraphs map 1:1 to NeMo modules: preprocessor, encoder, decoder, joint, and (optionally) a greedy decoder. In addition, we export two fused variants for on‑device efficiency: (1) `mel_encoder` which combines preprocessor+encoder, and (2) `joint_decision` which applies split/softmax/argmax to joint logits to output `token_id`, `token_prob`, and `duration`. -- Decoder state I/O: expose `h` and `c` separately as `[L=2, B, 640]` multi‑arrays to enable on‑device streaming. -- TDT duration logits are appended to the class dimension; downstream code must split `[V+1]` token logits from the last `5` duration logits. - -## Fixed 15‑second window (CoreML constraint) - -- Audio: `B=1`, `T_audio = 15s × 16kHz = 240,000` samples. -- Mel frames (center=True): `T_mel ≈ floor(T_audio / 160) + 1 = 1,500 + 1 = 1,501` → mel `[1, 128, 1501]`. -- Encoder length (×8 subsampling): `T_enc = ceil(1501 / 8) = 188` → encoder `[1, 1024, 188]` (wrapper transposes to `[1, 188, 1024]`). -- Decoder step budget: choose `U_max` to cap per‑window decoding (e.g., 256–384). Joint outputs then shape `[1, T_enc, U_max, 8192+1+5]`. - diff --git a/parakeet-tdt-v2-0.6b/coreml/context/silero_coreml_reference.md b/parakeet-tdt-v2-0.6b/coreml/context/silero_coreml_reference.md deleted file mode 100644 index 2efa8c5dad24884352d705092051bc1fedce9ca7..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/context/silero_coreml_reference.md +++ /dev/null @@ -1,53 +0,0 @@ -# Silero VAD CoreML Reference (Techniques to Reuse) - -This note distills patterns from `/models/vad/silero-vad/coreml` that we can reuse for Parakeet-TDT and highlights CoreML limits to plan around. - -## Tooling Patterns Worth Copying - -- **Dedicated CLI entry points**: `convert-coreml.py` performs all exports, while `compare-models.py` focuses solely on validation/plotting. Keeping convert vs. compare independent shortens iteration when only one side changes. -- **Module wrappers**: Lightweight `nn.Module` wrappers (`STFTModel`, `EncoderModel`, `DecoderModel`) map state-dict weights into export-friendly graphs. They surface state tensors explicitly (hidden/cell) and clamp activations where CoreML would otherwise overflow. -- **Shape contracts via CoreML types**: Inputs/outputs use `ct.TensorType` to document tensor layouts. Silero leans on `ct.RangeDim` for flexible windows; for Parakeet we can simplify by locking to the fixed 15 s window (240 k samples → 1501 mels → 188 encoder frames). -- **Metadata & publish scripts**: Conversion annotates author/version on each mlpackage and reports disk size. `compile_modelc.py` shows how to mass-compile mlpackages to `.mlmodelc` bundles via `xcrun`—handy when preparing release artifacts. -- **Multiple deployment variants**: The project exports both modular (STFT→Encoder→Decoder) and unified pipelines. The 256 ms unified variant demonstrates how to encode fixed iteration counts inside the traced graph (8 chunks + noisy-OR aggregation) for batch workflows. - -## Techniques Directly Applicable to Parakeet-TDT - -- **Separate convert & compare CLIs** - - `convert_parakeet_components.py` should mirror the Silero convert script: accept output directory, compute-unit/precision knobs, optional fused graph exports (e.g., preprocessor+encoder). - - A new `compare_parakeet_models.py` can follow the VAD wrapper pattern: run NeMo vs. CoreML components, collect per-layer diffs, and optionally chart mel/encoder/token probabilities over time. -- **Wrapper modules per component** - - We already have `PreprocessorWrapper`, `EncoderWrapper`, `DecoderWrapper`, `JointWrapper`; extend them with deterministic input padding/casts just like Silero’s wrappers to avoid CoreML runtime surprises. -- **Fixed-window CoreML shapes** - - Keep CoreML inputs fixed to the 15 s window. If we ever change the window length, re-export the models instead of relying on dynamic shape bounds. -- **Explicit state I/O** - - Follow Silero’s practice of exposing LSTM states as inputs/outputs every call. For Parakeet, that means separate `h`/`c` arrays of shape `[num_layers, batch, 640]`, enabling streaming decode loops outside CoreML. -- **Release automation** - - Borrow `compile_modelc.py` to generate `.mlmodelc`; consider a similar helper for quantization experiments once baseline parity is achieved. - -## Parakeet Components to Export - -1. **Audio front-end (Mel)** – same strategy as Silero STFT: wrap the NeMo preprocessor, trace with fixed 15 s audio (240 k samples), and export with constant shapes. -2. **Encoder (FastConformer)** – export with constant shapes for the 188 encoder frames corresponding to the 15 s window (transpose to time-major for CoreML compatibility). -3. **Decoder (RNNT Prediction Network)** – stateful LSTM; replicate Silero’s explicit state tensors but with `[2, 1, 640]` dims and integer targets. Provide zero-state helper for CoreML consumers. -4. **Joint network + TDT head** – trace head-only module; outputs `[B, T_enc, U, V+1+5]`. Document token vs. duration slices (see `tdt_decoding_notes.md`). -5. **Fused graphs** – export both: - - `mel_encoder` (preprocessor+encoder) to reduce I/O. - - `joint_decision` (joint + split/softmax/argmax) to avoid host-side post-processing on logits and return `token_id`, `token_prob`, and `duration` directly. - -## Likely CoreML Pain Points - -- **Joint tensor size**: At 15 s the joint produces ~188×U×(8192+6) floats. Even at `U=256`, that is >400 MB of fp32 activations. Plan for fp16 exports and/or tiled decoding (call joint per-step rather than full grid). -- **Dynamic loops**: RNNT greedy/beam search loops can’t be represented in a single CoreML graph (no `while` support). Expect to orchestrate decoding in Swift/Python by issuing repeated `decoder` and `joint` calls. -- **Large vocabulary softmax**: Full RNNT softmax over 8193 classes is CoreML-friendly but expensive. Avoid embedding beam-search logic in CoreML; keep it host-side. -- **Duration logits**: Splitting the final 5 logits inside CoreML is trivial, but combining them with token control flow should stay in host code (mirrors Silero’s noisy-OR aggregation done in Python before tracing). -- **Tokenizer & text post-processing**: SentencePiece decoding, punctuation fixes, and timestamp formatting remain in Python/Swift; CoreML returns logits only. -- **Feature normalization stats**: Stick to constant statistics baked into the wrapper as Silero does for STFT weights; do not rely on CoreML `LayerNormalization` with dynamic reductions over time. - -## Python / Host Responsibilities - -- Audio chunking, state caching, and greedy/beam decoding loops (leveraging exported decoder + joint). -- Token → text conversion (`sentencepiece`), duration bucket interpretation, punctuation/TDT alignment heuristics. -- Validation tooling (diff calculations, plotting) analogous to Silero’s `compare-models.py`. -- Optional post-processing (e.g., noise suppression, segmentation) that require dynamic control flow. - -Reuse these patterns to keep Parakeet’s conversion pipeline maintainable while respecting CoreML’s graph restrictions. diff --git a/parakeet-tdt-v2-0.6b/coreml/context/tdt_decoding_notes.md b/parakeet-tdt-v2-0.6b/coreml/context/tdt_decoding_notes.md deleted file mode 100644 index a1c5fb6ada701bc349921fbf288504b95fe34840..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/context/tdt_decoding_notes.md +++ /dev/null @@ -1,36 +0,0 @@ -# TDT Decoding Notes (RNNT + Duration Buckets) - -Parakeet‑TDT extends RNNT by appending 5 duration logits to the joint’s class axis. These describe coarse duration buckets associated with an emitted symbol. This note summarizes how to consume them alongside standard RNNT greedy decoding. - -## Joint output - -- Shape: `logits[B, T_enc, U, V+1+5]` where: - - `V=8192` BPE vocab, `+1` RNNT blank, `+5` TDT duration buckets. -- Split last dimension: - - `token_logits = logits[..., :V+1]` - - `duration_logits = logits[..., V+1:] # shape [..., 5]` - -## Greedy decoding sketch - -- Initialize decoder state `(h, c)` and `u=0`; iterate over encoder time `t=0..T_enc-1`. -- At each `(t,u)`: - - Either use raw joint logits: retrieve `token_logits[t, u]` and pick `k = argmax(token_logits)`. - - Or call the fused `JointDecisionWrapper` CoreML model to get `token_id[t,u]`, `token_prob[t,u]`, and `duration[t,u]` directly. - - If `k` is blank: advance `t += 1` (RNNT time advance), keep `u`. - - Else (emit token): append token `k` to hypothesis, update decoder state via `DecoderWrapper` with that token, increment `u += 1`. - - Duration (optional): `d = argmax(duration_logits[t, u_prev])`. Map `d ∈ {0..4}` to bucket durations per training config. Use to assign sub‑frame timestamp estimates. - -## Using duration buckets - -- Bucket IDs 0..4 correspond to the configured `tdt_durations = [0,1,2,3,4]` used during training. -- A simple heuristic for timestamps per emitted token: - - Keep track of current mel/encoder frame index. - - When emitting a token at `(t,u)`, associate bucket `d = argmax(duration_logits[t,u])`. - - Convert bucket to time by multiplying with the encoder frame stride (`8× hop_length / sr = 8×0.01 s = 80 ms` per encoder step) or use mel frame stride if preferred. -- For better quality, prefer server‑side timestamp decoding identical to NeMo’s implementation and use duration buckets as a hint when post‑processing on device. - -## Notes - -- NeMo trains the joint with `num_extra_outputs=5`. These logits are not part of the token softmax and should not affect argmax over `V+1`. -- The model card claims accurate word/segment timestamps; TDT buckets contribute to that, but exact use may vary per downstream aligner. -- If durations are not needed, you can ignore the last 5 logits entirely. diff --git a/parakeet-tdt-v2-0.6b/coreml/convert-parakeet.py b/parakeet-tdt-v2-0.6b/coreml/convert-parakeet.py deleted file mode 100644 index eaaa19ce95913996b3772611443ad6a7c0be7044..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/convert-parakeet.py +++ /dev/null @@ -1,619 +0,0 @@ -#!/usr/bin/env python3 -"""CLI for exporting Parakeet TDT v2 components to CoreML.""" -from __future__ import annotations - -import json -from dataclasses import asdict -from pathlib import Path -from typing import Dict, Optional, Tuple - -import coremltools as ct -import numpy as np -import soundfile as sf -import torch -import typer - -import nemo.collections.asr as nemo_asr - -from individual_components import ( - DecoderWrapper, - EncoderWrapper, - ExportSettings, - JointWrapper, - JointDecisionWrapper, - JointDecisionSingleStep, - PreprocessorWrapper, - MelEncoderWrapper, - _coreml_convert, -) - -DEFAULT_MODEL_ID = "nvidia/parakeet-tdt-0.6b-v2" -AUTHOR = "Fluid Inference" - - -def _compute_length(seconds: float, sample_rate: int) -> int: - return int(round(seconds * sample_rate)) - - -def _prepare_audio( - validation_audio: Optional[Path], - sample_rate: int, - max_samples: int, - seed: Optional[int], -) -> torch.Tensor: - if validation_audio is None: - if seed is not None: - torch.manual_seed(seed) - audio = torch.randn(1, max_samples, dtype=torch.float32) - return audio - - data, sr = sf.read(str(validation_audio), dtype="float32") - if sr != sample_rate: - raise typer.BadParameter( - f"Validation audio sample rate {sr} does not match model rate {sample_rate}" - ) - - if data.ndim > 1: - data = data[:, 0] - - if data.size == 0: - raise typer.BadParameter("Validation audio is empty") - - if data.size < max_samples: - pad_width = max_samples - data.size - data = np.pad(data, (0, pad_width)) - elif data.size > max_samples: - data = data[:max_samples] - - audio = torch.from_numpy(data).unsqueeze(0).to(dtype=torch.float32) - return audio - - -def _save_mlpackage(model: ct.models.MLModel, path: Path, description: str) -> None: - # Ensure iOS 17+ target for MLProgram ops and ANE readiness - try: - model.minimum_deployment_target = ct.target.iOS17 - except Exception: - pass - model.short_description = description - model.author = AUTHOR - path.parent.mkdir(parents=True, exist_ok=True) - model.save(str(path)) - - -def _tensor_shape(tensor: torch.Tensor) -> Tuple[int, ...]: - return tuple(int(dim) for dim in tensor.shape) - - -def _parse_compute_units(name: str) -> ct.ComputeUnit: - """Parse a human-friendly compute units string into ct.ComputeUnit. - - Accepted (case-insensitive): ALL, CPU_ONLY, CPU_AND_GPU, CPU_AND_NE. - """ - normalized = str(name).strip().upper() - mapping = { - "ALL": ct.ComputeUnit.ALL, - "CPU_ONLY": ct.ComputeUnit.CPU_ONLY, - "CPU_AND_GPU": ct.ComputeUnit.CPU_AND_GPU, - "CPU_AND_NE": ct.ComputeUnit.CPU_AND_NE, - "CPU_AND_NEURALENGINE": ct.ComputeUnit.CPU_AND_NE, - } - if normalized not in mapping: - raise typer.BadParameter( - f"Unknown compute units '{name}'. Choose from: " + ", ".join(mapping.keys()) - ) - return mapping[normalized] - - -def _parse_compute_precision(name: Optional[str]) -> Optional[ct.precision]: - """Parse compute precision string into ct.precision or None. - - Accepted (case-insensitive): FLOAT32, FLOAT16. If None/empty, returns None (tool default). - """ - if name is None: - return None - normalized = str(name).strip().upper() - if normalized == "": - return None - mapping = { - "FLOAT32": ct.precision.FLOAT32, - "FLOAT16": ct.precision.FLOAT16, - } - if normalized not in mapping: - raise typer.BadParameter( - f"Unknown compute precision '{name}'. Choose from: " + ", ".join(mapping.keys()) - ) - return mapping[normalized] - - -# Validation logic removed; use compare-compnents.py for comparisons. - - -# Fixed export choices: CPU_ONLY + FP32, min target iOS17 - - -app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) - - -@app.command() -def convert( - nemo_path: Optional[Path] = typer.Option( - None, - "--nemo-path", - exists=True, - resolve_path=True, - help="Path to parakeet-tdt-0.6b-v2 .nemo checkpoint (skip to auto-download)", - ), - model_id: str = typer.Option( - DEFAULT_MODEL_ID, - "--model-id", - help="Model identifier to download when --nemo-path is omitted", - ), - output_dir: Path = typer.Option(Path("parakeet_coreml"), help="Directory where mlpackages and metadata will be written"), - preprocessor_cu: str = typer.Option( - "CPU_ONLY", - "--preprocessor-cu", - help="Compute units for preprocessor (default CPU_ONLY)", - ), - mel_encoder_cu: str = typer.Option( - "CPU_ONLY", - "--mel-encoder-cu", - help="Compute units for fused mel+encoder (default CPU_ONLY)", - ), - compute_precision: Optional[str] = typer.Option( - None, - "--compute-precision", - help="Export precision: FLOAT32 (default) or FLOAT16 to shrink non-quantized weights.", - ), -) -> None: - """Export all Parakeet sub-modules to CoreML with a fixed 15-second window.""" - # Runtime CoreML contract keeps U=1 so the prediction net matches the streaming decoder. - export_settings = ExportSettings( - output_dir=output_dir, - compute_units=ct.ComputeUnit.CPU_ONLY, # Default: CPU-only for all components - deployment_target=ct.target.iOS17, # iOS 17+ features and kernels - compute_precision=_parse_compute_precision(compute_precision), - max_audio_seconds=15.0, - max_symbol_steps=1, - ) - - typer.echo("Export configuration:") - typer.echo(asdict(export_settings)) - - output_dir.mkdir(parents=True, exist_ok=True) - pre_cu = _parse_compute_units(preprocessor_cu) - melenc_cu = _parse_compute_units(mel_encoder_cu) - - if nemo_path is not None: - typer.echo(f"Loading NeMo model from {nemo_path}
") - asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from( - str(nemo_path), map_location="cpu" - ) - checkpoint_meta = { - "type": "file", - "path": str(nemo_path), - } - else: - typer.echo(f"Downloading NeMo model via {model_id}
") - asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained( - model_id, map_location="cpu" - ) - checkpoint_meta = { - "type": "pretrained", - "model_id": model_id, - } - asr_model.eval() - - sample_rate = int(asr_model.cfg.preprocessor.sample_rate) - max_samples = _compute_length(export_settings.max_audio_seconds, sample_rate) - # Prefer a bundled 15s 16kHz audio if available - default_audio = (Path(__file__).parent / "audio" / "yc_first_minute_16k_15s.wav").resolve() - if not default_audio.exists(): - raise typer.BadParameter(f"Expected 15s trace audio at {default_audio}; add the file to proceed.") - typer.echo(f"Using trace audio: {default_audio}") - audio_tensor = _prepare_audio(default_audio, sample_rate, max_samples, seed=None) - audio_length = torch.tensor([max_samples], dtype=torch.int32) - - preprocessor = PreprocessorWrapper(asr_model.preprocessor.eval()) - encoder = EncoderWrapper(asr_model.encoder.eval()) - decoder = DecoderWrapper(asr_model.decoder.eval()) - joint = JointWrapper(asr_model.joint.eval()) - - decoder_export_flag = getattr(asr_model.decoder, "_rnnt_export", False) - asr_model.decoder._rnnt_export = True - - try: - with torch.inference_mode(): - mel_ref, mel_length_ref = preprocessor(audio_tensor, audio_length) - mel_length_ref = mel_length_ref.to(dtype=torch.int32) - encoder_ref, encoder_length_ref = encoder(mel_ref, mel_length_ref) - encoder_length_ref = encoder_length_ref.to(dtype=torch.int32) - - # Clone Tensors to drop the inference tensor flag before tracing - mel_ref = mel_ref.clone() - mel_length_ref = mel_length_ref.clone() - encoder_ref = encoder_ref.clone() - encoder_length_ref = encoder_length_ref.clone() - - vocab_size = int(asr_model.tokenizer.vocab_size) - num_extra = int(asr_model.joint.num_extra_outputs) - decoder_hidden = int(asr_model.decoder.pred_hidden) - decoder_layers = int(asr_model.decoder.pred_rnn_layers) - - targets = torch.full( - (1, export_settings.max_symbol_steps), - fill_value=asr_model.decoder.blank_idx, - dtype=torch.int32, - ) - target_lengths = torch.tensor( - [export_settings.max_symbol_steps], dtype=torch.int32 - ) - zero_state = torch.zeros( - decoder_layers, - 1, - decoder_hidden, - dtype=torch.float32, - ) - - with torch.inference_mode(): - decoder_ref, h_ref, c_ref = decoder(targets, target_lengths, zero_state, zero_state) - joint_ref = joint(encoder_ref, decoder_ref) - - decoder_ref = decoder_ref.clone() - h_ref = h_ref.clone() - c_ref = c_ref.clone() - joint_ref = joint_ref.clone() - - typer.echo("Tracing and converting preprocessor
") - # Ensure tracing happens on CPU explicitly - preprocessor = preprocessor.cpu() - audio_tensor = audio_tensor.cpu() - audio_length = audio_length.cpu() - traced_preprocessor = torch.jit.trace( - preprocessor, (audio_tensor, audio_length), strict=False - ) - traced_preprocessor.eval() - preprocessor_inputs = [ - # Allow variable-length audio up to the fixed 15s window using RangeDim - ct.TensorType( - name="audio_signal", - shape=(1, ct.RangeDim(1, max_samples)), - dtype=np.float32, - ), - ct.TensorType(name="audio_length", shape=(1,), dtype=np.int32), - ] - preprocessor_outputs = [ - ct.TensorType(name="mel", dtype=np.float32), - ct.TensorType(name="mel_length", dtype=np.int32), - ] - # Preprocessor compute units (parametrized; default CPU_ONLY) - preprocessor_model = _coreml_convert( - traced_preprocessor, - preprocessor_inputs, - preprocessor_outputs, - export_settings, - compute_units_override=pre_cu, - ) - preprocessor_path = output_dir / "parakeet_preprocessor.mlpackage" - _save_mlpackage( - preprocessor_model, - preprocessor_path, - "Parakeet preprocessor (15 s window)", - ) - - typer.echo("Tracing and converting encoder
") - traced_encoder = torch.jit.trace( - encoder, (mel_ref, mel_length_ref), strict=False - ) - traced_encoder.eval() - encoder_inputs = [ - ct.TensorType(name="mel", shape=_tensor_shape(mel_ref), dtype=np.float32), - ct.TensorType(name="mel_length", shape=(1,), dtype=np.int32), - ] - encoder_outputs = [ - ct.TensorType(name="encoder", dtype=np.float32), - ct.TensorType(name="encoder_length", dtype=np.int32), - ] - # Encoder: CPU only - encoder_model = _coreml_convert( - traced_encoder, - encoder_inputs, - encoder_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - encoder_path = output_dir / "parakeet_encoder.mlpackage" - _save_mlpackage( - encoder_model, - encoder_path, - "Parakeet encoder (15 s window)", - ) - - # Optional fused export: Preprocessor + Encoder - typer.echo("Tracing and converting fused mel+encoder
") - mel_encoder = MelEncoderWrapper(preprocessor, encoder) - traced_mel_encoder = torch.jit.trace( - mel_encoder, (audio_tensor, audio_length), strict=False - ) - traced_mel_encoder.eval() - mel_encoder_inputs = [ - # Keep fixed 15s window for fused Mel+Encoder - ct.TensorType(name="audio_signal", shape=(1, max_samples), dtype=np.float32), - ct.TensorType(name="audio_length", shape=(1,), dtype=np.int32), - ] - mel_encoder_outputs = [ - ct.TensorType(name="encoder", dtype=np.float32), - ct.TensorType(name="encoder_length", dtype=np.int32), - ] - # Fused mel+encoder compute units (parametrized; default CPU_ONLY) - mel_encoder_model = _coreml_convert( - traced_mel_encoder, - mel_encoder_inputs, - mel_encoder_outputs, - export_settings, - compute_units_override=melenc_cu, - ) - mel_encoder_path = output_dir / "parakeet_mel_encoder.mlpackage" - _save_mlpackage( - mel_encoder_model, - mel_encoder_path, - "Parakeet fused Mel+Encoder (15 s window)", - ) - - typer.echo("Tracing and converting decoder
") - traced_decoder = torch.jit.trace( - decoder, - (targets, target_lengths, zero_state, zero_state), - strict=False, - ) - traced_decoder.eval() - decoder_inputs = [ - ct.TensorType(name="targets", shape=_tensor_shape(targets), dtype=np.int32), - ct.TensorType(name="target_length", shape=(1,), dtype=np.int32), - ct.TensorType(name="h_in", shape=_tensor_shape(zero_state), dtype=np.float32), - ct.TensorType(name="c_in", shape=_tensor_shape(zero_state), dtype=np.float32), - ] - decoder_outputs = [ - ct.TensorType(name="decoder", dtype=np.float32), - ct.TensorType(name="h_out", dtype=np.float32), - ct.TensorType(name="c_out", dtype=np.float32), - ] - # Decoder: CPU only - decoder_model = _coreml_convert( - traced_decoder, - decoder_inputs, - decoder_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - decoder_path = output_dir / "parakeet_decoder.mlpackage" - _save_mlpackage( - decoder_model, - decoder_path, - "Parakeet decoder (RNNT prediction network)", - ) - - typer.echo("Tracing and converting joint
") - traced_joint = torch.jit.trace( - joint, - (encoder_ref, decoder_ref), - strict=False, - ) - traced_joint.eval() - joint_inputs = [ - ct.TensorType(name="encoder", shape=_tensor_shape(encoder_ref), dtype=np.float32), - ct.TensorType(name="decoder", shape=_tensor_shape(decoder_ref), dtype=np.float32), - ] - joint_outputs = [ - ct.TensorType(name="logits", dtype=np.float32), - ] - # Joint: CPU only - joint_model = _coreml_convert( - traced_joint, - joint_inputs, - joint_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - joint_path = output_dir / "parakeet_joint.mlpackage" - _save_mlpackage( - joint_model, - joint_path, - "Parakeet joint network (RNNT)", - ) - - # Joint + decision head (split logits, softmax, argmax) - typer.echo("Tracing and converting joint decision head
") - vocab_size = int(asr_model.tokenizer.vocab_size) - num_extra = int(asr_model.joint.num_extra_outputs) - joint_decision = JointDecisionWrapper(joint, vocab_size=vocab_size, num_extra=num_extra) - traced_joint_decision = torch.jit.trace( - joint_decision, - (encoder_ref, decoder_ref), - strict=False, - ) - traced_joint_decision.eval() - joint_decision_inputs = [ - ct.TensorType(name="encoder", shape=_tensor_shape(encoder_ref), dtype=np.float32), - ct.TensorType(name="decoder", shape=_tensor_shape(decoder_ref), dtype=np.float32), - ] - joint_decision_outputs = [ - ct.TensorType(name="token_id", dtype=np.int32), - ct.TensorType(name="token_prob", dtype=np.float32), - ct.TensorType(name="duration", dtype=np.int32), - ] - # JointDecision: CPU only - joint_decision_model = _coreml_convert( - traced_joint_decision, - joint_decision_inputs, - joint_decision_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - joint_decision_path = output_dir / "parakeet_joint_decision.mlpackage" - _save_mlpackage( - joint_decision_model, - joint_decision_path, - "Parakeet joint + decision head (split, softmax, argmax)", - ) - - # Single-step JointDecision for [1,1024,1] x [1,640,1] -> [1,1,1] - typer.echo("Tracing and converting single-step joint decision
") - jd_single = JointDecisionSingleStep(joint, vocab_size=vocab_size, num_extra=num_extra) - # Create single-step slices from refs - enc_step = encoder_ref[:, :, :1].contiguous() - dec_step = decoder_ref[:, :, :1].contiguous() - traced_jd_single = torch.jit.trace( - jd_single, - (enc_step, dec_step), - strict=False, - ) - traced_jd_single.eval() - jd_single_inputs = [ - ct.TensorType(name="encoder_step", shape=(1, enc_step.shape[1], 1), dtype=np.float32), - ct.TensorType(name="decoder_step", shape=(1, dec_step.shape[1], 1), dtype=np.float32), - ] - jd_single_outputs = [ - ct.TensorType(name="token_id", dtype=np.int32), - ct.TensorType(name="token_prob", dtype=np.float32), - ct.TensorType(name="duration", dtype=np.int32), - ] - # Single-step JointDecision: CPU only - jd_single_model = _coreml_convert( - traced_jd_single, - jd_single_inputs, - jd_single_outputs, - export_settings, - compute_units_override=ct.ComputeUnit.CPU_ONLY, - ) - jd_single_path = output_dir / "parakeet_joint_decision_single_step.mlpackage" - _save_mlpackage( - jd_single_model, - jd_single_path, - "Parakeet single-step joint decision (current frame)", - ) - - metadata: Dict[str, object] = { - "model_id": model_id, - "sample_rate": sample_rate, - "max_audio_seconds": export_settings.max_audio_seconds, - "max_audio_samples": max_samples, - "max_symbol_steps": export_settings.max_symbol_steps, - "vocab_size": vocab_size, - "joint_extra_outputs": num_extra, - "checkpoint": checkpoint_meta, - "coreml": { - "compute_units": export_settings.compute_units.name, - "compute_precision": ( - export_settings.compute_precision.name - if export_settings.compute_precision is not None - else "FLOAT32" - ), - }, - "components": { - "preprocessor": { - "inputs": { - "audio_signal": list(_tensor_shape(audio_tensor)), - "audio_length": [1], - }, - "outputs": { - "mel": list(_tensor_shape(mel_ref)), - "mel_length": [1], - }, - "path": preprocessor_path.name, - }, - "encoder": { - "inputs": { - "mel": list(_tensor_shape(mel_ref)), - "mel_length": [1], - }, - "outputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "encoder_length": [1], - }, - "path": encoder_path.name, - }, - "mel_encoder": { - "inputs": { - "audio_signal": [1, max_samples], - "audio_length": [1], - }, - "outputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "encoder_length": [1], - }, - "path": mel_encoder_path.name, - }, - "decoder": { - "inputs": { - "targets": list(_tensor_shape(targets)), - "target_length": [1], - "h_in": list(_tensor_shape(zero_state)), - "c_in": list(_tensor_shape(zero_state)), - }, - "outputs": { - "decoder": list(_tensor_shape(decoder_ref)), - "h_out": list(_tensor_shape(h_ref)), - "c_out": list(_tensor_shape(c_ref)), - }, - "path": decoder_path.name, - }, - "joint": { - "inputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "decoder": list(_tensor_shape(decoder_ref)), - }, - "outputs": { - "logits": list(_tensor_shape(joint_ref)), - }, - "path": joint_path.name, - }, - "joint_decision": { - "inputs": { - "encoder": list(_tensor_shape(encoder_ref)), - "decoder": list(_tensor_shape(decoder_ref)), - }, - "outputs": { - "token_id": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[1], - _tensor_shape(decoder_ref)[1], - ], - "token_prob": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[1], - _tensor_shape(decoder_ref)[1], - ], - "duration": [ - _tensor_shape(encoder_ref)[0], - _tensor_shape(encoder_ref)[1], - _tensor_shape(decoder_ref)[1], - ], - }, - "path": joint_decision_path.name, - }, - "joint_decision_single_step": { - "inputs": { - "encoder_step": [1, _tensor_shape(encoder_ref)[2-1], 1], - "decoder_step": [1, _tensor_shape(decoder_ref)[2-1], 1], - }, - "outputs": { - "token_id": [1, 1, 1], - "token_prob": [1, 1, 1], - "duration": [1, 1, 1], - }, - "path": jd_single_path.name, - }, - }, - } - - metadata_path = output_dir / "metadata.json" - metadata_path.write_text(json.dumps(metadata, indent=2)) - typer.echo(f"Export complete. Metadata written to {metadata_path}") - - finally: - asr_model.decoder._rnnt_export = decoder_export_flag - - -if __name__ == "__main__": - app() diff --git a/parakeet-tdt-v2-0.6b/coreml/individual_components.py b/parakeet-tdt-v2-0.6b/coreml/individual_components.py deleted file mode 100644 index 5640330bbaa9e73ef18644458bcf023971ca70cf..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/individual_components.py +++ /dev/null @@ -1,229 +0,0 @@ -#!/usr/bin/env python3 -"""Export Parakeet TDT v2 RNNT components into CoreML and validate outputs.""" -from __future__ import annotations - -from dataclasses import dataclass -from pathlib import Path -from typing import Optional, Tuple - -import coremltools as ct -import torch - - -@dataclass -class ExportSettings: - output_dir: Path - compute_units: ct.ComputeUnit - deployment_target: Optional[ct.target.iOS17] - compute_precision: Optional[ct.precision] - max_audio_seconds: float - max_symbol_steps: int - - -@dataclass -class ValidationSettings: - audio_path: Optional[Path] - seconds: float - seed: Optional[int] - rtol: float - atol: float - skip: bool - - -@dataclass -class ValidationDiff: - name: str - max_abs_diff: float - max_rel_diff: float - - -@dataclass -class ValidationResult: - source: str - audio_num_samples: int - audio_seconds: float - token_length: int - atol: float - rtol: float - diffs: Tuple[ValidationDiff, ...] - - -class PreprocessorWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, audio_signal: torch.Tensor, length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - mel, mel_length = self.module(input_signal=audio_signal, length=length.to(dtype=torch.long)) - return mel, mel_length - - -class EncoderWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, features: torch.Tensor, length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - encoded, encoded_lengths = self.module(audio_signal=features, length=length.to(dtype=torch.long)) - return encoded, encoded_lengths - - -class DecoderWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward( - self, - targets: torch.Tensor, - target_lengths: torch.Tensor, - h_in: torch.Tensor, - c_in: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - state = [h_in, c_in] - decoder_output, _, new_state = self.module( - targets=targets.to(dtype=torch.long), - target_length=target_lengths.to(dtype=torch.long), - states=state, - ) - return decoder_output, new_state[0], new_state[1] - - -class JointWrapper(torch.nn.Module): - def __init__(self, module: torch.nn.Module) -> None: - super().__init__() - self.module = module - - def forward(self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor) -> torch.Tensor: - # Input: encoder_outputs [B, D, T], decoder_outputs [B, D, U] - # Transpose to match what projection layers expect - encoder_outputs = encoder_outputs.transpose(1, 2) # [B, T, D] - decoder_outputs = decoder_outputs.transpose(1, 2) # [B, U, D] - - # Apply projections - enc_proj = self.module.enc(encoder_outputs) # [B, T, 640] - dec_proj = self.module.pred(decoder_outputs) # [B, U, 640] - - # Explicit broadcasting along T and U to avoid converter ambiguity - x = enc_proj.unsqueeze(2) + dec_proj.unsqueeze(1) # [B, T, U, 640] - x = self.module.joint_net[0](x) # ReLU - x = self.module.joint_net[1](x) # Dropout (no-op in eval) - out = self.module.joint_net[2](x) # Linear -> logits [B, T, U, 8198] - return out - - -class MelEncoderWrapper(torch.nn.Module): - """Fused wrapper: waveform -> mel -> encoder. - - Inputs: - - audio_signal: [B, S] - - audio_length: [B] - - Outputs: - - encoder: [B, D, T_enc] - - encoder_length: [B] - """ - def __init__(self, preprocessor: PreprocessorWrapper, encoder: EncoderWrapper) -> None: - super().__init__() - self.preprocessor = preprocessor - self.encoder = encoder - - def forward(self, audio_signal: torch.Tensor, audio_length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - mel, mel_length = self.preprocessor(audio_signal, audio_length) - encoded, enc_len = self.encoder(mel, mel_length.to(dtype=torch.int32)) - return encoded, enc_len - - -class JointDecisionWrapper(torch.nn.Module): - """Joint + decision head: outputs label id, label prob, duration frames. - - Splits joint logits into token logits and duration logits, applies softmax - over tokens, argmax for both heads, and gathers probability of the chosen token. - - Inputs: - - encoder_outputs: [B, D, T] - - decoder_outputs: [B, D, U] - - Returns: - - token_id: [B, T, U] int32 - - token_prob: [B, T, U] float32 - - duration: [B, T, U] int32 (frames; duration buckets as defined by the model) - """ - def __init__(self, joint: JointWrapper, vocab_size: int, num_extra: int) -> None: - super().__init__() - self.joint = joint - self.vocab_with_blank = int(vocab_size) + 1 - self.num_extra = int(num_extra) - - def forward(self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor): - logits = self.joint(encoder_outputs, decoder_outputs) - token_logits = logits[..., : self.vocab_with_blank] - duration_logits = logits[..., -self.num_extra :] - - # Token selection - token_ids = torch.argmax(token_logits, dim=-1).to(dtype=torch.int32) - token_probs_all = torch.softmax(token_logits, dim=-1) - # gather expects int64 (long) indices; cast only for gather - token_prob = torch.gather( - token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) - ).squeeze(-1) - - # Duration prediction (duration bucket argmax → frame increments) - duration = torch.argmax(duration_logits, dim=-1).to(dtype=torch.int32) - return token_ids, token_prob, duration - - -class JointDecisionSingleStep(torch.nn.Module): - """Single-step variant for streaming: encoder_step [1, 1024, 1] -> [1,1,1]. - - Inputs: - - encoder_step: [B=1, D=1024, T=1] - - decoder_step: [B=1, D=640, U=1] - - Returns: - - token_id: [1, 1, 1] int32 - - token_prob: [1, 1, 1] float32 - - duration: [1, 1, 1] int32 - """ - def __init__(self, joint: JointWrapper, vocab_size: int, num_extra: int) -> None: - super().__init__() - self.joint = joint - self.vocab_with_blank = int(vocab_size) + 1 - self.num_extra = int(num_extra) - - def forward(self, encoder_step: torch.Tensor, decoder_step: torch.Tensor): - # Reuse JointWrapper which expects [B, D, T] and [B, D, U] - logits = self.joint(encoder_step, decoder_step) # [1, 1, 1, V+extra] - token_logits = logits[..., : self.vocab_with_blank] - duration_logits = logits[..., -self.num_extra :] - - token_ids = torch.argmax(token_logits, dim=-1, keepdim=False).to(dtype=torch.int32) - token_probs_all = torch.softmax(token_logits, dim=-1) - token_prob = torch.gather( - token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) - ).squeeze(-1) - duration = torch.argmax(duration_logits, dim=-1, keepdim=False).to(dtype=torch.int32) - return token_ids, token_prob, duration - - -def _coreml_convert( - traced: torch.jit.ScriptModule, - inputs, - outputs, - settings: ExportSettings, - compute_units_override: Optional[ct.ComputeUnit] = None, -) -> ct.models.MLModel: - cu = compute_units_override if compute_units_override is not None else settings.compute_units - kwargs = { - "convert_to": "mlprogram", - "inputs": inputs, - "outputs": outputs, - "compute_units": cu, - } - print("Converting:", traced.__class__.__name__) - print("Conversion kwargs:", kwargs) - if settings.deployment_target is not None: - kwargs["minimum_deployment_target"] = settings.deployment_target - if settings.compute_precision is not None: - kwargs["compute_precision"] = settings.compute_precision - return ct.convert(traced, **kwargs) diff --git a/parakeet-tdt-v2-0.6b/coreml/plots/compare-components/decoder_steps_l2.png 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b/parakeet-tdt-v2-0.6b/coreml/pyproject.toml deleted file mode 100644 index d73aaacc5f26aa71ce35b13f3dd9fc69b9616bc7..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/pyproject.toml +++ /dev/null @@ -1,251 +0,0 @@ -[project] -name = "parakeet-coreml" -version = "0.1.0" -description = "Add your description here" -readme = "README.md" -requires-python = "==3.10.12" -dependencies = [ - "absl-py==2.3.0", - "accelerate==1.8.1", - "aiohappyeyeballs==2.6.1", - "aiohttp==3.12.13", - "aiosignal==1.3.2", - "alembic==1.16.2", - "annotated-types==0.7.0", - "antlr4-python3-runtime==4.9.3", - "anyio==4.9.0", - "appnope==0.1.4", - "argon2-cffi-bindings==21.2.0", - "argon2-cffi==25.1.0", - "arrow==1.3.0", - "asttokens==3.0.0", - "async-lru==2.0.5", - "async-timeout==5.0.1", - "attrs==25.3.0", - "audioread==3.0.1", - "babel==2.17.0", - "backports-datetime-fromisoformat==2.0.3", - "beautifulsoup4==4.13.4", - "bleach==6.2.0", - "braceexpand==0.1.7", - "cattrs==25.1.1", - "certifi==2025.6.15", - "cffi==1.17.1", - "charset-normalizer==3.4.2", - "click==8.2.1", - "cloudpickle==3.1.1", - "colorlog==6.9.0", - "comm==0.2.2", - "contourpy==1.3.2", - "coremltools==9.0b1", - "cycler==0.12.1", - "cytoolz==1.0.1", - "datasets==3.6.0", - "debugpy==1.8.14", - "decorator==5.2.1", - "defusedxml==0.7.1", - "dill==0.3.8", - "distance==0.1.3", - "docopt==0.6.2", - "editdistance==0.8.1", - "einops==0.8.1", - "exceptiongroup==1.3.0", - "executing==2.2.0", - "fastjsonschema==2.21.1", - "fiddle==0.3.0", - "filelock==3.18.0", - "fonttools==4.58.4", - "fqdn==1.5.1", - "frozenlist==1.7.0", - "fsspec==2024.12.0", - "future==1.0.0", - "g2p-en==2.1.0", - "gitdb==4.0.12", - "gitpython==3.1.44", - "graphviz==0.21", - "grpcio==1.73.1", - "h11==0.16.0", - "hf-xet==1.1.5", - "httpcore==1.0.9", - "httpx==0.28.1", - "huggingface-hub==0.33.1", - "hydra-core==1.3.2", - "idna==3.10", - "inflect==7.5.0", - "intervaltree==3.1.0", - "ipykernel==6.29.5", - "ipython==8.37.0", - "ipywidgets==8.1.7", - "isoduration==20.11.0", - "jedi==0.19.2", - "jinja2==3.1.6", - "jiwer==4.0.0", - "joblib==1.5.1", - "json5==0.12.0", - "jsonpointer==3.0.0", - "jsonschema==4.24.0", - "jsonschema-specifications==2025.4.1", - "jupyter==1.1.1", - "jupyter-console==6.6.3", - "jupyter-events==0.12.0", - "jupyter-lsp==2.2.5", - "jupyter-client==8.6.3", - "jupyter-core==5.8.1", - "jupyter-server==2.16.0", - "jupyter-server-terminals==0.5.3", - "jupyterlab==4.4.4", - "jupyterlab-pygments==0.3.0", - "jupyterlab-server==2.27.3", - "jupyterlab-widgets==3.0.15", - "kaldi-python-io==1.2.2", - "kaldiio==2.18.1", - "kiwisolver==1.4.8", - "lazy-loader==0.4", - "levenshtein==0.27.1", - "lhotse==1.30.3", - "libcst==1.8.2", - "librosa==0.11.0", - "lightning==2.4.0", - "lightning-utilities==0.14.3", - "lilcom==1.8.1", - "llvmlite==0.44.0", - "loguru==0.7.3", - "mako==1.3.10", - "markdown==3.8.2", - "markdown-it-py==3.0.0", - "markupsafe==3.0.2", - "marshmallow==4.0.0", - "matplotlib==3.10.3", - "matplotlib-inline==0.1.7", - "mdurl==0.1.2", - "mediapy==1.1.6", - "mistune==3.1.3", - "more-itertools==10.7.0", - "mpmath==1.3.0", - "msgpack==1.1.1", - "multidict==6.6.2", - "multiprocess==0.70.16", - "nbclient==0.10.2", - "nbconvert==7.16.6", - "nbformat==5.10.4", - "nemo-toolkit==2.3.1", - "nest-asyncio==1.6.0", - "networkx==3.4.2", - "nltk==3.9.1", - "notebook==7.4.3", - "notebook-shim==0.2.4", - "num2words==0.5.14", - "numba==0.61.0", - "numpy==1.26.4", - "omegaconf==2.3.0", - "onnx==1.17.0", - "optuna==4.4.0", - "overrides==7.7.0", - "packaging==24.2", - "pandas==2.3.0", - "pandocfilters==1.5.1", - "parso==0.8.4", - "peft==0.15.2", - "pexpect==4.9.0", - "pillow==11.2.1", - "plac==1.4.5", - "platformdirs==4.3.8", - "pooch==1.8.2", - "prometheus-client==0.22.1", - "prompt-toolkit==3.0.51", - "propcache==0.3.2", - "psutil==7.0.0", - "ptyprocess==0.7.0", - "pure-eval==0.2.3", - "pyaml==25.5.0", - "pyannote-core==5.0.0", - "pyannote-database==5.1.3", - "pyannote-metrics==3.2.1", - "pyarrow==20.0.0", - "pybind11==2.13.6", - "pycparser==2.22", - "pydantic==2.11.7", - "pydantic-core==2.33.2", - "pydub==0.25.1", - "pygments==2.19.2", - "pyloudnorm==0.1.1", - "pyparsing==3.2.3", - "python-dateutil==2.9.0.post0", - "python-json-logger==3.3.0", - "pytorch-lightning==2.5.2", - "pytz==2025.2", - "pyyaml==6.0.2", - "pyzmq==27.0.0", - "rapidfuzz==3.13.0", - "referencing==0.36.2", - "regex==2024.11.6", - "requests==2.32.4", - "resampy==0.4.3", - "rfc3339-validator==0.1.4", - "rfc3986-validator==0.1.1", - "rich==14.0.0", - "rpds-py==0.25.1", - "ruamel-yaml==0.18.14", - "ruamel-yaml-clib==0.2.12", - "sacremoses==0.1.1", - "safetensors==0.5.3", - "scikit-learn==1.5.1", - "scipy==1.15.3", - "send2trash==1.8.3", - "sentencepiece==0.2.0", - "sentry-sdk==2.32.0", - "setproctitle==1.3.6", - "shellingham==1.5.4", - "six==1.17.0", - "smmap==5.0.2", - "sniffio==1.3.1", - "sortedcontainers==2.4.0", - "soundfile==0.13.1", - "soupsieve==2.7", - "sox==1.5.0", - "soxr==0.5.0.post1", - "sqlalchemy==2.0.41", - "stack-data==0.6.3", - "tabulate==0.9.0", - "tensorboard==2.19.0", - "tensorboard-data-server==0.7.2", - "termcolor==3.1.0", - "terminado==0.18.1", - "text-unidecode==1.3", - "texterrors==0.5.1", - "threadpoolctl==3.6.0", - "tinycss2==1.4.0", - "tokenizers==0.21.2", - "tomli==2.2.1", - "toolz==1.0.0", - "torch==2.7.0", - "torchmetrics==1.7.3", - "tornado==6.5.1", - "tqdm==4.67.1", - "traitlets==5.14.3", - "transformers==4.51.3", - "typeguard==4.4.4", - "typer==0.16.0", - "types-python-dateutil==2.9.0.20250516", - "typing-inspection==0.4.1", - "typing-extensions==4.14.0", - "tzdata==2025.2", - "uri-template==1.3.0", - "urllib3==2.5.0", - "wandb==0.20.1", - "wcwidth==0.2.13", - "webcolors==24.11.1", - "webdataset==1.0.2", - "webencodings==0.5.1", - "websocket-client==1.8.0", - "werkzeug==3.1.3", - "wget==3.2", - "widgetsnbextension==4.0.14", - "wrapt==1.17.2", - "xxhash==3.5.0", - "yarl==1.20.1", - "pip>=25.1.1", - "seaborn>=0.13.2", - "pyannote-audio>=3.3.2", - "funasr>=1.2.6", -] diff --git a/parakeet-tdt-v2-0.6b/coreml/quantize_coreml.py b/parakeet-tdt-v2-0.6b/coreml/quantize_coreml.py deleted file mode 100644 index 2067bd179da56ba63fe260b8334c4adc56321970..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/quantize_coreml.py +++ /dev/null @@ -1,1207 +0,0 @@ -#!/usr/bin/env python3 -"""Quantize CoreML mlpackages and compare quality, compression, latency, and compile time. - -This script focuses on the fused models: - - parakeet_mel_encoder.mlpackage (waveform -> encoder) - - parakeet_joint_decision.mlpackage (joint + softmax/argmax) - -It also quantizes the rest of the components in the directory for completeness. - -Variants tried by default (examples): - - int8-linear (per-channel) - - palettize 6-bit (Mel-only) - - jd-only variants (int8 and palette) - - prune + int8 - -Outputs: - - A new directory per variant under // with quantized mlpackages - - quantization_summary.json with aggregate metrics (baseline vs each variant) - - Plots saved under /plots/ and mirrored to /plots/quantize// - - fused_quality.png / fused_latency.png / fused_compression.png / fused_size.png (latency chart also shows compile time) - - all_components_quality.png / all_components_latency.png / all_components_compression.png / all_components_size.png (latency chart also shows compile time) - -Notes: - - Uses CoreMLTools optimize.coreml linear/palettize/prune for MLProgram models. - - Keeps the fixed 15-second window shapes as per context/coreml_component_io.md. - - Run via `uv run` to ensure reproducible environment. - - Sets minimum deployment target to iOS 17 for all outputs and loads models with `compute_units=ALL` by default to enable ANE. - - Tracks offline compile time (mlpackage->mlmodelc) and includes that metric in plots. -""" -from __future__ import annotations - -import json -import platform -import shutil -import subprocess -import time -from dataclasses import dataclass -from pathlib import Path -from typing import Dict, List, Optional, Set, Tuple - -import numpy as np -import soundfile as sf -import typer - -import coremltools as ct -from coremltools.optimize.coreml import ( - OptimizationConfig, - OpLinearQuantizerConfig, - OpPalettizerConfig, - OpThresholdPrunerConfig, - linear_quantize_weights, - palettize_weights, - prune_weights, -) - - -# Optional plotting -try: - import matplotlib - matplotlib.use("Agg") - import matplotlib.pyplot as plt - HAS_MPL = True -except Exception: - HAS_MPL = False - - -BASE_DIR = Path(__file__).resolve().parent -BYTES_IN_MB = 1024 * 1024 - - -DISPLAY_LABEL_OVERRIDES = { - "mel6bit-palettize": "mel6bit", - "enc6bit-palettize": "enc6bit", - "int8-linear": "int8 linear", -} - - -# When no explicit components are provided, we derive the set of -# components to quantize from the selected variants' whitelists. -# If any selected variant has no whitelist (i.e., applies globally), -# we quantize all components. -DEFAULT_TARGET_COMPONENTS: Tuple[str, str] = () - - -# Formatting helpers ------------------------------------------------------- - - -def _format_labels(labels: List[str]) -> List[str]: - formatted: List[str] = [] - for label in labels: - pretty = DISPLAY_LABEL_OVERRIDES.get(label, label) - pretty = pretty.replace("_", " ") - pretty = pretty.replace("-", "\n") - formatted.append(pretty) - return formatted - - -def _friendly_component_name(name: str) -> str: - return name.replace("_", " ").title() - - -def _get_metric_series( - metrics: Dict[str, Dict[str, List[float]]], - component: str, - key: str, - length: int, -) -> List[float]: - values = metrics.get(component, {}).get(key) - if values is None: - return [float("nan")] * length - if len(values) >= length: - return list(values[:length]) - padded = list(values) - padded.extend([float("nan")] * (length - len(values))) - return padded - - -def _plot_bar_rows( - out_path: Path, - display_labels: List[str], - rows: List[Dict[str, object]], - title_suffix: str, -) -> None: - if not HAS_MPL: - return - if not rows or not display_labels: - return - - n_labels = len(display_labels) - fig_width = max(8.0, 1.3 * n_labels) - fig_height = max(2.6 * len(rows), 3.0) - - fig, axes = plt.subplots(len(rows), 1, figsize=(fig_width, fig_height), squeeze=False) - axes = axes.flatten() - x = np.arange(n_labels) - - for ax, row in zip(axes, rows): - values = np.asarray(row.get("values", [float("nan")] * n_labels), dtype=np.float64) - color = row.get("color") - bars = ax.bar(x, values, width=0.6, color=color) - ax.set_title(str(row.get("title", ""))) - ax.set_xticks(x) - ax.set_xticklabels(display_labels, rotation=25, ha="right") - ylim = row.get("ylim") - if isinstance(ylim, tuple) and len(ylim) == 2: - ax.set_ylim(ylim) - ax.grid(axis="y", linestyle="--", alpha=0.3) - # Add value labels for bars - for bar in bars: - h = bar.get_height() - if np.isnan(h): - continue - ax.annotate( - f"{h:.2f}", - xy=(bar.get_x() + bar.get_width() / 2, h), - xytext=(0, 3), - textcoords="offset points", - ha="center", - va="bottom", - fontsize=8, - ) - - if title_suffix: - fig.suptitle(title_suffix, fontsize=12) - fig.tight_layout(rect=(0, 0, 1, 0.95)) - else: - fig.tight_layout() - - out_path.parent.mkdir(parents=True, exist_ok=True) - plt.savefig(out_path) - plt.close(fig) - - -def _plot_fused_category_charts( - plot_dir: Path, - labels: List[str], - mel_quality: List[float], - mel_latency_ms: List[float], - mel_compression: List[float], - mel_size_mb: List[float], - jd_acc: List[float], - jd_latency_ms: List[float], - jd_compression: List[float], - jd_size_mb: List[float], - mel_compile_ms: Optional[List[float]] = None, - jd_compile_ms: Optional[List[float]] = None, - title_suffix: str = "", -) -> List[Path]: - if not HAS_MPL: - return [] - outputs: List[Path] = [] - display = _format_labels(labels) - prefix = f"Fused Components — {title_suffix}" if title_suffix else "Fused Components" - - quality_path = plot_dir / "fused_quality.png" - _plot_bar_rows( - quality_path, - display, - [ - { - "title": "MelEncoder quality (1 - norm err)", - "values": mel_quality, - "color": "C0", - "ylim": (0.0, 1.05), - }, - { - "title": "JointDecision token-id match rate", - "values": jd_acc, - "color": "C0", - "ylim": (0.0, 1.05), - }, - ], - f"{prefix} — Quality", - ) - outputs.append(quality_path) - - compression_path = plot_dir / "fused_compression.png" - _plot_bar_rows( - compression_path, - display, - [ - { - "title": "MelEncoder compression ratio", - "values": mel_compression, - "color": "C2", - }, - { - "title": "JointDecision compression ratio", - "values": jd_compression, - "color": "C2", - }, - ], - f"{prefix} — Compression", - ) - outputs.append(compression_path) - - size_path = plot_dir / "fused_size.png" - _plot_bar_rows( - size_path, - display, - [ - { - "title": "MelEncoder size (MB)", - "values": mel_size_mb, - "color": "C4", - }, - { - "title": "JointDecision size (MB)", - "values": jd_size_mb, - "color": "C4", - }, - ], - f"{prefix} — Size", - ) - outputs.append(size_path) - - latency_rows = [ - { - "title": "MelEncoder latency (ms)", - "values": mel_latency_ms, - "color": "C1", - }, - { - "title": "JointDecision latency (ms)", - "values": jd_latency_ms, - "color": "C1", - }, - ] - if mel_compile_ms is not None and jd_compile_ms is not None: - latency_rows.extend( - [ - { - "title": "MelEncoder compile (ms)", - "values": mel_compile_ms, - "color": "C3", - }, - { - "title": "JointDecision compile (ms)", - "values": jd_compile_ms, - "color": "C3", - }, - ] - ) - - latency_path = plot_dir / "fused_latency.png" - _plot_bar_rows( - latency_path, - display, - latency_rows, - f"{prefix} — Latency", - ) - outputs.append(latency_path) - - return outputs - - -def _plot_all_component_category_charts( - plot_dir: Path, - labels: List[str], - metrics: Dict[str, Dict[str, List[float]]], - title_suffix: str = "", -) -> List[Path]: - if not HAS_MPL: - return [] - outputs: List[Path] = [] - display = _format_labels(labels) - prefix = f"Component Breakdown — {title_suffix}" if title_suffix else "Component Breakdown" - comp_order = [ - "preprocessor", - "encoder", - "mel_encoder", - "decoder", - "joint", - "joint_decision", - ] - n = len(labels) - - quality_rows: List[Dict[str, object]] = [] - for comp in comp_order: - friendly = _friendly_component_name(comp) - if comp == "joint_decision": - values = _get_metric_series(metrics, comp, "acc", n) - title = f"{friendly} token-id match rate" - else: - values = _get_metric_series(metrics, comp, "quality", n) - title = f"{friendly} quality (1 - norm err)" - quality_rows.append({"title": title, "values": values, "color": "C0", "ylim": (0.0, 1.05)}) - - compression_rows: List[Dict[str, object]] = [] - for comp in comp_order: - friendly = _friendly_component_name(comp) - compression_rows.append( - { - "title": f"{friendly} compression ratio", - "values": _get_metric_series(metrics, comp, "compression", n), - "color": "C2", - } - ) - - size_rows: List[Dict[str, object]] = [] - for comp in comp_order: - friendly = _friendly_component_name(comp) - size_rows.append( - { - "title": f"{friendly} size (MB)", - "values": _get_metric_series(metrics, comp, "size_mb", n), - "color": "C4", - } - ) - - latency_rows: List[Dict[str, object]] = [] - for comp in comp_order: - friendly = _friendly_component_name(comp) - latency_rows.append( - { - "title": f"{friendly} latency (ms)", - "values": _get_metric_series(metrics, comp, "latency_ms", n), - "color": "C1", - } - ) - - compile_rows: List[Dict[str, object]] = [] - for comp in comp_order: - friendly = _friendly_component_name(comp) - compile_rows.append( - { - "title": f"{friendly} compile (ms)", - "values": _get_metric_series(metrics, comp, "compile_ms", n), - "color": "C3", - } - ) - - quality_path = plot_dir / "all_components_quality.png" - _plot_bar_rows(quality_path, display, quality_rows, f"{prefix} — Quality") - outputs.append(quality_path) - - compression_path = plot_dir / "all_components_compression.png" - _plot_bar_rows(compression_path, display, compression_rows, f"{prefix} — Compression") - outputs.append(compression_path) - - size_path = plot_dir / "all_components_size.png" - _plot_bar_rows(size_path, display, size_rows, f"{prefix} — Size") - outputs.append(size_path) - - latency_path = plot_dir / "all_components_latency.png" - _plot_bar_rows(latency_path, display, latency_rows, f"{prefix} — Latency") - outputs.append(latency_path) - - compile_path = plot_dir / "all_components_compile.png" - _plot_bar_rows(compile_path, display, compile_rows, f"{prefix} — Compile") - outputs.append(compile_path) - - return outputs - - -app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) - - -@dataclass -class VariantConfig: - name: str - # List of (op_kind, opt_config) steps applied in order; op_kind in {'linear','palettize','prune'} - steps: List[Tuple[str, OptimizationConfig]] - category: str - whitelist: Optional[List[str]] = None # component names to apply; others copied - - -def _dir_size_bytes(path: Path) -> int: - total = 0 - for p in path.rglob("*"): - if p.is_file(): - try: - total += p.stat().st_size - except OSError: - pass - return total - - -def _load_metadata(input_dir: Path) -> Dict[str, object]: - meta_path = input_dir / "metadata.json" - if not meta_path.exists(): - raise typer.BadParameter(f"Expected metadata.json in {input_dir}") - return json.loads(meta_path.read_text()) - - -def _prepare_audio( - seconds: float, - sample_rate: int, - audio_path: Optional[Path], -) -> Tuple[np.ndarray, np.ndarray]: - max_samples = int(round(seconds * sample_rate)) - if audio_path is None: - # random but deterministic sample - rng = np.random.default_rng(1234) - audio = rng.standard_normal(size=(1, max_samples), dtype=np.float32).astype(np.float32) - else: - data, sr = sf.read(str(audio_path), dtype="float32") - if sr != sample_rate: - raise typer.BadParameter( - f"Validation audio sample rate {sr} != expected {sample_rate}" - ) - if data.ndim > 1: - data = data[:, 0] - if data.size < max_samples: - data = np.pad(data, (0, max_samples - data.size)) - elif data.size > max_samples: - data = data[:max_samples] - audio = data.reshape(1, -1).astype(np.float32, copy=False) - length = np.array([max_samples], dtype=np.int32) - return audio, length - - -def _predict_latency(model: ct.models.MLModel, inputs: Dict[str, np.ndarray], runs: int = 10, warmup: int = 3) -> Tuple[float, float]: - # Warmup - for _ in range(max(0, warmup)): - _ = model.predict(inputs) - # Timed runs - times: List[float] = [] - for _ in range(max(1, runs)): - t0 = time.perf_counter() - _ = model.predict(inputs) - t1 = time.perf_counter() - times.append((t1 - t0) * 1000.0) # ms - arr = np.array(times, dtype=np.float64) - return float(arr.mean()), float(arr.std(ddof=1) if arr.size > 1 else 0.0) - - -def _max_abs_rel(a: np.ndarray, b: np.ndarray) -> Tuple[float, float]: - a = np.asarray(a, dtype=np.float32) - b = np.asarray(b, dtype=np.float32) - if a.size == 0: - return 0.0, 0.0 - diff = np.abs(a - b) - max_abs = float(diff.max()) - denom = np.maximum(np.abs(a), np.abs(b)) - with np.errstate(divide="ignore", invalid="ignore"): - rel = np.where(denom == 0.0, 0.0, diff / denom) - max_rel = float(rel.max()) - return max_abs, max_rel - - -def _save_mlpackage(model: ct.models.MLModel, path: Path, description: str) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - # Ensure iOS 17 target for proper MLProgram ops (e.g., blockwise shift/scale) - try: - model.minimum_deployment_target = ct.target.iOS17 - except Exception: - pass - model.short_description = description - model.author = "Fluid Inference" - model.save(str(path)) - - -def _offline_compile_time_ms(model_path: Path) -> float: - """Compile mlpackage -> mlmodelc and return wall time in ms (host offline compile). - - Returns NaN on failure. - """ - compiled_dir: Optional[Path] = None - expected_dir = model_path.with_suffix(".mlmodelc") - try: - # Delete any prior compile artifact in-place so we can measure a fresh build - if expected_dir.exists(): - shutil.rmtree(expected_dir, ignore_errors=True) - - t0 = time.perf_counter() - compiled_path = ct.utils.compile_model(str(model_path)) - t1 = time.perf_counter() - compiled_dir = Path(compiled_path) - return (t1 - t0) * 1000.0 - except Exception: - return float("nan") - finally: - if ( - compiled_dir is not None - and compiled_dir.exists() - and compiled_dir == expected_dir - ): - shutil.rmtree(compiled_dir, ignore_errors=True) - -def _chip_spec_string(compute_units: str) -> str: - try: - chip = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"]).decode().strip() - except Exception: - chip = platform.processor() or platform.machine() - mac_ver = platform.mac_ver()[0] or platform.platform() - return f"Host: {chip} ‱ macOS {mac_ver} ‱ CoreMLTools {ct.__version__} ‱ ComputeUnits={compute_units} ‱ Min Target: iOS17" - - -def _quantize_dir( - input_dir: Path, - output_dir: Path, - variant: VariantConfig, - global_whitelist: Optional[Set[str]] = None, -) -> Dict[str, str]: - """Quantize all mlpackages in input_dir into output_dir using given variant config. - - Returns a map of component name -> saved relative path. - """ - meta = _load_metadata(input_dir) - comps = meta.get("components", {}) - saved: Dict[str, str] = {} - for name, cfg in comps.items(): - src_name = cfg.get("path") - if not src_name: - continue - src_path = input_dir / src_name - if not src_path.exists(): - continue - dst_path = output_dir / src_name - # Use CPU+GPU for preprocessor to avoid NE preprocessor input size issues; others use CPU+NE - cu = ct.ComputeUnit.CPU_AND_GPU if name == "preprocessor" else ct.ComputeUnit.CPU_AND_NE - base_model = ct.models.MLModel(str(src_path), compute_units=cu) - # Target iOS17 when running optimizations so the right ops are chosen - try: - base_model.minimum_deployment_target = ct.target.iOS17 - except Exception: - pass - - if name == "decoder": - typer.echo(f"[{variant.name}] Skipping decoder quantization; copying baseline: {src_name}") - _save_mlpackage(base_model, dst_path, "Baseline copy (decoder quantization disabled) - decoder") - saved[name] = dst_path.name - continue - - skip_reasons: List[str] = [] - if variant.whitelist is not None and name not in variant.whitelist: - skip_reasons.append("not targeted by variant") - if global_whitelist is not None and name not in global_whitelist: - skip_reasons.append("not in requested components") - - if skip_reasons: - reason = "; ".join(skip_reasons) - typer.echo(f"[{variant.name}] Skipping {name} ({reason}); copying baseline: {src_name}") - _save_mlpackage(base_model, dst_path, f"Baseline copy ({reason}) - {name}") - saved[name] = dst_path.name - continue - - typer.echo(f"[{variant.name}] Quantizing {name}: {src_name}") - - try: - q_model = base_model - for step_kind, step_cfg in variant.steps: - if step_kind == 'linear': - q_model = linear_quantize_weights(q_model, step_cfg) - elif step_kind == 'palettize': - q_model = palettize_weights(q_model, step_cfg) - elif step_kind == 'prune': - q_model = prune_weights(q_model, step_cfg) - else: - raise ValueError(f"Unknown variant step: {step_kind}") - except Exception as e: - # If quantization fails (e.g., unsupported op), fall back to copying baseline. - typer.echo(f" ! Failed to quantize {name} with {variant.name}: {e}. Copying baseline.") - _save_mlpackage(base_model, dst_path, f"Baseline copy (failed to quantize) - {name}") - else: - _save_mlpackage(q_model, dst_path, f"{variant.name} quantized - {name}") - saved[name] = dst_path.name - # Persist a variant metadata shim - out_meta = { - "variant": variant.name, - "base_dir": str(input_dir.resolve()), - "components": saved, - } - (output_dir / "quantization_metadata.json").write_text(json.dumps(out_meta, indent=2)) - return saved - - -@app.command() -def quantize( - input_dir: Path = typer.Option(Path("parakeet_coreml"), help="Directory containing baseline mlpackages + metadata.json"), - output_root: Path = typer.Option(Path("parakeet_coreml_quantized"), help="Root output dir for quantized variants"), - validation_audio: Optional[Path] = typer.Option(None, exists=True, resolve_path=True, help="Optional 15s, 16kHz wav for evaluation (defaults to bundled audio if present)"), - compute_units: str = typer.Option("CPU_AND_NE", help="Compute units for evaluation of non-preprocessor models. Preprocessor is forced to CPU_AND_GPU."), - runs: int = typer.Option(10, help="Timed runs per model for latency measurement"), - categories: Optional[List[str]] = typer.Option( - None, - "--category", - "-c", - help="Only run quantization variants in these categories (e.g., linear, mel-palettize). Can be repeated.", - ), - components: Optional[List[str]] = typer.Option( - None, - "--component", - "-m", - help="Component names to quantize. Defaults to mel_encoder and joint_decision. Use 'all' to keep every component enabled.", - ), -) -> None: - """Quantize models, then compare quality, compression, latency, and compile time. - - Variants include int8-linear (per-channel/per-tensor/block), palettization (6-bit), - jd-only probes, and prune+int8. Baseline is the pre-converted models. - """ - meta = _load_metadata(input_dir) - sr = int(meta.get("sample_rate", 16000)) - seconds = float(meta.get("max_audio_seconds", 15.0)) - - components_meta = meta.get("components", {}) - component_lookup = {name.lower(): name for name in components_meta.keys()} - # Defer computing component_filter until after variant/category selection so - # that, by default, we quantize exactly the components targeted by the - # selected variants (instead of a hard-coded subset). - component_filter: Optional[Set[str]] = None - - # Default audio if present - default_audio = (BASE_DIR / "audio" / "yc_first_minute_16k_15s.wav").resolve() - audio_path = validation_audio if validation_audio is not None else (default_audio if default_audio.exists() else None) - if audio_path is not None and validation_audio is None: - typer.echo(f"Using default validation audio: {audio_path}") - audio, audio_len = _prepare_audio(seconds, sr, audio_path) - - # Load baseline models and helpers for inputs - # Baseline models for input preparation - # Force preprocessor to CPU+GPU and all other components to CPU+NE for evaluation - pre_base = ct.models.MLModel(str(input_dir / "parakeet_preprocessor.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_GPU) - enc_base = ct.models.MLModel(str(input_dir / "parakeet_encoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - mel_encoder_base = ct.models.MLModel(str(input_dir / "parakeet_mel_encoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - decoder_base = ct.models.MLModel(str(input_dir / "parakeet_decoder.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - joint_decision_base = ct.models.MLModel(str(input_dir / "parakeet_joint_decision.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - joint_base = ct.models.MLModel(str(input_dir / "parakeet_joint.mlpackage"), compute_units=ct.ComputeUnit.CPU_AND_NE) - - # Prepare typical inputs once using baseline models - pre_out = pre_base.predict({"audio_signal": audio, "audio_length": audio_len}) - mel_ref = np.array(pre_out["mel"], dtype=np.float32, copy=True) - mel_len = np.array(pre_out["mel_length"], dtype=np.int32, copy=True) - - enc_out = enc_base.predict({"mel": mel_ref, "mel_length": mel_len}) - encoder_ref = np.array(enc_out["encoder"], dtype=np.float32, copy=True) - encoder_len = np.array(enc_out["encoder_length"], dtype=np.int32, copy=True) - - # Decoder inputs from metadata - dec_in = meta["components"]["decoder"]["inputs"] - targets_shape = tuple(int(x) for x in dec_in["targets"]) # e.g., (1, 256) - h_shape = tuple(int(x) for x in dec_in["h_in"]) # e.g., (2, 1, 640) - # Use zeros as targets for reproducibility without needing blank idx - targets = np.zeros(targets_shape, dtype=np.int32) - target_length = np.array([targets_shape[1]], dtype=np.int32) - h0 = np.zeros(h_shape, dtype=np.float32) - c0 = np.zeros(h_shape, dtype=np.float32) - dec_out = decoder_base.predict({ - "targets": targets, - "target_length": target_length, - "h_in": h0, - "c_in": c0, - }) - decoder_ref = np.array(dec_out["decoder"], dtype=np.float32, copy=True) - - # Baseline sizes per component - pre_base_size = _dir_size_bytes(input_dir / "parakeet_preprocessor.mlpackage") - enc_base_size = _dir_size_bytes(input_dir / "parakeet_encoder.mlpackage") - mel_base_size = _dir_size_bytes(input_dir / "parakeet_mel_encoder.mlpackage") - dec_base_size = _dir_size_bytes(input_dir / "parakeet_decoder.mlpackage") - joint_base_size = _dir_size_bytes(input_dir / "parakeet_joint.mlpackage") - jd_base_size = _dir_size_bytes(input_dir / "parakeet_joint_decision.mlpackage") - - # Baseline latencies - pre_base_inputs = {"audio_signal": audio, "audio_length": audio_len} - enc_base_inputs = {"mel": mel_ref, "mel_length": mel_len} - mel_base_inputs = {"audio_signal": audio, "audio_length": audio_len} - dec_base_inputs = {"targets": targets, "target_length": target_length, "h_in": h0, "c_in": c0} - joint_base_inputs = {"encoder": encoder_ref, "decoder": decoder_ref} - jd_base_inputs = {"encoder": encoder_ref, "decoder": decoder_ref} - pre_base_ms, _ = _predict_latency(pre_base, pre_base_inputs, runs=runs) - enc_base_ms, _ = _predict_latency(enc_base, enc_base_inputs, runs=runs) - mel_base_ms, _ = _predict_latency(mel_encoder_base, mel_base_inputs, runs=runs) - dec_base_ms, _ = _predict_latency(decoder_base, dec_base_inputs, runs=runs) - joint_base_ms, _ = _predict_latency(joint_base, joint_base_inputs, runs=runs) - jd_base_ms, _ = _predict_latency(joint_decision_base, jd_base_inputs, runs=runs) - # Cache baseline joint logits for comparisons - logits_base = np.array(joint_base.predict(joint_base_inputs)["logits"], dtype=np.float32, copy=True) - - # Variants - variants: List[VariantConfig] = [ - VariantConfig( - name="int8-linear", - steps=[( - "linear", - OptimizationConfig(global_config=OpLinearQuantizerConfig(mode="linear", granularity="per_channel")), - )], - category="linear", - ), - # 6-bit palettization for MelEncoder only - VariantConfig( - name="mel6bit-palettize", - steps=[( - "palettize", - OptimizationConfig( - global_config=OpPalettizerConfig(mode="kmeans", nbits=6) - ), - )], - category="mel-palettize", - whitelist=["mel_encoder"], - ), - # 6-bit palettization for Encoder only - VariantConfig( - name="enc6bit-palettize", - steps=[( - "palettize", - OptimizationConfig( - global_config=OpPalettizerConfig(mode="kmeans", nbits=6) - ), - )], - category="encoder-palettize", - whitelist=["encoder"], - ), - # (removed) Global palettization variants - ] - - available_categories = {variant.category for variant in variants} - category_lookup = {cat.lower(): cat for cat in available_categories} - selected_categories: Set[str] - if categories: - normalized_categories = [cat.strip().lower() for cat in categories if cat.strip()] - if normalized_categories: - invalid_categories = [cat for cat in normalized_categories if cat not in category_lookup] - if invalid_categories: - available_str = ", ".join(sorted(available_categories)) or "none" - bad = ", ".join(sorted(set(invalid_categories))) - raise typer.BadParameter( - f"Unknown category (--category): {bad}. Available categories: {available_str}." - ) - selected_categories = {category_lookup[cat] for cat in normalized_categories} - variants = [variant for variant in variants if variant.category in selected_categories] - else: - selected_categories = available_categories - else: - selected_categories = available_categories - - if not variants: - typer.echo("No quantization variants matched the requested categories; nothing to do.") - raise typer.Exit(code=0) - - typer.echo("Running variant categories: " + ", ".join(sorted(selected_categories))) - - # Resolve the component whitelist now that variants are known. - if components: - normalized_components = [comp.strip().lower() for comp in components if comp.strip()] - if any(comp == "all" for comp in normalized_components): - component_filter = None - else: - resolved: List[str] = [] - invalid: List[str] = [] - for comp in normalized_components: - match = component_lookup.get(comp) - if match is None: - invalid.append(comp) - else: - resolved.append(match) - if invalid: - available = ", ".join(sorted(component_lookup.values())) or "none" - bad = ", ".join(sorted(set(invalid))) - raise typer.BadParameter( - f"Unknown component(s) for --component: {bad}. Available components: {available}." - ) - component_filter = set(resolved) - else: - # Derive from selected variants' whitelists - derived: Set[str] = set() - has_global = False - for v in variants: - if v.whitelist is None: - has_global = True - break - derived.update(v.whitelist) - component_filter = None if has_global or not derived else derived - - if component_filter is None: - typer.echo("Quantizing components: all components (derived)") - else: - typer.echo("Quantizing components: " + ", ".join(sorted(component_filter)) + " (derived)") - - # Aggregate results (baseline + variants) - summary: Dict[str, Dict[str, object]] = {} - variants_names: List[str] = [] - # Build arrays including baseline as first label - fused_labels: List[str] = ["baseline"] - mel_quality_scores: List[float] = [1.0] - mel_latency_ms: List[float] = [mel_base_ms] - mel_compression: List[float] = [1.0] - mel_size_mb: List[float] = [float(mel_base_size) / BYTES_IN_MB] - # Offline compile time (host) for fused models - mel_compile_ms: List[float] = [_offline_compile_time_ms(input_dir / "parakeet_mel_encoder.mlpackage")] - jd_accuracy: List[float] = [1.0] - jd_latency_ms: List[float] = [jd_base_ms] - jd_compression: List[float] = [1.0] - jd_size_mb: List[float] = [float(jd_base_size) / BYTES_IN_MB] - jd_compile_ms: List[float] = [_offline_compile_time_ms(input_dir / "parakeet_joint_decision.mlpackage")] - - # For the all-components chart, collect per-component metrics similarly - all_metrics: Dict[str, Dict[str, List[float]]] = { - "preprocessor": { - "quality": [1.0], - "compression": [1.0], - "latency_ms": [pre_base_ms], - "compile_ms": [_offline_compile_time_ms(input_dir / "parakeet_preprocessor.mlpackage")], - "size_mb": [float(pre_base_size) / BYTES_IN_MB], - }, - "encoder": { - "quality": [1.0], - "compression": [1.0], - "latency_ms": [enc_base_ms], - "compile_ms": [_offline_compile_time_ms(input_dir / "parakeet_encoder.mlpackage")], - "size_mb": [float(enc_base_size) / BYTES_IN_MB], - }, - "mel_encoder": { - "quality": [1.0], - "compression": [1.0], - "latency_ms": [mel_base_ms], - "compile_ms": [mel_compile_ms[0]], - "size_mb": [float(mel_base_size) / BYTES_IN_MB], - }, - "decoder": { - "quality": [1.0], - "compression": [1.0], - "latency_ms": [dec_base_ms], - "compile_ms": [_offline_compile_time_ms(input_dir / "parakeet_decoder.mlpackage")], - "size_mb": [float(dec_base_size) / BYTES_IN_MB], - }, - "joint": { - "quality": [1.0], - "compression": [1.0], - "latency_ms": [joint_base_ms], - "compile_ms": [_offline_compile_time_ms(input_dir / "parakeet_joint.mlpackage")], - "size_mb": [float(joint_base_size) / BYTES_IN_MB], - }, - "joint_decision": { - "acc": [1.0], - "compression": [1.0], - "latency_ms": [jd_base_ms], - "compile_ms": [jd_compile_ms[0]], - "size_mb": [float(jd_base_size) / BYTES_IN_MB], - }, - } - - # Populate baseline entry - summary["baseline"] = { - "components": { - "preprocessor": { - "quality": 1.0, - "latency_ms": pre_base_ms, - "size_bytes": float(pre_base_size), - "size_mb": float(pre_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["preprocessor"]["compile_ms"][0], - }, - "encoder": { - "quality": 1.0, - "latency_ms": enc_base_ms, - "size_bytes": float(enc_base_size), - "size_mb": float(enc_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["encoder"]["compile_ms"][0], - }, - "mel_encoder": { - "quality": 1.0, - "latency_ms": mel_base_ms, - "size_bytes": float(mel_base_size), - "size_mb": float(mel_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["mel_encoder"]["compile_ms"][0], - }, - "decoder": { - "quality": 1.0, - "latency_ms": dec_base_ms, - "size_bytes": float(dec_base_size), - "size_mb": float(dec_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["decoder"]["compile_ms"][0], - }, - "joint": { - "quality": 1.0, - "latency_ms": joint_base_ms, - "size_bytes": float(joint_base_size), - "size_mb": float(joint_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["joint"]["compile_ms"][0], - }, - "joint_decision": { - "acc": 1.0, - "latency_ms": jd_base_ms, - "size_bytes": float(jd_base_size), - "size_mb": float(jd_base_size) / BYTES_IN_MB, - "compression_ratio": 1.0, - "compile_ms": all_metrics["joint_decision"]["compile_ms"][0], - }, - } - } - - for var in variants: - variants_names.append(var.name) - out_dir = output_root / var.name - out_dir_exists = out_dir.exists() - out_dir.mkdir(parents=True, exist_ok=True) - - expected_components = [] - for comp_cfg in meta.get("components", {}).values(): - rel = comp_cfg.get("path") - if rel: - expected_components.append(out_dir / rel) - - missing = [p for p in expected_components if not p.exists()] - if out_dir_exists and not missing: - typer.echo(f"[{var.name}] Output already present at {out_dir}; skipping quantization step.") - else: - if out_dir_exists and missing: - missing_names = ", ".join(sorted(p.name for p in missing)) or "unknown" - typer.echo(f"[{var.name}] Output directory exists but is incomplete (missing: {missing_names}). Re-quantizing.") - shutil.rmtree(out_dir, ignore_errors=True) - out_dir.mkdir(parents=True, exist_ok=True) - _quantize_dir(input_dir, out_dir, var, component_filter) - - # Load quantized models and pre-compute offline compile time - pre_q_path = out_dir / "parakeet_preprocessor.mlpackage" - enc_q_path = out_dir / "parakeet_encoder.mlpackage" - mel_q_path = out_dir / "parakeet_mel_encoder.mlpackage" - dec_q_path = out_dir / "parakeet_decoder.mlpackage" - joint_q_path = out_dir / "parakeet_joint.mlpackage" - jd_q_path = out_dir / "parakeet_joint_decision.mlpackage" - - pre_compile_ms = _offline_compile_time_ms(pre_q_path) - enc_compile_ms = _offline_compile_time_ms(enc_q_path) - mel_compile_q_ms = _offline_compile_time_ms(mel_q_path) - dec_compile_ms = _offline_compile_time_ms(dec_q_path) - joint_compile_ms = _offline_compile_time_ms(joint_q_path) - jd_compile_q_ms = _offline_compile_time_ms(jd_q_path) - - # Match compute units for quantized artifacts: preprocessor on CPU+GPU; others on CPU+NE - pre_q = ct.models.MLModel(str(pre_q_path), compute_units=ct.ComputeUnit.CPU_AND_GPU) - enc_q = ct.models.MLModel(str(enc_q_path), compute_units=ct.ComputeUnit.CPU_AND_NE) - mel_q = ct.models.MLModel(str(mel_q_path), compute_units=ct.ComputeUnit.CPU_AND_NE) - dec_q = ct.models.MLModel(str(dec_q_path), compute_units=ct.ComputeUnit.CPU_AND_NE) - joint_q = ct.models.MLModel(str(joint_q_path), compute_units=ct.ComputeUnit.CPU_AND_NE) - jd_q = ct.models.MLModel(str(jd_q_path), compute_units=ct.ComputeUnit.CPU_AND_NE) - - # Preprocessor quality vs baseline - pre_q_out = pre_q.predict(pre_base_inputs) - mel_q_in = np.array(pre_q_out["mel"], dtype=np.float32, copy=True) - a_pre, r_pre = _max_abs_rel(mel_ref, mel_q_in) - l2_pre_ref = float(np.linalg.norm(mel_ref)) - l2_pre_err = float(np.linalg.norm(mel_ref - mel_q_in)) - pre_norm_err = (l2_pre_err / (l2_pre_ref + 1e-8)) if l2_pre_ref > 0 else 0.0 - pre_quality = float(max(0.0, 1.0 - pre_norm_err)) - pre_q_ms, _ = _predict_latency(pre_q, pre_base_inputs, runs=runs) - pre_q_size = _dir_size_bytes(out_dir / "parakeet_preprocessor.mlpackage") - all_metrics["preprocessor"]["quality"].append(pre_quality) - all_metrics["preprocessor"]["latency_ms"].append(pre_q_ms) - all_metrics["preprocessor"]["compression"].append(float(pre_base_size) / float(pre_q_size if pre_q_size > 0 else 1)) - all_metrics["preprocessor"].setdefault("compile_ms", []).append(pre_compile_ms) - all_metrics["preprocessor"]["size_mb"].append(float(pre_q_size) / BYTES_IN_MB) - - # Encoder quality vs baseline (feed baseline mel to both) - enc_q_out = enc_q.predict({"mel": mel_ref, "mel_length": mel_len}) - encoder_q = np.array(enc_q_out["encoder"], dtype=np.float32, copy=True) - l2_enc_ref = float(np.linalg.norm(encoder_ref)) - l2_enc_err = float(np.linalg.norm(encoder_ref - encoder_q)) - enc_norm_err = (l2_enc_err / (l2_enc_ref + 1e-8)) if l2_enc_ref > 0 else 0.0 - enc_quality = float(max(0.0, 1.0 - enc_norm_err)) - enc_q_ms, _ = _predict_latency(enc_q, enc_base_inputs, runs=runs) - enc_q_size = _dir_size_bytes(out_dir / "parakeet_encoder.mlpackage") - all_metrics["encoder"]["quality"].append(enc_quality) - all_metrics["encoder"]["latency_ms"].append(enc_q_ms) - all_metrics["encoder"]["compression"].append(float(enc_base_size) / float(enc_q_size if enc_q_size > 0 else 1)) - all_metrics["encoder"].setdefault("compile_ms", []).append(enc_compile_ms) - all_metrics["encoder"]["size_mb"].append(float(enc_q_size) / BYTES_IN_MB) - - # MelEncoder quality - mel_q_out = mel_q.predict(mel_base_inputs) - enc_q_fused = np.array(mel_q_out["encoder"], dtype=np.float32, copy=True) - a_mel, r_mel = _max_abs_rel(encoder_ref, enc_q_fused) - # Normalize error into a [0,1] quality score: 1 / (1 + normalized L2) - # Use relative measure derived from L2 norms as more stable than max. - l2_ref = float(np.linalg.norm(encoder_ref)) - l2_err = float(np.linalg.norm(encoder_ref - enc_q_fused)) - norm_err = (l2_err / (l2_ref + 1e-8)) if l2_ref > 0 else 0.0 - mel_quality = float(max(0.0, 1.0 - norm_err)) - mel_q_ms, _ = _predict_latency(mel_q, mel_base_inputs, runs=runs) - mel_q_size = _dir_size_bytes(out_dir / "parakeet_mel_encoder.mlpackage") - mel_ratio = float(mel_base_size) / float(mel_q_size if mel_q_size > 0 else 1) - mel_size_mb.append(float(mel_q_size) / BYTES_IN_MB) - all_metrics["mel_encoder"]["quality"].append(mel_quality) - all_metrics["mel_encoder"]["latency_ms"].append(mel_q_ms) - all_metrics["mel_encoder"]["compression"].append(float(mel_base_size) / float(mel_q_size if mel_q_size > 0 else 1)) - all_metrics["mel_encoder"].setdefault("compile_ms", []).append(mel_compile_q_ms) - all_metrics["mel_encoder"]["size_mb"].append(float(mel_q_size) / BYTES_IN_MB) - - # JointDecision quality: token-id and duration match rates - jd_base_out = joint_decision_base.predict(jd_base_inputs) - token_id_base = np.array(jd_base_out["token_id"], dtype=np.int32, copy=True) - duration_base = np.array(jd_base_out["duration"], dtype=np.int32, copy=True) - token_prob_base = np.array(jd_base_out["token_prob"], dtype=np.float32, copy=True) - - jd_q_out = jd_q.predict(jd_base_inputs) - token_id_q = np.array(jd_q_out["token_id"], dtype=np.int32, copy=True) - duration_q = np.array(jd_q_out["duration"], dtype=np.int32, copy=True) - token_prob_q = np.array(jd_q_out["token_prob"], dtype=np.float32, copy=True) - - # Accuracy metrics - id_match = float((token_id_q == token_id_base).mean()) - dur_match = float((duration_q == duration_base).mean()) - # Aggregate a single "accuracy" number as token-id match rate (primary) - jd_acc = id_match - jd_q_ms, _ = _predict_latency(jd_q, jd_base_inputs, runs=runs) - jd_q_size = _dir_size_bytes(out_dir / "parakeet_joint_decision.mlpackage") - jd_ratio = float(jd_base_size) / float(jd_q_size if jd_q_size > 0 else 1) - jd_size_mb.append(float(jd_q_size) / BYTES_IN_MB) - all_metrics["joint_decision"].setdefault("acc", []).append(jd_acc) - all_metrics["joint_decision"]["latency_ms"].append(jd_q_ms) - all_metrics["joint_decision"]["compression"].append(float(jd_base_size) / float(jd_q_size if jd_q_size > 0 else 1)) - all_metrics["joint_decision"].setdefault("compile_ms", []).append(jd_compile_q_ms) - all_metrics["joint_decision"]["size_mb"].append(float(jd_q_size) / BYTES_IN_MB) - - # Decoder quality vs baseline - dec_q_out = dec_q.predict(dec_base_inputs) - decoder_q = np.array(dec_q_out["decoder"], dtype=np.float32, copy=True) - l2_dec_ref = float(np.linalg.norm(decoder_ref)) - l2_dec_err = float(np.linalg.norm(decoder_ref - decoder_q)) - dec_norm_err = (l2_dec_err / (l2_dec_ref + 1e-8)) if l2_dec_ref > 0 else 0.0 - dec_quality = float(max(0.0, 1.0 - dec_norm_err)) - dec_q_ms, _ = _predict_latency(dec_q, dec_base_inputs, runs=runs) - dec_q_size = _dir_size_bytes(out_dir / "parakeet_decoder.mlpackage") - all_metrics["decoder"]["quality"].append(dec_quality) - all_metrics["decoder"]["latency_ms"].append(dec_q_ms) - all_metrics["decoder"]["compression"].append(float(dec_base_size) / float(dec_q_size if dec_q_size > 0 else 1)) - all_metrics["decoder"].setdefault("compile_ms", []).append(dec_compile_ms) - all_metrics["decoder"]["size_mb"].append(float(dec_q_size) / BYTES_IN_MB) - - # Joint quality vs baseline (compare logits) - joint_q_out = joint_q.predict(joint_base_inputs) - logits_q = np.array(joint_q_out["logits"], dtype=np.float32, copy=True) - l2_joint_ref = float(np.linalg.norm(logits_base)) - l2_joint_err = float(np.linalg.norm(logits_base - logits_q)) - joint_norm_err = (l2_joint_err / (l2_joint_ref + 1e-8)) if l2_joint_ref > 0 else 0.0 - joint_quality = float(max(0.0, 1.0 - joint_norm_err)) - joint_q_ms, _ = _predict_latency(joint_q, joint_base_inputs, runs=runs) - joint_q_size = _dir_size_bytes(out_dir / "parakeet_joint.mlpackage") - all_metrics["joint"]["quality"].append(joint_quality) - all_metrics["joint"]["latency_ms"].append(joint_q_ms) - all_metrics["joint"]["compression"].append(float(joint_base_size) / float(joint_q_size if joint_q_size > 0 else 1)) - all_metrics["joint"].setdefault("compile_ms", []).append(joint_compile_ms) - all_metrics["joint"]["size_mb"].append(float(joint_q_size) / BYTES_IN_MB) - - # Decoder deltas for JSON - a_dec, r_dec = _max_abs_rel(decoder_ref, decoder_q) - # Joint deltas for JSON - a_joint, r_joint = _max_abs_rel(logits_base, logits_q) - - # Store metrics - summary[var.name] = { - "components": { - "preprocessor": { - "quality": pre_quality, - "latency_ms": pre_q_ms, - "size_bytes": float(pre_q_size), - "size_mb": float(pre_q_size) / BYTES_IN_MB, - "compression_ratio": float(pre_base_size) / float(pre_q_size if pre_q_size > 0 else 1), - "max_abs": a_pre, - "max_rel": r_pre, - "compile_ms": pre_compile_ms, - }, - "encoder": { - "quality": enc_quality, - "latency_ms": enc_q_ms, - "size_bytes": float(enc_q_size), - "size_mb": float(enc_q_size) / BYTES_IN_MB, - "compression_ratio": float(enc_base_size) / float(enc_q_size if enc_q_size > 0 else 1), - "compile_ms": enc_compile_ms, - }, - "mel_encoder": { - "quality": mel_quality, - "latency_ms": mel_q_ms, - "size_bytes": float(mel_q_size), - "size_mb": float(mel_q_size) / BYTES_IN_MB, - "compression_ratio": mel_ratio, - "max_abs": a_mel, - "max_rel": r_mel, - "compile_ms": mel_compile_q_ms, - }, - "decoder": { - "quality": dec_quality, - "latency_ms": dec_q_ms, - "size_bytes": float(dec_q_size), - "size_mb": float(dec_q_size) / BYTES_IN_MB, - "compression_ratio": float(dec_base_size) / float(dec_q_size if dec_q_size > 0 else 1), - "max_abs": a_dec, - "max_rel": r_dec, - "compile_ms": dec_compile_ms, - }, - "joint": { - "quality": joint_quality, - "latency_ms": joint_q_ms, - "size_bytes": float(joint_q_size), - "size_mb": float(joint_q_size) / BYTES_IN_MB, - "compression_ratio": float(joint_base_size) / float(joint_q_size if joint_q_size > 0 else 1), - "max_abs": a_joint, - "max_rel": r_joint, - "compile_ms": joint_compile_ms, - }, - "joint_decision": { - "acc": jd_acc, - "duration_match": dur_match, - "prob_mae": float(np.mean(np.abs(token_prob_q - token_prob_base))), - "latency_ms": jd_q_ms, - "size_bytes": float(jd_q_size), - "size_mb": float(jd_q_size) / BYTES_IN_MB, - "compression_ratio": jd_ratio, - "compile_ms": jd_compile_q_ms, - }, - } - } - - fused_labels.append(var.name) - mel_quality_scores.append(mel_quality) - mel_latency_ms.append(mel_q_ms) - mel_compression.append(mel_ratio) - jd_accuracy.append(jd_acc) - jd_latency_ms.append(jd_q_ms) - jd_compression.append(jd_ratio) - mel_compile_ms.append(mel_compile_q_ms) - jd_compile_ms.append(jd_compile_q_ms) - - # Write summary JSON - out_root = output_root - out_root.mkdir(parents=True, exist_ok=True) - (out_root / "quantization_summary.json").write_text(json.dumps(summary, indent=2)) - - # Plot - plot_dir = out_root / "plots" - title_suffix = _chip_spec_string(compute_units) - fused_paths = _plot_fused_category_charts( - plot_dir, - fused_labels, - mel_quality_scores, - mel_latency_ms, - mel_compression, - mel_size_mb, - jd_accuracy, - jd_latency_ms, - jd_compression, - jd_size_mb, - mel_compile_ms, - jd_compile_ms, - title_suffix, - ) - component_paths = _plot_all_component_category_charts( - plot_dir, - fused_labels, - all_metrics, - title_suffix, - ) - - typer.echo(f"Wrote summary JSON: {out_root / 'quantization_summary.json'}") - if HAS_MPL: - all_plot_paths = fused_paths + component_paths - repo_plot_dir = BASE_DIR / "plots" / "quantize" / compute_units.lower() - repo_plot_dir.mkdir(parents=True, exist_ok=True) - for path in all_plot_paths: - typer.echo(f"Wrote plot: {path}") - if path.exists(): - dest = repo_plot_dir / path.name - shutil.copy2(path, dest) - typer.echo(f"Mirrored plot: {dest}") - else: - typer.echo("matplotlib unavailable; skipped plotting.") - - -if __name__ == "__main__": - app() diff --git a/parakeet-tdt-v2-0.6b/coreml/speech_to_text_streaming_infer_rnnt.py b/parakeet-tdt-v2-0.6b/coreml/speech_to_text_streaming_infer_rnnt.py deleted file mode 100644 index 07d8fcc04df44b41c758ad52f518d36a1ad797ae..0000000000000000000000000000000000000000 --- a/parakeet-tdt-v2-0.6b/coreml/speech_to_text_streaming_infer_rnnt.py +++ /dev/null @@ -1,341 +0,0 @@ -"""Streaming inference helper that stitches Parakeet CoreML components using the RNNT greedy loop from Nemo.""" -from __future__ import annotations - -import argparse -import json -import logging -from pathlib import Path -from typing import Iterable, List, Optional, Sequence - -import coremltools as ct -import librosa -import numpy as np -import torch - -from parakeet_components import CoreMLModelBundle - -LOGGER = logging.getLogger("parakeet_streaming") - - -class BatchedHyps: - """Minimal port of Nemo's batched hypothesis buffer.""" - - def __init__( - self, - batch_size: int, - init_length: int, - device: torch.device, - float_dtype: torch.dtype, - ) -> None: - if init_length <= 0: - raise ValueError("init_length must be > 0") - self._max_length = init_length - self.current_lengths = torch.zeros(batch_size, device=device, dtype=torch.long) - self.transcript = torch.zeros((batch_size, self._max_length), device=device, dtype=torch.long) - self.timestamps = torch.zeros((batch_size, self._max_length), device=device, dtype=torch.long) - self.scores = torch.zeros(batch_size, device=device, dtype=float_dtype) - self.last_timestamp = torch.full((batch_size,), -1, device=device, dtype=torch.long) - self.last_timestamp_lasts = torch.zeros(batch_size, device=device, dtype=torch.long) - self._batch_indices = torch.arange(batch_size, device=device) - self._ones = torch.ones_like(self._batch_indices) - - def add_results( - self, - active_mask: torch.Tensor, - labels: torch.Tensor, - time_indices: torch.Tensor, - scores: torch.Tensor, - ) -> None: - self.scores = torch.where(active_mask, self.scores + scores, self.scores) - self.transcript[self._batch_indices, self.current_lengths] = labels - self.timestamps[self._batch_indices, self.current_lengths] = time_indices - torch.where( - torch.logical_and(active_mask, self.last_timestamp == time_indices), - self.last_timestamp_lasts + 1, - self.last_timestamp_lasts, - out=self.last_timestamp_lasts, - ) - torch.where( - torch.logical_and(active_mask, self.last_timestamp != time_indices), - self._ones, - self.last_timestamp_lasts, - out=self.last_timestamp_lasts, - ) - torch.where(active_mask, time_indices, self.last_timestamp, out=self.last_timestamp) - self.current_lengths += active_mask - - -class CoreMLStreamingDecoder: - """Use exported decoder and joint CoreML models with Nemo's greedy RNNT loop.""" - - def __init__( - self, - decoder_model: ct.models.MLModel, - joint_model: ct.models.MLModel, - *, - vocab_size: int, - blank_id: int, - num_layers: int, - hidden_size: int, - durations: Sequence[int] = (0, 1, 2, 3, 4), - max_symbols: int = 10, - device: torch.device = torch.device("cpu"), - ) -> None: - self.decoder_model = decoder_model - self.joint_model = joint_model - self.vocab_size = vocab_size - self.blank_id = blank_id - self.num_layers = num_layers - self.hidden_size = hidden_size - self.durations = torch.tensor(durations, dtype=torch.long, device=device) - self.max_symbols = max_symbols - self.device = device - - def _predict_decoder(self, labels: torch.Tensor, h_in: np.ndarray, c_in: np.ndarray) -> tuple[torch.Tensor, np.ndarray, np.ndarray]: - outputs = self.decoder_model.predict( - { - "targets": np.array([labels.cpu().numpy()], dtype=np.int32), - "target_lengths": np.array([labels.numel()], dtype=np.int32), - "h_in": h_in, - "c_in": c_in, - } - ) - decoder_output = torch.from_numpy(outputs["decoder_output"]).to(self.device) - return decoder_output, outputs["h_out"], outputs["c_out"] - - def _predict_joint(self, encoder_frame: torch.Tensor, decoder_output: torch.Tensor) -> torch.Tensor: - outputs = self.joint_model.predict( - { - "encoder_outputs": encoder_frame.unsqueeze(1).cpu().numpy().astype(np.float32), - "decoder_outputs": decoder_output.cpu().numpy().astype(np.float32), - } - ) - logits = torch.from_numpy(outputs["logits"]).to(self.device) - return logits.squeeze(1).squeeze(1) - - def decode(self, encoder_output: torch.Tensor, encoder_lengths: torch.Tensor) -> List[List[int]]: - batch_size, max_time, _ = encoder_output.shape - encoder_output = encoder_output.to(self.device) - encoder_lengths = encoder_lengths.to(self.device) - - float_dtype = encoder_output.dtype - batch_indices = torch.arange(batch_size, device=self.device) - labels = torch.full((batch_size,), fill_value=self.blank_id, device=self.device, dtype=torch.long) - time_indices = torch.zeros_like(labels) - safe_time_indices = torch.zeros_like(labels) - time_indices_current = torch.zeros_like(labels) - last_timesteps = encoder_lengths - 1 - active_mask = encoder_lengths > 0 - advance_mask = torch.empty_like(active_mask) - active_mask_prev = torch.empty_like(active_mask) - became_inactive = torch.empty_like(active_mask) - - hyps = BatchedHyps( - batch_size=batch_size, - init_length=max_time * self.max_symbols if self.max_symbols else max_time, - device=self.device, - float_dtype=float_dtype, - ) - - h_in = np.zeros((self.num_layers, batch_size, self.hidden_size), dtype=np.float32) - c_in = np.zeros((self.num_layers, batch_size, self.hidden_size), dtype=np.float32) - - while active_mask.any(): - active_mask_prev.copy_(active_mask) - decoder_output, h_in, c_in = self._predict_decoder(labels, h_in, c_in) - logits = self._predict_joint(encoder_output[batch_indices, safe_time_indices], decoder_output) - - scores, labels = logits[:, : self.vocab_size].max(dim=-1) - duration_indices = logits[:, self.vocab_size : self.vocab_size + len(self.durations)].argmax(dim=-1) - durations = self.durations[duration_indices] - - blank_mask = labels == self.blank_id - durations.masked_fill_(torch.logical_and(durations == 0, blank_mask), 1) - time_indices_current.copy_(time_indices) - time_indices += durations - torch.minimum(time_indices, last_timesteps, out=safe_time_indices) - torch.less(time_indices, encoder_lengths, out=active_mask) - torch.logical_and(active_mask, blank_mask, out=advance_mask) - - while advance_mask.any(): - torch.where(advance_mask, time_indices, time_indices_current, out=time_indices_current) - logits = self._predict_joint(encoder_output[batch_indices, safe_time_indices], decoder_output) - more_scores, more_labels = logits[:, : self.vocab_size].max(dim=-1) - labels = torch.where(advance_mask, more_labels, labels) - scores = torch.where(advance_mask, more_scores, scores) - duration_indices = logits[:, self.vocab_size : self.vocab_size + len(self.durations)].argmax(dim=-1) - durations = self.durations[duration_indices] - blank_mask = labels == self.blank_id - durations.masked_fill_(torch.logical_and(durations == 0, blank_mask), 1) - torch.where(advance_mask, time_indices + durations, time_indices, out=time_indices) - torch.minimum(time_indices, last_timesteps, out=safe_time_indices) - torch.less(time_indices, encoder_lengths, out=active_mask) - torch.logical_and(active_mask, blank_mask, out=advance_mask) - - torch.ne(active_mask, active_mask_prev, out=became_inactive) - hyps.add_results(active_mask, labels, time_indices_current, scores) - - if self.max_symbols is not None: - force_blank = torch.logical_and( - active_mask, - torch.logical_and( - torch.logical_and(labels != self.blank_id, hyps.last_timestamp_lasts >= self.max_symbols), - hyps.last_timestamp == time_indices, - ), - ) - time_indices += force_blank - torch.minimum(time_indices, last_timesteps, out=safe_time_indices) - torch.less(time_indices, encoder_lengths, out=active_mask) - - results: List[List[int]] = [] - for hyp in hyps.transcript: - tokens = [int(token) for token in hyp.tolist() if 0 < token < self.vocab_size] - results.append(tokens) - return results - - -class StreamingTranscriber: - def __init__( - self, - bundle: CoreMLModelBundle, - *, - blank_id: Optional[int] = None, - num_layers: int = 2, - hidden_size: int = 640, - durations: Sequence[int] = (0, 1, 2, 3, 4), - ) -> None: - self.preprocessor = ct.models.MLModel(str(bundle.preprocessor), compute_units=ct.ComputeUnit.CPU_ONLY) - self.encoder = ct.models.MLModel(str(bundle.encoder), compute_units=ct.ComputeUnit.CPU_ONLY) - self.decoder = ct.models.MLModel(str(bundle.decoder), compute_units=ct.ComputeUnit.CPU_ONLY) - self.joint = ct.models.MLModel(str(bundle.joint), compute_units=ct.ComputeUnit.CPU_ONLY) - self.tokenizer = self._load_tokenizer(bundle.tokenizer) - - vocab_size = max(self.tokenizer.keys()) + 1 - if blank_id is None: - blank_id = vocab_size - 1 - self.decoder_helper = CoreMLStreamingDecoder( - self.decoder, - self.joint, - vocab_size=vocab_size, - blank_id=blank_id, - num_layers=num_layers, - hidden_size=hidden_size, - durations=durations, - ) - self.blank_id = blank_id - - @staticmethod - def _load_tokenizer(tokenizer_path: Optional[Path]) -> dict[int, str]: - if tokenizer_path is None: - raise ValueError("Tokenizer JSON is required") - with Path(tokenizer_path).open() as f: - data = json.load(f) - return {int(k): v for k, v in data.items()} - - def _tokens_to_text(self, tokens: Iterable[int]) -> str: - pieces: List[str] = [] - for token in tokens: - piece = self.tokenizer.get(token) - if piece is None: - continue - if piece.startswith("▁"): - if pieces: - pieces.append(" ") - pieces.append(piece[1:]) - else: - pieces.append(piece) - return "".join(pieces).strip() - - def _preprocess(self, audio: np.ndarray) -> tuple[np.ndarray, int]: - audio_2d = audio.reshape(1, -1).astype(np.float32) - length = np.array([audio_2d.shape[-1]], dtype=np.int32) - outputs = self.preprocessor.predict({ - "audio_signal": audio_2d, - "audio_length": length, - }) - return outputs["melspectrogram"], int(outputs["melspectrogram_length"][0]) - - def _encode(self, mel: np.ndarray, mel_length: int) -> tuple[torch.Tensor, torch.Tensor]: - outputs = self.encoder.predict({ - "melspectrogram": mel.astype(np.float32), - "melspectrogram_length": np.array([mel_length], dtype=np.int32), - }) - encoder_output = outputs["encoder_output"] - if encoder_output.ndim == 3: - encoder_output = np.transpose(encoder_output, (0, 2, 1)) - length = torch.tensor(outputs["encoder_output_length"], dtype=torch.long) - return torch.from_numpy(encoder_output.astype(np.float32)), length - - def transcribe(self, audio_path: Path) -> str: - audio, _ = librosa.load(str(audio_path), sr=16000) - mel, mel_length = self._preprocess(audio) - encoder_output, encoder_length = self._encode(mel, mel_length) - token_ids = self.decoder_helper.decode(encoder_output, encoder_length)[0] - return self._tokens_to_text(token_ids) - - def transcribe_many(self, audio_paths: Sequence[Path]) -> List[str]: - results: List[str] = [] - for path in audio_paths: - LOGGER.info("Transcribing %s", path) - results.append(self.transcribe(path)) - return results - - -def _resolve_bundle(args: argparse.Namespace) -> CoreMLModelBundle: - base = Path(args.model_dir) if args.model_dir else None - if base is None and not all([args.preprocessor, args.encoder, args.decoder, args.joint, args.tokenizer]): - raise ValueError("Either --model-dir or explicit model paths are required") - return CoreMLModelBundle( - preprocessor=Path(args.preprocessor) if args.preprocessor else base / "Melspectrogram.mlpackage", - encoder=Path(args.encoder) if args.encoder else base / "ParakeetEncoder.mlpackage", - decoder=Path(args.decoder) if args.decoder else base / "ParakeetDecoder.mlpackage", - joint=Path(args.joint) if args.joint else base / "RNNTJoint.mlpackage", - tokenizer=Path(args.tokenizer) if args.tokenizer else base / "tokenizer.json", - ) - - -def _build_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser(description="Streaming RNNT inference with CoreML components") - parser.add_argument("--model-dir", type=Path, help="Directory containing exported CoreML models") - parser.add_argument("--preprocessor", type=Path, help="Path to the preprocessor .mlpackage") - parser.add_argument("--encoder", type=Path, help="Path to the encoder .mlpackage") - parser.add_argument("--decoder", type=Path, help="Path to the decoder .mlpackage") - parser.add_argument("--joint", type=Path, help="Path to the joint .mlpackage") - parser.add_argument("--tokenizer", type=Path, help="Path to tokenizer JSON") - parser.add_argument("audio", nargs="+", help="Audio files to transcribe") - parser.add_argument("--blank-id", type=int, help="Blank token id") - parser.add_argument("--num-layers", type=int, default=2, help="Prediction network layer count") - parser.add_argument("--hidden-size", type=int, default=640, help="Prediction network hidden size") - parser.add_argument("--durations", type=int, nargs="+", default=[0, 1, 2, 3, 4], help="RNNT duration bucket values") - parser.add_argument("--verbose", "-v", action="count", default=0, help="Increase log verbosity") - return parser - - -def _configure_logging(verbosity: int) -> None: - level = logging.WARNING - (10 * verbosity) - logging.basicConfig(level=max(logging.DEBUG, level), format="[%(levelname)s] %(message)s") - - -def main(argv: Optional[Sequence[str]] = None) -> None: - parser = _build_parser() - args = parser.parse_args(argv) - _configure_logging(args.verbose) - - try: - bundle = _resolve_bundle(args) - transcriber = StreamingTranscriber( - bundle, - blank_id=args.blank_id, - num_layers=args.num_layers, - hidden_size=args.hidden_size, - durations=tuple(args.durations), - ) - transcripts = transcriber.transcribe_many([Path(p) for p in args.audio]) - for path, text in zip(args.audio, transcripts): - print(f"{path}: {text}") - except ValueError as exc: - parser.error(str(exc)) - - -if __name__ == "__main__": # pragma: no cover - main() diff --git a/parakeet-tdt-v2-0.6b/coreml/uv.lock b/parakeet-tdt-v2-0.6b/coreml/uv.lock deleted file mode 100644 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+0,0 @@ -[project] -name = "parakeet-tdt-ctc-110m-coreml" -version = "1.0.0" -description = "NVIDIA Parakeet-TDT-CTC-110M converted to CoreML format for Apple Silicon" -readme = "README.md" -requires-python = ">=3.10" -license = { text = "Apache-2.0" } -authors = [ - { name = "FluidInference" } -] -keywords = ["asr", "speech-recognition", "coreml", "apple-silicon", "nvidia", "parakeet"] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "License :: OSI Approved :: Apache Software License", - "Operating System :: MacOS", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Multimedia :: Sound/Audio :: Speech", -] - -dependencies = [ - "coremltools>=7.0", - "numpy>=1.24.0", - "scipy>=1.10.0", -] - -[project.optional-dependencies] -convert = [ - "torch>=2.0.0", - "nemo-toolkit[asr]>=1.20.0", -] -audio = [ - "librosa>=0.10.0", -] -dev = [ - "pytest>=7.0.0", - "ruff>=0.1.0", -] - -[project.urls] -Homepage = "https://huggingface.co/FluidInference/parakeet-tdt-ctc-110m-coreml" -Repository = "https://huggingface.co/FluidInference/parakeet-tdt-ctc-110m-coreml" -Issues = "https://github.com/FluidInference/fluidaudio/issues" - -[project.scripts] -parakeet-inference = "scripts.inference:main" - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build.targets.wheel] -packages = ["scripts"] - -[tool.ruff] -line-length = 100 -target-version = "py310" - -[tool.ruff.lint] -select = ["E", "F", "I", "N", "W"] -ignore = ["E501"] diff --git a/scripts/run_benchmarks.py b/scripts/run_benchmarks.py deleted file mode 100644 index d8e7f9c40658f6325c539d2ee11f8a25d3e9d220..0000000000000000000000000000000000000000 --- a/scripts/run_benchmarks.py +++ /dev/null @@ -1,273 +0,0 @@ -#!/usr/bin/env python3 -""" -FluidAudio Benchmark Suite - -Runs ASR, VAD, and Diarization benchmarks and saves results to JSON. -Compare results against Documentation/Benchmarks.md baselines. - -Usage: - python run_benchmarks.py # Run all benchmarks - python run_benchmarks.py --quick # Quick smoke test - python run_benchmarks.py --asr-only # ASR benchmark only - python run_benchmarks.py --vad-only # VAD benchmark only - python run_benchmarks.py --diar-only # Diarization only -""" - -import argparse -import json -import subprocess -import sys -from datetime import datetime -from pathlib import Path - - -# Baseline values from Documentation/Benchmarks.md -BASELINES = { - "asr": { - "wer_percent": 5.8, - "rtfx_min": 200, # M4 Pro: ~210x - "description": "LibriSpeech test-clean, Parakeet TDT 0.6B" - }, - "vad": { - "f1_percent": 85.0, - "rtfx_min": 500, - "description": "VOiCES dataset, Silero VAD" - }, - "diarization": { - "der_percent": 17.7, - "rtfx_min": 1.0, - "description": "AMI SDM, pyannote-based" - } -} - - -def run_command(cmd: list[str], output_file: Path | None = None) -> tuple[int, str]: - """Run a command and optionally save output.""" - print(f"Running: {' '.join(cmd)}") - - result = subprocess.run( - cmd, - capture_output=True, - text=True - ) - - output = result.stdout + result.stderr - - if output_file: - output_file.write_text(output) - - return result.returncode, output - - -def build_release() -> bool: - """Build the project in release mode.""" - print("\n" + "=" * 60) - print("Building release...") - print("=" * 60) - - returncode, _ = run_command(["swift", "build", "-c", "release"]) - - if returncode != 0: - print("ERROR: Build failed!") - return False - - print("Build successful.") - return True - - -def run_asr_benchmark(output_dir: Path, quick: bool = False) -> dict | None: - """Run ASR benchmark on LibriSpeech test-clean.""" - print("\n" + "=" * 60) - print("ASR Benchmark (LibriSpeech test-clean)") - print("=" * 60) - - max_files = "100" if quick else "all" - output_json = output_dir / f"asr_results.json" - - cmd = [ - "swift", "run", "-c", "release", "fluidaudio", "asr-benchmark", - "--subset", "test-clean", - "--max-files", max_files, - "--output", str(output_json) - ] - - returncode, output = run_command(cmd, output_dir / "asr_log.txt") - - if returncode != 0: - print(f"ERROR: ASR benchmark failed!") - return None - - if output_json.exists(): - return json.loads(output_json.read_text()) - - return None - - -def run_vad_benchmark(output_dir: Path, quick: bool = False) -> dict | None: - """Run VAD benchmark.""" - print("\n" + "=" * 60) - print("VAD Benchmark") - print("=" * 60) - - dataset = "mini50" if quick else "voices-subset" - output_json = output_dir / f"vad_results.json" - - cmd = [ - "swift", "run", "-c", "release", "fluidaudio", "vad-benchmark", - "--dataset", dataset, - "--all-files", - "--threshold", "0.5", - "--output", str(output_json) - ] - - returncode, output = run_command(cmd, output_dir / "vad_log.txt") - - if returncode != 0: - print(f"ERROR: VAD benchmark failed!") - return None - - if output_json.exists(): - return json.loads(output_json.read_text()) - - return None - - -def run_diarization_benchmark(output_dir: Path, quick: bool = False) -> dict | None: - """Run diarization benchmark on AMI SDM.""" - print("\n" + "=" * 60) - print("Diarization Benchmark (AMI SDM)") - print("=" * 60) - - output_json = output_dir / f"diarization_results.json" - - cmd = [ - "swift", "run", "-c", "release", "fluidaudio", "diarization-benchmark", - "--auto-download", - "--output", str(output_json) - ] - - if quick: - cmd.extend(["--single-file", "ES2004a"]) - - returncode, output = run_command(cmd, output_dir / "diarization_log.txt") - - if returncode != 0: - print(f"ERROR: Diarization benchmark failed!") - return None - - if output_json.exists(): - return json.loads(output_json.read_text()) - - return None - - -def compare_results(results: dict) -> None: - """Compare results against baselines.""" - print("\n" + "=" * 60) - print("Results vs Baselines (Documentation/Benchmarks.md)") - print("=" * 60) - - if "asr" in results and results["asr"]: - asr = results["asr"] - baseline = BASELINES["asr"] - wer = asr.get("wer", asr.get("average_wer", 0)) * 100 - rtfx = asr.get("rtfx", asr.get("median_rtfx", 0)) - - wer_status = "✓" if wer <= baseline["wer_percent"] * 1.1 else "✗" - rtfx_status = "✓" if rtfx >= baseline["rtfx_min"] * 0.8 else "✗" - - print(f"\nASR ({baseline['description']}):") - print(f" WER: {wer:.1f}% (baseline: {baseline['wer_percent']}%) {wer_status}") - print(f" RTFx: {rtfx:.1f}x (baseline: {baseline['rtfx_min']}x+) {rtfx_status}") - - if "vad" in results and results["vad"]: - vad = results["vad"] - baseline = BASELINES["vad"] - f1 = vad.get("f1_score", 0) - rtfx = vad.get("rtfx", 0) - - f1_status = "✓" if f1 >= baseline["f1_percent"] * 0.9 else "✗" - rtfx_status = "✓" if rtfx >= baseline["rtfx_min"] * 0.5 else "✗" - - print(f"\nVAD ({baseline['description']}):") - print(f" F1: {f1:.1f}% (baseline: {baseline['f1_percent']}%+) {f1_status}") - print(f" RTFx: {rtfx:.1f}x (baseline: {baseline['rtfx_min']}x+) {rtfx_status}") - - if "diarization" in results and results["diarization"]: - diar = results["diarization"] - baseline = BASELINES["diarization"] - der = diar.get("der", diar.get("average_der", 0)) * 100 - rtfx = diar.get("rtfx", diar.get("average_rtfx", 0)) - - der_status = "✓" if der <= baseline["der_percent"] * 1.2 else "✗" - rtfx_status = "✓" if rtfx >= baseline["rtfx_min"] else "✗" - - print(f"\nDiarization ({baseline['description']}):") - print(f" DER: {der:.1f}% (baseline: {baseline['der_percent']}%) {der_status}") - print(f" RTFx: {rtfx:.1f}x (baseline: {baseline['rtfx_min']}x+) {rtfx_status}") - - -def main(): - parser = argparse.ArgumentParser(description="FluidAudio Benchmark Suite") - parser.add_argument("--quick", action="store_true", help="Quick smoke test with smaller datasets") - parser.add_argument("--asr-only", action="store_true", help="Run ASR benchmark only") - parser.add_argument("--vad-only", action="store_true", help="Run VAD benchmark only") - parser.add_argument("--diar-only", action="store_true", help="Run diarization benchmark only") - parser.add_argument("--output-dir", type=str, help="Output directory for results") - args = parser.parse_args() - - # Determine which benchmarks to run - run_all = not (args.asr_only or args.vad_only or args.diar_only) - - # Setup output directory - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - if args.output_dir: - output_dir = Path(args.output_dir) - else: - output_dir = Path("benchmark-results") / timestamp - - output_dir.mkdir(parents=True, exist_ok=True) - - print("=" * 60) - print("FluidAudio Benchmark Suite") - print("=" * 60) - print(f"Mode: {'Quick' if args.quick else 'Full'}") - print(f"Output: {output_dir}") - print(f"Time: {timestamp}") - - # Build first - if not build_release(): - sys.exit(1) - - results = {} - - # Run benchmarks - if run_all or args.asr_only: - results["asr"] = run_asr_benchmark(output_dir, args.quick) - - if run_all or args.vad_only: - results["vad"] = run_vad_benchmark(output_dir, args.quick) - - if run_all or args.diar_only: - results["diarization"] = run_diarization_benchmark(output_dir, args.quick) - - # Save combined results - combined_output = output_dir / "benchmark_results.json" - combined_output.write_text(json.dumps({ - "timestamp": timestamp, - "mode": "quick" if args.quick else "full", - "baselines": BASELINES, - "results": results - }, indent=2)) - - # Compare against baselines - compare_results(results) - - print("\n" + "=" * 60) - print("Benchmark complete!") - print("=" * 60) - print(f"Results saved to: {combined_output}") - - -if __name__ == "__main__": - main() diff --git a/special_tokens_map.json b/special_tokens_map.json deleted file mode 100644 index 815fbdc56abc60a0f29e51ff2373c998d7779805..0000000000000000000000000000000000000000 --- a/special_tokens_map.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "pad_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "unk_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - } -} diff --git a/tokenizer.json b/tokenizer.json deleted file mode 100644 index 21e87f185d71820f5089456aa9c79c97c0a0ce8b..0000000000000000000000000000000000000000 --- a/tokenizer.json +++ /dev/null @@ -1,2456 +0,0 @@ -{ - "version": "1.0", - "truncation": null, - "padding": null, - "added_tokens": [ - { - "id": 0, - "content": "", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 1024, - "content": "", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - } - ], - "normalizer": { - "type": "Sequence", - "normalizers": [ - { - "type": "Precompiled", - "precompiled_charsmap": 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- "in e", - "i ne", - "▁her e", - "▁he re", - "▁h ere", - "▁w ould", - "ca use", - "ri ght", - "r ight", - "s o", - "os t", - "o st", - "ur e", - "u re", - "▁ha s", - "▁h as", - "e ct", - "▁th ink", - "▁f e", - "on g", - "o ng", - "▁se e", - "▁s ee", - "▁wh en", - "▁wh o", - "▁we re", - "▁w ere", - "▁real ly", - "▁re ally", - "▁the ir", - "▁w ant", - "on e", - "o ne", - "op le", - "o ple", - "▁the n", - "▁th en", - "▁tim e", - "▁s a", - "a p", - "▁t e", - "▁ te", - "▁H e", - "▁y e", - "c k", - "▁he r", - "▁h er", - "▁ her", - "▁th ing", - "▁t hing", - "▁ thing", - "▁r ight", - "▁ right", - "▁wh ich", - "it t", - "ic e", - "i ce", - "ac t", - "a ct", - "▁pe ople", - "t y", - "▁tw o", - "▁ J", - "▁ im", - "th er", - "t her", - "c i", - "os e", - "o se", - "▁c l", - "▁ qu", - "▁m an", - "▁al so", - "re e", - "r ee", - "▁e n", - "▁ en", - "u d", - "▁h ow", - "re at", - "a k", - "h ing", - "a g", - "▁an y", - "▁ any", - "f f", - "ac e", - "a ce", - "pe r", - "p er", - "▁be cause", - "▁ very", - "ow n", - "o wn", - "▁a d", - "▁ ad", - "▁ac t", - "▁a ct", - "▁ act", - "▁be en", - "▁b een", - "▁no w", - "▁n ow", - "▁a g", - "▁ ag", - "▁int o", - "▁com p", - "ar s", - "ion s", - "i ons", - "ar e", - "a re", - "it e", - "i te", - "i v", - "▁the se", - "▁th ese", - "ay s", - "a ys", - "e p", - "▁Th is", - "▁sh e", - "▁s he", - "an s", - "a h", - "ee n", - "e en", - "▁o ver", - "r y", - "▁l o", - "ag e", - "a ge", - "▁p r", - "▁s p", - "u e", - "▁c o", - "▁ co", - "ic k", - "i ck", - "be r", - "b er", - "▁d id", - "i p", - "ac h", - "a ch", - "▁b ack", - "▁n o", - "▁con t", - "▁co nt", - "▁c ont", - "▁o ther", - "▁ other", - "▁ever y", - "▁e very", - "p t", - "▁ne ed", - "▁h im", - "▁ U", - "▁I n", - "▁wor k", - "ir st", - "▁p art", - "▁loo k", - "▁l ook", - "itt le", - "b le", - "i z", - "▁u n", - "▁ un", - "▁m ake", - "ome t", - "om et", - "nd er", - "n der", - "is h", - "n a", - "▁l ittle", - "▁of f", - "▁o ff", - "▁th an", - "▁go t", - "▁g ot", - "ual ly", - "u ally", - "▁pe r", - "▁p er", - "▁ per", - "▁go od", - "▁g ood", - "▁w ay", - "▁cou ld", - "▁c ould", - "▁a c", - "▁ ac", - "▁im p", - "ab le", - "a ble", - "▁wh ere", - "if f", - "i ff", - "▁Th at", - "▁re s", - "▁r es", - "▁ res", - "ou nt", - "p l", - "an ce", - "a nce", - "▁f irst", - "▁r o", - "▁ ro", - "▁pr e", - "▁p re", - "as s", - "▁sa y", - "▁s ay", - "in t", - "i nt", - "ate d", - "at ed", - "ir e", - "i re", - "uc h", - "u ch", - "as e", - "a se", - "▁some t", - "▁s omet", - "ou nd", - "o und", - "▁do wn", - "▁d own", - "▁d iff", - "se l", - "s el", - "▁g u", - "▁a m", - "▁ am", - "res s", - "r ess", - "▁lo t", - "▁l ot", - "en ce", - "e nce", - "▁d is", - "or m", - "i x", - "▁p o", - "v ing", - "ent y", - "en ty", - "▁ K", - "▁sp e", - "▁s pe", - "un d", - "u nd", - "h e", - "▁m uch", - "▁a r", - "▁ ar", - "rou nd", - "ro und", - "r ound", - "▁a pp", - "c o", - "ar k", - "▁ne w", - "▁n ew", - "ate r", - "at er", - "a ter", - "ul t", - "en d", - "e nd", - "▁ev en", - "▁e ven", - "▁st art", - "ation s", - "at ions", - "rou gh", - "r ough", - "il e", - "i le", - "ft er", - "f ter", - "▁we ll", - "▁w ell", - "b e", - "▁The y", - "▁th ree", - "ig n", - "il d", - "i ld", - "▁sa id", - "ou gh", - "an g", - "a ng", - "▁to o", - "▁t oo", - "ad e", - "▁b l", - "en s", - "▁in c", - "i a", - "▁th ose", - "▁m o", - "▁t ake", - "▁th rough", - "▁f l", - "▁k ind", - "▁thing s", - "▁th ings", - "▁be t", - "▁b et", - "▁on ly", - "▁S t", - "▁le t", - "▁l et", - "ces s", - "c ess", - "▁C h", - "ar y", - "a ry", - "ve l", - "v el", - "▁I f", - "x t", - "oth er", - "ot her", - "o ther", - "a v", - "ic al", - "or d", - "▁ag ain", - "▁somet hing", - "▁some thing", - "on na", - "f ore", - "▁m ay", - "t ing", - "▁b u", - "▁diff ere", - "ur n", - "▁g onna", - "▁do es", - "uc t", - "u ct", - "o g", - "▁tw enty", - "▁g r", - "▁ gr", - "▁Y e", - "w n", - "▁sh ould", - "▁com m", - "▁c omm", - "it ion", - "▁un der", - "▁u nder", - "▁he l", - "▁h el", - "or y", - "o ry", - "▁f o", - "▁us e", - "▁u se", - "▁ use", - "ig h", - "i gh", - "if e", - "▁act ually", - "▁t al", - "▁ca ll", - "▁c all", - "ent s", - "en ts", - "i ous", - "ul l", - "u ll", - "▁The re", - "▁Th ere", - "▁Ye ah", - "▁mo st", - "▁m ost", - "▁k e", - "▁ ke", - "or s", - "ve d", - "v ed", - "y s", - "▁s c", - "▁ha pp", - "op e", - "o pe", - "▁hel p", - "at ch", - "▁Wh at", - "▁re m", - "▁r em", - "pl e", - "p le", - "▁No w", - "▁N ow", - "▁b r", - "oo l", - "o ol", - "ot h", - "o th", - "▁fo ur", - "▁f our", - "sel f", - "▁st r", - "n e", - "th ing", - "t hing", - "▁p ut", - "ia l", - "i al", - "▁g reat", - "a il", - "u b", - "n ing", - "▁s m", - "▁fe el", - "▁f ive", - "od y", - "und red", - "is s", - "an k", - "ge t", - "g et", - "ak ing", - "a king", - "▁man y", - "▁m any", - "▁h undred", - "▁year s", - "▁ye ars", - "▁be ing", - "▁com e", - "▁c ome", - "▁me an", - "il y", - "i ly", - "▁differe nt", - "▁a fter", - "▁se r", - "▁s er", - "▁sh ow", - "f orm", - "f ul", - "o y", - "▁s ix", - "▁v ide", - "▁ V", - "▁it s", - "▁ its", - "▁po int", - "▁d ay", - "▁ day", - "▁de s", - "▁d es", - "on s", - "▁b it", - "▁be l", - "▁b el", - "▁be fore", - "▁a w", - "▁en d", - "▁e nd", - "▁ end", - "▁O h", - "▁st ill", - "at h", - "a th", - "▁lo ng", - "▁l ong", - "▁ '", - "is e", - "i se", - "o b", - "d ay", - "▁ad d", - "f t", - "ve s", - "v es", - "ce s", - "c es", - "ad y", - "▁c r", - "▁ar ound", - "▁a round", - "▁tr y", - "▁t ry", - "le s", - "l es", - "ver s", - "v ers", - "k ay", - "ia n", - "i an", - "ate s", - "at es", - "▁fin d", - "▁f ind", - "w ard", - "▁A s", - "▁e ight", - "li c", - "l ic", - "▁s ame", - "▁po s", - "▁p os", - "▁e m", - "▁ em", - "▁m ade", - "▁su pp", - "▁l ife", - "▁B e", - "pe ct", - "p ect", - "▁de c", - "▁pl ay", - "ang e", - "an ge", - "▁at t", - "▁per s", - "▁p ers", - "w ays", - "▁h igh", - "▁ha nd", - "▁h and", - "▁ne xt", - "▁con s", - "▁c ons", - "▁o wn", - "▁ own", - "▁in v", - "ow er", - "▁in d", - "▁ ind", - "er t", - "n g", - "av e", - "a ve", - "▁ye ar", - "▁b ig", - "at ing", - "a ting", - "▁wor ld", - "▁re l", - "▁r el", - "▁sur e", - "▁su re", - "▁s ure", - "▁tr a", - "▁t ra", - "e w", - "ere d", - "er ed", - "e red", - "▁f in", - "▁We ll", - "▁W ell", - "▁s l", - "▁do ing", - "b s", - "▁se t", - "▁s et", - "▁re c", - "u al", - "ci al", - "c ial", - "▁p h", - "▁ ph", - "er m", - "▁lo ve", - "p h", - "▁re al", - "▁la st", - "▁l ast", - "ic t", - "i ct", - "▁b o", - "▁r a", - "▁ ra", - "ib le", - "i ble", - "▁w r", - "m er", - "▁cou nt", - "▁c ount", - "it ies", - "▁al ways", - "ine t", - "in et", - "ment s", - "m ents", - "u c", - "▁m ight", - "▁int er", - "▁in ter", - "▁vide o", - "g in", - "▁te ll", - "▁t ell", - "▁ne ver", - "▁n ever", - "ven t", - "ve nt", - "v ent", - "▁imp ort", - "ie d", - "i ed", - "▁s y", - "▁H ow", - "ical ly", - "ic ally", - "ough t", - "ou ght", - "▁th ir", - "▁re p", - "▁r ep", - "k s", - "i b", - "▁f am", - "j ect", - "▁b as", - "▁S he", - "▁g ive", - "ake s", - "ak es", - "▁n inet", - "▁re g", - "▁m in", - "▁o p", - "▁ op", - "▁de f", - "▁did n", - "t e", - "▁cou r", - "▁co ur", - "▁c our", - "▁wh y", - "▁en t", - "▁e nt", - "▁ ent", - "▁pl ace", - "▁in s", - "▁ca r", - "▁c ar", - "ath er", - "at her", - "a ther", - "▁pers on", - "ul ar", - "▁ins t", - "▁in st", - "▁pro d", - "▁pr od", - "le ct", - "l ect", - "▁A l", - "▁to day", - "▁be c", - "▁su r", - "▁s ur", - "▁Al l", - "▁A ll", - "▁an other", - "▁bu s", - "▁b us", - "▁ke ep", - "el l", - "e ll", - "es e", - "e se", - "ri end", - "▁qu est", - "▁tal k", - "al s", - "ing s", - "▁mo n", - "▁m on", - "co nd", - "c ond", - "ol d", - "o ld", - "▁ac c", - "▁l a", - "▁n um", - "ide nt", - "id ent", - "▁ch e", - "▁c he", - "in ess", - "i ness", - "▁t urn", - "▁e ar", - "▁N o", - "ous and", - "▁bet ter", - "if ic", - "▁lo o", - "▁l oo", - "▁g l", - "o c", - "▁import ant", - "ite d", - "it ed", - "▁A n", - "▁th ousand", - "il ity", - "ll ow", - "▁use d", - "▁us ed", - "▁g en", - "▁s im", - "l i", - "▁happ en", - "▁U n", - "▁L et", - "a ir", - "oc k", - "o ck", - "ab ly", - "g g", - "▁w atch", - "▁F or", - "▁s w", - "re n", - "r en", - "ut e", - "u te", - "e ver", - "▁po l", - "▁p ol", - "▁sc h", - "▁s ch", - "▁Wh en", - "▁su ch", - "▁s uch", - "▁f if", - "▁h ome", - "▁cl e", - "▁c le", - "▁cont in", - "ous e", - "ou se", - "o use", - "▁f riend", - "ur ing", - "▁O kay", - "g r", - "▁ab le", - "▁a ble", - "▁ able", - "▁st ud", - "▁e ff", - "h ip", - "b ody", - "▁to p", - "▁t op", - "n ess", - "▁exp er", - "▁ex per", - "▁pre t", - "▁pr et", - "▁bo th", - "▁b oth", - "▁don e", - "▁do ne", - "▁d one", - "c ri", - "▁m ark", - "▁wh ile", - "▁o ld", - "▁ old", - "ro s", - "r os", - "on t", - "o nt", - "▁se cond", - "at ive", - "▁th ought", - "▁be st", - "▁b est", - "▁fo und", - "▁f ound", - "ie w", - "i ew", - "▁bel ie", - "▁e ach", - "ere st", - "er est", - "▁tr i", - "▁t ri", - "▁e as", - "▁c a", - "▁ ca", - "▁f act", - "▁car e", - "▁ca re", - "▁c are", - "▁f un", - "at ter", - "ure s", - "ur es", - "u res", - "▁he ad", - "▁le ar", - "▁w ater", - "▁h ard", - "▁fe w", - "▁f ew", - "▁s ide", - "w een", - "▁ex p", - "▁aw ay", - "it s", - "i ts", - "▁ex t", - "▁e xt", - "l ud", - "▁r un", - "▁tr ans", - "in ce", - "i nce", - "▁s k", - "▁op en", - "c us", - "▁bet ween", - "▁call ed", - "▁we e", - "▁w ee", - "▁pret ty", - "as on", - "▁f ar", - "em ber", - "om m", - "▁inter est", - "▁int erest", - "an y", - "ne r", - "n er", - "u ff", - "▁pre s", - "▁pr es", - "▁p res", - "▁c ur", - "▁ch ild", - "e e", - "▁to get", - "▁toget her", - "ol og", - "▁G od", - "on d", - "o nd", - "▁ch ar", - "▁look ing", - "▁loo king", - "st em", - "a z", - "ce nt", - "c ent", - "▁o b", - "▁ ob", - "▁as s", - "▁ ass", - "l and", - "▁does n", - "▁bus iness", - "▁cour se", - "▁te n", - "▁t en", - "▁ ten", - "p s", - "ar ch", - "ce d", - "c ed", - "m s", - "iz e", - "n ce", - "▁re f", - "▁n ame", - "ros s", - "▁gr ow", - "one y", - "▁we nt", - "▁w ent", - "ic s", - "te en", - "t een", - "▁co u", - "▁c ou", - "▁pro b", - "▁pr ob", - "▁re t", - "▁r et", - "▁gu ys", - "▁c ame", - "as h", - "le d", - "l ed", - "▁E ur", - "ue s", - "u es", - "▁ ide", - "g an", - "▁every thing", - "▁get ting", - "▁as k", - "▁co r", - "▁c or", - "▁bu ild", - "▁s ign", - "▁sm all", - "uc k", - "u ck", - "▁e l", - "▁ el", - "▁co l", - "▁c ol", - "▁I s", - "ation al", - "st and", - "c y", - "▁con f", - "d er", - "▁br e", - "▁b re", - "▁ca p", - "▁c ap", - "▁mo d", - "▁m od", - "et s", - "e ts", - "i ke", - "▁num ber", - "▁comp le", - "▁com ple", - "ert ain", - "▁ev er", - "▁e ver", - "▁ ever", - "▁col l", - "▁co ll", - "▁h um", - "▁Eur ope", - "▁cr e", - "▁c re", - "▁me t", - "▁m et", - "▁ex am", - "▁mo ve", - "▁p ass", - "▁le ft", - "▁sy stem", - "▁inc lud", - "▁Th ank", - "ce pt", - "▁w om", - "▁prod uct", - "te n", - "t en", - "▁res t", - "▁re st", - "▁r est", - "▁prob ably", - "▁dr i", - "▁d ri", - "▁D o", - "▁gen er", - "▁any thing", - "▁la r", - "▁l ar", - "▁M y", - "▁sch ool", - "▁le ad", - "▁su b", - "▁s ub", - "▁t y", - "▁ ty", - "▁pl an", 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