from __future__ import annotations import argparse import hashlib import json import re from collections import defaultdict from pathlib import Path import numpy as np import onnx import onnxruntime as ort from onnx import TensorProto, helper, numpy_helper ROOT = Path(__file__).resolve().parent BASELINE = ROOT / ".work/models/shared/decoder_unified_gather_qdq_int8.onnx" DEFAULT_SOURCE = ROOT / ".work/models/model-opt/decoder_gather_before_dq_int8.onnx" DEFAULT_DESTINATION = ROOT / ".work/models/model-opt/decoder_static_fp16_matmul.onnx" DEFAULT_REPORT = ROOT / ".work/reports/model-fp16-matmul-optimization.json" VISION_TOKENS = 256 KEEP_QDQ_MATMUL_NAMES = {"/lm_head/MatMul"} def sha256(path: Path) -> str: digest = hashlib.sha256() with path.open("rb") as source: for chunk in iter(lambda: source.read(1024 * 1024), b""): digest.update(chunk) return digest.hexdigest() def dequantize_to_fp16( quantized: onnx.TensorProto, scale: onnx.TensorProto, zero_point: onnx.TensorProto, axis: int, ) -> np.ndarray: values = numpy_helper.to_array(quantized).astype(np.float32) scales = numpy_helper.to_array(scale).astype(np.float32) zeros = numpy_helper.to_array(zero_point).astype(np.float32) broadcast_shape = [1] * values.ndim broadcast_shape[axis] = scales.size return ((values - zeros.reshape(broadcast_shape)) * scales.reshape(broadcast_shape)).astype(np.float16) def rewrite_matmul_weights(model: onnx.ModelProto, selected_layers: set[int]) -> dict: graph = model.graph initializers = {value.name: value for value in graph.initializer} consumers: dict[str, list[onnx.NodeProto]] = defaultdict(list) for node in graph.node: for name in node.input: consumers[name].append(node) dq_by_matmul_name: dict[str, onnx.NodeProto] = {} fp16_initializers: list[onnx.TensorProto] = [] removable_initializers: set[str] = set() quantized_elements = 0 for node in graph.node: if node.op_type != "DequantizeLinear" or len(node.input) < 3: continue quantized = initializers.get(node.input[0]) scale = initializers.get(node.input[1]) zero_point = initializers.get(node.input[2]) if quantized is None or scale is None or zero_point is None: continue matches = [consumer for consumer in consumers[node.output[0]] if consumer.op_type == "MatMul"] if not matches: continue if len(matches) != 1: raise RuntimeError(f"Expected one MatMul consumer for {node.name}, found {len(matches)}") matmul = matches[0] if matmul.name in KEEP_QDQ_MATMUL_NAMES: continue layer_match = re.search(r"/decoder/layers\.(\d+)/", matmul.name) if layer_match is None: raise RuntimeError(f"Unexpected non-layer MatMul {matmul.name}") if int(layer_match.group(1)) not in selected_layers: continue if matmul.input[1] != node.output[0]: raise RuntimeError(f"Quantized weight is not RHS input for {matmul.name}") axis = next((attribute.i for attribute in node.attribute if attribute.name == "axis"), 1) fp16_name = f"{quantized.name}_static_fp16" fp16 = dequantize_to_fp16(quantized, scale, zero_point, axis) fp16_initializers.append(numpy_helper.from_array(fp16, fp16_name)) matmul.input[1] = fp16_name dq_by_matmul_name[matmul.name] = node removable_initializers.update(node.input[:3]) quantized_elements += fp16.size expected_matmuls = 7 * len(selected_layers) if len(dq_by_matmul_name) != expected_matmuls: raise RuntimeError(f"Expected {expected_matmuls} selected MatMuls, found {len(dq_by_matmul_name)}") rewritten: list[onnx.NodeProto] = [] removed_dq_names = {node.name for node in dq_by_matmul_name.values()} for node in graph.node: if node.name in removed_dq_names: continue if node.name not in dq_by_matmul_name: rewritten.append(node) continue input_fp16 = f"{node.input[0]}__for_{node.name.replace('/', '_')}_fp16" output_float = node.output[0] output_fp16 = f"{output_float}__fp16" rewritten.append( helper.make_node( "Cast", [node.input[0]], [input_fp16], name=f"{node.name}/CastInputToFp16", to=TensorProto.FLOAT16, ) ) node.input[0] = input_fp16 node.output[0] = output_fp16 rewritten.append(node) rewritten.append( helper.make_node( "Cast", [output_fp16], [output_float], name=f"{node.name}/CastOutputToFp32", to=TensorProto.FLOAT, ) ) retained = [value for value in graph.initializer if value.name not in removable_initializers] del graph.initializer[:] graph.initializer.extend(retained) graph.initializer.extend(fp16_initializers) del graph.node[:] graph.node.extend(rewritten) return { "matmuls_rewritten": len(dq_by_matmul_name), "layers_rewritten": sorted(selected_layers), "matmuls_kept_qdq": sorted(KEEP_QDQ_MATMUL_NAMES), "weight_elements": quantized_elements, "runtime_fp32_weight_bytes_avoided": quantized_elements * 4, "static_fp16_weight_bytes": quantized_elements * 2, "source_int8_weight_bytes_removed": quantized_elements, "estimated_live_weight_bytes_avoided": quantized_elements * 3, } def feeds(session: ort.InferenceSession, past_length: int, *, prefill: bool) -> dict[str, np.ndarray]: rng = np.random.default_rng(20260717 + past_length) result: dict[str, np.ndarray] = {} for value in session.get_inputs(): if value.name == "vision_embeds": length = VISION_TOKENS if prefill else 0 result[value.name] = rng.normal(0, 0.2, [1, length, 512]).astype(np.float32) elif value.name == "token_ids": result[value.name] = np.array([[1 if prefill else 4]], dtype=np.int32) elif value.name == "position_ids": result[value.name] = ( np.arange(VISION_TOKENS + 1, dtype=np.int32)[None, :] if prefill else np.array([[past_length]], dtype=np.int32) ) else: result[value.name] = rng.normal(0, 0.02, [1, 2, past_length, 64]).astype(np.float32) return result def validate_cpu(baseline: Path, candidate: Path) -> dict: baseline_session = ort.InferenceSession(str(baseline), providers=["CPUExecutionProvider"]) candidate_session = ort.InferenceSession(str(candidate), providers=["CPUExecutionProvider"]) checks = {} for label, past_length, prefill in ( ("prefill", 0, True), ("step_257", 257, False), ("step_383", 383, False), ): model_feeds = feeds(baseline_session, past_length, prefill=prefill) expected = baseline_session.run(None, model_feeds) actual = candidate_session.run(None, model_feeds) max_abs = [float(np.max(np.abs(left - right))) for left, right in zip(expected, actual)] checks[label] = { "logits_max_abs": max_abs[0], "all_outputs_max_abs": max(max_abs), "top_token_baseline": int(expected[0][0, -1].argmax()), "top_token_candidate": int(actual[0][0, -1].argmax()), } if checks[label]["top_token_baseline"] != checks[label]["top_token_candidate"]: raise RuntimeError(f"CPU top-token parity failed for {label}: {checks[label]}") return checks def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--baseline", type=Path, default=BASELINE) parser.add_argument("--source", type=Path, default=DEFAULT_SOURCE) parser.add_argument("--destination", type=Path, default=DEFAULT_DESTINATION) parser.add_argument("--report", type=Path, default=DEFAULT_REPORT) parser.add_argument( "--layers", default="1,2,3", help="Comma-separated decoder layers whose seven MatMuls execute in FP16", ) arguments = parser.parse_args() arguments.destination.parent.mkdir(parents=True, exist_ok=True) arguments.report.parent.mkdir(parents=True, exist_ok=True) selected_layers = {int(value) for value in arguments.layers.split(",") if value != ""} if not selected_layers.issubset(set(range(6))): raise ValueError(f"Invalid decoder layers {sorted(selected_layers)}") model = onnx.load(arguments.source) optimization = rewrite_matmul_weights(model, selected_layers) model.producer_name = "vibe-manga-baberu-webgpu-static-fp16-matmul" model.producer_version = "1" onnx.checker.check_model(model) onnx.save(model, arguments.destination) parity = validate_cpu(arguments.baseline, arguments.destination) report = { "source": { "path": str(arguments.source.relative_to(ROOT)), "bytes": arguments.source.stat().st_size, "sha256": sha256(arguments.source), }, "optimized": { "path": str(arguments.destination.relative_to(ROOT)), "bytes": arguments.destination.stat().st_size, "sha256": sha256(arguments.destination), }, "capability": { "layers": 6, "hidden_size": 512, "kv_heads": 2, "vocabulary": 14630, "max_new_tokens": 128, "architecture_changed": False, }, "static_fp16_matmul": optimization, "cpu_validation": parity, } arguments.report.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") print(json.dumps(report, indent=2)) if __name__ == "__main__": main()