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Batch inference example for JaneGPT v2 Intent Classifier.
Classifies multiple inputs efficiently.
"""
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import time
import json
from pathlib import Path
from typing import List, Dict
import torch
from model.classifier import JaneGPTClassifier
def classify_batch(
classifier: JaneGPTClassifier,
texts: List[str],
context: dict = None
) -> List[Dict]:
"""
Classify a batch of texts.
Note: Current implementation processes sequentially.
For true batch processing with padding, see classify_batch_parallel().
Args:
classifier: Loaded JaneGPTClassifier
texts: List of user utterances
context: Optional shared context
Returns:
List of result dictionaries
"""
results = []
for text in texts:
intent, confidence = classifier.predict(text, context)
results.append({
"text": text,
"intent": intent,
"confidence": round(confidence, 4),
})
return results
def classify_batch_parallel(
classifier: JaneGPTClassifier,
texts: List[str],
context: dict = None
) -> List[Dict]:
"""
Classify a batch of texts in parallel (single forward pass).
More efficient for large batches on GPU.
Args:
classifier: Loaded JaneGPTClassifier
texts: List of user utterances
context: Optional shared context
Returns:
List of result dictionaries
"""
if not classifier.is_ready:
raise RuntimeError("Model not loaded")
# Format and tokenize all inputs
all_ids = []
for text in texts:
formatted = classifier._format_input(text, context)
ids = classifier.tokenizer.encode(formatted).ids
if len(ids) > classifier.MAX_LEN:
ids = ids[:classifier.MAX_LEN]
else:
ids = ids + [classifier.PAD_ID] * (classifier.MAX_LEN - len(ids))
all_ids.append(ids)
# Create batch tensor
batch_tensor = torch.tensor(all_ids, dtype=torch.long, device=classifier.device)
# Single forward pass
with torch.no_grad():
logits, _ = classifier.model(batch_tensor)
probs = torch.softmax(logits, dim=-1)
confidences, predicted = torch.max(probs, dim=-1)
# Build results
results = []
for i, text in enumerate(texts):
idx = predicted[i].item()
conf = confidences[i].item()
intent = classifier.id_to_intent.get(idx, 'chat')
results.append({
"text": text,
"intent": intent,
"confidence": round(conf, 4),
})
return results
def main():
# Load model
classifier = JaneGPTClassifier()
print(f"Model loaded: {classifier}\n")
# Example batch
commands = [
"turn up the volume",
"make it louder",
"open chrome",
"play shape of you",
"search for python tutorials on google",
"set brightness to 50",
"take a screenshot",
"set a reminder for 10 minutes",
"mute",
"read this for me",
"explain what's on my screen",
"undo that",
"shut down",
"hello",
"what can you do",
"close notepad",
"skip to the next song",
"dim the screen",
"pause the music",
"what time is it",
]
# --- Sequential processing ---
print("=" * 65)
print(" Sequential Batch Processing")
print("=" * 65)
start = time.perf_counter()
results = classify_batch(classifier, commands)
elapsed = time.perf_counter() - start
print(f"\n {'Text':<42} {'Intent':<20} {'Conf':>6}")
print(f" {'-'*68}")
for r in results:
print(f" {r['text']:<42} {r['intent']:<20} {r['confidence']:>5.1%}")
print(f"\n Processed {len(commands)} commands in {elapsed*1000:.1f}ms")
print(f" Average: {elapsed/len(commands)*1000:.1f}ms per command")
# --- Parallel processing ---
print(f"\n{'=' * 65}")
print(" Parallel Batch Processing (single forward pass)")
print("=" * 65)
start = time.perf_counter()
results_parallel = classify_batch_parallel(classifier, commands)
elapsed_parallel = time.perf_counter() - start
print(f"\n Processed {len(commands)} commands in {elapsed_parallel*1000:.1f}ms")
print(f" Average: {elapsed_parallel/len(commands)*1000:.1f}ms per command")
print(f" Speedup: {elapsed/elapsed_parallel:.1f}x faster than sequential")
# Verify both methods give same results
match = all(
r1['intent'] == r2['intent']
for r1, r2 in zip(results, results_parallel)
)
print(f" Results match: {'YES' if match else 'NO'}")
# --- Save results to JSON ---
output_file = Path("examples/batch_results.json")
with open(output_file, 'w') as f:
json.dump(results, f, indent=2)
print(f"\n Results saved to: {output_file}")
# --- Batch with context ---
print(f"\n{'=' * 65}")
print(" Context-Aware Batch")
print("=" * 65)
# Simulate: user just adjusted volume, now giving follow-up commands
context = {"last_intent": "volume_up"}
follow_ups = [
"not enough",
"too much",
"a bit more",
"the other one",
"perfect",
]
print(f"\n Context: last_intent = volume_up\n")
ctx_results = classify_batch(classifier, follow_ups, context)
for r in ctx_results:
print(f" {r['text']:<42} {r['intent']:<20} {r['confidence']:>5.1%}")
if __name__ == "__main__":
main() |