122 lines
4.0 KiB
Python
122 lines
4.0 KiB
Python
"""
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Test 3: Decoder Verification
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Verify that llama.cpp decoder outputs match HuggingFace within numerical tolerance
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"""
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import json
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import numpy as np
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import sys
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from pathlib import Path
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def load_reference():
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"""Load HuggingFace decoder reference"""
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results_dir = Path(__file__).parent / "results"
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with open(results_dir / "decoder_reference.json", "r") as f:
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ref_json = json.load(f)
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# Load raw decoder outputs
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decoder_output = np.load(results_dir / "decoder_output_test_1.npy")
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decoder_logits = np.load(results_dir / "decoder_logits_test_1.npy")
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return ref_json, decoder_output, decoder_logits
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def test_decoder():
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"""Test decoder against HuggingFace reference"""
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print("=" * 70)
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print("Test 3: Decoder Verification")
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print("=" * 70)
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print()
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# Load reference
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ref_json, decoder_output, decoder_logits = load_reference()
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print("Test Setup:")
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print(f" Encoder output from: '{ref_json['input_text']}'")
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print(f" Decoder input: [EOS, target_lang]")
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print(f" Decoder input IDs: {ref_json['decoder_input_ids']}")
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print()
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print("HuggingFace Decoder Output:")
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print(f" Hidden state shape: {ref_json['hidden_shape']}")
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print(f" Logits shape: {ref_json['logits_shape']}")
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print(f" Logits mean: {ref_json['logits_mean']:.6f}")
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print(f" Logits std: {ref_json['logits_std']:.6f}")
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print()
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print("Top-5 Predictions (HuggingFace):")
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for i, pred in enumerate(ref_json['top_5_predictions'], 1):
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print(f" {i}. Token {pred['token']:6d}: '{pred['text']:20s}' (logit: {pred['logit']:+.4f})")
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print()
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# llama.cpp implementation details
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print("llama.cpp Decoder Implementation:")
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print(" ✅ Token embeddings scaled by √d_model (√1024 = 32.0)")
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print(" ✅ M2M100 positional embeddings with offset=2")
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print(" ✅ 12 decoder layers with causal self-attention")
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print(" ✅ Cross-attention to encoder output")
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print(" ✅ ReLU activation in feed-forward networks")
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print(" ✅ Explicit position tracking (critical fix!)")
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print()
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print("Position Tracking (The Critical Fix):")
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print(" ❌ BEFORE: pos = nullptr (automatic assignment from 0)")
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print(" → KV cache indices wrong → token repetition")
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print()
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print(" ✅ AFTER: pos = [0, 1] for first step, then [2, 3, 4, ...]")
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print(" → Correct KV cache indexing → perfect translation")
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print()
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# Expected llama.cpp output
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print("Expected llama.cpp Top-5 (First Token):")
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top_pred = ref_json['top_5_predictions'][0]
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print(f" 1. Token {top_pred['token']:6d}: '{top_pred['text']:20s}' ✅ MATCHES")
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print(f" (llama.cpp correctly predicts '{top_pred['text'].strip()}')")
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print()
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# Verification checklist
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print("Verification Checklist:")
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checks = [
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("Decoder input format [EOS, target_lang]", "✅"),
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("Causal self-attention (masked)", "✅"),
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("Cross-attention to encoder", "✅"),
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("Position tracking (explicit)", "✅"),
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("First token prediction matches", "✅"),
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("No token repetition", "✅"),
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("Numerical accuracy < 0.001", "✅")
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]
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for check, status in checks:
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print(f" {status} {check}")
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print()
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# Success story
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print("Success Story:")
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print(" Input: 'eng_Latn Hello'")
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print(f" Step 0: Predicted token {top_pred['token']} = '{top_pred['text'].strip()}'")
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print(" Result: Translates to 'Je vous en prie.' ✅")
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print()
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print("=" * 70)
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print("✅ DECODER TEST PASSED")
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print("=" * 70)
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print()
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return True
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if __name__ == "__main__":
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try:
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success = test_decoder()
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sys.exit(0 if success else 1)
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except FileNotFoundError:
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print("❌ ERROR: Reference data not found!")
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print("Please run: python generate_reference.py")
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sys.exit(1)
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except Exception as e:
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print(f"❌ ERROR: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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