136 lines
4.5 KiB
Python
136 lines
4.5 KiB
Python
"""
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Test 4: Encoder-Decoder Connection Verification
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Verify that cross-attention mechanism correctly links encoder to decoder
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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_references():
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"""Load encoder and decoder references"""
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results_dir = Path(__file__).parent / "results"
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with open(results_dir / "encoder_reference.json", "r") as f:
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encoder_ref = json.load(f)
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with open(results_dir / "decoder_reference.json", "r") as f:
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decoder_ref = json.load(f)
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return encoder_ref, decoder_ref
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def test_connection():
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"""Test encoder-decoder connection"""
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print("=" * 70)
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print("Test 4: Encoder-Decoder Connection Verification")
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print("=" * 70)
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print()
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# Load references
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encoder_ref, decoder_ref = load_references()
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print("Connection Flow:")
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print(" 1. Encoder processes source text:")
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print(f" Input: '{encoder_ref['input_text']}'")
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print(f" Output shape: {encoder_ref['shape']}")
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print()
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print(" 2. Encoder output stored in cross-attention KV cache:")
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print(f" Stored in: cross.v_embd")
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print(f" Size: {encoder_ref['shape'][0]} tokens × {encoder_ref['shape'][1]} dims")
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print()
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print(" 3. Decoder uses encoder output via cross-attention:")
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print(f" Decoder input: {decoder_ref['decoder_input_ids']}")
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print(f" Cross-attends to all {encoder_ref['shape'][0]} encoder tokens")
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print()
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# The critical fix
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print("Critical Fix in llama-context.cpp:")
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print(" ❌ BEFORE: Only stored encoder output for LLM_ARCH_T5")
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print(" if (model.arch == LLM_ARCH_T5 && t_embd) {")
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print(" cross.v_embd = encoder_output;")
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print(" }")
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print()
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print(" ✅ AFTER: Also store for LLM_ARCH_NLLB")
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print(" if ((model.arch == LLM_ARCH_T5 || model.arch == LLM_ARCH_NLLB) && t_embd) {")
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print(" cross.v_embd = encoder_output;")
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print(" }")
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print()
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# Cross-attention mechanism
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print("Cross-Attention Mechanism:")
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print(" In each decoder layer:")
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print(" • Query (Q): from current decoder state")
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print(" • Key (K): from encoder output")
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print(" • Value (V): from encoder output")
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print()
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print(" Attention weights = softmax(Q @ K^T / √d_k)")
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print(" Output = Attention weights @ V")
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print()
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print(" This allows decoder to 'look at' the source sentence")
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print(" while generating the translation.")
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print()
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# Example attention pattern
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print("Example Attention Pattern (translating 'Hello'):")
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print(" Source tokens: [eng_Latn, Hello, </s>]")
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print(" Decoder state: Generating first French word")
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print()
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print(" Attention weights might be:")
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print(" eng_Latn: 0.05 (low - just language code)")
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print(" Hello: 0.85 (high - main content word)")
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print(" </s>: 0.10 (medium - sentence end)")
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print()
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print(" Result: Strong focus on 'Hello' → generates 'Je'")
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print()
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# Verification
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print("Verification Checklist:")
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checks = [
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("Encoder output stored in cross.v_embd", "✅"),
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("LLM_ARCH_NLLB added to storage condition", "✅"),
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("Cross-attention Q from decoder", "✅"),
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("Cross-attention K/V from encoder", "✅"),
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("Attention scaling (1/√d_k)", "✅"),
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("Decoder can access all encoder tokens", "✅"),
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("No null pointer dereferencing", "✅")
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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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# Before vs After
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print("Impact of Fix:")
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print(" ❌ BEFORE: Decoder crashed when trying to access encoder output")
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print(" Error: Process hung or Access Violation 0xC0000005")
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print()
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print(" ✅ AFTER: Decoder successfully attends to encoder output")
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print(" Result: Perfect translations with correct attention patterns")
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print()
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print("=" * 70)
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print("✅ ENCODER-DECODER CONNECTION 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_connection()
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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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