b2txt25/model_training_nnn/test_xla_model.py

#!/usr/bin/env python3
"""
XLA Model Verification Script
验证XLA优化后的模型输出与原始模型保持一致
"""

import torch
import torch.nn as nn
import sys
import os

# Add the model training directory to path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

from rnn_model import TripleGRUDecoder

def create_test_data(batch_size=4, seq_len=100, neural_dim=512, n_days=10):
    """Create synthetic test data matching expected model inputs"""
    # Create random neural features
    features = torch.randn(batch_size, seq_len, neural_dim)

    # Create random day indices (should be valid indices < n_days)
    day_indices = torch.randint(0, n_days, (batch_size,))

    return features, day_indices

def test_model_consistency():
    """Test that XLA-optimized model produces consistent outputs"""

    print("Testing XLA-optimized TripleGRUDecoder consistency...")

    # Model parameters (matching typical configuration)
    neural_dim = 512
    n_units = 768
    n_days = 10
    n_classes = 40  # Typical phoneme count
    batch_size = 4
    seq_len = 100
    patch_size = 14
    patch_stride = 1

    # Create model
    model = TripleGRUDecoder(
        neural_dim=neural_dim,
        n_units=n_units,
        n_days=n_days,
        n_classes=n_classes,
        rnn_dropout=0.0,  # Disable dropout for consistent testing
        input_dropout=0.0,
        patch_size=patch_size,
        patch_stride=patch_stride
    )

    # Set to eval mode for consistent results
    model.eval()

    # Create test data
    features, day_indices = create_test_data(batch_size, seq_len, neural_dim, n_days)

    print(f"Test data shapes:")
    print(f"  Features: {features.shape}")
    print(f"  Day indices: {day_indices.shape}")
    print(f"  Day indices values: {day_indices.tolist()}")

    # Test inference mode (most commonly used)
    print("\n=== Testing Inference Mode ===")
    with torch.no_grad():
        try:
            # Run inference mode
            clean_logits = model(features, day_indices, states=None, return_state=False, mode='inference')

            print(f"Clean logits shape: {clean_logits.shape}")
            print(f"Clean logits range: [{clean_logits.min().item():.4f}, {clean_logits.max().item():.4f}]")
            print("✓ Inference mode successful")

            # Test with return_state=True
            clean_logits_with_state, noise_hidden = model(features, day_indices, states=None, return_state=True, mode='inference')

            # Verify consistency
            assert torch.allclose(clean_logits, clean_logits_with_state, rtol=1e-5, atol=1e-6), "Inconsistent outputs with/without return_state"
            print("✓ return_state consistency verified")

        except Exception as e:
            print(f"✗ Inference mode failed: {e}")
            raise

    # Test full mode (training)
    print("\n=== Testing Full Mode ===")
    with torch.no_grad():
        try:
            # Run full mode
            clean_logits, noisy_logits, noise_output = model(features, day_indices, states=None, return_state=False, mode='full')

            print(f"Clean logits shape: {clean_logits.shape}")
            print(f"Noisy logits shape: {noisy_logits.shape}")
            print(f"Noise output shape: {noise_output.shape}")
            print("✓ Full mode successful")

            # Test with return_state=True
            (clean_logits_with_state, noisy_logits_with_state, noise_output_with_state), noise_hidden = model(
                features, day_indices, states=None, return_state=True, mode='full')

            # Verify consistency
            assert torch.allclose(clean_logits, clean_logits_with_state, rtol=1e-5, atol=1e-6), "Inconsistent clean logits"
            assert torch.allclose(noisy_logits, noisy_logits_with_state, rtol=1e-5, atol=1e-6), "Inconsistent noisy logits"
            assert torch.allclose(noise_output, noise_output_with_state, rtol=1e-5, atol=1e-6), "Inconsistent noise output"
            print("✓ return_state consistency verified")

        except Exception as e:
            print(f"✗ Full mode failed: {e}")
            raise

    # Test multiple runs for consistency
    print("\n=== Testing Multiple Run Consistency ===")
    with torch.no_grad():
        try:
            # Run same input multiple times
            results = []
            for i in range(3):
                result = model(features, day_indices, states=None, return_state=False, mode='inference')
                results.append(result)

            # Verify all runs produce identical results
            for i in range(1, len(results)):
                assert torch.allclose(results[0], results[i], rtol=1e-7, atol=1e-8), f"Inconsistent results between runs 0 and {i}"

            print("✓ Multiple runs produce identical results")

        except Exception as e:
            print(f"✗ Multiple run consistency failed: {e}")
            raise

    # Test different batch sizes
    print("\n=== Testing Different Batch Sizes ===")
    with torch.no_grad():
        try:
            for test_batch_size in [1, 2, 8]:
                test_features, test_day_indices = create_test_data(test_batch_size, seq_len, neural_dim, n_days)
                result = model(test_features, test_day_indices, states=None, return_state=False, mode='inference')

                expected_shape = (test_batch_size, (seq_len - patch_size) // patch_stride + 1, n_classes)
                assert result.shape == expected_shape, f"Unexpected shape for batch_size={test_batch_size}: {result.shape} vs {expected_shape}"

                print(f"✓ Batch size {test_batch_size}: {result.shape}")

        except Exception as e:
            print(f"✗ Batch size testing failed: {e}")
            raise

    print("\n🎉 All tests passed! XLA-optimized model is working correctly.")
    return True

if __name__ == "__main__":
    test_model_consistency()