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Update n_samples and output in test script

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Hidde L
2025-10-14 22:38:20 +02:00
parent d0f955c2d3
commit da0e4420d1
1 changed files with 12 additions and 9 deletions
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21
Programs/Source/bert_inference.mpc
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@@ -22,7 +22,7 @@ from datasets import load_dataset
MODEL_NAME = 'M-FAC/bert-tiny-finetuned-qnli' # BERT-tiny (2 layers, 128 hidden)
MAX_LENGTH = 64 # Maximum sequence length
N_SAMPLES = 1 # Number of samples to evaluate
N_SAMPLES = 25 # Number of samples to evaluate
BATCH_SIZE = 1 # Batch size for MPC inference (increase for better performance)
# GLUE task configuration
@@ -343,8 +343,7 @@ _ = optimizer.reveal_correctness(test_embeddings_one, pt_probabilities_sfix_one,
# Compare layers
print_ln("\nLayer-by-layer comparison (Sample 0 only):")
print_ln("Layer | Total Absolute Difference | First 8 Values")
print_ln("-" * 80)
print_ln("=" * 100)
for idx, (mpc_layer, pt_layer) in enumerate(layers_to_compare):
layer_id = f"{idx}.{type(pt_layer).__name__}"
@@ -364,12 +363,16 @@ for idx, (mpc_layer, pt_layer) in enumerate(layers_to_compare):
# Get MPC values
mpc_output = mpc_layer.Y[0].get_vector().reveal()
# Compute sum of absolute differences
diff = sum(abs(pt_at_runtime - mpc_output))
# Compute detailed statistics
total_abs_diff = sum(abs(pt_at_runtime - mpc_output))
pt_magnitude = sum(abs(pt_at_runtime))
# Print layer comparison with first 8 values
print_ln("%s | Avg. Diff: %s", layer_id, diff / sum(pt_values.shape))
print_ln(" PyTorch: %s", pt_at_runtime[:8])
print_ln(" MP-SPDZ: %s", mpc_output[:8])
# Print layer comparison
print_ln("\n%s", layer_id)
print_ln(" Shape: %s, Elements: %s", pt_values.shape, len(pt_at_runtime))
print_ln(" Total Abs Diff: %s", total_abs_diff)
print_ln(" PT Total Magnitude: %s", pt_magnitude)
print_ln(" First 8 PT: %s", pt_at_runtime[:8])
print_ln(" First 8 MPC: %s", mpc_output[:8])
print_ln("\n=== Inference Complete ===")