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Inconsistent Results After ONNX Runtime Optimization #23199

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matchaaShaw opened this issue Dec 26, 2024 · 0 comments
Open

Inconsistent Results After ONNX Runtime Optimization #23199

matchaaShaw opened this issue Dec 26, 2024 · 0 comments
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model:transformer issues related to a transformer model: BERT, GPT2, Hugging Face, Longformer, T5, etc.

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@matchaaShaw
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matchaaShaw commented Dec 26, 2024

Describe the issue

I am encountering an issue where the output of the model after optimization using ONNX Runtime is inconsistent with the original model.

  • Actual Behavior:
AssertionError: 
Not equal to tolerance rtol=0.001, atol=0.001

Mismatched elements: 12 / 1056 (1.14%)
Max absolute difference: 0.00350001
Max relative difference: 0.07692306
 x: array([0.42  , 0.42  , 0.42  , ..., 0.1155, 0.126 , 0.084 ], dtype=float32)
 y: array([0.42  , 0.42  , 0.42  , ..., 0.1155, 0.126 , 0.084 ], dtype=float32)
  • Expected Behavior:
    The optimized model should produce identical results for all outputs when compared to the original model, within the specified tolerance.

To reproduce

  1. Download the model
  2. run the following script:
import onnx
import onnxruntime as ort
from onnxruntime.transformers import optimizer
import numpy as np

model_path = "20730.onnx"
optimized_model_path = f"./opt.onnx"
sess_options = ort.SessionOptions()
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL

original_session = ort.InferenceSession(model_path, sess_options, providers=["CUDAExecutionProvider"])
input_data = {"v5_0": np.random.rand(55, 7, 1, 40).astype(np.float32)}
original_output_names = [output.name for output in original_session.get_outputs()]
original_result = original_session.run(original_output_names, input_data)
original_result2 = original_session.run(original_output_names, input_data)
for r1, r2 in zip(original_result, original_result2):
    np.testing.assert_allclose(r1, r2, rtol=1e-3, atol=1e-3)

optimized_model = optimizer.optimize_model(model_path, opt_level=99)
optimized_model.save_model_to_file(optimized_model_path)
optimized_session = ort.InferenceSession(optimized_model_path, providers=["CUDAExecutionProvider"])
optimized_output_names = [output.name for output in optimized_session.get_outputs()]
optimized_result = optimized_session.run(optimized_output_names, input_data)

for r1, r2 in zip(original_result, optimized_result):
    np.testing.assert_allclose(r1, r2, atol=1e-3, rtol=1e-3)

Urgency

No response

Platform

Linux

OS Version

Ubuntu 20.04

ONNX Runtime Installation

Built from Source

ONNX Runtime Version or Commit ID

5c1b7cc

ONNX Runtime API

Python

Architecture

X64

Execution Provider

CUDA

Execution Provider Library Version

No response

@github-actions github-actions bot added the model:transformer issues related to a transformer model: BERT, GPT2, Hugging Face, Longformer, T5, etc. label Dec 26, 2024
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Labels
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