fbf5d09a0c
### Fix random failure of ortmodule_api.py::test_unused_parameters
Fix FAILED
orttraining_test_ortmodule_api.py::test_unused_parameters[model1-none_pt_params1]
for orttraining-linux-gpu-ci-pipeline CI pipeline
```
=================================== FAILURES ===================================
________________ test_unused_parameters[model1-none_pt_params1] ________________
model = UnusedMiddleParameterNet(
(fc1): Linear(in_features=784, out_features=500, bias=True)
(relu): ReLU()
(fc2): Linear(in_features=500, out_features=400, bias=True)
(fc3): Linear(in_features=500, out_features=10, bias=True)
)
none_pt_params = ['fc2.weight', 'fc2.bias']
@pytest.mark.parametrize(
"model, none_pt_params",
[
(UnusedBeginParameterNet(784, 500, 400, 10), ["fc1.weight", "fc1.bias"]),
(UnusedMiddleParameterNet(784, 500, 400, 10), ["fc2.weight", "fc2.bias"]),
(UnusedEndParameterNet(784, 500, 400, 10), ["fc2.weight", "fc2.bias"]),
],
)
def test_unused_parameters(model, none_pt_params):
device = "cuda"
N, D_in, H1, H2, D_out = 64, 784, 500, 400, 10
model = model.to(device)
ort_model = ORTModule(copy.deepcopy(model))
# Make sure model runs without any exception
for _ in range(5):
x = torch.randn(N, D_in, device=device)
y = copy.deepcopy(x)
out_pt = model(x)
out_ort = ort_model(y)
loss_pt = out_pt.sum()
loss_pt.backward()
loss_ort = out_ort.sum()
loss_ort.backward()
_test_helpers.assert_values_are_close(out_ort, out_pt)
> _test_helpers.assert_gradients_match_and_reset_gradient(ort_model, model, none_pt_params=none_pt_params)
orttraining_test_ortmodule_api.py:4050:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_test_helpers.py:216: in assert_gradients_match_and_reset_gradient
assert_values_are_close(ort_param.grad, pt_param.grad, rtol=rtol, atol=atol)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
```
Initially the test runs very well. As we insert more and more tests,
when running ortmodule_api.py::test_unused_parameters, the random
generated data got changed, and now it is more easily to generate an
input data that produce a result the break existing rtol and atol.
The example data, 0.1041 only have very minor diff, e.g. abs_diff:
2.2649765014648438e-06.
> The torch.allclose judge it is not equal because: abs_diff> 0.1041 *
rtol + atol = 1.041e-1 * 1e-5 + 1e-6 =-2.041e-6.
> Additionally, according to math
[here](
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
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