Add unit test to check backward function for conv, checks there is no graph breaks #1709
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Signed-off-by: Xavier Dupre <[email protected]>
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## main #1709 +/- ##
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- Coverage 74.70% 74.65% -0.05%
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Files 242 243 +1
Lines 25862 25933 +71
Branches 4661 4678 +17
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+ Hits 19319 19360 +41
- Misses 5674 5699 +25
- Partials 869 874 +5 ☔ View full report in Codecov by Sentry. |
Signed-off-by: Xavier Dupre <[email protected]>
xadupre
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| def train_loop( | ||
| model: Any, | ||
| *args, | ||
| loss_fn: Any | None = None, | ||
| optimizer: Any | None = None, | ||
| dump_onnx_models: bool = False, | ||
| dump_prefix: str = "dump_train_loop", | ||
| dump_clean_first: bool = True, | ||
| ) -> tuple[Any, tuple[Any, ...]] | tuple[Any, tuple[Any, ...], list[str]]: |
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Returning tuples with varying lengths Note
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In the OpInfo data structure, I have seen a field that says supports_grad or something which may make it easier for us to generate backward tests. @xiaowuhu do you have some ideas?
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This seems be a different scenario than the OpInfo way. Here, we need to go through the aot-compile-training-backward process which is an e2e scenario, although it is not a straight forward way. But this requirement will only benefit not more than 20 backward functions, so I think it is OK.
Signed-off-by: Xavier Dupre <[email protected]>
Signed-off-by: Xavier Dupre <[email protected]>
Signed-off-by: Xavier Dupre <[email protected]>
| expected_results, expected_gradients = onnxscript.tools.training_helper.train_loop( # pylint: disable=unbalanced-tuple-unpacking | ||
| model, *input_tensors | ||
| ) | ||
| results, gradients, onnx_models = onnxscript.tools.training_helper.train_loop( |
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RUFF/F841 Warning
| expected_results, expected_gradients = onnxscript.tools.training_helper.train_loop( # pylint: disable=unbalanced-tuple-unpacking | ||
| model, *input_tensors | ||
| ) | ||
| results, gradients, onnx_models = onnxscript.tools.training_helper.train_loop( |
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PYLINT/W0612 Warning
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