[ONNX] Refactor AvgPool to support dynamic shapes#105683
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titaiwangms wants to merge 5 commits intogh/titaiwangms/32/basefrom
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[ONNX] Refactor AvgPool to support dynamic shapes#105683titaiwangms wants to merge 5 commits intogh/titaiwangms/32/basefrom
titaiwangms wants to merge 5 commits intogh/titaiwangms/32/basefrom
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/105683
Note: Links to docs will display an error until the docs builds have been completed. ✅ 4 Unrelated FailuresAs of commit c0776b3: UNSTABLE - The following jobs failed but were likely due to flakiness present on trunk and has been marked as unstable:
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LGTM.
IIUC, the ceil_mode failure is a bug on ORT side, which will be addressed there?
In #87892, to pick up the corner cases found in #71549, the PR falls back the implementation of AvgPool to the way opset 9 implementing. However, it introduces a regression on dynamic shape cases found in #101397. This PR refactors the AvgPool op with the same implementation we have in onnxscript: microsoft/onnxscript#754. However, the corner case with `ceil_mode` remains unsolved in onnxruntime: microsoft/onnxruntime#16203. The calculuation on the last value of each dimension is different between ORT and PyTorch. But the fix can be proved in: microsoft/onnxruntime#16752, and it supports AvgPool since opset19. [ghstack-poisoned]
In #87892, to pick up the corner cases found in #71549, the PR falls back the implementation of AvgPool to the way opset 9 implementing. However, it introduces a regression on dynamic shape cases found in #101397. This PR refactors the AvgPool op with the same implementation we have in onnxscript: microsoft/onnxscript#754. However, the corner case with `ceil_mode` remains unsolved in onnxruntime: microsoft/onnxruntime#16203. The calculuation on the last value of each dimension is different between ORT and PyTorch. But the fix can be proved in: microsoft/onnxruntime#16752, and it supports AvgPool since opset19. [ghstack-poisoned]
In #87892, to pick up the corner cases found in #71549, the PR falls back the implementation of AvgPool to the way opset 9 implementing. However, it introduces a regression on dynamic shape cases found in #101397. This PR refactors the AvgPool op with the same implementation we have in onnxscript: microsoft/onnxscript#754. However, the corner case with `ceil_mode` remains unsolved in onnxruntime: microsoft/onnxruntime#16203. The calculuation on the last value of each dimension is different between ORT and PyTorch. But the fix can be proved in: microsoft/onnxruntime#16752, and it supports AvgPool since opset19. [ghstack-poisoned]
In #87892, to pick up the corner cases found in #71549, the PR falls back the implementation of AvgPool to the way opset 9 implementing. However, it introduces a regression on dynamic shape cases found in #101397. This PR refactors the AvgPool op with the same implementation we have in onnxscript: microsoft/onnxscript#754. However, the corner case with `ceil_mode` remains unsolved in onnxruntime: microsoft/onnxruntime#16203. The calculuation on the last value of each dimension is different between ORT and PyTorch. But the fix can be proved in: microsoft/onnxruntime#16752, and it supports AvgPool since opset19. [ghstack-poisoned]
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Stack from ghstack (oldest at bottom):
In #87892, to pick up the corner cases found in #71549, the PR falls back the implementation of AvgPool to the way opset 9 implementing. However, it introduces a regression on dynamic shape cases found in #101397. This PR refactors the AvgPool op with the same implementation we have in onnxscript: microsoft/onnxscript#754.
However, the corner case with
count_include_padremains unsolved in onnxruntime: microsoft/onnxruntime#16203. The calculuation on the last value of each dimension is different between ORT and PyTorch. But the fix can be proved in: microsoft/onnxruntime#16752, and it supports AvgPool since opset19.