Pytorch Conv Transpose Padding Fix #7958
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March 18, 2020 19:30
# Conflicts: # python/tvm/relay/frontend/tensorflow.py
…-conv-transpose-pad-fix
…rameter for conv transpose operations * updating pytorch converter to correctly convert conv1d to conv1d in tvm inestead of a flattened conv2d unless under circumstances of grouped convolution * updating pytorch converter to correctly convert conv1d transpose to conv1d transpose in tvm instead of a flattened conv2d transpose * added tests to cover these latest additions
…-conv-transpose-pad-fix
…-conv-transpose-pad-fix
…he#34) SYSOL-584 Pytorch Conv Transpose Padding Fix Approved-by: Alicja Kwasniewska Approved-by: Mikael Sevenier
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Link to discussion: https://discuss.tvm.apache.org/t/pytorch-conv-transpose-padding-fix/9873 |
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Thanks, can we close #7912? |
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Yes. Thanks.
…On Sat, May 1, 2021 at 3:47 PM masahi ***@***.***> wrote:
Thanks, can we close #7912 <#7912>?
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…anspose-padding-fix # Conflicts: # tests/python/frontend/pytorch/test_forward.py
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@masahi How can we pass the tvm-ci/pr-merge step? Looking at the details the error comes up as:
I do not believe the changes in this PR cause this issue. |
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I think just retriggering ci will sometimes fix the problem. |
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@Jeffrey-Sima sorry missed your last comment, yes please try rebase and retrigger ci |
…anspose-padding-fix
masahi
approved these changes
May 14, 2021
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thanks @Jeffrey-Sima |
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Jun 17, 2021
* fix conv transpose import from TF * fix String::fromwe() to String::from() * * fixing pytorch converter to take into account the output_padding parameter for conv transpose operations * updating pytorch converter to correctly convert conv1d to conv1d in tvm inestead of a flattened conv2d unless under circumstances of grouped convolution * updating pytorch converter to correctly convert conv1d transpose to conv1d transpose in tvm instead of a flattened conv2d transpose * added tests to cover these latest additions * * removing print statements used for debugging * * fixing typos and formatting * * fixing formatting * * fixing grammar * * formatting fixes * * updated formatting after running pylint and python_format checks Co-authored-by: Mikael Sevenier <mikael.sevenier@sima.ai>
trevor-m
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to neo-ai/tvm
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Jun 17, 2021
* fix conv transpose import from TF * fix String::fromwe() to String::from() * * fixing pytorch converter to take into account the output_padding parameter for conv transpose operations * updating pytorch converter to correctly convert conv1d to conv1d in tvm inestead of a flattened conv2d unless under circumstances of grouped convolution * updating pytorch converter to correctly convert conv1d transpose to conv1d transpose in tvm instead of a flattened conv2d transpose * added tests to cover these latest additions * * removing print statements used for debugging * * fixing typos and formatting * * fixing formatting * * fixing grammar * * formatting fixes * * updated formatting after running pylint and python_format checks Co-authored-by: Mikael Sevenier <mikael.sevenier@sima.ai>
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Background
torch.nn.ConvTranspose2doperator.torch.nn.ConvTranspose2doperator and thetvm.relay.nn.conv2d_transposeoperator, the output_padding parameter in
tvm.relay.nn.conv2d_transposewould always default to0 regardless of what output padding was set in
torch.nn.ConvTranspose2d.tvm/python/tvm/relay/frontend/pytorch.py, the import logic for convolution layers was missing the output_padding parameter.The Fix
tvm/relay/frontend/pytorch.py.PyTorchOpConverter class is updated so that when it constructed the relay convolution op it supplied the output_padding attribute in the cases where it was creating convolution transpose operations.
torch.nn.ConvTranspose1Doperations intotvm.relay.nn.conv2d_transpose. This was fixed so now they wereconverted into
tvm.relay.nn.conv1d_transposeoperations.torch.nn.Conv1doperations were being converted intotvm.relay.nn.conv2doperations. This was fixed so that they are now converted into tvm.relay.nn.conv1doperations. There is a slight caveat where because tvm does not support grouped 1D convolution as stated in the
description of
tvm.relay.nn.conv1d, in that case we convert the operation to 2D convolution which does havesupport for grouped convolution. After the 2D convolution, we then squeeze the output to get the correct shape and
values for a grouped 1D convolution.
Test Coverage
test_forward_conv_transposetest intvm/tests/python/frontend/pytorch/test_forward.py.