Handle non-contiguous inputs with mkldnn convolution.#16300
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soumith
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Jan 24, 2019
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I wonder if there's a generic way to test for non-contiguous inputs. Do we do it currently for generically written tests in test_nn.py? |
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@dzhulgakov we only test non contiguous inputs to NN modules, but it didn't actually matter in this case: there was already an explicit test for non-contiguous convolution weights (although not biases, I added a test for that), but it didn't exercise mkldnn because we only use mkldnn for float32 and we run autograd tests in float64... A test revamp would at least check float32 as well (and if it made sense, direct calls to different convolution algorithms). |
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Summary: Fixes pytorch/pytorch#16018. Backwards appears to be fine because the derivative is written in terms of mkldnn_convolution itself. Pull Request resolved: pytorch/pytorch#16300 Differential Revision: D13797776 Pulled By: gchanan fbshipit-source-id: 68a990b8a3c186412a99d176931314806c9ed7bf
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Summary: Fixes pytorch#16018. Backwards appears to be fine because the derivative is written in terms of mkldnn_convolution itself. Pull Request resolved: pytorch#16300 Differential Revision: D13797776 Pulled By: gchanan fbshipit-source-id: 68a990b8a3c186412a99d176931314806c9ed7bf
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Fixes #16018.
Backwards appears to be fine because the derivative is written in terms of mkldnn_convolution itself.