Fix some backwards definitions wrt keepdim. #10382
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Before we had 0-dim tensors in TH, we were flexible in what we accepted wrt to the difference between size [] and size [1] tensors in backwards functions because they were identical in TH. This often masks shape issues, adds greatly to code complexity and thus IMO isn't worth keeping.
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colesbury
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looks good
do we have test cases that cover these regressions?
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| Tensor index_select_backward(Tensor grad, int64_t dim, Tensor indices, IntList sizes, bool keepdim) { | ||
| if (!keepdim) { | ||
| if (!keepdim && sizes.size() > 0) { |
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| if (dims_to_unsqueeze[i]) | ||
| res = res.unsqueeze(i); | ||
| if (dims_to_unsqueeze[i]) { | ||
| res = res.unsqueeze(i); |
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PenghuiCheng
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Summary: Before we had 0-dim tensors in TH, we were flexible in what we accepted wrt to the difference between size [] and size [1] tensors in backwards functions because they were identical in TH. So, we had backwards definitions that were technically incorrect, but happened to work. This often masks shape issues, adds greatly to code complexity and thus IMO isn't worth keeping. Pull Request resolved: pytorch#10382 Differential Revision: D9244618 Pulled By: gchanan fbshipit-source-id: 2c29c53a8ffe8710843451202cad6b4323af10e8
goodlux
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Aug 15, 2018
Summary: Before we had 0-dim tensors in TH, we were flexible in what we accepted wrt to the difference between size [] and size [1] tensors in backwards functions because they were identical in TH. So, we had backwards definitions that were technically incorrect, but happened to work. This often masks shape issues, adds greatly to code complexity and thus IMO isn't worth keeping. Pull Request resolved: pytorch#10382 Differential Revision: D9244618 Pulled By: gchanan fbshipit-source-id: 2c29c53a8ffe8710843451202cad6b4323af10e8
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Before we had 0-dim tensors in TH, we were flexible in what we accepted wrt to the difference between size [] and size [1] tensors in backwards functions because they were identical in TH. So, we had backwards definitions that were technically incorrect, but happened to work. This often masks shape issues, adds greatly to code complexity and thus IMO isn't worth keeping.