use all_weights instead of _parameters in _flat_weights in rnn #15766
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soumith
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Jan 6, 2019
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failing tests |
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Build and asan failures seem unrelated (cannot install moreutils, temporary failure in name resolution), cuda9_cudnn7_py2_test looks real, I'll take a look next week. |
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Remaining MacOS failures are unrelated. |
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Summary: **WIP** Attempt 2 at #14831 This adds `nn.LSTM` to the jit standard library. Necessary changes to the module itself are detailed in comments. The main limitation is the lack of a true `PackedSequence`, instead this PR uses an ordinary `tuple` to stand in for `PackedSequence`. Most of the new code in `rnn.py` is copied to `nn.LSTM` from `nn.RNNBase` to specialize it for LSTM since `hx` is a `Tuple[Tensor, Tensor]` (rather than just a `Tensor` as in the other RNN modules) for LSTM. As a hack it adds an internal annotation `@_parameter_list` to mark that a function returns all the parameters of a module. The weights for `RNN` modules are passed to the corresponding op as a `List[Tensor]`. In Python this has to be gathered dynamically since Parameters could be moved from CPU to GPU or be deleted and replaced (i.e. if someone calls `weight_norm` on their module, #15766), but in the JIT parameter lists are immutable, hence a builtin to handle this differently in Python/JIT. Pull Request resolved: #15744 Differential Revision: D14173198 Pulled By: driazati fbshipit-source-id: 4ee8113159b3a8f29a9f56fe661cfbb6b30dffcd
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Fixes #15749