Made a += b for lists do an in place add #21896
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pytorchbot
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oncall: jit
smessmer
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Jun 18, 2019
| // Get the appropriate builtin op for this augmented assignment | ||
| // If the RHS is a tensor, return the corresponding ATen in-place op | ||
| // If it's a list of scalars, then return the corresponding list augment op | ||
| Symbol getAugOp(const AugAssign& stmt, bool isTensor) { |
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not sure what you're doing here, this seems unrelated. Can we have this in a separate PR?
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This is what's needed to make a += b do the inplace add instead of desugaring it into a = a + b.
smessmer
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Jun 25, 2019
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looks good now. Thanks.
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@Chillee is landing this pull request. If you are a Facebook employee, you can view this diff on Phabricator.
zdevito
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Jun 27, 2019
Summary: In talks with smessmer, we decided that it'd be better to put the logic in `list`, as optimal behavior requires knowing `.capacity()` Results on my cpu (for the benchmark here: https://twitter.com/VahidK/status/1138674536679821312) now look like this: ``` Pytorch batch_gather took 0.018311 seconds. Pytorch batch_gather jit took 0.013921 seconds. Pytorch vectorized batch_gather took 0.001384 seconds. ``` Previously, `batch_gather jit` took 3x as long as `batch_gather`. Some logic taken from pytorch/pytorch#21690. Note that these two PR's are somewhat orthogonal. That PR handles this benchmark by looking at the alias analysis, while this PR specializes for `+=`. Note that we can't jit the vectorized version as we think `torch.arange` returns a float tensor. Pull Request resolved: pytorch/pytorch#21896 Differential Revision: D15998628 Pulled By: Chillee fbshipit-source-id: b0085960da4613578b94deb98ac62c0a4532a8c3
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xzhu1900
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Jul 5, 2019
Summary: In talks with smessmer, we decided that it'd be better to put the logic in `list`, as optimal behavior requires knowing `.capacity()` Results on my cpu (for the benchmark here: https://twitter.com/VahidK/status/1138674536679821312) now look like this: ``` Pytorch batch_gather took 0.018311 seconds. Pytorch batch_gather jit took 0.013921 seconds. Pytorch vectorized batch_gather took 0.001384 seconds. ``` Previously, `batch_gather jit` took 3x as long as `batch_gather`. Some logic taken from pytorch#21690. Note that these two PR's are somewhat orthogonal. That PR handles this benchmark by looking at the alias analysis, while this PR specializes for `+=`. Note that we can't jit the vectorized version as we think `torch.arange` returns a float tensor. Pull Request resolved: pytorch#21896 Differential Revision: D15998628 Pulled By: Chillee fbshipit-source-id: b0085960da4613578b94deb98ac62c0a4532a8c3
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In talks with @smessmer, we decided that it'd be better to put the logic in
list, as optimal behavior requires knowing.capacity()Results on my cpu (for the benchmark here: https://twitter.com/VahidK/status/1138674536679821312) now look like this:
Previously,
batch_gather jittook 3x as long asbatch_gather.Some logic taken from #21690. Note that these two PR's are somewhat orthogonal. That PR handles this benchmark by looking at the alias analysis, while this PR specializes for
+=.Note that we can't jit the vectorized version as we think
torch.arangereturns a float tensor.