Use `append_cxx_flag_if_supported` to determine whether or not `-Werror` is supported
Do not suppress deprecation warnings if glog is not used/installed, as the way check is written right now, it will suppress deprecations even if `glog` is not installed.
Similarly, do not suppress deprecations on MacOS simply because we are compiling with protobuf.
Fix deprecation warnings in:
- MPS by replacing `MTLResourceOptionCPUCacheModeDefault`->`MTLResourceCPUCacheModeDefaultCache`
- In GTests by replacing `TYPED_TEST_CASE`->`TYPED_TEST_SUITE`
- In `codegen/onednn/interface.cpp`, by using passing `Stack` by reference rathern than pointer.
Do not guard calls to `append_cxx_flag_if_supported` with `if(CLANG)` or `if(GCC)`.
Fix some deprecated calls in `Metal` hide more complex exception under `C10_CLANG_DIAGNOSTIC_IGNORE`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97584
Approved by: https://github.com/kit1980
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66746
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
Test Plan: Sandcastle
Reviewed By: malfet
Differential Revision: D31705361
fbshipit-source-id: 33fd22eb03086d114e2c98e56703e8ec84460268
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66234
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
bypass_size_limit
allow-large-files
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D30652629
fbshipit-source-id: 0ae6c4bbbb554bad42e372792a6430e1acf15e3e
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56830
Opt into formatting on GitHub and format everything. This is a trial run before turning on formatting for more and eventually all of the codebase.
Test Plan: CI
Reviewed By: zertosh
Differential Revision: D27979080
fbshipit-source-id: a80f0c48691c08ae8ca0af06377b87e6a2351151
Summary:
All pretty minor. I avoided renaming `class DestructableMock` to `class DestructibleMock` and similar such symbol renames (in this PR).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49815
Reviewed By: VitalyFedyunin
Differential Revision: D25734507
Pulled By: mruberry
fbshipit-source-id: bbe8874a99d047e9d9814bf92ea8c036a5c6a3fd
Summary:
Enabled torch.nn.functional.log_softmax and torch.nn.CrossEntropyLoss for bfloat16 data type.
In order to do that, following dependency have to be enabled.
- RNE (round to nearest even)
- AccumulateType
- bfloat16 arithmetic operator overload
Also, we implement std::numeric_limits fully support for bfloat16 data type
background for dependency:
- RNE vs truncate
From torch.nn.CrossEntropyLoss test. input_size=(128, 1000)
RNE result:
float output: tensor(7.3981, dtype=torch.float32, grad_fn=<NllLossBackward>)
bfloat16 output: tensor(7.3125, dtype=torch.bfloat16, grad_fn=<NllLossBackward>)
truncate result:
float output: tensor(7.3981, dtype=torch.float32, grad_fn=<NllLossBackward>)
bfloat16 output: tensor(5.8750, dtype=torch.bfloat16, grad_fn=<NllLossBackward>)
- scalar_t vs AccumulateType (AccumulateType of bfloat16 is float)
AccumulateType is essential to keep accuracy, especially for reduction related operation.
we have verified it with both local case and real topology. It turns out that bfloat16 type accumulator would cause huge relative error when elements number is large, even more than 50%.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24457
Differential Revision: D17113018
Pulled By: ezyang
fbshipit-source-id: 8d61297ca118f9b5c6730a01efcf3a3704d2f206
Summary:
Enable Add, sub, mul, and div on CPU for bfloat16 type.
Tested via unit tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22851
Differential Revision: D16256757
Pulled By: izdeby
fbshipit-source-id: 8b62f7581fc0ca0d2cff48ab40d877a9fcf70a5b
