This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
Because ANDROID>=21 is assumed in CI tests, it is time to remove old workarounds. math_compat.h contains solely wrapper math functions for ANDROID, so we can remove its usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116167
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65222
When compiling against the Android SDK with `--D_FORTIFY_SOURCE=2`, the compiler will complain that the `dst` size is a larger size than the `src` size due to the function templating using two differently sized objects. There is a `TORCH_CHECK` to ensure we don't go through with these `memcpy`'s, but in the interest of making the compiler happy, lets switch the `memcpy` to take `sizeof(src)`.
Test Plan: CI
Reviewed By: bertmaher, lanza
Differential Revision: D30992678
fbshipit-source-id: b3e7aa992a3650e1051abad05be800b684e6332b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64887
BufHandle has exactly the same functionality and should be used instead.
Differential Revision:
D30889483
D30889483
Test Plan: Imported from OSS
Reviewed By: navahgar
Pulled By: ZolotukhinM
fbshipit-source-id: 365fe8e396731b88920535a3de96bd3301aaa3f3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64077
We were assuming kernel dimensions fit in 32 bits (the old fuser made
this assumption too), but we should be able to support 64.
ghstack-source-id: 136933272
Test Plan: unit tests; new IR level test with huge sizes
Reviewed By: ZolotukhinM
Differential Revision: D30596689
fbshipit-source-id: 23b7e393a2ebaecb0c391a6b1f0c4b05a98bcc94
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63195
This helps us to later switch from using KernelArena with raw pointers
to shared pointers without having to change all our source files at
once.
The changes are mechanical and should not affect any functionality.
With this PR, we're changing the following:
* `Add*` --> `AddPtr`
* `new Add(...)` --> `alloc<Add>(...)`
* `dynamic_cast<Add*>` --> `to<Add>`
* `static_cast<Add*>` --> `static_to<Add>`
Due to some complications with args forwarding, some places became more
verbose, e.g.:
* `new Block({})` --> `new Block(std::vector<ExprPtr>())`
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D30292779
Pulled By: ZolotukhinM
fbshipit-source-id: 150301c7d2df56b608b035827b6a9a87f5e2d9e9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62336
This PR was generated by removing `const` for all types of nodes in NNC IR, and fixing compilation errors that were the result of this change.
This is the first step in making all NNC mutations in-place.
Test Plan: Imported from OSS
Reviewed By: iramazanli
Differential Revision: D30049829
Pulled By: navahgar
fbshipit-source-id: ed14e2d2ca0559ffc0b92ac371f405579c85dd63
Summary:
This PR suppresses clang-tidy warnings in the codebase (for now) so that we can re-enable clang-tidy checks on master.
I ran this script to add the `NOLINTNEXTLINE` comments (on a devserver):
```bash
python3 setup.py develop
# Uses same script that's run on CI and adds the -j (parallel), -s (add comments), -k (continue if diagnostic errors are found) options
python3 tools/clang_tidy.py \
-j \
-s \
-k \
-v \
--paths torch/csrc/ \
-g"-torch/csrc/jit/passes/onnx/helper.cpp" \
-g"-torch/csrc/jit/passes/onnx/shape_type_inference.cpp" \
-g"-torch/csrc/jit/serialization/onnx.cpp" \
-g"-torch/csrc/jit/serialization/export.cpp" \
-g"-torch/csrc/jit/serialization/import.cpp" \
-g"-torch/csrc/jit/serialization/import_legacy.cpp" \
-g"-torch/csrc/onnx/init.cpp" \
-g"-torch/csrc/cuda/nccl.*" \
-g"-torch/csrc/cuda/python_nccl.cpp" \
-g"-torch/csrc/autograd/FunctionsManual.cpp" \
-g"-torch/csrc/generic/*.cpp" \
-g"-torch/csrc/jit/codegen/cuda/runtime/*" \
-g"-torch/csrc/deploy/interpreter/interpreter.cpp" \
-g"-torch/csrc/deploy/interpreter/interpreter.h" \
-g"-torch/csrc/deploy/interpreter/interpreter_impl.h" \
-g"-torch/csrc/deploy/interpreter/test_main.cpp"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60649
Test Plan: Verified changes by re-running the script (without the `-s` option) and seeing no warnings/errors.
Reviewed By: walterddr, janeyx99
Differential Revision: D29504258
Pulled By: 1ntEgr8
fbshipit-source-id: 78310b30ee8213b73ddb4771ad874665323e7a4e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57334
Here's a possibly controversial PR. These counters got in the way of
generalizing the fuser tests to handle arbitrary devices, and I guess I'm just
generally skeptical that they provide much value. While true that they let us
observe whether fusion groups were created, we already have assertions based on
the shape of the graph, and I'm not sure that I trust those any less than these
counters.
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision: D29471484
Pulled By: bertmaher
fbshipit-source-id: f6d76f6e72dbfb581acff1d834b0c74500941b57
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57551
The new method allows to pass input and output arguments by `void*`
pointers instead of CallArgs. That helps to reduce the invocation
overhead. Currently this is only supported in LLVM codegen.
Relanding #55113 (the entire stack) which was reverted because I forgot
to guard a new test with `ifdef LLVM`.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D28195049
Pulled By: ZolotukhinM
fbshipit-source-id: 035b77ae996dbbcd542b4b0e4c011b41e8d7828b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55113
The new method allows to pass input and output arguments by `void*`
pointers instead of CallArgs. That helps to reduce the invocation
overhead. Currently this is only supported in LLVM codegen.
Differential Revision: D27487549
Test Plan: Imported from OSS
Reviewed By: bertmaher
Pulled By: ZolotukhinM
fbshipit-source-id: d8f3d92262cde1c155beefb629454370d9af2f89
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55825
The mask has never been used (in vectorization we generate an explicit
`IfThenElse` construct when we need to mask out some elements). The PR
removes it and cleans up all its traces from tests.
Differential Revision: D27717776
Test Plan: Imported from OSS
Reviewed By: navahgar
Pulled By: ZolotukhinM
fbshipit-source-id: 41d1feeea4322da75b3999d661801c2a7f82b9db
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49697
Mostly mechanical move. This refactoring helps to hide unnecessary
details from the SimpleIREval interface and make it more similar to a
pure 'codegen'.
Test Plan: Imported from OSS
Reviewed By: nickgg
Differential Revision: D25668696
Pulled By: ZolotukhinM
fbshipit-source-id: 423247bfcdfa88403e8ec92152f00110bb9da19c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49357
This is a follow-up fix for PR #48679, where the previous PR
adds support for integer inputs to aten::abs by promoting integers to
float and then demote the result back to integers. This PR supports
integer inputs to aten::abs more efficiently in the SimpleIREvaluator
by allowing implementing integer inputs for kAbs (renamed from kFabs).
- Rename kFabs to kAbs
- Add support for integer input to kAbs in SimpleIREvalator (note that:
llvm_codegen and cuda_codegen already supports integer inputs to kAbs)
Test Plan:
- `PYTORCH_TENSOREXPR_DONT_USE_LLVM=1 python test/test_jit_fuser_te.py
TestTEFuser.test_unary_ops`
- `python test/test_jit_fuser_te.py TestTEFuser.test_unary_ops`
Imported from OSS
Reviewed By: eellison
Differential Revision: D25545791
fbshipit-source-id: e52f51a352d149f66ce8341fb3beb479be08a230
Summary:
This is a fast log implementations
benchmark:
```
buck run mode/opt //caffe2/benchmarks/cpp/tensorexpr:tensorexpr_bench -c 'fbcode.caffe2_gpu_type=none'
```
Test Plan: buck test mode/no-gpu //caffe2/test/cpp/tensorexpr:tensorexpr -- *.fastLogFloat
Reviewed By: bertmaher
Differential Revision: D25445815
fbshipit-source-id: 20696eacd12a55e797f606f4a6dbbd94c9652888
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49396
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49271
Two things:
1. These throw exceptions in their constructor, which causes a segfault (*), so
move the exceptions to ::make.
2. They technically support FP types but the rules are complicated so let's not
bother.
(*) The reason for the segfault: all Exprs including these inherit from
KernelScopedObject, whose constructor adds the object to a list for destruction
at the end of the containing KernelArena's lifetime. But if the derived-class
constructor throws, the object is deleted even though it's still in the
KernelArena's list. So when the KernelArena is itself deleted, it double-frees
the pointer and dies. I've also fixed And, Or, and Xor in this diff.
ghstack-source-id: 118594998
Test Plan: `buck test //caffe2/test:jit`
Reviewed By: bwasti
Differential Revision: D25512052
fbshipit-source-id: 42670b3be0cc1600dc5cda6811f7f270a2c88bba
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49184
Adds BitCasting to NNC. This will enable fast approximation algorithms implemented directly in TensorExpressions
Test Plan: buck test mode/no-gpu //caffe2/test/cpp/tensorexpr:tensorexpr
Reviewed By: bertmaher
Differential Revision: D25466476
fbshipit-source-id: f063ab29ba7bab2dcce463e499f2d4a16bdc1f0e
Summary: Adds BitCasting to NNC. This will enable fast approximation algorithms implemented directly in TensorExpressions
Test Plan: buck test mode/no-gpu //caffe2/test/cpp/tensorexpr:tensorexpr
Reviewed By: bertmaher
Differential Revision: D25441716
fbshipit-source-id: c97b871697bc5931d09cda4a9cb0a81bb420f4e2
Summary:
Add missing types for bitwise_ops in `SimpleIREvaluator`
This is the first part of fixes for issue https://github.com/pytorch/pytorch/issues/48053.
- Original implementation of bitwise_ops supports only int operands, the
fix all support for integral types supported by the IR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48179
Test Plan: `python test/test_jit_fuser_te.py TestTEFuser.test_bitwise_ops`
Reviewed By: ZolotukhinM
Differential Revision: D25126944
Pulled By: penguinwu
fbshipit-source-id: 04dc7fc00c93b2bf1bd9f9cd09f7252357840b85
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45388
Classes defined in these files are closely related, so it is reasonable
to have them all in one file. The change is purely a code move.
Differential Revision: D23952867
Test Plan: Imported from OSS
Reviewed By: nickgg
Pulled By: ZolotukhinM
fbshipit-source-id: 12cfaa968bdfc4dff00509e34310a497c7b59155
Summary:
Awhile back when commonizing the Let and LetStmt nodes, I ended up removing both and adding a separate VarBinding section the Block. At the time I couldn't find a counter example, but I found it today: Local Vars and Allocations dependencies may go in either direction and so we need to support interleaving of those statements.
So, I've removed all the VarBinding logic and reimplemented Let statements. ZolotukhinM I think you get to say "I told you so". No new tests, existing tests should cover this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42634
Reviewed By: mruberry
Differential Revision: D22969771
Pulled By: nickgg
fbshipit-source-id: a46c5193357902d0f59bf30ab103fe123b1503f1
