Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35994
prim::rpc_async was optimized out if we don't takes its returned future and wait on the future.
Test Plan: `
Differential Revision: D7850846
fbshipit-source-id: e4e46506ab608f2e072027d6c10c49a4d784b14a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115
This commit runs the newly added tools/clang_format.py on the JIT
codebase and includes all of the formatting changes thus produced.
Testing:
Ran the script, CI.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D20568523
Pulled By: SplitInfinity
fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34515
Once upon a time we thought this was necessary. In reality it is not, so
removing it.
For backcompat, our public interface (defined in `api/`) still has
typedefs to the old `script::` names.
There was only one collision: `Pass` as a `Stmt` and `Pass` as a graph
transform. I renamed one of them.
Test Plan: Imported from OSS
Differential Revision: D20353503
Pulled By: suo
fbshipit-source-id: 48bb911ce75120a8c9e0c6fb65262ef775dfba93
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33921
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.intern.facebook.com/intern/diff/D20153092/)!
Test Plan: Imported from OSS
Differential Revision: D20177227
Pulled By: jamesr66a
fbshipit-source-id: 87f3e484c4f873d60f76f50f6789c1b4a73bdfde
Summary:
Separating CUDA fuser from CPU fuser.
1. New node in IR - prim::CudaFusionGroup:
This enables the cuda fuser to co-exist along side the old fuser. Allows us
to incrementally build and expand cuda fuser.
2. copied FuseGraph optimization passes to CudaFuserGraph:
We will re-factor & reuse Chunk/Concat in the old fuser logic, which is
handled in the optimization pass at this moment. Unfortunately many code in
the pass is tightly binded with the legacy fuser, which makes code sharing
difficult.
The CudaFusionGraph will support only a subset of operations comparing to
legacy fuser (CUDA only). It is registered as a custom pass post fusion via
```torch._C._jit_register_cuda_fuser()```
To have it in effect, you should also turn off fusion on GPU via
```torch._C._jit_override_can_fuse_on_gpu(False)```
3. We don't have codegen in this PR yet (WIP). Currently we just fall back to
the old fuser.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33527
Differential Revision: D20171598
Pulled By: ZolotukhinM
fbshipit-source-id: 9a3c0f06f46da7eaa80ae7551c04869f5b03ef71
Summary:
[This check](019ffdca31/torch/csrc/jit/ir/alias_analysis.cpp (L772)) wasn't being triggered for None outputs of tuples, because `mustBeNone` would return false if `num_outputs != 1`. This caused an assertion to fail in alias analysis. It's kind of a convoluted case to repro and I wasn't able to make a succinct one, but I tested internally and it fixed the bug.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34268
Differential Revision: D20261539
Pulled By: eellison
fbshipit-source-id: 95edea10e2971727cfd3f3bc2b6bdf9dbadca6a9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33705
The fact that there were two overloads appears to be a historical
artifact that dates back to when goldsborough originally added these
bindings in the first place. If TensorOptions is made optional,
then you only need one overload, not two, as they are exactly redundant
with each other. When MemoryFormat was added, it was made a little
harder to do this, as the C++ syntax at::empty_like(t, memory_format) would
not work if you collapsed the overload; but now it works because TensorOptions
supports MemoryFormat.
The upshot is, I can get rid of all the overloads and just have one overload.
Amazingly, this change is backwards compatible, as the test attests. While
I was at it, I also deleted the overload name from the functions entirely.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D20073355
Pulled By: bhosmer
fbshipit-source-id: c6a8908213b32ccf6737ea864d135e2cce34f56b
Summary:
**Summary**
This commit adds an implementation of `Tensor.tolist()` to the JIT interpreter.
**Testing**
This commit adds several unit tests that test that this function works correctly for
0D, 1D, 2D and 3D tensors of type `float`, `int` and `bool`.
```
(base) meghanl-mbp:pytorch meghanl$ python test/test_jit.py TestList.test_to_list -v
Fail to import hypothesis in common_utils, tests are not derandomized
test_to_list (jit.test_list_dict.TestList)
Unit tests for Tensor.tolist() function. ... ok
----------------------------------------------------------------------
Ran 1 test in 0.329s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33472
Differential Revision: D20109738
Pulled By: SplitInfinity
fbshipit-source-id: a6e3fee5e3201d5e1f0c4ca45048488ae2bf5e33
