Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35039
This is the initial step towards merging ivalue future and rpc future
Test Plan: Imported from OSS
Differential Revision: D20537164
Pulled By: wanchaol
fbshipit-source-id: d4f148c88e49ed6b0881ca4b4dd945ea24166183
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34842
This PR (hopefully the last one of such kind) is merging changes from a
side branch where tensor expessions based fuser work has been done so
far. This PR is is a squashed version of changes in the side branch,
which is available here: https://github.com/bertmaher/pytorch
Differential Revision: D20478208
Test Plan: Imported from OSS
Pulled By: ZolotukhinM
fbshipit-source-id: 21556e009f1fd88099944732edba72ac40e9b9c0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34227
This PR adds a CUDA support to tensor expressions.
Differential Revision: D20251836
Test Plan: Imported from OSS
Pulled By: ZolotukhinM
fbshipit-source-id: ab36a55834cceff30c8371fef6cca1054a32f017
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34226
LLVM and Cuda backends are added in subsequent PRs, so at this point the fuser is pretty useless, but it still can be tested and its logic is not going to change with addition of the codegens.
Differential Revision: D20251838
Test Plan: Imported from OSS
Pulled By: ZolotukhinM
fbshipit-source-id: 82b0d221fa89904ed526689d02a6c7676a8ce8de
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34319
Removes prepacking ops and install them as attributes of the top level
module. Needs to run freezing as the first pass.
Test Plan:
python test/test_xnnpack_integration.py
Imported from OSS
Differential Revision: D20290726
fbshipit-source-id: 633ceaa867ff7d5c8e69bd814c0362018394cb3a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34048
Rewrites the graph to insert xnnpack prepack and packed run ops for
conv2d and linear.
Test Plan:
python test/test_xnnpack_integration.py
Imported from OSS
Differential Revision: D20185658
fbshipit-source-id: c4c073c912ad33e822e7beb4ed86c9f895129d55
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34671
Like the python arg parser, this tries to convert to the schema in order.
It introduces schema_match_exception which gets thrown when the schema doesn't match,
allowing the overload handler to try the next option.
Behavior will not 100% match the schema argument parser but should work for
simple cases using custom binding.
Test Plan: Imported from OSS
Differential Revision: D20432206
Pulled By: zdevito
fbshipit-source-id: 280839a2205ea3497db3a9b5741fccc1e2bff9a8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34677
1. Remove remaining uses of `script::` namespace from the codebase,
2. Add one more typedef for `script::ExtraFilesMap` which is part of the
public interface.
Pull Request resolved: #34580
Test Plan: Imported from OSS
Reviewed By: zdevito
Differential Revision: D20431739
Pulled By: suo
fbshipit-source-id: a29d369c755b6506c53447ca1f286b6339222c9a
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33927
Test Plan:
test will be added in later PRs
Imported from OSS
Differential Revision: D20354879
fbshipit-source-id: 03976f4b86c46dbdc4e45764a1e72f1a3855a404
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/33853
Quant fusion relies on inline, but inline will break the CallFunction("linaer", ...) into a if block
it will be hard to recognize this block and swap it with quantized::linear, in order to
preserve the op, we will swap all quantized functional linear into aten::linear.
They might produce different backward graph, but this is called in the step before we get quantized
model, so it shouldn't affect anything.
We'll integrate this with convert_script later in the new "finalize_quant" API
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20343873
fbshipit-source-id: 423e03bf893b79267d2dc97bc997ee1bfe54ec0f
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 patch enables folding GetAttr nodes with their corresponding
values. _jit_pass_freeze_module API returns a new TorchScipt module
where all function calls and get attributes are inlined.
Usage:
frozen_model = torch._C._freeze_module(scrited_model._c)
frozen_model.forward(...)
This API currently optimizes the forward method. We will follow up to
to preserve and optimize methods and attributes that are annotated as
torch.jit.interface.
Several future improvements to JIT optimizations are required to maximize
clean up/de-sugar the graph and eliminate redundancies.
Ideally, we want to produce a graph that can easily be lowered to
GLOW and other low-level backends.
__
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32178
Differential Revision: D19419640
Pulled By: bzinodev
fbshipit-source-id: 52baffaba9bca2cd60a8e747baa68d57711ad42b
