Summary:
This PR introduces frame ids that will allow us to associate profiling information with its corresponding run.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33788
Differential Revision: D20164897
Pulled By: Krovatkin
fbshipit-source-id: 8172ff9f4d188b339e2ff98a80bbe4a2b306a8aa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35849
This change harmonizes some aspects of the api.
- torch::utils::Future callback should have no args, like ivalue::future.
Many of the lines of this change are related to fixing that up downstream.
No args makes the api simpler to use, particularly since many/most of the
downstream use cases ignore the passed-in args. It's simple enough to
appropriately capture the future in the lambda if necessary.
- Add error/hasError methods to ivalue::Future.
- Use c10::optional underneath for error to ivalue::Future.
- Change markCompleted(error) to setError(error) to ivalue::Future.
- Add setValue(FutureError) version to torch::utils::Future
ghstack-source-id: 101684435
Test Plan: buck test mode/dev-nosan caffe2/test/...
Differential Revision: D20803251
fbshipit-source-id: e3d925287bd9a80d649843eef5f270163f448269
Summary:
**Summary:** This PR contains the infrastructure of a new CUDA fuser. This CUDA fuser is based on many of the same principles of TensorExpressions and Halide, however the implementation is ground up. The fusion pass itself is similar to the default CUDA fuser, however, it has undergone some refactoring and is using the new code generation infrastructure. For those who are interested in how the code generation in this PR works, I would recommend reviewing _test/cpp/jit/test_gpu_fusion.cpp_ as well as the long comment section at the beginning of _torch/csrc/jit/codegen/cuda/transform_replay.h_ One of the largest differences between our approach and that of TVM/Halide, is the concept of "TensorView". TensorView from a high level should be thought of similarly to how we think of working with Tensors in PyTorch. It's an N-D object which can undergo transformations that change its dimensionality. Dimensionality changes are done through the operations split/merge/reorder/computeAt. These transformations are similar to split/fuse/reorder/compute_at of TVM, they modify how a tensor is iterated over to generate GPU code. Interestingly, in our scheme these transformations are applied to tensors and only impact how that tensor is generated.
**Warning:** This PR is purposefully not feature complete with the current fuser. We wanted to separate out the infrastructure from the fusion capabilities. Once in, smaller incremental PRs will be submitted to expand capabilities of the fuser.
**Short term goals:**
Parity with current CUDA fuser (including performance):
- Dynamic shapes (no recompilation)
- Implicit handling of braodcast (broadcasted tensors are treated as tensors of the braodcasted size in the generated code)
- Dropout
**Mid-term goals:**
- Transposes fused with pointwise operations where transpose involves only 2 axes (across the fused operation).
- 1-D reductions fused with pointwise operations
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34785
Reviewed By: ZolotukhinM
Differential Revision: D20650977
Pulled By: soumith
fbshipit-source-id: ee39c95a880e1b9822e874ed4cc180971572bf63
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35523
In this PR we extend ThreadLocalState to cover dispatch keys and
ThreadLocalDebugInfo and move it from JIT interpreter down to
thread management (at::launch) and autograd (backward threads) code
Test Plan: unit tests (CI)
Reviewed By: dzhulgakov
Differential Revision: D20615714
fbshipit-source-id: 16a9fc96a25cb6c2629230b1187fbf78786ac565
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34710
Extending RecordFunction API to support new recording scopes (such as TorchScript functions), as well as giving more flexibility to set sampling rate.
Test Plan: unit test (test_misc.cpp/testRecordFunction)
Reviewed By: gdankel, dzhulgakov
Differential Revision: D20158523
fbshipit-source-id: a9e0819d21cc06f4952d92d43246587c36137582
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/34360
The distributed autograd context sets up a thread local context id
which is used to perform appropriate book keeping and autograd recording of RPC
functions in the forward pass.
However, if we use torch.jit._fork within the distributed autograd context, the
code executed within torch.jit._fork will lose this context since it is run in
a separate JIT thread and the thread local is not set in that thread.
To fix this problem, we pass in the distributed autograd context to
torch.jit._fork similar to what we did in
https://github.com/pytorch/pytorch/pull/16101.
ghstack-source-id: 100445465
Test Plan: waitforbuildbot
Differential Revision: D20301352
fbshipit-source-id: aa3fffe69c2b40722c66213351a4e0d77484a621
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34623
The bandaid of "AT_WARN" keeps introducing new warnings. Let's get rid
of it entirely.
Close#34502
Test Plan: Imported from OSS
Differential Revision: D20420112
Pulled By: albanD
fbshipit-source-id: 7160c113cb4deb2d2f50a375356f423fe5e86f50
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33834
This changes how we report Tracebacks to make them more clear when
there are both serialized and non-serialized ranges. It now looks like:
```
Traceback (most recent call last):
File "foo.py", line 25, in <module>
s2(a, b)
File "/scratch/zdevito/pytorch/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
RuntimeError: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript, serialized code (most recent call last):
File "code/__torch__.py", line 7, in forward
x: Tensor,
y: Tensor) -> Tensor:
return (self).bar(x, y, )
~~~~~~~~~ <--- HERE
def bar(self: __torch__.Moo,
x: Tensor,
File "code/__torch__.py", line 11, in bar
x: Tensor,
y: Tensor) -> Tensor:
_0 = (self).baz(x, y, )
~~~~~~~~~ <--- HERE
_1 = torch.ones([3], dtype=None, layout=None, device=None, pin_memory=None)
return torch.add(_0, _1, alpha=1)
File "code/__torch__.py", line 17, in baz
x: Tensor,
y: Tensor) -> Tensor:
return torch.add(x, y, alpha=1)
~~~~~~~~~ <--- HERE
Traceback of TorchScript, original code (most recent call last):
File "foo.py", line 11, in forward
def forward(self, x, y):
return self.bar(x, y)
~~~~~~~~ <--- HERE
File "foo.py", line 9, in bar
def bar(self, x, y):
return self.baz(x, y) + torch.ones(3)
~~~~~~~~ <--- HERE
File "foo.py", line 7, in baz
def baz(self, x, y):
return x + y
~~~~~ <--- HERE
RuntimeError: The size of tensor a (4) must match the size of tensor b (5) at non-singleton dimension 1
```
It follows Python convension of having the most important information last
and reading from the bottom up.
Changes:
* Moved the error message to the end, to copy Python
* Report original traceback separate from serialized traceback
* Make sure root functions have names in the interpreter trace.
Test Plan: Imported from OSS
Differential Revision: D20126136
Pulled By: zdevito
fbshipit-source-id: fd01f9985e5d74e04c4d064c02e8bc320f4fac13
