Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73875
Previously we had a few settings:
- getExecutor - which toggled between Profiling Executor and Legacy
- getGraphOptimize - if true, overrides PE/Legacy to run with simple executor (no optimizations)
and then...
- getProfilingMode - which would set PE to 0 specializtions.
The last mode is redundant with getGraphOptimize, we should just remove it and use getGraphOptimize in these cases. It would lead to potentially invalid combinations of logic - what does mean if getProfilingMode is true but getExecutor is set to false ? This would lead to a bug in specialize_autograd_zero in this case, see: https://github.com/pytorch/pytorch/blob/master/torch%2Fcsrc%2Fjit%2Fpasses%2Fspecialize_autogradzero.cpp#L93.
The tests here are failing but get fixed with the PR above it, so i'll squash for landing.
Test Plan: Imported from OSS
Reviewed By: cpuhrsch
Differential Revision: D34938130
Pulled By: eellison
fbshipit-source-id: 1a9c0ae7f6d1cfddc2ed3499a5af611053ae5e1b
(cherry picked from commit cf69ce3d155ba7d334022c42fb2cee54bb088c23)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71666
When JIT autodiff is constructing a gradient computation graph, it will only add gradients for tensors that require_grad. Previously, require_grad information was **not** propagated to the subgraph that autodiff used; as a result, autodiff would calculate *all* gradients, even if requires_grad had never been set during profiling runs. In certain cases, this can lead to performance issues. For example, during training, the gradient of the input data is not needed, but is still computed.
This propagates requires_grad to the subgraph passed into autodiff, so that autodiff will not compute unnecessary gradients.
Test: `./bin/test_jit --gtest_filter="AutodiffRemoveUnusedGradientsTest.Linear"`
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D33725304
Pulled By: davidberard98
fbshipit-source-id: ca7ab4c9a6a26f94f93aff2d5a4135e125323ba1
(cherry picked from commit a97fe0556da1d74d04250c7cbcd1b8e9d8b41ebe)
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:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
Fixes https://github.com/pytorch/pytorch/issues/46373
As noted in https://github.com/pytorch/pytorch/issues/46373, there needs to be a flag passed into the engine that indicates whether it was executed through the backward api or grad api. Tentatively named the flag `accumulate_grad` since functionally, backward api accumulates grad into .grad while grad api captures the grad and returns it.
Moving changes not necessary to the python api (cpp, torchscript) to a new PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46855
Reviewed By: ngimel
Differential Revision: D24649054
Pulled By: soulitzer
fbshipit-source-id: 6925d5a67d583eeb781fc7cfaec807c410e1fc65
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:
Clamp input tensor values to [3, 3] to limit how small `tanh` gradint can get
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35196
Test Plan: CI + `bin/test_jit --gtest_filter=JitTest.ADFormulas --gtest_repeat=60000 --gtest_break_on_failure`
Differential Revision: D20611256
Pulled By: malfet
fbshipit-source-id: 8640faa5d8567d6c6df8cc5df80c2e65407116eb
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/29665
Our intention is to merge the static distinction between Tensor and
Variable. Ordinarily, this would entail merging the methods of Tensor
and Variable. But there are a lot of "private"-ish methods on Variable
that we don't actually want to dump onto the Tensor class. So, as prep
work, we move all of those methods off of Variable and into
the torch::autograd::impl namespace (impl as in, please don't use this
end users). This ends up being a fairly large patch because all of
the call sites have to play ball too.
While I was on the topic, I also moved any of the touched functions into
the C++ file, so that modifying them would not trigger a recompilation of
all of torch.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D18496169
Pulled By: ezyang
fbshipit-source-id: afb203252620ec274be596b3e7b1d84d321bad3a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26572
Combined with isinstance specialization this allows a degree of polymorphic
functions to work without needing to use our weirder overload hacks.
We do not define any operators on Any, so the only thing you can do with it
is to put it in containers or type refine it using an isinstance check.
Any is restricted from appearing in non-argument position because we
cannot restore type tags if it ends up as a field in a class.
Test Plan: Imported from OSS
Differential Revision: D17530643
Pulled By: zdevito
fbshipit-source-id: f06f78ce84819f7773953a492f3d4c49219ee94c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24284
This PR finishes the unification of all Tensor types into a single object.
ProfiledTensorType is renamed to TensorType and the old TensorType is
deleted.
Notes:
* Fixes bug in merge for VaryingShape by changing its representation to an
optional list of optional ints.
* Removes ProfiledTensorType::create(type) invocations that can now
simply be expect calls on tensor type.
Test Plan: Imported from OSS
Differential Revision: D16794034
Pulled By: zdevito
fbshipit-source-id: 10362398d0bb166d0d385d74801e95d9b87d9dfc
Summary:
This PR removes SymbolicVariable from all tests as well as the specialize_autogradzero and canonicalize_ops passes. These passes used SymbolicVariable in a relatively simple way compared to its few remaining uses.
Removing SymbolicVariable means graphs must be constructed by other methods. IRParser was preferred for tests, but tests requiring pointers to graph internals or differentiation use direct construction instead. See https://github.com/pytorch/pytorch/issues/23989, which was discovered during this process, for why IRParser cannot be used when differentiation is required. Direct construction was also used in the updated passes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24007
Test Plan: Only refactors existing tests and preserves current checks; no additional testing needed.
Differential Revision: D16906045
Pulled By: mruberry
fbshipit-source-id: b67df4611562cd7618f969890e2b6840750c7266
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24801
This is to fix the ODR-violations in fbcode static builds, which have been broken for several months.
This PR is unfortunately quite large, but the changes are only mechanical:
1. Tests defined in header files -> tests defined in cpp files
2. Remove the `torch::jit::testing` namespace -> `torch::jit`.
3. Single `test.h` file that aggregates all tests.
4. Separate out files for gtest and python versions of the tests instead of using a build flag
5. Add a readme for how to add a new test, and explaining a bit about why the cpp tests are the way they are.
Test Plan: Imported from OSS
Differential Revision: D16878605
Pulled By: suo
fbshipit-source-id: 27b5c077dadd990a5f74e25d01731f9c1f491603
