Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56830
Opt into formatting on GitHub and format everything. This is a trial run before turning on formatting for more and eventually all of the codebase.
Test Plan: CI
Reviewed By: zertosh
Differential Revision: D27979080
fbshipit-source-id: a80f0c48691c08ae8ca0af06377b87e6a2351151
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55572
We used to have VariableVersion default constructor
`VariableVersion(uint32_t version=0)`. But sometimes
we override the version_counter right after it's constructed.
E.g in SavedVariable/TensorImpl.
Thus we should make DISABLED the default constructor and else
where using explicit `VariableVersion(uint32_t)` constructor.
Note this PR effectively changes SavedVariable constructor (which overrides
version_counter_ inside) to use the DISABLED constructor and we
can see the gains in reduced instruction counts.
```
// benchmark code
timer = Timer(
"y = x * x",
"""
x = torch.rand((3, 3)).requires_grad_()
""",
language=Language.PYTHON,
)
λ ~ python compare.py
No CUDA runtime is found, using CUDA_HOME='/public/apps/cuda/10.2'
<torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts
object at 0x7f06c48b3a50>
7236 lookdict_unicode_nodummy
2600 torch::autograd::VariableType::(...)
100 0x0000000017751750
-5 unlink_chunk.isra.0
-100 0x000000001773e750
-402 _int_malloc
-1600 operator delete(...)
-1600 c10::intrusive_ptr_target::release_resources()
-2400 c10::VariableVersion::VersionCounter::~VersionCounter()
-3600 torch::autograd::SavedVariable::operator=(...)
-4800 operator new(...)
-6400 torch::autograd::SavedVariable::SavedVariable(...)
-7200 torch::autograd::SavedVariable::SavedVariable()
-8400 free
-16800 malloc
-24400 _int_free
Total: -67771
```
Note there're for other callsites(esp. view related) we just keep it unchanged by
explicitly calling `VariableVersion(uint32_t)` but we should be
able to optimize those in the followup PRs.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D27669074
Pulled By: ailzhang
fbshipit-source-id: a4deb297cc89142ae8bd683284516c881ddf3c87
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54403
A few important points about InferenceMode behavior:
1. All tensors created in InferenceMode are inference tensors except for view ops.
- view ops produce output has the same is_inference_tensor property as their input.
Namely view of normal tensor inside InferenceMode produce a normal tensor, which is
exactly the same as creating a view inside NoGradMode. And view of
inference tensor outside InferenceMode produce inference tensor as output.
2. All ops are allowed inside InferenceMode, faster than normal mode.
3. Inference tensor cannot be saved for backward.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D27316483
Pulled By: ailzhang
fbshipit-source-id: e03248a66d42e2d43cfe7ccb61e49cc4afb2923b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55333
Reapplying without using enum class in a bitfield. See new
comments about gcc bug.
ghstack-source-id: 125776904
Test Plan: Carefully review OSS test failure logs this time
Reviewed By: kimishpatel, bhosmer
Differential Revision: D27576623
fbshipit-source-id: 68fb00e5ff5215e56c8b9bc02717e1e7b2fedf9b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54896
This should help performance. (For example, it improves total
time spent in a C++ benchmark that just adds 2 tensors in place by
about 10%.)
ghstack-source-id: 125659451
Reviewed By: bhosmer
Differential Revision: D27404164
fbshipit-source-id: e1dce8c02100ee4ce22510298c7e0d0f192be201
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53388
Most of this method did not depend on the template parameter. No need to include it in the .h file or duplicate it in the generated code.
ghstack-source-id: 123211590
Test Plan: Existing CI should cover this
Reviewed By: smessmer
Differential Revision: D26851985
fbshipit-source-id: 115e00fa3fde547c4c0009f2679d4b1e9bdda5df
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52344
This line is a bug-prone use of std::move combined with a reference to the moved-from parameter in the same series of function call arguments. This is normally a problem because the order of evaluation is undefined -- if the move happens before the call to `storage.device()`, we may have problems. It is not a problem here because we are merely forwarding from one `Storage&&` parameter to another.
ghstack-source-id: 121837267
Test Plan: See no clang-tidy/HowToEven warning on the diff, I hope
Reviewed By: bhosmer
Differential Revision: D26436550
fbshipit-source-id: da85d79be854ff42c5a0cab9649ba82295816eca
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51049
This diff makes it OK to query has_storage() on all TensorImpls. I added debug assertions that storage_ is indeed never set on them, which is required for this to be correct.
ghstack-source-id: 120714380
Test Plan: CI
Reviewed By: ezyang
Differential Revision: D26008498
fbshipit-source-id: b3f55f0b57b04636d13b09aa55bb720c6529542c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50290
This was reverted because it landed after D24772023 (b73c018598), which
changed the implementation of `dim()`, without rebasing on top of it,
and thus broke the build.
ghstack-source-id: 119608505
Test Plan: CI
Reviewed By: ezyang
Differential Revision: D25852810
fbshipit-source-id: 9735a095d539a3a6dc530b7b3bb758d4872d05a8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50176
UndefinedTensorImpl was the only type that overrode this, and IIUC we don't need to do it.
ghstack-source-id: 119609531
Test Plan: CI, internal benchmarks
Reviewed By: ezyang
Differential Revision: D25817370
fbshipit-source-id: 985a99dcea2e0daee3ca3fc315445b978f3bf680
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47507
This introduces a new SizesAndStrides class as a helper for
TensorImpl, in preparation for changing its representation.
ghstack-source-id: 119313559
Test Plan:
Added new automated tests as well.
Run framework overhead benchmarks. Results seem to be neutral-ish.
Reviewed By: ezyang
Differential Revision: D24762557
fbshipit-source-id: 6cc0ede52d0a126549fb51eecef92af41c3e1a98
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49770
Seems like the performance cost of making this commonly-called method virtual isn't worth having use of undefined tensors crash a bit earlier (they'll still fail to dispatch).
ghstack-source-id: 119528065
Test Plan: framework overhead benchmarks
Reviewed By: ezyang
Differential Revision: D25687465
fbshipit-source-id: 89aabce165a594be401979c04236114a6f527b59
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48877
Setting `Storage` in the TensorImpl ctor only to set it again in
`copy_tensor_metadata` wastes one refcount bump.
ghstack-source-id: 117937872
Test Plan:
internal benchmark. compared results with perf, saw 0.15%
reduction in percent of total time spent in
`TensorImpl::shallow_copy_and_detach`.
Reviewed By: bhosmer
Differential Revision: D25353529
fbshipit-source-id: e85d3a139ccd44cbd059c14edb19b22b962881a9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48681
This should reduce reference counting traffic when creating views.
The code duplication here is unfortunate and I'm open to suggestions on how to reduce it. It's especially regrettable that we create a footgun for subclasses of TensorImpl: they can accidentally override only one of the two overloads and get confusing behavior.
ghstack-source-id: 117896685
Test Plan: internal benchmarks
Reviewed By: ezyang
Differential Revision: D25259741
fbshipit-source-id: 55f99b16b50f9791fdab85cbc81d7cd14e31c4cf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48680
It seems a bit long to put into the header (and is virtual anyway).
ghstack-source-id: 117894350
Test Plan: CI
Reviewed By: bhosmer
Differential Revision: D25259848
fbshipit-source-id: e3eed1f2483fc3c1ff51459159bf3bfed9d6f363
Summary:
This PR moves `DispatchKey::Autograd` to an alias dispatch key mapping to `AutogradCPU, AutogradCUDA, AutogradXLA, AutogradOther, AutogradPrivate*` keys.
A few things are handled in this PR:
- Update alias dispatch key mapping and precompute dispatchTable logic
- Move `Autograd` key from `always_included` set to TensorImpl constructor.
- Update `dummyTensor` constructor to take `requires_grad` as optional argument so that it's closer to the real application in op_registration_test.
- Use `BackendSelect` key for both backend select before and after autograd layer. (1 liner in backend_select codegen)
A few planned followups ordered by priority:
- [cleanup] Update `test_dispatch.py` to include testing `Autograd`.
- [cleanup] Add Math alias key and move catchAll to Math. (to remove 2.2 in `computeDispatchTableEntryWithDebug`)
- [new feature] Add support for Math in native_functions.yaml
- [cleanup] Add iterator like functionality to DispatchKeySet
- [cleanup/large] Only add Autograd backend keys when tensor requires grad. (cc: ljk53 ?)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43070
Reviewed By: ezyang
Differential Revision: D23281535
Pulled By: ailzhang
fbshipit-source-id: 9ad00b17142e9b83304f63cf599f785500f28f71
Summary:
Update the API to access grad in cpp to avoid unexpected thread safety issues.
In particular, with the current API, a check like `t.grad().defined()` is not thread safe.
- This introduces `t.mutable_grad()` that should be used when getting a mutable version of the saved gradient. This function is **not** thread safe.
- The `Tensor& grad()` API is now removed. We could not do a deprecation cycle as most of our call side use non-const Tensors that use the non-const overload. This would lead to most calls hitting the warning. This would be too verbose for all the users.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40887
Reviewed By: ezyang
Differential Revision: D22343932
Pulled By: albanD
fbshipit-source-id: d5eb909bb743bc20caaf2098196e18ca4110c5d2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32734
VariableTensorId is the only key with this treatment today,
but BackendSelect and CompoundOp are coming soon.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D19628091
Pulled By: ezyang
fbshipit-source-id: 250753f90528fa282af7a18d8d2f7736382754bd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30874
These have all been disabled at this point, so there is no difference in the generated code.
Test Plan: Imported from OSS
Differential Revision: D18855990
Pulled By: gchanan
fbshipit-source-id: 03796b2978e23ef9060063f33241a1cbb39f1cf3
Summary:
This improved multi-d microbenchmark by ~100 ns, empty_tensor_restride used to be 13% of iteration time, now about 5%
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30452
Test Plan: Covered by existing tests
Differential Revision: D18704233
Pulled By: ngimel
fbshipit-source-id: be527f09183bc31e9d1f63fd49bfbe0998fe167f
