Summary:
Follow up to https://github.com/pytorch/pytorch/issues/68095
This also changes the files from the ATen folder to include c10's `Export.h` instead since they can't ever be exporting `TORCH_PYTHON_API`.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69585
Reviewed By: mrshenli
Differential Revision: D32958594
Pulled By: albanD
fbshipit-source-id: 1ec7ef63764573fa2b486928955e3a1172150061
Summary:
This PR suppresses clang-tidy warnings in the codebase (for now) so that we can re-enable clang-tidy checks on master.
I ran this script to add the `NOLINTNEXTLINE` comments (on a devserver):
```bash
python3 setup.py develop
# Uses same script that's run on CI and adds the -j (parallel), -s (add comments), -k (continue if diagnostic errors are found) options
python3 tools/clang_tidy.py \
-j \
-s \
-k \
-v \
--paths torch/csrc/ \
-g"-torch/csrc/jit/passes/onnx/helper.cpp" \
-g"-torch/csrc/jit/passes/onnx/shape_type_inference.cpp" \
-g"-torch/csrc/jit/serialization/onnx.cpp" \
-g"-torch/csrc/jit/serialization/export.cpp" \
-g"-torch/csrc/jit/serialization/import.cpp" \
-g"-torch/csrc/jit/serialization/import_legacy.cpp" \
-g"-torch/csrc/onnx/init.cpp" \
-g"-torch/csrc/cuda/nccl.*" \
-g"-torch/csrc/cuda/python_nccl.cpp" \
-g"-torch/csrc/autograd/FunctionsManual.cpp" \
-g"-torch/csrc/generic/*.cpp" \
-g"-torch/csrc/jit/codegen/cuda/runtime/*" \
-g"-torch/csrc/deploy/interpreter/interpreter.cpp" \
-g"-torch/csrc/deploy/interpreter/interpreter.h" \
-g"-torch/csrc/deploy/interpreter/interpreter_impl.h" \
-g"-torch/csrc/deploy/interpreter/test_main.cpp"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60649
Test Plan: Verified changes by re-running the script (without the `-s` option) and seeing no warnings/errors.
Reviewed By: walterddr, janeyx99
Differential Revision: D29504258
Pulled By: 1ntEgr8
fbshipit-source-id: 78310b30ee8213b73ddb4771ad874665323e7a4e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50321
Quantization team reported that when there are two empty tensors are replicated among ranks, the two empty tensors start to share storage after resizing.
The root cause is unflatten_dense_tensor unflattened the empty tensor as view of flat tensor and thus share storage with other tensors.
This PR is trying to avoid unflatten the empty tensor as view of flat tensor so that empty tensor will not share storage with other tensors.
Test Plan: unit test
Reviewed By: pritamdamania87
Differential Revision: D25859503
fbshipit-source-id: 5b760b31af6ed2b66bb22954cba8d1514f389cca
Summary:
* adds TORCH_API and AT_CUDA_API in places
* refactor code generation Python logic to separate
caffe2/torch outputs
* fix hip and asan
* remove profiler_cuda from hip
* fix gcc warnings for enums
* Fix PythonOp::Kind
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19554
Differential Revision: D15082727
Pulled By: kostmo
fbshipit-source-id: 83a8a99717f025ab44b29608848928d76b3147a4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17991
changes:
-Breaks bc: Tensor::type() now returns DeprecatedTypeProperties& rather than Type&.
-Added DeprecatedTypeProperties, it serves as a temporary replacement for Type as the return value of Tensor::type(). This contributes to making Type just for dispatch purposes so that we can make it dtype agnostic.
-Tensor::dispatch_type() now returns Type& like Tensor::type() used to do.
-Changed callsites of Tensor::type() appropriately.
Reviewed By: ezyang
Differential Revision: D14443117
fbshipit-source-id: 239ccb7a09626279a71d1a37f8f82e7f57bf7d9e
Summary:
Anywhere we used #include "foo.h", we now say #include <foo.h>
Paths are adjusted to be rooted out of aten/src, torch/lib, or
the root level directory.
I modified CMakeLists.txt by hand to remove TH and THC from
the include paths.
I used the following script to do the canonicalization:
```
import subprocess
import re
import os.path
files = subprocess.check_output(['git', 'ls-files']).decode('utf-8').rstrip().split('\n')
for fn in files:
if not any(fn.endswith(suff) for suff in ['.cu', '.cpp', '.in', '.h', '.hpp', '.cu', '.cuh', '.cc']):
continue
if not any(fn.startswith(pref) for pref in ["aten/", "torch/"]):
continue
with open(fn, 'r') as f:
c = f.read()
def fmt(p):
return "#include <{}>".format(p)
def repl(m):
p = m.group(1)
if p in ["dlfcn.h", "unistd.h", "nvrtc.h", "cuda.h", "cuda_runtime.h", "cstdint", "cudnn.h", "Python.h", "cusparse.h", "cuda_runtime_api.h", "cuda_fp16.h", "cublas_v2.h", "stdint.h", "curand_kernel.h"]:
return fmt(p)
if any(p.startswith(pref) for pref in ["torch/csrc", "c10/", "ATen/", "caffe2/", "TH/", "THC/", "Eigen/", "gtest/", "zdl/", "gloo/", "onnx/", "miopen/"]):
return fmt(p)
for root in ["aten/src", "torch/lib", ""]:
for bad_root in [os.path.dirname(fn), "aten/src/TH", "aten/src/THC", "torch/csrc"]:
new_p = os.path.relpath(os.path.join(bad_root, p), root)
if not new_p.startswith("../") and (os.path.exists(os.path.join(root, new_p)) or os.path.exists(os.path.join(root, new_p + ".in"))):
return fmt(new_p)
print("ERROR: ", fn, p)
return m.group(0)
new_c = re.sub(r'#include "([^"]+)"', repl, c)
if new_c != c:
print(fn)
with open(fn, 'w') as f:
f.write(new_c)
```
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14849
Reviewed By: dzhulgakov
Differential Revision: D13363445
Pulled By: ezyang
fbshipit-source-id: 52361f878a672785f9306c9e9ab2513128092b68
Summary:
When go to mixed precision fp16 training, DDP randomly hangs. Initially, I thought this smells like a similar NCCL bug I filed a while ago. It turns out it's not. Again, I am seeing different rank process has different size. How could this even happen?
It turns out that take_tensors will generate a list of bucketed tensors in an un deterministic order, because, the key to the map is a pointer. An interesting bug digging and fix.
Now fp16 DDP training should be fully working now.
Also, added another take_tensor fine grained helper that aims to improve the performance of DDP, making it a TODO to replace the DDP take_tensors with that.
Fixed: https://github.com/pytorch/pytorch/issues/12150
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13496
Differential Revision: D12920985
Pulled By: teng-li
fbshipit-source-id: 26f3edae7be45a80fa7b2410a2e5a1baab212d9c
Summary:
I got some tensor->variable conversion exceptions from `torch/csrc/autograd/variable.h`, which used the `TORCH_ASSERTM` macros instead of `AT_CHECK`, so they didn't have backtraces. This was such a substantial loss for debugability that I decided to update the whole codebase to use the backtrace-enabled ATen macros instead of `TORCH_ASSERT` and `JIT_ASSERT`, the latter having been an alias of the former.
ezyang apaszke zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9575
Differential Revision: D8924566
Pulled By: goldsborough
fbshipit-source-id: 7a4013b13eec9dbf024cef94cf49fca72f61d441
