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/37776
* Remove type-specific size tracking in favor of byte size tracking in Storage and StorageImpl
* Changed numel() and set_numel() to nbytes() and set_nbytes()
* Added enum argument to Storage/StorageImpl constructor to indicate new meaning of the size parameter
* Update all callers of the changed API
Part of issue https://github.com/pytorch/pytorch/issues/33950
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37028
Differential Revision: D21171334
Pulled By: ezyang
fbshipit-source-id: 37329a379de9a3a83cc5e9007e455a3e1c2d10b8
Summary:
Stacked PRs
* #32958 - Make zip serialization the default
* **#32244 - Fix some bugs with zipfile serialization**
It includes the following changes:
* Split up tests so that we can test both serialization methods
* Loading something within a buffer doesn't work anymore, so those tests are only on the old serialization method (it's possible but introduces a big slowdown since it requires a linear scan of the entire zipfile to find the magic number at the end)
* Call `readinto` on a buffer if possible instead of `read` + a copy
* Disable CRC-32 checks on read (there was some issue where miniz said the CRC was wrong but `zipinfo` and `unzip` said the zip file was fine)
](https://our.intern.facebook.com/intern/diff/19418935/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32244
Pulled By: driazati
Reviewed By: eellison
Differential Revision: D19418935
fbshipit-source-id: df140854f52ecd04236225417d625374fd99f573
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25527
Master GH issue: https://github.com/pytorch/pytorch/issues/23110.
This change builds upon https://github.com/pytorch/pytorch/pull/24876 and
provides all the autograd hooks needed for a forward pass with distributed rpc
for builtin operators. This change does not address distributed rpc for python
UDFs and that will be addressed in follow up PRs.
Summary of changes:
1. Attach send autograd functions when a request is sent from the client and
response is sent from the server.
2. Attach receive autograd functions when a request is received on the server
and a response is received on the client.
3. Generate a globally unique autograd_message_id for each send/recv autograd
function pair to uniquely identify them.
ghstack-source-id: 91240466
Test Plan: unit tests.
Differential Revision: D17148077
fbshipit-source-id: 192d8a3f552ed7cc939f55dcca332965c9bd3233
Summary:
Serialization.cpp fails on big endian machines.
This patch fixes the endian bugs and also makes the pytorch
model files portable across different endian architectures.
x86 generated model file can be read on s390 arch.
First problem, is serialization.cpp forgets to convert "size" value
of the storage elements to the native byte order.
torch.load throws an assertion as a result
(see the first stack trace below).
Second problem is when it reads the model from storage (doRead)
it decodes values to little endian which is the wrong order
on a big endian machine. The decode should be
to THP_nativeByteOrder() instead
(see the model dump below)
```loaded_model = torch.load( opt.model_file, map_location=torch.device("cpu"))
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 422, in load
return _load(f, map_location, pickle_module, **pickle_load_args)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 616, in _load
deserialized_objects[key]._set_from_file(f, offset, f_should_read_directly)
RuntimeError: storage has wrong size: expected 2305843009213693952 got 32
(the very long number is actually 32 in the wrong endianness)
```
Model file load on x86 (correct output)
```>>> import torch
>>> torch.load('400f2k_best.model', map_location=torch.device("cpu"))
{'epoch': 24, 'model_type': 'emb_aec', 'classifier_model': OrderedDict([('model.0.weight', tensor([[ 2.4608e-01, -1.1174e-01, -1.0854e-01, 4.0124e-01, -1.5261e-02,
-1.2206e-01, 1.3229e-01, -1.2615e-01, -5.2773e-01, 2.6333e-01,
-3.1462e-03, -1.4902e-01, 9.8545e-02, -1.5789e-01, -2.2625e-01,
-1.0776e-01, -9.0895e-02, -3.8530e-01, 9.1152e-01, -3.9720e-01,
-8.5848e-01, -4.7837e-02, -1.5178e-01, 8.5023e-02, 1.5013e-01,
-9.9294e-02, -2.7422e-01, -4.3986e-01, -4.4297e-01, -3.9570e-01,
```
Model file load on s390x (wrong endianness; notice the exponents)
```>>> import torch
>>> torch.load( "400f2k_best.model", map_location=torch.device("cpu"))
{'epoch': 24, 'model_type': 'emb_aec', 'classifier_model': OrderedDict([('model.0.weight', tensor([[ 9.2780e+21, -9.7722e-11, 4.1350e+33, 7.782e+34, 4.2056e-31,
9.0784e+18, 1.1846e-32, 3.3320e-32, -4.8288e-28, -7.2679e+12,
1.5379e-16, -5.2604e+12, -4.7240e+17, 4.6092e-21, -1.8360e-20,
-2.7712e-31, 1.4548e-16, -2.5089e-27, 7.9094e-10, 7.1977e+34,
1.1930e+26, 8.4536e+15, 2.7757e+23, -5.8455e-10, -1.5611e+09,
-1.1311e-23, 6.6451e+19, -2.0970e+20, 3.4878e-19, -1.0857e-12,
7.8098e+22, 5.3998e-35],
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26383
Differential Revision: D17480891
fbshipit-source-id: f40569c7b9c4a1935dceb41f1a2508ce21ea3491
Summary:
Fixes#15308. Before this change, `torch.save` and `torch.load` would
initialize the CUDA context on GPU 0 if it hadn't been initialized
already, even if the serialized tensors are only on GPU 1.
This PR fixes that bug by using CUDAGuard in the storage serialization
path.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15807
Differential Revision: D13593201
Pulled By: zou3519
fbshipit-source-id: 4addc91ea5a5278d56a03f3d422577ee39e99897
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:
Previously, doRead/doWrite were functions that could return partial reads/writes,
and we checked for this case inconsistently in the call sites of serialization.cpp.
Now, these functions do NOT return the amount of bytes read/written, and instead
handle the necessary checking loop themselves.
Fixes#12042. Maybe.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12143
Differential Revision: D10097027
Pulled By: ezyang
fbshipit-source-id: fd222ab8a825bed352153648ad396acfe124a3e1
Summary:
This is necessary to allow us to use the complex header
which defines real (and is very sad if real is macro'ed).
We should also fix accreal, ureal, Real and REAL, but
only 'real' is the real blocker.
```
codemod -d aten/src/TH --extensions c,cc,cpp,cu,cuh,h,TARGETS,py,hpp '\breal\b' scalar_t
codemod -d aten/src/THC --extensions c,cc,cpp,cu,cuh,h,TARGETS,py,hpp '\breal\b' scalar_t
codemod -d aten/src/THNN --extensions c,cc,cpp,cu,cuh,h,TARGETS,py,hpp '\breal\b' scalar_t
codemod -d aten/src/THCUNN --extensions c,cc,cpp,cu,cuh,h,TARGETS,py,hpp '\breal\b' scalar_t
```
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11163
Reviewed By: SsnL
Differential Revision: D9619906
Pulled By: ezyang
fbshipit-source-id: 922cb3a763c0bffecbd81200c1cefc6b8ea70942
* Don't override Tensor, Storage macros defined outside torch/csrc in torch/csrc.
This PR does the following:
1) Removes THSTensor macros in torch/csrc, which aren't used.
2) For macros defined outside of torch/csrc (THTensor, THTensor_, THStorage, THStorage_):
a) No longer override them, i.e. previously THTensor could actually be THCTensor if a generic file was included from a file including THCP.h.
b) Instead, introduce new macros THW* (e.g. THWTensor) to represent a (potentially empty) wildcard character.
In addition to making this code easier to read and codemod, this allows us to more freely change TH/THC; for example:
currently in the THC random code, the state is casted to THByteTensor*; this happens to work because the macros don't happen to override THByteTensor.
But if THByteTensor just becomes an alias of THTensor (which is the plan for a single tensor type), then this no longer works.
The whole thing is a bit of a mess previously because you really have to understand which macros and redefined and which aren't.
We could also rename the macros that live in torch/csrc (e.g. the THPTensor macros), but since that is more self contained, I punted for now.
* Don't change the plugin.
Changelist:
- Move *.c to *.cpp
- Change includes of ".c" to ".cpp"
- A bunch of cmake configuration modifying CMAKE_C_FLAGS changed
to CMAKE_CXX_FLAGS or add_compile_options, because if you do CMAKE_C_FLAGS it only applies when you compile C code
- Explicitly cast void* to T* in a number of places
- Delete extern "C" { ... } blocks; instead, properly apply TH_API to everything that should have it (TH_API handles extern "C")
- Stop using stdatomic.h, instead, use <atomic>. This resulted in a bunch of placement-new/delete to be "totally properly correct"
- Refactor of THLongStorageView to not have static constructor methods (since it no longer has a copy/move constructor)
- Documentation about how the TH C interface (and extern C business) works
- Note that THD master_worker mode is dead
- C++ headers in TH libraries are given .hpp suffix, to make it less likely that you'll confuse them with the C-compatible headers (now suffixed .h)
- New function THCStream_stream and THCStream_device to project out fields of THCStream instead of accessing fields directly
- New function THStorage_(retainIfLive), which is equivalent to a retain but only if the refcount is greater than zero.
- In general, I tried to avoid using hpp headers outside of ATen/TH. However, there were a few places where I gave up and depended on the headers for my own sanity. See Note [TH abstraction violation] for all the sites where this occurred. All other sites were refactored to use functions
- Some extra Werror fixes (char* versus const char*)
