mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
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
183 lines
6.3 KiB
C++
183 lines
6.3 KiB
C++
#ifndef TH_GENERIC_FILE
|
|
#define TH_GENERIC_FILE "torch/csrc/generic/serialization.cpp"
|
|
#else
|
|
|
|
#ifdef THC_GENERIC_FILE
|
|
#include <c10/cuda/CUDAGuard.h>
|
|
#endif
|
|
|
|
// save_save is necessary since the old eager format saved storages as
|
|
// [size + data], but the v1.5 eager format removes this since size is saved in
|
|
// the filesize.
|
|
template <class io>
|
|
void THPStorage_(writeFileRaw)(THWStorage *self, io fd, bool save_size)
|
|
{
|
|
#ifdef THC_GENERIC_FILE
|
|
c10::cuda::CUDAGuard guard(self->device());
|
|
#endif
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
|
|
scalar_t *data;
|
|
int64_t size_bytes = self->nbytes();
|
|
int64_t numel = size_bytes / sizeof(scalar_t);
|
|
#ifndef THC_GENERIC_FILE
|
|
data = THWStorage_(data)(LIBRARY_STATE self);
|
|
#else
|
|
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
|
|
std::unique_ptr<char[]> cpu_data(new char[size_bytes]);
|
|
data = (scalar_t*)cpu_data.get();
|
|
THCudaCheck(cudaMemcpy(
|
|
data,
|
|
THWStorage_(data)(LIBRARY_STATE self),
|
|
size_bytes,
|
|
cudaMemcpyDeviceToHost));
|
|
#endif
|
|
if (save_size) {
|
|
if (torch::utils::THP_nativeByteOrder() ==
|
|
torch::utils::THPByteOrder::THP_LITTLE_ENDIAN)
|
|
doWrite(fd, &numel, sizeof(int64_t));
|
|
else {
|
|
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
|
|
int64_t nsize; // convert big endian cpu to little endian storage
|
|
torch::utils::THP_encodeInt64Buffer(
|
|
(uint8_t*)&nsize,
|
|
(const int64_t*)&numel,
|
|
torch::utils::THPByteOrder::THP_LITTLE_ENDIAN,
|
|
1);
|
|
doWrite(fd, &nsize, sizeof(int64_t));
|
|
}
|
|
}
|
|
// fast track for bytes and little endian
|
|
if (sizeof(scalar_t) == 1 ||
|
|
torch::utils::THP_nativeByteOrder() ==
|
|
torch::utils::THPByteOrder::THP_LITTLE_ENDIAN) {
|
|
doWrite(fd, data, size_bytes);
|
|
} else {
|
|
int64_t buffer_size = std::min(numel, (int64_t)5000);
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
|
|
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
|
|
std::unique_ptr<uint8_t[]> le_buffer(new uint8_t[buffer_size * sizeof(scalar_t)]);
|
|
for (int64_t i = 0; i < numel; i += buffer_size) {
|
|
size_t to_convert = std::min(numel - i, buffer_size);
|
|
// NOLINTNEXTLINE(bugprone-branch-clone)
|
|
if (sizeof(scalar_t) == 2) {
|
|
torch::utils::THP_encodeInt16Buffer(
|
|
(uint8_t*)le_buffer.get(),
|
|
(const int16_t*)data + i,
|
|
torch::utils::THPByteOrder::THP_LITTLE_ENDIAN,
|
|
to_convert);
|
|
} else if (sizeof(scalar_t) == 4) {
|
|
torch::utils::THP_encodeInt32Buffer(
|
|
(uint8_t*)le_buffer.get(),
|
|
(const int32_t*)data + i,
|
|
torch::utils::THPByteOrder::THP_LITTLE_ENDIAN,
|
|
to_convert);
|
|
} else if (sizeof(scalar_t) == 8) {
|
|
torch::utils::THP_encodeInt64Buffer(
|
|
(uint8_t*)le_buffer.get(),
|
|
(const int64_t*)data + i,
|
|
torch::utils::THPByteOrder::THP_LITTLE_ENDIAN,
|
|
to_convert);
|
|
}
|
|
doWrite(fd, le_buffer.get(), to_convert * sizeof(scalar_t));
|
|
}
|
|
}
|
|
}
|
|
|
|
template void THPStorage_(writeFileRaw<int>)(THWStorage *self, int fd, bool save_size);
|
|
template void THPStorage_(writeFileRaw<PyObject*>)(THWStorage *self, PyObject* fd, bool save_size);
|
|
|
|
template <class io>
|
|
THWStorage * THPStorage_(readFileRaw)(io file, THWStorage *_storage)
|
|
{
|
|
#ifdef THC_GENERIC_FILE
|
|
c10::cuda::OptionalCUDAGuard guard;
|
|
if (_storage != nullptr) {
|
|
guard.set_device(_storage->device());
|
|
}
|
|
#endif
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
|
|
scalar_t *data;
|
|
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
|
|
int64_t size;
|
|
doRead(file, &size, sizeof(int64_t));
|
|
if (torch::utils::THP_nativeByteOrder() ==
|
|
torch::utils::THPByteOrder::THP_BIG_ENDIAN) {
|
|
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
|
|
int64_t nsize; // convert little endian storage to big endian cpu
|
|
nsize = size;
|
|
torch::utils::THP_decodeInt64Buffer(
|
|
&size, (const uint8_t*)&nsize, torch::utils::THP_nativeByteOrder(), 1);
|
|
}
|
|
THWStoragePtr storage;
|
|
if (_storage == nullptr) {
|
|
storage = THWStorage_(newWithSize)(LIBRARY_STATE size);
|
|
} else {
|
|
int64_t _storage_numel = _storage->nbytes() / sizeof(scalar_t);
|
|
THPUtils_assert(
|
|
_storage_numel == size,
|
|
"storage has wrong size: expected %ld got %ld",
|
|
size,
|
|
_storage_numel);
|
|
storage = _storage;
|
|
}
|
|
|
|
#ifndef THC_GENERIC_FILE
|
|
data = THWStorage_(data)(LIBRARY_STATE storage);
|
|
#else
|
|
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
|
|
std::unique_ptr<char[]> cpu_data(new char[size * sizeof(scalar_t)]);
|
|
data = (scalar_t*)cpu_data.get();
|
|
#endif
|
|
|
|
// fast track for bytes and little endian
|
|
if (sizeof(scalar_t) == 1 ||
|
|
torch::utils::THP_nativeByteOrder() ==
|
|
torch::utils::THPByteOrder::THP_LITTLE_ENDIAN) {
|
|
doRead(file, data, storage->nbytes());
|
|
} else {
|
|
int64_t buffer_size = std::min(size, (int64_t)5000);
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
|
|
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
|
|
std::unique_ptr<uint8_t[]> le_buffer(new uint8_t[buffer_size * sizeof(scalar_t)]);
|
|
|
|
|
|
for (int64_t i = 0; i < size; i += buffer_size) {
|
|
size_t to_convert = std::min(size - i, buffer_size);
|
|
doRead(file, le_buffer.get(), sizeof(scalar_t) * to_convert);
|
|
|
|
// NOLINTNEXTLINE(bugprone-branch-clone)
|
|
if (sizeof(scalar_t) == 2) {
|
|
torch::utils::THP_decodeInt16Buffer(
|
|
(int16_t*)data + i,
|
|
le_buffer.get(),
|
|
torch::utils::THP_nativeByteOrder(),
|
|
to_convert);
|
|
} else if (sizeof(scalar_t) == 4) {
|
|
torch::utils::THP_decodeInt32Buffer(
|
|
(int32_t*)data + i,
|
|
le_buffer.get(),
|
|
torch::utils::THP_nativeByteOrder(),
|
|
to_convert);
|
|
} else if (sizeof(scalar_t) == 8) {
|
|
torch::utils::THP_decodeInt64Buffer(
|
|
(int64_t*)data + i,
|
|
le_buffer.get(),
|
|
torch::utils::THP_nativeByteOrder(),
|
|
to_convert);
|
|
}
|
|
}
|
|
}
|
|
|
|
#ifdef THC_GENERIC_FILE
|
|
THCudaCheck(cudaMemcpy(THWStorage_(data)(LIBRARY_STATE storage), data, size * sizeof(scalar_t), cudaMemcpyHostToDevice));
|
|
#endif
|
|
return storage.release();
|
|
}
|
|
|
|
template THWStorage* THPStorage_(readFileRaw<int>)(int fd, THWStorage* storage);
|
|
template THWStorage* THPStorage_(readFileRaw<PyObject*>)(PyObject* fd, THWStorage* storage);
|
|
|
|
#endif
|