`torch/csrc/utils.h` should be device-independent. Currently, it contains CUDA-related implementations, which indirectly causes the [failure of ROCm testing](https://github.com/pytorch/pytorch/pull/151914#issuecomment-2839691038) (The reason is that the ROCm test environment shouldn`t expose HIP-related header files, which causes the JIT compilation to fail during testing)
Therefore, move CUDA-related implementations to `torch/csrc/cuda/utils.h`.
**Question:**
This change may introduce BC-breack.
I searched for this function globally on github and I think the impact is very small.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152521
Approved by: https://github.com/Skylion007, https://github.com/albanD
ghstack dependencies: #152512, #152513
This PR is to export specific function symbols into .dll shared library on Windows platform to support Windows build for [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch).
TORCH_API/TORCH_PYTHON_API/PYBIND11_EXPORT are macros that decorate the function as dllexport while compilation, so that the function symbol will be exported into the .dll shared library file on Windows platform. It is necessary for other libraries (such as IPEX) to import and call these functions through dynamic linking of PyTorch on Windows platform.
The code changes of this PR adds decorators to export specific functions used by IPEX.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98054
Approved by: https://github.com/ezyang
Summary:
This is part 1 of the effort to support `share_memory_()` in C++ aten library.
This allows C++ code to in place replace the tensor storage to shm based.
For now fd based shm is the only implementation supported to simplify memory management in general.
This first part intentionally avoids public api changes (to `TensorBase`, see comments in `StorageUtil.h`) such that we can get the core features usable outside pt/csrc first. The API addition to `Tensor` or `TensorBase` would involve more distracting changes and make the change harder to review.
Test Plan:
```
buck test caffe2:StorageUtils_test
```
Differential Revision: D43467616
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95228
Approved by: https://github.com/ezyang
Summary:
Move TH<C>GenerateByteType includes into torch/csrc (the only place they are used), and we can remove TH folder altogether!
The only thing left in THC are includes left for bc compatibility.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69929
Reviewed By: mruberry
Differential Revision: D33133013
Pulled By: ngimel
fbshipit-source-id: 78c87cf93d2d641631b0f71051ace318bf4ec3c1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69041
`TH_CONCAT_{N}` is still being used by THP so I've moved that into
it's own header but all the compiled code is gone.
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D32872477
Pulled By: ngimel
fbshipit-source-id: 06c82d8f96dbcee0715be407c61dfc7d7e8be47a
Summary:
This renames `WindowsTorchApiMacro.h` to `Export.h` to mirror the c10 header `c10/macros/Export.h` and also updates it to use `C10_EXPORT`/`C10_IMPORT`. This also removes the `THP_API` macro from `THP_export.h` which appears to serve the same purpose.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68095
Reviewed By: jbschlosser
Differential Revision: D32810881
Pulled By: albanD
fbshipit-source-id: d6949ccd0d80d6c3e5ec1264207611fcfe2503e3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65545
Introduce 2bit qtensor. The new dtype added for this is c10::quint2x4
The underlying storage for this is still uint8_t, so we pack 4 2-bit values in a byte while quantizing it.
Kernels that use this dtype should be aware of the packing format. (4 2-bit values in one byte)
Test Plan: `buck test mode/dev-asan caffe2/test/:quantization -- test_qtensor`
Reviewed By: supriyar
Differential Revision: D31148141
fbshipit-source-id: 1dc1de719e097adaf93fee47c6d1b8010a3eae6c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44678
This is a prototype PR that introduces 4 bit qtensors. The new dtype added for this is c10::quint4x2
The underlying storage for this is still uint8_t, so we pack 2 4-bit values in a byte while quantizing it.
This change uses most of the existing scaffolding for qtensor storage. We allocate storage
based on the dtype before creating a new qtensor.
It also adds a dispatch mechanism for this dtype so we can use this to get the bitwidth, qmin and qmax info
while quantizing and packing the qtensor (when we add 2-bit qtensor)
Kernels that use this dtype should be aware of the packing format.
Test Plan:
Locally tested
```
x = torch.ones((100, 100), dtype=torch.float)
qx_8bit = torch.quantize_per_tensor(x, scale=1.0, zero_point=2, dtype=torch.quint8)
qx = torch.quantize_per_tensor(x, scale=1.0, zero_point=2, dtype=torch.quint4x2)
torch.save(x, "temp.p")
print('Size float (B):', os.path.getsize("temp.p"))
os.remove('temp.p')
torch.save(qx_8bit, "temp.p")
print('Size quantized 8bit(B):', os.path.getsize("temp.p"))
os.remove('temp.p')
torch.save(qx, "temp.p")
print('Size quantized 4bit(B):', os.path.getsize("temp.p"))
os.remove('temp.p')
```
Size float (B): 40760
Size quantized 8bit(B): 10808
Size quantized 4bit(B): 5816
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D23993134
fbshipit-source-id: 073bf262f9680416150ba78ed2d932032275946d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35614
Python 2 has reached end-of-life and is no longer supported by PyTorch.
Now we can clean up a lot of cruft that we put in place to support it.
These changes were all done manually, and I skipped anything that seemed
like it would take more than a few seconds, so I think it makes sense to
review it manually as well.
Test Plan: CI
Differential Revision: D20842876
Pulled By: dreiss
fbshipit-source-id: 18abf0d324ed2185ec6d27c864e935d856dcc6ad
Summary:
Following up on this: https://github.com/pytorch/pytorch/pull/35851 cross dtype storage copy is not being used internally, so I have not included cross dtype copy for complex.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35771
Differential Revision: D21319650
Pulled By: anjali411
fbshipit-source-id: 07c72996ee598eba0cf401ad61534494d6f5b5b3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29143
THP_CORE macro is a very old macro that appeared to have served
two purposes:
1. The torch-python equivalent of CAFFE2_BUILD_MAIN_LIB, to toggle
symbol visibility headers
2. Some sort of ad hoc way of hiding certain definitions from headers
so external clients can't get at them.
It did (2) in a very confusing manner, because we set THP_CORE in both
torch and torch-python (it shouldn't do anything in torch). In this
PR I just get rid of use case (2) entirely (so everything shows up in
headers all the time), and then redo (1) using a new THP_BUILD_MAIN_LIB
macro. This cleans up some of the macro definitions and makes my life
easier for working on #27215.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D18309594
Pulled By: ezyang
fbshipit-source-id: adcb6d7cb387cd818480137e2b94e5e761dbfefc
Summary:
This is the first commit from a series of planned changes in order to add boolean tensors to PyTorch. The whole plan looks like this:
0. Storage Implementation (this change)
1. Tensor Creation.
2. Tensor Conversions.
3. Tensor Indexing.
4. Tensor Operations.
5. Back compatibility related changes.
This feature was requested by the community:
https://github.com/pytorch/pytorch/issues/4764https://github.com/pytorch/pytorch/issues/4219https://github.com/pytorch/pytorch/issues/4288
**Change**:
Added boolean type to the Storage class for CPU and CUDA backends.
**Tested via**:
1. unit tests
2. running this:
-> import torch
-> torch.BoolStorage
<class 'torch.BoolStorage'>
-> torch.cuda.BoolStorage
<class 'torch.cuda.BoolStorage'>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16810
Reviewed By: gchanan
Differential Revision: D14087246
Pulled By: izdeby
fbshipit-source-id: 042642ced1cb0fd1bb6bff05f9ca871a5c54ee5e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14248
This diff also introduces a horrifying hack to override CUDA's DeviceGuardImpl
with a HIPGuardImplMasqueradingAsCUDA, to accommodate PyTorch's current
behavior of pretending CUDA is HIP when you build with ROCm enabled.
Reviewed By: bddppq
Differential Revision: D13145293
fbshipit-source-id: ee0e207b6fd132f0d435512957424a002d588f02
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13125
Previously, it returned a vector of THCStream*, which we eventually turned
into CUDAStream. No need to spatter the conversion code everywhere: just
do it correctly to begin with. An important side effect of doing it this
way is that we no longer pass nullptr to CUDAStream; instead, we create
the default stream. I will rely on this in a later patch.
Reviewed By: gchanan
Differential Revision: D10853224
fbshipit-source-id: f6bd6594eba4626eb41a4a5e67fc64c9bbb46a1a
Summary:
How did we get so many uses of `NULL` again?
ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11047
Differential Revision: D9566799
Pulled By: goldsborough
fbshipit-source-id: 83469f352ac69aa65bdaf1a1a21f922d892e0db3
* 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.
- Remove some uses of mega-header THP.h
- Use HANDLE_TH_ERRORS in functions that may throw
- Move NumPy includes to common header
- Delete unused allocator
* Use ATen infer_size implementation rather than TH.
The only substantitive difference between the two implementations is in how empty sizes are handled;
in ATen these are treated as scalars (i.e., can be expanded to anything), whereas in TH they are treated
as a special case of empty tensors (i.e., can't be expanded to anything). Therefore, this change is
necessary to support scalars (0-dimensional tensors). We could also take a bool parameter for determining
how we treat empty tensors but this seems unnecessary: if one tries to expand an empty tensors (as a result
of an infer_size calculation), the expansion will fail.
* Make changes for review.
* Attempt to fix windows build.
* long -> int.
The Tensor and Variable classes are being merged in Python. This means
that all interfaces to C++ must accept Variables where they previously
accepted Tensors.
Implements basic and advanced indexing using ATen tensors/variables.
Basic indexing is translated at the Python-binding level
(python_variable_indexing.cpp) to slice/squeeze/unsqueeze/select calls.
Advanced indexing is implemented in ATen in terms of take() and put()
calls.
