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.
There were two implementations of THPUtils_checkLong/THPUtils_unpackLong; one
that was a macro and one that was not, which is hella bad if you accidentally
include the macro before the real definition. Now we always use the inline
function.
A reasonable follow-up task would be to un-macro-ify the rest of these functions.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Setting torch.utils.backcompat.broadcast.warning.enabled=True
will cause Python warnings in the case where broadcast occurs
but previously 1-d view style pointwise ops occured.
Moves THPObjectPtr into a separate header, so that it can be included
independently. Currently, utils.h requries all of THP.h. Also adds RAII
structs for acquiring and releasing the GIL.
See issue #20
The torch.Size class is a tuple subclass which distinguishes sizes from
other tuples so that torch.Tensor(size) is interpreted as size instead
of data.
