## Motivation
Add `__torch_function__` override protocol supporting to the factory functions in defined in pytorch_torch_funcions_manual.cpp.
## Solution
By moving the PythonArg parser from the tensor_new.cpp and add the torch function handle dispatching for these API in `torch` name space.
as_tensor
sparse_coo_tensor
_sparse_coo_tensor_unsafe
sparce_csr_tensor
_sparce_csr_tensor_unsafe.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75639
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73850
Previously, torch.Tensor was treated as if it were torch.FloatTensor
(where Float is whatever the default dtype was). This is not good
behavior for tensor subclasses, which inherit from torch.Tensor and
will want to super() call into it and will only notice later that
only float works as a dtype. So in this PR I relax the behavior
for this case to make the torch.Tensor constructor more useful for
subclasses.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D34707396
Pulled By: ezyang
fbshipit-source-id: a995d601007b6fcd0317d89f66ca7e08c4d6053e
(cherry picked from commit e8d0d7b3e8b17681b931cbe4f5729de2e80cf3de)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73822
I guess hypothetically the logic duplication here is a faux
amis because we could say that the constructor and new method
should evolve APIs independently... but nah, it's not worth it.
There is only very slight differences between the two functions:
different error messages, and the new method does extra checks
to make sure the requested types are consistent with the base
Tensor. But I need to refactor this code and I really don't want
to do the refactor twice. So dedupe first.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D34665171
Pulled By: ezyang
fbshipit-source-id: bd40ec7f6e694bfeff4e4aaab2f4e95cea250b65
(cherry picked from commit 10a03926d8d8f36506c9a3d62cf2c380f559b00b)
Summary:
Reland of https://github.com/pytorch/pytorch/pull/72623 that was reverted for the tls cleanup was removed.
From close inspection on the counting of the number of available keys, I think there is one more since the guard is actually one after the last usable key. With this update assert, the last updated key will still be <=63 which will fit just fine.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72832
Reviewed By: H-Huang
Differential Revision: D34228571
Pulled By: albanD
fbshipit-source-id: ce5e10a841ea87386727346cfc8d9327252574c4
(cherry picked from commit 59d3b86353)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65331
ghstack-source-id: 148862595
This is a performance optimization for the use case:
```
tensor = torch.tensor(<large_data>, device='meta')
```
where the current implementation requires a superfluous memory allocation on CPU even though the target device is a meta.
Test Plan: Run existing tests since no behavioral change is introduced.
Reviewed By: ezyang
Differential Revision: D31055036
fbshipit-source-id: 04d6c13594a71fc65bf2fbd567ee71833a879851
(cherry picked from commit 489d0a151a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62030
Remove dtype tracking from Python Storage interface, remove all the different `<type>Storage` classes except for `ByteStorage`, and update serialization accordingly, while maintaining as much FC/BC as possible
Fixes https://github.com/pytorch/pytorch/issues/47442
* **THE SERIALIZATION FORMAT IS FULLY FC/BC.** We worked very hard to make sure this is the case. We will probably want to break FC at some point to make the serialization structure of tensors make more sense, but not today.
* There is now only a single torch.ByteStorage class. Methods like `Tensor.set_` no longer check that the dtype of storage is appropriate.
* As we no longer know what dtype of a storage is, we've **removed** the size method from Storage, replacing it with nbytes. This is to help catch otherwise silent errors where you confuse number of elements with number of bytes.
* `Storage._new_shared` takes a `nbytes` kwarg and will reject previous positional only calls. `Storage._new_with_file` and `_set_from_file` require explicit element size arguments.
* It's no longer possible to convert storages to different types using the float/double/etc methods. Instead, do the conversion using a tensor.
* It's no longer possible to allocate a typed storage directly using FloatStorage/DoubleStorage/etc constructors. Instead, construct a tensor and extract its storage. The classes still exist but they are used purely for unpickling.
* The preexisting serialization format stores dtype with storage, and in fact this dtype is used to determine the dtype of the tensor overall.
To accommodate this case, we introduce a new TypedStorage concept that exists only during unpickling time which is used to temporarily store the dtype so we can construct a tensor. **If you overrode the handling of pickling/unpickling, you MUST add handling for TypedStorage** or your serialization code will degrade to standard file-based serialization.
Original pull request: https://github.com/pytorch/pytorch/pull/59671
Reviewed By: soulitzer, ngimel
Differential Revision: D29466819
Pulled By: ezyang
fbshipit-source-id: 4a14e5d3c2b08e06e558683d97f7378a3180b00e
Summary:
Refactor:
```
TORCH_CHECK ( key == a ||
key == b ||
key == c,
"expected key to be in ", a, " or ", b , " or ", c,
" but got ", key);
```
into
```
TORCH_CHECK( key_set.has(key),
"expected key to be in ", key_set,
" but got ", key );
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65535
Reviewed By: wconstab
Differential Revision: D31144239
Pulled By: malfet
fbshipit-source-id: 68a053041a38f043e688e491889dd7ee258f3db3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64360
This PR adds a (private) enable_python_mode context manager.
(see torch/utils/_python_dispatch.py).
enable_python_mode accepts the type of a __torch_dispatch__ object
as its argument. Whenever an operator gets called inside of the
context manager, it dispatches to the __torch_dispatch__ of
the passed-in type.
Example usage:
```
with enable_python_mode(LoggingTensor):
z = torch.empty([])
assert isinstance(z, LoggingTensor)
```
There are quite a few changes that were made to support this.
First, we added TorchDispatchTypeObject, a C++ struct that represents the
type of a `__torch_dispatch__` object (e.g. LoggingTensor).
It holds both the PyObject* representing the class and a PyInterpreter*
so we know which Python interpreter it came from.
Next, we updated the concrete_dispatch_fn in python_variable.cpp to accept
a `const std::shared_ptr<TorchDispatchTypeObject>&` argument. When this
is null, dispatching happens as usual. When it is non-null, we prepend
the TorchDispatchTypeObject's PyObject* to the overloaded args list so that
it is considered first for dispatch.
To get that to work, we changed how `handle_torch_dispatch_no_python_arg_parser`
works. The "overloaded args list" previously only consisted of Tensor PyObjects,
but now it can have types in addition to Tensors!
- We renamed `append_overloaded_arg` to `append_overloaded_arg`
- We added a new `append_overloaded_type` that appends a type to
overloaded_args
- We added special handling in `handle_torch_dispatch_no_python_arg_parser`
and `append_overloaded_arg` to handle types in addition to Tensors.
Then, there is PythonMode and PythonModeTLS.
- We reuse the DispatchKey::Python dispatch key as a mode key
- We use PythonMode::enter and PythonMode::exit to enable/disable
DispatchKey::Python and set the PythonModeTLS.
- PythonModeTLS stores a TorchDispatchTypeObject as metadata.
- PythonMode is in libtorch_python, and PythonModeTLS is in ATen.
This split is due to the libtorch_python library boundary (because we need
to save TLS in ATen/ThreadLocalState)
- We modify the PythonFallbackKernel to look up
the relevant TorchDispatchTypeObject (if Python Mode is active) and
dispatch using it.
There are two more miscellaneous changes:
- internal_new_from_data (torch/csrc/utils/tensor_new.cpp) gets an
exclude guard. enable_python_mode currently does not handle
torch.tensor and the exclude guard is to prevent a bug.
Future:
- This PR does not allow for the nesting of Python modes. In the future we
should be able to enable this with a more sane no_dispatch API and by changing
the TLS to a stack. For now I did not need this for CompositeImplicitAutograd testing.
Test Plan: - new tests
Reviewed By: ezyang
Differential Revision: D30698082
Pulled By: zou3519
fbshipit-source-id: 7094a90eee6aa51f8b71bc4d91cfb6f49e9691f8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63496
This PR adds a (private) enable_python_mode context manager.
(see torch/utils/_python_dispatch.py).
enable_python_mode accepts the type of a __torch_dispatch__ object
as its argument. Whenever an operator gets called inside of the
context manager, it dispatches to the __torch_dispatch__ of
the passed-in type.
Example usage:
```
with enable_python_mode(LoggingTensor):
z = torch.empty([])
assert isinstance(z, LoggingTensor)
```
There are quite a few changes that were made to support this.
First, we added TorchDispatchTypeObject, a C++ struct that represents the
type of a `__torch_dispatch__` object (e.g. LoggingTensor).
It holds both the PyObject* representing the class and a PyInterpreter*
so we know which Python interpreter it came from.
Next, we updated the concrete_dispatch_fn in python_variable.cpp to accept
a `const std::shared_ptr<TorchDispatchTypeObject>&` argument. When this
is null, dispatching happens as usual. When it is non-null, we prepend
the TorchDispatchTypeObject's PyObject* to the overloaded args list so that
it is considered first for dispatch.
To get that to work, we changed how `handle_torch_dispatch_no_python_arg_parser`
works. The "overloaded args list" previously only consisted of Tensor PyObjects,
but now it can have types in addition to Tensors!
- We renamed `append_overloaded_arg` to `append_overloaded_arg`
- We added a new `append_overloaded_type` that appends a type to
overloaded_args
- We added special handling in `handle_torch_dispatch_no_python_arg_parser`
and `append_overloaded_arg` to handle types in addition to Tensors.
Then, there is PythonMode and PythonModeTLS.
- We reuse the DispatchKey::Python dispatch key as a mode key
- We use PythonMode::enter and PythonMode::exit to enable/disable
DispatchKey::Python and set the PythonModeTLS.
- PythonModeTLS stores a TorchDispatchTypeObject as metadata.
- PythonMode is in libtorch_python, and PythonModeTLS is in ATen.
This split is due to the libtorch_python library boundary (because we need
to save TLS in ATen/ThreadLocalState)
- We modify the PythonFallbackKernel to look up
the relevant TorchDispatchTypeObject (if Python Mode is active) and
dispatch using it.
There are two more miscellaneous changes:
- internal_new_from_data (torch/csrc/utils/tensor_new.cpp) gets an
exclude guard. enable_python_mode currently does not handle
torch.tensor and the exclude guard is to prevent a bug.
Future:
- This PR does not allow for the nesting of Python modes. In the future we
should be able to enable this with a more sane no_dispatch API and by changing
the TLS to a stack. For now I did not need this for CompositeImplicitAutograd testing.
Test Plan: - new tests
Reviewed By: malfet, albanD
Differential Revision: D30543236
Pulled By: zou3519
fbshipit-source-id: ef5444d96a5a957d1657b7e37dce80f9a497d452
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62423
Fixes https://github.com/facebookresearch/functorch/issues/7.
functorch uses FuncTorchDynamicLayerBackMode as a mode key to wrap all
tensors returned from operators in special TensorWrapper tensor
extension.
The problem with this is that TensorWrapper does not have storage so
accessing the data_ptr (for recursive_store) internal asserts.
As a quick hack, the guard added prevents functorch from wrapping the
empty tensor in a TensorWrapper and instead when `tensor.to` is called later,
the tensor gets wrapped. This is effectively what Ed proposed in
https://github.com/facebookresearch/functorch/issues/7#issuecomment-847501020
In the long term we probably want some better way of extending
`internal_new_from_data` for cases like this (where there is a
mode-based dispatch key for a C++ tensor extension -- the Python case
may be different).
Test Plan: - Verified that this fixes functorch's problem
Reviewed By: malfet
Differential Revision: D29992607
Pulled By: zou3519
fbshipit-source-id: 82b713156a37d7470f8fc46e3803ee7353689a33
Summary:
Switches most of the simple for loops outside of `jit` directories to use `c10::irange`.
Generated with D28874212.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59481
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D28909681
fbshipit-source-id: ec9ab1bd602933238d9d0f73d4d8d027b75d9d85
Summary:
`NULL` return from `PyObject_GetAttrString` should never get ignored without handling the exception, as behavior of subsequent Python C API calls are undefined until `PyErr_Fetch` or `PyErr_Clear` is called.
This accidentally leads to `list` type being incorrectly identified as `Tensor`
Fixes https://github.com/pytorch/pytorch/issues/58520
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58631
Reviewed By: albanD
Differential Revision: D28559454
Pulled By: malfet
fbshipit-source-id: 46f044b5f0f94264779a6108474d04a8ba851c53
Summary:
…evice.
Previously, it was possible for torch.Tensor(tensor, device) or Tensor.new(tensor, device) to map to IntArrayRef or PyObject*.
PyObject* was not a problem because that would error out later.
But IntArrayRef would create an uninitialized tensor, which is confusing.
Fixes https://github.com/pytorch/pytorch/issues/47112
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58108
Reviewed By: agolynski, mruberry
Differential Revision: D28372426
Pulled By: gchanan
fbshipit-source-id: 795ab4f0561939d002a661c5cc14c6cdb579f31a
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
