Summary:
With this change, the optimizer is almost twice as fast as before. As the result of the first call is never used, it looks like a copy paste error and therefore can be removed. In addition, this duplicate call is not present in the Python implementation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72773
Reviewed By: samdow
Differential Revision: D34214312
Pulled By: albanD
fbshipit-source-id: 4f4de08633c7236f3ccce8a2a74e56500003281b
(cherry picked from commit 4a63f812ab)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68693
Generation of python bindings for native functions is split over 8
different files. One for each namespace, with the torch namespace
split into 3 shards, and methods in their own file as well. This
change ensures that editing any single (non-method) operator only
causes one of these files to be rebuilt.
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D32596270
Pulled By: albanD
fbshipit-source-id: 0570ec69e7476b8f1bc21138ba18fe8f95ebbe3f
(cherry picked from commit ba0fc71a3a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70326
See D24145988 for context: it allows loops such as for(int i=0;i<10;i++) to be expressed as for(const auto i : c10::irange(10)). This is nice because it auto-types the loops and adds const-safety to the iteration variable.
Test Plan: buck run //caffe2/torch/fb/sparsenn:test
Reviewed By: r-barnes
Differential Revision: D33243400
fbshipit-source-id: b1f1b4163f4bf662031baea9e5268459b40c69a3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66933
This PR exposes `torch.lu` as `torch.linalg.lu_factor` and
`torch.linalg.lu_factor_ex`.
This PR also adds support for matrices with zero elements both in
the size of the matrix and the batch. Note that this function simply
returns empty tensors of the correct size in this case.
We add a test and an OpInfo for the new function.
This PR also adds documentation for this new function in line of
the documentation in the rest of `torch.linalg`.
Fixes https://github.com/pytorch/pytorch/issues/56590
Fixes https://github.com/pytorch/pytorch/issues/64014
cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano
Test Plan: Imported from OSS
Reviewed By: gchanan
Differential Revision: D32834069
Pulled By: mruberry
fbshipit-source-id: 51ef12535fa91d292f419acf83b800b86ee9c7eb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69421
I've hit a lot of build issues in D32671972, and I've come to realize that a lot of it boils down to header hygene. `function.h` includes `profiler.h` *solely* to transitively include `record_function.h` which winds up leaking the profiler symbols. Moreover several files are relying on transitive includes to get access to `getTime`. As long as I have to touch all the places that use `getTime`, I may as well also move them to the new namespace.
Test Plan: Unit tests and CI.
Reviewed By: aaronenyeshi, albanD
Differential Revision: D32865907
fbshipit-source-id: f87d6fd5afb784dca2146436e72c69e34623020e
Summary:
Follow up to https://github.com/pytorch/pytorch/issues/68095
This also changes the files from the ATen folder to include c10's `Export.h` instead since they can't ever be exporting `TORCH_PYTHON_API`.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69585
Reviewed By: mrshenli
Differential Revision: D32958594
Pulled By: albanD
fbshipit-source-id: 1ec7ef63764573fa2b486928955e3a1172150061
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66933
This PR exposes `torch.lu` as `torch.linalg.lu_factor` and
`torch.linalg.lu_factor_ex`.
This PR also adds support for matrices with zero elements both in
the size of the matrix and the batch. Note that this function simply
returns empty tensors of the correct size in this case.
We add a test and an OpInfo for the new function.
This PR also adds documentation for this new function in line of
the documentation in the rest of `torch.linalg`.
Fixes https://github.com/pytorch/pytorch/issues/56590
Fixes https://github.com/pytorch/pytorch/issues/64014
cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D32521980
Pulled By: mruberry
fbshipit-source-id: 26a49ebd87f8a41472f8cd4e9de4ddfb7f5581fb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63568
This PR adds the first solver with structure to `linalg`. This solver
has an API compatible with that of `linalg.solve` preparing these for a
possible future merge of the APIs. The new API:
- Just returns the solution, rather than the solution and a copy of `A`
- Removes the confusing `transpose` argument and replaces it by a
correct handling of conj and strides within the call
- Adds a `left=True` kwarg. This can be achieved via transposes of the
inputs and the result, but it's exposed for convenience.
This PR also implements a dataflow that minimises the number of copies
needed before calling LAPACK / MAGMA / cuBLAS and takes advantage of the
conjugate and neg bits.
This algorithm is implemented for `solve_triangular` (which, for this, is
the most complex of all the solvers due to the `upper` parameters).
Once more solvers are added, we will factor out this calling algorithm,
so that all of them can take advantage of it.
Given the complexity of this algorithm, we implement some thorough
testing. We also added tests for all the backends, which was not done
before.
We also add forward AD support for `linalg.solve_triangular` and improve the
docs of `linalg.solve_triangular`. We also fix a few issues with those of
`torch.triangular_solve`.
Resolves https://github.com/pytorch/pytorch/issues/54258
Resolves https://github.com/pytorch/pytorch/issues/56327
Resolves https://github.com/pytorch/pytorch/issues/45734
cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D32588230
Pulled By: mruberry
fbshipit-source-id: 69e484849deb9ad7bb992cc97905df29c8915910
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68672
This PR adds `python_module: sparse` to `native_function.yaml`.
These functions would appear in `torch._C._sparse` namespace instead of
just `torch`.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D32517813
fbshipit-source-id: 7c3d6df57a24d7c7354d0fefe1b628dc89be9431
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63568
This PR adds the first solver with structure to `linalg`. This solver
has an API compatible with that of `linalg.solve` preparing these for a
possible future merge of the APIs. The new API:
- Just returns the solution, rather than the solution and a copy of `A`
- Removes the confusing `transpose` argument and replaces it by a
correct handling of conj and strides within the call
- Adds a `left=True` kwarg. This can be achieved via transposes of the
inputs and the result, but it's exposed for convenience.
This PR also implements a dataflow that minimises the number of copies
needed before calling LAPACK / MAGMA / cuBLAS and takes advantage of the
conjugate and neg bits.
This algorithm is implemented for `solve_triangular` (which, for this, is
the most complex of all the solvers due to the `upper` parameters).
Once more solvers are added, we will factor out this calling algorithm,
so that all of them can take advantage of it.
Given the complexity of this algorithm, we implement some thorough
testing. We also added tests for all the backends, which was not done
before.
We also add forward AD support for `linalg.solve_triangular` and improve the
docs of `linalg.solve_triangular`. We also fix a few issues with those of
`torch.triangular_solve`.
Resolves https://github.com/pytorch/pytorch/issues/54258
Resolves https://github.com/pytorch/pytorch/issues/56327
Resolves https://github.com/pytorch/pytorch/issues/45734
cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano
Test Plan: Imported from OSS
Reviewed By: zou3519, JacobSzwejbka
Differential Revision: D32283178
Pulled By: mruberry
fbshipit-source-id: deb672e6e52f58b76536ab4158073927a35e43a8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67803
* Addresses comments from #63589
[ONNX] remove torch::onnx::PRODUCER_VERSION (#67107)
Use constants from version.h instead.
This simplifies things since we no longer have to update
PRODUCER_VERSION for each release.
Also add TORCH_VERSION to version.h so that a string is available for
this purpose.
[ONNX] Set `ir_version` based on opset_version. (#67128)
This increases the odds that the exported ONNX model will be usable.
Before this change, we were setting the IR version to a value which may
be higher than what the model consumer supports.
Also some minor clean-up in the test code:
* Fix string replacement.
* Use a temporary file so as to not leave files around in the test
current working directory.
Test Plan: Imported from OSS
Reviewed By: msaroufim
Differential Revision: D32181306
Pulled By: malfet
fbshipit-source-id: 02f136d34ef8f664ade0bc1985a584f0e8c2b663
Co-authored-by: BowenBao <bowbao@microsoft.com>
Co-authored-by: Gary Miguel <garymiguel@microsoft.com>
Co-authored-by: Nikita Shulga <nshulga@fb.com>
Summary:
Summary : Inserted a check for the momentum and print "None" in case is not defined. See https://github.com/pytorch/pytorch/issues/65143
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67335
Test Plan:
The code below now prints `torch::nn::BatchNorm2d(128, eps=1e-05, momentum=None, affine=true, track_running_stats=true)` without generating errors.
```
torch::nn::BatchNorm2d m(torch::nn::BatchNormOptions(128).momentum(c10::nullopt));
std::cerr << *m << "\n";
```
Fixes https://github.com/pytorch/pytorch/issues/65143
Reviewed By: mruberry
Differential Revision: D32067820
Pulled By: ngimel
fbshipit-source-id: f40f9bbe090aa78e00f6c3a57deae393d946b88d
Summary:
The documentation of torch.nn.Upsample stated that `align_corners` only affects `linear`, `bilinear` and `trilinear`.
This PR updates the documentation for the Python `Upsample` module and the C++ `UpsampleOptions` struct to reflect that `bicubic` is also affected by `align_corners`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66756
Reviewed By: zou3519
Differential Revision: D31731148
Pulled By: jbschlosser
fbshipit-source-id: 3ec277fc3fbdf8414d0de327d8c57ba07342a5b9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63612
This makes Tensor inherit from a new class TensorBase, that provides a subset of Tensor that doesn't
directly depend on native_functions.yaml. Code that only includes TensorBase.h with thus not need to
be rebuilt every time someone changes an operator signature.
Making `Tensor` inherit from this class means that `const TensorBase&` parameters will be callable
with an ordinary `Tensor`. I've also made `Tensor` constructible and assignable from `TensorBase` to
minimize friction in code mixing the two types.
To help enforce that `Tensor.h` and `Functions.h` aren't accidentally included, I've added an error
into `Operators.h` if `TORCH_ASSERT_NO_OPERATORS` is defined. We can either set this in the build
system for certain folders, or just define it at the top of any file.
I've also included an example of manually special-casing the commonly used `contiguous` operator.
The inline function's slow path defers to `TensorBase::__dispatch_contiguous` which is defined in
`Tensor.cpp`. I've made it so `OptionalTensorRef` is constructible from `TensorBase`, so I can
materialize a `Tensor` for use in dispatch without actually increasing its refcount.
Test Plan: Imported from OSS
Reviewed By: gchanan
Differential Revision: D30728580
Pulled By: ezyang
fbshipit-source-id: 2cbc8eee08043382ee6904ea8e743b1286921c03
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63995
JIT methods already have name() in their interface, and Py methods have names in their implementation. I'm adding this for a particular case where someone tried to use name() on a JIT method that we're replacing with an IMethod.
Test Plan: add case to imethod API test
Reviewed By: suo
Differential Revision: D30559401
fbshipit-source-id: 76236721f5cd9a9d9d488ddba12bfdd01d679a2c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63345
This diff did the following few things to enable the tests:
1. Exposed IMethod as TORCH_API.
2. Linked torch_deploy to test_api if USE_DEPLOY == 1.
3. Generated torch::deploy examples when building torch_deploy library.
Test Plan: ./build/bin/test_api --gtest_filter=IMethodTest.*
Reviewed By: ngimel
Differential Revision: D30346257
Pulled By: alanwaketan
fbshipit-source-id: 932ae7d45790dfb6e00c51893933a054a0fad86d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63380
Crosses off some more of #62011, see the test in the stacked PR #63381
Test Plan: Imported from OSS
Reviewed By: malfet, seemethere
Differential Revision: D30455843
Pulled By: driazati
fbshipit-source-id: d473545d05ffa0b2476968f0b1c55f3a16a2c755
Summary:
Replace for loop with for `irange` loop. Also fix some unused variable warnings in range loop cases
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62928
Reviewed By: driazati
Differential Revision: D30171904
Pulled By: malfet
fbshipit-source-id: 1b437a0f7e3515f4a2e324f3450e93312f1933ae
