This PR adds a function for computing the LDL decomposition and a function that can solve systems of linear equations using this decomposition. The result of `torch.linalg.ldl_factor_ex` is in a compact form and it's required to use it only through `torch.linalg.ldl_solve`. In the future, we could provide `ldl_unpack` function that transforms the compact representation into explicit matrices.
Fixes https://github.com/pytorch/pytorch/issues/54847.
cc @jianyuh @nikitaved @pearu @mruberry @walterddr @IvanYashchuk @xwang233 @Lezcano
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69828
Approved by: https://github.com/Lezcano, https://github.com/mruberry, https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60838
Rewrote `addmm_out_sparse_csr_dense_cuda` implementation using new cusparse descriptors.
`addmm` now works without conversions with both 32-bit and 64-bit indices.
The dense tensors can have a row- or column-major layout. If the dense tensors are a contiguous slice of a larger tensor, the storage is used directly without temporary copies.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D30643191
Pulled By: cpuhrsch
fbshipit-source-id: 5555f5b59b288daa3a3987d322a93dada63b46c8
Summary:
This PR enables Half, BFloat16, ComplexFloat, and ComplexDouble support for matrix-matrix multiplication of COO sparse matrices.
The change is applied only to CUDA 11+ builds.
`cusparseSpGEMM` also supports `CUDA_C_16F` (complex float16) and `CUDA_C_16BF` (complex bfloat16). PyTorch also supports the complex float16 dtype (`ScalarType::ComplexHalf`), but there is no convenient dispatch, so this dtype is omitted in this PR.
cc nikitaved pearu cpuhrsch IvanYashchuk ezyang anjali411 dylanbespalko mruberry Lezcano
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59980
Reviewed By: ngimel
Differential Revision: D30994115
Pulled By: cpuhrsch
fbshipit-source-id: 4f55b99e8e25079d6273b4edf95ad6fa85aeaf24
Summary:
This PR enables Half, BFloat16, ComplexFloat, and ComplexDouble support for matrix-matrix multiplication of COO sparse matrices.
The change is applied only to CUDA 11+ builds.
`cusparseSpGEMM` also supports `CUDA_C_16F` (complex float16) and `CUDA_C_16BF` (complex bfloat16). PyTorch also supports the complex float16 dtype (`ScalarType::ComplexHalf`), but there is no convenient dispatch, so this dtype is omitted in this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59980
Reviewed By: ngimel
Differential Revision: D29699456
Pulled By: cpuhrsch
fbshipit-source-id: 407ae53392acb2f92396a62a57cbaeb0fe6e950b
Summary:
Before this PR `CUDA11OrLater` was incorrectly set to `False` when `torch.version.cuda == "11.0"`.
`torch.version.cuda` returns major and minor CUDA versions, it doesn't return patch info.
LooseVersion comparison was calling `[11, 0] >= [11, 0, 0]` which evaluates to `False`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60010
Reviewed By: mruberry
Differential Revision: D29147107
Pulled By: ezyang
fbshipit-source-id: bd9ed076337b4d32bf1c3376b8f7ae15dbc4d08d
Summary:
[distutils](https://docs.python.org/3/library/distutils.html) is on its way out and will be deprecated-on-import for Python 3.10+ and removed in Python 3.12 (see [PEP 632](https://www.python.org/dev/peps/pep-0632/)). There's no reason for us to keep it around since all the functionality we want from it can be found in `setuptools` / `sysconfig`. `setuptools` includes a copy of most of `distutils` (which is fine to use according to the PEP), that it uses under the hood, so this PR also uses that in some places.
Fixes#56527
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57040
Pulled By: driazati
Reviewed By: nikithamalgifb
Differential Revision: D28051356
fbshipit-source-id: 1ca312219032540e755593e50da0c9e23c62d720
Summary:
Also modify the `tf32_on_and_off` decorator to make it support function without `device` argument.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52871
Reviewed By: ngimel
Differential Revision: D27286674
Pulled By: mruberry
fbshipit-source-id: 14f6d558271bd6a1d0bc40691c170d47e81de1ff
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
Summary:
- The thresholds of some tests are bumped up. Depending on the random generator, sometimes these tests fail with things like 0.0059 is not smaller than 0.005. I ran `test_nn.py` and `test_torch.py` for 10+ times to check these are no longer flaky.
- Add `tf32_on_and_off` to new `matrix_exp` tests.
- Disable TF32 on test suites other than `test_nn.py` and `test_torch.py`
cc: ptrblck
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44240
Reviewed By: mruberry
Differential Revision: D23882498
Pulled By: ngimel
fbshipit-source-id: 44a9ec08802c93a2efaf4e01d7487222478b6df8
Summary:
Older versions of MIOpen (<=2.2) don't have the `miopenGetVersion` api, but MIOpen is always a part of the ROCm builds, so do NOT set `lib` to None for ROCm builds. `__cudnn_version` will be `None` for older versions of MIOpen.
Setting `lib` to `None` ends up printing the following erroneous warning when running unit tests:
```
/root/.local/lib/python3.6/site-packages/torch/backends/cudnn/__init__.py:120: UserWarning: cuDNN/MIOpen library not found. Check your LD_LIBRARY_PATH
}.get(sys.platform, 'LD_LIBRARY_PATH')))
```
Eg.: https://ci.pytorch.org/jenkins/job/pytorch-builds/job/py3.6-clang7-rocmdeb-ubuntu16.04-test2/18387/consoleFull
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33837
Differential Revision: D20369285
Pulled By: xw285cornell
fbshipit-source-id: e82e6f8f5bccb486213cf868f40aece41ce11f98
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30445
Create distributed and rpc directories under caffe/test for better management
of unit tests.
Differential Revision: D18702786
fbshipit-source-id: e9daeed0cfb846ef68806f6decfcb57c0e0e3606
