PR #90689 replaces NVTX with NVTX3. However, the torch::nvtoolsext is created only when the third party NVTX is used.
This is clear a logical error. We now move the creation code out of the branch to cover all cases. This should fix the issues reported in the comments of #90689.
It would be better to move configurations of the failed FRL jobs to CI tests so that we can find such issues early before merging.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97582
Approved by: https://github.com/peterbell10
Summary:
Extra C binding module for flatbuffer was introduced because
not all dependencies of Pytorch want (or can) bundle in flatbuffer.
However, flatbuffer is in by default now so this separate binding is not longer needed.
Test Plan: existing unit tests
Differential Revision: D44352583
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97476
Approved by: https://github.com/dbort
Use `append_cxx_flag_if_supported` to determine whether or not `-Werror` is supported
Do not suppress deprecation warnings if glog is not used/installed, as the way check is written right now, it will suppress deprecations even if `glog` is not installed.
Similarly, do not suppress deprecations on MacOS simply because we are compiling with protobuf.
Fix deprecation warnings in:
- MPS by replacing `MTLResourceOptionCPUCacheModeDefault`->`MTLResourceCPUCacheModeDefaultCache`
- In GTests by replacing `TYPED_TEST_CASE`->`TYPED_TEST_SUITE`
- In `codegen/onednn/interface.cpp`, by using passing `Stack` by reference rathern than pointer.
Do not guard calls to `append_cxx_flag_if_supported` with `if(CLANG)` or `if(GCC)`.
Fix some deprecated calls in `Metal` hide more complex exception under `C10_CLANG_DIAGNOSTIC_IGNORE`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97584
Approved by: https://github.com/kit1980
Preferring dash over underscore in command-line options. Add `--command-arg-name` to the argument parser. The old arguments with underscores `--command_arg_name` are kept for backward compatibility.
Both dashes and underscores are used in the PyTorch codebase. Some argument parsers only have dashes or only have underscores in arguments. For example, the `torchrun` utility for distributed training only accepts underscore arguments (e.g., `--master_port`). The dashes are more common in other command-line tools. And it looks to be the default choice in the Python standard library:
`argparse.BooleanOptionalAction`: 4a9dff0e5a/Lib/argparse.py (L893-L895)
```python
class BooleanOptionalAction(Action):
def __init__(...):
if option_string.startswith('--'):
option_string = '--no-' + option_string[2:]
_option_strings.append(option_string)
```
It adds `--no-argname`, not `--no_argname`. Also typing `_` need to press the shift or the caps-lock key than `-`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94505
Approved by: https://github.com/ezyang, https://github.com/seemethere
The main changes are:
1. Remove outdated checks for old compiler versions because they can't support C++17.
2. Remove outdated CMake checks because it now requires 3.18.
3. Remove outdated CUDA checks because we are moving to CUDA 11.
Almost all changes are in CMake files for easy audition.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90599
Approved by: https://github.com/soumith
Headers under torch/csrc/distributed may be referened with relative path, e.g., "<c10d/...>". However, relative path cannot be gracefully handled by Meta internal build when the NCCL PG is hipified to support AMD/RCCL because the "hipified" header files are generated in other directories. Moreover, using absolute path for header inclusion is the state-of-the-art in most components in Pytorch. Thus, this patch refactors all header paths in torch/csrc/distributed to be absolute.
See D39835774 for more details about Meta internal complication.
**How to test**: commit 9e5d199 removes -I./torch/csrc/distributed in compile options. Thus use it to verify we don't miss any relative path use of torch/csrc/distributed headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85780
Approved by: https://github.com/kumpera, https://github.com/huydhn
Headers under torch/csrc/distributed may be referened with relative path, e.g., "<c10d/...>". However, relative path cannot be gracefully handled by Meta internal build when the NCCL PG is hipified to support AMD/RCCL because the "hipified" header files are generated in other directories. Moreover, using absolute path for header inclusion is the state-of-the-art in most components in Pytorch. Thus, this patch refactors all header paths in torch/csrc/distributed to be absolute.
See D39835774 for more details about Meta internal complication.
**How to test**: commit 9e5d199 removes -I./torch/csrc/distributed in compile options. Thus use it to verify we don't miss any relative path use of torch/csrc/distributed headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85780
Approved by: https://github.com/kumpera
Move functorch/functorch into `functorch` folder
- Add functorch/CMakeLists.txt that adds `functorch` native python exension
- Modify `setup.py` to package pytorch and functorch together into a single wheel
- Modify `functorch.__version__` is not equal to that of `torch.__version__`
- Add dummy `functorch/setup.py` file for the projects that still want to build it
Differential Revision: [D39058811](https://our.internmc.facebook.com/intern/diff/D39058811)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83464
Approved by: https://github.com/zou3519
- [x] Direct dependency on UCX is completely removed, UCC active set API always enabled
- [x] Remove `TORCH_UCC_PROFILING_ENABLE`, always enable profiling
- [x] Fixes profiling of `recv` and `all_gather`
- [x] Use the NCCL TL of UCC on CUDA, as the UCP TL is not well supported on CUDA
Most tests are passing, but there are a few skipped tests:
- `scatter` and `gather` are not supported by the UCP TL of UCC on CPU tensors
- A few flaky tests in PyTorch's CI environment
- Profiler-related failures, some of them will be fixed by @Fuzzkatt in https://github.com/pytorch/pytorch/pull/84368
After this PR is merged, I will continue to work on these skipped failures.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83285
Approved by: https://github.com/vtlam, https://github.com/malfet, https://github.com/kwen2501
The pyi, selected_mobile_ops and nvfuser code generators were missing
some dependencies outright. The autograd codegen had some effort to
list out specific files that it depends on, but this has clearly
fallen out of sync so it's safer to just depend on the entire folder.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83683
Approved by: https://github.com/albanD
- Modifies the current cmake build definitions to use `find_package` to find UCX and UCC installed in the system
- Install UCX and UCC in CUDA dockers
- Build PyTorch with `USE_UCC=1` in pipelines
- Currently, we are not running unit tests with the UCC PG. Those tests will be added in future PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81583
Approved by: https://github.com/vtlam, https://github.com/malfet
Summary:
This diff integrates UCC process group as a native component of Pytorch Distributed core. It is based on the existing torch-ucc (https://github.com/facebookresearch/torch_ucc) as the wrapper for UCC collective communication library.
The environment and cmake variables are named in mirroring to the existing process groups such as NCCL and Gloo. Specifically,
- USE_UCC: enables UCC PG. This defaults to OFF, so there is no breakage of existing builds that do not have UCX/UCC external libraries.
- USE_SYSTEM_UCC: uses external UCX and UCC shared libraries that are set accordingly with UCX_HOME and UCC_HOME.
Currently, this diff only supports USE_SYSTEM_UCC=ON, i.e., requiring users to specify external libraries for UCX and UCC. In subsequent diffs, we will add UCX and UCC repos as third-party dependencies in pytorch/third-party.
Test Plan:
Passed Torch-UCC tests that invoke UCC process group. For example:
$ sh test/start_test.sh test/torch_allreduce_test.py --backend gloo --use-cuda
...
Test allreduce: succeeded
Differential Revision: D36973688
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79918
Approved by: https://github.com/kwen2501, https://github.com/kingchc
(reopening due to botched merge)
The cuDNN V8 API (main support merged in https://github.com/pytorch/pytorch/pull/60755) potentially exposes many more kernels with benchmark=True. While these additional kernels can improve performance, it is often unnecessary to run every kernel returned by the heuristic and doing so may degrade the user experience by causing the first model iteration to be very slow. To alleviate this issue, this PR introduces torch.backends.cudnn.benchmark_limit. benchmark_limit specifies the maximum number of working cuDNN kernels to try for a given workload, with the default being 10 (similar to what TensorFlow does). benchmark_limit = 0 yields the current behavior of trying every kernel returned by the heuristic.
CC @ptrblck @ngimel @xwang233
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77002
Approved by: https://github.com/ngimel
Re-landing #68111/#74596
## Description
v0.5 PR of this [RFC](https://github.com/pytorch/pytorch/issues/49444).
On the basis of #50256, the below improvements are included:
* The [v0.5 release branch](https://github.com/oneapi-src/oneDNN/releases/tag/graph-v0.5) of the oneDNN Graph API is used
* The fuser now works with the profiling graph executor. We have inserted type check nodes to guard the profiled tensor properties.
### User API:
The optimization pass is disabled by default. Users could enable it by:
```
torch.jit.enable_onednn_fusion(True)
```
`torch.jit.freeze` should be used after tracing (recommended) or scripting a model.
### Performance:
[pytorch/benchmark](https://github.com/pytorch/benchmark) tool is used to compare the performance:
* SkyLake 8180 (1 socket of 28 cores):
* SkyLake 8180 (single thread):
* By mapping hardswish to oneDNN Graph, it’s 8% faster than PyTorch JIT (NNC + OFI)
** We expect performance gain after mapping transpose, contiguous & view to oneDNN graph ops
### Directory structure of the integration code
Fuser-related code is placed under:
```
torch/csrc/jit/codegen/onednn/
```
Optimization pass registration is done in:
```
torch/csrc/jit/passes/onednn_graph_fuser.h
```
CMake for the integration code is in:
```
caffe2/CMakeLists.txt
cmake/public/mkldnn.cmake
cmake/Modules/FindMKLDNN.cmake
```
## Limitations
* In this PR, we only support Pytorch-oneDNN-Graph integration on Linux platform. Support on Windows and MacOS will be enabled as a next step.
* We have only optimized the inference use-case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76622
Approved by: https://github.com/eellison
This PR introduces 3 BC changes:
First, this PR propagates `BUILD_CAFFE2` flag to `libtorch` and `libtorch_python`, which is necessary for non-caffe2 ONNX runtimes when using `ONNX_ATEN_FALLBACK` operator export type.
Second, as a complement of https://github.com/pytorch/pytorch/pull/68490, this PR refactors Caffe2's Aten ops symbolics to consider not only the `operator_export_type` (aka `ONNX_ATEN_FALLBACK`) to emit Caffe2 Aten ops, but also whether `BUILD_CAFFE2` (which is called `torch.onnx._CAFFE2_ATEN_FALLBACK` in python binding) is set.
Lastly, it renames `onnx::ATen` to `aten::ATen` for ONNX spec consistency in a BC fashion.
ONNX doesn't have `ATen` op on its spec, but PyTorch ONNX converter emits them. Non-Caffe2 backend engines would be mislead by such operator's name/domain. A non-ideal workaround would be to have Aten ops handled based on its name and ignore the (non-complaint) domain. Moreover, users could incorrectly file bugs to either ONNX or ONNX Runtime when they inspect the model and notice the presence of an unspecified ONNX operator.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73954
Approved by: https://github.com/BowenBao, https://github.com/malfet, https://github.com/garymm, https://github.com/jiafatom
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74387
Make temporary python bindings for flatbuffer to test ScriptModule save / load.
(Note: this ignores all push blocking failures!)
Test Plan: unittest
Reviewed By: iseeyuan
Differential Revision: D34968080
fbshipit-source-id: d23b16abda6e4b7ecf6b1198ed6e00908a3db903
(cherry picked from commit 5cbbc390c5f54146a1c469106ab4a6286c754325)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73991
Automatically generate `datapipe.pyi` via CMake and removing the generated .pyi file from Git. Users should have the .pyi file locally after building for the first time.
I will also be adding an internal equivalent diff for buck.
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D34868001
Pulled By: NivekT
fbshipit-source-id: 448c92da659d6b4c5f686407d3723933c266c74f
(cherry picked from commit 306dbc5f469e63bc141dac57ef310e6f0e16d9cd)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68490
The use of ATEN as a fallback operator during ONNX conversion is important for increasing operator coverage or even provide more efficient implementations over some ONNX ops.
Currently this feature is available through `OperatorExportTypes.ONNX_ATEN_FALLBACK`,
but it also performs changes to the graph that are runnable by Caffe2, only.
This PR introduces restricts caffe2-specific graph transformations for `ONNX_ATEN_FALLBACK`
operator export type for when pytorch is built with caffe2 support (aka BUILD_CAFFE2=1 during build)
The first version of this PR introduced a new operator export type `ONNX_ATEN__STRICT_FALLBACK`,
which essentially is the same as `ONNX_ATEN_FALLBACK` but without caffe2 transformations.
It was preferred to not introduce a new operator export type, but to refine the existing aten fallback one
## BC-breaking note
### The global constant `torch.onnx.PYTORCH_ONNX_CAFFE2_BUNDLE` is removed in favor of
a less visible `torch.onnx._CAFFE2_ATEN_FALLBACK`.
`PYTORCH_ONNX_CAFFE2_BUNDLE` is really a dead code flag always set to False.
One alternative would be fixing it, but #66658 disables Caffe2 build by default.
Making a Caffe2 feature a private one seems to make more sense for future deprecation.
### The method `torch.onnx.export` now defaults to ONNX when `operator_export_type` is not specified.
Previously `torch.onnx.export's operator_export_type` intended to default to `ONNX_ATEN_FALLBACK` when `PYTORCH_ONNX_CAFFE2_BUNDLE` was set, but it would never happen as `PYTORCH_ONNX_CAFFE2_BUNDLE` is always undefined
Co-authored-by: Nikita Shulga <nshulga@fb.com>
Test Plan: Imported from OSS
Reviewed By: jansel
Differential Revision: D32483781
Pulled By: malfet
fbshipit-source-id: e9b447db9466b369e77d747188685495aec3f124
(cherry picked from commit 5fb1eb1b19)
Summary:
`include_directories` is old-style CMake which adds the include path to every file being compiled. This instead makes `python`, `numpy` and `pybind11` into targets that only `torch_python` and `caffe2_pybind_state` are linked to. So, python libraries can't be accidentally included elsewhere.
Resubmit of https://github.com/pytorch/pytorch/issues/65654, Closes https://github.com/pytorch/pytorch/issues/65828
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69085
Reviewed By: anjali411
Differential Revision: D33776456
Pulled By: malfet
fbshipit-source-id: 018b0f6cd5a4f8c9e36df961deff832bc4afd479
(cherry picked from commit 57063107d6)
