Before this PR, calling a triton kernel would look like:
```py
kernel.run(a, b, xnumel, grid=grid(xnumel), stream=stream0)
```
where the `grid=` was passed as a callable (function closure) arg. This PR removes the grid arg:
```py
kernel.run(a, b, xnumel, stream=stream0)
```
instead now the grid computation is included in the kernel launcher, with something like:
```py
def launcher(in_ptr0, out_ptr0, xnumel, stream):
grid_0 = ((xnumel + 1023) >> 10)
grid_1 = 1
grid_2 = 1
runner(grid_0, grid_1, grid_2, stream, function, metadata, None, launch_enter_hook, launch_exit_hook, in_ptr0, out_ptr0, xnumel)
```
This should be faster, since we remove multiple function/dict calls and are able to specialize the grid computation for each `triton.Config`.
It also allows us to unify the handling of grids between the Python and C++ wrapper code. Before this, C++ wrapper code didn't actually support dynamic grid sizes and instead burned in a static grid.
This unification allows this PR to be a net deletion of code.
Note the attached diff contains some minor fbcode-only changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147583
Approved by: https://github.com/eellison, https://github.com/shunting314
This PR aims to add the functionality support of max-autotune for XPU. The current triton templates and configurations are not well optimized for XPU, so the performance is not ready yet. Also the `mm_plus_mm` template have accuracy issues in some cases. We will address these issues in the next PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143266
Approved by: https://github.com/EikanWang, https://github.com/jansel
This PR aims to add the functionality support of max-autotune for XPU. The current triton templates and configurations are not well optimized for XPU, so the performance is not ready yet. Also the `mm_plus_mm` template have accuracy issues in some cases. We will address these issues in the next PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143266
Approved by: https://github.com/EikanWang, https://github.com/jansel
Summary: For the second-pass, we don't have to rerun the whole inductor flow again. This PR moves that second-pass to the codegen time. This change not only speeds up the compilation, but also removes kernel scheduling inconsistency between the two passes. Another future improvement is to make the second-pass reuse the scheduler and do the wrapper codegen only.
This is a copy of https://github.com/pytorch/pytorch/pull/113762 to land in github first.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114067
Approved by: https://github.com/chenyang78
Enables additional inductor UTs on ROCm and un skips outdated skips.
I have also removed a group of failures in `test_torchinductor_opinfo` which are now passing for CUDA and ROCm
```
- # The following 3 tests fail on CUDA with AssertionError: expected size 5==5, stride 5==1 at dim=0
- # linalg._svd's return value has different strides on CUDA vs CPU which causes this
- # In test_meta.py there is a mechanism to skipping strides checks for some ops
- # (including _linalg_svd), possibly we should have something similar here
- "linalg.cond": {f32, f64},
- "linalg.svdvals": {f32, f64},
- "linalg.matrix_rank": {f32, f64},
- "linalg.svd": {f32, f64},
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104624
Approved by: https://github.com/malfet
Summary:
Add a new path in `post_grad.py` for replacing addmm + ReLU / GELU activation with the corresponding `_addmm_activation` call (with `use_gelu=False` or `True`, respectively). The replacement is done only on `max_autotune_gemm=False` and when the activation is fusible.
Test Plan:
$ python test/inductor/test_pattern_matcher.py -k test_addmm_activation -v
(__main__.TestPaternMatcher.test_addmm_activation) ... /data/users/aakhundov/pytorch/torch/_inductor/compile_fx.py:128: UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.
warnings.warn(
Using FallbackKernel: aten._addmm_activation.default
Using FallbackKernel: aten._addmm_activation.default
/data/users/aakhundov/pytorch/torch/_dynamo/eval_frame.py:373: UserWarning: changing options to `torch.compile()` may require calling `torch._dynamo.reset()` to take effect
warnings.warn(
frames [('total', 1), ('ok', 1)]
stats [('calls_captured', 2), ('unique_graphs', 1)]
aot_autograd [('total', 1), ('ok', 1)]
inductor []
ok
----------------------------------------------------------------------
Ran 1 test in 13.415s
OK
Reviewers: @eellison
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104132
Approved by: https://github.com/eellison, https://github.com/jansel
Summary: bias_addmm is not backed up by a cpp funciton, so turn
autotune_cublasLt for cpp_wrapper + max_autotune. We can add a cpp
function implementation if there is a performance need.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103004
Approved by: https://github.com/jansel
PR to enable default workflow PyTorch 2.0 unit tests for the ROCm stack.
- Enables all the dynamo unit test suites
- Enables some of the inductor unit test suites
- `test_config`
- `test_cpp_wrapper` (cpu only)
- `test_minifier`
- `test_standalone_compile`
- `test_torchinductor_dynamic_shapes`
- `test_torchinductor_opinfo`
- `test_torchinductor`
- `test_triton_wrapper`
- Introduces TEST_WITH_ROCM conditions for unit test skip/fail dictionaries in test_torchinductor_dynamic_shapes.py and test_torchinductor_opinfo.py
Note this PR follows on from the discussions for the previous UT enablement PR https://github.com/pytorch/pytorch/pull/97988, we have opted to only enable a few inductor suites at the moment to ease the upstreaming effort as these files are changing very quickly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100981
Approved by: https://github.com/jithunnair-amd, https://github.com/malfet
