Summary:
Currently only `num_warps` and `num_stages` are supported as one of the kernel options for inductor auto-tuning using `TritonTemplate`. In order to allow warp-specialization kernel options should allow specifying `num_consumer_groups` and `num_buffers_warp_spec` as well.
Test Plan:
## Unit test
Added tests for `test_triton_template_warp_specialization` to verify generated kenrnel contains configs for `num_consumer_groups` and `num_buffers_warp_spec`.
## Functional Testing
Specific to flexattention.
```
import torch
from torch.nn.attention.flex_attention import flex_attention
from triton.testing import do_bench
make_tensor = lambda: torch.rand(8, 16, 8192, 128, device="cuda", dtype=torch.bfloat16)
q, k, v = make_tensor(), make_tensor(), make_tensor()
flex_compiled = torch.compile(flex_attention, fullgraph=True)
print(do_bench(lambda: flex_compiled(q, k, v, kernel_options={"num_warps": 4})))
```
triton do_bench results:
- default compile: 15.176783561706543
- with warp-spec: 9.452800750732422
## Extra notes
- generated triton kernel using `TORCH_LOGS=output_code`: P1740612877
- TTGIR for fused kernel: P1740614685
Differential Revision: D70212243
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148503
Approved by: https://github.com/eellison
Summary:
Relands D69965761 / https://github.com/pytorch/pytorch/pull/147583
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.
Differential [disconnected] Revision: D70471332
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148305
Approved by: https://github.com/shunting314, https://github.com/eellison
Summary:
Relands D69965761 / https://github.com/pytorch/pytorch/pull/147583
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.
Differential Revision: D70471332
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148305
Approved by: https://github.com/shunting314, https://github.com/eellison
#147620 enabled `force_shape_pad` for triton kernel benchmark. Intel GPU supports this scenario. Hence, we need to enable the case in this PR. Otherwise, there would be a test case regression for Intel GPU as #147620 has been landed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148237
Approved by: https://github.com/jansel
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
