[triton_heuristics] Optimize the triton launcher in pt2 #158897

xuzhao9 · 2025-07-23T03:47:46Z

Summary:
We observed ~10us PT2-Triton launch overhead regression after pin update.

Before Triton pin-update:
{F1980557238}

After Triton pin-update:
{F1980557240}

The root cause is because #145051 adds _get_args_with_constexprs to the cubin launcher caller function, which is on the critical path.

Note that the static_cuda_launcher.py does not require constants to be passed to the cubin launcher (https://www.internalfb.com/code/fbsource/[2f2a11827c50e03a21b75ddd34771f34667fba98]/fbcode/caffe2/torch/_inductor/runtime/static_cuda_launcher.py?lines=215), there is no need to pass in constexprs to the generated launcher code.

Analysis: https://docs.google.com/document/d/1PHaSmx2w59K8qpjw5_qzKWShfEgptf_Zpv_DL7YxiWU/edit?tab=t.0

Test Plan:
Before:

$ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 

1.863x

After:

buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency
  x_val    nop_python_function-walltime    nop_triton_kernel-walltime    nop_triton_compiled_kernel_run-walltime    nop_inductor_kernel-walltime    nop_inductor_kernel_cudagraph-walltime
-------  ------------------------------  ----------------------------  -----------------------------------------  ------------------------------  ----------------------------------------
      0                      0.00747067                       1.92589                                   0.726509                         4.35459                                  0.204205
     19                      0.00747823                       7.36852                                   1.26241                          6.28208                                  0.239278
average                      0.00747445                       4.6472                                    0.994459                         5.31834                                  0.221741

$ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 

1.985x

Rollback Plan:

Differential Revision: D78783302

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben

pytorch-bot · 2025-07-23T03:47:50Z

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/158897

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❌ 7 New Failures, 1 Unrelated Failure

As of commit 195e7da with merge base 4c01991 ():

NEW FAILURES - The following jobs have failed:

Lint / lintrunner-noclang / linux-job (gh)
>>> Lint for torch/_inductor/runtime/triton_heuristics.py:
pull / linux-jammy-cuda12.8-py3.10-gcc11 / test (default, 2, 5, linux.g6.4xlarge.experimental.nvidia.gpu) (gh)
inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_triton_dtype_dynamic_False_backend_inductor
pull / linux-jammy-cuda12.8-py3.10-gcc11 / test (default, 3, 5, linux.g6.4xlarge.experimental.nvidia.gpu) (gh)
inductor/test_kernel_benchmark.py::TestKernelBenchmark::test_remove_inductor_deps
pull / linux-jammy-cuda12.8-py3.10-gcc11 / test (distributed, 1, 3, linux.g4dn.12xlarge.nvidia.gpu) (gh)
distributed/test_c10d_functional_native.py::TestWithNCCL::test_all_gather_into_tensor_coalesced
pull / linux-jammy-cuda12.8-py3.10-gcc11 / test (distributed, 2, 3, linux.g4dn.12xlarge.nvidia.gpu) (gh)
distributed/test_dynamo_distributed.py::TestMultiProc::test_asymmetric_compilation
pull / linux-jammy-cuda12.8-py3.10-gcc11 / test (distributed, 3, 3, linux.g4dn.12xlarge.nvidia.gpu) (gh)
distributed/test_inductor_collectives.py::TestCollectivesMultiProc::test_all_to_all_recompute_is_always_banned_override_with_ac_False
pull / linux-jammy-cuda12.8-py3.10-gcc11 / test (pr_time_benchmarks, 1, 1, linux.g4dn.metal.nvidia.gpu) (gh)
Process completed with exit code 1.

UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:

pull / linux-jammy-py3_9-clang9-xla / test (xla, 1, 1, linux.12xlarge, unstable) (gh) (#158876)
/var/lib/jenkins/workspace/xla/torch_xla/csrc/runtime/BUILD:476:14: Compiling torch_xla/csrc/runtime/xla_util_test.cpp failed: (Exit 1): gcc failed: error executing CppCompile command (from target //torch_xla/csrc/runtime:xla_util_test) /usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections ... (remaining 229 arguments skipped)

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facebook-github-bot · 2025-07-23T03:48:00Z

This pull request was exported from Phabricator. Differential Revision: D78783302

github-actions · 2025-07-23T03:49:01Z

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Summary: We observed ~10us PT2-Triton launch overhead regression after pin update. Before Triton pin-update: {F1980557238} After Triton pin-update: {F1980557240} The root cause is because pytorch#145051 adds `_get_args_with_constexprs` to the cubin launcher caller function, which is on the critical path. Note that the static_cuda_launcher.py does not require constants to be passed to the cubin launcher (https://www.internalfb.com/code/fbsource/[2f2a11827c50e03a21b75ddd34771f34667fba98]/fbcode/caffe2/torch/_inductor/runtime/static_cuda_launcher.py?lines=215), there is no need to pass in constexprs to the generated launcher code. I am pretty sure that this will work for StaticallyLaunchedCudaKernel, but needs more investigation into whether it will work for triton.compile.CompiledKernel Analysis: https://docs.google.com/document/d/1PHaSmx2w59K8qpjw5_qzKWShfEgptf_Zpv_DL7YxiWU/edit?tab=t.0 Test Plan: Before: ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.893x ``` ``` $ buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00760921 1.80298 0.623282 5.25024 0.203722 19 0.00799885 4.78223 1.00226 5.8213 0.239084 average 0.00780403 3.29261 0.812769 5.53577 0.221403 ``` After: ``` buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00747067 1.92589 0.726509 4.35459 0.204205 19 0.00747823 7.36852 1.26241 6.28208 0.239278 average 0.00747445 4.6472 0.994459 5.31834 0.221741 ``` ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.985x ``` Rollback Plan: Differential Revision: D78783302

facebook-github-bot · 2025-07-23T06:01:06Z

This pull request was exported from Phabricator. Differential Revision: D78783302

Summary: We observed ~10us PT2-Triton launch overhead regression after pin update. Before Triton pin-update: {F1980557238} After Triton pin-update: {F1980557240} The root cause is because pytorch#145051 adds `_get_args_with_constexprs` to the cubin launcher caller function, which is on the critical path. Note that the static_cuda_launcher.py does not require constants to be passed to the cubin launcher (https://www.internalfb.com/code/fbsource/[2f2a11827c50e03a21b75ddd34771f34667fba98]/fbcode/caffe2/torch/_inductor/runtime/static_cuda_launcher.py?lines=215), there is no need to pass in constexprs to the generated launcher code. I am pretty sure that this will work for StaticallyLaunchedCudaKernel, but needs more investigation into whether it will work for triton.compile.CompiledKernel Analysis: https://docs.google.com/document/d/1PHaSmx2w59K8qpjw5_qzKWShfEgptf_Zpv_DL7YxiWU/edit?tab=t.0 Test Plan: Before: ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.893x ``` ``` $ buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00760921 1.80298 0.623282 5.25024 0.203722 19 0.00799885 4.78223 1.00226 5.8213 0.239084 average 0.00780403 3.29261 0.812769 5.53577 0.221403 ``` After: ``` buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00747067 1.92589 0.726509 4.35459 0.204205 19 0.00747823 7.36852 1.26241 6.28208 0.239278 average 0.00747445 4.6472 0.994459 5.31834 0.221741 ``` ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.985x ``` Rollback Plan: Differential Revision: D78783302

facebook-github-bot · 2025-07-23T06:02:02Z

This pull request was exported from Phabricator. Differential Revision: D78783302

facebook-github-bot · 2025-07-25T19:34:22Z

This pull request was exported from Phabricator. Differential Revision: D78783302

Summary: We observed ~10us PT2-Triton launch overhead regression after pin update. Before Triton pin-update: {F1980557238} After Triton pin-update: {F1980557240} The root cause is because #145051 adds `_get_args_with_constexprs` to the cubin launcher caller function, which is on the critical path. Note that the static_cuda_launcher.py does not require constants to be passed to the cubin launcher (https://www.internalfb.com/code/fbsource/[2f2a11827c50e03a21b75ddd34771f34667fba98]/fbcode/caffe2/torch/_inductor/runtime/static_cuda_launcher.py?lines=215), there is no need to pass in constexprs to the generated launcher code. The new launcher code needs to work on three cases: - StaticallyLaunchedCudaKernel - triton.compile.CompiledKernel - [WIP] AOTInductor Analysis: https://docs.google.com/document/d/1PHaSmx2w59K8qpjw5_qzKWShfEgptf_Zpv_DL7YxiWU/edit?tab=t.0 Test Plan: Before: ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.893x ``` ``` $ buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00760921 1.80298 0.623282 5.25024 0.203722 19 0.00799885 4.78223 1.00226 5.8213 0.239084 average 0.00780403 3.29261 0.812769 5.53577 0.221403 ``` After: ``` buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00747067 1.92589 0.726509 4.35459 0.204205 19 0.00747823 7.36852 1.26241 6.28208 0.239278 average 0.00747445 4.6472 0.994459 5.31834 0.221741 ``` ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.985x ``` Rollback Plan: Differential Revision: D78783302

Summary: We observed ~10us PT2-Triton launch overhead regression after pin update. Before Triton pin-update: {F1980557238} After Triton pin-update: {F1980557240} The root cause is because pytorch#145051 adds `_get_args_with_constexprs` to the cubin launcher caller function, which is on the critical path. Note that the static_cuda_launcher.py does not require constants to be passed to the cubin launcher (https://www.internalfb.com/code/fbsource/[2f2a11827c50e03a21b75ddd34771f34667fba98]/fbcode/caffe2/torch/_inductor/runtime/static_cuda_launcher.py?lines=215), there is no need to pass in constexprs to the generated launcher code. The new launcher code needs to work on three cases: - StaticallyLaunchedCudaKernel - triton.compile.CompiledKernel - [WIP] AOTInductor Analysis: https://docs.google.com/document/d/1PHaSmx2w59K8qpjw5_qzKWShfEgptf_Zpv_DL7YxiWU/edit?tab=t.0 Test Plan: Before: ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.893x ``` ``` $ buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00760921 1.80298 0.623282 5.25024 0.203722 19 0.00799885 4.78223 1.00226 5.8213 0.239084 average 0.00780403 3.29261 0.812769 5.53577 0.221403 ``` After: ``` buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00747067 1.92589 0.726509 4.35459 0.204205 19 0.00747823 7.36852 1.26241 6.28208 0.239278 average 0.00747445 4.6472 0.994459 5.31834 0.221741 ``` ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.985x ``` Rollback Plan: Differential Revision: D78783302

facebook-github-bot · 2025-07-25T22:19:51Z

This pull request was exported from Phabricator. Differential Revision: D78783302

Summary: We observed ~10us PT2-Triton launch overhead regression after pin update. Before Triton pin-update: {F1980557238} After Triton pin-update: {F1980557240} The root cause is because pytorch#145051 adds `_get_args_with_constexprs` to the cubin launcher caller function, which is on the critical path. Note that the static_cuda_launcher.py does not require constants to be passed to the cubin launcher (https://www.internalfb.com/code/fbsource/[2f2a11827c50e03a21b75ddd34771f34667fba98]/fbcode/caffe2/torch/_inductor/runtime/static_cuda_launcher.py?lines=215), there is no need to pass in constexprs to the generated launcher code. The new launcher code needs to work on three cases: - StaticallyLaunchedCudaKernel - triton.compile.CompiledKernel - [WIP] AOTInductor Analysis: https://docs.google.com/document/d/1PHaSmx2w59K8qpjw5_qzKWShfEgptf_Zpv_DL7YxiWU/edit?tab=t.0 Test Plan: Before: ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.893x ``` ``` $ buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00760921 1.80298 0.623282 5.25024 0.203722 19 0.00799885 4.78223 1.00226 5.8213 0.239084 average 0.00780403 3.29261 0.812769 5.53577 0.221403 ``` After: ``` buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00747067 1.92589 0.726509 4.35459 0.204205 19 0.00747823 7.36852 1.26241 6.28208 0.239278 average 0.00747445 4.6472 0.994459 5.31834 0.221741 ``` ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.985x ``` Rollback Plan: Differential Revision: D78783302

Differential Revision: D78789289

Summary: We observed ~10us PT2-Triton launch overhead regression after pin update. Before Triton pin-update: {F1980557238} After Triton pin-update: {F1980557240} The root cause is because pytorch#145051 adds `_get_args_with_constexprs` to the cubin launcher caller function, which is on the critical path. Note that the static_cuda_launcher.py does not require constants to be passed to the cubin launcher (https://www.internalfb.com/code/fbsource/[2f2a11827c50e03a21b75ddd34771f34667fba98]/fbcode/caffe2/torch/_inductor/runtime/static_cuda_launcher.py?lines=215), there is no need to pass in constexprs to the generated launcher code. The new launcher code needs to work on three cases: - StaticallyLaunchedCudaKernel - triton.compile.CompiledKernel - [WIP] AOTInductor Analysis: https://docs.google.com/document/d/1PHaSmx2w59K8qpjw5_qzKWShfEgptf_Zpv_DL7YxiWU/edit?tab=t.0 Test Plan: Before: ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.893x ``` ``` $ buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00760921 1.80298 0.623282 5.25024 0.203722 19 0.00799885 4.78223 1.00226 5.8213 0.239084 average 0.00780403 3.29261 0.812769 5.53577 0.221403 ``` After: ``` buck2 run mode/opt //pytorch/tritonbench:run -- --op launch_latency x_val nop_python_function-walltime nop_triton_kernel-walltime nop_triton_compiled_kernel_run-walltime nop_inductor_kernel-walltime nop_inductor_kernel_cudagraph-walltime ------- ------------------------------ ---------------------------- ----------------------------------------- ------------------------------ ---------------------------------------- 0 0.00747067 1.92589 0.726509 4.35459 0.204205 19 0.00747823 7.36852 1.26241 6.28208 0.239278 average 0.00747445 4.6472 0.994459 5.31834 0.221741 ``` ``` $ buck2 run mode/opt //pytorch/benchmark:pt2 -- --only BERT_pytorch --performance --backend=inductor --training --amp --disable-cudagraphs 1.985x ``` Rollback Plan: Differential Revision: D78783302

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