[inductor][Autotune] Add matrix_instr_nonkdim to triton_meta #122852
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/122852
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (2 Unrelated Failures)As of commit 6564757 with merge base 3e7fd45 ( FLAKY - The following jobs failed but were likely due to flakiness present on trunk:
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This pull request was exported from Phabricator. Differential Revision: D55456401 |
htyu
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Mar 28, 2024
…#122852) Summary: Previous work `pytorch#120742 to enable `matrix_instr_nonkdim` only dealt with the autotuner benchmarking, but failed to enable the parameter in Triton meta for real runs. `matrix_instr_nonkdim` needs to be visible to the compiler driver to set up the optimization pipeline, so it's unlike other kernel parameters such as `BLOCK_N` that can be just set inside the kernel itself. Test Plan: P1201466917 triton_heuristics.template( num_stages=1, num_warps=4, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())], 'matrix_instr_nonkdim': 16}, inductor_meta={'kernel_name': 'triton_tem_fused_mm_0', 'backend_hash': None}, ) Differential Revision: D55456401
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This pull request was exported from Phabricator. Differential Revision: D55456401 |
…#122852) Summary: Previous work `pytorch#120742 to enable `matrix_instr_nonkdim` only dealt with the autotuner benchmarking, but failed to enable the parameter in Triton meta for real runs. `matrix_instr_nonkdim` needs to be visible to the compiler driver to set up the optimization pipeline, so it's unlike other kernel parameters such as `BLOCK_N` that can be just set inside the kernel itself. Test Plan: P1201466917 triton_heuristics.template( num_stages=1, num_warps=4, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())], 'matrix_instr_nonkdim': 16}, inductor_meta={'kernel_name': 'triton_tem_fused_mm_0', 'backend_hash': None}, ) Differential Revision: D55456401
htyu
added a commit
to htyu/pytorch
that referenced
this pull request
Mar 28, 2024
…#122852) Summary: Previous work `pytorch#120742 to enable `matrix_instr_nonkdim` only dealt with the autotuner benchmarking, but failed to enable the parameter in Triton meta for real runs. `matrix_instr_nonkdim` needs to be visible to the compiler driver to set up the optimization pipeline, so it's unlike other kernel parameters such as `BLOCK_N` that can be just set inside the kernel itself. Test Plan: P1201466917 triton_heuristics.template( num_stages=1, num_warps=4, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())], 'matrix_instr_nonkdim': 16}, inductor_meta={'kernel_name': 'triton_tem_fused_mm_0', 'backend_hash': None}, ) Differential Revision: D55456401
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This pull request was exported from Phabricator. Differential Revision: D55456401 |
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This pull request was exported from Phabricator. Differential Revision: D55456401 |
xw285cornell
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Mar 28, 2024
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@pytorchbot merge -f "failure unrelated" |
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htyu
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Mar 28, 2024
Summary: Previous work `#120742 to enable `matrix_instr_nonkdim` only dealt with the autotuner benchmarking, but failed to enable the parameter in Triton meta for real runs. `matrix_instr_nonkdim` needs to be visible to the compiler driver to set up the optimization pipeline, so it's unlike other kernel parameters such as `BLOCK_N` that can be just set inside the kernel itself. Test Plan: P1201466917 triton_heuristics.template( num_stages=1, num_warps=4, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())], 'matrix_instr_nonkdim': 16}, inductor_meta={'kernel_name': 'triton_tem_fused_mm_0', 'backend_hash': None}, ) Differential Revision: D55456401
jerrymannil
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Apr 16, 2024
…#122852) Summary: Previous work `pytorch#120742 to enable `matrix_instr_nonkdim` only dealt with the autotuner benchmarking, but failed to enable the parameter in Triton meta for real runs. `matrix_instr_nonkdim` needs to be visible to the compiler driver to set up the optimization pipeline, so it's unlike other kernel parameters such as `BLOCK_N` that can be just set inside the kernel itself. Test Plan: P1201466917 triton_heuristics.template( num_stages=1, num_warps=4, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())], 'matrix_instr_nonkdim': 16}, inductor_meta={'kernel_name': 'triton_tem_fused_mm_0', 'backend_hash': None}, ) Perf : Before: 1.693ms 0.134GB 79.28GB/s After: 1.577ms 0.134GB 85.12GB/s Differential Revision: D55456401 Pull Request resolved: pytorch#122852 Approved by: https://github.com/xw285cornell
sanketpurandare
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that referenced
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Apr 22, 2024
…#122852) Summary: Previous work `pytorch#120742 to enable `matrix_instr_nonkdim` only dealt with the autotuner benchmarking, but failed to enable the parameter in Triton meta for real runs. `matrix_instr_nonkdim` needs to be visible to the compiler driver to set up the optimization pipeline, so it's unlike other kernel parameters such as `BLOCK_N` that can be just set inside the kernel itself. Test Plan: P1201466917 triton_heuristics.template( num_stages=1, num_warps=4, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())], 'matrix_instr_nonkdim': 16}, inductor_meta={'kernel_name': 'triton_tem_fused_mm_0', 'backend_hash': None}, ) Perf : Before: 1.693ms 0.134GB 79.28GB/s After: 1.577ms 0.134GB 85.12GB/s Differential Revision: D55456401 Pull Request resolved: pytorch#122852 Approved by: https://github.com/xw285cornell
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Summary: Previous work
https://github.com/pytorch/pytorch/pull/120742to enablematrix_instr_nonkdimonly dealt with the autotuner benchmarking, but failed to enable the parameter in Triton meta for real runs.matrix_instr_nonkdimneeds to be visible to the compiler driver to set up the optimization pipeline, so it's unlike other kernel parameters such asBLOCK_Nthat can be just set inside the kernel itself.Test Plan:
P1201466917
triton_heuristics.template(
num_stages=1,
num_warps=4,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())], 'matrix_instr_nonkdim': 16},
inductor_meta={'kernel_name': 'triton_tem_fused_mm_0', 'backend_hash': None},
)
Perf :
Before: 1.693ms 0.134GB 79.28GB/s
After: 1.577ms 0.134GB 85.12GB/s
Differential Revision: D55456401
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov @ColinPeppler @amjames @desertfire @chauhang