Add a Blackwell-specific scaled persistent + TMA template for GEMMs #432
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Summary: Taking inspiration from D77053488, Triton's [persistent matmul](https://triton-lang.org/main/getting-started/tutorials/09-persistent-matmul.html#) tutorial, and Triton's [block scaled matmul](https://triton-lang.org/main/getting-started/tutorials/10-block-scaled-matmul.html) tutorial, write and benchmark a persistent + TMA Triton kernel for FP8 workloads on Blackwell which enables warp specialization and flattening. Recall the following for FP8 workloads: - Input shapes: `torch.float8_e4m3fn` (`tl.float8e4nv`) - Output: `torch.float16` (per-tensor, `tl.float16`) or `torch.bfloat16` (per-row, `tl.bfloat16`) - Accumulation: `torch.float32` (`tl.float32`). Note the following limitations: - `(K, N)` (second matrix in GEMM) needs to be a multiple of 16 - `num_warps >= 4`: TMA instructions expect at least a 128-thread (4 warps) group. 1 warp = 32 threads, so we need 4 warps per thread block to ensure correct functionality. Note that the current kernel is being autotuned on just one config; this will be changed in a future diff. Differential Revision: D81470285
Summary: Support per-row scaling for the FP8 Blackwell persistent + TMA kernel with warp specialization. Differential Revision: D82516347
Summary: Add epilogue subtiling to Blackwell FP8 persistent + TMA kernel in TritonBench. Epilogue subtiling breaks computation in the epilogue into multiple sub-tiles, allowing TMA to more efficiently overlap expensive computation (e.g. stores, scaling). Reviewed By: NikhilAPatel Differential Revision: D82462670
Summary: Add a Blackwell-specific scaled persistent + TMA Triton template to Inductor. This diff builds on D82515450 by adding a new set of mixins which inherit the scaling epilogue and add scaled persistent + TMA kwargs to the template. This diff also adds a benchmark for the scaled Blackwell persistent + TMA template to TritonBench `fp8_gemm`. Note that this diff is a minimal extension to the above diff; rather than adding a new kernel for the scaled version, we opted to simply extend the epilogue to account for scaling. This template is accurate for per-tensor and per-row scaling but may require modifications for other scaling modes, such as deepseek-style scaling, which apply scaling prior to the GEMM computation. In addition, note that epilogue subtiling is currently unsupported for both the scaled and non-scaled Blackwell templates, and functionality will be added in a subsequent diff. Differential Revision: D82597111
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@jananisriram has exported this pull request. If you are a Meta employee, you can view the originating diff in D82597111. |
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…TMA template for GEMMs Summary: X-link: meta-pytorch/tritonbench#432 Add a Blackwell-specific scaled persistent + TMA Triton template to Inductor. This diff builds on D82515450 by adding a new set of mixins which inherit the scaling epilogue and add scaled persistent + TMA kwargs to the template. This diff also adds a benchmark for the scaled Blackwell persistent + TMA template to TritonBench `fp8_gemm`. Note that this diff is a minimal extension to the above diff; rather than adding a new kernel for the scaled version, we opted to simply extend the epilogue to account for scaling. This template is accurate for per-tensor and per-row scaling but may require modifications for other scaling modes, such as deepseek-style scaling, which apply scaling prior to the GEMM computation. In addition, note that epilogue subtiling is currently unsupported for both the scaled and non-scaled Blackwell templates, and functionality will be added in a subsequent diff. Test Plan: Verified that the scaled Blackwell template adds the scaling epilogue to the generated Triton kernel by inspecting the Inductor-generated Triton kernel. Benchmarking command: ``` TRITON_PRINT_AUTOTUNING=1 TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor TRITON_CACHE_DIR=~/personal/cache_dir_triton TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -c fbcode.nvcc_arch=b200a -c fbcode.enable_gpu_sections=true -c fbcode.platform010_cuda_version=12.8 -- --op fp8_gemm --only torch_fp8_gemm,blackwell_pt2_fp8_gemm --metrics tflops,accuracy --input-loader=/home/jananisriram/personal/fp8_shapes_testing.json --scaling_rowwise --output="/home/jananisriram/personal/fp8_shapes_testing_results.csv" --atol=1e-2 --rtol=0.5 2>&1 | tee ~/personal/fp8_shapes_testing.log ``` Rollback Plan: Differential Revision: D82597111
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…TMA template for GEMMs (#163147) Summary: X-link: meta-pytorch/tritonbench#432 Add a Blackwell-specific scaled persistent + TMA Triton template to Inductor. This diff builds on D82515450 by adding a new set of mixins which inherit the scaling epilogue and add scaled persistent + TMA kwargs to the template. Note that this diff is a minimal extension to the above diff; rather than adding a new kernel for the scaled version, we opted to simply extend the epilogue to account for scaling. This template is accurate for per-tensor and per-row scaling but may require modifications for other scaling modes, such as deepseek-style scaling, which apply scaling prior to the GEMM computation. In addition, note that epilogue subtiling is currently unsupported for both the scaled and non-scaled Blackwell templates, and functionality will be added in a subsequent diff. Test Plan: Verified that the scaled Blackwell template adds the scaling epilogue to the generated Triton kernel by inspecting the Inductor-generated Triton kernel. Differential Revision: D82597111
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…TMA template for GEMMs (#163147) Summary: X-link: meta-pytorch/tritonbench#432 Add a Blackwell-specific scaled persistent + TMA Triton template to Inductor. This diff builds on D82515450 by adding a new set of mixins which inherit the scaling epilogue and add scaled persistent + TMA kwargs to the template. Note that this diff is a minimal extension to the above diff; rather than adding a new kernel for the scaled version, we opted to simply extend the epilogue to account for scaling. This template is accurate for per-tensor and per-row scaling but may require modifications for other scaling modes, such as deepseek-style scaling, which apply scaling prior to the GEMM computation. In addition, note that epilogue subtiling is currently unsupported for both the scaled and non-scaled Blackwell templates, and functionality will be added in a subsequent diff. Test Plan: Verified that the scaled Blackwell template adds the scaling epilogue to the generated Triton kernel by inspecting the Inductor-generated Triton kernel. Reviewed By: njriasan Differential Revision: D82597111
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…TMA template for GEMMs (#163147) Summary: X-link: meta-pytorch/tritonbench#432 Add a Blackwell-specific scaled persistent + TMA Triton template to Inductor. This diff builds on D82515450 by adding a new set of mixins which inherit the scaling epilogue and add scaled persistent + TMA kwargs to the template. Note that this diff is a minimal extension to the above diff; rather than adding a new kernel for the scaled version, we opted to simply extend the epilogue to account for scaling. This template is accurate for per-tensor and per-row scaling but may require modifications for other scaling modes, such as deepseek-style scaling, which apply scaling prior to the GEMM computation. In addition, note that epilogue subtiling is currently unsupported for both the scaled and non-scaled Blackwell templates, and functionality will be added in a subsequent diff. Test Plan: Verified that the scaled Blackwell template adds the scaling epilogue to the generated Triton kernel by inspecting the Inductor-generated Triton kernel. Reviewed By: njriasan Differential Revision: D82597111
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…TMA template for GEMMs (#163147) Summary: X-link: meta-pytorch/tritonbench#432 Add a Blackwell-specific scaled persistent + TMA Triton template to Inductor. This diff builds on D82515450 by adding a new set of mixins which inherit the scaling epilogue and add scaled persistent + TMA kwargs to the template. This diff also adds a benchmark for the scaled Blackwell persistent + TMA template to TritonBench `fp8_gemm`. Note that this diff is a minimal extension to the above diff; rather than adding a new kernel for the scaled version, we opted to simply extend the epilogue to account for scaling. This template is accurate for per-tensor and per-row scaling but may require modifications for other scaling modes, such as deepseek-style scaling, which apply scaling prior to the GEMM computation. In addition, note that epilogue subtiling is currently unsupported for both the scaled and non-scaled Blackwell templates, and functionality will be added in a subsequent diff. Test Plan: Verified that the scaled Blackwell template adds the scaling epilogue to the generated Triton kernel by inspecting the Inductor-generated Triton kernel. Benchmarking command: ``` TRITON_PRINT_AUTOTUNING=1 TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor TRITON_CACHE_DIR=~/personal/cache_dir_triton TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -c fbcode.nvcc_arch=b200a -c fbcode.enable_gpu_sections=true -c fbcode.platform010_cuda_version=12.8 -- --op fp8_gemm --only torch_fp8_gemm,blackwell_pt2_fp8_gemm --metrics tflops,accuracy --input-loader=/home/jananisriram/personal/fp8_shapes_testing.json --scaling_rowwise --output="/home/jananisriram/personal/fp8_shapes_testing_results.csv" --atol=1e-2 --rtol=0.5 2>&1 | tee ~/personal/fp8_shapes_testing.log ``` Rollback Plan: Differential Revision: D82597111 Pull Request resolved: #163147 Approved by: https://github.com/njriasan
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…TMA template for GEMMs (pytorch#163147) Summary: X-link: meta-pytorch/tritonbench#432 Add a Blackwell-specific scaled persistent + TMA Triton template to Inductor. This diff builds on D82515450 by adding a new set of mixins which inherit the scaling epilogue and add scaled persistent + TMA kwargs to the template. This diff also adds a benchmark for the scaled Blackwell persistent + TMA template to TritonBench `fp8_gemm`. Note that this diff is a minimal extension to the above diff; rather than adding a new kernel for the scaled version, we opted to simply extend the epilogue to account for scaling. This template is accurate for per-tensor and per-row scaling but may require modifications for other scaling modes, such as deepseek-style scaling, which apply scaling prior to the GEMM computation. In addition, note that epilogue subtiling is currently unsupported for both the scaled and non-scaled Blackwell templates, and functionality will be added in a subsequent diff. Test Plan: Verified that the scaled Blackwell template adds the scaling epilogue to the generated Triton kernel by inspecting the Inductor-generated Triton kernel. Benchmarking command: ``` TRITON_PRINT_AUTOTUNING=1 TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor TRITON_CACHE_DIR=~/personal/cache_dir_triton TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -c fbcode.nvcc_arch=b200a -c fbcode.enable_gpu_sections=true -c fbcode.platform010_cuda_version=12.8 -- --op fp8_gemm --only torch_fp8_gemm,blackwell_pt2_fp8_gemm --metrics tflops,accuracy --input-loader=/home/jananisriram/personal/fp8_shapes_testing.json --scaling_rowwise --output="/home/jananisriram/personal/fp8_shapes_testing_results.csv" --atol=1e-2 --rtol=0.5 2>&1 | tee ~/personal/fp8_shapes_testing.log ``` Rollback Plan: Differential Revision: D82597111 Pull Request resolved: pytorch#163147 Approved by: https://github.com/njriasan
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…TMA template for GEMMs (pytorch#163147) Summary: X-link: meta-pytorch/tritonbench#432 Add a Blackwell-specific scaled persistent + TMA Triton template to Inductor. This diff builds on D82515450 by adding a new set of mixins which inherit the scaling epilogue and add scaled persistent + TMA kwargs to the template. This diff also adds a benchmark for the scaled Blackwell persistent + TMA template to TritonBench `fp8_gemm`. Note that this diff is a minimal extension to the above diff; rather than adding a new kernel for the scaled version, we opted to simply extend the epilogue to account for scaling. This template is accurate for per-tensor and per-row scaling but may require modifications for other scaling modes, such as deepseek-style scaling, which apply scaling prior to the GEMM computation. In addition, note that epilogue subtiling is currently unsupported for both the scaled and non-scaled Blackwell templates, and functionality will be added in a subsequent diff. Test Plan: Verified that the scaled Blackwell template adds the scaling epilogue to the generated Triton kernel by inspecting the Inductor-generated Triton kernel. Benchmarking command: ``` TRITON_PRINT_AUTOTUNING=1 TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor TRITON_CACHE_DIR=~/personal/cache_dir_triton TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -c fbcode.nvcc_arch=b200a -c fbcode.enable_gpu_sections=true -c fbcode.platform010_cuda_version=12.8 -- --op fp8_gemm --only torch_fp8_gemm,blackwell_pt2_fp8_gemm --metrics tflops,accuracy --input-loader=/home/jananisriram/personal/fp8_shapes_testing.json --scaling_rowwise --output="/home/jananisriram/personal/fp8_shapes_testing_results.csv" --atol=1e-2 --rtol=0.5 2>&1 | tee ~/personal/fp8_shapes_testing.log ``` Rollback Plan: Differential Revision: D82597111 Pull Request resolved: pytorch#163147 Approved by: https://github.com/njriasan
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…TMA template for GEMMs (pytorch#163147) Summary: X-link: meta-pytorch/tritonbench#432 Add a Blackwell-specific scaled persistent + TMA Triton template to Inductor. This diff builds on D82515450 by adding a new set of mixins which inherit the scaling epilogue and add scaled persistent + TMA kwargs to the template. This diff also adds a benchmark for the scaled Blackwell persistent + TMA template to TritonBench `fp8_gemm`. Note that this diff is a minimal extension to the above diff; rather than adding a new kernel for the scaled version, we opted to simply extend the epilogue to account for scaling. This template is accurate for per-tensor and per-row scaling but may require modifications for other scaling modes, such as deepseek-style scaling, which apply scaling prior to the GEMM computation. In addition, note that epilogue subtiling is currently unsupported for both the scaled and non-scaled Blackwell templates, and functionality will be added in a subsequent diff. Test Plan: Verified that the scaled Blackwell template adds the scaling epilogue to the generated Triton kernel by inspecting the Inductor-generated Triton kernel. Benchmarking command: ``` TRITON_PRINT_AUTOTUNING=1 TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor TRITON_CACHE_DIR=~/personal/cache_dir_triton TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -c fbcode.nvcc_arch=b200a -c fbcode.enable_gpu_sections=true -c fbcode.platform010_cuda_version=12.8 -- --op fp8_gemm --only torch_fp8_gemm,blackwell_pt2_fp8_gemm --metrics tflops,accuracy --input-loader=/home/jananisriram/personal/fp8_shapes_testing.json --scaling_rowwise --output="/home/jananisriram/personal/fp8_shapes_testing_results.csv" --atol=1e-2 --rtol=0.5 2>&1 | tee ~/personal/fp8_shapes_testing.log ``` Rollback Plan: Differential Revision: D82597111 Pull Request resolved: pytorch#163147 Approved by: https://github.com/njriasan
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| def blackwell_pt2_fp8_gemm(self, a, b, scale_a, scale_b): |
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Include scaling type in the name? If not clarify how this is generic. Does everything in this file work with all kinds of scaling (I assume its an input generation problem).
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Summary:
Add a Blackwell-specific scaled persistent + TMA Triton template to Inductor. This diff builds on D82515450 by adding a new set of mixins which inherit the scaling epilogue and add scaled persistent + TMA kwargs to the template.
This diff also adds a benchmark for the scaled Blackwell persistent + TMA template to TritonBench
fp8_gemm.Note that this diff is a minimal extension to the above diff; rather than adding a new kernel for the scaled version, we opted to simply extend the epilogue to account for scaling. This template is accurate for per-tensor and per-row scaling but may require modifications for other scaling modes, such as deepseek-style scaling, which apply scaling prior to the GEMM computation.
In addition, note that epilogue subtiling is currently unsupported for both the scaled and non-scaled Blackwell templates, and functionality will be added in a subsequent diff.
Differential Revision: D82597111