Support cublasLt Fp8 Approx Gelu epilogue fusion. #7751
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reedwm
requested changes
Dec 14, 2023
| ENTRY test { | ||
| x = f8e4m3fn[16,32] parameter(0) | ||
| y = f8e4m3fn[32,16] parameter(1) | ||
| x_f32 = bf16[16,32] convert(x) |
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This should be named x_bf16. And similarly for y. And same for the other test
reedwm
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Dec 14, 2023
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Dec 15, 2023
Imported from GitHub PR openxla/xla#7751 Due to fast accumulation being turned on in the forward mode, the cublasLt fp8 gemm with gelu epilogue can efficiently operate with a fused kernel. Compared against the XLA-generated gelu kernel on H100, the performance demonstrates some improvement for size of [8192, 4096] x [4096, 16384] + gelu: Execution time for matmul using cublasLt and gelu (XLA): 1.28ms Execution time for matmul_gelu using cublasLt: 1.25ms Copybara import of the project: -- e8abce3b41f68cae1bb625cdecd5885413a0781d by Shu Wang <shuw@nvidia.com>: Support cublasLt Fp8 Approx Gelu epilogue fusion. -- 818127cf582af7ceba014d88bdf027857fc8f0e5 by shuw <shuw@nvidia.com>: Remove F32 check -- 5ce3108a9bc8459e20456d23a3ae493ef7a6a387 by shuw <shuw@nvidia.com>: Improve based on review #1 Merging this change closes #7751 PiperOrigin-RevId: 591236441
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Due to fast accumulation being turned on in the forward mode, the cublasLt fp8 gemm with gelu epilogue can efficiently operate with a fused kernel. Compared against the XLA-generated gelu kernel on H100, the performance demonstrates some improvement for size of [8192, 4096] x [4096, 16384] + gelu:
Execution time for matmul using cublasLt and gelu (XLA): 1.28ms
Execution time for matmul_gelu using cublasLt: 1.25ms