124 lines
7.4 KiB
Plaintext
124 lines
7.4 KiB
Plaintext
/*
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* Copyright (c) 2025 by FlashInfer team.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <flashinfer/cutlass_utils.cuh>
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#include "pytorch_extension_utils.h"
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using namespace flashinfer;
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#define DISPATCH_PYTORCH_INPUT_OUTPUT_DTYPE(input_dtype, output_dtype, c_type_in, c_type_out, ...) \
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[&]() -> bool { \
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return DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(output_dtype, c_type_out, [&] { \
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return DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP8(input_dtype, c_type_in, \
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[&] { return __VA_ARGS__(); }); \
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}); \
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}()
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#define DISPATCH_SCALE_GRANULARITY(scale_granularity_m, scale_granularity_n, scale_granularity_k, \
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SCALE_GRANULARITY_M, SCALE_GRANULARITY_N, SCALE_GRANULARITY_K, \
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...) \
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[&]() -> bool { \
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constexpr int SCALE_GRANULARITY_K = 128; \
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if (scale_granularity_k != 128) { \
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TORCH_CHECK( \
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false, \
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"SM120 requires scale_granularity_k=128. CUTLASS enforces ScaleGranularityK must equal " \
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"tile shape K dimension (128 for both Cooperative and PingPong schedules)."); \
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return false; \
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} \
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/* Match SM100's approach: support only (1,128,128) and (128,128,128) */ \
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if (scale_granularity_m == 1 && scale_granularity_n == 128) { \
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constexpr int SCALE_GRANULARITY_M = 1; \
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constexpr int SCALE_GRANULARITY_N = 128; \
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return __VA_ARGS__(); \
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} else if (scale_granularity_m == 128 && scale_granularity_n == 128) { \
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constexpr int SCALE_GRANULARITY_M = 128; \
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constexpr int SCALE_GRANULARITY_N = 128; \
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return __VA_ARGS__(); \
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} \
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TORCH_CHECK(false, "SM120: Unsupported scale granularity combination (", scale_granularity_m, \
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",", scale_granularity_n, ",", scale_granularity_k, ")"); \
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return false; \
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}()
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#define DISPATCH_SCALE_MAJOR_K(scale_major_mode, SCALE_MAJOR_K, ...) \
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[&]() -> bool { \
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if (scale_major_mode == "K") { \
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constexpr bool SCALE_MAJOR_K = true; \
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return __VA_ARGS__(); \
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} else if (scale_major_mode == "MN") { \
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constexpr bool SCALE_MAJOR_K = false; \
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return __VA_ARGS__(); \
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} \
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TORCH_CHECK(false, "Unsupported Scale Major Mode"); \
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return false; \
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}()
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namespace flashinfer {
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namespace group_gemm {
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template <int ScaleGranularityM, int ScaleGranularityN, int ScaleGranularityK, bool ScaleMajorK,
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typename DTypeIn, typename DTypeOut>
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cudaError_t CutlassFP8GroupwiseScaledGroupGEMMSM120(
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void* int_buffer, size_t int_buffer_size_in_bytes, void* float_buffer,
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size_t float_buffer_size_in_bytes, DTypeIn* A, DTypeIn* B, float* SFA, float* SFB, DTypeOut* D,
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int* m_indptr, int max_m, int n, int k, int num_groups, cudaStream_t stream);
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} // namespace group_gemm
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} // namespace flashinfer
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void CutlassGroupGemmFP8GroupwiseScaledSM120(
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at::Tensor int_workspace_buffer, at::Tensor float_workspace_buffer, at::Tensor A, at::Tensor B,
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at::Tensor SFA, at::Tensor SFB, at::Tensor D, at::Tensor m_indptr, int64_t n, int64_t k,
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int64_t scale_granularity_m, int64_t scale_granularity_n, int64_t scale_granularity_k,
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std::string scale_major_mode) {
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const c10::cuda::OptionalCUDAGuard device_guard(float_workspace_buffer.device());
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auto stream = at::cuda::getCurrentCUDAStream();
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int num_groups = m_indptr.size(0) - 1;
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// Ensure scales are contiguous
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// Note: We keep the original shape and let the kernel's layout handle interpretation
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at::Tensor SFA_contig = SFA.is_contiguous() ? SFA : SFA.contiguous();
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at::Tensor SFB_contig = SFB.is_contiguous() ? SFB : SFB.contiguous();
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// Get max_m from SFA shape
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int max_m = SFA.size(SFA.dim() > 1 ? 1 : 0);
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DISPATCH_PYTORCH_INPUT_OUTPUT_DTYPE(A.scalar_type(), D.scalar_type(), c_type_in, c_type_out, [&] {
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return DISPATCH_SCALE_MAJOR_K(scale_major_mode, SCALE_MAJOR_K, [&] {
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return DISPATCH_SCALE_GRANULARITY(
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scale_granularity_m, scale_granularity_n, scale_granularity_k, SCALE_GRANULARITY_M,
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SCALE_GRANULARITY_N, SCALE_GRANULARITY_K, [&] {
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using cutlass_t_in = cutlass_dtype_t<c_type_in>;
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using cutlass_t_out = cutlass_dtype_t<c_type_out>;
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auto status = flashinfer::group_gemm::CutlassFP8GroupwiseScaledGroupGEMMSM120<
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SCALE_GRANULARITY_M, SCALE_GRANULARITY_N, SCALE_GRANULARITY_K, SCALE_MAJOR_K,
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cutlass_t_in, cutlass_t_out>(
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static_cast<int*>(int_workspace_buffer.data_ptr()),
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int_workspace_buffer.element_size() * int_workspace_buffer.size(0),
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static_cast<float*>(float_workspace_buffer.data_ptr()),
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float_workspace_buffer.element_size() * float_workspace_buffer.size(0),
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static_cast<cutlass_t_in*>(A.data_ptr()), static_cast<cutlass_t_in*>(B.data_ptr()),
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static_cast<float*>(SFA_contig.data_ptr()),
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static_cast<float*>(SFB_contig.data_ptr()),
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static_cast<cutlass_t_out*>(D.data_ptr()), static_cast<int*>(m_indptr.data_ptr()),
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max_m, n, k, num_groups, stream);
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return status == cudaSuccess;
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});
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});
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});
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}
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