76 lines
4.2 KiB
Plaintext
76 lines
4.2 KiB
Plaintext
/*
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* Copyright (c) 2024 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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if (input_dtype == output_dtype) { \
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return DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(input_dtype, c_type_in, [&] { \
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using c_type_out = c_type_in; \
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return __VA_ARGS__(); \
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}); \
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} else { \
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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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}()
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namespace flashinfer {
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namespace group_gemm {
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template <typename DTypeIn, typename DTypeOut>
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cudaError_t CutlassSegmentGEMMSM90Run(void* float_buffer, size_t float_buffer_size_in_bytes,
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void* int_buffer, size_t int_buffer_size_in_bytes,
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void* all_problems, int64_t batch_size, void* x, void* w,
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void* y, void* x_stride, void* w_stride, void* y_stride,
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bool weight_column_major, cudaStream_t stream);
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} // namespace group_gemm
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} // namespace flashinfer
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void CutlassSegmentGEMMSM90(at::Tensor float_workspace_buffer, at::Tensor int_workspace_buffer,
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at::Tensor all_problems, at::Tensor x_ptr, at::Tensor w_ptr,
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at::Tensor y_ptr, at::Tensor x_stride, at::Tensor weight_stride,
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at::Tensor y_stride, at::Tensor empty_x_data, at::Tensor empty_y_data,
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bool weight_column_major) {
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unsigned int batch_size = x_ptr.size(0);
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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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DISPATCH_PYTORCH_INPUT_OUTPUT_DTYPE(
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empty_x_data.scalar_type(), empty_y_data.scalar_type(), c_type_in, c_type_out, [&] {
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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 =
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flashinfer::group_gemm::CutlassSegmentGEMMSM90Run<cutlass_t_in, cutlass_t_out>(
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float_workspace_buffer.data_ptr(),
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float_workspace_buffer.element_size() * float_workspace_buffer.size(0),
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int_workspace_buffer.data_ptr(),
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int_workspace_buffer.element_size() * int_workspace_buffer.size(0),
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all_problems.data_ptr(), batch_size, x_ptr.data_ptr(), w_ptr.data_ptr(),
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y_ptr.data_ptr(), x_stride.data_ptr(), weight_stride.data_ptr(),
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y_stride.data_ptr(), weight_column_major, stream);
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TORCH_CHECK(status == cudaSuccess,
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"Failed to run CutlassSegmentGEMM: ", cudaGetErrorString(status));
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return true;
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});
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}
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