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csrc_musa/attention/attention_utils.muh
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57
csrc_musa/attention/attention_utils.muh
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/*
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* Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
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* Copyright (c) 2024 - 2024 Moore Threads Technology Co., Ltd("Moore Threads"). All rights reserved.
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* Copyright (c) 2023, The vLLM team.
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* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
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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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#pragma once
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#include "../musa_compat.h"
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#include "attention_dtypes.h"
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#include <float.h>
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#include <type_traits>
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namespace vllm {
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// Q*K^T operation.
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template<int THREAD_GROUP_SIZE, typename Vec, int N>
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inline __device__ float qk_dot_(const Vec (&q)[N], const Vec (&k)[N]) {
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using A_vec = typename FloatVec<Vec>::Type;
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// Compute the parallel products for Q*K^T (treat vector lanes separately).
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A_vec qk_vec = mul<A_vec, Vec, Vec>(q[0], k[0]);
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#pragma unroll
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for (int ii = 1; ii < N; ++ii) {
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qk_vec = fma(q[ii], k[ii], qk_vec);
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}
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// Finalize the reduction across lanes.
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float qk = sum(qk_vec);
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#pragma unroll
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for (int mask = THREAD_GROUP_SIZE / 2; mask >= 1; mask /= 2) {
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qk += VLLM_SHFL_XOR_SYNC(qk, mask);
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}
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return qk;
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}
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template<typename T, int THREAD_GROUP_SIZE>
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struct Qk_dot {
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template<typename Vec, int N>
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static inline __device__ float dot(const Vec (&q)[N], const Vec (&k)[N]) {
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return qk_dot_<THREAD_GROUP_SIZE>(q, k);
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}
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};
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} // namespace vllm
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