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1,120 changes: 1,120 additions & 0 deletions byte_infer_perf/llm_perf/backends/ROCM/paged_attn/csrc/attention.cu

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#pragma once

#include "attention_generic.cuh"
#include "dtype_float16.cuh"
#include "dtype_float32.cuh"
#include "dtype_bfloat16.cuh"
#include "dtype_fp8.cuh"
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/*
* Adapted from
* https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
* Copyright (c) 2023, The vLLM team.
* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once

#include <stdint.h>

namespace vllm {

// A vector type to store Q, K, V elements.
template <typename T, int VEC_SIZE>
struct Vec {};

// A vector type to store FP32 accumulators.
template <typename T>
struct FloatVec {};

// Template vector operations.
template <typename Acc, typename A, typename B>
inline __device__ Acc mul(A a, B b);

template <typename T>
inline __device__ float sum(T v);

template <typename T>
inline __device__ float dot(T a, T b) {
return sum(mul<T, T, T>(a, b));
}

template <typename A, typename T>
inline __device__ float dot(T a, T b) {
return sum(mul<A, T, T>(a, b));
}

template <typename T>
inline __device__ void zero(T& dst) {
constexpr int WORDS = sizeof(T) / 4;
union {
T raw;
uint32_t words[WORDS];
} tmp;

#pragma unroll
for (int ii = 0; ii < WORDS; ++ii) {
tmp.words[ii] = 0u;
}
dst = tmp.raw;
}

} // namespace vllm
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