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05c670e593
* [Sync] Update to latest code * Add new code files * Add new code files * update code * Try to fix build.sh * Try to fix build.sh * Update code * Update requirements.txt * Update code --------- Co-authored-by: Jiang-Jia-Jun <jiangjiajun@baidu.com>
237 lines
8.5 KiB
Plaintext
237 lines
8.5 KiB
Plaintext
// Copyright (c) 2024 PaddlePaddle Authors. 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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#pragma once
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#include "multi_head_latent_attention_kernel.h"
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template <size_t vec_size, typename T>
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struct softmax_state_t {
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AlignedVector<T, vec_size> o;
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T m;
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T d;
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__device__ __forceinline__ void init() {
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if constexpr (std::is_same<T, half>::value) {
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#pragma unroll
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for (int i = 0; i < vec_size / 2; ++i) {
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*((half2*)(&o) + i) = make_half2(0, 0);
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}
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} else if constexpr (std::is_same<T, __nv_bfloat16>::value) {
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#pragma unroll
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for (int i = 0; i < vec_size / 2; ++i) {
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*((nv_bfloat162*)(&o) + i) = make_bfloat162(0, 0);
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}
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}
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d = 1.f;
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if constexpr (std::is_same<T, half>::value) {
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m = __float2half(-5e4f);
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} else if constexpr (std::is_same<T, nv_bfloat16>::value) {
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m = __float2bfloat16(-3.38953e38f);
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}
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}
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__device__ __forceinline__ softmax_state_t() {
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init();
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}
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__device__ __forceinline__ void merge(const AlignedVector<T, vec_size>& other_o,
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T other_m,
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T other_d) {
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// using kType = typename cascade_attn_nv_type2_traits<T>::type;
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T m_prev = m, d_prev = d;
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m = m_prev > other_m ? m_prev : other_m;
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T scale1 = hexp(m_prev - m), scale2 = hexp(other_m - m);
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d = d_prev * scale1 + other_d * scale2;
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#pragma unroll
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for (size_t i = 0; i < vec_size; ++i) {
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o[i] = o[i] * scale1 + other_o[i] * scale2;
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}
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}
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__device__ __forceinline__ void normalize() {
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#pragma unroll
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for (size_t i = 0; i < vec_size; ++i) {
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o[i] /= d;
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}
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}
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};
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template <size_t vec_size, typename T, uint32_t num_tiles = 0>
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struct softmax_state_ts {
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uint32_t num_tiles_ = num_tiles;
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AlignedVector<T, vec_size> o[num_tiles];
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float m;
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float d;
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__device__ __forceinline__ void init() {
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#pragma unroll
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for (uint32_t tile_id = 0; tile_id < num_tiles_; ++tile_id) {
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if constexpr (std::is_same<T, half>::value) {
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#pragma unroll
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for (int i = 0; i < vec_size / 2; ++i) {
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*((half2*)(&o[tile_id]) + i) = make_half2(0, 0);
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}
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} else if constexpr (std::is_same<T, __nv_bfloat16>::value) {
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#pragma unroll
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for (int i = 0; i < vec_size / 2; ++i) {
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*((nv_bfloat162*)(&o[tile_id]) + i) = make_bfloat162(0, 0);
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}
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}
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}
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d = 1.f;
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if constexpr (std::is_same<T, half>::value) {
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m = -5e4f;
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} else if constexpr (std::is_same<T, nv_bfloat16>::value) {
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m = -3.38953e38f;
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}
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}
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__device__ __forceinline__ softmax_state_ts() {
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init();
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}
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__device__ __forceinline__ void normalize(const uint32_t tile_id) {
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#pragma unroll
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for (size_t i = 0; i < vec_size; i++) {
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o[tile_id][i] /= d;
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}
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}
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};
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template <SharedMemFillMode fill_mode, uint32_t HEAD_DIM_QK, uint32_t vec_size, uint32_t NUM_VEC_PER_HEAD, uint32_t bdx, uint32_t BLOCK_SIZE, uint32_t CACHE_VEC_SIZE, typename CacheT>
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__device__ __forceinline__ void produce_kv(CacheT *smem,
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CacheT *kv_base_gptr,
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const int * block_table_smem,
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const uint32_t seq_offset_gmem,
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const uint32_t seq_offset_smem,
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const uint32_t kv_head_idx,
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const uint32_t kv_num_heads,
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const uint32_t tidx,
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const uint32_t chunk_start,
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const uint32_t chunk_end) {
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int block_id = __ldg(&block_table_smem[seq_offset_gmem / BLOCK_SIZE]);
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if (block_id < 0) {
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block_id = 0;
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}
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const uint32_t block_offset = seq_offset_gmem % BLOCK_SIZE;
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// 8/16 T/int8 each time
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const uint32_t k_offset_base = ((block_id * kv_num_heads + kv_head_idx) * BLOCK_SIZE + block_offset) * HEAD_DIM_QK;
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const uint32_t smem_offset_base = seq_offset_smem * HEAD_DIM_QK;
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for(uint32_t vid = tidx; vid < NUM_VEC_PER_HEAD; vid += bdx) {
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pred_load<128, PrefetchMode::kPrefetch, fill_mode, CacheT>(
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smem + smem_offset_base + vid * CACHE_VEC_SIZE,
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kv_base_gptr + k_offset_base + vid * CACHE_VEC_SIZE,
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seq_offset_gmem < chunk_end
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);
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}
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}
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template <uint32_t vec_size, uint32_t NUM_VEC_PER_HEAD, uint32_t bdx, uint32_t bdy, uint32_t HEAD_DIM, uint32_t DEAL_EACH_TIME, uint32_t num_tile_v, typename T, typename CacheT>
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__device__ __forceinline__ void compute_qk(const T* cu_q_smem,
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const CacheT* k_smem,
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const uint32_t kv_idx_base,
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const uint32_t stage_idx,
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const uint32_t iter_base,
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const uint32_t iter_bound,
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const uint32_t tidx,
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const uint32_t gid,
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const float scale,
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float *s,
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softmax_state_ts<vec_size, T, num_tile_v>& st) {
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const CacheT* smem;
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AlignedVector<T, vec_size> q_vec;
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AlignedVector<T, vec_size> k_vec;
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float m_prev = st.m;
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// smem = base_smem + (stage_idx * DEAL_EACH_TIME + zid * tile_size) * HEAD_DIM;
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smem = k_smem + stage_idx * DEAL_EACH_TIME * HEAD_DIM;
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#pragma unroll
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for (uint32_t j = 0; j < DEAL_EACH_TIME; ++j) {
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if (iter_base + j < iter_bound) {
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if constexpr (std::is_same<T, half>::value) {
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s[j] = 0.f;
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} else if constexpr (std::is_same<T, __nv_bfloat16>::value) {
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s[j] = 0.f;
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}
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#pragma unroll
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for(uint32_t vid = tidx; vid < NUM_VEC_PER_HEAD; vid += bdx) {
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Load<T, vec_size>(cu_q_smem + vid * vec_size, &q_vec);
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Load<CacheT, vec_size>(smem + j * HEAD_DIM + vid * vec_size, &k_vec);
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for (uint32_t i = 0; i < vec_size; ++i) {
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s[j] += static_cast<float>(q_vec[i] * k_vec[i]);
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}
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}
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#pragma unroll
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for (uint32_t offset = bdx / 2; offset > 0; offset /= 2) {
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s[j] += __shfl_xor_sync(-1, s[j], offset, 32);
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}
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__syncthreads();
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} else {
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if constexpr (std::is_same<T, half>::value) {
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s[j] = -5e4f;
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} else if constexpr (std::is_same<T, __nv_bfloat16>::value) {
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s[j] = -3.38953e38f;
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}
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}
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st.m = st.m > s[j] ? st.m : s[j];
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}
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// T o_scale = hexp(m_prev - st.m);
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float o_scale = __expf(m_prev - st.m);
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st.d *= o_scale;
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#pragma unroll
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for (uint32_t j = 0; j < DEAL_EACH_TIME; ++j) {
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// s[j] = hexp(s[j] - st.m);
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s[j] = __expf(s[j] - st.m);
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st.d += s[j];
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}
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#pragma unroll
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for (uint32_t tile_id = 0; tile_id < num_tile_v; ++tile_id) {
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for (uint32_t i = 0; i < vec_size; ++i) {
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st.o[tile_id][i] *= o_scale;
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}
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}
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}
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template<uint32_t vec_size, uint32_t NUM_VEC_PER_HEAD, uint32_t bdx, uint32_t DEAL_EACH_TIME, uint32_t HEAD_DIM_QK, uint32_t num_tile, typename T, typename CacheT>
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__device__ __forceinline__ void compute_sv(const float *s,
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const CacheT *base_v_smem,
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const uint32_t stage_idx,
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const uint32_t iter_base,
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const uint32_t iter_bound,
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const uint32_t tidx,
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softmax_state_ts<vec_size, T, num_tile>& st) {
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const CacheT* v_smem;
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AlignedVector<T, vec_size> v_vec;
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#pragma unroll
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for (int j = 0; (j < DEAL_EACH_TIME) && (iter_base + j < iter_bound); ++j) {
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v_smem = base_v_smem + stage_idx * DEAL_EACH_TIME * HEAD_DIM_QK + j * HEAD_DIM_QK;
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for(uint32_t vid = tidx; vid < NUM_VEC_PER_HEAD; vid += bdx) {
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Load<T, vec_size>(v_smem + vid * vec_size, &v_vec);
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uint32_t tile_id = vid / bdx;
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#pragma unroll
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for (int reg_id = 0; reg_id < vec_size; ++reg_id) {
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st.o[tile_id][reg_id] += static_cast<T>(s[j]) * v_vec[reg_id];
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}
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}
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}
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}
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