877 lines
37 KiB
Plaintext
877 lines
37 KiB
Plaintext
/*
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* Adapted from
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* 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) 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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#include <torch/extension.h>
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#include <ATen/cuda/CUDAContext.h>
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#include <c10/cuda/CUDAGuard.h>
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#include <algorithm>
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#include "attention_dtypes.h"
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#include "attention_utils.cuh"
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#ifdef USE_ROCM
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#include <hip/hip_bf16.h>
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#include "../quantization/fp8/amd/quant_utils.cuh"
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typedef __hip_bfloat16 __nv_bfloat16;
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#else
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#include "../quantization/fp8/nvidia/quant_utils.cuh"
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#endif
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#ifndef USE_ROCM
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#define WARP_SIZE 32
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#else
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#define WARP_SIZE warpSize
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#endif
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#define MAX(a, b) ((a) > (b) ? (a) : (b))
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#define MIN(a, b) ((a) < (b) ? (a) : (b))
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#define DIVIDE_ROUND_UP(a, b) (((a) + (b) - 1) / (b))
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namespace vllm {
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// Utility function for attention softmax.
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template <int NUM_WARPS>
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inline __device__ float block_sum(float* red_smem, float sum) {
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// Decompose the thread index into warp / lane.
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int warp = threadIdx.x / WARP_SIZE;
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int lane = threadIdx.x % WARP_SIZE;
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// Compute the sum per warp.
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#pragma unroll
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for (int mask = WARP_SIZE / 2; mask >= 1; mask /= 2) {
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sum += VLLM_SHFL_XOR_SYNC(sum, mask);
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}
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// Warp leaders store the data to shared memory.
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if (lane == 0) {
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red_smem[warp] = sum;
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}
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// Make sure the data is in shared memory.
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__syncthreads();
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// The warps compute the final sums.
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if (lane < NUM_WARPS) {
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sum = red_smem[lane];
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}
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// Parallel reduction inside the warp.
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#pragma unroll
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for (int mask = NUM_WARPS / 2; mask >= 1; mask /= 2) {
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sum += VLLM_SHFL_XOR_SYNC(sum, mask);
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}
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// Broadcast to other threads.
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return VLLM_SHFL_SYNC(sum, 0);
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}
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// TODO(woosuk): Merge the last two dimensions of the grid.
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// Grid: (num_heads, num_seqs, max_num_partitions).
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template <typename scalar_t, typename cache_t, int HEAD_SIZE, int BLOCK_SIZE,
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int NUM_THREADS, vllm::Fp8KVCacheDataType KV_DTYPE,
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int PARTITION_SIZE = 0> // Zero means no partitioning.
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__device__ void paged_attention_kernel(
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float* __restrict__ exp_sums, // [num_seqs, num_heads, max_num_partitions]
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float* __restrict__ max_logits, // [num_seqs, num_heads,
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// max_num_partitions]
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scalar_t* __restrict__ out, // [num_seqs, num_heads, max_num_partitions,
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// head_size]
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const scalar_t* __restrict__ q, // [num_seqs, num_heads, head_size]
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const cache_t* __restrict__ k_cache, // [num_blocks, num_kv_heads,
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// head_size/x, block_size, x]
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const cache_t* __restrict__ v_cache, // [num_blocks, num_kv_heads,
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// head_size, block_size]
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const int num_kv_heads, // [num_heads]
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const float scale,
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const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
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const int* __restrict__ seq_lens, // [num_seqs]
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const int max_num_blocks_per_seq,
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const float* __restrict__ alibi_slopes, // [num_heads]
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const int q_stride, const int kv_block_stride, const int kv_head_stride,
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const float kv_scale) {
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const int seq_idx = blockIdx.y;
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const int partition_idx = blockIdx.z;
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const int max_num_partitions = gridDim.z;
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constexpr bool USE_PARTITIONING = PARTITION_SIZE > 0;
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const int seq_len = seq_lens[seq_idx];
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if (USE_PARTITIONING && partition_idx * PARTITION_SIZE >= seq_len) {
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// No work to do. Terminate the thread block.
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return;
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}
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const int num_seq_blocks = DIVIDE_ROUND_UP(seq_len, BLOCK_SIZE);
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const int num_blocks_per_partition =
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USE_PARTITIONING ? PARTITION_SIZE / BLOCK_SIZE : num_seq_blocks;
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// [start_block_idx, end_block_idx) is the range of blocks to process.
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const int start_block_idx =
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USE_PARTITIONING ? partition_idx * num_blocks_per_partition : 0;
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const int end_block_idx =
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MIN(start_block_idx + num_blocks_per_partition, num_seq_blocks);
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const int num_blocks = end_block_idx - start_block_idx;
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// [start_token_idx, end_token_idx) is the range of tokens to process.
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const int start_token_idx = start_block_idx * BLOCK_SIZE;
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const int end_token_idx =
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MIN(start_token_idx + num_blocks * BLOCK_SIZE, seq_len);
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const int num_tokens = end_token_idx - start_token_idx;
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constexpr int THREAD_GROUP_SIZE = MAX(WARP_SIZE / BLOCK_SIZE, 1);
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constexpr int NUM_THREAD_GROUPS =
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NUM_THREADS / THREAD_GROUP_SIZE; // Note: This assumes THREAD_GROUP_SIZE
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// divides NUM_THREADS
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assert(NUM_THREADS % THREAD_GROUP_SIZE == 0);
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constexpr int NUM_TOKENS_PER_THREAD_GROUP =
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DIVIDE_ROUND_UP(BLOCK_SIZE, WARP_SIZE);
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constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
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const int thread_idx = threadIdx.x;
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const int warp_idx = thread_idx / WARP_SIZE;
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const int lane = thread_idx % WARP_SIZE;
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const int head_idx = blockIdx.x;
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const int num_heads = gridDim.x;
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const int num_queries_per_kv = num_heads / num_kv_heads;
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const int kv_head_idx = head_idx / num_queries_per_kv;
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const float alibi_slope =
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alibi_slopes == nullptr ? 0.f : alibi_slopes[head_idx];
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// A vector type to store a part of a key or a query.
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// The vector size is configured in such a way that the threads in a thread
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// group fetch or compute 16 bytes at a time. For example, if the size of a
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// thread group is 4 and the data type is half, then the vector size is 16 /
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// (4 * sizeof(half)) == 2.
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constexpr int VEC_SIZE = MAX(16 / (THREAD_GROUP_SIZE * sizeof(scalar_t)), 1);
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using K_vec = typename Vec<scalar_t, VEC_SIZE>::Type;
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using Q_vec = typename Vec<scalar_t, VEC_SIZE>::Type;
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using Quant_vec = typename Vec<cache_t, VEC_SIZE>::Type;
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constexpr int NUM_ELEMS_PER_THREAD = HEAD_SIZE / THREAD_GROUP_SIZE;
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constexpr int NUM_VECS_PER_THREAD = NUM_ELEMS_PER_THREAD / VEC_SIZE;
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const int thread_group_idx = thread_idx / THREAD_GROUP_SIZE;
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const int thread_group_offset = thread_idx % THREAD_GROUP_SIZE;
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// Load the query to registers.
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// Each thread in a thread group has a different part of the query.
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// For example, if the the thread group size is 4, then the first thread in
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// the group has 0, 4, 8, ... th vectors of the query, and the second thread
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// has 1, 5, 9, ... th vectors of the query, and so on. NOTE(woosuk): Because
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// q is split from a qkv tensor, it may not be contiguous.
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const scalar_t* q_ptr = q + seq_idx * q_stride + head_idx * HEAD_SIZE;
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__shared__ Q_vec q_vecs[THREAD_GROUP_SIZE][NUM_VECS_PER_THREAD];
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#pragma unroll
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for (int i = thread_group_idx; i < NUM_VECS_PER_THREAD;
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i += NUM_THREAD_GROUPS) {
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const int vec_idx = thread_group_offset + i * THREAD_GROUP_SIZE;
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q_vecs[thread_group_offset][i] =
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*reinterpret_cast<const Q_vec*>(q_ptr + vec_idx * VEC_SIZE);
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}
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__syncthreads(); // TODO(naed90): possible speedup if this is replaced with a
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// memory wall right before we use q_vecs
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// Memory planning.
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extern __shared__ char shared_mem[];
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// NOTE(woosuk): We use FP32 for the softmax logits for better accuracy.
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float* logits = reinterpret_cast<float*>(shared_mem);
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// Workspace for reduction.
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__shared__ float red_smem[2 * NUM_WARPS];
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// x == THREAD_GROUP_SIZE * VEC_SIZE
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// Each thread group fetches x elements from the key at a time.
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constexpr int x = 16 / sizeof(cache_t);
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float qk_max = -FLT_MAX;
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// Iterate over the key blocks.
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// Each warp fetches a block of keys for each iteration.
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// Each thread group in a warp fetches a key from the block, and computes
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// dot product with the query.
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const int* block_table = block_tables + seq_idx * max_num_blocks_per_seq;
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for (int block_idx = start_block_idx + warp_idx; block_idx < end_block_idx;
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block_idx += NUM_WARPS) {
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// NOTE(woosuk): The block number is stored in int32. However, we cast it to
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// int64 because int32 can lead to overflow when this variable is multiplied
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// by large numbers (e.g., kv_block_stride).
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const int64_t physical_block_number =
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static_cast<int64_t>(block_table[block_idx]);
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// Load a key to registers.
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// Each thread in a thread group has a different part of the key.
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// For example, if the the thread group size is 4, then the first thread in
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// the group has 0, 4, 8, ... th vectors of the key, and the second thread
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// has 1, 5, 9, ... th vectors of the key, and so on.
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for (int i = 0; i < NUM_TOKENS_PER_THREAD_GROUP; i++) {
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const int physical_block_offset =
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(thread_group_idx + i * WARP_SIZE) % BLOCK_SIZE;
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const int token_idx = block_idx * BLOCK_SIZE + physical_block_offset;
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K_vec k_vecs[NUM_VECS_PER_THREAD];
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#pragma unroll
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for (int j = 0; j < NUM_VECS_PER_THREAD; j++) {
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const cache_t* k_ptr =
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k_cache + physical_block_number * kv_block_stride +
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kv_head_idx * kv_head_stride + physical_block_offset * x;
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const int vec_idx = thread_group_offset + j * THREAD_GROUP_SIZE;
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const int offset1 = (vec_idx * VEC_SIZE) / x;
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const int offset2 = (vec_idx * VEC_SIZE) % x;
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if constexpr (KV_DTYPE == Fp8KVCacheDataType::kAuto) {
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k_vecs[j] = *reinterpret_cast<const K_vec*>(
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k_ptr + offset1 * BLOCK_SIZE * x + offset2);
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} else {
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// Vector conversion from Quant_vec to K_vec.
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Quant_vec k_vec_quant = *reinterpret_cast<const Quant_vec*>(
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k_ptr + offset1 * BLOCK_SIZE * x + offset2);
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k_vecs[j] = fp8::scaled_convert<K_vec, Quant_vec, KV_DTYPE>(
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k_vec_quant, kv_scale);
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}
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}
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// Compute dot product.
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// This includes a reduction across the threads in the same thread group.
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float qk = scale * Qk_dot<scalar_t, THREAD_GROUP_SIZE>::dot(
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q_vecs[thread_group_offset], k_vecs);
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// Add the ALiBi bias if slopes are given.
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qk += (alibi_slope != 0) ? alibi_slope * (token_idx - seq_len + 1) : 0;
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if (thread_group_offset == 0) {
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// Store the partial reductions to shared memory.
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// NOTE(woosuk): It is required to zero out the masked logits.
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const bool mask = token_idx >= seq_len;
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logits[token_idx - start_token_idx] = mask ? 0.f : qk;
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// Update the max value.
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qk_max = mask ? qk_max : fmaxf(qk_max, qk);
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}
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}
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}
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// Perform reduction across the threads in the same warp to get the
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// max qk value for each "warp" (not across the thread block yet).
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// The 0-th thread of each thread group already has its max qk value.
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#pragma unroll
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for (int mask = WARP_SIZE / 2; mask >= THREAD_GROUP_SIZE; mask /= 2) {
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qk_max = fmaxf(qk_max, VLLM_SHFL_XOR_SYNC(qk_max, mask));
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}
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if (lane == 0) {
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red_smem[warp_idx] = qk_max;
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}
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__syncthreads();
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// TODO(woosuk): Refactor this part.
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// Get the max qk value for the sequence.
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qk_max = lane < NUM_WARPS ? red_smem[lane] : -FLT_MAX;
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#pragma unroll
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for (int mask = NUM_WARPS / 2; mask >= 1; mask /= 2) {
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qk_max = fmaxf(qk_max, VLLM_SHFL_XOR_SYNC(qk_max, mask));
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}
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// Broadcast the max qk value to all threads.
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qk_max = VLLM_SHFL_SYNC(qk_max, 0);
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// Get the sum of the exp values.
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float exp_sum = 0.f;
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for (int i = thread_idx; i < num_tokens; i += NUM_THREADS) {
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float val = __expf(logits[i] - qk_max);
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logits[i] = val;
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exp_sum += val;
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}
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exp_sum = block_sum<NUM_WARPS>(&red_smem[NUM_WARPS], exp_sum);
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// Compute softmax.
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const float inv_sum = __fdividef(1.f, exp_sum + 1e-6f);
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for (int i = thread_idx; i < num_tokens; i += NUM_THREADS) {
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logits[i] *= inv_sum;
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}
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__syncthreads();
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// If partitioning is enabled, store the max logit and exp_sum.
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if (USE_PARTITIONING && thread_idx == 0) {
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float* max_logits_ptr = max_logits +
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seq_idx * num_heads * max_num_partitions +
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head_idx * max_num_partitions + partition_idx;
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*max_logits_ptr = qk_max;
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float* exp_sums_ptr = exp_sums + seq_idx * num_heads * max_num_partitions +
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head_idx * max_num_partitions + partition_idx;
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*exp_sums_ptr = exp_sum;
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}
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// Each thread will fetch 16 bytes from the value cache at a time.
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constexpr int V_VEC_SIZE = MIN(16 / sizeof(scalar_t), BLOCK_SIZE);
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using V_vec = typename Vec<scalar_t, V_VEC_SIZE>::Type;
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using L_vec = typename Vec<scalar_t, V_VEC_SIZE>::Type;
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using V_quant_vec = typename Vec<cache_t, V_VEC_SIZE>::Type;
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using Float_L_vec = typename FloatVec<L_vec>::Type;
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constexpr int NUM_V_VECS_PER_ROW = BLOCK_SIZE / V_VEC_SIZE;
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constexpr int NUM_ROWS_PER_ITER = WARP_SIZE / NUM_V_VECS_PER_ROW;
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constexpr int NUM_ROWS_PER_THREAD =
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DIVIDE_ROUND_UP(HEAD_SIZE, NUM_ROWS_PER_ITER);
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// NOTE(woosuk): We use FP32 for the accumulator for better accuracy.
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float accs[NUM_ROWS_PER_THREAD];
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#pragma unroll
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for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
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accs[i] = 0.f;
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}
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scalar_t zero_value;
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zero(zero_value);
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for (int block_idx = start_block_idx + warp_idx; block_idx < end_block_idx;
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block_idx += NUM_WARPS) {
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// NOTE(woosuk): The block number is stored in int32. However, we cast it to
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// int64 because int32 can lead to overflow when this variable is multiplied
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// by large numbers (e.g., kv_block_stride).
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const int64_t physical_block_number =
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static_cast<int64_t>(block_table[block_idx]);
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const int physical_block_offset = (lane % NUM_V_VECS_PER_ROW) * V_VEC_SIZE;
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const int token_idx = block_idx * BLOCK_SIZE + physical_block_offset;
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L_vec logits_vec;
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from_float(logits_vec, *reinterpret_cast<Float_L_vec*>(logits + token_idx -
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start_token_idx));
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const cache_t* v_ptr = v_cache + physical_block_number * kv_block_stride +
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kv_head_idx * kv_head_stride;
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#pragma unroll
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for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
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const int row_idx = lane / NUM_V_VECS_PER_ROW + i * NUM_ROWS_PER_ITER;
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if (row_idx < HEAD_SIZE) {
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const int offset = row_idx * BLOCK_SIZE + physical_block_offset;
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V_vec v_vec;
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if constexpr (KV_DTYPE == Fp8KVCacheDataType::kAuto) {
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v_vec = *reinterpret_cast<const V_vec*>(v_ptr + offset);
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} else {
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V_quant_vec v_quant_vec =
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*reinterpret_cast<const V_quant_vec*>(v_ptr + offset);
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// Vector conversion from V_quant_vec to V_vec.
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v_vec = fp8::scaled_convert<V_vec, V_quant_vec, KV_DTYPE>(v_quant_vec,
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kv_scale);
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}
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if (block_idx == num_seq_blocks - 1) {
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// NOTE(woosuk): When v_vec contains the tokens that are out of the
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// context, we should explicitly zero out the values since they may
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// contain NaNs. See
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// https://github.com/vllm-project/vllm/issues/641#issuecomment-1682544472
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scalar_t* v_vec_ptr = reinterpret_cast<scalar_t*>(&v_vec);
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#pragma unroll
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for (int j = 0; j < V_VEC_SIZE; j++) {
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v_vec_ptr[j] = token_idx + j < seq_len ? v_vec_ptr[j] : zero_value;
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}
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}
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accs[i] += dot(logits_vec, v_vec);
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}
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}
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}
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// Perform reduction within each warp.
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#pragma unroll
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for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
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float acc = accs[i];
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#pragma unroll
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for (int mask = NUM_V_VECS_PER_ROW / 2; mask >= 1; mask /= 2) {
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acc += VLLM_SHFL_XOR_SYNC(acc, mask);
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}
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accs[i] = acc;
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}
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// NOTE(woosuk): A barrier is required because the shared memory space for
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// logits is reused for the output.
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__syncthreads();
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// Perform reduction across warps.
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float* out_smem = reinterpret_cast<float*>(shared_mem);
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#pragma unroll
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for (int i = NUM_WARPS; i > 1; i /= 2) {
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int mid = i / 2;
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// Upper warps write to shared memory.
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if (warp_idx >= mid && warp_idx < i) {
|
|
float* dst = &out_smem[(warp_idx - mid) * HEAD_SIZE];
|
|
#pragma unroll
|
|
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
|
|
const int row_idx = lane / NUM_V_VECS_PER_ROW + i * NUM_ROWS_PER_ITER;
|
|
if (row_idx < HEAD_SIZE && lane % NUM_V_VECS_PER_ROW == 0) {
|
|
dst[row_idx] = accs[i];
|
|
}
|
|
}
|
|
}
|
|
__syncthreads();
|
|
|
|
// Lower warps update the output.
|
|
if (warp_idx < mid) {
|
|
const float* src = &out_smem[warp_idx * HEAD_SIZE];
|
|
#pragma unroll
|
|
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
|
|
const int row_idx = lane / NUM_V_VECS_PER_ROW + i * NUM_ROWS_PER_ITER;
|
|
if (row_idx < HEAD_SIZE && lane % NUM_V_VECS_PER_ROW == 0) {
|
|
accs[i] += src[row_idx];
|
|
}
|
|
}
|
|
}
|
|
__syncthreads();
|
|
}
|
|
|
|
// Write the final output.
|
|
if (warp_idx == 0) {
|
|
scalar_t* out_ptr =
|
|
out + seq_idx * num_heads * max_num_partitions * HEAD_SIZE +
|
|
head_idx * max_num_partitions * HEAD_SIZE + partition_idx * HEAD_SIZE;
|
|
#pragma unroll
|
|
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
|
|
const int row_idx = lane / NUM_V_VECS_PER_ROW + i * NUM_ROWS_PER_ITER;
|
|
if (row_idx < HEAD_SIZE && lane % NUM_V_VECS_PER_ROW == 0) {
|
|
from_float(*(out_ptr + row_idx), accs[i]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Grid: (num_heads, num_seqs, 1).
|
|
template <typename scalar_t, typename cache_t, int HEAD_SIZE, int BLOCK_SIZE,
|
|
int NUM_THREADS,
|
|
vllm::Fp8KVCacheDataType KV_DTYPE>
|
|
__global__ void paged_attention_v1_kernel(
|
|
scalar_t* __restrict__ out, // [num_seqs, num_heads, head_size]
|
|
const scalar_t* __restrict__ q, // [num_seqs, num_heads, head_size]
|
|
const cache_t* __restrict__ k_cache, // [num_blocks, num_kv_heads,
|
|
// head_size/x, block_size, x]
|
|
const cache_t* __restrict__ v_cache, // [num_blocks, num_kv_heads,
|
|
// head_size, block_size]
|
|
const int num_kv_heads, // [num_heads]
|
|
const float scale,
|
|
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
|
|
const int* __restrict__ seq_lens, // [num_seqs]
|
|
const int max_num_blocks_per_seq,
|
|
const float* __restrict__ alibi_slopes, // [num_heads]
|
|
const int q_stride, const int kv_block_stride, const int kv_head_stride,
|
|
const float kv_scale) {
|
|
paged_attention_kernel<scalar_t, cache_t, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS,
|
|
KV_DTYPE>(
|
|
/* exp_sums */ nullptr, /* max_logits */ nullptr, out, q, k_cache,
|
|
v_cache, num_kv_heads, scale, block_tables, seq_lens,
|
|
max_num_blocks_per_seq, alibi_slopes, q_stride, kv_block_stride,
|
|
kv_head_stride, kv_scale);
|
|
}
|
|
|
|
// Grid: (num_heads, num_seqs, max_num_partitions).
|
|
template <typename scalar_t, typename cache_t, int HEAD_SIZE, int BLOCK_SIZE,
|
|
int NUM_THREADS, vllm::Fp8KVCacheDataType KV_DTYPE,
|
|
int PARTITION_SIZE>
|
|
__global__ void paged_attention_v2_kernel(
|
|
float* __restrict__ exp_sums, // [num_seqs, num_heads, max_num_partitions]
|
|
float* __restrict__ max_logits, // [num_seqs, num_heads,
|
|
// max_num_partitions]
|
|
scalar_t* __restrict__ tmp_out, // [num_seqs, num_heads,
|
|
// max_num_partitions, head_size]
|
|
const scalar_t* __restrict__ q, // [num_seqs, num_heads, head_size]
|
|
const cache_t* __restrict__ k_cache, // [num_blocks, num_kv_heads,
|
|
// head_size/x, block_size, x]
|
|
const cache_t* __restrict__ v_cache, // [num_blocks, num_kv_heads,
|
|
// head_size, block_size]
|
|
const int num_kv_heads, // [num_heads]
|
|
const float scale,
|
|
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
|
|
const int* __restrict__ seq_lens, // [num_seqs]
|
|
const int max_num_blocks_per_seq,
|
|
const float* __restrict__ alibi_slopes, // [num_heads]
|
|
const int q_stride, const int kv_block_stride, const int kv_head_stride,
|
|
const float kv_scale) {
|
|
paged_attention_kernel<scalar_t, cache_t, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS,
|
|
KV_DTYPE, PARTITION_SIZE>(
|
|
exp_sums, max_logits, tmp_out, q, k_cache, v_cache, num_kv_heads, scale,
|
|
block_tables, seq_lens, max_num_blocks_per_seq, alibi_slopes, q_stride,
|
|
kv_block_stride, kv_head_stride, kv_scale);
|
|
}
|
|
|
|
// Grid: (num_heads, num_seqs).
|
|
template <typename scalar_t, int HEAD_SIZE, int NUM_THREADS,
|
|
int PARTITION_SIZE>
|
|
__global__ void paged_attention_v2_reduce_kernel(
|
|
scalar_t* __restrict__ out, // [num_seqs, num_heads, head_size]
|
|
const float* __restrict__ exp_sums, // [num_seqs, num_heads,
|
|
// max_num_partitions]
|
|
const float* __restrict__ max_logits, // [num_seqs, num_heads,
|
|
// max_num_partitions]
|
|
const scalar_t* __restrict__ tmp_out, // [num_seqs, num_heads,
|
|
// max_num_partitions, head_size]
|
|
const int* __restrict__ seq_lens, // [num_seqs]
|
|
const int max_num_partitions) {
|
|
const int num_heads = gridDim.x;
|
|
const int head_idx = blockIdx.x;
|
|
const int seq_idx = blockIdx.y;
|
|
const int seq_len = seq_lens[seq_idx];
|
|
const int num_partitions = DIVIDE_ROUND_UP(seq_len, PARTITION_SIZE);
|
|
if (num_partitions == 1) {
|
|
// No need to reduce. Only copy tmp_out to out.
|
|
scalar_t* out_ptr =
|
|
out + seq_idx * num_heads * HEAD_SIZE + head_idx * HEAD_SIZE;
|
|
const scalar_t* tmp_out_ptr =
|
|
tmp_out + seq_idx * num_heads * max_num_partitions * HEAD_SIZE +
|
|
head_idx * max_num_partitions * HEAD_SIZE;
|
|
for (int i = threadIdx.x; i < HEAD_SIZE; i += blockDim.x) {
|
|
out_ptr[i] = tmp_out_ptr[i];
|
|
}
|
|
// Terminate the thread block.
|
|
return;
|
|
}
|
|
|
|
constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
|
|
const int warp_idx = threadIdx.x / WARP_SIZE;
|
|
const int lane = threadIdx.x % WARP_SIZE;
|
|
|
|
// Size: 2 * num_partitions.
|
|
extern __shared__ char shared_mem[];
|
|
// Workspace for reduction.
|
|
__shared__ float red_smem[2 * NUM_WARPS];
|
|
|
|
// Load max logits to shared memory.
|
|
float* shared_max_logits = reinterpret_cast<float*>(shared_mem);
|
|
const float* max_logits_ptr = max_logits +
|
|
seq_idx * num_heads * max_num_partitions +
|
|
head_idx * max_num_partitions;
|
|
float max_logit = -FLT_MAX;
|
|
for (int i = threadIdx.x; i < num_partitions; i += blockDim.x) {
|
|
const float l = max_logits_ptr[i];
|
|
shared_max_logits[i] = l;
|
|
max_logit = fmaxf(max_logit, l);
|
|
}
|
|
__syncthreads();
|
|
|
|
// Get the global max logit.
|
|
// Reduce within the warp.
|
|
#pragma unroll
|
|
for (int mask = WARP_SIZE / 2; mask >= 1; mask /= 2) {
|
|
max_logit = fmaxf(max_logit, VLLM_SHFL_XOR_SYNC(max_logit, mask));
|
|
}
|
|
if (lane == 0) {
|
|
red_smem[warp_idx] = max_logit;
|
|
}
|
|
__syncthreads();
|
|
// Reduce across warps.
|
|
max_logit = lane < NUM_WARPS ? red_smem[lane] : -FLT_MAX;
|
|
#pragma unroll
|
|
for (int mask = NUM_WARPS / 2; mask >= 1; mask /= 2) {
|
|
max_logit = fmaxf(max_logit, VLLM_SHFL_XOR_SYNC(max_logit, mask));
|
|
}
|
|
// Broadcast the max value to all threads.
|
|
max_logit = VLLM_SHFL_SYNC(max_logit, 0);
|
|
|
|
// Load rescaled exp sums to shared memory.
|
|
float* shared_exp_sums =
|
|
reinterpret_cast<float*>(shared_mem + sizeof(float) * num_partitions);
|
|
const float* exp_sums_ptr = exp_sums +
|
|
seq_idx * num_heads * max_num_partitions +
|
|
head_idx * max_num_partitions;
|
|
float global_exp_sum = 0.0f;
|
|
for (int i = threadIdx.x; i < num_partitions; i += blockDim.x) {
|
|
float l = shared_max_logits[i];
|
|
float rescaled_exp_sum = exp_sums_ptr[i] * expf(l - max_logit);
|
|
global_exp_sum += rescaled_exp_sum;
|
|
shared_exp_sums[i] = rescaled_exp_sum;
|
|
}
|
|
__syncthreads();
|
|
global_exp_sum = block_sum<NUM_WARPS>(&red_smem[NUM_WARPS], global_exp_sum);
|
|
const float inv_global_exp_sum = __fdividef(1.0f, global_exp_sum + 1e-6f);
|
|
|
|
// Aggregate tmp_out to out.
|
|
const scalar_t* tmp_out_ptr =
|
|
tmp_out + seq_idx * num_heads * max_num_partitions * HEAD_SIZE +
|
|
head_idx * max_num_partitions * HEAD_SIZE;
|
|
scalar_t* out_ptr =
|
|
out + seq_idx * num_heads * HEAD_SIZE + head_idx * HEAD_SIZE;
|
|
#pragma unroll
|
|
for (int i = threadIdx.x; i < HEAD_SIZE; i += NUM_THREADS) {
|
|
float acc = 0.0f;
|
|
for (int j = 0; j < num_partitions; ++j) {
|
|
acc += to_float(tmp_out_ptr[j * HEAD_SIZE + i]) * shared_exp_sums[j] *
|
|
inv_global_exp_sum;
|
|
}
|
|
from_float(out_ptr[i], acc);
|
|
}
|
|
}
|
|
|
|
} // namespace vllm
|
|
|
|
#define LAUNCH_PAGED_ATTENTION_V1(HEAD_SIZE) \
|
|
VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize( \
|
|
((void*)vllm::paged_attention_v1_kernel< \
|
|
T, CACHE_T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS, KV_DTYPE>), \
|
|
shared_mem_size); \
|
|
vllm::paged_attention_v1_kernel<T, CACHE_T, HEAD_SIZE, BLOCK_SIZE, \
|
|
NUM_THREADS, KV_DTYPE> \
|
|
<<<grid, block, shared_mem_size, stream>>>( \
|
|
out_ptr, query_ptr, key_cache_ptr, value_cache_ptr, num_kv_heads, \
|
|
scale, block_tables_ptr, seq_lens_ptr, max_num_blocks_per_seq, \
|
|
alibi_slopes_ptr, q_stride, kv_block_stride, kv_head_stride, \
|
|
kv_scale);
|
|
|
|
// TODO(woosuk): Tune NUM_THREADS.
|
|
template <typename T, typename CACHE_T, int BLOCK_SIZE,
|
|
vllm::Fp8KVCacheDataType KV_DTYPE, int NUM_THREADS = 128>
|
|
void paged_attention_v1_launcher(
|
|
torch::Tensor& out, torch::Tensor& query, torch::Tensor& key_cache,
|
|
torch::Tensor& value_cache, int num_kv_heads, float scale,
|
|
torch::Tensor& block_tables, torch::Tensor& seq_lens, int max_seq_len,
|
|
const c10::optional<torch::Tensor>& alibi_slopes, float kv_scale) {
|
|
int num_seqs = query.size(0);
|
|
int num_heads = query.size(1);
|
|
int head_size = query.size(2);
|
|
int max_num_blocks_per_seq = block_tables.size(1);
|
|
int q_stride = query.stride(0);
|
|
int kv_block_stride = key_cache.stride(0);
|
|
int kv_head_stride = key_cache.stride(1);
|
|
|
|
int thread_group_size = MAX(WARP_SIZE / BLOCK_SIZE, 1);
|
|
assert(head_size % thread_group_size == 0);
|
|
|
|
// NOTE: alibi_slopes is optional.
|
|
const float* alibi_slopes_ptr =
|
|
alibi_slopes
|
|
? reinterpret_cast<const float*>(alibi_slopes.value().data_ptr())
|
|
: nullptr;
|
|
|
|
T* out_ptr = reinterpret_cast<T*>(out.data_ptr());
|
|
T* query_ptr = reinterpret_cast<T*>(query.data_ptr());
|
|
CACHE_T* key_cache_ptr = reinterpret_cast<CACHE_T*>(key_cache.data_ptr());
|
|
CACHE_T* value_cache_ptr = reinterpret_cast<CACHE_T*>(value_cache.data_ptr());
|
|
int* block_tables_ptr = block_tables.data_ptr<int>();
|
|
int* seq_lens_ptr = seq_lens.data_ptr<int>();
|
|
|
|
constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
|
|
int padded_max_seq_len =
|
|
DIVIDE_ROUND_UP(max_seq_len, BLOCK_SIZE) * BLOCK_SIZE;
|
|
int logits_size = padded_max_seq_len * sizeof(float);
|
|
int outputs_size = (NUM_WARPS / 2) * head_size * sizeof(float);
|
|
// Python-side check in vllm.worker.worker._check_if_can_support_max_seq_len
|
|
// Keep that in sync with the logic here!
|
|
int shared_mem_size = std::max(logits_size, outputs_size);
|
|
|
|
dim3 grid(num_heads, num_seqs, 1);
|
|
dim3 block(NUM_THREADS);
|
|
const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
|
|
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
|
|
switch (head_size) {
|
|
// NOTE(woosuk): To reduce the compilation time, we only compile for the
|
|
// head sizes that we use in the model. However, we can easily extend this
|
|
// to support any head size which is a multiple of 16.
|
|
case 64:
|
|
LAUNCH_PAGED_ATTENTION_V1(64);
|
|
break;
|
|
case 80:
|
|
LAUNCH_PAGED_ATTENTION_V1(80);
|
|
break;
|
|
case 96:
|
|
LAUNCH_PAGED_ATTENTION_V1(96);
|
|
break;
|
|
case 112:
|
|
LAUNCH_PAGED_ATTENTION_V1(112);
|
|
break;
|
|
case 128:
|
|
LAUNCH_PAGED_ATTENTION_V1(128);
|
|
break;
|
|
case 256:
|
|
LAUNCH_PAGED_ATTENTION_V1(256);
|
|
break;
|
|
default:
|
|
TORCH_CHECK(false, "Unsupported head size: ", head_size);
|
|
break;
|
|
}
|
|
}
|
|
|
|
#define CALL_V1_LAUNCHER(T, CACHE_T, BLOCK_SIZE, KV_DTYPE) \
|
|
paged_attention_v1_launcher<T, CACHE_T, BLOCK_SIZE, KV_DTYPE>( \
|
|
out, query, key_cache, value_cache, num_kv_heads, scale, block_tables, \
|
|
seq_lens, max_seq_len, alibi_slopes, kv_scale);
|
|
|
|
// NOTE(woosuk): To reduce the compilation time, we omitted block sizes
|
|
// 1, 2, 4, 64, 128, 256.
|
|
#define CALL_V1_LAUNCHER_BLOCK_SIZE(T, CACHE_T, KV_DTYPE) \
|
|
switch (block_size) { \
|
|
case 8: \
|
|
CALL_V1_LAUNCHER(T, CACHE_T, 8, KV_DTYPE); \
|
|
break; \
|
|
case 16: \
|
|
CALL_V1_LAUNCHER(T, CACHE_T, 16, KV_DTYPE); \
|
|
break; \
|
|
case 32: \
|
|
CALL_V1_LAUNCHER(T, CACHE_T, 32, KV_DTYPE); \
|
|
break; \
|
|
default: \
|
|
TORCH_CHECK(false, "Unsupported block size: ", block_size); \
|
|
break; \
|
|
}
|
|
|
|
void paged_attention_v1(
|
|
torch::Tensor& out, // [num_seqs, num_heads, head_size]
|
|
torch::Tensor& query, // [num_seqs, num_heads, head_size]
|
|
torch::Tensor&
|
|
key_cache, // [num_blocks, num_heads, head_size/x, block_size, x]
|
|
torch::Tensor&
|
|
value_cache, // [num_blocks, num_heads, head_size, block_size]
|
|
int num_kv_heads, // [num_heads]
|
|
float scale,
|
|
torch::Tensor& block_tables, // [num_seqs, max_num_blocks_per_seq]
|
|
torch::Tensor& seq_lens, // [num_seqs]
|
|
int block_size, int max_seq_len,
|
|
const c10::optional<torch::Tensor>& alibi_slopes,
|
|
const std::string& kv_cache_dtype, float kv_scale){
|
|
|
|
DISPATCH_BY_KV_CACHE_DTYPE(query.dtype(), kv_cache_dtype,
|
|
CALL_V1_LAUNCHER_BLOCK_SIZE)}
|
|
#define LAUNCH_PAGED_ATTENTION_V2(HEAD_SIZE) \
|
|
vllm::paged_attention_v2_kernel<T, CACHE_T, HEAD_SIZE, BLOCK_SIZE, \
|
|
NUM_THREADS, KV_DTYPE, PARTITION_SIZE> \
|
|
<<<grid, block, shared_mem_size, stream>>>( \
|
|
exp_sums_ptr, max_logits_ptr, tmp_out_ptr, query_ptr, key_cache_ptr, \
|
|
value_cache_ptr, num_kv_heads, scale, block_tables_ptr, \
|
|
seq_lens_ptr, max_num_blocks_per_seq, alibi_slopes_ptr, q_stride, \
|
|
kv_block_stride, kv_head_stride, kv_scale); \
|
|
vllm::paged_attention_v2_reduce_kernel<T, HEAD_SIZE, NUM_THREADS, \
|
|
PARTITION_SIZE> \
|
|
<<<reduce_grid, block, reduce_shared_mem_size, stream>>>( \
|
|
out_ptr, exp_sums_ptr, max_logits_ptr, tmp_out_ptr, seq_lens_ptr, \
|
|
max_num_partitions);
|
|
|
|
template <typename T, typename CACHE_T, int BLOCK_SIZE,
|
|
vllm::Fp8KVCacheDataType KV_DTYPE, int NUM_THREADS = 128,
|
|
int PARTITION_SIZE = 512>
|
|
void paged_attention_v2_launcher(
|
|
torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,
|
|
torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
|
|
torch::Tensor& value_cache, int num_kv_heads, float scale,
|
|
torch::Tensor& block_tables, torch::Tensor& seq_lens, int max_seq_len,
|
|
const c10::optional<torch::Tensor>& alibi_slopes, float kv_scale) {
|
|
int num_seqs = query.size(0);
|
|
int num_heads = query.size(1);
|
|
int head_size = query.size(2);
|
|
int max_num_blocks_per_seq = block_tables.size(1);
|
|
int q_stride = query.stride(0);
|
|
int kv_block_stride = key_cache.stride(0);
|
|
int kv_head_stride = key_cache.stride(1);
|
|
|
|
int thread_group_size = MAX(WARP_SIZE / BLOCK_SIZE, 1);
|
|
assert(head_size % thread_group_size == 0);
|
|
|
|
// NOTE: alibi_slopes is optional.
|
|
const float* alibi_slopes_ptr =
|
|
alibi_slopes
|
|
? reinterpret_cast<const float*>(alibi_slopes.value().data_ptr())
|
|
: nullptr;
|
|
|
|
T* out_ptr = reinterpret_cast<T*>(out.data_ptr());
|
|
float* exp_sums_ptr = reinterpret_cast<float*>(exp_sums.data_ptr());
|
|
float* max_logits_ptr = reinterpret_cast<float*>(max_logits.data_ptr());
|
|
T* tmp_out_ptr = reinterpret_cast<T*>(tmp_out.data_ptr());
|
|
T* query_ptr = reinterpret_cast<T*>(query.data_ptr());
|
|
CACHE_T* key_cache_ptr = reinterpret_cast<CACHE_T*>(key_cache.data_ptr());
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CACHE_T* value_cache_ptr = reinterpret_cast<CACHE_T*>(value_cache.data_ptr());
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int* block_tables_ptr = block_tables.data_ptr<int>();
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int* seq_lens_ptr = seq_lens.data_ptr<int>();
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|
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constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
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int max_num_partitions = DIVIDE_ROUND_UP(max_seq_len, PARTITION_SIZE);
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int logits_size = PARTITION_SIZE * sizeof(float);
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int outputs_size = (NUM_WARPS / 2) * head_size * sizeof(float);
|
|
|
|
// For paged attention v2 kernel.
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dim3 grid(num_heads, num_seqs, max_num_partitions);
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int shared_mem_size = std::max(logits_size, outputs_size);
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|
// For paged attention v2 reduce kernel.
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|
dim3 reduce_grid(num_heads, num_seqs);
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|
int reduce_shared_mem_size = 2 * max_num_partitions * sizeof(float);
|
|
|
|
dim3 block(NUM_THREADS);
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|
const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
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|
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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|
switch (head_size) {
|
|
// NOTE(woosuk): To reduce the compilation time, we only compile for the
|
|
// head sizes that we use in the model. However, we can easily extend this
|
|
// to support any head size which is a multiple of 16.
|
|
case 64:
|
|
LAUNCH_PAGED_ATTENTION_V2(64);
|
|
break;
|
|
case 80:
|
|
LAUNCH_PAGED_ATTENTION_V2(80);
|
|
break;
|
|
case 96:
|
|
LAUNCH_PAGED_ATTENTION_V2(96);
|
|
break;
|
|
case 112:
|
|
LAUNCH_PAGED_ATTENTION_V2(112);
|
|
break;
|
|
case 128:
|
|
LAUNCH_PAGED_ATTENTION_V2(128);
|
|
break;
|
|
case 256:
|
|
LAUNCH_PAGED_ATTENTION_V2(256);
|
|
break;
|
|
default:
|
|
TORCH_CHECK(false, "Unsupported head size: ", head_size);
|
|
break;
|
|
}
|
|
}
|
|
|
|
#define CALL_V2_LAUNCHER(T, CACHE_T, BLOCK_SIZE, KV_DTYPE) \
|
|
paged_attention_v2_launcher<T, CACHE_T, BLOCK_SIZE, KV_DTYPE>( \
|
|
out, exp_sums, max_logits, tmp_out, query, key_cache, value_cache, \
|
|
num_kv_heads, scale, block_tables, seq_lens, max_seq_len, alibi_slopes, \
|
|
kv_scale);
|
|
|
|
// NOTE(woosuk): To reduce the compilation time, we omitted block sizes
|
|
// 1, 2, 4, 64, 128, 256.
|
|
#define CALL_V2_LAUNCHER_BLOCK_SIZE(T, CACHE_T, KV_DTYPE) \
|
|
switch (block_size) { \
|
|
case 8: \
|
|
CALL_V2_LAUNCHER(T, CACHE_T, 8, KV_DTYPE); \
|
|
break; \
|
|
case 16: \
|
|
CALL_V2_LAUNCHER(T, CACHE_T, 16, KV_DTYPE); \
|
|
break; \
|
|
case 32: \
|
|
CALL_V2_LAUNCHER(T, CACHE_T, 32, KV_DTYPE); \
|
|
break; \
|
|
default: \
|
|
TORCH_CHECK(false, "Unsupported block size: ", block_size); \
|
|
break; \
|
|
}
|
|
|
|
void paged_attention_v2(
|
|
torch::Tensor& out, // [num_seqs, num_heads, head_size]
|
|
torch::Tensor& exp_sums, // [num_seqs, num_heads, max_num_partitions]
|
|
torch::Tensor& max_logits, // [num_seqs, num_heads, max_num_partitions]
|
|
torch::Tensor&
|
|
tmp_out, // [num_seqs, num_heads, max_num_partitions, head_size]
|
|
torch::Tensor& query, // [num_seqs, num_heads, head_size]
|
|
torch::Tensor&
|
|
key_cache, // [num_blocks, num_heads, head_size/x, block_size, x]
|
|
torch::Tensor&
|
|
value_cache, // [num_blocks, num_heads, head_size, block_size]
|
|
int num_kv_heads, // [num_heads]
|
|
float scale,
|
|
torch::Tensor& block_tables, // [num_seqs, max_num_blocks_per_seq]
|
|
torch::Tensor& seq_lens, // [num_seqs]
|
|
int block_size, int max_seq_len,
|
|
const c10::optional<torch::Tensor>& alibi_slopes,
|
|
const std::string& kv_cache_dtype, float kv_scale) {
|
|
DISPATCH_BY_KV_CACHE_DTYPE(query.dtype(), kv_cache_dtype,
|
|
CALL_V2_LAUNCHER_BLOCK_SIZE)
|
|
}
|
|
|
|
#undef WARP_SIZE
|
|
#undef MAX
|
|
#undef MIN
|
|
#undef DIVIDE_ROUND_UP
|