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vllm/csrc/attention/attention_kernels.cu
TJian 6ccc0bfffb
Merge EmbeddedLLM/vllm-rocm into vLLM main (#1836) 
Co-authored-by: Philipp Moritz <pcmoritz@gmail.com>
Co-authored-by: Amir Balwel <amoooori04@gmail.com>
Co-authored-by: root <kuanfu.liu@akirakan.com>
Co-authored-by: tjtanaa <tunjian.tan@embeddedllm.com>
Co-authored-by: kuanfu <kuanfu.liu@embeddedllm.com>
Co-authored-by: miloice <17350011+kliuae@users.noreply.github.com>
2023-12-07 23:16:52 -08:00

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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
* 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.
*/
#ifdef USE_ROCM
#include <hip/hip_runtime.h>
#endif
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include "attention_dtypes.h"
#include "attention_utils.cuh"
#include <algorithm>
#ifndef USE_ROCM
#define WARP_SIZE 32
#else
#define WARP_SIZE warpSize
#endif
#define MAX(a, b) ((a) > (b) ? (a) : (b))
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define DIVIDE_ROUND_UP(a, b) (((a) + (b) - 1) / (b))
namespace vllm {
// Utility function for attention softmax.
template<int NUM_WARPS>
inline __device__ float block_sum(float* red_smem, float sum) {
// Decompose the thread index into warp / lane.
int warp = threadIdx.x / WARP_SIZE;
int lane = threadIdx.x % WARP_SIZE;
// Compute the sum per warp.
#pragma unroll
for (int mask = WARP_SIZE / 2; mask >= 1; mask /= 2) {
sum += VLLM_SHFL_XOR_SYNC(sum, mask);
}
// Warp leaders store the data to shared memory.
if (lane == 0) {
red_smem[warp] = sum;
}
// Make sure the data is in shared memory.
__syncthreads();
// The warps compute the final sums.
if (lane < NUM_WARPS) {
sum = red_smem[lane];
}
// Parallel reduction inside the warp.
#pragma unroll
for (int mask = NUM_WARPS / 2; mask >= 1; mask /= 2) {
sum += VLLM_SHFL_XOR_SYNC(sum, mask);
}
// Broadcast to other threads.
return VLLM_SHFL_SYNC(sum, 0);
}
// TODO(woosuk): Merge the last two dimensions of the grid.
// Grid: (num_heads, num_seqs, max_num_partitions).
template<
typename scalar_t,
int HEAD_SIZE,
int BLOCK_SIZE,
int NUM_THREADS,
int PARTITION_SIZE = 0> // Zero means no partitioning.
__device__ void paged_attention_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__ out, // [num_seqs, num_heads, max_num_partitions, head_size]
const scalar_t* __restrict__ q, // [num_seqs, num_heads, head_size]
const scalar_t* __restrict__ k_cache, // [num_blocks, num_kv_heads, head_size/x, block_size, x]
const scalar_t* __restrict__ v_cache, // [num_blocks, num_kv_heads, head_size, block_size]
const int* __restrict__ head_mapping, // [num_heads]
const float scale,
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
const int* __restrict__ context_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 int seq_idx = blockIdx.y;
const int partition_idx = blockIdx.z;
const int max_num_partitions = gridDim.z;
constexpr bool USE_PARTITIONING = PARTITION_SIZE > 0;
const int context_len = context_lens[seq_idx];
if (USE_PARTITIONING && partition_idx * PARTITION_SIZE >= context_len) {
// No work to do. Terminate the thread block.
return;
}
const int num_context_blocks = DIVIDE_ROUND_UP(context_len, BLOCK_SIZE);
const int num_blocks_per_partition = USE_PARTITIONING ? PARTITION_SIZE / BLOCK_SIZE : num_context_blocks;
// [start_block_idx, end_block_idx) is the range of blocks to process.
const int start_block_idx = USE_PARTITIONING ? partition_idx * num_blocks_per_partition : 0;
const int end_block_idx = MIN(start_block_idx + num_blocks_per_partition, num_context_blocks);
const int num_blocks = end_block_idx - start_block_idx;
// [start_token_idx, end_token_idx) is the range of tokens to process.
const int start_token_idx = start_block_idx * BLOCK_SIZE;
const int end_token_idx = MIN(start_token_idx + num_blocks * BLOCK_SIZE, context_len);
const int num_tokens = end_token_idx - start_token_idx;
constexpr int THREAD_GROUP_SIZE = MAX(WARP_SIZE / BLOCK_SIZE, 1);
constexpr int NUM_THREAD_GROUPS = NUM_THREADS / THREAD_GROUP_SIZE; // Note: This assumes THREAD_GROUP_SIZE divides NUM_THREADS
assert(NUM_THREADS % THREAD_GROUP_SIZE == 0);
constexpr int NUM_TOKENS_PER_THREAD_GROUP = DIVIDE_ROUND_UP(BLOCK_SIZE, WARP_SIZE);
constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
const int thread_idx = threadIdx.x;
const int warp_idx = thread_idx / WARP_SIZE;
const int lane = thread_idx % WARP_SIZE;
const int head_idx = blockIdx.x;
const int num_heads = gridDim.x;
const int kv_head_idx = head_mapping[head_idx];
const float alibi_slope = alibi_slopes == nullptr ? 0.f : alibi_slopes[head_idx];
// A vector type to store a part of a key or a query.
// The vector size is configured in such a way that the threads in a thread group
// fetch or compute 16 bytes at a time.
// For example, if the size of a thread group is 4 and the data type is half,
// then the vector size is 16 / (4 * sizeof(half)) == 2.
constexpr int VEC_SIZE = MAX(16 / (THREAD_GROUP_SIZE * sizeof(scalar_t)), 1);
using K_vec = typename Vec<scalar_t, VEC_SIZE>::Type;
using Q_vec = typename Vec<scalar_t, VEC_SIZE>::Type;
constexpr int NUM_ELEMS_PER_THREAD = HEAD_SIZE / THREAD_GROUP_SIZE;
constexpr int NUM_VECS_PER_THREAD = NUM_ELEMS_PER_THREAD / VEC_SIZE;
const int thread_group_idx = thread_idx / THREAD_GROUP_SIZE;
const int thread_group_offset = thread_idx % THREAD_GROUP_SIZE;
// Load the query to registers.
// Each thread in a thread group has a different part of the query.
// For example, if the the thread group size is 4, then the first thread in the group
// has 0, 4, 8, ... th vectors of the query, and the second thread has 1, 5, 9, ...
// th vectors of the query, and so on.
// NOTE(woosuk): Because q is split from a qkv tensor, it may not be contiguous.
const scalar_t* q_ptr = q + seq_idx * q_stride + head_idx * HEAD_SIZE;
__shared__ Q_vec q_vecs[THREAD_GROUP_SIZE][NUM_VECS_PER_THREAD];
#pragma unroll
for (int i = thread_group_idx; i < NUM_VECS_PER_THREAD; i += NUM_THREAD_GROUPS) {
const int vec_idx = thread_group_offset + i * THREAD_GROUP_SIZE;
q_vecs[thread_group_offset][i] = *reinterpret_cast<const Q_vec*>(q_ptr + vec_idx * VEC_SIZE);
}
__syncthreads(); // TODO(naed90): possible speedup if this is replaced with a memory wall right before we use q_vecs
// Memory planning.
extern __shared__ char shared_mem[];
// NOTE(woosuk): We use FP32 for the softmax logits for better accuracy.
float* logits = reinterpret_cast<float*>(shared_mem);
// Workspace for reduction.
__shared__ float red_smem[2 * NUM_WARPS];
// x == THREAD_GROUP_SIZE * VEC_SIZE
// Each thread group fetches x elements from the key at a time.
constexpr int x = 16 / sizeof(scalar_t);
float qk_max = -FLT_MAX;
// Iterate over the key blocks.
// Each warp fetches a block of keys for each iteration.
// Each thread group in a warp fetches a key from the block, and computes
// dot product with the query.
const int* block_table = block_tables + seq_idx * max_num_blocks_per_seq;
for (int block_idx = start_block_idx + warp_idx; block_idx < end_block_idx; block_idx += NUM_WARPS) {
// NOTE(woosuk): The block number is stored in int32. However, we cast it to int64
// because int32 can lead to overflow when this variable is multiplied by large numbers
// (e.g., kv_block_stride).
const int64_t physical_block_number = static_cast<int64_t>(block_table[block_idx]);
// Load a key to registers.
// Each thread in a thread group has a different part of the key.
// For example, if the the thread group size is 4, then the first thread in the group
// has 0, 4, 8, ... th vectors of the key, and the second thread has 1, 5, 9, ... th
// vectors of the key, and so on.
for (int i = 0; i < NUM_TOKENS_PER_THREAD_GROUP; i++) {
const int physical_block_offset = (thread_group_idx + i * WARP_SIZE) % BLOCK_SIZE;
const int token_idx = block_idx * BLOCK_SIZE + physical_block_offset;
K_vec k_vecs[NUM_VECS_PER_THREAD];
#pragma unroll
for (int j = 0; j < NUM_VECS_PER_THREAD; j++) {
const scalar_t* k_ptr = k_cache + physical_block_number * kv_block_stride
+ kv_head_idx * kv_head_stride
+ physical_block_offset * x;
const int vec_idx = thread_group_offset + j * THREAD_GROUP_SIZE;
const int offset1 = (vec_idx * VEC_SIZE) / x;
const int offset2 = (vec_idx * VEC_SIZE) % x;
k_vecs[j] = *reinterpret_cast<const K_vec*>(k_ptr + offset1 * BLOCK_SIZE * x + offset2);
}
// Compute dot product.
// This includes a reduction across the threads in the same thread group.
float qk = scale * Qk_dot<scalar_t, THREAD_GROUP_SIZE>::dot(q_vecs[thread_group_offset], k_vecs);
// Add the ALiBi bias if slopes are given.
qk += (alibi_slope != 0) ? alibi_slope * (token_idx - context_len + 1) : 0;
if (thread_group_offset == 0) {
// Store the partial reductions to shared memory.
// NOTE(woosuk): It is required to zero out the masked logits.
const bool mask = token_idx >= context_len;
logits[token_idx - start_token_idx] = mask ? 0.f : qk;
// Update the max value.
qk_max = mask ? qk_max : fmaxf(qk_max, qk);
}
}
}
// Perform reduction across the threads in the same warp to get the
// max qk value for each "warp" (not across the thread block yet).
// The 0-th thread of each thread group already has its max qk value.
#pragma unroll
for (int mask = WARP_SIZE / 2; mask >= THREAD_GROUP_SIZE; mask /= 2) {
qk_max = fmaxf(qk_max, VLLM_SHFL_XOR_SYNC(qk_max, mask));
}
if (lane == 0) {
red_smem[warp_idx] = qk_max;
}
__syncthreads();
// TODO(woosuk): Refactor this part.
// Get the max qk value for the sequence.
qk_max = lane < NUM_WARPS ? red_smem[lane] : -FLT_MAX;
#pragma unroll
for (int mask = NUM_WARPS / 2; mask >= 1; mask /= 2) {
qk_max = fmaxf(qk_max, VLLM_SHFL_XOR_SYNC(qk_max, mask));
}
// Broadcast the max qk value to all threads.
qk_max = VLLM_SHFL_SYNC(qk_max, 0);
// Get the sum of the exp values.
float exp_sum = 0.f;
for (int i = thread_idx; i < num_tokens; i += NUM_THREADS) {
float val = __expf(logits[i] - qk_max);
logits[i] = val;
exp_sum += val;
}
exp_sum = block_sum<NUM_WARPS>(&red_smem[NUM_WARPS], exp_sum);
// Compute softmax.
const float inv_sum = __fdividef(1.f, exp_sum + 1e-6f);
for (int i = thread_idx; i < num_tokens; i += NUM_THREADS) {
logits[i] *= inv_sum;
}
__syncthreads();
// If partitioning is enabled, store the max logit and exp_sum.
if (USE_PARTITIONING && thread_idx == 0) {
float* max_logits_ptr = max_logits + seq_idx * num_heads * max_num_partitions
+ head_idx * max_num_partitions
+ partition_idx;
*max_logits_ptr = qk_max;
float* exp_sums_ptr = exp_sums + seq_idx * num_heads * max_num_partitions
+ head_idx * max_num_partitions
+ partition_idx;
*exp_sums_ptr = exp_sum;
}
// Each thread will fetch 16 bytes from the value cache at a time.
constexpr int V_VEC_SIZE = MIN(16 / sizeof(scalar_t), BLOCK_SIZE);
using V_vec = typename Vec<scalar_t, V_VEC_SIZE>::Type;
using L_vec = typename Vec<scalar_t, V_VEC_SIZE>::Type;
using Float_L_vec = typename FloatVec<L_vec>::Type;
constexpr int NUM_V_VECS_PER_ROW = BLOCK_SIZE / V_VEC_SIZE;
constexpr int NUM_ROWS_PER_ITER = WARP_SIZE / NUM_V_VECS_PER_ROW;
constexpr int NUM_ROWS_PER_THREAD = DIVIDE_ROUND_UP(HEAD_SIZE, NUM_ROWS_PER_ITER);
// NOTE(woosuk): We use FP32 for the accumulator for better accuracy.
float accs[NUM_ROWS_PER_THREAD];
#pragma unroll
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
accs[i] = 0.f;
}
scalar_t zero_value;
zero(zero_value);
for (int block_idx = start_block_idx + warp_idx; block_idx < end_block_idx; block_idx += NUM_WARPS) {
// NOTE(woosuk): The block number is stored in int32. However, we cast it to int64
// because int32 can lead to overflow when this variable is multiplied by large numbers
// (e.g., kv_block_stride).
const int64_t physical_block_number = static_cast<int64_t>(block_table[block_idx]);
const int physical_block_offset = (lane % NUM_V_VECS_PER_ROW) * V_VEC_SIZE;
const int token_idx = block_idx * BLOCK_SIZE + physical_block_offset;
L_vec logits_vec;
from_float(logits_vec, *reinterpret_cast<Float_L_vec*>(logits + token_idx - start_token_idx));
const scalar_t* v_ptr = v_cache + physical_block_number * kv_block_stride
+ kv_head_idx * kv_head_stride;
#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) {
const int offset = row_idx * BLOCK_SIZE + physical_block_offset;
V_vec v_vec = *reinterpret_cast<const V_vec*>(v_ptr + offset);
if (block_idx == num_context_blocks - 1) {
// NOTE(woosuk): When v_vec contains the tokens that are out of the context,
// we should explicitly zero out the values since they may contain NaNs.
// See https://github.com/vllm-project/vllm/issues/641#issuecomment-1682544472
scalar_t* v_vec_ptr = reinterpret_cast<scalar_t*>(&v_vec);
#pragma unroll
for (int j = 0; j < V_VEC_SIZE; j++) {
v_vec_ptr[j] = token_idx + j < context_len ? v_vec_ptr[j] : zero_value;
}
}
accs[i] += dot(logits_vec, v_vec);
}
}
}
// Perform reduction within each warp.
#pragma unroll
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
float acc = accs[i];
#pragma unroll
for (int mask = NUM_V_VECS_PER_ROW / 2; mask >= 1; mask /= 2) {
acc += VLLM_SHFL_XOR_SYNC(acc, mask);
}
accs[i] = acc;
}
// NOTE(woosuk): A barrier is required because the shared memory space for logits
// is reused for the output.
__syncthreads();
// Perform reduction across warps.
float* out_smem = reinterpret_cast<float*>(shared_mem);
#pragma unroll
for (int i = NUM_WARPS; i > 1; i /= 2) {
int mid = i / 2;
// Upper warps write to shared memory.
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,
int HEAD_SIZE,
int BLOCK_SIZE,
int NUM_THREADS>
__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 scalar_t* __restrict__ k_cache, // [num_blocks, num_kv_heads, head_size/x, block_size, x]
const scalar_t* __restrict__ v_cache, // [num_blocks, num_kv_heads, head_size, block_size]
const int* __restrict__ head_mapping, // [num_heads]
const float scale,
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
const int* __restrict__ context_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) {
paged_attention_kernel<scalar_t, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS>(
/* exp_sums */ nullptr, /* max_logits */ nullptr,
out, q, k_cache, v_cache, head_mapping, scale, block_tables, context_lens,
max_num_blocks_per_seq, alibi_slopes, q_stride, kv_block_stride, kv_head_stride);
}
// Grid: (num_heads, num_seqs, max_num_partitions).
template<
typename scalar_t,
int HEAD_SIZE,
int BLOCK_SIZE,
int NUM_THREADS,
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 scalar_t* __restrict__ k_cache, // [num_blocks, num_kv_heads, head_size/x, block_size, x]
const scalar_t* __restrict__ v_cache, // [num_blocks, num_kv_heads, head_size, block_size]
const int* __restrict__ head_mapping, // [num_heads]
const float scale,
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
const int* __restrict__ context_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) {
paged_attention_kernel<scalar_t, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS, PARTITION_SIZE>(
exp_sums, max_logits, tmp_out, q, k_cache, v_cache, head_mapping, scale,
block_tables, context_lens, max_num_blocks_per_seq, alibi_slopes,
q_stride, kv_block_stride, kv_head_stride);
}
// 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__ context_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 context_len = context_lens[seq_idx];
const int num_partitions = DIVIDE_ROUND_UP(context_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, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS>), \
shared_mem_size); \
vllm::paged_attention_v1_kernel<T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS> \
<<<grid, block, shared_mem_size, stream>>>( \
out_ptr, \
query_ptr, \
key_cache_ptr, \
value_cache_ptr, \
head_mapping_ptr, \
scale, \
block_tables_ptr, \
context_lens_ptr, \
max_num_blocks_per_seq, \
alibi_slopes_ptr, \
q_stride, \
kv_block_stride, \
kv_head_stride);
// TODO(woosuk): Tune NUM_THREADS.
template<
typename T,
int BLOCK_SIZE,
int NUM_THREADS = 128>
void paged_attention_v1_launcher(
torch::Tensor& out,
torch::Tensor& query,
torch::Tensor& key_cache,
torch::Tensor& value_cache,
torch::Tensor& head_mapping,
float scale,
torch::Tensor& block_tables,
torch::Tensor& context_lens,
int max_context_len,
const c10::optional<torch::Tensor>& alibi_slopes) {
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());
T* key_cache_ptr = reinterpret_cast<T*>(key_cache.data_ptr());
T* value_cache_ptr = reinterpret_cast<T*>(value_cache.data_ptr());
int* head_mapping_ptr = reinterpret_cast<int*>(head_mapping.data_ptr());
int* block_tables_ptr = block_tables.data_ptr<int>();
int* context_lens_ptr = context_lens.data_ptr<int>();
constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
int padded_max_context_len = DIVIDE_ROUND_UP(max_context_len, BLOCK_SIZE) * BLOCK_SIZE;
int logits_size = padded_max_context_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 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, BLOCK_SIZE) \
paged_attention_v1_launcher<T, BLOCK_SIZE>( \
out, \
query, \
key_cache, \
value_cache, \
head_mapping, \
scale, \
block_tables, \
context_lens, \
max_context_len, \
alibi_slopes);
// NOTE(woosuk): To reduce the compilation time, we omitted block sizes
// 1, 2, 4, 64, 128, 256.
#define CALL_V1_LAUNCHER_BLOCK_SIZE(T) \
switch (block_size) { \
case 8: \
CALL_V1_LAUNCHER(T, 8); \
break; \
case 16: \
CALL_V1_LAUNCHER(T, 16); \
break; \
case 32: \
CALL_V1_LAUNCHER(T, 32); \
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]
torch::Tensor& head_mapping, // [num_heads]
float scale,
torch::Tensor& block_tables, // [num_seqs, max_num_blocks_per_seq]
torch::Tensor& context_lens, // [num_seqs]
int block_size,
int max_context_len,
const c10::optional<torch::Tensor>& alibi_slopes) {
if (query.dtype() == at::ScalarType::Float) {
CALL_V1_LAUNCHER_BLOCK_SIZE(float);
} else if (query.dtype() == at::ScalarType::Half) {
CALL_V1_LAUNCHER_BLOCK_SIZE(uint16_t);
} else if (query.dtype() == at::ScalarType::BFloat16) {
CALL_V1_LAUNCHER_BLOCK_SIZE(__nv_bfloat16);
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
}
}
#define LAUNCH_PAGED_ATTENTION_V2(HEAD_SIZE) \
vllm::paged_attention_v2_kernel<T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS, 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, \
head_mapping_ptr, \
scale, \
block_tables_ptr, \
context_lens_ptr, \
max_num_blocks_per_seq, \
alibi_slopes_ptr, \
q_stride, \
kv_block_stride, \
kv_head_stride); \
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, \
context_lens_ptr, \
max_num_partitions);
template<
typename T,
int BLOCK_SIZE,
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,
torch::Tensor& head_mapping,
float scale,
torch::Tensor& block_tables,
torch::Tensor& context_lens,
int max_context_len,
const c10::optional<torch::Tensor>& alibi_slopes) {
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());
T* key_cache_ptr = reinterpret_cast<T*>(key_cache.data_ptr());
T* value_cache_ptr = reinterpret_cast<T*>(value_cache.data_ptr());
int* head_mapping_ptr = reinterpret_cast<int*>(head_mapping.data_ptr());
int* block_tables_ptr = block_tables.data_ptr<int>();
int* context_lens_ptr = context_lens.data_ptr<int>();
constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
int max_num_partitions = DIVIDE_ROUND_UP(max_context_len, PARTITION_SIZE);
int logits_size = PARTITION_SIZE * sizeof(float);
int outputs_size = (NUM_WARPS / 2) * head_size * sizeof(float);
// For paged attention v2 kernel.
dim3 grid(num_heads, num_seqs, max_num_partitions);
int shared_mem_size = std::max(logits_size, outputs_size);
// For paged attention v2 reduce kernel.
dim3 reduce_grid(num_heads, num_seqs);
int reduce_shared_mem_size = 2 * max_num_partitions * sizeof(float);
dim3 block(NUM_THREADS);
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_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, BLOCK_SIZE) \
paged_attention_v2_launcher<T, BLOCK_SIZE>( \
out, \
exp_sums, \
max_logits, \
tmp_out, \
query, \
key_cache, \
value_cache, \
head_mapping, \
scale, \
block_tables, \
context_lens, \
max_context_len, \
alibi_slopes);
// NOTE(woosuk): To reduce the compilation time, we omitted block sizes
// 1, 2, 4, 64, 128, 256.
#define CALL_V2_LAUNCHER_BLOCK_SIZE(T) \
switch (block_size) { \
case 8: \
CALL_V2_LAUNCHER(T, 8); \
break; \
case 16: \
CALL_V2_LAUNCHER(T, 16); \
break; \
case 32: \
CALL_V2_LAUNCHER(T, 32); \
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]
torch::Tensor& head_mapping, // [num_heads]
float scale,
torch::Tensor& block_tables, // [num_seqs, max_num_blocks_per_seq]
torch::Tensor& context_lens, // [num_seqs]
int block_size,
int max_context_len,
const c10::optional<torch::Tensor>& alibi_slopes) {
if (query.dtype() == at::ScalarType::Float) {
CALL_V2_LAUNCHER_BLOCK_SIZE(float);
} else if (query.dtype() == at::ScalarType::Half) {
CALL_V2_LAUNCHER_BLOCK_SIZE(uint16_t);
} else if (query.dtype() == at::ScalarType::BFloat16) {
CALL_V2_LAUNCHER_BLOCK_SIZE(__nv_bfloat16);
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
}
}
#undef WARP_SIZE
#undef MAX
#undef MIN
#undef DIVIDE_ROUND_UP
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