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390 lines
17 KiB
Plaintext
390 lines
17 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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#include "helper.h"
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#include "speculate_msg.h"
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#ifndef PD_BUILD_STATIC_OP
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#define PD_BUILD_STATIC_OP(name) PD_BUILD_OP(static_op_##name)
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#endif
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__device__ __forceinline__ bool in_need_block_list_schedule(const int &qid,
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int *need_block_list,
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const int &need_block_len) {
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bool res = false;
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for (int i = 0; i < need_block_len; i++) {
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if (qid == need_block_list[i]) {
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res = true;
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need_block_list[i] = -1;
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break;
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}
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}
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return res;
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}
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__global__ void speculate_free_and_reschedule(bool *stop_flags,
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int *seq_lens_this_time,
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int *seq_lens_decoder,
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int *block_tables,
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int *encoder_block_lens,
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bool *is_block_step,
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int *step_block_list, // [bsz]
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int *step_len,
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int *recover_block_list,
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int *recover_len,
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int *need_block_list,
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int *need_block_len,
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int *used_list_len,
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int *free_list,
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int *free_list_len,
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int64_t *first_token_ids,
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int* accept_num,
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const int bsz,
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const int block_size,
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const int block_num_per_seq,
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const int max_decoder_block_num,
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const int max_draft_tokens) {
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typedef cub::BlockReduce<cub::KeyValuePair<int, int>, 256> BlockReduce;
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__shared__ typename BlockReduce::TempStorage temp_storage;
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__shared__ bool step_max_block_flag;
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__shared__ int in_need_block_list_len;
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const int tid = threadIdx.x;
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if (tid < bsz) {
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if (tid == 0) {
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step_max_block_flag = false;
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in_need_block_list_len = 0;
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}
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int *block_table_now = block_tables + tid * block_num_per_seq;
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int max_possible_block_idx = (seq_lens_decoder[tid] + max_draft_tokens + 1 ) / block_size;
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if (stop_flags[tid]) {
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// 回收block块
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first_token_ids[tid] = -1;
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const int encoder_block_len = encoder_block_lens[tid];
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const int decoder_used_len = used_list_len[tid];
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if (decoder_used_len > 0) {
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const int ori_free_list_len =
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atomicAdd(free_list_len, decoder_used_len);
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#ifdef DEBUG_STEP
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printf(
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"free block seq_id: %d, free block num: %d, "
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"encoder_block_len: %d, ori_free_list_len: %d\n",
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tid,
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decoder_used_len,
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encoder_block_len,
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ori_free_list_len);
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#endif
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for (int i = 0; i < decoder_used_len; i++) {
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free_list[ori_free_list_len + i] =
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block_table_now[encoder_block_len + i];
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block_table_now[encoder_block_len + i] = -1;
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}
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encoder_block_lens[tid] = 0;
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used_list_len[tid] = 0;
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}
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} else if (seq_lens_this_time[tid] != 0 && max_possible_block_idx < block_num_per_seq &&
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block_table_now[(seq_lens_decoder[tid] + max_draft_tokens +
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1) /
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block_size] == -1) {
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// 统计需要分配block的位置和总数
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#ifdef DEBUG_STEP
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printf("step seq_id:%d, ##### pin 1 #####\n", tid);
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#endif
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const int ori_need_block_len = atomicAdd(need_block_len, 1);
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need_block_list[ori_need_block_len] = tid;
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#ifdef DEBUG_STEP
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printf("seq_id: %d need block\n", tid);
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#endif
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}
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}
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#ifdef DEBUG_STEP
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printf("step seq_id:%d, ##### pin 2 #####\n", tid);
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#endif
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__syncthreads();
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// 调度block，直到满足need_block_len
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while (need_block_len[0] > free_list_len[0]) {
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if (tid == 0) {
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printf("need_block_len: %d, free_list_len: %d\n",
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need_block_len[0],
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free_list_len[0]);
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}
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// 调度block，根据used_list_len从大到小回收block，直到满足need_block_len，已解码到最后一个block的query不参与调度（马上就结束）
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const int used_block_num =
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tid < bsz ? used_list_len[tid] : 0;
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cub::KeyValuePair<int, int> kv_pair = {tid, used_block_num};
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kv_pair = BlockReduce(temp_storage).Reduce(kv_pair, cub::ArgMax());
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if (tid == 0) {
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if (kv_pair.value == 0) {
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step_max_block_flag = true;
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} else {
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const int encoder_block_len = encoder_block_lens[kv_pair.key];
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printf("step max_id: %d, max_num: %d, encoder_block_len: %d\n",
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kv_pair.key,
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kv_pair.value,
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encoder_block_len);
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int *block_table_now =
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block_tables + kv_pair.key * block_num_per_seq;
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// 回收调度位的block
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for (int i = 0; i < kv_pair.value; i++) {
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free_list[free_list_len[0] + i] =
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block_table_now[encoder_block_len + i];
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block_table_now[encoder_block_len + i] = -1;
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}
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step_block_list[step_len[0]] = kv_pair.key;
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// 如果调度位置本次也需要block，对应的处理
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if (in_need_block_list_schedule(
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kv_pair.key,
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need_block_list,
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need_block_len[0] + in_need_block_list_len)) {
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need_block_len[0] -= 1;
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in_need_block_list_len += 1;
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}
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step_len[0] += 1;
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free_list_len[0] += kv_pair.value;
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stop_flags[kv_pair.key] = true;
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seq_lens_this_time[kv_pair.key] = 0;
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seq_lens_decoder[kv_pair.key] = 0;
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encoder_block_lens[kv_pair.key] = 0;
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used_list_len[kv_pair.key] = 0;
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printf(
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"free block seq_id: %d, free block num: %d, "
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"now_free_list_len: %d\n",
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(int)kv_pair.key,
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(int)kv_pair.value,
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(int)free_list_len[0]);
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}
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}
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__syncthreads();
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}
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#ifdef DEBUG_STEP
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printf("step seq_id:%d, ##### pin 3 #####\n", tid);
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#endif
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// 为需要block的位置分配block，每个位置分配一个block
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if (tid < need_block_len[0] + in_need_block_list_len) {
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const int need_block_id = need_block_list[tid];
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if (need_block_id != -1) {
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if (!stop_flags[need_block_id]) {
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// 如果需要的位置正好是上一步中被释放的位置，不做处理
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used_list_len[need_block_id] += 1;
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const int ori_free_list_len = atomicSub(free_list_len, 1);
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int *block_table_now =
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block_tables + need_block_id * block_num_per_seq;
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block_table_now[(seq_lens_decoder[need_block_id] +
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max_draft_tokens + 1) /
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block_size] = free_list[ori_free_list_len - 1];
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}
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need_block_list[tid] = -1;
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}
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}
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__syncthreads();
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// reset need_block_len
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if (tid == 0) {
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need_block_len[0] = 0;
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}
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}
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// 为不修改接口调用方式，入参暂不改变
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void SpeculateStepSchedule(const paddle::Tensor &stop_flags,
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const paddle::Tensor &seq_lens_this_time,
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const paddle::Tensor &ori_seq_lens_encoder,
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const paddle::Tensor &seq_lens_encoder,
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const paddle::Tensor &seq_lens_decoder,
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const paddle::Tensor &block_tables, // [bsz, block_num_per_seq]
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const paddle::Tensor &encoder_block_lens,
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const paddle::Tensor &is_block_step,
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const paddle::Tensor &step_block_list,
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const paddle::Tensor &step_lens,
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const paddle::Tensor &recover_block_list,
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const paddle::Tensor &recover_lens,
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const paddle::Tensor &need_block_list,
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const paddle::Tensor &need_block_len,
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const paddle::Tensor &used_list_len,
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const paddle::Tensor &free_list,
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const paddle::Tensor &free_list_len,
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const paddle::Tensor &input_ids,
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const paddle::Tensor &pre_ids,
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const paddle::Tensor &step_idx,
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const paddle::Tensor &next_tokens,
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const paddle::Tensor &first_token_ids,
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const paddle::Tensor &accept_num,
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const int block_size,
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const int encoder_decoder_block_num,
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const int max_draft_tokens) {
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auto cu_stream = seq_lens_this_time.stream();
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const int bsz = seq_lens_this_time.shape()[0];
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const int block_num_per_seq = block_tables.shape()[1];
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const int length = input_ids.shape()[1];
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const int pre_id_length = pre_ids.shape()[1];
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constexpr int BlockSize = 256; // bsz <= 256
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const int max_decoder_block_num = length / block_size - encoder_decoder_block_num; // 最大输出长度对应的block - 服务为解码分配的block数量
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auto step_lens_inkernel = paddle::full({1}, 0, paddle::DataType::INT32, stop_flags.place());
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auto step_bs_list = GetEmptyTensor({bsz}, paddle::DataType::INT32, stop_flags.place());
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#ifdef DEBUG_STEP
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printf(
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"bsz: %d, block_num_per_seq: %d, length: %d, max_decoder_block_num: "
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"%d\n",
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bsz,
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block_num_per_seq,
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length,
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max_decoder_block_num);
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#endif
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speculate_free_and_reschedule<<<1, BlockSize, 0, cu_stream>>>(
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const_cast<bool *>(stop_flags.data<bool>()),
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const_cast<int *>(seq_lens_this_time.data<int>()),
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const_cast<int *>(seq_lens_decoder.data<int>()),
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const_cast<int *>(block_tables.data<int>()),
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const_cast<int *>(encoder_block_lens.data<int>()),
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const_cast<bool *>(is_block_step.data<bool>()),
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const_cast<int *>(step_bs_list.data<int>()),
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const_cast<int *>(step_lens_inkernel.data<int>()),
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const_cast<int *>(recover_block_list.data<int>()),
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const_cast<int *>(recover_lens.data<int>()),
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const_cast<int *>(need_block_list.data<int>()),
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const_cast<int *>(need_block_len.data<int>()),
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const_cast<int *>(used_list_len.data<int>()),
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const_cast<int *>(free_list.data<int>()),
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const_cast<int *>(free_list_len.data<int>()),
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const_cast<int64_t *>(first_token_ids.data<int64_t>()),
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const_cast<int *>(accept_num.data<int>()),
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bsz,
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block_size,
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block_num_per_seq,
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max_decoder_block_num,
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max_draft_tokens);
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#ifdef DEBUG_STEP
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cudaDeviceSynchronize();
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#endif
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// save output
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auto step_lens_cpu = step_lens_inkernel.copy_to(paddle::CPUPlace(), false);
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if (step_lens_cpu.data<int>()[0] > 0) {
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auto step_bs_list_cpu = step_bs_list.copy_to(paddle::CPUPlace(), false);
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auto next_tokens = paddle::full({bsz}, -1, paddle::DataType::INT64, paddle::CPUPlace());
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for (int i = 0; i < step_lens_cpu.data<int>()[0]; i++) {
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const int step_bid = step_bs_list_cpu.data<int>()[i];
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next_tokens.data<int64_t>()[step_bid] = -3; // need reschedule
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}
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const int rank_id = static_cast<int>(stop_flags.place().GetDeviceId());
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printf("reschedule rank_id: %d, step_lens: %d", rank_id, step_lens_cpu.data<int>()[0]);
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const int64_t* x_data = next_tokens.data<int64_t>();
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static struct speculate_msgdata msg_sed;
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int msg_queue_id = rank_id;
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if (const char* inference_msg_queue_id_env_p =
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std::getenv("INFERENCE_MSG_QUEUE_ID")) {
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std::string inference_msg_queue_id_env_str(
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inference_msg_queue_id_env_p);
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int inference_msg_queue_id_from_env =
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std::stoi(inference_msg_queue_id_env_str);
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msg_queue_id = inference_msg_queue_id_from_env;
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} else {
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std::cout << "Failed to got INFERENCE_MSG_QUEUE_ID at env, use default."
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<< std::endl;
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}
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int inference_msg_id_from_env = 1;
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if (const char* inference_msg_id_env_p = std::getenv("INFERENCE_MSG_ID")) {
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std::string inference_msg_id_env_str(inference_msg_id_env_p);
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inference_msg_id_from_env = std::stoi(inference_msg_id_env_str);
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if (inference_msg_id_from_env == 2) {
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// 2 and -2 is perserve for no-output indication.
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throw std::runtime_error(
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" INFERENCE_MSG_ID cannot be 2, please use other number.");
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}
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if (inference_msg_id_from_env < 0) {
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throw std::runtime_error(
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" INFERENCE_MSG_ID cannot be negative, please use other "
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"number.");
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}
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} else {
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}
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// static key_t key = ftok("/dev/shm", msg_queue_id);
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static key_t key = ftok("./", msg_queue_id);
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static int msgid = msgget(key, IPC_CREAT | 0666);
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msg_sed.mtype = 1;
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msg_sed.mtext[0] = inference_msg_id_from_env;
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msg_sed.mtext[1] = bsz;
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for (int i = 2; i < bsz + 2; i++) {
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msg_sed.mtext[i] = (int)x_data[i - 2];
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}
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if ((msgsnd(msgid, &msg_sed, (MAX_BSZ + 2) * 4, 0)) == -1) {
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printf("full msg buffer\n");
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}
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}
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}
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PD_BUILD_STATIC_OP(speculate_step_reschedule)
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.Inputs({"stop_flags",
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"seq_lens_this_time",
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"ori_seq_lens_encoder",
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"seq_lens_encoder",
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"seq_lens_decoder",
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"block_tables",
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"encoder_block_lens",
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"is_block_step",
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"step_block_list",
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"step_lens",
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"recover_block_list",
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"recover_lens",
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"need_block_list",
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"need_block_len",
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"used_list_len",
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"free_list",
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"free_list_len",
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"input_ids",
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"pre_ids",
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"step_idx",
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"next_tokens",
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"first_token_ids",
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"accept_num"})
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.Attrs({"block_size: int",
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"encoder_decoder_block_num: int",
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"max_draft_tokens: int"})
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.Outputs({"stop_flags_out",
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"seq_lens_this_time_out",
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"seq_lens_encoder_out",
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"seq_lens_decoder_out",
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"block_tables_out",
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"encoder_block_lens_out",
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"is_block_step_out",
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"step_block_list_out",
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"step_lens_out",
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"recover_block_list_out",
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"recover_lens_out",
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"need_block_list_out",
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"need_block_len_out",
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"used_list_len_out",
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"free_list_out",
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"free_list_len_out",
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"input_ids_out",
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"first_token_ids_out"})
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.SetInplaceMap({{"stop_flags", "stop_flags_out"},
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{"seq_lens_this_time", "seq_lens_this_time_out"},
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{"seq_lens_encoder", "seq_lens_encoder_out"},
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{"seq_lens_decoder", "seq_lens_decoder_out"},
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{"block_tables", "block_tables_out"},
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{"encoder_block_lens", "encoder_block_lens_out"},
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{"is_block_step", "is_block_step_out"},
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{"step_block_list", "step_block_list_out"},
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{"step_lens", "step_lens_out"},
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{"recover_block_list", "recover_block_list_out"},
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{"recover_lens", "recover_lens_out"},
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{"need_block_list", "need_block_list_out"},
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{"need_block_len", "need_block_len_out"},
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{"used_list_len", "used_list_len_out"},
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{"free_list", "free_list_out"},
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{"free_list_len", "free_list_len_out"},
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{"input_ids", "input_ids_out"},
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{"first_token_ids", "first_token_ids_out"}})
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.SetKernelFn(PD_KERNEL(SpeculateStepSchedule));
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