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[LLM] First commit the llm deployment code
This commit is contained in:
jiangjiajun
481
custom_ops/gpu_ops/speculate_decoding/speculate_step.cu
Normal file
481
custom_ops/gpu_ops/speculate_decoding/speculate_step.cu
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@@ -0,0 +1,481 @@
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// 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" // NOLINT
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// #define DEBUG_STEP
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__device__ bool speculate_free_and_dispatch_block(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_dispatch_block(
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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] && !is_block_step[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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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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__syncthreads();
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while (need_block_len[0] > free_list_len[0]) {
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#ifdef DEBUG_STEP
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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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#endif
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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 && !is_block_step[tid] &&
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(step_max_block_flag ||
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used_list_len[tid] != max_decoder_block_num)
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? used_list_len[tid]
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: 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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// #ifdef DEBUG_STEP
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printf("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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// #endif
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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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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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if (speculate_free_and_dispatch_block(
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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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is_block_step[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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// Note(@wufeisheng): when step, accept num will not be 0 so
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// that next step even if this batch member is stepped, save
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// output still stream output, so accept num should be set to 0
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accept_num[kv_pair.key] = 0;
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}
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}
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__syncthreads();
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}
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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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#ifdef DEBUG_STEP
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printf("need_block_id %d\n", need_block_id);
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printf("ori_free_list_len %d\n", ori_free_list_len);
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printf("max_draft_tokens %d\n", max_draft_tokens);
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printf("seq_lens_decoder[need_block_id] %d\n",
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seq_lens_decoder[need_block_id]);
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printf("free_list[ori_free_list_len - 1] %d\n",
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free_list[ori_free_list_len - 1]);
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#endif
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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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// 计算可以复原的query id
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if (tid == 0) {
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int ori_free_list_len = free_list_len[0];
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int ori_step_len = step_len[0];
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if (ori_step_len > 0) {
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int ori_step_block_id = step_block_list[ori_step_len - 1];
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int tmp_used_len = used_list_len[ori_step_block_id];
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// 比之前调度时多分配一个block，防止马上恢复刚调度的query(比如回收的seq_id在need_block_list中）
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const int max_decoder_block_num_this_seq =
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max_decoder_block_num - encoder_block_lens[ori_step_block_id];
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int used_len = tmp_used_len + 1 < max_decoder_block_num_this_seq
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? tmp_used_len + 1
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: max_decoder_block_num_this_seq;
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if (ori_step_len > 0 && ori_free_list_len >= used_len) {
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// #ifdef DEBUG_STEP
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printf(
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"recover seq_id: %d, free_list_len: %d, used_list_len: "
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"%d\n",
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ori_step_block_id,
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ori_free_list_len,
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used_len);
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// #endif
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recover_block_list[recover_len[0]] = ori_step_block_id;
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is_block_step[ori_step_block_id] = false;
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used_list_len[ori_step_block_id] = used_len;
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ori_free_list_len -= used_len;
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step_block_list[ori_step_len - 1] = -1;
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step_len[0] -= 1;
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recover_len[0] += 1;
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ori_step_len = step_len[0];
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if (ori_step_len > 0) {
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ori_step_block_id = step_block_list[ori_step_len - 1];
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tmp_used_len = used_list_len[ori_step_block_id];
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used_len = tmp_used_len + 1 < max_decoder_block_num_this_seq
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? tmp_used_len + 1
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: max_decoder_block_num_this_seq;
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}
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}
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}
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need_block_len[0] = 0;
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}
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}
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// 根据上一步计算出的可以复原的query_id进行状态恢复
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__global__ void speculate_recover_block(int *recover_block_list, // [bsz]
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int *recover_len,
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bool *stop_flags,
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int *seq_lens_this_time,
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int *ori_seq_lens_encoder,
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int *seq_lens_encoder,
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int *seq_lens_decoder,
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int *block_tables,
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int *free_list,
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int *free_list_len,
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int64_t *input_ids,
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int64_t *pre_ids,
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int64_t *step_idx,
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int *encoder_block_lens,
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int *used_list_len,
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const int64_t *next_tokens,
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const int64_t *first_token_ids,
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const int bsz,
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const int block_num_per_seq,
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const int length,
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const int pre_id_length) {
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const int bid = blockIdx.x;
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const int tid = threadIdx.x;
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__shared__ int ori_free_list_len;
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if (bid < recover_len[0]) {
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const int recover_id = recover_block_list[bid];
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const int ori_seq_len_encoder = ori_seq_lens_encoder[recover_id];
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const int step_idx_now = step_idx[recover_id];
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const int seq_len = ori_seq_len_encoder + step_idx_now;
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const int encoder_block_len = encoder_block_lens[recover_id];
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const int decoder_used_len = used_list_len[recover_id];
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int *block_table_now = block_tables + recover_id * block_num_per_seq;
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int64_t *input_ids_now = input_ids + recover_id * length;
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int64_t *pre_ids_now = pre_ids + recover_id * pre_id_length;
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if (tid == 0) {
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seq_lens_this_time[recover_id] = seq_len;
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seq_lens_encoder[recover_id] = seq_len;
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stop_flags[recover_id] = false;
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// input_ids_now[ori_seq_len_encoder + step_idx_now - 1] =
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// next_tokens[recover_id]; // next tokens
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input_ids_now[0] =
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first_token_ids[recover_id]; // set first prompt token
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const int ori_free_list_len_tid0 =
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atomicSub(free_list_len, decoder_used_len);
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ori_free_list_len = ori_free_list_len_tid0;
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#ifdef DEBUG_STEP
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printf(
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"seq_id: %d, ori_seq_len_encoder: %d, step_idx_now: %d, "
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"seq_len: %d, ori_free_list_len_tid0: %d, "
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"ori_free_list_len: %d\n",
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recover_id,
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ori_seq_len_encoder,
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step_idx_now,
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seq_len,
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ori_free_list_len_tid0,
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ori_free_list_len);
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#endif
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}
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__syncthreads();
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// 恢复block table
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for (int i = tid; i < decoder_used_len; i += blockDim.x) {
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block_table_now[encoder_block_len + i] =
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free_list[ori_free_list_len - i - 1];
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}
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// 恢复input_ids
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for (int i = tid; i < step_idx_now; i += blockDim.x) {
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input_ids_now[ori_seq_len_encoder + i] = pre_ids_now[i + 1];
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}
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}
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if (bid == 0 && tid == 0) {
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recover_len[0] = 0;
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}
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}
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void SpeculateStepPaddle(
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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];
|
||||
constexpr int BlockSize = 256; // bsz <= 256
|
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const int max_decoder_block_num = length / block_size;
|
||||
// const int max_decoder_block_num = 2048 / block_size -
|
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// encoder_decoder_block_num;
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#ifdef DEBUG_STEP
|
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printf(
|
||||
"bsz: %d, block_num_per_seq: %d, length: %d, max_decoder_block_num: "
|
||||
"%d\n",
|
||||
bsz,
|
||||
block_num_per_seq,
|
||||
length,
|
||||
max_decoder_block_num);
|
||||
#endif
|
||||
speculate_free_and_dispatch_block<<<1, BlockSize, 0, cu_stream>>>(
|
||||
const_cast<bool *>(stop_flags.data<bool>()),
|
||||
const_cast<int *>(seq_lens_this_time.data<int>()),
|
||||
const_cast<int *>(seq_lens_decoder.data<int>()),
|
||||
const_cast<int *>(block_tables.data<int>()),
|
||||
const_cast<int *>(encoder_block_lens.data<int>()),
|
||||
const_cast<bool *>(is_block_step.data<bool>()),
|
||||
const_cast<int *>(step_block_list.data<int>()),
|
||||
const_cast<int *>(step_lens.data<int>()),
|
||||
const_cast<int *>(recover_block_list.data<int>()),
|
||||
const_cast<int *>(recover_lens.data<int>()),
|
||||
const_cast<int *>(need_block_list.data<int>()),
|
||||
const_cast<int *>(need_block_len.data<int>()),
|
||||
const_cast<int *>(used_list_len.data<int>()),
|
||||
const_cast<int *>(free_list.data<int>()),
|
||||
const_cast<int *>(free_list_len.data<int>()),
|
||||
const_cast<int64_t *>(first_token_ids.data<int64_t>()),
|
||||
const_cast<int *>(accept_num.data<int>()),
|
||||
bsz,
|
||||
block_size,
|
||||
block_num_per_seq,
|
||||
max_decoder_block_num,
|
||||
max_draft_tokens);
|
||||
#ifdef DEBUG_STEP
|
||||
cudaDeviceSynchronize();
|
||||
#endif
|
||||
auto cpu_recover_lens = recover_lens.copy_to(paddle::CPUPlace(), false);
|
||||
const int grid_size = cpu_recover_lens.data<int>()[0];
|
||||
#ifdef DEBUG_STEP
|
||||
printf("grid_size2 %d\n", grid_size);
|
||||
#endif
|
||||
if (grid_size > 0) {
|
||||
speculate_recover_block<<<grid_size, BlockSize, 0, cu_stream>>>(
|
||||
const_cast<int *>(recover_block_list.data<int>()),
|
||||
const_cast<int *>(recover_lens.data<int>()),
|
||||
const_cast<bool *>(stop_flags.data<bool>()),
|
||||
const_cast<int *>(seq_lens_this_time.data<int>()),
|
||||
const_cast<int *>(ori_seq_lens_encoder.data<int>()),
|
||||
const_cast<int *>(seq_lens_encoder.data<int>()),
|
||||
const_cast<int *>(seq_lens_decoder.data<int>()),
|
||||
const_cast<int *>(block_tables.data<int>()),
|
||||
const_cast<int *>(free_list.data<int>()),
|
||||
const_cast<int *>(free_list_len.data<int>()),
|
||||
const_cast<int64_t *>(input_ids.data<int64_t>()),
|
||||
const_cast<int64_t *>(pre_ids.data<int64_t>()),
|
||||
const_cast<int64_t *>(step_idx.data<int64_t>()),
|
||||
const_cast<int *>(encoder_block_lens.data<int>()),
|
||||
const_cast<int *>(used_list_len.data<int>()),
|
||||
next_tokens.data<int64_t>(),
|
||||
first_token_ids.data<int64_t>(),
|
||||
bsz,
|
||||
block_num_per_seq,
|
||||
length,
|
||||
pre_id_length);
|
||||
#ifdef DEBUG_STEP
|
||||
cudaDeviceSynchronize();
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
PD_BUILD_STATIC_OP(speculate_step_paddle)
|
||||
.Inputs({"stop_flags",
|
||||
"seq_lens_this_time",
|
||||
"ori_seq_lens_encoder",
|
||||
"seq_lens_encoder",
|
||||
"seq_lens_decoder",
|
||||
"block_tables",
|
||||
"encoder_block_lens",
|
||||
"is_block_step",
|
||||
"step_block_list",
|
||||
"step_lens",
|
||||
"recover_block_list",
|
||||
"recover_lens",
|
||||
"need_block_list",
|
||||
"need_block_len",
|
||||
"used_list_len",
|
||||
"free_list",
|
||||
"free_list_len",
|
||||
"input_ids",
|
||||
"pre_ids",
|
||||
"step_idx",
|
||||
"next_tokens",
|
||||
"first_token_ids",
|
||||
"accept_num"})
|
||||
.Attrs({"block_size: int",
|
||||
"encoder_decoder_block_num: int",
|
||||
"max_draft_tokens: int"})
|
||||
.Outputs({"stop_flags_out",
|
||||
"seq_lens_this_time_out",
|
||||
"seq_lens_encoder_out",
|
||||
"seq_lens_decoder_out",
|
||||
"block_tables_out",
|
||||
"encoder_block_lens_out",
|
||||
"is_block_step_out",
|
||||
"step_block_list_out",
|
||||
"step_lens_out",
|
||||
"recover_block_list_out",
|
||||
"recover_lens_out",
|
||||
"need_block_list_out",
|
||||
"need_block_len_out",
|
||||
"used_list_len_out",
|
||||
"free_list_out",
|
||||
"free_list_len_out",
|
||||
"input_ids_out",
|
||||
"first_token_ids_out"})
|
||||
.SetInplaceMap({{"stop_flags", "stop_flags_out"},
|
||||
{"seq_lens_this_time", "seq_lens_this_time_out"},
|
||||
{"seq_lens_encoder", "seq_lens_encoder_out"},
|
||||
{"seq_lens_decoder", "seq_lens_decoder_out"},
|
||||
{"block_tables", "block_tables_out"},
|
||||
{"encoder_block_lens", "encoder_block_lens_out"},
|
||||
{"is_block_step", "is_block_step_out"},
|
||||
{"step_block_list", "step_block_list_out"},
|
||||
{"step_lens", "step_lens_out"},
|
||||
{"recover_block_list", "recover_block_list_out"},
|
||||
{"recover_lens", "recover_lens_out"},
|
||||
{"need_block_list", "need_block_list_out"},
|
||||
{"need_block_len", "need_block_len_out"},
|
||||
{"used_list_len", "used_list_len_out"},
|
||||
{"free_list", "free_list_out"},
|
||||
{"free_list_len", "free_list_len_out"},
|
||||
{"input_ids", "input_ids_out"},
|
||||
{"first_token_ids", "first_token_ids_out"}})
|
||||
.SetKernelFn(PD_KERNEL(SpeculateStepPaddle));
|
||||
Reference in New Issue
Block a user