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https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 00:33:03 +08:00
[Feature] Support block scheduler v1 for FD (#2928)
* Support FD block scheduler v1 * Support FD block scheduler v1 * Support FD block scheduler v1 * Fix according to copilot review * Fix according to review * Remove is_dummy * Fix bug when real_bsz=1 * Fix infer first token cost time --------- Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
This commit is contained in:
chenjian
@@ -284,6 +284,32 @@ void UpdateInputes(const paddle::Tensor &stop_flags,
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const paddle::Tensor &next_tokens,
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const paddle::Tensor &is_block_step);
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void UpdateInputesV1(const paddle::Tensor &stop_flags,
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const paddle::Tensor ¬_need_stop, // only on cpu
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const paddle::Tensor &seq_lens_this_time,
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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 &step_seq_lens_decoder,
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const paddle::Tensor &prompt_lens,
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const paddle::Tensor &topk_ids,
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const paddle::Tensor &input_ids,
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const paddle::Tensor &block_tables,
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const paddle::Tensor &stop_nums,
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const paddle::Tensor &next_tokens,
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const paddle::Tensor &is_block_step,
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const int block_size);
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void RecoverDecodeTask(const paddle::Tensor &stop_flags,
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const paddle::Tensor &seq_lens_this_time,
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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 &step_seq_lens_decoder,
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const paddle::Tensor &block_tables,
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const paddle::Tensor &is_block_step,
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const int block_size);
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paddle::Tensor
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GroupSwigluWithMasked(const paddle::Tensor &fc1_out_tensor,
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const paddle::Tensor &token_nums_per_expert);
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@@ -941,6 +967,18 @@ PYBIND11_MODULE(fastdeploy_ops, m) {
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*/
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m.def("update_inputs", &UpdateInputes, "update_inputs function");
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/**
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* update_inputs_v1.cu
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* update_inputs_v1
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*/
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m.def("update_inputs_v1", &UpdateInputesV1, "update inputs for scheduler v1 function");
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/**
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* recover_decode_task.cu
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* recover_decode_task
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*/
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m.def("recover_decode_task", &RecoverDecodeTask, "recover decode task for scheduler v1 function");
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/**
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* extract_text_token_output.cu
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* extract_text_token_output
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91
custom_ops/gpu_ops/recover_decode_task.cu
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91
custom_ops/gpu_ops/recover_decode_task.cu
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@@ -0,0 +1,91 @@
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// Copyright (c) 2025 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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__global__ void recover_decode_task(bool *stop_flags,
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int *seq_lens_this_time,
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int *seq_lens_encoder,
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int *seq_lens_decoder,
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int *step_seq_lens_decoder,
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int *block_tables,
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bool *is_block_step,
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const int bsz,
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const int block_num_per_seq,
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const int block_size) {
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int thread_idx = threadIdx.x;
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if (thread_idx < bsz) {
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if(is_block_step[thread_idx] == true) {
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int *block_table_now = block_tables + thread_idx * block_num_per_seq;
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if (block_table_now[step_seq_lens_decoder[thread_idx] / block_size] != -1) {
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// can be recovered for decoding
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is_block_step[thread_idx] = false;
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seq_lens_this_time[thread_idx]= 1;
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stop_flags[thread_idx] = false;
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seq_lens_encoder[thread_idx] = 0;
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seq_lens_decoder[thread_idx] = step_seq_lens_decoder[thread_idx];
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}
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}
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}
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}
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void RecoverDecodeTask(const paddle::Tensor &stop_flags,
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const paddle::Tensor &seq_lens_this_time,
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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 &step_seq_lens_decoder,
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const paddle::Tensor &block_tables,
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const paddle::Tensor &is_block_step,
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const int block_size) {
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#ifdef PADDLE_WITH_CUSTOM_DEVICE
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auto dev_ctx = static_cast<const phi::CustomContext*>(paddle::experimental::DeviceContextPool::Instance().Get(seq_lens_this_time.place()));
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auto cu_stream = dev_ctx->stream();
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#else
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auto cu_stream = seq_lens_this_time.stream();
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#endif
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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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recover_decode_task<<<1, 1024, 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_encoder.data<int>()),
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const_cast<int *>(seq_lens_decoder.data<int>()),
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const_cast<int *>(step_seq_lens_decoder.data<int>()),
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const_cast<int *>(block_tables.data<int>()),
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const_cast<bool *>(is_block_step.data<bool>()),
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bsz,
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block_num_per_seq,
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block_size);
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}
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PD_BUILD_STATIC_OP(recover_decode_task)
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.Inputs({"stop_flags",
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"seq_lens_this_time",
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"seq_lens_encoder",
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"seq_lens_decoder",
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"step_seq_lens_decoder",
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"block_tables",
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"is_block_step"})
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.Attrs({"block_size: int"})
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.Outputs({"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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"stop_flags_out",
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"is_block_step_out"})
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.SetInplaceMap({{"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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{"stop_flags", "stop_flags_out"},
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{"is_block_step", "is_block_step_out"}})
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.SetKernelFn(PD_KERNEL(RecoverDecodeTask));
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176
custom_ops/gpu_ops/update_inputs_v1.cu
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176
custom_ops/gpu_ops/update_inputs_v1.cu
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@@ -0,0 +1,176 @@
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// Copyright (c) 2025 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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template <int THREADBLOCK_SIZE>
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__global__ void update_inputs_kernel_v1(bool *not_need_stop,
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int *seq_lens_this_time,
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int *seq_lens_encoder,
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int *seq_lens_decoder,
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int *step_seq_lens_decoder,
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int64_t *prompt_lens,
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int64_t *topk_ids,
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int64_t *input_ids,
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int *block_tables,
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const int64_t *stop_nums,
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bool *stop_flags,
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bool *is_block_step,
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const int64_t *next_tokens,
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const int bsz,
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const int max_bsz,
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const int input_ids_stride,
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const int block_num_per_seq,
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const int block_size) {
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int thread_idx = threadIdx.x;
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typedef cub::BlockReduce<int64_t, THREADBLOCK_SIZE> BlockReduce;
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__shared__ typename BlockReduce::TempStorage temp_storage;
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bool stop_flag_now = false;
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int64_t stop_flag_now_int = 0;
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if (thread_idx < max_bsz) {
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if (thread_idx < bsz) {
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stop_flag_now = stop_flags[thread_idx];
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stop_flag_now_int = static_cast<int64_t>(stop_flag_now);
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} else {
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stop_flag_now_int = 1;
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}
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}
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if (thread_idx < bsz) {
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if(stop_flag_now) {
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seq_lens_this_time[thread_idx] = 0; // stop at next step
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seq_lens_decoder[thread_idx] = 0;
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seq_lens_encoder[thread_idx] = 0;
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} else {
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if (seq_lens_this_time[thread_idx] + seq_lens_decoder[thread_idx] >= prompt_lens[thread_idx]) {
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// decoding
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seq_lens_decoder[thread_idx] += seq_lens_this_time[thread_idx];
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seq_lens_this_time[thread_idx] = 1;
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seq_lens_encoder[thread_idx] = 0;
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int64_t *input_ids_now = input_ids + thread_idx * input_ids_stride;
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input_ids_now[0] = next_tokens[thread_idx];
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// to judge whether block is not enough
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int *block_table_now = block_tables + thread_idx * block_num_per_seq;
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if (seq_lens_this_time[thread_idx] != 0 && block_table_now[seq_lens_decoder[thread_idx] / block_size] == -1) {
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// should be scheduled by server
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is_block_step[thread_idx] = true;
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seq_lens_this_time[thread_idx]= 0;
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stop_flags[thread_idx] = true;
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step_seq_lens_decoder[thread_idx] = seq_lens_decoder[thread_idx];
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seq_lens_decoder[thread_idx] = 0;
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stop_flag_now_int = 1;
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}
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} else
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{
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stop_flags[thread_idx] = true;
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seq_lens_this_time[thread_idx] = 0;
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seq_lens_decoder[thread_idx] = 0;
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seq_lens_encoder[thread_idx] = 0;
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topk_ids[thread_idx] = -1;
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stop_flag_now_int = 1;
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}
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}
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}
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__syncthreads();
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int64_t stop_sum = BlockReduce(temp_storage).Sum(stop_flag_now_int);
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if (thread_idx == 0) {
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not_need_stop[0] = stop_sum < stop_nums[0];
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}
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}
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void UpdateInputesV1(const paddle::Tensor &stop_flags,
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const paddle::Tensor ¬_need_stop, // only on cpu
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const paddle::Tensor &seq_lens_this_time,
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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 &step_seq_lens_decoder,
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const paddle::Tensor &prompt_lens,
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const paddle::Tensor &topk_ids,
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const paddle::Tensor &input_ids,
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const paddle::Tensor &block_tables,
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const paddle::Tensor &stop_nums,
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const paddle::Tensor &next_tokens,
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const paddle::Tensor &is_block_step,
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const int block_size) {
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#ifdef PADDLE_WITH_CUSTOM_DEVICE
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auto dev_ctx = static_cast<const phi::CustomContext*>(paddle::experimental::DeviceContextPool::Instance().Get(input_ids.place()));
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auto cu_stream = dev_ctx->stream();
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#else
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auto cu_stream = input_ids.stream();
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#endif
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const int max_bsz = stop_flags.shape()[0];
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const int now_bsz = seq_lens_this_time.shape()[0];
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const int input_ids_stride = input_ids.shape()[1];
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const int block_num_per_seq = block_tables.shape()[1];
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auto not_need_stop_gpu = not_need_stop.copy_to(stop_flags.place(), false);
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update_inputs_kernel_v1<1024><<<1, 1024, 0, cu_stream>>>(
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const_cast<bool *>(not_need_stop_gpu.data<bool>()),
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const_cast<int *>(seq_lens_this_time.data<int>()),
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const_cast<int *>(seq_lens_encoder.data<int>()),
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const_cast<int *>(seq_lens_decoder.data<int>()),
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const_cast<int *>(step_seq_lens_decoder.data<int>()),
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const_cast<int64_t *>(prompt_lens.data<int64_t>()),
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const_cast<int64_t *>(topk_ids.data<int64_t>()),
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const_cast<int64_t *>(input_ids.data<int64_t>()),
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const_cast<int *>(block_tables.data<int>()),
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stop_nums.data<int64_t>(),
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const_cast<bool *>(stop_flags.data<bool>()),
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const_cast<bool *>(is_block_step.data<bool>()),
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next_tokens.data<int64_t>(),
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now_bsz,
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max_bsz,
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input_ids_stride,
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block_num_per_seq,
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block_size);
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auto not_need_stop_cpu =
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not_need_stop_gpu.copy_to(not_need_stop.place(), false);
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bool *not_need_stop_data = const_cast<bool *>(not_need_stop.data<bool>());
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not_need_stop_data[0] = not_need_stop_cpu.data<bool>()[0];
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}
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PD_BUILD_STATIC_OP(update_inputs_v1)
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.Inputs({"stop_flags",
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"not_need_stop",
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"seq_lens_this_time",
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"seq_lens_encoder",
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"seq_lens_decoder",
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"step_seq_lens_decoder",
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"prompt_lens",
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"topk_ids",
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"input_ids",
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"block_tables",
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"stop_nums",
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"next_tokens",
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"is_block_step"})
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.Attrs({"block_size: int"})
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.Outputs({"not_need_stop_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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"step_seq_lens_decoder_out",
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"topk_ids_out",
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"input_ids_out",
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"stop_flags_out",
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"is_block_step_out"})
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.SetInplaceMap({{"not_need_stop", "not_need_stop_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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{"topk_ids", "topk_ids_out"},
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{"input_ids", "input_ids_out"},
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{"stop_flags", "stop_flags_out"},
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{"step_seq_lens_decoder", "step_seq_lens_decoder_out"},
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{"is_block_step", "is_block_step_out"}})
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.SetKernelFn(PD_KERNEL(UpdateInputesV1));
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