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[Cherry-Pick][Speculative Decoding] Add MTP logprob support for PD disaggregation (#7442) (#7464)

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* support mtp logprob in pd

* fix

* fix

* fix

* fix xpu bugs
This commit is contained in:
GoldPancake
2026-04-17 21:37:42 +08:00
committed by GitHub
parent 185708b566
commit 650d1e49aa
7 changed files with 389 additions and 78 deletions
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custom_ops/gpu_ops/speculate_decoding/draft_model/mtp_save_first_token_with_topk.cc
+218
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@@ -0,0 +1,218 @@
// Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <stdio.h>
#include <string.h>
#include <sys/ipc.h>
#include <sys/msg.h>
#include <sys/types.h>
#include "paddle/extension.h"
#include "../../custom_ftok.h"
#include "../speculate_logprob_msg.h"
#ifndef PD_BUILD_STATIC_OP
#define PD_BUILD_STATIC_OP(name) PD_BUILD_OP(static_op_##name)
#endif
void MTPSaveFirstTokenWithTopK(const paddle::Tensor& sampled_token_ids,
const paddle::Tensor& logprob_token_ids,
const paddle::Tensor& logprob_scores,
const paddle::Tensor& logprob_ranks,
const paddle::Tensor& token_num_per_batch,
const paddle::Tensor& cu_batch_token_offset,
const paddle::Tensor& not_need_stop,
const paddle::Tensor& seq_lens_decoder,
const paddle::Tensor& prompt_lens,
const paddle::Tensor& preempted_idx,
int message_flag, // Target: 3, Draft: 4
int64_t rank_id,
bool save_each_rank) {
if (!save_each_rank && rank_id > 0) {
return;
}
int max_draft_tokens = sampled_token_ids.shape()[1];
int bsz = token_num_per_batch.shape()[0];
auto sampled_token_ids_cpu =
sampled_token_ids.copy_to(paddle::CPUPlace(), false);
auto logprob_token_ids_cpu =
logprob_token_ids.copy_to(paddle::CPUPlace(), false);
auto logprob_scores_cpu = logprob_scores.copy_to(paddle::CPUPlace(), false);
auto logprob_ranks_cpu = logprob_ranks.copy_to(paddle::CPUPlace(), false);
auto token_num_per_batch_cpu =
token_num_per_batch.copy_to(paddle::CPUPlace(), false);
auto cu_batch_token_offset_cpu =
cu_batch_token_offset.copy_to(paddle::CPUPlace(), false);
auto seq_lens_decoder_cpu =
seq_lens_decoder.copy_to(paddle::CPUPlace(), true);
auto prompt_lens_cpu = prompt_lens.copy_to(paddle::CPUPlace(), true);
int64_t* sampled_token_ids_data = sampled_token_ids_cpu.data<int64_t>();
int64_t* logprob_token_ids_data = logprob_token_ids_cpu.data<int64_t>();
float* logprob_scores_data = logprob_scores_cpu.data<float>();
int64_t* logprob_ranks_data = logprob_ranks_cpu.data<int64_t>();
int* token_num_per_batch_data = token_num_per_batch_cpu.data<int>();
int* cu_batch_token_offset_data = cu_batch_token_offset_cpu.data<int>();
int* seq_lens_decoder_data = seq_lens_decoder_cpu.data<int>();
int64_t* prompt_lens_data = prompt_lens_cpu.data<int64_t>();
const int32_t* preempted_idx_data = preempted_idx.data<int32_t>();
static struct msgdata msg_sed;
int msg_queue_id = 1;
if (const char* inference_msg_queue_id_env_p =
std::getenv("INFERENCE_MSG_QUEUE_ID")) {
std::string inference_msg_queue_id_env_str(inference_msg_queue_id_env_p);
int inference_msg_queue_id_from_env =
std::stoi(inference_msg_queue_id_env_str);
msg_queue_id = inference_msg_queue_id_from_env;
#ifdef SPECULATE_SAVE_WITH_OUTPUT_DEBUG
std::cout << "Your INFERENCE_MSG_QUEUE_ID is: "
<< inference_msg_queue_id_from_env << std::endl;
#endif
} else {
#ifdef SPECULATE_SAVE_WITH_OUTPUT_DEBUG
std::cout << "Failed to got INFERENCE_MSG_QUEUE_ID at env, use default."
<< std::endl;
#endif
}
int inference_msg_id_from_env = 1;
if (const char* inference_msg_id_env_p = std::getenv("INFERENCE_MSG_ID")) {
std::string inference_msg_id_env_str(inference_msg_id_env_p);
inference_msg_id_from_env = std::stoi(inference_msg_id_env_str);
if (inference_msg_id_from_env == 2) {
// 2 and -2 is perserve for no-output indication.
throw std::runtime_error(
" INFERENCE_MSG_ID cannot be 2, please use other number.");
}
if (inference_msg_id_from_env < 0) {
throw std::runtime_error(
" INFERENCE_MSG_ID cannot be negative, please use other "
"number.");
}
#ifdef SPECULATE_SAVE_WITH_OUTPUT_DEBUG
std::cout << "Your INFERENCE_MSG_ID is: " << inference_msg_id_from_env
<< std::endl;
#endif
} else {
#ifdef SPECULATE_SAVE_WITH_OUTPUT_DEBUG
std::cout << "Failed to got INFERENCE_MSG_ID at env, use (int)1 as default."
<< std::endl;
#endif
}
static key_t key = custom_ftok("/dev/shm", msg_queue_id);
static int msgid = msgget(key, IPC_CREAT | 0666);
#ifdef SPECULATE_SAVE_WITH_OUTPUT_DEBUG
std::cout << "save_output_key: " << key << std::endl;
std::cout << "save msgid: " << msgid << std::endl;
#endif
msg_sed.mtype = 1;
msg_sed.meta[0] = not_need_stop.data<bool>()[0] ? inference_msg_id_from_env
: -inference_msg_id_from_env;
msg_sed.meta[1] = message_flag;
msg_sed.meta[2] = bsz;
int max_num_logprobs = logprob_token_ids.shape()[1];
for (int i = 0; i < bsz; i++) {
int cur_token_num;
if (seq_lens_decoder_data[i] < prompt_lens_data[i] ||
token_num_per_batch_data[i] == 0) {
// chunk prefill or stop slots
cur_token_num = 0;
} else {
cur_token_num = token_num_per_batch_data[i] + 1;
}
msg_sed.meta[3 + i] = cur_token_num;
if (preempted_idx_data[i] == 1) {
msg_sed.meta[3 + i] = -9;
}
auto* cur_batch_msg_sed = &msg_sed.mtext[i];
int token_offset = cu_batch_token_offset_data[i];
for (int j = 0; j < cur_token_num; j++) {
auto* cur_tokens = &cur_batch_msg_sed->tokens[j * (SPEC_LOGPROB_K + 1)];
auto* cur_scores = &cur_batch_msg_sed->scores[j * (SPEC_LOGPROB_K + 1)];
if (j == 0) {
// first token has full logprobs
for (int k = 0; k < SPEC_LOGPROB_K + 1; k++) {
if (k == 0) {
cur_tokens[k] =
(int)sampled_token_ids_data[i * max_draft_tokens + j];
cur_scores[k] =
logprob_scores_data[(token_offset + j) * (SPEC_LOGPROB_K + 1) +
k];
} else if (k < max_num_logprobs) {
// only for first token
cur_tokens[k] =
(int)logprob_token_ids_data[(token_offset + j) *
(SPEC_LOGPROB_K + 1) +
k];
cur_scores[k] =
logprob_scores_data[(token_offset + j) * (SPEC_LOGPROB_K + 1) +
k];
} else {
cur_tokens[k] = -1;
cur_scores[k] = 0.0;
}
}
cur_batch_msg_sed->ranks[j] = (int)logprob_ranks_data[token_offset + j];
} else {
// draft token only has token_id
cur_tokens[0] = (int)sampled_token_ids_data[i * max_draft_tokens + j];
}
}
}
#ifdef SPECULATE_SAVE_WITH_OUTPUT_DEBUG
std::cout << "msg data: " << std::endl;
std::cout << "stop_flag: " << msg_sed.meta[0]
<< ", message_flag: " << msg_sed.meta[1]
<< ", bsz: " << msg_sed.meta[2] << std::endl;
for (int i = 0; i < bsz; i++) {
int cur_token_num = msg_sed.meta[3 + i];
auto* cur_batch_msg_sed = &msg_sed.mtext[i];
std::cout << "batch " << i << " token_num: " << cur_token_num << std::endl;
for (int j = 0; j < cur_token_num; j++) {
auto* cur_tokens = &cur_batch_msg_sed->tokens[j * (SPEC_LOGPROB_K + 1)];
auto* cur_scores = &cur_batch_msg_sed->scores[j * (SPEC_LOGPROB_K + 1)];
std::cout << "tokens: ";
for (int k = 0; k < SPEC_LOGPROB_K + 1; k++) {
std::cout << cur_tokens[k] << " ";
}
std::cout << std::endl;
std::cout << "scores: ";
for (int k = 0; k < SPEC_LOGPROB_K + 1; k++) {
std::cout << cur_scores[k] << " ";
}
std::cout << std::endl;
std::cout << "ranks: " << cur_batch_msg_sed->ranks[j] << std::endl;
}
}
std::cout << std::endl;
#endif
if (msgsnd(msgid, &msg_sed, sizeof(msg_sed) - sizeof(long), 0) == -1) {
printf("full msg buffer\n");
}
}
PD_BUILD_STATIC_OP(mtp_save_first_token_with_topk)
.Inputs({"sampled_token_ids",
"logprob_token_ids",
"logprob_scores",
"logprob_ranks",
"token_num_per_batch",
"cu_batch_token_offset",
"not_need_stop",
"seq_lens_decoder",
"prompt_lens",
"preempted_idx"})
.Attrs({"message_flag: int", "rank_id: int64_t", "save_each_rank: bool"})
.SetKernelFn(PD_KERNEL(MTPSaveFirstTokenWithTopK));
custom_ops/gpu_ops/speculate_decoding/speculate_get_output_with_topk.cc
+16 -29
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@@ -19,27 +19,12 @@
#include <sys/types.h>
#include "paddle/extension.h"
#include "../custom_ftok.h"
#include "speculate_logprob_msg.h"
#ifndef PD_BUILD_STATIC_OP
#define PD_BUILD_STATIC_OP(name) PD_BUILD_OP(static_op_##name)
#endif
#define MAX_BSZ 512
#define K 20
#define MAX_DRAFT_TOKEN_NUM 6
struct batch_msgdata {
int tokens[MAX_DRAFT_TOKEN_NUM * (K + 1)];
float scores[MAX_DRAFT_TOKEN_NUM * (K + 1)];
int ranks[MAX_DRAFT_TOKEN_NUM];
};
struct msgdata {
long mtype;
int meta[3 + MAX_BSZ]; // stop_flag, message_flag, bsz, batch_token_nums
batch_msgdata mtext[MAX_BSZ];
};
void SpeculateGetOutMmsgTopK(const paddle::Tensor& output_tokens,
const paddle::Tensor& output_scores,
const paddle::Tensor& output_ranks,
@@ -93,22 +78,22 @@ void SpeculateGetOutMmsgTopK(const paddle::Tensor& output_tokens,
output_tokens_data[1] = (int64_t)msg_rcv.meta[1];
output_tokens_data[2] = (int64_t)msg_rcv.meta[2];
int output_tokens_offset = 3 + MAX_BSZ;
int output_tokens_offset = 3 + SPEC_LOGPROB_MAX_BSZ;
for (int i = 0; i < bsz; i++) {
int cur_token_num = msg_rcv.meta[3 + i];
output_tokens_data[3 + i] = (int64_t)cur_token_num; // batch_token_nums
auto* cur_output_token = output_tokens_data + output_tokens_offset +
i * (MAX_DRAFT_TOKEN_NUM * (K + 1));
i * (MAX_DRAFT_TOKEN_NUM * (SPEC_LOGPROB_K + 1));
auto* cur_output_score =
output_scores_data + i * (MAX_DRAFT_TOKEN_NUM * (K + 1));
output_scores_data + i * (MAX_DRAFT_TOKEN_NUM * (SPEC_LOGPROB_K + 1));
auto* cur_batch_msg_rcv = &msg_rcv.mtext[i];
for (int j = 0; j < cur_token_num; j++) {
for (int k = 0; k < real_k + 1; k++) {
cur_output_token[j * (K + 1) + k] =
(int64_t)cur_batch_msg_rcv->tokens[j * (K + 1) + k];
cur_output_score[j * (K + 1) + k] =
cur_batch_msg_rcv->scores[j * (K + 1) + k];
cur_output_token[j * (SPEC_LOGPROB_K + 1) + k] =
(int64_t)cur_batch_msg_rcv->tokens[j * (SPEC_LOGPROB_K + 1) + k];
cur_output_score[j * (SPEC_LOGPROB_K + 1) + k] =
cur_batch_msg_rcv->scores[j * (SPEC_LOGPROB_K + 1) + k];
}
output_ranks_data[i * MAX_DRAFT_TOKEN_NUM + j] =
(int64_t)cur_batch_msg_rcv->ranks[j];
@@ -124,17 +109,19 @@ void SpeculateGetOutMmsgTopK(const paddle::Tensor& output_tokens,
std::cout << "batch " << i << " token_num: " << cur_token_num << std::endl;
for (int j = 0; j < cur_token_num; j++) {
std::cout << "tokens: ";
for (int k = 0; k < K + 1; k++) {
for (int k = 0; k < SPEC_LOGPROB_K + 1; k++) {
std::cout << output_tokens_data[output_tokens_offset +
i * MAX_DRAFT_TOKEN_NUM * (K + 1) +
j * (K + 1) + k]
i * MAX_DRAFT_TOKEN_NUM *
(SPEC_LOGPROB_K + 1) +
j * (SPEC_LOGPROB_K + 1) + k]
<< " ";
}
std::cout << std::endl;
std::cout << "scores: ";
for (int k = 0; k < K + 1; k++) {
std::cout << output_scores_data[i * MAX_DRAFT_TOKEN_NUM * (K + 1) +
j * (K + 1) + k]
for (int k = 0; k < SPEC_LOGPROB_K + 1; k++) {
std::cout << output_scores_data[i * MAX_DRAFT_TOKEN_NUM *
(SPEC_LOGPROB_K + 1) +
j * (SPEC_LOGPROB_K + 1) + k]
<< " ";
}
std::cout << std::endl;
custom_ops/gpu_ops/speculate_decoding/speculate_logprob_msg.h
+39
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@@ -0,0 +1,39 @@
// Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <stdio.h>
#include <string.h>
#include <sys/ipc.h>
#include <sys/msg.h>
#include <sys/types.h>
#include "paddle/extension.h"
#define SPEC_LOGPROB_MAX_BSZ 512
#define SPEC_LOGPROB_K 20
#define MAX_DRAFT_TOKEN_NUM 6
struct batch_msgdata {
int tokens[MAX_DRAFT_TOKEN_NUM * (SPEC_LOGPROB_K + 1)];
float scores[MAX_DRAFT_TOKEN_NUM * (SPEC_LOGPROB_K + 1)];
int ranks[MAX_DRAFT_TOKEN_NUM];
};
struct msgdata {
long mtype;
// stop_flag, message_flag, bsz, batch_token_nums
int meta[3 + SPEC_LOGPROB_MAX_BSZ];
batch_msgdata mtext[SPEC_LOGPROB_MAX_BSZ];
};
custom_ops/gpu_ops/speculate_decoding/speculate_save_output_with_topk.cc
+17 -27
View File
@@ -19,27 +19,12 @@
#include <sys/types.h>
#include "paddle/extension.h"
#include "../custom_ftok.h"
#include "speculate_logprob_msg.h"
#ifndef PD_BUILD_STATIC_OP
#define PD_BUILD_STATIC_OP(name) PD_BUILD_OP(static_op_##name)
#endif
#define MAX_BSZ 512
#define K 20
#define MAX_DRAFT_TOKEN_NUM 6
struct batch_msgdata {
int tokens[MAX_DRAFT_TOKEN_NUM * (K + 1)];
float scores[MAX_DRAFT_TOKEN_NUM * (K + 1)];
int ranks[MAX_DRAFT_TOKEN_NUM];
};
struct msgdata {
long mtype;
int meta[3 + MAX_BSZ]; // stop_flag, message_flag, bsz, batch_token_nums
batch_msgdata mtext[MAX_BSZ];
};
void SpeculateSaveOutMmsgTopK(const paddle::Tensor& sampled_token_ids,
const paddle::Tensor& logprob_token_ids,
const paddle::Tensor& logprob_scores,
@@ -154,16 +139,21 @@ void SpeculateSaveOutMmsgTopK(const paddle::Tensor& sampled_token_ids,
auto* cur_batch_msg_sed = &msg_sed.mtext[i];
int token_offset = cu_batch_token_offset_data[i];
for (int j = 0; j < cur_token_num; j++) {
auto* cur_tokens = &cur_batch_msg_sed->tokens[j * (K + 1)];
auto* cur_scores = &cur_batch_msg_sed->scores[j * (K + 1)];
for (int k = 0; k < K + 1; k++) {
auto* cur_tokens = &cur_batch_msg_sed->tokens[j * (SPEC_LOGPROB_K + 1)];
auto* cur_scores = &cur_batch_msg_sed->scores[j * (SPEC_LOGPROB_K + 1)];
for (int k = 0; k < SPEC_LOGPROB_K + 1; k++) {
if (k == 0) {
cur_tokens[k] = (int)sampled_token_ids_data[i * max_draft_tokens + j];
cur_scores[k] = logprob_scores_data[(token_offset + j) * (K + 1) + k];
cur_scores[k] =
logprob_scores_data[(token_offset + j) * (SPEC_LOGPROB_K + 1) +
k];
} else if (k < max_num_logprobs) {
cur_tokens[k] =
(int)logprob_token_ids_data[(token_offset + j) * (K + 1) + k];
cur_scores[k] = logprob_scores_data[(token_offset + j) * (K + 1) + k];
cur_tokens[k] = (int)
logprob_token_ids_data[(token_offset + j) * (SPEC_LOGPROB_K + 1) +
k];
cur_scores[k] =
logprob_scores_data[(token_offset + j) * (SPEC_LOGPROB_K + 1) +
k];
} else {
cur_tokens[k] = -1;
cur_scores[k] = 0.0;
@@ -182,15 +172,15 @@ void SpeculateSaveOutMmsgTopK(const paddle::Tensor& sampled_token_ids,
auto* cur_batch_msg_sed = &msg_sed.mtext[i];
std::cout << "batch " << i << " token_num: " << cur_token_num << std::endl;
for (int j = 0; j < cur_token_num; j++) {
auto* cur_tokens = &cur_batch_msg_sed->tokens[j * (K + 1)];
auto* cur_scores = &cur_batch_msg_sed->scores[j * (K + 1)];
auto* cur_tokens = &cur_batch_msg_sed->tokens[j * (SPEC_LOGPROB_K + 1)];
auto* cur_scores = &cur_batch_msg_sed->scores[j * (SPEC_LOGPROB_K + 1)];
std::cout << "tokens: ";
for (int k = 0; k < K + 1; k++) {
for (int k = 0; k < SPEC_LOGPROB_K + 1; k++) {
std::cout << cur_tokens[k] << " ";
}
std::cout << std::endl;
std::cout << "scores: ";
for (int k = 0; k < K + 1; k++) {
for (int k = 0; k < SPEC_LOGPROB_K + 1; k++) {
std::cout << cur_scores[k] << " ";
}
std::cout << std::endl;
fastdeploy/model_executor/pre_and_post_process.py
+73 -2
View File
@@ -22,9 +22,14 @@ import paddle
from fastdeploy import envs
from fastdeploy.config import SpeculativeConfig
from fastdeploy.model_executor.ops.gpu import (
mtp_save_first_token,
mtp_save_first_token_with_topk,
)
from fastdeploy.platforms import current_platform
from fastdeploy.worker.input_batch import (
InputBatch,
ProposerInputBatch,
recover_batch_index_for_output,
recover_batch_index_for_sampler_output,
)
@@ -525,10 +530,76 @@ def save_output_specualate(
sampler_output: SamplerOutput,
model_output: ModelOutputData,
share_inputs: InputBatch,
proposer_share_inputs: ProposerInputBatch,
local_rank: int,
tensor_parallel_rank: int,
save_each_rank: bool = False,
skip_save_output: bool = False,
is_mtp_prefill: bool = False,
):
if not skip_save_output:
if is_mtp_prefill:
if tensor_parallel_rank == 0:
skip_chunk_prefill = bool(int(envs.ENABLE_V1_KVCACHE_SCHEDULER))
if sampler_output.logprobs_tensors is None:
recover_proposer_share_inputs_map = recover_batch_index_for_output(
proposer_share_inputs,
proposer_share_inputs.index_to_batch_id,
proposer_share_inputs.enable_pd_reorder,
[
"base_model_draft_tokens",
"seq_lens_decoder",
"prompt_lens",
"step_idx",
],
)
mtp_save_first_token(
recover_proposer_share_inputs_map["base_model_draft_tokens"],
proposer_share_inputs["not_need_stop"],
recover_proposer_share_inputs_map["seq_lens_decoder"],
recover_proposer_share_inputs_map["prompt_lens"],
recover_proposer_share_inputs_map["step_idx"],
local_rank,
save_each_rank,
skip_chunk_prefill,
)
else:
recover_share_inputs_map = recover_batch_index_for_output(
share_inputs,
model_output.index_to_batch_id,
model_output.enable_pd_reorder,
[
"sampled_token_ids",
"accept_tokens_cpu",
"accept_num_cpu",
"seq_lens_decoder_cpu",
"prompt_lens_cpu",
"last_preempted_idx",
],
)
recover_batch_index_for_sampler_output(
sampler_output, model_output.index_to_batch_id, model_output.enable_pd_reorder
)
recover_proposer_share_inputs_map = recover_batch_index_for_output(
proposer_share_inputs,
proposer_share_inputs.index_to_batch_id,
proposer_share_inputs.enable_pd_reorder,
["base_model_draft_tokens"],
)
mtp_save_first_token_with_topk(
recover_proposer_share_inputs_map["base_model_draft_tokens"],
sampler_output.logprobs_tensors.logprob_token_ids,
sampler_output.logprobs_tensors.logprobs,
sampler_output.logprobs_tensors.selected_token_ranks,
recover_share_inputs_map["accept_num_cpu"],
sampler_output.cu_batch_token_offset,
model_output.not_need_stop,
recover_share_inputs_map["seq_lens_decoder_cpu"],
recover_share_inputs_map["prompt_lens_cpu"],
recover_share_inputs_map["last_preempted_idx"],
3, # mtype
model_output.mp_rank,
save_each_rank,
)
else:
if sampler_output.logprobs_tensors is None:
recover_share_inputs = recover_batch_index_for_output(
share_inputs,
fastdeploy/spec_decode/mtp.py
+20 -18
View File
@@ -62,7 +62,6 @@ else:
eagle_get_self_hidden_states,
eagle_gather_hidden_states,
hybrid_mtp_ngram,
mtp_save_first_token,
mtp_step_paddle,
share_external_data,
speculate_get_logits,
@@ -835,23 +834,26 @@ class MTPProposer(Proposer):
)
if self.role == "prefill" and self.parallel_config.tensor_parallel_rank == 0:
skip_save = bool(int(envs.ENABLE_V1_KVCACHE_SCHEDULER))
recover_model_output_map = recover_batch_index_for_output(
self.model_inputs,
self.model_inputs.index_to_batch_id,
self.model_inputs.enable_pd_reorder,
["base_model_draft_tokens", "seq_lens_decoder", "prompt_lens", "step_idx"],
)
mtp_save_first_token(
recover_model_output_map["base_model_draft_tokens"],
self.model_inputs["not_need_stop"],
recover_model_output_map["seq_lens_decoder"],
recover_model_output_map["prompt_lens"],
recover_model_output_map["step_idx"],
self.local_rank,
self.parallel_config.use_ep,
skip_save,
)
if current_platform.is_xpu():
# Note(wangyanpeng): mtp_save_first_token for GPU platforms has been moved to model_runner.
# Only XPU platform is retained here.
skip_save = bool(int(envs.ENABLE_V1_KVCACHE_SCHEDULER))
recover_model_output_map = recover_batch_index_for_output(
self.model_inputs,
self.model_inputs.index_to_batch_id,
self.model_inputs.enable_pd_reorder,
["base_model_draft_tokens", "seq_lens_decoder", "prompt_lens", "step_idx"],
)
mtp_save_first_token(
recover_model_output_map["base_model_draft_tokens"],
self.model_inputs["not_need_stop"],
recover_model_output_map["seq_lens_decoder"],
recover_model_output_map["prompt_lens"],
recover_model_output_map["step_idx"],
self.local_rank,
self.parallel_config.use_ep,
skip_save,
)
# Ensure only save first token once.
paddle.assign(
paddle.where(
fastdeploy/worker/gpu_model_runner.py
+6 -2
View File
@@ -2478,13 +2478,17 @@ class GPUModelRunner(ModelRunnerBase):
sampler_output,
):
if self.speculative_decoding:
skip_save_output = self.spec_method == SpecMethod.MTP and self.scheduler_config.splitwise_role == "prefill"
save_output_specualate(
sampler_output=sampler_output,
model_output=model_output_data,
share_inputs=self.share_inputs,
proposer_share_inputs=self.proposer.model_inputs,
local_rank=self.local_rank,
tensor_parallel_rank=self.parallel_config.tensor_parallel_rank,
save_each_rank=self.parallel_config.use_ep,
skip_save_output=skip_save_output,
is_mtp_prefill=(
self.spec_method == SpecMethod.MTP and self.scheduler_config.splitwise_role == "prefill"
),
)
else:
save_output_normal(