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FastDeploy/custom_ops/gpu_ops/get_output_msg_with_topk.cc
2025-06-09 19:20:15 +08:00

110 lines
3.4 KiB
C++

// Copyright (c) 2024 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"
#ifndef PD_BUILD_STATIC_OP
#define PD_BUILD_STATIC_OP(name) PD_BUILD_OP(static_op_##name)
#endif
#define MAX_BSZ 512
#define K 10
struct msgdata {
long mtype;
int mtext[MAX_BSZ * (K + 1) + 2]; // stop_flag, bsz, tokens
float mtext_f[MAX_BSZ * (K + 1)]; // score
};
void GetOutputTopK(const paddle::Tensor& x,
const paddle::Tensor& scores,
int k,
int64_t rank_id,
bool wait_flag) {
if (rank_id > 0) {
return;
}
static struct msgdata msg_rcv;
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);
#ifdef GET_OUTPUT_DEBUG
std::cout << "Your INFERENCE_MSG_QUEUE_ID is: "
<< inference_msg_queue_id_from_env << std::endl;
#endif
msg_queue_id = inference_msg_queue_id_from_env;
}
static key_t key = ftok("/dev/shm", msg_queue_id);
static int msgid = msgget(key, IPC_CREAT | 0666);
#ifdef GET_OUTPUT_DEBUG
std::cout << "get_output_key: " << key << std::endl;
std::cout << "get_output msgid: " << msgid << std::endl;
#endif
int64_t* out_data = const_cast<int64_t*>(x.data<int64_t>());
float* scores_data = const_cast<float*>(scores.data<float>());
int ret = -1;
if (!wait_flag) {
ret = msgrcv(msgid,
&msg_rcv,
(MAX_BSZ * (K + 1) + 2) * 4 + MAX_BSZ * (K + 1) * 4,
0,
IPC_NOWAIT);
} else {
ret = msgrcv(msgid,
&msg_rcv,
(MAX_BSZ * (K + 1) + 2) * 4 + MAX_BSZ * (K + 1) * 4,
0,
0);
}
if (ret == -1) {
// read none
out_data[0] = -2;
out_data[1] = 0;
return;
}
int bsz = msg_rcv.mtext[1];
out_data[0] = (int64_t)msg_rcv.mtext[0];
out_data[1] = (int64_t)msg_rcv.mtext[1];
for (int i = 0; i < bsz; i++) {
for (int j = 0; j < k + 1; j++) {
const int64_t offset = i * (K + 1) + j;
out_data[offset + 2] = (int64_t)msg_rcv.mtext[offset + 2];
scores_data[offset] = msg_rcv.mtext_f[offset];
}
}
return;
}
PD_BUILD_STATIC_OP(get_output_topk)
.Inputs({"x", "scores"})
.Attrs({"k: int", "rank_id: int64_t", "wait_flag: bool"})
.Outputs({"x_out", "scores_out"})
.SetInplaceMap({{"x", "x_out"}, {"scores", "scores_out"}})
.SetKernelFn(PD_KERNEL(GetOutputTopK));