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https://github.com/PaddlePaddle/FastDeploy.git
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d33105baeb
* online chat support logprobs * check xpu * check vl_gpu_model_runner and xpu_model_runner * get_worker() check platform
114 lines
3.7 KiB
C++
114 lines
3.7 KiB
C++
// 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 <stdio.h>
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#include <string.h>
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#include <sys/ipc.h>
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#include <sys/msg.h>
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#include <sys/types.h>
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#include "paddle/extension.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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#define MAX_BSZ 512
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#define K 20
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struct msgdata {
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long mtype;
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int mtext[MAX_BSZ * (K + 1) + 2]; // stop_flag, bsz, tokens
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float mtext_f[MAX_BSZ * (K + 1)]; // score
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int mtext_ranks[MAX_BSZ]; // ranks
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};
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void GetOutputTopK(const paddle::Tensor& x,
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const paddle::Tensor& scores,
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const paddle::Tensor& ranks,
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int k,
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int64_t rank_id,
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bool wait_flag) {
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if (rank_id > 0) {
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return;
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}
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static struct msgdata msg_rcv;
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int msg_queue_id = 1;
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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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#ifdef GET_OUTPUT_DEBUG
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std::cout << "Your INFERENCE_MSG_QUEUE_ID is: "
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<< inference_msg_queue_id_from_env << std::endl;
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#endif
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msg_queue_id = inference_msg_queue_id_from_env;
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}
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static key_t key = ftok("/dev/shm", msg_queue_id);
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static int msgid = msgget(key, IPC_CREAT | 0666);
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#ifdef GET_OUTPUT_DEBUG
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std::cout << "get_output_key: " << key << std::endl;
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std::cout << "get_output msgid: " << msgid << std::endl;
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#endif
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int64_t* out_data = const_cast<int64_t*>(x.data<int64_t>());
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float* scores_data = const_cast<float*>(scores.data<float>());
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int64_t* ranks_data = const_cast<int64_t*>(ranks.data<int64_t>());
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int ret = -1;
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if (!wait_flag) {
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ret = msgrcv(msgid,
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&msg_rcv,
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(MAX_BSZ * (K + 1) + 2) * 4 + MAX_BSZ * (K + 1) * 4 + MAX_BSZ * 4,
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0,
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IPC_NOWAIT);
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} else {
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ret = msgrcv(msgid,
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&msg_rcv,
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(MAX_BSZ * (K + 1) + 2) * 4 + MAX_BSZ * (K + 1) * 4 + MAX_BSZ * 4,
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0,
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0);
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}
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if (ret == -1) {
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// read none
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out_data[0] = -2;
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out_data[1] = 0;
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return;
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}
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int bsz = msg_rcv.mtext[1];
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out_data[0] = (int64_t)msg_rcv.mtext[0];
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out_data[1] = (int64_t)msg_rcv.mtext[1];
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for (int i = 0; i < bsz; i++) {
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for (int j = 0; j < k + 1; j++) {
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const int64_t offset = i * (K + 1) + j;
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out_data[offset + 2] = (int64_t)msg_rcv.mtext[offset + 2];
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scores_data[offset] = msg_rcv.mtext_f[offset];
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}
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ranks_data[i] = (int64_t)msg_rcv.mtext_ranks[i];
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}
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return;
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}
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PD_BUILD_STATIC_OP(get_output_topk)
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.Inputs({"x", "scores", "ranks"})
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.Attrs({"k: int", "rank_id: int64_t", "wait_flag: bool"})
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.Outputs({"x_out", "scores_out", "ranks_out"})
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.SetInplaceMap({{"x", "x_out"}, {"scores", "scores_out"}, {"ranks", "ranks_out"}})
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.SetKernelFn(PD_KERNEL(GetOutputTopK));
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