general_response_op.cpp

// Copyright (c) 2020 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 "core/general-server/op/general_response_op.h"
#include <algorithm>
#include <iostream>
#include <memory>
#include <sstream>
#include "core/general-server/op/general_infer_helper.h"
#include "core/predictor/framework/infer.h"
#include "core/predictor/framework/memory.h"
#include "core/predictor/framework/resource.h"
#include "core/util/include/timer.h"

namespace baidu {
namespace paddle_serving {
namespace serving {

using baidu::paddle_serving::Timer;
using baidu::paddle_serving::predictor::MempoolWrapper;
using baidu::paddle_serving::predictor::general_model::Tensor;
using baidu::paddle_serving::predictor::general_model::Response;
using baidu::paddle_serving::predictor::general_model::Request;
using baidu::paddle_serving::predictor::general_model::FetchInst;
using baidu::paddle_serving::predictor::general_model::ModelOutput;
using baidu::paddle_serving::predictor::InferManager;
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;

int GeneralResponseOp::inference() {
  const std::vector<std::string> pre_node_names = pre_names();
  VLOG(2) << "pre node names size: " << pre_node_names.size();

  const Request *req = dynamic_cast<const Request *>(get_request_message());
  // response inst with only fetch_var_names
  Response *res = mutable_data<Response>();

  Timer timeline;
  // double response_time = 0.0;
  // timeline.Start();
  int64_t start = timeline.TimeStampUS();

  VLOG(2) << "start to call load general model_conf op";
  baidu::paddle_serving::predictor::Resource &resource =
      baidu::paddle_serving::predictor::Resource::instance();

  VLOG(2) << "get resource pointer done.";
  std::shared_ptr<PaddleGeneralModelConfig> model_config =
      resource.get_general_model_config();

  std::vector<int> fetch_index;
  fetch_index.resize(req->fetch_var_names_size());
  for (int i = 0; i < req->fetch_var_names_size(); ++i) {
    fetch_index[i] =
        model_config->_fetch_alias_name_to_index[req->fetch_var_names(i)];
  }

  const GeneralBlob *input_blob;
  for (uint32_t pi = 0; pi < pre_node_names.size(); ++pi) {
    const std::string &pre_name = pre_node_names[pi];
    VLOG(2) << "pre names[" << pi << "]: " << pre_name << " ("
            << pre_node_names.size() << ")";
    input_blob = get_depend_argument<GeneralBlob>(pre_name);
    // fprintf(stderr, "input(%s) blob address %x\n", pre_names.c_str(),
    // input_blob);
    if (!input_blob) {
      LOG(ERROR) << "Failed mutable depended argument, op: " << pre_name;
      return -1;
    }

    const TensorVector *in = &input_blob->tensor_vector;
    int batch_size = input_blob->GetBatchSize();
    VLOG(2) << "input batch size: " << batch_size;

    ModelOutput *output = res->add_outputs();
    output->set_engine_name(
        pre_name);  // To get the order of model return values
    for (int i = 0; i < batch_size; ++i) {
      FetchInst *fetch_inst = output->add_insts();
      for (auto &idx : fetch_index) {
        Tensor *tensor = fetch_inst->add_tensor_array();
        // currently only response float tensor or lod_tensor
        tensor->set_elem_type(1);
        if (model_config->_is_lod_fetch[idx]) {
          VLOG(2) << "out[" << idx << " is lod_tensor";
          tensor->add_shape(-1);
        } else {
          VLOG(2) << "out[" << idx << "] is tensor";
          for (int k = 1; k < in->at(idx).shape.size(); ++k) {
            VLOG(2) << "shape[" << k - 1 << "]: " << in->at(idx).shape[k];
            tensor->add_shape(in->at(idx).shape[k]);
          }
        }
      }
    }

    int var_idx = 0;
    for (auto &idx : fetch_index) {
      int cap = 1;
      for (int j = 1; j < in->at(idx).shape.size(); ++j) {
        cap *= in->at(idx).shape[j];
      }
      if (in->at(idx).dtype == paddle::PaddleDType::INT64) {
        int64_t *data_ptr = static_cast<int64_t *>(in->at(idx).data.data());
        if (model_config->_is_lod_fetch[idx]) {
          for (int j = 0; j < batch_size; ++j) {
            for (int k = in->at(idx).lod[0][j]; k < in->at(idx).lod[0][j + 1];
                 k++) {
              FetchInst *fetch_p = output->mutable_insts(j);
              fetch_p->mutable_tensor_array(var_idx)->add_int64_data(
                  data_ptr[k]);
            }
          }
        } else {
          int var_size = in->at(idx).shape[0];
          if (var_size == batch_size) {
            for (int j = 0; j < batch_size; ++j) {
              for (int k = j * cap; k < (j + 1) * cap; ++k) {
                FetchInst *fetch_p = output->mutable_insts(j);
                fetch_p->mutable_tensor_array(var_idx)->add_int64_data(
                    data_ptr[k]);
              }
            }
          } else {
            for (int j = 0; j < batch_size; ++j) {
              FetchInst *fetch_p = output->mutable_insts(j);
              fetch_p->mutable_tensor_array(var_idx)->add_int64_data(
                  data_ptr[0]);
            }
          }
        }
        var_idx++;
      } else if (in->at(idx).dtype == paddle::PaddleDType::FLOAT32) {
        float *data_ptr = static_cast<float *>(in->at(idx).data.data());
        if (model_config->_is_lod_fetch[idx]) {
          for (int j = 0; j < batch_size; ++j) {
            for (int k = in->at(idx).lod[0][j]; k < in->at(idx).lod[0][j + 1];
                 k++) {
              FetchInst *fetch_p = output->mutable_insts(j);
              fetch_p->mutable_tensor_array(var_idx)->add_float_data(
                  data_ptr[k]);
            }
          }
        } else {
          int var_size = in->at(idx).shape[0];
          if (var_size == batch_size) {
            for (int j = 0; j < batch_size; ++j) {
              for (int k = j * cap; k < (j + 1) * cap; ++k) {
                FetchInst *fetch_p = output->mutable_insts(j);
                fetch_p->mutable_tensor_array(var_idx)->add_float_data(
                    data_ptr[k]);
              }
            }
          } else {
            for (int j = 0; j < batch_size; ++j) {
              FetchInst *fetch_p = output->mutable_insts(j);
              fetch_p->mutable_tensor_array(var_idx)->add_float_data(
                  data_ptr[0]);
            }
          }
        }
        var_idx++;
      }
    }
  }

  if (req->profile_server()) {
    int64_t end = timeline.TimeStampUS();
    // TODO(barriery): multi-model profile_time.
    // At present, only the response_op is multi-input, so here we get
    // the profile_time by hard coding. It needs to be replaced with
    // a more elegant way.
    for (uint32_t pi = 0; pi < pre_node_names.size(); ++pi) {
      input_blob = get_depend_argument<GeneralBlob>(pre_node_names[pi]);
      VLOG(2) << "p size for input blob: " << input_blob->p_size;
      ModelOutput *output = res->mutable_outputs(pi);
      int profile_time_idx = -1;
      if (pi == 0) {
        profile_time_idx = 0;
      } else {
        profile_time_idx = input_blob->p_size - 2;
      }
      for (; profile_time_idx < input_blob->p_size; ++profile_time_idx) {
        res->add_profile_time(input_blob->time_stamp[profile_time_idx]);
      }
    }
    // TODO(guru4elephant): find more elegant way to do this
    res->add_profile_time(start);
    res->add_profile_time(end);
  }

  return 0;
}

DEFINE_OP(GeneralResponseOp);

}  // namespace serving
}  // namespace paddle_serving
}  // namespace baidu