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114 lines
3.6 KiB
114 lines
3.6 KiB
// Copyright (c) 2018 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 "paddle/contrib/inference/paddle_inference_api_anakin_engine.h"
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#include <cuda.h>
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namespace paddle {
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PaddleInferenceAnakinPredictor::PaddleInferenceAnakinPredictor(
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const AnakinConfig &config) {
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CHECK(Init(config));
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}
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bool PaddleInferenceAnakinPredictor::Init(const AnakinConfig &config) {
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if (!(graph_.load(config.model_file))) {
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return false;
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}
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graph_.ResetBatchSize("input_0", config.max_batch_size);
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// optimization for graph
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if (!(graph_.Optimize())) {
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return false;
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}
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// construct executer
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executor_.init(graph_);
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return true;
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}
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bool PaddleInferenceAnakinPredictor::Run(
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const std::vector<PaddleTensor> &inputs,
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std::vector<PaddleTensor> *output_data) {
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for (const auto &input : inputs) {
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if (input.dtype != PaddleDType::FLOAT32) {
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LOG(ERROR) << "Only support float type inputs. " << input.name
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<< "'s type is not float";
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return false;
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}
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auto d_tensor_in_p = executor_.get_in(input.name);
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float *d_data_p = d_tensor_in_p->mutable_data();
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if (cudaMemcpy(d_data_p,
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static_cast<float *>(input.data.data),
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d_tensor_in_p->valid_size() * sizeof(float),
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cudaMemcpyHostToDevice) != 0) {
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LOG(ERROR) << "copy data from CPU to GPU error";
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return false;
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}
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}
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executor_.prediction();
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if (output_data->empty()) {
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LOG(ERROR) << "At least one output should be set with tensors' names.";
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return false;
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}
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for (auto &output : *output_data) {
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auto *tensor = executor_.get_out(output.name);
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output.shape = tensor->shape();
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// Copy data from GPU -> CPU
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if (cudaMemcpy(output.data.data,
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tensor->mutable_data(),
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tensor->valid_size() * sizeof(float),
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cudaMemcpyDeviceToHost) != 0) {
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LOG(ERROR) << "copy data from GPU to CPU error";
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return false;
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}
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}
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return true;
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}
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anakin::Net<anakin::NV, anakin::saber::AK_FLOAT, anakin::Precision::FP32>
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&PaddleInferenceAnakinPredictor::get_executer() {
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return executor_;
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}
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// the cloned new Predictor of anakin share the same net weights from original
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// Predictor
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std::unique_ptr<PaddlePredictor> PaddleInferenceAnakinPredictor::Clone() {
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VLOG(3) << "Anakin Predictor::clone";
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std::unique_ptr<PaddlePredictor> cls(new PaddleInferenceAnakinPredictor());
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// construct executer from other graph
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auto anakin_predictor_p =
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dynamic_cast<PaddleInferenceAnakinPredictor *>(cls.get());
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if (!anakin_predictor_p) {
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LOG(ERROR) << "fail to call Init";
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return nullptr;
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}
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anakin_predictor_p->get_executer().init(graph_);
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return std::move(cls);
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}
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// A factory to help create difference predictor.
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template <>
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std::unique_ptr<PaddlePredictor>
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CreatePaddlePredictor<AnakinConfig, PaddleEngineKind::kAnakin>(
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const AnakinConfig &config) {
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VLOG(3) << "Anakin Predictor create.";
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std::unique_ptr<PaddlePredictor> x(
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new PaddleInferenceAnakinPredictor(config));
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return x;
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};
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} // namespace paddle
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