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https://github.com/PaddlePaddle/FastDeploy.git
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0c292c0766
* Seperate option from backends * Seperate option from backends * Seperate option from backends * Seperate option from backends
86 lines
2.9 KiB
C++
Executable File
86 lines
2.9 KiB
C++
Executable File
// Copyright (c) 2022 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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#pragma once
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#include <iostream>
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#include <memory>
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#include <string>
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#include <vector>
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#include "fastdeploy/backends/backend.h"
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#include "fastdeploy/backends/poros/option.h"
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#include "fastdeploy/backends/poros/common/compile.h"
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#include "fastdeploy/backends/poros/common/poros_module.h"
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namespace fastdeploy {
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// Convert data type from fastdeploy to poros
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at::ScalarType GetPorosDtype(const FDDataType& fd_dtype);
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// Convert data type from poros to fastdeploy
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FDDataType GetFdDtype(const at::ScalarType& dtype);
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// at::ScalarType to std::string for FDERROR
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std::string AtType2String(const at::ScalarType& dtype);
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// Create at::Tensor
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// is_backend_cuda specify if Poros use GPU Device
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// While is_backend_cuda = true, and tensor.device = Device::GPU
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at::Tensor CreatePorosValue(FDTensor& tensor, bool is_backend_cuda = false);
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// Copy memory data from at::Tensor to fastdeploy::FDTensor
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void CopyTensorToCpu(const at::Tensor& tensor, FDTensor* fd_tensor,
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bool is_backend_cuda = false);
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class PorosBackend : public BaseBackend {
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public:
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PorosBackend() {}
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virtual ~PorosBackend() = default;
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void BuildOption(const PorosBackendOption& option);
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bool
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InitFromTorchScript(const std::string& model_file,
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const PorosBackendOption& option = PorosBackendOption());
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bool InitFromPoros(const std::string& model_file,
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const PorosBackendOption& option = PorosBackendOption());
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bool Compile(const std::string& model_file,
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std::vector<std::vector<FDTensor>>& prewarm_tensors,
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const PorosBackendOption& option = PorosBackendOption());
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bool Infer(std::vector<FDTensor>& inputs, std::vector<FDTensor>* outputs,
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bool copy_to_fd = true) override;
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int NumInputs() const { return _numinputs; }
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int NumOutputs() const { return _numoutputs; }
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TensorInfo GetInputInfo(int index) override;
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TensorInfo GetOutputInfo(int index) override;
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std::vector<TensorInfo> GetInputInfos() override;
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std::vector<TensorInfo> GetOutputInfos() override;
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private:
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baidu::mirana::poros::PorosOptions _options;
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std::unique_ptr<baidu::mirana::poros::PorosModule> _poros_module;
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std::vector<std::vector<c10::IValue>> _prewarm_datas;
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int _numinputs = 1;
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int _numoutputs = 1;
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};
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} // namespace fastdeploy
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