mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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Support Poros Backend (#188)
* Add poros backend * Add torch lib * Add python3 lib * set c++ 14 for poros * fixed bugs * fixed grammer bugs * fixed grammer bugs * fixed code bugs * fixed code bugs * fixed CreatePorosValue bug * Add AtType2String for Log * fixed trt_option * fixed poros.cmake path * fixed grammer bug * fixed grammer bug * fixed ambiguous reference * fixed ambiguous reference * fixed reference error * fixed include files * rm ENABLE_TRT_BACKEND in poros * update CMakeLists.txt * fixed CMakeLists.txt * Add libtorch.so in CMakeLists.txt * Fixed CMakeLists.txt * Fixed CMakeLists.txt * Fixed copy bug * Fixed copy bug * Fixed copy bug * Fixed Cmake * Fixed Cmake * debug * debug * debug * debug * debug * debug * debug utils * debug utils * copy to cpu * rm log info * test share mem * test share mem * test share mem * test multi outputs * test multi outputs * test multi outputs * test multi outputs * test multi outputs * test multi outputs * test multi outputs * time cost * time cost * fixed bug * time collect * mem copy * mem copy * rm time log * rm share mem * fixed multi inputs bug * add set_input_dtypes func * add SetInputDtypes * fixed bug * fixed bug * fixed prewarm data order * debug * debug * debug * debug * debug * debug * debug * debug * debug * debug * debug * fixed bug * Add compile func * Add compile func * Add compile func * Add is_dynamic option * Add is_dynamic option * Add is_dynamic option * Add is_dynamic option * rm infer log * add cuda11.6 poros lib * fixed bug * fixed bug * fixed multi outputs * fixed multi outputs * fixed multi outputs * fixed multi outputs * fixed multi outputs * fixed multi outputs * fixed multi outputs * fixed multi outputs * fixed multi outputs * fixed multi outputs * fixed multi outputs * rm logs * test * test * test * add test log * add test log * add test log * add test log * support cpu * support cpu * support cpu * support cpu * support member variable definition * rm useless log * fixed name * resolve conflict * resolve conflict * resolve conflict * fixed cmake * add GetInputInfos&GetOutputInfos * add GetInputInfos&GetOutputInfos * fixed bug * fixed runtime.py * add compile func * add np * deal with comments * rm to_inter func * add property
This commit is contained in:
WJJ1995
186
fastdeploy/backends/poros/utils.cc
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186
fastdeploy/backends/poros/utils.cc
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// 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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#include "fastdeploy/backends/poros/poros_backend.h"
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#ifdef WITH_GPU
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#include <cuda_runtime_api.h>
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#endif
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namespace fastdeploy {
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std::string AtType2String(const at::ScalarType& dtype) {
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std::string out;
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switch (dtype) {
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case at::kByte:
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out = "at::kByte";
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break;
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case at::kChar:
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out = "at::kChar";
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break;
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case at::kShort:
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out = "at::kShort";
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break;
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case at::kInt:
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out = "at::kInt";
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break;
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case at::kLong:
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out = "at::kLong";
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break;
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case at::kHalf:
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out = "at::kHalf";
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break;
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case at::kFloat:
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out = "at::kFloat";
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break;
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case at::kDouble:
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out = "at::kDouble";
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break;
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default:
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out = "at::UNKNOWN";
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}
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return out;
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}
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at::ScalarType GetPorosDtype(const FDDataType& fd_dtype) {
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if (fd_dtype == FDDataType::FP32) {
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return at::kFloat;
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} else if (fd_dtype == FDDataType::FP64) {
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return at::kDouble;
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} else if (fd_dtype == FDDataType::INT32) {
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return at::kInt;
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} else if (fd_dtype == FDDataType::INT64) {
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return at::kLong;
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}
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FDERROR << "Unrecognized fastdeply data type:" << Str(fd_dtype) << "."
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<< std::endl;
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return at::kFloat;
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}
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FDDataType GetFdDtype(const at::ScalarType& poros_dtype) {
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if (poros_dtype == at::kFloat) {
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return FDDataType::FP32;
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} else if (poros_dtype == at::kDouble) {
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return FDDataType::FP64;
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} else if (poros_dtype == at::kInt) {
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return FDDataType::INT32;
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} else if (poros_dtype == at::kLong) {
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return FDDataType::INT64;
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}
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FDERROR << "Unrecognized poros data type:" << AtType2String(poros_dtype)
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<< "." << std::endl;
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return FDDataType::FP32;
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}
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at::Tensor CreatePorosValue(FDTensor& tensor, bool is_backend_cuda) {
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FDASSERT(tensor.device == Device::GPU || tensor.device == Device::CPU,
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"Only support tensor which device is CPU or GPU for PorosBackend.");
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auto data_type = GetPorosDtype(tensor.dtype);
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size_t numel = tensor.Numel();
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at::Tensor poros_value;
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if (is_backend_cuda) {
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poros_value = std::move(
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at::empty(tensor.shape, {at::kCUDA}).to(data_type).contiguous());
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} else {
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poros_value = std::move(
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at::empty(tensor.shape, {at::kCPU}).to(data_type).contiguous());
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}
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if (data_type == at::kFloat) {
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if (is_backend_cuda) {
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cudaMemcpy(poros_value.data_ptr(), static_cast<void*>(tensor.Data()),
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numel * sizeof(float), cudaMemcpyHostToDevice);
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} else {
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memcpy(poros_value.data_ptr(), static_cast<void*>(tensor.Data()),
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numel * sizeof(float));
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}
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} else if (data_type == at::kInt) {
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if (is_backend_cuda) {
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cudaMemcpy(poros_value.data_ptr(), static_cast<void*>(tensor.Data()),
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numel * sizeof(int32_t), cudaMemcpyHostToDevice);
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} else {
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memcpy(poros_value.data_ptr(), static_cast<void*>(tensor.Data()),
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numel * sizeof(int32_t));
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}
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} else if (data_type == at::kLong) {
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if (is_backend_cuda) {
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cudaMemcpy(poros_value.data_ptr(), static_cast<void*>(tensor.Data()),
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numel * sizeof(int64_t), cudaMemcpyHostToDevice);
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} else {
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memcpy(poros_value.data_ptr(), static_cast<void*>(tensor.Data()),
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numel * sizeof(int64_t));
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}
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} else if (data_type == at::kDouble) {
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if (is_backend_cuda) {
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cudaMemcpy(poros_value.data_ptr(), static_cast<void*>(tensor.Data()),
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numel * sizeof(double), cudaMemcpyHostToDevice);
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} else {
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memcpy(poros_value.data_ptr(), static_cast<void*>(tensor.Data()),
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numel * sizeof(double));
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}
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} else {
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FDASSERT(false,
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"Unrecognized data type while calling "
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"PorosBackend::CreatePorosValue().");
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}
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return poros_value;
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}
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void CopyTensorToCpu(const at::Tensor& tensor, FDTensor* fd_tensor,
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bool is_backend_cuda) {
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const auto data_type = tensor.scalar_type();
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std::vector<int64_t> shape;
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auto sizes = tensor.sizes();
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for (size_t i = 0; i < sizes.size(); i++) {
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shape.push_back(sizes[i]);
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}
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auto fd_dtype = GetFdDtype(data_type);
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fd_tensor->Resize(shape, fd_dtype);
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size_t numel = tensor.numel();
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// at::Tensor -> FDTensor
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if (data_type == at::kFloat) {
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if (is_backend_cuda) {
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cudaMemcpy(fd_tensor->Data(), tensor.data_ptr(), numel * sizeof(float),
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cudaMemcpyDeviceToHost);
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} else {
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memcpy(fd_tensor->Data(), tensor.data_ptr(), numel * sizeof(float));
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}
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return;
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} else if (data_type == at::kInt) {
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if (is_backend_cuda) {
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cudaMemcpy(fd_tensor->Data(), tensor.data_ptr(), numel * sizeof(int32_t),
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cudaMemcpyDeviceToHost);
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} else {
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memcpy(fd_tensor->Data(), tensor.data_ptr(), numel * sizeof(int32_t));
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}
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return;
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} else if (data_type == at::kLong) {
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if (is_backend_cuda) {
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cudaMemcpy(fd_tensor->Data(), tensor.data_ptr(), numel * sizeof(int64_t),
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cudaMemcpyDeviceToHost);
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} else {
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memcpy(fd_tensor->Data(), tensor.data_ptr(), numel * sizeof(int64_t));
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}
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return;
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} else if (data_type == at::kDouble) {
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if (is_backend_cuda) {
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cudaMemcpy(fd_tensor->Data(), tensor.data_ptr(), numel * sizeof(double),
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cudaMemcpyDeviceToHost);
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} else {
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memcpy(fd_tensor->Data(), tensor.data_ptr(), numel * sizeof(double));
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}
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return;
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}
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}
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} // namespace fastdeploy
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