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* TRT backend use pinned memory * refine fd tensor pinned memory logic * TRT enable pinned memory configurable * paddle inference support pinned memory * pinned memory pybindings Co-authored-by: Jason <jiangjiajun@baidu.com>
61 lines
1.4 KiB
C++
61 lines
1.4 KiB
C++
// 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 <memory>
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#include <new>
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#include <numeric>
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#include <string>
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#include <vector>
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#include "fastdeploy/utils/utils.h"
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namespace fastdeploy {
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class FASTDEPLOY_DECL FDHostAllocator {
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public:
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bool operator()(void** ptr, size_t size) const;
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};
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class FASTDEPLOY_DECL FDHostFree {
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public:
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void operator()(void* ptr) const;
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};
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#ifdef WITH_GPU
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class FASTDEPLOY_DECL FDDeviceAllocator {
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public:
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bool operator()(void** ptr, size_t size) const;
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};
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class FASTDEPLOY_DECL FDDeviceFree {
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public:
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void operator()(void* ptr) const;
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};
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class FASTDEPLOY_DECL FDDeviceHostAllocator {
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public:
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bool operator()(void** ptr, size_t size) const;
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};
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class FASTDEPLOY_DECL FDDeviceHostFree {
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public:
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void operator()(void* ptr) const;
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};
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#endif
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} // namespace fastdeploy
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