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* check in cmake * move backend_ops to csrc/backend_ops * check in preprocess, model, some codebase and their c-apis * check in CMakeLists.txt * check in parts of test_csrc * commit everything else * add readme * update core's BUILD_INTERFACE directory * skip codespell on third_party * update trt_net and ort_net's CMakeLists * ignore clion's build directory * check in pybind11 * add onnx.proto. Remove MMDeploy's dependency on ncnn's source code * export MMDeployTargets only when MMDEPLOY_BUILD_SDK is ON * remove useless message * target include directory is wrong * change target name from mmdeploy_ppl_net to mmdeploy_pplnn_net * skip install directory * update project's cmake * remove useless code * set CMAKE_BUILD_TYPE to Release by force if it isn't set by user * update custom ops CMakeLists * pass object target's source lists * fix lint end-of-file * fix lint: trailing whitespace * fix codespell hook * remove bicubic_interpolate to csrc/backend_ops/ * set MMDEPLOY_BUILD_SDK OFF * change custom ops build command * add spdlog installation command * update docs on how to checkout pybind11 * move bicubic_interpolate to backend_ops/tensorrt directory * remove useless code * correct cmake * fix typo * fix typo * fix install directory * correct sdk's readme * set cub dir when cuda version < 11.0 * change directory where clang-format will apply to * fix build command * add .clang-format * change clang-format style from google to file * reformat csrc/backend_ops * format sdk's code * turn off clang-format for some files * add -Xcompiler=-fno-gnu-unique * fix trt topk initialize * check in config for sdk demo * update cmake script and csrc's readme * correct config's path * add cuda include directory, otherwise compile failed in case of tensorrt8.2 * clang-format onnx2ncnn.cpp Co-authored-by: zhangli <lzhang329@gmail.com> Co-authored-by: grimoire <yaoqian@sensetime.com>
70 lines
1.8 KiB
C++
70 lines
1.8 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#ifndef MMDEPLOY_SRC_DEVICE_CUDA_LINEARALLOCATOR_H_
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#define MMDEPLOY_SRC_DEVICE_CUDA_LINEARALLOCATOR_H_
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#include "default_allocator.h"
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namespace mmdeploy::cuda {
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class LinearAllocator {
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public:
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explicit LinearAllocator(std::size_t size) : size_(size) {
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base_ = static_cast<uint8_t*>(gDefaultAllocator().Allocate(size));
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ptr_ = base_;
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}
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~LinearAllocator() { gDefaultAllocator().Deallocate(base_, size_); }
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[[nodiscard]] void* Allocate(std::size_t n) {
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std::optional<std::lock_guard<std::mutex> > lock;
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if (mutex_) {
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lock.emplace(*mutex_);
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}
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++count_;
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total_ += n;
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auto ptr = static_cast<void*>(ptr_);
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std::size_t space = base_ + size_ - ptr_;
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if (std::align(16, n, ptr, space)) {
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ERROR("success n={}, total={}, count={}", n, total_, count_);
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ptr_ = static_cast<uint8_t*>(ptr) + n;
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return ptr;
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}
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ERROR("fallback {}, total={}, count={}", n, total_, count_);
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return gDefaultAllocator().Allocate(n);
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}
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void Deallocate(void* _p, std::size_t n) {
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std::optional<std::lock_guard<std::mutex> > lock;
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if (mutex_) {
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lock.emplace(*mutex_);
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}
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auto p = static_cast<uint8_t*>(_p);
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if (!(base_ <= p && p < ptr_)) {
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gDefaultAllocator().Deallocate(_p, n);
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}
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total_ -= n;
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--count_;
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ERROR("deallocate total={}, count={}", total_, count_);
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if (total_ == 0) {
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assert(count_ == 0);
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ptr_ = base_;
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}
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}
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private:
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std::size_t size_;
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uint8_t* base_;
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uint8_t* ptr_;
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std::size_t total_{};
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std::size_t count_{};
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std::optional<std::mutex> mutex_;
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};
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inline LinearAllocator& gLinearAllocator() {
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static LinearAllocator v(1U << 30);
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return v;
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}
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} // namespace mmdeploy::cuda
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#endif // MMDEPLOY_SRC_DEVICE_CUDA_LINEARALLOCATOR_H_
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