Use cuda virtual memory management and merge blocks #36189
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| // Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
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| #ifdef PADDLE_WITH_CUDA | ||
| #include <cuda.h> | ||
| #include <cuda_runtime.h> | ||
| #endif | ||
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| #include <string> | ||
| #include "paddle/fluid/memory/allocation/cuda_virtual_mem_allocator.h" | ||
| #include "paddle/fluid/platform/enforce.h" | ||
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| #ifdef PADDLE_WITH_CUDA | ||
| #include "paddle/fluid/platform/cuda_device_guard.h" | ||
| #include "paddle/fluid/platform/dynload/cuda_driver.h" | ||
| #include "paddle/fluid/platform/gpu_info.h" | ||
| #endif | ||
| #if CUDA_VERSION >= 10020 | ||
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| namespace paddle { | ||
| namespace memory { | ||
| namespace allocation { | ||
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| CUDAVirtualMemAllocator::CUDAVirtualMemAllocator( | ||
| const platform::CUDAPlace& place) | ||
| : place_(place) { | ||
| CUmemAllocationProp prop = {}; | ||
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| prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; | ||
| prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; | ||
| prop.location.id = place.device; | ||
| prop_ = prop; | ||
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| access_desc_.resize(platform::GetCUDADeviceCount()); | ||
| for (int dev_id = 0; dev_id < platform::GetCUDADeviceCount(); ++dev_id) { | ||
| if (place.device != dev_id) { | ||
| int capable = 0; | ||
| PADDLE_ENFORCE_CUDA_SUCCESS( | ||
| cudaDeviceCanAccessPeer(&capable, place.device, dev_id)); | ||
| if (!capable) { | ||
| continue; | ||
| } | ||
| } | ||
| access_desc_[dev_id].location.type = CU_MEM_LOCATION_TYPE_DEVICE; | ||
| access_desc_[dev_id].location.id = dev_id; | ||
| access_desc_[dev_id].flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; | ||
| } | ||
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| granularity_ = 0; | ||
| for (int dev_id = 0; dev_id < platform::GetCUDADeviceCount(); ++dev_id) { | ||
| size_t granularity; | ||
| prop.location.id = dev_id; | ||
| PADDLE_ENFORCE_CUDA_SUCCESS( | ||
| paddle::platform::dynload::cuMemGetAllocationGranularity( | ||
| &granularity, &prop, CU_MEM_ALLOC_GRANULARITY_MINIMUM)); | ||
| granularity_ = std::max(granularity, granularity_); | ||
| } | ||
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| size_t actual_avail, actual_total; | ||
| paddle::platform::CUDADeviceGuard guard(place.device); | ||
| PADDLE_ENFORCE_CUDA_SUCCESS(cudaMemGetInfo(&actual_avail, &actual_total)); | ||
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| virtual_mem_size_ = (actual_total + granularity_ - 1) & ~(granularity_ - 1); | ||
|
There was a problem hiding this comment. 这是为了申请一个虚拟地址空间，大小恰好等于GPU的显存大小。确保一次性开辟的虚拟地址空间够用。 There was a problem hiding this comment. virtual_mem_size_ = (actual_total + granularity_ - 1) & ~(granularity_ - 1); |
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| PADDLE_ENFORCE_CUDA_SUCCESS(paddle::platform::dynload::cuMemAddressReserve( | ||
| &virtual_mem_base_, virtual_mem_size_, 0, 0, 0)); | ||
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| virtual_mem_alloced_offset_ = 0; | ||
| } | ||
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| bool CUDAVirtualMemAllocator::IsAllocThreadSafe() const { return false; } | ||
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| void CUDAVirtualMemAllocator::FreeImpl(Allocation* allocation) { | ||
| PADDLE_ENFORCE_EQ( | ||
| BOOST_GET_CONST(platform::CUDAPlace, allocation->place()), place_, | ||
| platform::errors::PermissionDenied( | ||
| "GPU memory is freed in incorrect device. This may be a bug")); | ||
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| auto iter = virtual_2_physical_map_.find( | ||
| reinterpret_cast<CUdeviceptr>(allocation->ptr())); | ||
| if (iter == virtual_2_physical_map_.end()) { | ||
| PADDLE_THROW(platform::errors::InvalidArgument( | ||
| "Can not find virtual memory address at %s", allocation->ptr())); | ||
| } | ||
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| int prev_id; | ||
| cudaGetDevice(&prev_id); | ||
| if (prev_id != place_.device) { | ||
| cudaSetDevice(place_.device); | ||
| } | ||
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| auto result = | ||
| paddle::platform::dynload::cuMemUnmap(iter->first, iter->second.second); | ||
| if (result != CUDA_ERROR_DEINITIALIZED) { | ||
| PADDLE_ENFORCE_CUDA_SUCCESS(result); | ||
| } | ||
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| if (result != CUDA_ERROR_DEINITIALIZED) { | ||
| PADDLE_ENFORCE_CUDA_SUCCESS(platform::RecordedCuMemRelease( | ||
| iter->second.first, iter->second.second, place_.device)); | ||
| } | ||
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| if (prev_id != place_.device) { | ||
| cudaSetDevice(prev_id); | ||
| } | ||
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| virtual_2_physical_map_.erase(iter); | ||
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| delete allocation; | ||
| } | ||
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| Allocation* CUDAVirtualMemAllocator::AllocateImpl(size_t size) { | ||
| size = (size + granularity_ - 1) & ~(granularity_ - 1); | ||
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| CUdeviceptr ptr = virtual_mem_base_ + virtual_mem_alloced_offset_; | ||
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| if (ptr + size > virtual_mem_base_ + virtual_mem_size_) { | ||
| PADDLE_THROW_BAD_ALLOC(platform::errors::ResourceExhausted( | ||
| "\n\nOut of memory error on GPU Virtual Memory %d. " | ||
| "Cannot allocate %s memory on GPU Virtual Memory %d, %s memory has " | ||
| "been allocated and " | ||
| "available memory is only %s.\n\n" | ||
| "Please decrease the batch size of your model.\n\n", | ||
| place_.device, string::HumanReadableSize(size), place_.device, | ||
| string::HumanReadableSize(virtual_mem_alloced_offset_), | ||
| string::HumanReadableSize(virtual_mem_size_ - | ||
| virtual_mem_alloced_offset_), | ||
| place_.device)); | ||
| return nullptr; | ||
| } | ||
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| CUmemGenericAllocationHandle handle; | ||
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| paddle::platform::CUDADeviceGuard guard(place_.device); | ||
| auto result = | ||
| platform::RecordedCuMemCreate(&handle, size, &prop_, 0, place_.device); | ||
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| if (result != CUDA_SUCCESS) { | ||
| if (result == CUDA_ERROR_OUT_OF_MEMORY) { | ||
| size_t actual_avail, actual_total; | ||
| PADDLE_ENFORCE_CUDA_SUCCESS(cudaMemGetInfo(&actual_avail, &actual_total)); | ||
| size_t actual_allocated = actual_total - actual_avail; | ||
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| PADDLE_THROW_BAD_ALLOC(platform::errors::ResourceExhausted( | ||
| "\n\nOut of memory error on GPU %d. " | ||
| "Cannot allocate %s memory on GPU %d, %s memory has been allocated " | ||
| "and " | ||
| "available memory is only %s.\n\n" | ||
| "Please check whether there is any other process using GPU %d.\n" | ||
| "1. If yes, please stop them, or start PaddlePaddle on another GPU.\n" | ||
| "2. If no, please decrease the batch size of your model.\n\n", | ||
| place_.device, string::HumanReadableSize(size), place_.device, | ||
| string::HumanReadableSize(actual_allocated), | ||
| string::HumanReadableSize(actual_avail), place_.device)); | ||
| } else { | ||
| PADDLE_ENFORCE_CUDA_SUCCESS(result); | ||
| } | ||
| return nullptr; | ||
| } | ||
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| result = paddle::platform::dynload::cuMemMap(ptr, size, 0, handle, 0); | ||
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| if (result != CUDA_SUCCESS) { | ||
| platform::RecordedCuMemRelease(handle, size, place_.device); | ||
| PADDLE_ENFORCE_CUDA_SUCCESS(result); | ||
| return nullptr; | ||
| } | ||
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| result = paddle::platform::dynload::cuMemSetAccess( | ||
| ptr, size, access_desc_.data(), access_desc_.size()); | ||
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| if (result != CUDA_SUCCESS) { | ||
| paddle::platform::dynload::cuMemUnmap(ptr, size); | ||
| platform::RecordedCuMemRelease(handle, size, place_.device); | ||
| PADDLE_ENFORCE_CUDA_SUCCESS(result); | ||
| return nullptr; | ||
| } | ||
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| virtual_2_physical_map_.emplace(ptr, std::make_pair(handle, size)); | ||
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| virtual_mem_alloced_offset_ += size; | ||
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| return new Allocation(reinterpret_cast<void*>(ptr), size, | ||
| platform::Place(place_)); | ||
| } | ||
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| } // namespace allocation | ||
| } // namespace memory | ||
| } // namespace paddle | ||
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| #endif | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,61 @@ | ||
| // Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
|
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| #pragma once | ||
|
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| #ifdef PADDLE_WITH_CUDA | ||
| #include <cuda_runtime.h> | ||
| #include "paddle/fluid/platform/cuda_device_guard.h" | ||
| #endif | ||
|
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| #include <mutex> // NOLINT | ||
| #include "paddle/fluid/memory/allocation/allocator.h" | ||
| #include "paddle/fluid/platform/place.h" | ||
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| #if CUDA_VERSION >= 10020 | ||
|
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| namespace paddle { | ||
| namespace memory { | ||
| namespace allocation { | ||
|
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| class CUDAVirtualMemAllocator : public Allocator { | ||
| public: | ||
| explicit CUDAVirtualMemAllocator(const platform::CUDAPlace& place); | ||
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| bool IsAllocThreadSafe() const override; | ||
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| protected: | ||
| void FreeImpl(Allocation* allocation) override; | ||
| Allocation* AllocateImpl(size_t size) override; | ||
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| private: | ||
| platform::CUDAPlace place_; | ||
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| CUdeviceptr virtual_mem_base_; | ||
| size_t virtual_mem_size_; | ||
| size_t virtual_mem_alloced_offset_; | ||
| size_t granularity_; | ||
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| CUmemAllocationProp prop_; | ||
| std::vector<CUmemAccessDesc> access_desc_; | ||
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| std::map<CUdeviceptr, std::pair<CUmemGenericAllocationHandle, size_t>> | ||
| virtual_2_physical_map_; | ||
| }; | ||
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| } // namespace allocation | ||
| } // namespace memory | ||
| } // namespace paddle | ||
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| #endif |
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in which case it may raise exception?
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https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/memory/allocation/allocator_facade.cc#L277
类似于这里，所以异常都这么来处理。
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I see, plz add comments on that.