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[cherry-pick] Thread-local Allocator, test=release/2.0 (#24061)
* cherry-pick of DeviceContext Split, test=develop (#23737) * New feature: thread local allocator, test=develop (#23989) * add the thread_local_allocator, test=develop * refactor the thread_local_allocator, test=develop * provides option setting strategy, test=develop * add boost dependency to cuda_stream, test=develop * declare the stream::Priority as enum class, test=develop * deal with PADDLE_ENFORCE_CUDA_SUCCESS macro in pr #23816
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// Copyright (c) 2020 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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#include "paddle/fluid/memory/allocation/thread_local_allocator.h" | ||
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namespace paddle { | ||
namespace memory { | ||
namespace allocation { | ||
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ThreadLocalAllocatorImpl::ThreadLocalAllocatorImpl(const platform::Place& p) | ||
: place_(p) { | ||
if (platform::is_gpu_place(place_)) { | ||
buddy_allocator_.reset(new memory::detail::BuddyAllocator( | ||
std::unique_ptr<memory::detail::SystemAllocator>( | ||
new memory::detail::GPUAllocator( | ||
boost::get<platform::CUDAPlace>(place_).device)), | ||
platform::GpuMinChunkSize(), platform::GpuMaxChunkSize())); | ||
} else { | ||
LOG(FATAL) << "Thread local allocator only supports CUDAPlace now."; | ||
} | ||
} | ||
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std::shared_ptr<ThreadLocalAllocatorImpl> ThreadLocalCUDAAllocatorPool::Get( | ||
int gpu_id) { | ||
auto pos = std::distance(devices_.begin(), | ||
std::find(devices_.begin(), devices_.end(), gpu_id)); | ||
PADDLE_ENFORCE_LT( | ||
pos, devices_.size(), | ||
platform::errors::InvalidArgument( | ||
"The position of device should be less than the size of devices.")); | ||
std::call_once(*init_flags_[pos], [this, pos, gpu_id] { | ||
platform::SetDeviceId(devices_[pos]); | ||
allocators_[pos].reset( | ||
new ThreadLocalAllocatorImpl(platform::CUDAPlace(gpu_id))); | ||
}); | ||
return allocators_[pos]; | ||
} | ||
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ThreadLocalCUDAAllocatorPool::ThreadLocalCUDAAllocatorPool() | ||
: devices_(platform::GetSelectedDevices()) { | ||
auto gpu_num = devices_.size(); | ||
allocators_.resize(gpu_num); | ||
init_flags_.reserve(gpu_num); | ||
for (size_t i = 0; i < gpu_num; ++i) { | ||
init_flags_.emplace_back(new std::once_flag()); | ||
} | ||
} | ||
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ThreadLocalAllocation* ThreadLocalAllocatorImpl::AllocateImpl(size_t size) { | ||
VLOG(10) << "ThreadLocalAllocatorImpl::AllocateImpl " << size; | ||
void* ptr = buddy_allocator_->Alloc(size); | ||
auto* tl_allocation = new ThreadLocalAllocation(ptr, size, place_); | ||
tl_allocation->SetThreadLocalAllocatorImpl(shared_from_this()); | ||
return tl_allocation; | ||
} | ||
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void ThreadLocalAllocatorImpl::FreeImpl(ThreadLocalAllocation* allocation) { | ||
VLOG(10) << "ThreadLocalAllocatorImpl::FreeImpl " << allocation; | ||
buddy_allocator_->Free(allocation->ptr()); | ||
delete allocation; | ||
} | ||
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} // namespace allocation | ||
} // namespace memory | ||
} // namespace paddle |
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100
paddle/fluid/memory/allocation/thread_local_allocator.h
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// Copyright (c) 2020 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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#include <memory> | ||
#include <vector> | ||
#include "paddle/fluid/memory/allocation/allocator.h" | ||
#include "paddle/fluid/memory/detail/buddy_allocator.h" | ||
#include "paddle/fluid/memory/detail/system_allocator.h" | ||
#include "paddle/fluid/platform/gpu_info.h" | ||
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namespace paddle { | ||
namespace memory { | ||
namespace allocation { | ||
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class ThreadLocalAllocatorImpl; | ||
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class ThreadLocalAllocation : public Allocation { | ||
public: | ||
ThreadLocalAllocation(void* ptr, size_t size, platform::Place place) | ||
: Allocation(ptr, size, place) {} | ||
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void SetThreadLocalAllocatorImpl( | ||
std::shared_ptr<ThreadLocalAllocatorImpl> allocator) { | ||
allocator_ = allocator; | ||
} | ||
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std::shared_ptr<ThreadLocalAllocatorImpl> GetAllocator() { | ||
return allocator_; | ||
} | ||
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private: | ||
std::shared_ptr<ThreadLocalAllocatorImpl> allocator_; | ||
}; | ||
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class ThreadLocalAllocatorImpl | ||
: public std::enable_shared_from_this<ThreadLocalAllocatorImpl> { | ||
public: | ||
explicit ThreadLocalAllocatorImpl(const platform::Place& p); | ||
ThreadLocalAllocation* AllocateImpl(size_t size); | ||
void FreeImpl(ThreadLocalAllocation* allocation); | ||
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private: | ||
std::unique_ptr<memory::detail::BuddyAllocator> buddy_allocator_; | ||
platform::Place place_; | ||
}; | ||
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class ThreadLocalCUDAAllocatorPool { | ||
public: | ||
static ThreadLocalCUDAAllocatorPool& Instance() { | ||
static thread_local ThreadLocalCUDAAllocatorPool pool; | ||
return pool; | ||
} | ||
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std::shared_ptr<ThreadLocalAllocatorImpl> Get(int gpu_id); | ||
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private: | ||
ThreadLocalCUDAAllocatorPool(); | ||
std::vector<int> devices_; | ||
std::vector<std::unique_ptr<std::once_flag>> init_flags_; | ||
std::vector<std::shared_ptr<ThreadLocalAllocatorImpl>> allocators_; | ||
}; | ||
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class ThreadLocalCUDAAllocator : public Allocator { | ||
public: | ||
explicit ThreadLocalCUDAAllocator(const platform::CUDAPlace& p) | ||
: gpu_id_(p.device) {} | ||
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bool IsAllocThreadSafe() const override { return true; } | ||
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protected: | ||
Allocation* AllocateImpl(size_t size) override { | ||
return ThreadLocalCUDAAllocatorPool::Instance().Get(gpu_id_)->AllocateImpl( | ||
size); | ||
} | ||
void FreeImpl(Allocation* allocation) override { | ||
auto* tl_allocation = static_cast<ThreadLocalAllocation*>(allocation); | ||
auto allocator_impl = tl_allocation->GetAllocator(); | ||
allocator_impl->FreeImpl(tl_allocation); | ||
} | ||
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private: | ||
int gpu_id_; | ||
}; | ||
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} // namespace allocation | ||
} // namespace memory | ||
} // namespace paddle |
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paddle/fluid/memory/allocation/thread_local_allocator_test.cc
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// Copyright (c) 2020 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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#include "paddle/fluid/memory/allocation/thread_local_allocator.h" | ||
#include <algorithm> | ||
#include <condition_variable> // NOLINT | ||
#include <functional> | ||
#include <iostream> | ||
#include <thread> // NOLINT | ||
#include <utility> | ||
#include "gtest/gtest.h" | ||
#include "paddle/fluid/memory/malloc.h" | ||
#include "paddle/fluid/platform/gpu_info.h" | ||
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DECLARE_double(fraction_of_gpu_memory_to_use); | ||
DECLARE_string(allocator_strategy); | ||
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namespace paddle { | ||
namespace memory { | ||
namespace allocation { | ||
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TEST(ThreadLocalAllocator, cross_scope_release) { | ||
FLAGS_fraction_of_gpu_memory_to_use = 0.1; | ||
FLAGS_allocator_strategy = "thread_local"; | ||
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const size_t thread_num = 5; | ||
const std::vector<int> devices = platform::GetSelectedDevices(); | ||
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std::vector<std::vector<void *>> allocator_addresses(devices.size()); | ||
std::vector<std::vector<AllocationPtr>> thread_allocations(devices.size()); | ||
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for (size_t i = 0; i < devices.size(); ++i) { | ||
allocator_addresses[i].resize(thread_num); | ||
thread_allocations[i].resize(thread_num); | ||
} | ||
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std::vector<std::thread> threads(thread_num); | ||
std::mutex mutex; | ||
std::condition_variable cv; | ||
bool flag = false; | ||
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for (size_t i = 0; i < threads.size(); ++i) { | ||
threads[i] = std::thread([&, i]() { | ||
{ | ||
std::unique_lock<std::mutex> lock(mutex); | ||
cv.wait(lock, [&] { return flag; }); | ||
} | ||
for (size_t j = 0; j < devices.size(); ++j) { | ||
thread_allocations[j][i] = | ||
memory::Alloc(platform::CUDAPlace(devices[j]), 10); | ||
auto tl_allocator_impl = | ||
ThreadLocalCUDAAllocatorPool::Instance().Get(devices[j]); | ||
allocator_addresses[j][i] = tl_allocator_impl.get(); | ||
} | ||
}); | ||
} | ||
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{ | ||
std::lock_guard<std::mutex> lock(mutex); | ||
flag = true; | ||
cv.notify_all(); | ||
} | ||
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for (auto &th : threads) { | ||
th.join(); | ||
} | ||
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for (auto &addresses : allocator_addresses) { | ||
std::sort(addresses.begin(), addresses.end()); | ||
ASSERT_EQ(std::adjacent_find(addresses.begin(), addresses.end(), | ||
std::equal_to<void *>()), | ||
addresses.end()); | ||
} | ||
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::testing::FLAGS_gtest_death_test_style = "threadsafe"; | ||
ASSERT_EXIT(([&]() { thread_allocations.clear(); }(), exit(0)), | ||
::testing::ExitedWithCode(0), ".*"); | ||
} | ||
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} // namespace allocation | ||
} // namespace memory | ||
} // namespace paddle |
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