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!527 gpu dynamic memory pool supports multi-allReduce

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Merge pull request !527 from limingqi107/master
pull/527/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent 31c985b244 2891f0d20d
commit ca3aa6071a
15 changed files with 117 additions and 108 deletions
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122
mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
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@ -111,7 +111,8 @@ void GPUKernelRuntime::AssignMemory(session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(context_ptr);
MS_EXCEPTION_IF_NULL(mem_manager_);
mem_manager_->ResetDynamicMemory();
AssignStaticMemory(graph);
AssignStaticMemoryInput(graph);
AssignStaticMemoryValueNode(graph);
bool is_enable_dynamic_mem = context_ptr->enable_dynamic_mem_pool();
if (is_enable_dynamic_mem) {
// Use the dynamic memory pool.
@ -181,7 +182,7 @@ void GPUKernelRuntime::InitKernelOutputAddress(const session::KernelGraph *graph
bool GPUKernelRuntime::LaunchKernelDynamic(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto graph_id = graph->graph_id();
// The inputs and outputs memory of communication kernel are special, so separate processing.
// The inputs and outputs memory of communication kernel need be continuous, so separate processing.
AllocCommunicationOpDynamicRes(graph);
auto &kernels = graph->execution_order();
@ -229,15 +230,12 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod
for (size_t i = 0; i < output_sizes.size(); ++i) {
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i);
MS_EXCEPTION_IF_NULL(device_address);
auto device_ptr = device_address->ptr_;
if (device_ptr == nullptr) {
device_ptr = mem_manager_->MallocMemFromMemPool(output_sizes[i]);
MS_EXCEPTION_IF_NULL(device_ptr);
device_address->ptr_ = device_ptr;
if (device_address->ptr_ == nullptr) {
mem_manager_->MallocMemFromMemPool(device_address, output_sizes[i]);
}
kernel::AddressPtr output = std::make_shared<kernel::Address>();
MS_EXCEPTION_IF_NULL(output);
output->addr = device_ptr;
output->addr = device_address->ptr_;
output->size = output_sizes[i];
kernel_outputs->push_back(output);
}
@ -267,7 +265,6 @@ void GPUKernelRuntime::AllocCommunicationOpDynamicRes(const session::KernelGraph
if (kernel_name == kAllReduceOpName) {
AllocCommunicationOpInputDynamicRes(kernel);
AllocCommunicationOpOutputDynamicRes(kernel);
return;
}
}
}
@ -275,48 +272,30 @@ void GPUKernelRuntime::AllocCommunicationOpDynamicRes(const session::KernelGraph
void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfNodePtr &kernel) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(mem_manager_);
// The reference count of communication kernel input is not 0.
if (communication_op_input_ref_count_ != 0) {
MS_LOG(ERROR) << "The reference count of communication kernel input is not 0.";
return;
}
size_t total = 0;
std::vector<std::pair<mindspore::device::DeviceAddress *, size_t>> addr_size;
size_t total_size = 0;
std::vector<size_t> size_list;
DeviceAddressPtrList addr_list;
for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(kernel); ++i) {
auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i);
MS_EXCEPTION_IF_NULL(device_address);
// The inputs of communication kernel are not released.
if ((i == 0) && (device_address->ptr_ != nullptr)) {
MS_LOG(ERROR) << "The inputs of communication kernel are not released.";
return;
}
auto output_size = device_address->size_;
total += output_size;
addr_size.emplace_back(device_address.get(), output_size);
if (device_address->ptr_ != nullptr) {
MS_LOG(INFO) << "The inputs of communication kernel are not released.";
mem_manager_->FreeMemFromMemPool(device_address);
}
auto device_mem_ptr = mem_manager_->MallocMemFromMemPool(total);
MS_EXCEPTION_IF_NULL(device_mem_ptr);
for (const auto &iter : addr_size) {
MS_EXCEPTION_IF_NULL(iter.first);
iter.first->set_ptr(device_mem_ptr);
communication_op_input_ref_count_++;
device_mem_ptr = AddressOffset(device_mem_ptr, iter.second);
total_size += device_address->size_;
size_list.emplace_back(device_address->size_);
addr_list.emplace_back(device_address);
}
mem_manager_->MallocContinuousMemFromMemPool(addr_list, total_size, size_list);
}
void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::AnfNodePtr &kernel) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(mem_manager_);
// The reference count of communication kernel output is not 0.
if (communication_op_output_ref_count_ != 0) {
MS_LOG(ERROR) << "The reference count of communication kernel output is not 0.";
return;
}
size_t total = 0;
std::vector<std::pair<mindspore::device::DeviceAddress *, size_t>> addr_size;
size_t total_size = 0;
std::vector<size_t> size_list;
DeviceAddressPtrList addr_list;
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto output_sizes = kernel_mod->GetOutputSizeList();
@ -324,22 +303,15 @@ void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::Anf
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i);
MS_EXCEPTION_IF_NULL(device_address);
// The outputs of communication kernel are not released.
if ((i == 0) && (device_address->ptr_ != nullptr)) {
MS_LOG(ERROR) << "The outputs of communication kernel are not released.";
return;
}
total += output_sizes[i];
addr_size.emplace_back(device_address.get(), output_sizes[i]);
if (device_address->ptr_ != nullptr) {
MS_LOG(INFO) << "The outputs of communication kernel are not released.";
mem_manager_->FreeMemFromMemPool(device_address);
}
auto device_mem_ptr = mem_manager_->MallocMemFromMemPool(total);
MS_EXCEPTION_IF_NULL(device_mem_ptr);
for (const auto &iter : addr_size) {
MS_EXCEPTION_IF_NULL(iter.first);
iter.first->set_ptr(device_mem_ptr);
communication_op_output_ref_count_++;
device_mem_ptr = AddressOffset(device_mem_ptr, iter.second);
total_size += output_sizes[i];
size_list.emplace_back(output_sizes[i]);
addr_list.emplace_back(device_address);
}
mem_manager_->MallocContinuousMemFromMemPool(addr_list, total_size, size_list);
}
void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
@ -362,14 +334,10 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
}
kernel_ref_count_ptr->ref_count_dynamic_use_--;
if (kernel_ref_count_ptr->ref_count_dynamic_use_ == 0) {
// Reset the reference count.
kernel_ref_count_ptr->ref_count_dynamic_use_ = kernel_ref_count_ptr->ref_count_;
bool is_communication_op = false;
FreeCommunicationOpDynamicRes(kernel, i, &is_communication_op);
if (!is_communication_op) {
auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i);
mem_manager_->FreeMemFromMemPool(device_address);
}
// Reset the reference count.
kernel_ref_count_ptr->ref_count_dynamic_use_ = kernel_ref_count_ptr->ref_count_;
}
}
// Free the output of kernel, if output has no reference.
@ -393,40 +361,6 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
}
}
}
void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr &kernel, size_t input_idx,
bool *is_communication_op) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(mem_manager_);
// The inputs memory of communication kernel is one piece memory, need release together.
if (AnfAlgo::GetCNodeName(kernel) == kAllReduceOpName) {
communication_op_input_ref_count_--;
if (communication_op_input_ref_count_ == 0) {
auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, 0);
mem_manager_->FreeMemFromMemPool(device_address);
}
*is_communication_op = true;
return;
}
auto cnode = kernel->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
if (input_idx + 1 >= cnode->inputs().size()) {
MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " << cnode->inputs().size() - 1
<< ".";
}
auto input_node = cnode->input(input_idx + 1);
auto kernel_input = AnfAlgo::VisitKernel(input_node, 0);
// The outputs memory of communication kernel is one piece memory, need release together.
if (AnfAlgo::GetCNodeName(kernel_input.first) == kAllReduceOpName) {
communication_op_output_ref_count_--;
if (communication_op_output_ref_count_ == 0) {
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel_input.first, 0);
mem_manager_->FreeMemFromMemPool(device_address);
}
*is_communication_op = true;
}
}
} // namespace gpu
} // namespace device
} // namespace mindspore

3
mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h
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@ -60,9 +60,6 @@ class GPUKernelRuntime : public KernelRuntime {
void AllocCommunicationOpOutputDynamicRes(const mindspore::AnfNodePtr &kernel);
void FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel, const AddressPtrList &kernel_workspaces,
uint32_t graph_id);
void FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr &kernel, size_t input_idx, bool *is_communication_op);
size_t communication_op_input_ref_count_{0};
size_t communication_op_output_ref_count_{0};
std::unordered_map<uint32_t, MemReuseUtilPtr> mem_reuse_util_map_;
};
MS_REG_KERNEL_RUNTIME(kGPUDevice, GPUKernelRuntime);

4
mindspore/ccsrc/device/gpu/gpu_memory_manager.cc
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@ -29,6 +29,10 @@ void GPUMemoryManager::FreeMemFromMemPool(void *device_ptr) {
GPUMemoryAllocator::GetInstance().FreeTensorMem(device_ptr);
}
std::vector<void *> GPUMemoryManager::MallocContinuousMemFromMemPool(size_t total_size, std::vector<size_t> size_list) {
return GPUMemoryAllocator::GetInstance().AllocContinuousTensorMem(total_size, size_list);
}
void GPUMemoryManager::MallocDeviceMemory() {
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);

2
mindspore/ccsrc/device/gpu/gpu_memory_manager.h
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@ -16,6 +16,7 @@
#ifndef MINDSPORE_MINDSPORE_CCSRC_DEVICE_GPU_GPU_MEMORY_MANAGER_H_
#define MINDSPORE_MINDSPORE_CCSRC_DEVICE_GPU_GPU_MEMORY_MANAGER_H_
#include <vector>
#include "device/memory_manager.h"
namespace mindspore {
namespace device {
@ -30,6 +31,7 @@ class GPUMemoryManager : public MemoryManager {
void *MallocMemFromMemPool(size_t size) override;
void FreeMemFromMemPool(void *device_ptr) override;
std::vector<void *> MallocContinuousMemFromMemPool(size_t total_size, std::vector<size_t> size_list);
protected:
uint8_t *MallocStaticMem(size_t size, bool communication_mem) override;

2
mindspore/ccsrc/device/kernel_runtime.h
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@ -67,6 +67,7 @@ class KernelRuntime {
TypeId type_id) = 0;
virtual bool SyncStream() = 0;
void AssignStaticMemory(session::KernelGraph *graph);
void AssignStaticMemoryValueNode(session::KernelGraph *graph);
void AssignDynamicMemory(session::KernelGraph *graph);
void ReuseAssignDynamicMemory(session::KernelGraph *graph);
void AssignNodeOutputMem(int flag, const AnfNodePtr &node, int index);
@ -81,7 +82,6 @@ class KernelRuntime {
private:
void AssignStaticMemoryOutput(const session::KernelGraph *graph);
void AssignStaticMemoryValueNode(session::KernelGraph *graph);
void GenLaunchArgs(const mindspore::kernel::KernelMod &kernel_mod, const AnfNodePtr &kernel,
AddressPtrList *kernel_inputs, AddressPtrList *kernel_workspaces, AddressPtrList *kernel_outputs);
bool LaunchKernelMod(const session::KernelGraph &graph);

23
mindspore/ccsrc/device/memory_manager.cc
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@ -167,5 +167,28 @@ void MemoryManager::FreeMemFromMemPool(void *device_ptr) {
MS_LOG(ERROR) << "FreeMemFromMemPool device_ptr is null.";
}
}
void MemoryManager::MallocContinuousMemFromMemPool(const DeviceAddressPtrList addr_list, size_t total_size,
std::vector<size_t> size_list) {
auto device_ptr_list = MallocContinuousMemFromMemPool(total_size, size_list);
if (addr_list.size() != device_ptr_list.size()) {
MS_LOG(EXCEPTION) << "The size of device list is not equal to the size of address list.";
}
for (size_t i = 0; i < addr_list.size(); i++) {
MS_EXCEPTION_IF_NULL(device_ptr_list[i]);
MS_EXCEPTION_IF_NULL(addr_list[i]);
addr_list[i]->ptr_ = device_ptr_list[i];
addr_list[i]->from_mem_pool_ = true;
}
}
std::vector<void *> MemoryManager::MallocContinuousMemFromMemPool(size_t total_size, std::vector<size_t> size_list) {
if (total_size == 0) {
MS_LOG(ERROR) << "MallocContinuousMemFromMemPool total_size is 0.";
}
std::vector<void *> device_ptr_list;
device_ptr_list.emplace_back(nullptr);
return device_ptr_list;
}
} // namespace device
} // namespace mindspore

4
mindspore/ccsrc/device/memory_manager.h
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@ -17,6 +17,7 @@
#ifndef MINDSPORE_MINDSPORE_CCSRC_DEVICE_MEMORY_MANAGER_H_
#define MINDSPORE_MINDSPORE_CCSRC_DEVICE_MEMORY_MANAGER_H_
#include <memory>
#include <vector>
#include "pre_activate/mem_reuse/mem_reuse.h"
#include "pre_activate/mem_reuse/mem_reuse_allocator.h"
namespace mindspore {
@ -49,6 +50,9 @@ class MemoryManager {
virtual void *MallocMemFromMemPool(size_t size);
virtual void FreeMemFromMemPool(const DeviceAddressPtr address);
virtual void FreeMemFromMemPool(void *device_ptr);
virtual void MallocContinuousMemFromMemPool(const DeviceAddressPtrList addr_list, size_t total_size,
std::vector<size_t> size_list);
virtual std::vector<void *> MallocContinuousMemFromMemPool(size_t total_size, std::vector<size_t> size_list);
size_t GetCommonAlignSize(size_t input_size) const;
size_t GetCommunicationAlignSize(size_t input_size) const;

2
mindspore/ccsrc/kernel/gpu/arrays/transpose_gpu_kernel.h
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@ -44,7 +44,7 @@ class TransposeGpuFwdKernel : public GpuKernel {
"cudaMemcpyAsync input_shape failed");
CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpyAsync(input_axis, &input_axis_[0], workspace_size_, cudaMemcpyHostToDevice,
reinterpret_cast<cudaStream_t>(stream_ptr)),
"cudaMemcphalfyAsync input_axis failed");
"cudaMemcpyAsync input_axis failed");
int size = SizeToInt(input_size_ / sizeof(T));
CalTranspose(size, input, input_shape, input_axis, SizeToInt(shape_size_), output,
reinterpret_cast<cudaStream_t>(stream_ptr));

16
mindspore/ccsrc/kernel/gpu/cuda_impl/unary_op_impl.cu
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@ -60,6 +60,14 @@ __global__ void SquareKernel(T *input, T *output, size_t count) {
return;
}
template <typename T>
__global__ void ZeroslikeKernel(T *output, size_t count) {
T zero = 0.0;
for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
output[i] = zero;
}
return;
}
template <typename T>
void Exponential(T *input, T *output, size_t count, cudaStream_t cuda_stream) {
ExponentialKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
return;
@ -84,13 +92,21 @@ void Square(T *input, T *output, size_t count, cudaStream_t cuda_stream) {
SquareKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
return;
}
template <typename T>
void Zeroslike(T *output, size_t count, cudaStream_t cuda_stream) {
ZeroslikeKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(output, count);
return;
}
template void Exponential<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
template void Logarithm<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
template void Negative<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
template void Reciprocal<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
template void Square<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
template void Zeroslike<float>(float *output, size_t count, cudaStream_t cuda_stream);
template void Exponential<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
template void Logarithm<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
template void Negative<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
template void Reciprocal<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
template void Square<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
template void Zeroslike<half>(half *output, size_t count, cudaStream_t cuda_stream);

3
mindspore/ccsrc/kernel/gpu/cuda_impl/unary_op_impl.cuh
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@ -28,4 +28,7 @@ template <typename T>
void Reciprocal(T *input, T *output, size_t count, cudaStream_t cuda_stream);
template <typename T>
void Square(T *input, T *output, size_t count, cudaStream_t cuda_stream);
template <typename T>
void Zeroslike(T *output, size_t count, cudaStream_t cuda_stream);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_UNARYOPIMPL_H_

1
mindspore/ccsrc/kernel/gpu/math/unary_op_gpu_kernel.h
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@ -81,6 +81,7 @@ class UnaryOpGpuKernel : public GpuKernel {
break;
}
case UNARY_OP_ZEROSLIKE: {
Zeroslike(output_addr, output_size_ / sizeof(T), reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
default: {

31
mindspore/ccsrc/pre_activate/mem_reuse/mem_dynamic_allocator.cc
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@ -36,6 +36,37 @@ DeviceMemPtr DynamicMemPoolBestFit::AllocTensorMem(size_t size) {
return device_addr;
}
std::vector<DeviceMemPtr> DynamicMemPoolBestFit::AllocContinuousTensorMem(size_t total_size,
std::vector<size_t> size_list) {
// Pre-alloc the one whole piece memory.
auto device_addr = AllocTensorMem(total_size);
MS_EXCEPTION_IF_NULL(device_addr);
// Remove the pre-alloc memory.
auto mem_block = FindMemBlock(device_addr);
MS_EXCEPTION_IF_NULL(mem_block);
auto iter = mem_block->block_all_mem_buf_map_.find(device_addr);
if (iter == mem_block->block_all_mem_buf_map_.end()) {
MS_LOG(EXCEPTION) << "Can't find the device address[" << device_addr << "].";
}
auto mem_buf = iter->second;
MS_EXCEPTION_IF_NULL(mem_buf);
auto rest_size = mem_buf->size_ - total_size;
(void)mem_block->block_all_mem_buf_map_.erase(iter);
// Split the pre-alloc memory into continuous memory by the size list.
DynamicMemBufPtr continuous_mem_buf;
std::vector<DeviceMemPtr> device_addr_list;
auto buf_addr = device_addr;
for (size_t i = 0; i < size_list.size(); i++) {
continuous_mem_buf = std::make_shared<DynamicMemBuf>(buf_addr, kMemBufUsed, size_list[i]);
(void)mem_block->block_all_mem_buf_map_.emplace(buf_addr, continuous_mem_buf);
device_addr_list.emplace_back(buf_addr);
buf_addr = AddressOffset(buf_addr, size_list[i]);
}
// Update the size of the last memory buf.
continuous_mem_buf->size_ += rest_size;
return device_addr_list;
}
size_t DynamicMemPoolBestFit::AlignMemorySize(size_t size) const {
if (size == 0) {
return DYNAMIC_MEM_ALIGN_SIZE;

2
mindspore/ccsrc/pre_activate/mem_reuse/mem_dynamic_allocator.h
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@ -79,6 +79,8 @@ class DynamicMemPoolBestFit {
virtual ~DynamicMemPoolBestFit();
// The main program entry of memory alloc.
DeviceMemPtr AllocTensorMem(size_t size);
// The main program entry of continuous memory alloc.
std::vector<DeviceMemPtr> AllocContinuousTensorMem(size_t total_size, std::vector<size_t> size_list);
// The main program entry of memory free.
void FreeTensorMem(const DeviceMemPtr device_addr);
// Release the real device memory.

8
mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc
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@ -162,10 +162,6 @@ void MemReuseUtil::SetInputMap(const CNodePtr &kernel, KernelDef *kernel_def_ptr
if (iter == kernel_def_ptr->inputs_.end()) {
kernel_def_ptr->inputs_[key].push_back(ref_ptr);
} else {
if (std::any_of(iter->second.begin(), iter->second.end(),
[ref_ptr](const KernelRefCountPtr &it) { return (it.get() == ref_ptr.get()); })) {
break;
}
iter->second.push_back(ref_ptr);
}
}
@ -185,10 +181,6 @@ void MemReuseUtil::SetOutputMap(const CNodePtr &kernel, KernelDef *kernel_def_pt
if (iter == kernel_def_ptr->outputs_.end()) {
kernel_def_ptr->outputs_[key].push_back(kernel_ref);
} else {
if (std::any_of(iter->second.begin(), iter->second.end(),
[kernel_ref](const KernelRefCountPtr &it) { return (it == kernel_ref); })) {
break;
}
iter->second.push_back(kernel_ref);
}
}

2
tests/st/nccl/test_nccl_all_reduce_op.py
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@ -20,7 +20,7 @@ import mindspore.context as context
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.communication.management import init, NCCL_WORLD_COMM_GROUP, get_rank, get_group_size
context.set_context(mode=context.GRAPH_MODE, device_target='GPU', enable_dynamic_memory=False)
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
init('nccl')
rank = get_rank()

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