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Revert "Remove workspace_handle in conv_cudnn (#15186)"

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test=develop
This reverts commit 064512aa47.
revert-15207-remove_op_handle_lock_and_fix_var
chengduozh 6 years ago
parent 5d9edb4124
commit 358e657f68
8 changed files with 107 additions and 208 deletions
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2
paddle/fluid/framework/operator.h
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@ -391,7 +391,7 @@ class ExecutionContext {
PADDLE_ENFORCE(
dynamic_cast<platform::TemporaryAllocation*>(allocation_ptr) != nullptr,
"The AllocationPtr must be TemporaryAllocation.");
PADDLE_ENFORCE_GE(allocation_ptr->size(),
PADDLE_ENFORCE_EQ(allocation_ptr->size(),
framework::product(dim) * sizeof(T));
paddle::framework::Tensor temp_tensor(

103
paddle/fluid/operators/conv_cudnn_op.cu.cc
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@ -137,6 +137,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn conv algorithm ---------------------
cudnnConvolutionFwdAlgo_t algo;
auto handle = dev_ctx.cudnn_handle();
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
bool half_float = false;
#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
@ -157,8 +158,6 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
VLOG(5) << "NOT use cudnn_tensor_op_math";
}
#endif
Tensor cudnn_workspace;
void* cudnn_workspace_ptr = nullptr;
auto x_dims = framework::vectorize(input->dims());
auto f_dims = framework::vectorize(filter->dims());
@ -181,26 +180,21 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
.Var(kCUDNNFwdAlgoCache)
->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
}
cudnn_workspace =
ctx.AllocateTmpTensor<int8_t, platform::CUDADeviceContext>(
framework::make_ddim(
{static_cast<int64_t>(workspace_size_limit)}),
dev_ctx);
cudnn_workspace_ptr = static_cast<void*>(cudnn_workspace.data<int8_t>());
algo = algo_cache->GetAlgorithm(
x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
int returned_algo_count;
std::array<cudnnConvolutionFwdAlgoPerf_t, kNUM_CUDNN_FWD_ALGS>
fwd_perf_stat;
auto cudnn_find_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(
platform::dynload::cudnnFindConvolutionForwardAlgorithmEx(
handle, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, cudnn_output_desc,
output_data, kNUM_CUDNN_FWD_ALGS, &returned_algo_count,
fwd_perf_stat.data(), cudnn_workspace_ptr,
fwd_perf_stat.data(), cudnn_workspace,
workspace_size_limit));
};
workspace_handle.RunFunc(cudnn_find_func, workspace_size_limit);
VLOG(3) << "Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
@ -225,23 +219,17 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit,
"workspace_size to be allocated exceeds the limit");
// Allocate on GPU memory
if (!cudnn_workspace_ptr) {
cudnn_workspace =
ctx.AllocateTmpTensor<int8_t, platform::CUDADeviceContext>(
framework::make_ddim(
{static_cast<int64_t>(workspace_size_in_bytes)}),
dev_ctx);
cudnn_workspace_ptr = static_cast<void*>(cudnn_workspace.data<int8_t>());
}
// ------------------- cudnn conv forward ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
for (int i = 0; i < groups; i++) {
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
cudnn_filter_desc, filter_data + i * group_offset_filter,
cudnn_conv_desc, algo, cudnn_workspace_ptr, workspace_size_in_bytes,
cudnn_conv_desc, algo, cudnn_workspace, workspace_size_in_bytes,
&beta, cudnn_output_desc, output_data + i * group_offset_out));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
}
}
};
@ -365,20 +353,10 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
workspace_size_limit = max_user_size * 1024 * 1024;
}
Tensor cudnn_workspace;
void* cudnn_workspace_ptr = nullptr;
if ((input_data || filter_data) && exhaustive_search) {
cudnn_workspace =
ctx.AllocateTmpTensor<int8_t, platform::CUDADeviceContext>(
framework::make_ddim(
{static_cast<int64_t>(workspace_size_limit)}),
dev_ctx);
cudnn_workspace_ptr = static_cast<void*>(cudnn_workspace.data<int8_t>());
}
auto x_dims = framework::vectorize(input->dims());
auto f_dims = framework::vectorize(filter->dims());
auto handle = dev_ctx.cudnn_handle();
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
if (input_grad) {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
if (exhaustive_search) {
@ -396,22 +374,25 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
->GetMutable<
AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>();
}
data_algo = data_algo_cache->GetAlgorithm(
x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
int returned_algo_count;
std::array<cudnnConvolutionBwdDataAlgoPerf_t,
kNUM_CUDNN_BWD_DATA_ALGS>
data_perf_stat;
CUDNN_ENFORCE(platform::dynload::
auto cudnn_find_bd_data_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(
platform::dynload::
cudnnFindConvolutionBackwardDataAlgorithmEx(
handle, cudnn_filter_desc, filter_data,
cudnn_output_grad_desc, output_grad_data,
cudnn_conv_desc, cudnn_input_desc,
input_grad_data, kNUM_CUDNN_BWD_DATA_ALGS,
&returned_algo_count, data_perf_stat.data(),
cudnn_workspace_ptr, workspace_size_limit));
cudnn_conv_desc, cudnn_input_desc, input_grad_data,
kNUM_CUDNN_BWD_DATA_ALGS, &returned_algo_count,
data_perf_stat.data(), cudnn_workspace,
workspace_size_limit));
};
workspace_handle.RunFunc(cudnn_find_bd_data_func,
workspace_size_limit);
VLOG(3) << "Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
@ -462,23 +443,25 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
->GetMutable<
AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>();
}
filter_algo = f_algo_cache->GetAlgorithm(
x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
int returned_algo_count;
std::array<cudnnConvolutionBwdFilterAlgoPerf_t,
kNUM_CUDNN_BWD_FILTER_ALGS>
filter_perf_stat;
auto cudnn_find_bd_f_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(
platform::dynload::
cudnnFindConvolutionBackwardFilterAlgorithmEx(
handle, cudnn_input_desc, input_data,
cudnn_output_grad_desc, output_grad_data,
cudnn_conv_desc, cudnn_filter_desc, filter_grad_data,
kNUM_CUDNN_BWD_FILTER_ALGS, &returned_algo_count,
filter_perf_stat.data(), cudnn_workspace_ptr,
workspace_size_limit));
cudnn_conv_desc, cudnn_filter_desc,
filter_grad_data, kNUM_CUDNN_BWD_FILTER_ALGS,
&returned_algo_count, filter_perf_stat.data(),
cudnn_workspace, workspace_size_limit));
};
workspace_handle.RunFunc(cudnn_find_bd_f_func,
workspace_size_limit);
return filter_perf_stat[0].algo;
});
VLOG(3) << "cuDNN backward filter algo " << filter_algo;
@ -499,16 +482,6 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
}
// ------------------- cudnn conv workspace ---------------------
if (!cudnn_workspace_ptr) {
cudnn_workspace =
ctx.AllocateTmpTensor<int8_t, platform::CUDADeviceContext>(
framework::make_ddim(
{static_cast<int64_t>(workspace_size_in_bytes)}),
dev_ctx);
cudnn_workspace_ptr = static_cast<void*>(cudnn_workspace.data<int8_t>());
}
// ------------------- cudnn conv backward data ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
if (input_grad) {
@ -516,12 +489,15 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
// Because beta is zero, it is unnecessary to reset input_grad.
for (int i = 0; i < groups; i++) {
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc,
filter_data + i * group_offset_filter, cudnn_output_grad_desc,
output_grad_data + i * group_offset_out, cudnn_conv_desc, data_algo,
cudnn_workspace_ptr, workspace_size_in_bytes, &beta,
output_grad_data + i * group_offset_out, cudnn_conv_desc,
data_algo, cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_input_desc, input_grad_data + i * group_offset_in));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
}
}
// ------------------- cudnn conv backward filter ---------------------
@ -529,12 +505,15 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset filter_grad.
for (int i = 0; i < groups; i++) {
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
cudnn_output_grad_desc, output_grad_data + i * group_offset_out,
cudnn_conv_desc, filter_algo, cudnn_workspace_ptr,
workspace_size_in_bytes, &beta, cudnn_filter_desc,
filter_grad_data + i * group_offset_filter));
handle, &alpha, cudnn_input_desc,
input_data + i * group_offset_in, cudnn_output_grad_desc,
output_grad_data + i * group_offset_out, cudnn_conv_desc,
filter_algo, cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_filter_desc, filter_grad_data + i * group_offset_filter));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
}
}
}

20
paddle/fluid/platform/device_context.cc
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@ -92,24 +92,26 @@ platform::TemporaryAllocator& DeviceTemporaryAllocator::Get(
const platform::Place& place, const cudaStream_t& stream) {
PADDLE_ENFORCE(platform::is_gpu_place(place));
auto place_stream = std::make_pair(place, stream);
{
std::unique_lock<std::mutex> lock(mtx_);
auto it = device_allocator_.find(place_stream);
if (it == device_allocator_.end()) {
auto tmp_allocator = new TemporaryAllocator(place);
tmp_allocator->SetCallback([stream]() {
if (!device_allocator_.count(place_stream)) {
device_allocator_[place_stream].reset(new TemporaryAllocator(place));
device_allocator_[place_stream]->SetCallback([stream]() {
PADDLE_ENFORCE(cudaStreamSynchronize(stream));
PADDLE_ENFORCE(cudaGetLastError());
});
device_allocator_[place_stream].reset(tmp_allocator);
return *tmp_allocator;
} else {
return *it->second;
}
}
return *device_allocator_.at(place_stream);
}
template <>
platform::TemporaryAllocator& DeviceTemporaryAllocator::Get(
const platform::CUDADeviceContext& dev_ctx) {
auto place_stream = std::make_pair(dev_ctx.GetPlace(), dev_ctx.stream());
if (device_allocator_.count(place_stream)) {
return *device_allocator_.at(place_stream);
}
return Get(dev_ctx.GetPlace(), dev_ctx.stream());
}
#endif
@ -323,7 +325,7 @@ Place CUDADeviceContext::GetPlace() const { return place_; }
void CUDADeviceContext::Wait() const {
auto& allocator =
DeviceTemporaryAllocator::Instance().Get<CUDADeviceContext>(*this);
allocator.Release([this]() {
allocator.Release([=]() {
PADDLE_ENFORCE(cudaStreamSynchronize(stream_));
PADDLE_ENFORCE(cudaGetLastError());
});

2
paddle/fluid/platform/device_context.h
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@ -61,7 +61,7 @@ namespace platform {
* the allocations of temp_allocation_queue:
* - when the Stream calls cudaStreamSynchronize;
* - when the allocation size of opportunities exceeds a certain threshold
* (defined by FLAGS_limit_of_tmp_allocation).
* (defined by FLAGS_limit_of_temporary_allocation).
*
* */
class DeviceTemporaryAllocator {

61
paddle/fluid/platform/temporary_allocator.cc
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@ -15,15 +15,8 @@
#include "paddle/fluid/platform/temporary_allocator.h"
#include "paddle/fluid/memory/allocation/allocator_facade.h"
DEFINE_int64(limit_of_tmp_allocation, -1,
DEFINE_double(limit_of_temporary_allocation, -1,
"The up limit of temporary_allocation size.");
DEFINE_double(times_excess_than_required_tmp_allocation, 2,
"times_excess_than_required_tmp_allocation indicates the "
"max size the TemporaryAllocator can return. For example, "
"if the required memory size is N, and "
"times_excess_than_required_tmp_allocation is 2.0, "
"the TemporaryAllocator will return the available allocation "
"that the range of size is N ~ 2*N.");
namespace paddle {
namespace platform {
@ -36,25 +29,24 @@ TemporaryAllocation::TemporaryAllocation(
underlying_allocation_(std::move(underlying_allocation)) {}
TemporaryAllocator::TemporaryAllocator(platform::Place place) : place_(place) {
temp_mem_map_.reset(new std::multimap<size_t, TemporaryAllocation *>());
temp_mem_queue_.reset(new std::deque<TemporaryAllocation *>());
}
bool TemporaryAllocator::IsAllocThreadSafe() const { return true; }
void TemporaryAllocator::Release(const std::function<void()> &callback) {
std::unique_ptr<std::multimap<size_t, TemporaryAllocation *>> t_allocations;
std::shared_ptr<std::deque<TemporaryAllocation *>> t_allocations;
{
std::unique_lock<std::mutex> lock(mtx_);
callback();
t_allocations.swap(temp_mem_map_);
temp_mem_map_.reset(new std::multimap<size_t, TemporaryAllocation *>());
t_allocations = temp_mem_queue_;
temp_mem_queue_.reset(new std::deque<TemporaryAllocation *>());
wait_delete_mem_ = 0;
}
for (auto tmp : *t_allocations) {
VLOG(10) << "Delete temporary allocation " << tmp.second->ptr()
<< " size: " << tmp.second->size();
delete tmp.second;
VLOG(10) << "Delete temporary allocation " << tmp->ptr()
<< " size: " << tmp->size();
delete tmp;
}
}
@ -62,34 +54,28 @@ void TemporaryAllocator::Free(alloc::Allocation *allocation) {
auto *temp_allocation = dynamic_cast<TemporaryAllocation *>(allocation);
PADDLE_ENFORCE_NOT_NULL(temp_allocation);
if (platform::is_gpu_place(temp_allocation->place())) {
PADDLE_ENFORCE(platform::is_same_place(temp_allocation->place(), place_),
"The place should be the same.");
size_t wait_delete_mem = 0;
{
std::unique_lock<std::mutex> lock(mtx_);
temp_mem_map_->emplace(temp_allocation->size(), temp_allocation);
temp_mem_queue_->emplace_back(temp_allocation);
wait_delete_mem_ += temp_allocation->size();
wait_delete_mem = wait_delete_mem_;
VLOG(10) << "Move temporary allocation: " << temp_allocation->ptr()
<< " to delete queue: " << temp_allocation->size() << "; "
<< "wait_delete_mem: " << wait_delete_mem;
<< "wait_delete_mem: " << wait_delete_mem_;
}
if (FLAGS_limit_of_tmp_allocation > 0 &&
wait_delete_mem > static_cast<size_t>(FLAGS_limit_of_tmp_allocation)) {
PADDLE_ENFORCE(callback_ != nullptr, "The callback is non-initialized.");
if (FLAGS_limit_of_temporary_allocation > 0 &&
wait_delete_mem > FLAGS_limit_of_temporary_allocation) {
Release(callback_);
}
return;
}
VLOG(10) << "Delete temporary allocation " << temp_allocation->ptr()
<< " size: " << temp_allocation->size();
delete temp_allocation;
}
size_t TemporaryAllocator::TemporaryAllocationQueueSize() {
std::unique_lock<std::mutex> lock(mtx_);
return temp_mem_map_ ? temp_mem_map_->size() : 0;
return temp_mem_queue_ ? temp_mem_queue_->size() : 0;
}
void TemporaryAllocator::SetCallback(const std::function<void()> &callback) {
@ -98,27 +84,6 @@ void TemporaryAllocator::SetCallback(const std::function<void()> &callback) {
alloc::Allocation *TemporaryAllocator::AllocateImpl(
size_t size, alloc::Allocator::Attr attr) {
{
// Find available allocation in temp_mem_map.
std::unique_lock<std::mutex> lock(mtx_);
if (temp_mem_map_->size()) {
auto it = temp_mem_map_->lower_bound(size);
// FIXME(zcd): Not sure the best value of excess fraction.
if (it != temp_mem_map_->end() &&
it->first <
static_cast<size_t>(
size * FLAGS_times_excess_than_required_tmp_allocation)) {
auto tmp_ptr = it->second;
temp_mem_map_->erase(it);
wait_delete_mem_ -= tmp_ptr->size();
VLOG(10) << "Reuse temporary allocation: " << tmp_ptr->ptr() << ": "
<< tmp_ptr->size();
return tmp_ptr;
}
}
}
// If not find the the available allocation, get allocation from
// AllocatorFacadeInstance.
auto raw_allocation =
alloc::AllocatorFacade::Instance().Alloc(place_, size, attr);
auto temp_mem = new TemporaryAllocation(std::move(raw_allocation));

10
paddle/fluid/platform/temporary_allocator.h
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@ -15,7 +15,6 @@
#pragma once
#include <condition_variable> // NOLINT
#include <deque>
#include <map>
#include <mutex> // NOLINT
#include "paddle/fluid/memory/allocation/allocator.h"
#include "paddle/fluid/platform/lock_guard_ptr.h"
@ -40,7 +39,7 @@ class TemporaryAllocation : public memory::allocation::Allocation {
*
* There is one opportunity to free the allocations of temp_allocation_queue:
* - when the allocation size of opportunities exceeds a certain threshold
* (defined by FLAGS_limit_of_tmp_allocation).
* (defined by FLAGS_limit_of_temporary_allocation).
*
* */
class TemporaryAllocator : public memory::allocation::Allocator {
@ -63,10 +62,11 @@ class TemporaryAllocator : public memory::allocation::Allocator {
private:
platform::Place place_;
// When the allocation is not held by any variable, it should be placed
// to temp_mem_map immediately.
std::unique_ptr<std::multimap<size_t, TemporaryAllocation *>> temp_mem_map_{
nullptr};
// to temp_mem_queue immediately.
std::shared_ptr<std::deque<TemporaryAllocation *>> temp_mem_queue_{nullptr};
std::mutex mtx_;
size_t wait_delete_mem_{0};
std::function<void()> callback_;

58
paddle/fluid/platform/temporary_allocator_test.cc
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@ -18,8 +18,7 @@
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/tensor_util.h"
DECLARE_int64(limit_of_tmp_allocation);
DECLARE_double(times_excess_than_required_tmp_allocation);
DECLARE_double(limit_of_temporary_allocation);
namespace paddle {
namespace platform {
@ -36,7 +35,7 @@ class DummyOp : public framework::OperatorBase {
const platform::Place& place) const override {}
};
TEST(temporary_allocator, test_base_function) {
TEST(temporary_allocator, temporary_allocator) {
platform::CPUPlace cpu_place;
TemporaryAllocator alloc(cpu_place);
alloc.Allocate(100);
@ -60,10 +59,10 @@ TEST(temporary_allocator, test_base_function) {
#endif
}
TEST(temporary_allocator, test_flags_function) {
TEST(temporary_allocator, add_callback) {
#ifdef PADDLE_WITH_CUDA
const int64_t limit = FLAGS_limit_of_tmp_allocation;
FLAGS_limit_of_tmp_allocation = 10;
const double limit = FLAGS_limit_of_temporary_allocation;
FLAGS_limit_of_temporary_allocation = 10;
platform::CUDAPlace gpu_place(0);
TemporaryAllocator gpu_alloc(gpu_place);
@ -79,52 +78,7 @@ TEST(temporary_allocator, test_flags_function) {
});
{ gpu_alloc.Allocate(100); }
PADDLE_ENFORCE(deleted);
FLAGS_limit_of_tmp_allocation = limit;
#endif
}
TEST(temporary_allocator, test_reuse_tmp_allocation) {
#ifdef PADDLE_WITH_CUDA
platform::CUDAPlace gpu_place(0);
TemporaryAllocator gpu_alloc(gpu_place);
gpu_alloc.SetCallback([]() {});
void* tmp_allocation_ptr1 = nullptr;
{
PADDLE_ENFORCE_EQ(gpu_alloc.TemporaryAllocationQueueSize(), 0);
auto tmp_allocation1 = gpu_alloc.Allocate(100);
tmp_allocation_ptr1 = tmp_allocation1->ptr();
}
PADDLE_ENFORCE_EQ(gpu_alloc.TemporaryAllocationQueueSize(), 1);
auto tmp_allocation2 = gpu_alloc.Allocate(100);
void* tmp_allocation_ptr2 = tmp_allocation2->ptr();
PADDLE_ENFORCE_EQ(gpu_alloc.TemporaryAllocationQueueSize(), 0);
PADDLE_ENFORCE_EQ(tmp_allocation_ptr1, tmp_allocation_ptr2);
auto tmp_allocation3 = gpu_alloc.Allocate(100);
void* tmp_allocation_ptr3 = tmp_allocation2->ptr();
PADDLE_ENFORCE_EQ(tmp_allocation_ptr1, tmp_allocation_ptr3);
#endif
}
TEST(temporary_allocator, test_times_excess_than_required_tmp_allocation) {
#ifdef PADDLE_WITH_CUDA
platform::CUDAPlace gpu_place(0);
TemporaryAllocator gpu_alloc(gpu_place);
gpu_alloc.SetCallback([]() {});
double excess_fraction = FLAGS_times_excess_than_required_tmp_allocation;
void* tmp_allocation_ptr1 = nullptr;
{
PADDLE_ENFORCE_EQ(gpu_alloc.TemporaryAllocationQueueSize(), 0);
auto tmp_allocation1 =
gpu_alloc.Allocate(static_cast<size_t>(100 * excess_fraction - 1));
tmp_allocation_ptr1 = tmp_allocation1->ptr();
}
PADDLE_ENFORCE_EQ(gpu_alloc.TemporaryAllocationQueueSize(), 1);
auto tmp_allocation2 = gpu_alloc.Allocate(100);
void* tmp_allocation_ptr2 = tmp_allocation2->ptr();
PADDLE_ENFORCE_EQ(gpu_alloc.TemporaryAllocationQueueSize(), 0);
PADDLE_ENFORCE_EQ(tmp_allocation_ptr1, tmp_allocation_ptr2);
FLAGS_limit_of_temporary_allocation = limit;
#endif
}

3
python/paddle/fluid/__init__.py
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@ -155,8 +155,7 @@ def __bootstrap__():
'fraction_of_gpu_memory_to_use', 'cudnn_deterministic',
'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
'cudnn_exhaustive_search', 'memory_optimize_debug', 'selected_gpus',
'sync_nccl_allreduce', 'limit_of_tmp_allocation',
'times_excess_than_required_tmp_allocation'
'sync_nccl_allreduce'
]
core.init_gflags([sys.argv[0]] +

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