Merge pull request #15278 from chengduoZH/revert_remove_workspace_handle_in_conv2d_cudnn

Revert "Remove workspace_handle in conv_cudnn (#15186)"

paddle/fluid/framework/operator.h

@@ -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(

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -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;
-
-            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,
-                    workspace_size_limit));
+            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,
+                      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++) {
-      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,
-          &beta, cudnn_output_desc, output_data + i * group_offset_out));
+      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, 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::
-                                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));
+              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,
+                            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;
-
-              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));
+              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, 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++) {
-        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,
-            cudnn_input_desc, input_grad_data + i * group_offset_in));
+        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, 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++) {
-        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));
+        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, workspace_size_in_bytes, &beta,
+              cudnn_filter_desc, filter_grad_data + i * group_offset_filter));
+        };
+        workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
       }
     }
   }

paddle/fluid/platform/device_context.h

@@ -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 {

paddle/fluid/platform/temporary_allocator.cc

@@ -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,
-             "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.");
+DEFINE_double(limit_of_temporary_allocation, -1,
+              "The up limit of temporary_allocation size.");
 
 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));

paddle/fluid/platform/temporary_allocator.h

@@ -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_;

paddle/fluid/platform/temporary_allocator_test.cc

@@ -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
 }
 

python/paddle/fluid/__init__.py

@@ -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]] +