Merge pull request #13195 from PaddlePaddle/revert-13078-dev_CudnnHolder

Revert "Add CudnnHolder and use it in Conv and ConvTranspose op"

paddle/fluid/framework/rw_lock.h

@@ -56,76 +56,5 @@ struct RWLock {
 };
 #endif
 
-class RWLockGuard {
- public:
-  enum Status { kUnLock, kWRLock, kRDLock };
-
-  RWLockGuard(RWLock* rw_lock, Status init_status)
-      : lock_(rw_lock), status_(Status::kUnLock) {
-    switch (init_status) {
-      case Status::kRDLock: {
-        RDLock();
-        break;
-      }
-      case Status::kWRLock: {
-        WRLock();
-        break;
-      }
-      case Status::kUnLock: {
-        break;
-      }
-    }
-  }
-
-  void WRLock() {
-    switch (status_) {
-      case Status::kUnLock: {
-        lock_->WRLock();
-        status_ = Status::kWRLock;
-        break;
-      }
-      case Status::kWRLock: {
-        break;
-      }
-      case Status::kRDLock: {
-        PADDLE_THROW(
-            "Please unlock read lock first before invoking write lock.");
-        break;
-      }
-    }
-  }
-
-  void RDLock() {
-    switch (status_) {
-      case Status::kUnLock: {
-        lock_->RDLock();
-        status_ = Status::kRDLock;
-        break;
-      }
-      case Status::kRDLock: {
-        break;
-      }
-      case Status::kWRLock: {
-        PADDLE_THROW(
-            "Please unlock write lock first before invoking read lock.");
-        break;
-      }
-    }
-  }
-
-  void UnLock() {
-    if (status_ != Status::kUnLock) {
-      lock_->UNLock();
-      status_ = Status::kUnLock;
-    }
-  }
-
-  ~RWLockGuard() { UnLock(); }
-
- private:
-  RWLock* lock_;
-  Status status_;
-};
-
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -118,6 +118,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
         output_channels / groups * output_height * output_width * output_depth;
     int group_offset_filter = filter->numel() / groups;
     // ------------------- cudnn conv workspace ---------------------
+    void* cudnn_workspace = nullptr;
     size_t workspace_size_in_bytes;  // final workspace to allocate.
     size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
     if (user_workspace_size > 0) {
@@ -158,18 +159,20 @@ 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
+    platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
+    cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
     // ------------------- 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, workspace_size_in_bytes,
           &beta, cudnn_output_desc, output_data + i * group_offset_out));
-      };
-      dev_ctx.RunCudnnFuncWithWorkspace(cudnn_func, workspace_size_in_bytes);
     }
+    // Release the cudnn workspace
+    paddle::memory::Free(gpu, cudnn_workspace);
   }
 };
 
@@ -311,7 +314,11 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
               cudnn_filter_desc, filter_algo, &tmp_size));
       workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
     }
-
+    // ------------------- cudnn conv workspace ---------------------
+    // Already on GPU
+    void* cudnn_workspace = nullptr;
+    platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
+    cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
     // ------------------- cudnn conv backward data ---------------------
     ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
     if (input_grad) {
@@ -319,15 +326,12 @@ 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, workspace_size_in_bytes, &beta,
-              cudnn_input_desc, input_grad_data + i * group_offset_in));
-        };
-        dev_ctx.RunCudnnFuncWithWorkspace(cudnn_func, workspace_size_in_bytes);
+            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));
       }
     }
     // ------------------- cudnn conv backward filter ---------------------
@@ -335,17 +339,16 @@ 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, workspace_size_in_bytes, &beta,
-              cudnn_filter_desc, filter_grad_data + i * group_offset_filter));
-        };
-        dev_ctx.RunCudnnFuncWithWorkspace(cudnn_func, workspace_size_in_bytes);
+            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));
       }
     }
+    // Release the cudnn workspace
+    paddle::memory::Free(gpu, cudnn_workspace);
   }
 };
 

paddle/fluid/operators/conv_transpose_cudnn_op.cu.cc

@@ -76,6 +76,7 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
         conv_desc.descriptor<T>(paddings, strides, dilations);
 
     // ------------------- cudnn conv workspace ---------------------
+    void* cudnn_workspace = nullptr;
     size_t workspace_size_in_bytes;  // final workspace to allocate.
     size_t workspace_size_limit = kConvCUDNNWorkspaceLimitBytes;
     if (user_workspace_size > 0) {
@@ -99,21 +100,25 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
             handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
             cudnn_output_desc, algo, &workspace_size_in_bytes));
 
+    // Allocate on GPU memory
+    platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
+    cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
+
     // ------------------- cudnn conv transpose forward ---------------------
     int input_offset = input->numel() / input->dims()[0] / groups;
     int output_offset = output->numel() / output->dims()[0] / groups;
     int filter_offset = filter->numel() / groups;
     T alpha = 1.0f, beta = 0.0f;
     for (int g = 0; g < groups; g++) {
-      auto cudnn_func = [&](void* cudnn_workspace) {
       CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
           handle, &alpha, cudnn_filter_desc, filter_data + filter_offset * g,
           cudnn_input_desc, input_data + input_offset * g, cudnn_conv_desc,
           algo, cudnn_workspace, workspace_size_in_bytes, &beta,
           cudnn_output_desc, output_data + output_offset * g));
-      };
-      dev_ctx.RunCudnnFuncWithWorkspace(cudnn_func, workspace_size_in_bytes);
     }
+
+    // Release the cudnn workspace
+    paddle::memory::Free(gpu, cudnn_workspace);
   }
 };
 
@@ -201,6 +206,11 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
           std::max(workspace_size_in_bytes, bwd_filter_ws_size);
     }
 
+    // ------------------- cudnn conv workspace ---------------------
+    // Already on GPU
+    void* cudnn_workspace = nullptr;
+    platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
+    cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
     // ------------------- cudnn conv backward data ---------------------
     // FIXME(typhoonzero): template type T may not be the same as cudnn call.
     int input_offset = input->numel() / input->dims()[0] / groups;
@@ -212,15 +222,12 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
       T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
       // Because beta is zero, it is unnecessary to reset input_grad.
       for (int g = 0; g < groups; g++) {
-        auto cudnn_func = [&](void* cudnn_workspace) {
         CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
             handle, &alpha, cudnn_output_desc,
             output_grad_data + output_grad_offset * g, cudnn_filter_desc,
             filter_data + filter_offset * g, cudnn_conv_desc, data_algo,
             cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_input_desc,
             input_grad_data + input_offset * g));
-        };
-        dev_ctx.RunCudnnFuncWithWorkspace(cudnn_func, workspace_size_in_bytes);
       }
     }
 
@@ -230,17 +237,17 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
       // Because beta is zero, it is unnecessary to reset filter_grad.
       // Gradient with respect to the filter
       for (int g = 0; g < groups; g++) {
-        auto cudnn_func = [&](void* cudnn_workspace) {
         CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
             handle, &alpha, cudnn_output_desc,
             output_grad_data + output_grad_offset * g, cudnn_input_desc,
             input_data + input_offset * g, cudnn_conv_desc, filter_algo,
-              cudnn_workspace, workspace_size_in_bytes, &beta,
-              cudnn_filter_desc, filter_grad_data + filter_offset * g));
-        };
-        dev_ctx.RunCudnnFuncWithWorkspace(cudnn_func, workspace_size_in_bytes);
+            cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_filter_desc,
+            filter_grad_data + filter_offset * g));
       }
     }
+
+    // Release the cudnn workspace
+    paddle::memory::Free(gpu, cudnn_workspace);
   }
 };
 

paddle/fluid/platform/device_context.cc

@@ -16,9 +16,6 @@ limitations under the License. */
 #include <vector>
 
 #include "paddle/fluid/memory/memory.h"
-#ifdef PADDLE_WITH_CUDA
-#include "paddle/fluid/framework/rw_lock.h"
-#endif
 
 namespace paddle {
 namespace platform {
@@ -145,59 +142,7 @@ class EigenCudaStreamDevice : public Eigen::StreamInterface {
   mutable unsigned int* semaphore_;
 };
 
-class CudnnHolder {
- public:
-  CudnnHolder(const cudaStream_t* stream, const CUDAPlace& place)
-      : workspace_(nullptr), workspace_len_(0), stream_(stream), place_(place) {
-    PADDLE_ENFORCE(dynload::cudnnCreate(&cudnn_handle_));
-    PADDLE_ENFORCE(dynload::cudnnSetStream(cudnn_handle_, *stream_));
-  }
-
-  cudnnHandle_t cudnn_handle() const { return cudnn_handle_; }
-
-  void RunFunc(const std::function<void(void*)>& cudnn_func,
-               size_t required_workspace_len) {
-    std::lock_guard<std::mutex> lock(mtx_);
-    if (required_workspace_len > workspace_len_) {
-      ReallocateWorkspace(required_workspace_len);
-    }
-    cudnn_func(workspace_);
-  }
-
-  ~CudnnHolder() {
-    PADDLE_ENFORCE(dynload::cudnnDestroy(cudnn_handle_));
-    if (workspace_ != nullptr) {
-      paddle::memory::Free(place_, workspace_);
-    }
-  }
-
- private:
-  void ReallocateWorkspace(size_t required_workspace_len) {
-    if (required_workspace_len <= workspace_len_) {
-      return;
-    }
-    void* new_workspace = paddle::memory::Alloc(place_, required_workspace_len);
-    if (workspace_ != nullptr) {
-      // Maybe someone is using the current workspace
-      PADDLE_ENFORCE(cudaStreamSynchronize(*stream_));
-      paddle::memory::Free(place_, workspace_);
-    }
-    workspace_ = new_workspace;
-    workspace_len_ = required_workspace_len;
-  }
-
-  cudnnHandle_t cudnn_handle_;
-  void* workspace_;
-  size_t workspace_len_;
-
-  const cudaStream_t* stream_;  // not owned;
-  const CUDAPlace place_;
-
-  std::mutex mtx_;
-};
-
-CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
-    : place_(place), cudnn_holder_(nullptr) {
+CUDADeviceContext::CUDADeviceContext(CUDAPlace place) : place_(place) {
   SetDeviceId(place_.device);
   compute_capability = GetCUDAComputeCapability(place_.device);
   multi_process = GetCUDAMultiProcessors(place_.device);
@@ -209,7 +154,10 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
   PADDLE_ENFORCE(dynload::cublasCreate(&cublas_handle_));
   PADDLE_ENFORCE(dynload::cublasSetStream(cublas_handle_, stream_));
   if (dynload::HasCUDNN()) {
-    cudnn_holder_.reset(new CudnnHolder(&stream_, place));
+    PADDLE_ENFORCE(dynload::cudnnCreate(&cudnn_handle_));
+    PADDLE_ENFORCE(dynload::cudnnSetStream(cudnn_handle_, stream_));
+  } else {
+    cudnn_handle_ = nullptr;
   }
 }
 
@@ -217,6 +165,9 @@ CUDADeviceContext::~CUDADeviceContext() {
   SetDeviceId(place_.device);
   Wait();
   PADDLE_ENFORCE(dynload::cublasDestroy(cublas_handle_));
+  if (cudnn_handle_ != nullptr) {
+    PADDLE_ENFORCE(dynload::cudnnDestroy(cudnn_handle_));
+  }
   eigen_stream_.reset();
   eigen_device_.reset();
   PADDLE_ENFORCE(cudaStreamDestroy(stream_));
@@ -245,14 +196,7 @@ cublasHandle_t CUDADeviceContext::cublas_handle() const {
   return cublas_handle_;
 }
 
-cudnnHandle_t CUDADeviceContext::cudnn_handle() const {
-  return cudnn_holder_->cudnn_handle();
-}
-
-void CUDADeviceContext::RunCudnnFuncWithWorkspace(
-    const std::function<void(void*)>& cudnn_func, size_t workspace_len) const {
-  cudnn_holder_->RunFunc(cudnn_func, workspace_len);
-}
+cudnnHandle_t CUDADeviceContext::cudnn_handle() const { return cudnn_handle_; }
 
 cudaStream_t CUDADeviceContext::stream() const { return stream_; }
 

paddle/fluid/platform/device_context.h

@@ -69,7 +69,6 @@ struct DefaultDeviceContextType<platform::CPUPlace> {
 #ifdef PADDLE_WITH_CUDA
 
 class EigenCudaStreamDevice;
-class CudnnHolder;
 
 class CUDADeviceContext : public DeviceContext {
  public:
@@ -97,11 +96,6 @@ class CUDADeviceContext : public DeviceContext {
   /*! \brief  Return cudnn  handle in the device context. */
   cudnnHandle_t cudnn_handle() const;
 
-  /*! \brief  Run a cudnn function with the workspace provided by
-   * CUDADeviceContext */
-  void RunCudnnFuncWithWorkspace(const std::function<void(void*)>& cudnn_func,
-                                 size_t workspace_len) const;
-
   /*! \brief  Return cuda stream in the device context. */
   cudaStream_t stream() const;
 
@@ -117,8 +111,8 @@ class CUDADeviceContext : public DeviceContext {
 
   std::unique_ptr<Eigen::GpuDevice> eigen_device_;
   std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
-  std::unique_ptr<CudnnHolder> cudnn_holder_;
   cudaStream_t stream_;
+  cudnnHandle_t cudnn_handle_;
   cublasHandle_t cublas_handle_;
 
   int compute_capability;