Revert "Remove op handle lock"

test=develop

paddle/fluid/operators/math/blas_impl.cu.h

@@ -62,19 +62,27 @@ struct CUBlas<float> {
                       cudaDataType_t Atype, int lda, const void *B,
                       cudaDataType_t Btype, int ldb, const float *beta, void *C,
                       cudaDataType_t Ctype, int ldc) {
-// Because the gcc 4.8 doesn't expand template parameter pack that
-// appears in a lambda-expression, I can not use template parameter pack
-// here.
+    // Because the gcc 4.8 doesn't expand template parameter pack that
+    // appears in a lambda-expression, I can not use template parameter pack
+    // here.
+    auto cublas_call = [&]() {
 #if CUDA_VERSION >= 8000
       VLOG(5) << "use_tensor_op_math: "
-            << (dev_ctx->tensor_core_available() ? "True" : "False");
-    dev_ctx->TensorCoreCublasCallIfAvailable([&](cublasHandle_t handle) {
+              << (platform::TensorCoreAvailable() ? "True" : "False");
       PADDLE_ENFORCE(platform::dynload::cublasSgemmEx(
-          handle, transa, transb, m, n, k, alpha, A, Atype, lda, B, Btype, ldb,
-          beta, C, Ctype, ldc));
-    });
+          dev_ctx->cublas_handle(), transa, transb, m, n, k, alpha, A, Atype,
+          lda, B, Btype, ldb, beta, C, Ctype, ldc));
 #else
       PADDLE_THROW("cublasSgemmEx is supported on cuda >= 8.0");
+#endif
+    };
+
+#if CUDA_VERSION >= 9000
+    // NOTES: To use Tensor Core, we should change the cublas config,
+    // but the cublas may be hold by multi-thread.
+    dev_ctx->CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
+#else
+    cublas_call();
 #endif
   }
 };
@@ -162,10 +170,11 @@ struct CUBlas<platform::float16> {
                       cudaDataType_t Btype, int ldb, const void *beta, void *C,
                       cudaDataType_t Ctype, int ldc,
                       cudaDataType_t computeType) {
+    auto cublas_call = [&]() {
 #if CUDA_VERSION >= 8000
       cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
 #if CUDA_VERSION >= 9000
-    bool use_tensor_op_math = dev_ctx->tensor_core_available();
+      bool use_tensor_op_math = platform::TensorCoreAvailable();
       if (use_tensor_op_math) {
         algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
       }
@@ -173,13 +182,20 @@ struct CUBlas<platform::float16> {
               << (use_tensor_op_math ? "True" : "False");
 #endif  // CUDA_VERSION >= 9000
 
-    dev_ctx->TensorCoreCublasCallIfAvailable([&](cublasHandle_t handle) {
       PADDLE_ENFORCE(platform::dynload::cublasGemmEx(
-          handle, transa, transb, m, n, k, alpha, A, Atype, lda, B, Btype, ldb,
-          beta, C, Ctype, ldc, computeType, algo));
-    });
+          dev_ctx->cublas_handle(), transa, transb, m, n, k, alpha, A, Atype,
+          lda, B, Btype, ldb, beta, C, Ctype, ldc, computeType, algo));
 #else
       PADDLE_THROW("cublasGemmEx is supported on cuda >= 8.0");
+#endif
+    };
+
+#if CUDA_VERSION >= 9000
+    // NOTES: To use Tensor Core, we should change the cublas config,
+    // but the cublas may be hold by multi-thread.
+    dev_ctx->CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
+#else
+    cublas_call();
 #endif
   }
 };
@@ -207,10 +223,9 @@ void Blas<platform::CUDADeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
                        CUDA_R_32F, N);
   } else {
 #endif  // CUDA_VERSION >= 8000
-    context_.CublasCall([&](cublasHandle_t handle) {
-      CUBlas<T>::GEMM(handle, cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A,
-                      lda, &beta, C, N);
-    });
+
+    CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
+                    &alpha, B, ldb, A, lda, &beta, C, N);
 
 #if CUDA_VERSION >= 8000
   }
@@ -251,12 +266,9 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
       CUDA_R_16F, lda, &h_beta, C, CUDA_R_16F, N, CUDA_R_32F);
 #else
   // CUDA 7.5 does not support cublasGemmEx, hence we fall back to use hgemm
-
-  context_.CublasCall([&](cublasHandle_t handle) {
-    CUBlas<platform::float16>::GEMM(handle, cuTransB, cuTransA, N, M, K,
-                                    &h_alpha, h_B, ldb, h_A, lda, &h_beta, h_C,
-                                    N);
-  });
+  CUBlas<platform::float16>::GEMM(context_.cublas_handle(), cuTransB, cuTransA,
+                                  N, M, K, &h_alpha, h_B, ldb, h_A, lda,
+                                  &h_beta, h_C, N);
 #endif  // CUDA_VERSION >= 8000
 }
 
@@ -280,10 +292,8 @@ void Blas<platform::CUDADeviceContext>::GEMM(bool transA, bool transB, int M,
   } else {
 #endif  // CUDA_VERSION >= 8000
 
-    context_.CublasCall([&](cublasHandle_t handle) {
-      CUBlas<T>::GEMM(handle, cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A,
-                      lda, &beta, C, ldc);
-    });
+    CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
+                    &alpha, B, ldb, A, lda, &beta, C, ldc);
 
 #if CUDA_VERSION >= 8000
   }
@@ -301,19 +311,16 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
   cublasOperation_t cuTransA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
   cublasOperation_t cuTransB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
 
-  context_.CublasCall([&](cublasHandle_t handle) {
-    CUBlas<platform::float16>::GEMM(handle, cuTransB, cuTransA, N, M, K, &alpha,
-                                    B, ldb, A, lda, &beta, C, ldc);
-  });
+  CUBlas<platform::float16>::GEMM(context_.cublas_handle(), cuTransB, cuTransA,
+                                  N, M, K, &alpha, B, ldb, A, lda, &beta, C,
+                                  ldc);
 }
 
 template <>
 template <typename T>
 void Blas<platform::CUDADeviceContext>::AXPY(int n, T alpha, const T *x,
                                              T *y) const {
-  context_.CublasCall([&](cublasHandle_t handle) {
-    CUBlas<T>::AXPY(handle, n, &alpha, x, 1, y, 1);
-  });
+  CUBlas<T>::AXPY(context_.cublas_handle(), n, &alpha, x, 1, y, 1);
 }
 
 template <>
@@ -323,9 +330,8 @@ void Blas<platform::CUDADeviceContext>::GEMV(bool trans_a, int M, int N,
                                              T beta, T *C) const {
   cublasOperation_t cuTransA = !trans_a ? CUBLAS_OP_T : CUBLAS_OP_N;
 
-  context_.CublasCall([&](cublasHandle_t handle) {
-    CUBlas<T>::GEMV(handle, cuTransA, N, M, &alpha, A, N, B, 1, &beta, C, 1);
-  });
+  CUBlas<T>::GEMV(context_.cublas_handle(), cuTransA, N, M, &alpha, A, N, B, 1,
+                  &beta, C, 1);
 }
 
 template <>
@@ -347,28 +353,28 @@ void Blas<platform::CUDADeviceContext>::BatchedGEMM(
 
 #if CUDA_VERSION >= 9010
   if (FLAGS_enable_cublas_tensor_op_math && std::is_same<T, float>::value) {
+    auto cublas_call = [&]() {
       cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
-    bool use_tensor_op_math = context_.tensor_core_available();
+      bool use_tensor_op_math = platform::TensorCoreAvailable();
       if (use_tensor_op_math) {
         algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
       }
       VLOG(5) << "use_tensor_op_math: "
               << (use_tensor_op_math ? "True" : "False");
 
-    context_.TensorCoreCublasCallIfAvailable([&](cublasHandle_t handle) {
       PADDLE_ENFORCE(platform::dynload::cublasGemmStridedBatchedEx(
-          handle, cuTransB, cuTransA, N, M, K, &alpha, B, CUDA_R_32F, ldb,
-          strideB, A, CUDA_R_32F, lda, strideA, &beta, C, CUDA_R_32F, ldc,
-          strideC, batchCount, CUDA_R_32F, algo));
-    });
+          context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha, B,
+          CUDA_R_32F, ldb, strideB, A, CUDA_R_32F, lda, strideA, &beta, C,
+          CUDA_R_32F, ldc, strideC, batchCount, CUDA_R_32F, algo));
+    };
+    auto &dev_ctx = const_cast<platform::CUDADeviceContext &>(context_);
+    dev_ctx.CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
   } else {
 #endif  // CUDA_VERSION >= 9010
 
-    context_.CublasCall([&](cublasHandle_t handle) {
-      CUBlas<T>::GEMM_STRIDED_BATCH(handle, cuTransB, cuTransA, N, M, K, &alpha,
-                                    B, ldb, strideB, A, lda, strideA, &beta, C,
-                                    ldc, strideC, batchCount);
-    });
+    CUBlas<T>::GEMM_STRIDED_BATCH(context_.cublas_handle(), cuTransB, cuTransA,
+                                  N, M, K, &alpha, B, ldb, strideB, A, lda,
+                                  strideA, &beta, C, ldc, strideC, batchCount);
 
 #if CUDA_VERSION >= 9010
   }

paddle/fluid/platform/cuda_helper.h

-// Copyright (c) 2019 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.
-
-#pragma once
-
-#include <mutex>  // NOLINT
-
-#include "paddle/fluid/platform/dynload/cublas.h"
-#include "paddle/fluid/platform/macros.h"
-
-#if CUDA_VERSION < 9000
-enum cublasMath_t { CUBLAS_DEFAULT_MATH = 0 };
-#endif
-
-namespace paddle {
-namespace platform {
-
-class CublasHandleHolder {
- public:
-  CublasHandleHolder(cudaStream_t stream, cublasMath_t math_type) {
-    PADDLE_ENFORCE(dynload::cublasCreate(&handle_));
-    PADDLE_ENFORCE(dynload::cublasSetStream(handle_, stream));
-#if CUDA_VERSION >= 9000
-    if (math_type == CUBLAS_TENSOR_OP_MATH) {
-      PADDLE_ENFORCE(
-          dynload::cublasSetMathMode(handle_, CUBLAS_TENSOR_OP_MATH));
-    }
-#endif
-  }
-
-  ~CublasHandleHolder() { PADDLE_ENFORCE(dynload::cublasDestroy(handle_)); }
-
-  template <typename Callback>
-  inline void Call(Callback &&callback) const {
-    std::lock_guard<std::mutex> guard(mtx_);
-    callback(handle_);
-  }
-
- private:
-  DISABLE_COPY_AND_ASSIGN(CublasHandleHolder);
-
-  cublasHandle_t handle_;
-  mutable std::mutex mtx_;
-};
-
-}  // namespace platform
-}  // namespace paddle

paddle/fluid/platform/device_context.cc

@@ -245,15 +245,8 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
   eigen_stream_.reset(new EigenCudaStreamDevice());
   eigen_stream_->Reinitialize(&stream_, place);
   eigen_device_.reset(new Eigen::GpuDevice(eigen_stream_.get()));
-  cublas_handle_.reset(new CublasHandleHolder(stream_, CUBLAS_DEFAULT_MATH));
-
-  if (TensorCoreAvailable()) {
-#if CUDA_VERSION >= 9000
-    cublas_tensor_core_handle_.reset(
-        new CublasHandleHolder(stream_, CUBLAS_TENSOR_OP_MATH));
-#endif
-  }
-
+  PADDLE_ENFORCE(dynload::cublasCreate(&cublas_handle_));
+  PADDLE_ENFORCE(dynload::cublasSetStream(cublas_handle_, stream_));
   if (dynload::HasCUDNN()) {
     cudnn_holder_.reset(new CudnnHolder(&stream_, place));
   }
@@ -313,8 +306,7 @@ CUDADeviceContext::~CUDADeviceContext() {
   SetDeviceId(place_.device);
   Wait();
   WaitStreamCallback();
-  cublas_handle_.reset();
-  cublas_tensor_core_handle_.reset();
+  PADDLE_ENFORCE(dynload::cublasDestroy(cublas_handle_));
   eigen_stream_.reset();
   eigen_device_.reset();
   PADDLE_ENFORCE(cudaStreamDestroy(stream_));
@@ -343,8 +335,8 @@ Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
   return eigen_device_.get();
 }
 
-bool CUDADeviceContext::tensor_core_available() const {
-  return cublas_tensor_core_handle_ != nullptr;
+cublasHandle_t CUDADeviceContext::cublas_handle() const {
+  return cublas_handle_;
 }
 
 cudnnHandle_t CUDADeviceContext::cudnn_handle() const {

paddle/fluid/platform/device_context.h

@@ -20,7 +20,6 @@ limitations under the License. */
 #include "paddle/fluid/memory/malloc.h"
 #include "paddle/fluid/platform/temporary_allocator.h"
 #ifdef PADDLE_WITH_CUDA
-#include "paddle/fluid/platform/cuda_helper.h"
 #include "paddle/fluid/platform/dynload/cublas.h"
 #include "paddle/fluid/platform/dynload/cudnn.h"
 #include "paddle/fluid/platform/gpu_info.h"
@@ -210,6 +209,39 @@ class CudnnWorkspaceHandle {
   std::unique_ptr<std::lock_guard<std::mutex>> guard_;
 };
 
+#if CUDA_VERSION >= 9000
+class ScopedCublasMathMode {
+ public:
+  ScopedCublasMathMode(cublasHandle_t handle, cublasMath_t new_math_mode)
+      : handle_(handle) {
+    need_reset = false;
+    PADDLE_ENFORCE(
+        platform::dynload::cublasGetMathMode(handle_, &old_math_mode_),
+        "Failed to get old cublas math mode");
+    if (old_math_mode_ != new_math_mode) {
+      PADDLE_ENFORCE(
+          platform::dynload::cublasSetMathMode(handle_, new_math_mode),
+          "Failed to set old cublas math mode");
+      need_reset = true;
+    }
+  }
+
+  ~ScopedCublasMathMode() {
+    if (need_reset) {
+      PADDLE_ENFORCE(
+          platform::dynload::cublasSetMathMode(handle_, old_math_mode_),
+          "Failed to set old cublas math mode");
+    }
+  }
+
+ private:
+  cublasHandle_t handle_;
+  cublasMath_t old_math_mode_;
+  bool need_reset;
+};
+
+#endif
+
 class CUDADeviceContext : public DeviceContext {
  public:
   explicit CUDADeviceContext(CUDAPlace place);
@@ -230,25 +262,8 @@ class CUDADeviceContext : public DeviceContext {
   /*! \brief  Return eigen device in the device context. */
   Eigen::GpuDevice* eigen_device() const;
 
-  /*! \brief  Call cublas function safely. */
-  template <typename Callback>
-  inline void CublasCall(Callback&& callback) const {
-    cublas_handle_->Call(std::forward<Callback>(callback));
-  }
-
-  /*! \brief  Check whether tensor core is supported */
-  bool tensor_core_available() const;
-
-  /*! \brief  Call cublas function with Tensor Core safely. If
-      Tensor Core is not available, use DEFAULT_MATH instead. */
-  template <typename Callback>
-  inline void TensorCoreCublasCallIfAvailable(Callback&& callback) const {
-    if (cublas_tensor_core_handle_) {
-      cublas_tensor_core_handle_->Call(std::forward<Callback>(callback));
-    } else {
-      cublas_handle_->Call(std::forward<Callback>(callback));
-    }
-  }
+  /*! \brief  Return cublas handle in the device context. */
+  cublasHandle_t cublas_handle() const;
 
   /*! \brief  Return cudnn  handle in the device context. */
   cudnnHandle_t cudnn_handle() const;
@@ -267,6 +282,7 @@ class CUDADeviceContext : public DeviceContext {
 
   template <typename Callback>
   void RecordEvent(cudaEvent_t ev, Callback callback) {
+    std::lock_guard<std::mutex> guard(mtx_);
     callback();
     PADDLE_ENFORCE(cudaEventRecord(ev, stream_));
   }
@@ -278,6 +294,18 @@ class CUDADeviceContext : public DeviceContext {
 
   void WaitStreamCallback() const { callback_manager_->Wait(); }
 
+#if CUDA_VERSION >= 9000
+  /*! \brief CublasCall may need to change cublas's config,
+   *  but the cublas may be hold by multi-thread, so we should
+   *  add lock here. */
+  template <typename Callback>
+  void CublasCall(Callback callback, cublasMath_t new_math) {
+    std::lock_guard<std::mutex> guard(cublas_mtx_);
+    ScopedCublasMathMode scoped_cublas_math(cublas_handle_, new_math);
+    callback();
+  }
+#endif
+
  private:
   CUDAPlace place_;
 
@@ -285,9 +313,7 @@ class CUDADeviceContext : public DeviceContext {
   std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
   std::unique_ptr<CudnnHolder> cudnn_holder_;
   cudaStream_t stream_;
-
-  std::unique_ptr<CublasHandleHolder> cublas_handle_;
-  std::unique_ptr<CublasHandleHolder> cublas_tensor_core_handle_;
+  cublasHandle_t cublas_handle_;
 
   int compute_capability_;
   int runtime_version_;
@@ -295,10 +321,12 @@ class CUDADeviceContext : public DeviceContext {
   int multi_process_;
   int max_threads_per_mp_;
 
+  mutable std::mutex mtx_;
+
   // StreamCallbackManager is thread-safe
   std::unique_ptr<StreamCallbackManager> callback_manager_;
 
-  DISABLE_COPY_AND_ASSIGN(CUDADeviceContext);
+  mutable std::mutex cublas_mtx_;
 };
 
 template <>

paddle/fluid/platform/device_context_test.cu

@@ -43,6 +43,9 @@ TEST(Device, CUDADeviceContext) {
     ASSERT_NE(nullptr, gpu_device);
     cudnnHandle_t cudnn_handle = device_context->cudnn_handle();
     ASSERT_NE(nullptr, cudnn_handle);
+    cublasHandle_t cublas_handle = device_context->cublas_handle();
+    ASSERT_NE(nullptr, cublas_handle);
+    ASSERT_NE(nullptr, device_context->stream());
     delete device_context;
   }
 }