Convolution fusion operator. (#14449)

* Convolution fusion operator.
* Clean code
test=develop

cmake/operators.cmake

@@ -111,7 +111,7 @@ function(op_library TARGET)
 
     # Define operators that don't need pybind here.
     foreach(manual_pybind_op "compare_op" "logical_op" "nccl_op"
-"tensor_array_read_write_op" "tensorrt_engine_op")
+"tensor_array_read_write_op" "tensorrt_engine_op" "conv_fusion_op")
         if ("${TARGET}" STREQUAL "${manual_pybind_op}")
             set(pybind_flag 1)
         endif()

paddle/fluid/operators/CMakeLists.txt

@@ -34,7 +34,7 @@ if (WITH_GPU AND TENSORRT_FOUND)
     add_subdirectory(tensorrt)
 endif()
 
-register_operators(EXCLUDES warpctc_op)
+register_operators(EXCLUDES warpctc_op conv_fusion_op)
 
 # warpctc_cudnn need cudnn 7 above
 if (WITH_GPU)
@@ -43,6 +43,8 @@ if (WITH_GPU)
     else()
         op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
     endif()
+  op_library(conv_fusion_op)
+  file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(conv2d_fusion);\n")
 else()
     op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
 endif()

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -43,26 +43,6 @@ using DataLayout = platform::DataLayout;
 template <typename T>
 using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
 
-static constexpr char kCUDNNFwdAlgoCache[] = "kCUDNNFwdAlgoCache";
-static constexpr char kCUDNNBwdDataAlgoCache[] = "kCUDNNBwdDataAlgoCache";
-static constexpr char kCUDNNBwdFilterAlgoCache[] = "kCUDNNBwdFilterAlgoCache";
-
-static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES =
-    static_cast<size_t>(1024) * 1024 * 1024;
-
-#if CUDNN_VERSION_MIN(6, 0, 5)
-static constexpr size_t kNUM_CUDNN_FWD_ALGS = CUDNN_CONVOLUTION_FWD_ALGO_COUNT;
-static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS =
-    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT;
-static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS =
-    CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT;
-#else
-// cuDNN v5 has no CUDNN_CONVOLUTION_FWD_ALGO_COUNT etc.
-static constexpr size_t kNUM_CUDNN_FWD_ALGS = 7;
-static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS = 4;
-static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS = 5;
-#endif
-
 template <typename T>
 class CUDNNConvOpKernel : public framework::OpKernel<T> {
  public:

paddle/fluid/operators/conv_cudnn_op_cache.h

@@ -17,10 +17,31 @@ limitations under the License. */
 #include <functional>
 #include <unordered_map>
 #include <vector>
+#include "paddle/fluid/platform/cudnn_helper.h"
 
 namespace paddle {
 namespace operators {
 
+static constexpr char kCUDNNFwdAlgoCache[] = "kCUDNNFwdAlgoCache";
+static constexpr char kCUDNNBwdDataAlgoCache[] = "kCUDNNBwdDataAlgoCache";
+static constexpr char kCUDNNBwdFilterAlgoCache[] = "kCUDNNBwdFilterAlgoCache";
+
+static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES =
+    static_cast<size_t>(1024) * 1024 * 1024;
+
+#if CUDNN_VERSION_MIN(6, 0, 5)
+static constexpr size_t kNUM_CUDNN_FWD_ALGS = CUDNN_CONVOLUTION_FWD_ALGO_COUNT;
+static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS =
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT;
+static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS =
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT;
+#else
+// cuDNN v5 has no CUDNN_CONVOLUTION_FWD_ALGO_COUNT etc.
+static constexpr size_t kNUM_CUDNN_FWD_ALGS = 7;
+static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS = 4;
+static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS = 5;
+#endif
+
 template <typename TAlgorithm>
 class AlgorithmsCache {
  public:

paddle/fluid/operators/conv_fusion_op.cc

+/* Copyright (c) 2016 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. */
+
+#include <string>
+#include <vector>
+#include "paddle/fluid/operators/conv_op.h"
+#ifdef PADDLE_WITH_CUDA
+#include "paddle/fluid/platform/cudnn_helper.h"
+#endif
+
+namespace paddle {
+namespace operators {
+
+// This fused conv follows the equation:
+//   y = act ( alpha1 * conv(x) + alpha2 * z + bias ).
+//   here, y is Output,
+//         x is Input,
+//         z is ResidualData,
+//         bias is Bias
+class Conv2DFusionOpMaker : public Conv2DOpMaker {
+ protected:
+  void Apply() override {
+    AddAttr<std::string>(
+        "activation",
+        "The activation type can be 'identity', 'sigmoid', 'relu', 'relu6' "
+        "'relux' , 'tanh', 'band_pass'")
+        .SetDefault("relu");
+  }
+};
+// TODO(qingqing): add gradient operator for conv2d_fusion
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(conv2d_fusion, ops::ConvOp, ops::Conv2DFusionOpMaker,
+                  ops::ConvOpInferVarType, paddle::framework::EmptyGradOpMaker);

paddle/fluid/operators/conv_fusion_op.cu.cc

+/* Copyright (c) 2016 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. */
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/conv_cudnn_op_cache.h"
+#include "paddle/fluid/platform/cudnn_helper.h"
+
+DECLARE_uint64(conv_workspace_size_limit);
+DECLARE_bool(cudnn_exhaustive_search);
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
+using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
+using ScopedConvolutionDescriptor = platform::ScopedConvolutionDescriptor;
+using ScopedActivationDescriptor = platform::ScopedActivationDescriptor;
+using DataLayout = platform::DataLayout;
+template <typename T>
+using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
+
+template <typename T>
+class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    auto* input = ctx.Input<Tensor>("Input");
+    auto* filter = ctx.Input<Tensor>("Filter");
+    auto* bias = ctx.Input<Tensor>("Bias");
+    PADDLE_ENFORCE(bias, "The bias should not be null.");
+    auto* residual = ctx.Input<Tensor>("ResidualData");
+    auto* output = ctx.Output<Tensor>("Output");
+
+    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    const std::string activation = ctx.Attr<std::string>("activation");
+    int groups = ctx.Attr<int>("groups");
+    int64_t user_workspace_size =
+        static_cast<size_t>(ctx.Attr<int>("workspace_size_MB"));
+    bool exhaustive_search =
+        FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
+
+    const T* input_data = input->data<T>();
+    const T* filter_data = filter->data<T>();
+    const T* bias_data = bias->data<T>();
+    T* output_data = output->mutable_data<T>(ctx.GetPlace());
+    const T* residual_data = residual ? residual->data<T>() : output_data;
+
+    // ------------------- cudnn descriptors ---------------------
+    ScopedTensorDescriptor input_desc;
+    ScopedTensorDescriptor output_desc;
+    ScopedFilterDescriptor filter_desc;
+    ScopedTensorDescriptor bias_desc;
+    ScopedConvolutionDescriptor conv_desc;
+    ScopedActivationDescriptor act_desc;
+    DataLayout layout = DataLayout::kNCHW;
+    if (input->dims().size() == 5) {
+      layout = DataLayout::kNCDHW;
+    }
+
+    cudnnConvolutionDescriptor_t cudnn_conv_desc =
+        conv_desc.descriptor<T>(paddings, strides, dilations);
+    CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
+        cudnn_conv_desc, groups));
+
+    cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
+        layout, framework::vectorize2int(input->dims()));
+    cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
+        layout, framework::vectorize2int(output->dims()));
+    cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
+        layout, framework::vectorize2int(filter->dims()));
+    // Now only support NCHW
+    std::vector<int> bias_dim = {1, static_cast<int>(output->dims()[1]), 1, 1};
+    cudnnTensorDescriptor_t cudnn_bias_desc =
+        bias_desc.descriptor<T>(layout, bias_dim);
+    cudnnActivationDescriptor_t cudnn_act_desc =
+        act_desc.descriptor<T>(activation);
+
+    // ------------------- cudnn conv workspace ---------------------
+    size_t workspace_size_in_bytes;  // final workspace to allocate.
+    size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
+    if (FLAGS_conv_workspace_size_limit > 0 || user_workspace_size > 0) {
+      int64_t max_user_size =
+          std::max(static_cast<int64_t>(FLAGS_conv_workspace_size_limit),
+                   user_workspace_size);
+      workspace_size_limit = max_user_size * 1024 * 1024;
+    }
+
+    // ------------------- cudnn conv algorithm ---------------------
+    cudnnConvolutionFwdAlgo_t algo;
+    auto handle = dev_ctx.cudnn_handle();
+    auto workspace_handle = dev_ctx.cudnn_workspace_handle();
+
+    CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
+        cudnn_conv_desc, CUDNN_DEFAULT_MATH));
+
+    auto x_dims = framework::vectorize(input->dims());
+    auto f_dims = framework::vectorize(filter->dims());
+    if (activation == "identity") {
+      // Only the CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM algo is
+      // enabled with CUDNN_ACTIVATION_IDENTITY in cuDNN lib.
+      algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
+    } else if (!exhaustive_search) {
+      CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
+          handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
+          cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+          workspace_size_limit, &algo));
+      VLOG(3) << "cuDNN forward algo " << algo;
+    } else {
+      AlgorithmsCache<cudnnConvolutionFwdAlgo_t>* algo_cache = nullptr;
+      if (ctx.scope().FindVar(kCUDNNFwdAlgoCache)) {
+        algo_cache =
+            ctx.scope()
+                .FindVar(kCUDNNFwdAlgoCache)
+                ->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
+      } else {
+        algo_cache =
+            const_cast<framework::Scope&>(ctx.scope())
+                .Var(kCUDNNFwdAlgoCache)
+                ->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_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,
+                      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) {
+              const auto& stat = fwd_perf_stat[i];
+              VLOG(3) << stat.algo << ": " << stat.status << " " << stat.time
+                      << " " << stat.memory;
+            }
+            return fwd_perf_stat[0].algo;
+          });
+      VLOG(3) << "choose algo " << algo;
+    }
+
+    CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
+        handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
+        cudnn_output_desc, algo, &workspace_size_in_bytes));
+    PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit,
+                      "workspace_size to be allocated exceeds the limit");
+
+    // ------------------- cudnn conv+bias+act forward --------------------
+    ScalingParamType<T> alpha1 = 1.0f;
+    ScalingParamType<T> alpha2 = residual ? 1.0f : 0.0f;
+    auto cudnn_func = [&](void* cudnn_workspace) {
+      CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBiasActivationForward(
+          handle, &alpha1, cudnn_input_desc, input_data, cudnn_filter_desc,
+          filter_data, cudnn_conv_desc, algo, cudnn_workspace,
+          workspace_size_in_bytes, &alpha2, cudnn_output_desc, residual_data,
+          cudnn_bias_desc, bias_data, cudnn_act_desc, cudnn_output_desc,
+          output_data));
+    };
+    workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(conv2d_fusion, ops::CUDNNConvFusionOpKernel<float>,
+                        ops::CUDNNConvFusionOpKernel<double>);

paddle/fluid/operators/conv_op.cc

@@ -225,17 +225,9 @@ $$
        W_{out}= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
 $$
 )DOC");
+  Apply();
 }
 
-class ConvOpInferVarType : public framework::PassInDtypeAndVarTypeToOutput {
- protected:
-  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
-      const override {
-    return std::unordered_map<std::string, std::string>{
-        {"Input", /*->*/ "Output"}};
-  }
-};
-
 void Conv3DOpMaker::Make() {
   AddInput(
       "Input",
@@ -334,6 +326,7 @@ Example:
        W_{out}= \frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{ strides[2]}+ 1
   $$
 )DOC");
+  Apply();
 }
 
 void ConvOpGrad::InferShape(framework::InferShapeContext* ctx) const {

paddle/fluid/operators/conv_op.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <string>
 #include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
@@ -60,12 +61,27 @@ inline bool IsExpand(const std::vector<int64_t>& filter_dim,
 // operator implementations can reuse the code.
 class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  void Make() override;
+  void Make() final;
+
+ protected:
+  virtual void Apply() {}
 };
 
 class Conv3DOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  void Make() override;
+  void Make() final;
+
+ protected:
+  virtual void Apply() {}
+};
+
+class ConvOpInferVarType : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return std::unordered_map<std::string, std::string>{
+        {"Input", /*->*/ "Output"}};
+  }
 };
 
 class ConvOp : public framework::OperatorWithKernel {

paddle/fluid/platform/cudnn_helper.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <string>
 #include <vector>
 
 #include "paddle/fluid/framework/operator.h"
@@ -81,6 +82,16 @@ enum class PoolingMode {
   kAverageInclusive,
 };
 
+enum ActivationMode {
+  kNone,  // activation identity
+  kSigmoid,
+  kRelu,
+  kRelu6,
+  kReluX,
+  kTanh,
+  kBandPass,
+};
+
 #if CUDNN_VERSION < 6000
 #pragma message "CUDNN version under 6.0 is supported at best effort."
 #pragma message "We strongly encourage you to move to 6.0 and above."
@@ -120,6 +131,26 @@ inline cudnnPoolingMode_t GetPoolingMode(const PoolingMode& mode) {
 }
 #endif  // CUDNN_VERSION < 6000
 
+inline ActivationMode StringToActivationMode(const std::string& str) {
+  if (str == "identity") {
+    return ActivationMode::kNone;
+  } else if (str == "sigmoid") {
+    return ActivationMode::kSigmoid;
+  } else if (str == "relu") {
+    return ActivationMode::kRelu;
+  } else if (str == "relu6") {
+    return ActivationMode::kRelu6;
+  } else if (str == "relux") {
+    return ActivationMode::kReluX;
+  } else if (str == "tanh") {
+    return ActivationMode::kTanh;
+  } else if (str == "bandpass") {
+    return ActivationMode::kBandPass;
+  } else {
+    PADDLE_THROW("Unknown activation string: %s", str);
+  }
+}
+
 template <typename T>
 class CudnnDataType;
 
@@ -368,6 +399,58 @@ class ScopedSpatialTransformerDescriptor {
   DISABLE_COPY_AND_ASSIGN(ScopedSpatialTransformerDescriptor);
 };
 
+class ScopedActivationDescriptor {
+ public:
+  ScopedActivationDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnCreateActivationDescriptor(&desc_));
+  }
+  ~ScopedActivationDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnDestroyActivationDescriptor(desc_));
+  }
+
+  template <typename T>
+  inline cudnnActivationDescriptor_t descriptor(
+      const std::string& act, double value_max = static_cast<double>(0.)) {
+    double relu_ceiling = 0.0;
+    ActivationMode activation_mode = StringToActivationMode(act);
+    cudnnActivationMode_t mode;
+    switch (activation_mode) {
+#if CUDNN_VERSION >= 7100
+      case ActivationMode::kNone:
+        mode = CUDNN_ACTIVATION_IDENTITY;
+        break;
+#endif
+      case ActivationMode::kRelu6:
+        relu_ceiling = 6.0;
+        mode = CUDNN_ACTIVATION_CLIPPED_RELU;
+        break;
+      case ActivationMode::kReluX:
+        relu_ceiling = value_max;
+        mode = CUDNN_ACTIVATION_CLIPPED_RELU;
+        break;
+      case ActivationMode::kRelu:
+        mode = CUDNN_ACTIVATION_RELU;
+        break;
+      case ActivationMode::kSigmoid:
+        mode = CUDNN_ACTIVATION_SIGMOID;
+        break;
+      case ActivationMode::kTanh:
+        mode = CUDNN_ACTIVATION_TANH;
+        break;
+      default:
+        PADDLE_THROW("unrecognized activation mode: %d .",
+                     static_cast<int>(activation_mode));
+    }
+    CUDNN_ENFORCE(dynload::cudnnSetActivationDescriptor(
+        desc_, mode, CUDNN_NOT_PROPAGATE_NAN, relu_ceiling));
+    return desc_;
+  }
+
+ private:
+  cudnnActivationDescriptor_t desc_;
+  DISABLE_COPY_AND_ASSIGN(ScopedActivationDescriptor);
+};
+
 inline bool CanCUDNNBeUsed(const framework::ExecutionContext& ctx) {
   bool use_cudnn = ctx.Attr<bool>("use_cudnn");
   use_cudnn &= paddle::platform::is_gpu_place(ctx.GetPlace());

paddle/fluid/platform/dynload/cudnn.h

@@ -152,14 +152,15 @@ CUDNN_DNN_ROUTINE_EACH_R5(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
 #endif
 
 #if CUDNN_VERSION >= 7001
-#define CUDNN_DNN_ROUTINE_EACH_R7(__macro) \
-  __macro(cudnnSetConvolutionGroupCount);  \
-  __macro(cudnnSetConvolutionMathType);    \
-  __macro(cudnnCreateCTCLossDescriptor);   \
-  __macro(cudnnDestroyCTCLossDescriptor);  \
-  __macro(cudnnGetCTCLossDescriptor);      \
-  __macro(cudnnSetCTCLossDescriptor);      \
-  __macro(cudnnGetCTCLossWorkspaceSize);   \
+#define CUDNN_DNN_ROUTINE_EACH_R7(__macro)        \
+  __macro(cudnnSetConvolutionGroupCount);         \
+  __macro(cudnnSetConvolutionMathType);           \
+  __macro(cudnnConvolutionBiasActivationForward); \
+  __macro(cudnnCreateCTCLossDescriptor);          \
+  __macro(cudnnDestroyCTCLossDescriptor);         \
+  __macro(cudnnGetCTCLossDescriptor);             \
+  __macro(cudnnSetCTCLossDescriptor);             \
+  __macro(cudnnGetCTCLossWorkspaceSize);          \
   __macro(cudnnCTCLoss);
 CUDNN_DNN_ROUTINE_EACH_R7(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
 #endif

python/paddle/fluid/tests/unittests/test_conv2d_fusion_op.py

+#   Copyright (c) 2018 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+
+import paddle.fluid.core as core
+from op_test import OpTest
+
+from test_conv2d_op import conv2d_forward_naive
+
+
+class TestConv2dFusionOp(OpTest):
+    def setUp(self):
+        self.op_type = "conv2d_fusion"
+        self.exhaustive_search = False
+        self.data_format = "AnyLayout"
+        self.dtype = np.float32
+        self.activation = 'relu'
+        self.add_bias = True
+        self.add_residual_data = True
+
+        self.init_group()
+        self.init_dilation()
+        self.init_test_case()
+        self.init_bias_residual()
+        self.init_activation()
+        self.set_search_method()
+
+        conv2d_param = {
+            'stride': self.stride,
+            'pad': self.pad,
+            'dilation': self.dilations
+        }
+
+        input = np.random.random(self.input_size).astype(self.dtype)
+        filter = np.random.random(self.filter_size).astype(self.dtype)
+
+        output = conv2d_forward_naive(input, filter, self.groups,
+                                      conv2d_param).astype(self.dtype)
+
+        self.inputs = {
+            'Input': OpTest.np_dtype_to_fluid_dtype(input),
+            'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
+        }
+
+        if self.add_residual_data:
+            residual_data = np.random.random(output.shape).astype(self.dtype)
+            self.inputs['ResidualData'] = OpTest.np_dtype_to_fluid_dtype(
+                residual_data)
+            output += residual_data
+
+        if self.add_bias:
+            bias = np.random.random(self.filter_size[0]).astype(self.dtype)
+            self.inputs['Bias'] = OpTest.np_dtype_to_fluid_dtype(bias)
+            output = output + bias.reshape((1, bias.size, 1, 1))
+
+        assert self.activation in ['relu', 'identity']
+        if self.activation == 'relu':
+            output = np.maximum(output, 0)
+
+        self.attrs = {
+            'strides': self.stride,
+            'paddings': self.pad,
+            'groups': self.groups,
+            'dilations': self.dilations,
+            'data_format': self.data_format,
+            'exhaustive_search': self.exhaustive_search,
+            'activation': self.activation
+        }
+        self.outputs = {'Output': output}
+
+    def testcuda(self):
+        return core.is_compiled_with_cuda()
+
+    def test_check_output(self):
+        if self.testcuda():
+            place = core.CUDAPlace(0)
+            self.check_output_with_place(place, atol=1e-5)
+        else:
+            pass
+
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+    def init_dilation(self):
+        self.dilations = [1, 1]
+
+    def init_group(self):
+        self.groups = 1
+
+    def init_bias_residual(self):
+        self.add_bias = True
+        self.add_residual_data = True
+
+    def init_activation(self):
+        self.activation = 'relu'
+
+    def set_search_method(self):
+        self.exhaustive_search = False
+
+
+class TestWithoutResidual(TestConv2dFusionOp):
+    def init_bias_residual(self):
+        self.add_residual_data = False
+
+
+class TestIdentityActivation(TestConv2dFusionOp):
+    def init_activation(self):
+        self.activation = 'identity'
+
+
+class TestWithGroup(TestConv2dFusionOp):
+    def init_group(self):
+        self.groups = 3
+
+
+class TestWithDilation(TestConv2dFusionOp):
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 10, 10]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+    def init_dilation(self):
+        self.dilations = [2, 2]
+
+    def init_group(self):
+        self.groups = 3
+
+
+class TestCUDNNExhaustiveSearch(TestConv2dFusionOp):
+    def set_search_method(self):
+        self.exhaustive_search = True
+
+
+if __name__ == '__main__':
+    unittest.main()