Merge branch 'develop' into cross_entropy_over_beam

.pre-commit-config.yaml

@@ -22,7 +22,7 @@
     -   id: clang-format-with-version-check
         name: clang-format
         description: Format files with ClangFormat.
-        entry: ./.clang_format.hook -i
+        entry: bash ./.clang_format.hook -i
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
 -   repo: https://github.com/PaddlePaddle/pre-commit-golang

doc/design/cluster_train/README.md

@@ -54,17 +54,18 @@ The life cycle of a single task is illustrated below:
 <img src="src/paddle-task-states.png"/>
 
 1. When a new pass of training starts, all tasks will be placed in the todo queue.
-1. The master server will dispatch few tasks to each trainer at a time, puts them in the pending queue and waits for completion.
-1. The trainer will work on its tasks and tell the master server once a task is completed. The master server will dispatch a new task to that trainer.
-1. If a task timeout. the master server will move it back to the todo queue. The timeout count will increase by one. If the timeout count is above a threshold, the task is likely to cause a trainer to crash, so it will be discarded.
+1. Upon trainer requests for new task, the master server will dispatch a task from todo queue to it, put the task in the pending queue and wait for completion.
+1. The trainer will work on its task and tell the master server once the task is completed and ask for new task. The master server will dispatch a new task to that trainer.
+1. If a task fails for any reason in trainer, or takes longer than a specific period of time,  the master server will move the task back to the todo queue. The timeout count for that task will increase by one. If the timeout count is above a threshold, the task is likely to cause a trainer to crash, then it will be discarded.
 1. The master server will move completed task to the done queue. When the todo queue is empty, the master server will start a new pass by moving all tasks in the done queue to todo queue and reset the timeout counter of all tasks to zero.
 
 ### Trainer Process
 
 The trainer process will:
 
-- Receive tasks from the master.
-- Work on the tasks: calculate and upload gradient to parameter servers, and update local model by downloading new parameters from parameter servers.
+- Request tasks from the master.
+- Work on the tasks
+- Upload gradient to parameter servers, and update local model by downloading new parameters from parameter servers.
 
 ### Parameter Server Process
 
@@ -119,8 +120,8 @@ When the master is started by the Kubernetes, it executes the following steps at
 
 1. Grabs a unique *master* lock in etcd, which prevents concurrent master instantiations.
 1. Recovers the task queues from etcd if they already exist, otherwise, the master will create them.
-1. Watches the trainer prefix keys `/trainer/` on etcd to find the live trainers.
-1. Starts dispatching the tasks to the trainers, and updates task queue using an etcd transaction to ensure lock is held during the update.
+1. Write its ip address to */master/addr* so that trainers can discover it.
+1. Listens to trainers' request of task, dispatch one upon request, and updates task queue using an etcd transaction to ensure lock is held during the update.
 
 When the master server process is dead for any reason, Kubernetes will restart it. It will be online again with all states recovered from etcd in few minutes.
 
@@ -128,13 +129,11 @@ When the master server process is dead for any reason, Kubernetes will restart i
 
 When the trainer is started by the Kubernetes, it executes the following steps at startup:
 
-1. Watches the available parameter server prefix keys `/ps/` on etcd and waits until the count of parameter servers reaches the desired count.
-1. Generates a unique ID, and sets key `/trainer/<unique ID>` with its contact address as value. The key will be deleted when the lease expires, so the master will be aware of the trainer being online and offline.
-1. Waits for tasks from the master to start training.
+1. Watches the available parameter server prefix keys `/ps/` on etcd and waits until the count of parameter servers reaches the desired count */ps_desired*.
+1. Finds and watches */master/addr* to get master's address.
+1. Requests for tasks from the master to start training.
 
-If trainer's etcd lease expires, it will try set key `/trainer/<unique ID>` again so that the master server can discover the trainer again.
-
-When a trainer fails, Kuberentes would try to restart it. The recovered trainer would fetch tasks from the TODO queue and go on training.
+When a trainer fails, Kuberentes would try to restart it. The recovered trainer would fetch tasks from master and go on training.
 
 ### Parameter Server Process
 

go/master/client.go

@@ -63,13 +63,24 @@ func WithAddr(addr string) func(c *Client) error {
 // WithEtcd sets the client to use etcd for master discovery.
 func WithEtcd(endpoints []string, timeout time.Duration) func(*Client) error {
 	return func(c *Client) error {
-		cli, err := clientv3.New(clientv3.Config{
+		var cli *clientv3.Client
+		f := func() error {
+			var err error
+			cli, err = clientv3.New(clientv3.Config{
 				Endpoints:   endpoints,
 				DialTimeout: timeout,
 			})
-		if err != nil {
 			return err
 		}
+		for {
+			err := f()
+			if err != nil {
+				log.Warningln(err)
+			} else {
+				break
+			}
+			time.Sleep(time.Second)
+		}
 
 		ch := make(chan string, 1)
 		a, err := GetKey(cli, DefaultAddrPath, timeout)
@@ -101,9 +112,6 @@ func NewClient(opts ...func(*Client) error) (*Client, error) {
 		}
 	}
 	c.ch = make(chan record, c.bufSize)
-	// FIXME: connection is created asyncrosly in monitorMaster go routine,
-	//        ensure the connection is ready for use before calling c.addClient.
-	time.Sleep(time.Second)
 	return c, nil
 }
 

paddle/capi/CMakeLists.txt

@@ -53,7 +53,10 @@ add_custom_target(paddle_capi_whole ALL
 set_target_properties(paddle_capi_whole
   PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library})
 
+set(LINK_FLAGS " -Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/export.sym -Wl,--version-script ${CMAKE_CURRENT_SOURCE_DIR}/export.map")
+# TODO: merge mkl into paddle_capi_shared
 add_library(paddle_capi_shared SHARED ${CAPI_SOURCES})
+set_target_properties(paddle_capi_shared	PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
 target_include_directories(paddle_capi_shared PUBLIC ${CMAKE_CURRENT_BINARY_DIR})
 link_paddle_exe(paddle_capi_shared)
 

paddle/capi/export.map

+{
+	global:
+		paddle_*;
+	local:
+		*;
+};

paddle/cuda/include/hl_cuda_cudnn.h

@@ -214,7 +214,8 @@ extern void hl_conv_workspace(hl_tensor_descriptor input,
                               int* convBwdDataAlgo,
                               size_t* bwdDataLimitBytes,
                               int* convBwdFilterAlgo,
-                              size_t* bwdFilterLimitBytes);
+                              size_t* bwdFilterLimitBytes,
+                              bool useDilation);
 
 /**
  * @brief   destroy filter descriptor.
@@ -242,7 +243,9 @@ extern void hl_create_convolution_descriptor(hl_convolution_descriptor* conv,
                                              int padding_height,
                                              int padding_width,
                                              int stride_height,
-                                             int stride_width);
+                                             int stride_width,
+                                             int dilation_h = 1,
+                                             int dilation_w = 1);
 
 /**
  * @brief   reset convolution descriptor.
@@ -262,7 +265,9 @@ extern void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
                                             int padding_height,
                                             int padding_width,
                                             int stride_height,
-                                            int stride_width);
+                                            int stride_width,
+                                            int dilation_h = 1,
+                                            int dilation_w = 1);
 
 /**
  * @brief   destroy convolution descriptor.

paddle/cuda/include/stub/hl_cuda_cudnn_stub.h

@@ -78,7 +78,9 @@ inline void hl_create_convolution_descriptor(hl_convolution_descriptor* conv,
                                              int padding_height,
                                              int padding_width,
                                              int stride_height,
-                                             int stride_width) {}
+                                             int stride_width,
+                                             int dilation_h,
+                                             int dilation_w) {}
 
 inline void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
                                             hl_tensor_descriptor image,
@@ -86,7 +88,9 @@ inline void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
                                             int padding_height,
                                             int padding_width,
                                             int stride_height,
-                                            int stride_width) {}
+                                            int stride_width,
+                                            int dilation_h,
+                                            int dilation_w) {}
 
 inline void hl_destroy_convolution_descriptor(hl_convolution_descriptor conv) {}
 
@@ -99,7 +103,8 @@ inline void hl_conv_workspace(hl_tensor_descriptor input,
                               int* convBwdDataAlgo,
                               size_t* bwdDataLimitBytes,
                               int* convBwdFilterAlgo,
-                              size_t* bwdFilterLimitBytes) {}
+                              size_t* bwdFilterLimitBytes,
+                              bool useDilation) {}
 
 inline void hl_convolution_forward(hl_tensor_descriptor input,
                                    real* input_data,

paddle/cuda/src/hl_cuda_cudnn.cc

@@ -201,7 +201,8 @@ void hl_conv_workspace(hl_tensor_descriptor input,
                        int* convBwdDataAlgo,
                        size_t* bwdDataLimitBytes,
                        int* convBwdFilterAlgo,
-                       size_t* bwdFilterLimitBytes) {
+                       size_t* bwdFilterLimitBytes,
+                       bool useDilation) {
 #if CUDNN_VERSION >= 4000
 
   CHECK_NOTNULL(input);
@@ -213,12 +214,32 @@ void hl_conv_workspace(hl_tensor_descriptor input,
   size_t memoryLimitBytes =
       (1LL << 20) * FLAGS_cudnn_conv_workspace_limit_in_mb;
 
+  // For dilation
+  int algo = 0;
+
   // cudnn convolution forward configuration
   cudnnTensorDescriptor_t fwd_src_desc = GET_TENSOR_DESCRIPTOR(input);
   cudnnTensorDescriptor_t fwd_dest_desc = GET_TENSOR_DESCRIPTOR(output);
   cudnnFilterDescriptor_t fwd_filter_desc = GET_FILTER_DESCRIPTOR(filter);
   cudnnConvolutionDescriptor_t fwd_conv_desc = GET_CONVOLUTION_DESCRIPTOR(conv);
+  // cudnn convolution backward data configuration
+  cudnnFilterDescriptor_t bwd_data_filter_desc = GET_FILTER_DESCRIPTOR(filter);
+  cudnnTensorDescriptor_t bwd_data_diff_desc = GET_TENSOR_DESCRIPTOR(output);
+  cudnnTensorDescriptor_t bwd_data_grad_desc = GET_TENSOR_DESCRIPTOR(input);
+  cudnnConvolutionDescriptor_t bwd_data_conv_desc =
+      GET_CONVOLUTION_DESCRIPTOR(conv);
+  // cudnn convolution backward filter configuration
+  cudnnTensorDescriptor_t bwd_filter_src_desc = GET_TENSOR_DESCRIPTOR(input);
+  cudnnTensorDescriptor_t bwd_filter_diff_desc = GET_TENSOR_DESCRIPTOR(output);
+  cudnnConvolutionDescriptor_t bwd_filter_conv_desc =
+      GET_CONVOLUTION_DESCRIPTOR(conv);
+  cudnnFilterDescriptor_t bwd_filter_grad_desc = GET_FILTER_DESCRIPTOR(filter);
 
+  if (useDilation) {
+    convFwdAlgo = &algo;
+    convBwdDataAlgo = &algo;
+    convBwdFilterAlgo = &algo;
+  } else {
     CHECK_CUDNN(dynload::cudnnGetConvolutionForwardAlgorithm(
         t_resource.cudnn_handle,
         fwd_src_desc,
@@ -228,23 +249,6 @@ void hl_conv_workspace(hl_tensor_descriptor input,
         CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
         memoryLimitBytes,
         reinterpret_cast<cudnnConvolutionFwdAlgo_t*>(convFwdAlgo)));
-
-  CHECK_CUDNN(dynload::cudnnGetConvolutionForwardWorkspaceSize(
-      t_resource.cudnn_handle,
-      fwd_src_desc,
-      fwd_filter_desc,
-      fwd_conv_desc,
-      fwd_dest_desc,
-      static_cast<cudnnConvolutionFwdAlgo_t>(*convFwdAlgo),
-      fwdLimitBytes));
-
-  // cudnn convolution backward data configuration
-  cudnnFilterDescriptor_t bwd_data_filter_desc = GET_FILTER_DESCRIPTOR(filter);
-  cudnnTensorDescriptor_t bwd_data_diff_desc = GET_TENSOR_DESCRIPTOR(output);
-  cudnnTensorDescriptor_t bwd_data_grad_desc = GET_TENSOR_DESCRIPTOR(input);
-  cudnnConvolutionDescriptor_t bwd_data_conv_desc =
-      GET_CONVOLUTION_DESCRIPTOR(conv);
-
     CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataAlgorithm(
         t_resource.cudnn_handle,
         bwd_data_filter_desc,
@@ -254,23 +258,6 @@ void hl_conv_workspace(hl_tensor_descriptor input,
         CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
         memoryLimitBytes,
         reinterpret_cast<cudnnConvolutionBwdDataAlgo_t*>(convBwdDataAlgo)));
-
-  CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
-      t_resource.cudnn_handle,
-      bwd_data_filter_desc,
-      bwd_data_diff_desc,
-      bwd_data_conv_desc,
-      bwd_data_grad_desc,
-      static_cast<cudnnConvolutionBwdDataAlgo_t>(*convBwdDataAlgo),
-      bwdDataLimitBytes));
-
-  // cudnn convolution backward filter configuration
-  cudnnTensorDescriptor_t bwd_filter_src_desc = GET_TENSOR_DESCRIPTOR(input);
-  cudnnTensorDescriptor_t bwd_filter_diff_desc = GET_TENSOR_DESCRIPTOR(output);
-  cudnnConvolutionDescriptor_t bwd_filter_conv_desc =
-      GET_CONVOLUTION_DESCRIPTOR(conv);
-  cudnnFilterDescriptor_t bwd_filter_grad_desc = GET_FILTER_DESCRIPTOR(filter);
-
     CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
         t_resource.cudnn_handle,
         bwd_filter_src_desc,
@@ -280,6 +267,25 @@ void hl_conv_workspace(hl_tensor_descriptor input,
         CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
         memoryLimitBytes,
         reinterpret_cast<cudnnConvolutionBwdFilterAlgo_t*>(convBwdFilterAlgo)));
+  }
+
+  CHECK_CUDNN(dynload::cudnnGetConvolutionForwardWorkspaceSize(
+      t_resource.cudnn_handle,
+      fwd_src_desc,
+      fwd_filter_desc,
+      fwd_conv_desc,
+      fwd_dest_desc,
+      static_cast<cudnnConvolutionFwdAlgo_t>(*convFwdAlgo),
+      fwdLimitBytes));
+
+  CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
+      t_resource.cudnn_handle,
+      bwd_data_filter_desc,
+      bwd_data_diff_desc,
+      bwd_data_conv_desc,
+      bwd_data_grad_desc,
+      static_cast<cudnnConvolutionBwdDataAlgo_t>(*convBwdDataAlgo),
+      bwdDataLimitBytes));
 
   CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
       t_resource.cudnn_handle,
@@ -603,7 +609,9 @@ void hl_create_convolution_descriptor(hl_convolution_descriptor* conv,
                                       int padding_height,
                                       int padding_width,
                                       int stride_height,
-                                      int stride_width) {
+                                      int stride_width,
+                                      int dilation_h,
+                                      int dilation_w) {
   CHECK_NOTNULL(conv);
 
   cudnn_convolution_descriptor hl_conv = (cudnn_convolution_descriptor)malloc(
@@ -625,18 +633,24 @@ void hl_create_convolution_descriptor(hl_convolution_descriptor* conv,
                                                        padding_width,
                                                        stride_height,
                                                        stride_width,
-                                                       1,
-                                                       1,
+                                                       dilation_h,
+                                                       dilation_w,
                                                        mode,
                                                        data_type));
 #else
+  if (dilation_h > 1 || dilation_w > 1) {
+    LOG(FATAL)
+        << "Current cuDNN version does't support for dilation convolution. "
+        << "The dilation convolution requires cuDNN >= v6.0.";
+  }
+
   CHECK_CUDNN(dynload::cudnnSetConvolution2dDescriptor(hl_conv->desc,
                                                        padding_height,
                                                        padding_width,
                                                        stride_height,
                                                        stride_width,
-                                                       1,
-                                                       1,
+                                                       dilation_h,
+                                                       dilation_w,
                                                        mode));
 #endif
 
@@ -659,7 +673,9 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
                                      int padding_height,
                                      int padding_width,
                                      int stride_height,
-                                     int stride_width) {
+                                     int stride_width,
+                                     int dilation_h,
+                                     int dilation_w) {
   CHECK_NOTNULL(conv);
   CHECK_NOTNULL(image);
   CHECK_NOTNULL(filter);
@@ -678,8 +694,8 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
                                                        padding_width,
                                                        stride_height,
                                                        stride_width,
-                                                       1,
-                                                       1,
+                                                       dilation_h,
+                                                       dilation_w,
                                                        mode,
                                                        data_type));
 #else
@@ -688,8 +704,8 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
                                                        padding_width,
                                                        stride_height,
                                                        stride_width,
-                                                       1,
-                                                       1,
+                                                       dilation_h,
+                                                       dilation_w,
                                                        mode));
 #endif
 

paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp

@@ -1344,7 +1344,7 @@ void RecurrentGradientMachine::fillGenOutputs() {
       CHECK(!finalPaths_[i].empty());
       Path& path = finalPaths_[i][0];
       generator_.ids.insert(
-          generator_.ids.begin(), path.ids.begin(), path.ids.end());
+          generator_.ids.end(), path.ids.begin(), path.ids.end());
       starts[i + 1] = starts[i] + path.ids.size();
     }
   }
@@ -1399,8 +1399,8 @@ void RecurrentGradientMachine::createDataOutlinkCopySizeInfo(
              getBeamSize() > 1 ? finalPaths_.size() : finalPaths_[0].size());
     int* starts = inputSeqStartPos->getMutableData(false);
     int seqId = 0;
-    for (int i = 0; i < finalPaths_.size(); ++i) {
-      for (int j = 0; j < finalPaths_[i].size(); ++j) {
+    for (size_t i = 0; i < finalPaths_.size(); ++i) {
+      for (size_t j = 0; j < finalPaths_[i].size(); ++j) {
         copySize[seqId] = getBeamSize() > 1 ? starts[i + 1] - starts[i]
                                             : starts[j + 1] - starts[j];
         batchMachineStartPos_[seqId + 1] =

paddle/gserver/layers/ConvBaseLayer.cpp

@@ -32,9 +32,11 @@ bool ConvBaseLayer::init(const LayerMap& layerMap,
     const ConvConfig& conf = inputConfig.conv_conf();
     padding_.push_back(conf.padding());
     stride_.push_back(conf.stride());
+    dilation_.push_back(conf.dilation());
     filterSize_.push_back(conf.filter_size());
     paddingY_.push_back(conf.padding_y());
     strideY_.push_back(conf.stride_y());
+    dilationY_.push_back(conf.dilation_y());
     filterSizeY_.push_back(conf.filter_size_y());
     filterPixels_.push_back(filterSize_.back() * filterSizeY_.back());
     channels_.push_back(conf.channels());
@@ -89,7 +91,11 @@ size_t ConvBaseLayer::calOutputSize() {
   size_t layerSize = 0;
 
   auto setLayerSize = [&](IntV& inH, IntV& inW, IntV& outH, IntV& outW) {
+    size_t filterSizeY;
+    size_t filterSize;
     for (size_t i = 0; i < inputLayers_.size(); i++) {
+      filterSizeY = (filterSizeY_[i] - 1) * dilationY_[i] + 1;
+      filterSize = (filterSize_[i] - 1) * dilation_[i] + 1;
       inH.push_back(inputLayers_[i]->getOutput().getFrameHeight());
       inW.push_back(inputLayers_[i]->getOutput().getFrameWidth());
       const ConvConfig& conf = config_.inputs(i).conv_conf();
@@ -98,17 +104,17 @@ size_t ConvBaseLayer::calOutputSize() {
           inH[i] = conf.has_output_y() ? conf.output_y() : conf.output_x();
         if (inW[i] == 0) inW[i] = conf.output_x();
         outH.push_back(imageSize(
-            inH[i], filterSizeY_[i], paddingY_[i], strideY_[i], caffeMode_));
-        outW.push_back(imageSize(
-            inW[i], filterSize_[i], padding_[i], stride_[i], caffeMode_));
+            inH[i], filterSizeY, paddingY_[i], strideY_[i], caffeMode_));
+        outW.push_back(
+            imageSize(inW[i], filterSize, padding_[i], stride_[i], caffeMode_));
       } else {
         if (inH[i] == 0)
           inH[i] = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size();
         if (inW[i] == 0) inW[i] = conf.img_size();
         outH.push_back(outputSize(
-            inH[i], filterSizeY_[i], paddingY_[i], strideY_[i], caffeMode_));
+            inH[i], filterSizeY, paddingY_[i], strideY_[i], caffeMode_));
         outW.push_back(outputSize(
-            inW[i], filterSize_[i], padding_[i], stride_[i], caffeMode_));
+            inW[i], filterSize, padding_[i], stride_[i], caffeMode_));
       }
       CHECK_EQ(outH[i], outH[0]);
       CHECK_EQ(outW[i], outW[0]);

paddle/gserver/layers/ConvBaseLayer.h

@@ -40,6 +40,10 @@ protected:
   IntV stride_;
   /// The y dimension of the stride.
   IntV strideY_;
+  /// The x dimension of the dilation.
+  IntV dilation_;
+  /// The y dimension of the dilation.
+  IntV dilationY_;
   /// The x dimension of a filter kernel.
   IntV filterSize_;
   /// The y dimension of a filter kernel.

paddle/gserver/layers/ConvBaseOperator.cpp

@@ -59,7 +59,8 @@ void ConvBaseOperator::allocConvWorkSpace() {
                     &bwdDataAlgo_,
                     &bwdDataLimitBytes_,
                     &bwdFilterAlgo_,
-                    &bwdFilterLimitBytes_);
+                    &bwdFilterLimitBytes_,
+                    /*useDilation*/ false);
 
   size_t maxWorkSpace = 0;
   maxWorkSpace = std::max(fwdLimitBytes_, bwdDataLimitBytes_);

paddle/gserver/layers/ConvBaseProjection.cpp

@@ -41,6 +41,11 @@ void ConvBaseProjection::getConvParams() {
   strideH_ = conf.stride_y();
   strideW_ = conf.stride();
 
+  dilationH_ = conf.dilation_y();
+  dilationW_ = conf.dilation();
+  CHECK_GT(dilationH_, 0);
+  CHECK_GT(dilationW_, 0);
+
   filterH_ = conf.filter_size_y();
   filterW_ = conf.filter_size();
 
@@ -77,7 +82,9 @@ void ConvBaseProjection::initCudnn() {
                                    paddingH_,
                                    paddingW_,
                                    strideH_,
-                                   strideW_);
+                                   strideW_,
+                                   dilationH_,
+                                   dilationW_);
 
   // initialize all to default algorithms
   fwdAlgo_ = 0;
@@ -131,7 +138,9 @@ void ConvBaseProjection::reshapeTensorDesc(int batchSize) {
                                   paddingH_,
                                   paddingW_,
                                   strideH_,
-                                  strideW_);
+                                  strideW_,
+                                  dilationH_,
+                                  dilationW_);
 }
 
 void ConvBaseProjection::reshape(int batchSize) {
@@ -140,6 +149,10 @@ void ConvBaseProjection::reshape(int batchSize) {
   CHECK_EQ(calInputSize(), in_->value->getWidth());
 
   reshapeTensorDesc(batchSize);
+  bool useDilation = false;
+  if (dilationH_ > 1 || dilationW_ > 1) {
+    useDilation = true;
+  }
   hl_conv_workspace(imageDesc_,
                     outputDesc_,
                     filterDesc_,
@@ -149,7 +162,8 @@ void ConvBaseProjection::reshape(int batchSize) {
                     &bwdDataAlgo_,
                     &bwdDataLimitBytes_,
                     &bwdFilterAlgo_,
-                    &bwdFilterLimitBytes_);
+                    &bwdFilterLimitBytes_,
+                    useDilation);
 
   size_t maxWorkSpace = 0;
   maxWorkSpace = std::max(fwdLimitBytes_, bwdDataLimitBytes_);

paddle/gserver/layers/ConvBaseProjection.h

@@ -63,6 +63,7 @@ protected:
   int configChannels_, configNumFilters_;
   int paddingH_, paddingW_;
   int strideH_, strideW_;
+  int dilationH_, dilationW_;
   int filterH_, filterW_;
   /// One group offset of input data.
   int inputOffset_;

paddle/gserver/layers/ConvProjection.cpp

@@ -25,12 +25,12 @@ size_t ConvProjection::calOutputSize() {
   if (imageH_ == 0) imageH_ = configImgH_;
   if (imageW_ == 0) imageW_ = configImgW_;
   outputH_ = outputSize(imageH_,
-                        filterH_,
+                        (filterH_ - 1) * dilationH_ + 1,
                         paddingH_,
                         strideH_,
                         /* caffeMode */ true);
   outputW_ = outputSize(imageW_,
-                        filterW_,
+                        (filterW_ - 1) * dilationW_ + 1,
                         paddingW_,
                         strideW_,
                         /* caffeMode */ true);

paddle/gserver/layers/CrossEntropyOverBeam.cpp

@@ -331,6 +331,8 @@ void CrossEntropyOverBeam::splitBatchBeams() {
                          false,
                          false);
       beamPerSeq_[j].gold[i] = goldSequence_[i]->getData()[j];
+
+      CHECK_LE(beamPerSeq_[j].gold[i], seqStarts[j + 1] - seqStarts[j]);
     }
   }
 }

paddle/gserver/layers/SequenceSliceLayer.cpp

@@ -130,6 +130,8 @@ void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts,
   CHECK(starts || ends) << "At least one of the start or end indices "
                         << "should be given.";
 
+  bool hasSubseq = getInput(0).hasSubseq();
+
   outSeqStartPos_.resize(1, 0);
   outSubSeqStartPos_.resize(1, 0);
   selectedRows_.clear();
@@ -151,14 +153,13 @@ void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts,
         int seqLen = endPos - begPos + 1;
         CHECK_GT(seqLen, 0U);
         for (int m = begPos; m <= endPos; ++m) selectedRows_.push_back(m);
-        inputSeqInfoVec_.size() > 1
+        hasSubseq
             ? outSubSeqStartPos_.push_back(outSubSeqStartPos_.back() + seqLen)
             : outSeqStartPos_.push_back(outSeqStartPos_.back() + seqLen);
       }
       rowIdx++;
     }
-    if (inputSeqInfoVec_.size() > 1)
-      outSeqStartPos_.push_back(outSubSeqStartPos_.back());
+    if (hasSubseq) outSeqStartPos_.push_back(outSubSeqStartPos_.back());
   }
 
   if (useGpu_) {
@@ -175,7 +176,7 @@ void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts,
   output_.sequenceStartPositions->copyFrom(
       outSeqStartPos_.data(), outSeqStartPos_.size(), false);
 
-  if (inputSeqInfoVec_.size() > 1) {
+  if (hasSubseq) {
     ICpuGpuVector::resizeOrCreate(
         output_.subSequenceStartPositions, outSubSeqStartPos_.size(), false);
     output_.subSequenceStartPositions->copyFrom(
@@ -200,13 +201,15 @@ void SequenceSliceLayer::forward(PassType passType) {
       startIdsOnCpu_ = getInputValue(1);
       endIdsOnCpu_ = getInputValue(2);
     }
-  } else
+  } else {
     copySliceIdsToCpu();
+  }
 
-  // calculate the selected row indices in a batch,
-  // and build the output sequence information.
-  calSelectedRows(startIdsOnCpu_ ? startIdsOnCpu_ : nullptr,
-                  endIdsOnCpu_ ? endIdsOnCpu_ : nullptr);
+  /*
+   * calculate the selected row indices in a batch, and build the output
+   * sequence information.
+   */
+  calSelectedRows(startIdsOnCpu_, endIdsOnCpu_);
 
   resetOutput(selectedRows_.size(), getSize());
 

paddle/gserver/tests/test_LayerGrad.cpp

@@ -12,6 +12,9 @@ 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. */
 
+#ifndef PADDLE_ONLY_CPU
+#include <cudnn.h>
+#endif
 #include <gtest/gtest.h>
 #include <string>
 #include <vector>
@@ -189,10 +192,16 @@ TEST(Projection, scaling) {
 void testProjectionConv(size_t groups, bool isDeconv) {
   const int NUM_FILTERS = 18;
   const int FILTER_SIZE = 2;
-  const int FILTER_SIZE_Y = 4;
+  const int FILTER_SIZE_Y = 2;
   const int CHANNELS = 3;
   const int IMAGE_SIZE = 16;
 
+#if CUDNN_VERSION >= 6000
+  const int DILATION = 2;
+#else
+  const int DILATION = 1;
+#endif
+
   ProjectionConfig conf;
   if (isDeconv) {
     conf.set_type("convt");
@@ -209,6 +218,8 @@ void testProjectionConv(size_t groups, bool isDeconv) {
   conv->set_padding_y(1);
   conv->set_stride(2);
   conv->set_stride_y(2);
+  conv->set_dilation(DILATION);
+  conv->set_dilation_y(DILATION);
   conv->set_groups(groups);
   if (isDeconv) {
     conv->set_filter_channels(NUM_FILTERS / conv->groups());
@@ -217,12 +228,12 @@ void testProjectionConv(size_t groups, bool isDeconv) {
   }
   conv->set_img_size(IMAGE_SIZE);
   int output_x = outputSize(conv->img_size(),
-                            conv->filter_size(),
+                            (conv->filter_size() - 1) * DILATION + 1,
                             conv->padding(),
                             conv->stride(),
                             /* caffeMode */ true);
   int output_y = outputSize(conv->img_size(),
-                            conv->filter_size_y(),
+                            (conv->filter_size_y() - 1) * DILATION + 1,
                             conv->padding_y(),
                             conv->stride_y(),
                             /* caffeMode */ true);
@@ -424,27 +435,38 @@ void testConvLayer(const string& type, bool trans, bool useGpu) {
   config.layerConfig.set_partial_sum(1);
   config.layerConfig.set_shared_biases(true);
 
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288});
+  int dilation = 1;
+  if (type == "cudnn_conv") {
+#if CUDNN_VERSION >= 6000
+    dilation = 2;
+#else
+    dilation = 1;
+#endif
+  }
+
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 768, 192});
   LayerInputConfig* input = config.layerConfig.add_inputs();
   ConvConfig* conv = input->mutable_conv_conf();
   conv->set_filter_size(2);
-  conv->set_filter_size_y(3);
+  conv->set_filter_size_y(2);
   conv->set_channels(3);
   conv->set_padding(0);
   conv->set_padding_y(1);
   conv->set_stride(2);
   conv->set_stride_y(2);
+  conv->set_dilation(dilation);
+  conv->set_dilation_y(dilation);
   conv->set_groups(1);
   conv->set_filter_channels(conv->channels() / conv->groups());
   conv->set_img_size(16);
-  conv->set_img_size_y(8);
+  conv->set_img_size_y(16);
   conv->set_output_x(outputSize(conv->img_size(),
-                                conv->filter_size(),
+                                (conv->filter_size() - 1) * dilation + 1,
                                 conv->padding(),
                                 conv->stride(),
                                 /* caffeMode */ true));
   conv->set_output_y(outputSize(conv->img_size_y(),
-                                conv->filter_size_y(),
+                                (conv->filter_size_y() - 1) * dilation + 1,
                                 conv->padding_y(),
                                 conv->stride_y(),
                                 /* caffeMode */ true));

paddle/gserver/tests/test_SeqSliceLayerGrad.cpp

@@ -30,6 +30,8 @@ const int MAX_SEQ_NUM = 17;
 const int MAX_SEQ_LEN = 23;
 const int MAX_BEAM_SIZE = 13;
 
+const size_t SEED = (size_t)(time(NULL));
+
 vector<real> randSampling(real range, int n) {
   CHECK_GE(range, n);
   vector<real> num(range);
@@ -46,7 +48,7 @@ void genSeqInfo(vector<int>& seqStartPos, vector<int>& subSeqStartPos) {
   seqStartPos.resize(1, 0);
   subSeqStartPos.resize(1, 0);
 
-  srand((size_t)(time(NULL)));
+  srand(SEED);
   int seqNum = 1 + (rand() % MAX_SEQ_NUM);
   for (int i = 0; i < seqNum; ++i) {
     int subSeqNum = 1 + (rand() % MAX_SEQ_NUM);

paddle/operators/CMakeLists.txt

@@ -42,6 +42,7 @@ function(op_library TARGET)
 endfunction()
 
 add_subdirectory(math)
+
 cc_test(gather_test SRCS gather_test.cc DEPS tensor)
 op_library(gather_op SRCS gather_op.cc gather_op.cu)
 
@@ -67,6 +68,7 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
 
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
     DEPS framework_proto tensor op_registry operator net_op)
-op_library(uniform_random_op
-        SRCS uniform_random_op.cc uniform_random_op.cu)
+op_library(uniform_random_op SRCS uniform_random_op.cc uniform_random_op.cu)
+op_library(lookup_table_op SRCS lookup_table_op.cc lookup_table_op.cu)
 op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op)
+op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)

paddle/operators/fill_zeros_like_op.h

@@ -26,7 +26,7 @@ class FillZerosLikeKernel : public framework::OpKernel {
     auto* output = context.Output<framework::Tensor>("Dst");
     output->mutable_data<T>(context.GetPlace());
     auto t = framework::EigenVector<T>::Flatten(*output);
-    t.device(context.GetEigenDevice<Place>()) = t.constant(T(0));
+    t.device(context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
   }
 };
 

paddle/operators/lookup_table_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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/operators/lookup_table_op.h"
+
+namespace paddle {
+namespace operators {
+
+class LookupTableOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &context) const override {
+    auto table_t = context.Input<Tensor>("W");
+    auto ids_t = context.Input<Tensor>("Ids");
+    auto output_t = context.Output<Tensor>("Out");
+
+    output_t->Resize({ids_t->dims()[0], table_t->dims()[1]});
+  }
+};
+
+class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  LookupTableOpMaker(framework::OpProto *proto,
+                     framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("W",
+             "An input represents embedding tensors,"
+             " which is a learnable parameter.");
+    AddInput("Ids",
+             "An input with type int32 or int64"
+             "contains the ids to be looked up in W.");
+    AddOutput("Out", "The lookup results, which have the same type with W.");
+    AddComment(
+        "This operator is used to perform lookups on the parameter W,"
+        "then concatenated into a dense tensor.");
+  }
+};
+
+class LookupTableOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &context) const override {
+    auto table = context.Input<Tensor>("W");
+    auto d_table = context.Output<Tensor>(framework::GradVarName("W"));
+    d_table->Resize(table->dims());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(lookup_table, ops::LookupTableOp, ops::LookupTableOpMaker,
+            lookup_table_grad, ops::LookupTableOpGrad);
+
+REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>);
+REGISTER_OP_CPU_KERNEL(lookup_table_grad, ops::LookupTableGradKernel<float>);

paddle/operators/lookup_table_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/platform/assert.h"
+#include "paddle/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T, int BlockDimX, int BlockDimY, int GridDimX>
+__global__ void LookupTable(T* output, const T* table, const int32_t* ids,
+                            const int N, const int K, const int D) {
+  int idx = threadIdx.x;
+  int idy = blockIdx.x + threadIdx.y * GridDimX;
+
+  while (idy < K) {
+    int id = ids[idy];
+    PADDLE_ASSERT(id >= 0);
+    PADDLE_ASSERT(id < N);
+    T* out = output + idy * D;
+    const T* tab = table + id * D;
+    for (int i = idx; i < D; i += BlockDimX) {
+      out[i] = tab[i];
+    }
+    idy += BlockDimY * GridDimX;
+  }
+}
+
+template <typename T, int BlockDimX, int BlockDimY, int GridDimX>
+__global__ void LookupTableGrad(T* table, const T* output, const int32_t* ids,
+                                const int N, const int K, const int D) {
+  int idx = threadIdx.x;
+  int idy = blockIdx.x + threadIdx.y * GridDimX;
+
+  while (idy < K) {
+    int id = ids[idy];
+    PADDLE_ASSERT(id >= 0);
+    PADDLE_ASSERT(id < N);
+    const T* out = output + idy * D;
+    T* tab = table + id * D;
+    for (int i = idx; i < D; i += BlockDimX) {
+      paddle::platform::CudaAtomicAdd(&tab[i], out[i]);
+    }
+    idy += BlockDimY * GridDimX;
+  }
+}
+
+template <typename T>
+class LookupTableCUDAKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto table_t = context.Input<Tensor>("W");
+    auto ids_t = context.Input<Tensor>("Ids");
+    auto output_t = context.Output<Tensor>("Out");
+
+    size_t N = table_t->dims()[0];
+    size_t D = table_t->dims()[1];
+    size_t K = product(ids_t->dims());
+    auto ids = ids_t->data<int32_t>();
+    auto table = table_t->data<T>();
+    auto output = output_t->mutable_data<T>(context.GetPlace());
+
+    dim3 threads(128, 8);
+    dim3 grids(8, 1);
+    LookupTable<T, 128, 8, 8><<<grids, threads>>>(output, table, ids, N, K, D);
+  }
+};
+
+template <typename T>
+class LookupTableGradCUDAKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto ids_t = context.Input<Tensor>("Ids");
+    auto d_output_t = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto d_table_t = context.Output<Tensor>(framework::GradVarName("W"));
+
+    int N = d_table_t->dims()[0];
+    int D = d_table_t->dims()[1];
+    int K = product(ids_t->dims());
+    const int32_t* ids = ids_t->data<int32_t>();
+    const T* d_output = d_output_t->data<T>();
+    T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
+
+    auto t = framework::EigenVector<T>::Flatten(*d_table_t);
+    t.device(context.GetEigenDevice<platform::GPUPlace>()) =
+        t.constant(static_cast<T>(0));
+
+    dim3 threads(128, 8);
+    dim3 grids(8, 1);
+    LookupTableGrad<T, 128, 8, 8><<<grids, threads>>>(d_table, d_output, ids, N,
+                                                      K, D);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(lookup_table, ops::LookupTableCUDAKernel<float>);
+REGISTER_OP_GPU_KERNEL(lookup_table_grad,
+                       ops::LookupTableGradCUDAKernel<float>);

paddle/operators/lookup_table_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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 "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class LookupTableKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto table_t = context.Input<Tensor>("W");      // float tensor
+    auto ids_t = context.Input<Tensor>("Ids");      // int tensor
+    auto output_t = context.Output<Tensor>("Out");  // float tensor
+
+    size_t N = table_t->dims()[0];
+    size_t D = table_t->dims()[1];
+    auto ids = ids_t->data<int32_t>();
+    auto table = table_t->data<T>();
+    auto output = output_t->mutable_data<T>(context.GetPlace());
+    for (size_t i = 0; i < product(ids_t->dims()); ++i) {
+      PADDLE_ENFORCE_LT(ids[i], N);
+      PADDLE_ENFORCE_GE(ids[i], 0);
+      memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
+    }
+  }
+};
+
+template <typename T>
+class LookupTableGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto ids_t = context.Input<Tensor>("Ids");
+    auto d_output_t = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto d_table_t = context.Output<Tensor>(framework::GradVarName("W"));
+
+    size_t N = d_table_t->dims()[0];
+    size_t D = d_table_t->dims()[1];
+    auto ids = ids_t->data<int32_t>();
+    const T* d_output = d_output_t->data<T>();
+    T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
+
+    auto t = framework::EigenVector<T>::Flatten(*d_table_t);
+    t.device(context.GetEigenDevice<platform::CPUPlace>()) =
+        t.constant(static_cast<T>(0));
+
+    for (size_t i = 0; i < product(ids_t->dims()); ++i) {
+      PADDLE_ENFORCE_LT(ids[i], N);
+      PADDLE_ENFORCE_GE(ids[i], 0);
+      for (size_t j = 0; j < D; ++j) {
+        d_table[ids[i] * D + j] += d_output[i * D + j];
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/minus_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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/operators/minus_op.h"
+#include "paddle/operators/net_op.h"
+
+namespace paddle {
+namespace operators {
+
+class MinusOp : public framework::OperatorWithKernel {
+ public:
+  MinusOp(const std::string &type, const framework::VariableNameMap &inputs,
+          const framework::VariableNameMap &outputs,
+          const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    auto *left_tensor = ctx.Input<framework::Tensor>("X");
+    auto *right_tensor = ctx.Input<framework::Tensor>("Y");
+
+    PADDLE_ENFORCE_EQ(
+        framework::product(left_tensor->dims()),
+        framework::product(right_tensor->dims()),
+        "Minus operator must take two tensor with same num of elements");
+    ctx.Output<framework::Tensor>("Out")->Resize(left_tensor->dims());
+  }
+};
+
+class MinusOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  MinusOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The left tensor of minus operator.").NotInGradient();
+    AddInput("Y", "The right tensor of minus operator.").NotInGradient();
+    AddOutput("Out", "The output tensor of minus operator.").NotInGradient();
+
+    AddComment(R"DOC(Minus Operator
+
+Equation: Out = X - Y
+)DOC");
+  }
+};
+template <typename AttrType>
+class MinusGradOp : public NetOp {
+ public:
+  MinusGradOp(const std::string &type, const framework::VariableNameMap &inputs,
+              const framework::VariableNameMap &outputs,
+              const framework::AttributeMap &attrs)
+      : NetOp(type, inputs, outputs, attrs) {
+    auto out_grad = Input(framework::GradVarName("Out"));
+    auto x_grad = Output(framework::GradVarName("X"));
+    auto y_grad = Output(framework::GradVarName("Y"));
+
+    // x_grad = out_grad
+    AppendOp(framework::OpRegistry::CreateOp("identity", {{"X", {out_grad}}},
+                                             {{"Out", {x_grad}}}, {}));
+
+    framework::AttributeMap scale_attr;
+    scale_attr["scale"] = static_cast<AttrType>(-1);
+    AppendOp(framework::OpRegistry::CreateOp("scale", {{"X", {out_grad}}},
+                                             {{"Out", {y_grad}}}, scale_attr));
+    CompleteAddOp(false);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+USE_OP(scale);
+USE_OP_ITSELF(identity);
+namespace ops = paddle::operators;
+REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad,
+            ops::MinusGradOp<float>);
+REGISTER_OP_CPU_KERNEL(minus,
+                       ops::MinusKernel<paddle::platform::CPUPlace, float>);

paddle/operators/minus_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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/operators/minus_op.h"
+
+REGISTER_OP_GPU_KERNEL(
+    minus, paddle::operators::MinusKernel<paddle::platform::GPUPlace, float>);

paddle/operators/minus_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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 "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class MinusKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* left_tensor = context.Input<framework::Tensor>("X");
+    auto* right_tensor = context.Input<framework::Tensor>("Y");
+    auto* out_tensor = context.Output<framework::Tensor>("Out");
+
+    out_tensor->mutable_data<T>(context.GetPlace());
+    auto& dev = context.GetEigenDevice<Place>();
+    framework::EigenVector<T>::Flatten(*out_tensor).device(dev) =
+        framework::EigenVector<T>::Flatten(*left_tensor) -
+        framework::EigenVector<T>::Flatten(*right_tensor);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/net_op_test.cc

@@ -79,7 +79,7 @@ TEST(NetOp, Clone) {
   ASSERT_NE(new_net_op, nullptr);
   ASSERT_TRUE(new_net_op->IsNetOp());
   auto* new_net = static_cast<NetOp*>(new_net_op.get());
-  ASSERT_EQ(2, new_net->ops_.size());
+  ASSERT_EQ(2UL, new_net->ops_.size());
   ASSERT_EQ(new_net->ops_[0]->Type(), "empty");
   ASSERT_EQ(new_net->ops_[1]->Type(), "empty2");
 }

paddle/operators/rowwise_add_op.cu

@@ -18,3 +18,6 @@
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
     rowwise_add, ops::RowwiseAddKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    rowwise_add_grad,
+    ops::RowwiseAddGradKernel<paddle::platform::GPUPlace, float>);

paddle/platform/cuda_helper.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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 <cuda.h>
+
+namespace paddle {
+namespace platform {
+
+#define CUDA_ATOMIC_WRAPPER(op, T) \
+  __device__ __forceinline__ T CudaAtomic##op(T* address, const T val)
+
+#define USE_CUDA_ATOMIC(op, T) \
+  CUDA_ATOMIC_WRAPPER(op, T) { return atomic##op(address, val); }
+
+// For atomicAdd.
+USE_CUDA_ATOMIC(Add, float);
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 600
+USE_CUDA_ATOMIC(Add, double);
+#else
+CUDA_ATOMIC_WRAPPER(Add, double) {
+  unsigned long long int* address_as_ull =
+      reinterpret_cast<unsigned long long int*>(address);
+  unsigned long long int old = *address_as_ull, assumed;
+
+  do {
+    assumed = old;
+    old = atomicCAS(address_as_ull, assumed,
+                    __double_as_longlong(val + __longlong_as_double(assumed)));
+
+    // Note: uses integer comparison to avoid hang in case of NaN
+  } while (assumed != old);
+
+  return __longlong_as_double(old);
+}
+#endif
+
+}  // namespace platform
+}  // namespace paddle

paddle/pybind/CMakeLists.txt

@@ -15,5 +15,7 @@ cc_library(paddle_pybind SHARED
     uniform_random_op
     gaussian_random_op
     fill_zeros_like_op
-    scale_op)
+    lookup_table_op
+    scale_op
+    minus_op)
 endif(WITH_PYTHON)

paddle/pybind/pybind.cc

@@ -42,8 +42,10 @@ USE_OP(fill_zeros_like);
 USE_OP_ITSELF(recurrent_op);
 USE_OP(gaussian_random);
 USE_OP(uniform_random);
+USE_OP(lookup_table);
 USE_OP(scale);
 USE_OP_ITSELF(identity);
+USE_OP(minus);
 USE_CPU_ONLY_OP(gather);
 
 namespace paddle {

proto/ModelConfig.proto

@@ -82,6 +82,9 @@ message ConvConfig {
 
   // if not set, use img_size
   optional uint32 img_size_y = 14;
+
+  optional uint32 dilation = 15 [ default = 1 ];
+  optional uint32 dilation_y = 16 [ default = 1 ];
 }
 
 message PoolConfig {

python/paddle/trainer/config_parser.py

@@ -870,12 +870,16 @@ class Conv(Cfg):
                  caffe_mode=True,
                  filter_size_y=None,
                  padding_y=None,
-                 stride_y=None):
+                 stride_y=None,
+                 dilation=None,
+                 dilation_y=None):
         self.add_keys(locals())
         if filter_size_y is None:
             self.filter_size_y = filter_size
         if padding_y is None:
             self.padding_y = padding
+        if dilation_y is None:
+            self.dilation_y = dilation
         if stride_y is None:
             self.stride_y = stride
         if output_x is not None:

python/paddle/trainer_config_helpers/layers.py

@@ -2344,6 +2344,7 @@ def img_conv_layer(input,
                    groups=1,
                    stride=1,
                    padding=0,
+                   dilation=1,
                    bias_attr=None,
                    param_attr=None,
                    shared_biases=True,
@@ -2351,6 +2352,7 @@ def img_conv_layer(input,
                    filter_size_y=None,
                    stride_y=None,
                    padding_y=None,
+                   dilation_y=None,
                    trans=False,
                    layer_type=None):
     """
@@ -2415,6 +2417,11 @@ def img_conv_layer(input,
     :type padding: int|tuple|list
     :param padding_y: The y dimension of the padding.
     :type padding_y: int
+    :param dilation: The x dimension of the dilation. Or input a tuple for two
+                    image dimension
+    :type dilation: int|tuple|list
+    :param dilation_y: The y dimension of the dilation.
+    :type dilation_y: int
     :param bias_attr: Convolution bias attribute. None means default bias.
                       False means no bias.
     :type bias_attr: ParameterAttribute|False
@@ -2462,6 +2469,13 @@ def img_conv_layer(input,
         else:
             padding_y = padding
 
+    if dilation_y is None:
+        if isinstance(dilation, collections.Sequence):
+            assert len(dilation) == 2
+            dilation, dilation_y = dilation
+        else:
+            dilation_y = dilation
+
     if param_attr.attr.get('initial_smart'):
         # special initial for conv layers.
         init_w = (2.0 / (filter_size**2 * num_channels))**0.5
@@ -2471,6 +2485,8 @@ def img_conv_layer(input,
         param_attr.attr["initial_smart"] = False
 
     if layer_type:
+        if dilation > 1 or dilation_y > 1:
+            assert layer_type in ["cudnn_conv", "cudnn_convt"]
         if trans:
             assert layer_type in ["exconvt", "cudnn_convt"]
         else:
@@ -2486,11 +2502,13 @@ def img_conv_layer(input,
             conv=Conv(
                 filter_size=filter_size,
                 padding=padding,
+                dilation=dilation,
                 stride=stride,
                 channels=num_channels,
                 groups=groups,
                 filter_size_y=filter_size_y,
                 padding_y=padding_y,
+                dilation_y=dilation_y,
                 stride_y=stride_y),
             **param_attr.attr),
         active_type=act.name,

python/paddle/trainer_config_helpers/tests/configs/img_layers.py

@@ -12,6 +12,7 @@ img_conv = img_conv_layer(
     num_filters=64,
     filter_size=(32, 32),
     padding=(1, 1),
+    dilation=(1, 1),
     stride=(1, 1),
     act=LinearActivation())
 img_bn = batch_norm_layer(input=img_conv, act=ReluActivation())

python/paddle/v2/framework/tests/CMakeLists.txt

@@ -28,4 +28,6 @@ py_test(test_uniform_random_op SRCS test_uniform_random_op.py)
 py_test(test_recurrent_op SRCS test_recurrent_op.py)
 py_test(test_sgd_op SRCS test_sgd_op.py)
 py_test(test_gradient_checker SRCS test_gradient_checker.py)
+py_test(test_lookup_table SRCS test_lookup_table.py)
 py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py)
+py_test(mnist SRCS mnist.py)

python/paddle/v2/framework/tests/gradient_checker.py

@@ -23,6 +23,10 @@ def grad_var_name(var_name):
     return var_name + "@GRAD"
 
 
+def empty_var_name():
+    return "@EMPTY@"
+
+
 def get_numeric_gradient(op,
                          input_values,
                          output_name,
@@ -176,7 +180,7 @@ class GradientChecker(unittest.TestCase):
         ]
         return outs
 
-    def compare_grad(self, forward_op, input_value):
+    def compare_grad(self, forward_op, input_value, no_grad_set=None):
         """ Compare the input gradients between CPU and GPU for the given forward
         operator.
 
@@ -184,15 +188,20 @@ class GradientChecker(unittest.TestCase):
         :type forward_op: Operator
         :param input_value: input values.
         :type input_value: dict{string:numpy.array}
+        :param no_grad_set: the set of variables names without gradients.
+        :type no_grad_set: a set of string
         :raises: AssertionError, there is different gradient value.
         """
-        backward_op = core.Operator.backward(forward_op, set())
+        if no_grad_set is None:
+            no_grad_set = set()
+        backward_op = core.Operator.backward(forward_op, no_grad_set)
         # return if not compile with GPU or not implementing GPU kernel
         if not (core.is_compile_gpu() and backward_op.support_gpu()):
             return
 
         outputs = backward_op.outputs()
         out_names = [item for k in outputs for item in outputs[k]]
+        out_names = filter(lambda x: x != empty_var_name(), out_names)
         cpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
                                         out_names, core.CPUPlace())
         gpu_grads = self.__get_gradient(forward_op, backward_op, input_value,

python/paddle/v2/framework/tests/mnist.py

+import paddle.v2.framework.core as core
+from paddle.v2.framework.op import Operator
+import numpy
+import paddle.v2 as paddle
+
+BATCH_SIZE = 100
+
+scope = core.Scope()
+place = core.CPUPlace()
+# if you want to test GPU training, you can use gpu place
+# place = core.GPUPlace(0)
+dev_ctx = core.DeviceContext.create(place)
+
+init_net = core.Net.create()
+forward_net = core.Net.create()
+backward_net = None
+optimize_net = core.Net.create()
+
+
+def atomic_id():
+    id = 0
+    while True:
+        yield id
+        id += 1
+
+
+uniq_id = atomic_id().next
+
+
+def data_layer(name, dims):
+    var = scope.new_var(name)
+    tensor = var.get_tensor()
+    tensor.set_dims(dims)  # 1 is batch size holder.
+    return name
+
+
+def feed_data(name, data):
+    assert isinstance(data, numpy.ndarray)
+    tensor = scope.find_var(name).get_tensor()
+    tensor.set_dims(data.shape)
+    if data.dtype == numpy.dtype('int32'):
+        tensor.alloc_int(place)
+    elif data.dtype == numpy.dtype('float32'):
+        tensor.alloc_float(place)
+    else:
+        raise ValueError("data type not supported")
+    tensor.set(data, place)
+
+
+def grad_var_name(var_name):
+    return var_name + "@GRAD"
+
+
+def sgd_optimizer(net, param_name, learning_rate=0.005):
+    grad_name = grad_var_name(param_name)
+    optimize_op = Operator(
+        "sgd",
+        param=param_name,
+        grad=grad_name,
+        param_out=param_name,
+        learning_rate=learning_rate)
+    net.append_op(optimize_op)
+
+
+# should use operator and add these to the init_network
+def init_param(net, param_name, dims):
+    scope.new_var(param_name)
+    op = Operator(
+        "uniform_random", Out=param_name, dims=dims, min=-0.5, max=0.5, seed=10)
+    op.infer_shape(scope)
+    net.append_op(op)
+
+
+# fc_layer
+def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
+    """
+    Add a fc layer to net
+
+    :param input: input variable name.
+    :type input: str
+    :param size: fully connected layer size.
+    :param act: activation name
+    :param param: parameter attribute, used for initialize parameters.
+    :param bias: bias attribute. False will not have a bias.
+    :param name: the name of fc layer. If not set, model will generate a
+    readable name
+    :return: output variable name.
+    """
+    if name is None:
+        name = 'fc_%d' % uniq_id()
+    if not isinstance(name, str):
+        raise ValueError("name should be string")
+
+    input_dims = scope.find_var(input).get_tensor().get_dims()
+
+    w_name = param or name + ".w"
+    init_param(net=init_net, param_name=w_name, dims=[input_dims[1], size])
+    sgd_optimizer(net=optimize_net, param_name=w_name, learning_rate=0.01)
+
+    pre_activation = name + ".mul.out"
+    scope.new_var(pre_activation)
+    mul_op = Operator("mul", X=input, Y=w_name, Out=pre_activation)
+    net.append_op(mul_op)
+
+    # create bias variable if needed
+    if bias:
+        bias_name = name + ".b"
+        init_param(net=init_net, param_name=bias_name, dims=[size])
+        sgd_optimizer(
+            net=optimize_net, param_name=bias_name, learning_rate=0.001)
+        bias_out = name + ".rowwise_add.out"
+        scope.new_var(bias_out)
+        rowwise_append_op = Operator(
+            "rowwise_add", X=pre_activation, b=bias_name, Out=bias_out)
+        net.append_op(rowwise_append_op)
+        pre_activation = bias_out
+
+    activation_op = Operator(act, X=pre_activation, Y=name)
+    net.append_op(activation_op)
+    scope.new_var(name)
+    net.infer_shape(scope)
+    return name
+
+
+def cross_entropy_layer(net, input, label):
+    cost_name = 'cross_entropy_%d' % uniq_id()
+    cross_entropy_op = Operator(
+        "onehot_cross_entropy", X=input, label=label, Y=cost_name)
+    net.append_op(cross_entropy_op)
+    scope.new_var(cost_name)
+    net.infer_shape(scope)
+    return cost_name
+
+
+def create_backward_net(forward_net):
+    net = core.Operator.backward(forward_net, set())
+    for input in net.inputs()["all"]:
+        var = scope.new_var(input)
+        var.get_tensor()
+    for output in net.outputs()["all"]:
+        var = scope.new_var(output)
+        var.get_tensor()
+    return net
+
+
+def debug_print_op(op):
+    print("===============" + op.type() + "==============")
+    print("***inputs:***")
+    for input in op.inputs()["all"]:
+        print input, scope.find_var(input).get_tensor().get_dims()
+    print("\n***outputs:***")
+    for output in op.outputs()["all"]:
+        print output, scope.find_var(output).get_tensor().get_dims()
+    print("")
+    print("")
+
+
+def set_cost(cost):
+    cost_shape = numpy.array(scope.find_var(cost).get_tensor()).shape
+    cost_grad = \
+        scope.find_var(grad_var_name(cost)).get_tensor()
+    cost_grad.set_dims(cost_shape)
+    cost_grad.alloc_float(place)
+    cost_grad.set(numpy.ones(cost_shape).astype("float32"), place)
+
+
+def get_cost_mean(cost):
+    cost_data = numpy.array(scope.find_var(cost).get_tensor())
+    return cost_data.sum() / len(cost_data)
+
+
+def error_rate(predict, label):
+    predict_var = numpy.array(scope.find_var(predict).get_tensor()).argmax(
+        axis=1)
+    label = numpy.array(scope.find_var(label).get_tensor())
+    error_num = numpy.sum(predict_var != label)
+    return error_num / float(len(label))
+
+
+images = data_layer(name='pixel', dims=[BATCH_SIZE, 784])
+labels = data_layer(name='label', dims=[BATCH_SIZE])
+fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid")
+fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid")
+predict = fc_layer(net=forward_net, input=fc2, size=100, act="softmax")
+cost = cross_entropy_layer(net=forward_net, input=predict, label=labels)
+
+init_net.complete_add_op(True)
+forward_net.complete_add_op(True)
+backward_net = create_backward_net(forward_net)
+optimize_net.complete_add_op(True)
+
+print(init_net)
+print(forward_net)
+print(backward_net)
+print(optimize_net)
+
+debug_print_op(forward_net)
+debug_print_op(backward_net)
+debug_print_op(optimize_net)
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=8192),
+    batch_size=BATCH_SIZE)
+
+
+def test(cost_name):
+    test_reader = paddle.batch(
+        paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
+    cost = []
+    error = []
+    for data in test_reader():
+        image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
+        label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
+        feed_data(images, image_data)
+        feed_data(labels, label_data)
+
+        forward_net.infer_shape(scope)
+        forward_net.run(scope, dev_ctx)
+        cost.append(get_cost_mean(cost_name))
+        error.append(error_rate(predict, "label"))
+    print("cost=" + str(sum(cost) / float(len(cost))) + " error_rate=" + str(
+        sum(error) / float(len(error))))
+
+
+PASS_NUM = 1
+
+init_net.run(scope, dev_ctx)
+for pass_id in range(PASS_NUM):
+    batch_id = 0
+
+    for data in train_reader():
+        image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
+        label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
+        feed_data(images, image_data)
+        feed_data(labels, label_data)
+
+        forward_net.infer_shape(scope)
+        forward_net.run(scope, dev_ctx)
+        set_cost(cost)
+        backward_net.infer_shape(scope)
+        backward_net.run(scope, dev_ctx)
+
+        optimize_net.run(scope, dev_ctx)
+        if batch_id % 100 == 0:
+            print("pass[" + str(pass_id) + "] batch_id[" + str(batch_id) + "]")
+            test(cost)
+
+        batch_id = batch_id + 1

python/paddle/v2/framework/tests/test_lookup_table.py

+import unittest
+import numpy as np
+from op_test_util import OpTestMeta
+from gradient_checker import GradientChecker, create_op
+
+
+class TestSigmoidOp(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = 'lookup_table'
+        table = np.random.random((17, 31)).astype('float32')
+        ids = np.random.randint(0, 17, 4).astype('int32')
+        self.inputs = {'W': table, 'Ids': ids}
+        self.outputs = {'Out': table[ids]}
+
+
+class TestSigmoidGradOp(GradientChecker):
+    def test_grad(self):
+        op = create_op('lookup_table')
+        table = np.random.random((17, 31)).astype('float32')
+        ids = np.random.randint(0, 17, 4).astype('int32')
+        inputs = {'W': table, 'Ids': ids}
+        # comapre gradients 
+        self.compare_grad(op, inputs, set(['Ids']))
+        # check gradients 
+        self.check_grad(op, inputs, set('W'), 'Out')
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/framework/tests/test_minus_op.py

+import unittest
+import numpy as np
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
+
+
+class MinusOpTest(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = "minus"
+        self.inputs = {
+            'X': np.random.random((32, 84)).astype("float32"),
+            'Y': np.random.random((32, 84)).astype("float32")
+        }
+        self.outputs = {'Out': (self.inputs['X'] - self.inputs['Y'])}
+
+
+class MinusGradTest(GradientChecker):
+    def test_left(self):
+        op = create_op("minus")
+        inputs = {
+            "X": np.random.random((10, 10)).astype("float32"),
+            "Y": np.random.random((10, 10)).astype("float32")
+        }
+        self.check_grad(op, inputs, ["X", 'Y'], "Out")
+
+
+if __name__ == '__main__':
+    unittest.main()