commit event

src/framework/cl/cl_deleter.h

@@ -30,6 +30,13 @@ struct CLMemDeleter {
   }
 };
 
+struct CLEventDeleter {
+  template <class T>
+  void operator()(T *clEventObj) {
+    clReleaseEvent(clEventObj);
+  }
+};
+
 struct CLCommQueueDeleter {
   template <class T>
   void operator()(T *clQueueObj) {

src/framework/cl/cl_engine.h

@@ -81,6 +81,12 @@ class CLEngine {
     return std::move(program_ptr);
   }
 
+  std::unique_ptr<_cl_event, CLEventDeleter> CreateEvent(cl_context context) {
+    cl_event event = clCreateUserEvent(context, status_);
+    std::unique_ptr<_cl_event, CLEventDeleter> event_ptr(event);
+    return std::move(event_ptr);
+  }
+
   bool BuildProgram(cl_program program) {
     cl_int status;
     status = clBuildProgram(program, 0, 0, "-cl-fast-relaxed-math", 0, 0);

src/framework/cl/cl_image.h

@@ -21,6 +21,7 @@ limitations under the License. */
 #include "framework/cl/cl_half.h"
 #include "framework/cl/cl_tool.h"
 #include "framework/cl/cl_deleter.h"
+#include "framework/cl/cl_engine.h"
 #include "framework/ddim.h"
 #include "framework/tensor.h"
 
@@ -97,6 +98,8 @@ class CLImage {
       InitCLImage(context, command_queue, tensor_data_, tensor_dims_);
     }
 
+    cl_event_ = CLEngine::Instance()->CreateEvent(context);
+
 
 //    InitCLImage(context, command_queue, nullptr, dim);
     initialized_ = true;
@@ -157,6 +160,8 @@ class CLImage {
 
   const ImageType GetImageType() const { return image_type_; }
 
+  cl_event GetClEvent() const { return cl_event_.get(); }
+
  private:
   ImageType image_type_ = Invalid;
   void InitCLImage2C(cl_context context, cl_command_queue command_queue,
@@ -295,6 +300,7 @@ class CLImage {
 
   bool initialized_ = false;
   std::unique_ptr<_cl_mem, CLMemDeleter> cl_image_;
+  std::unique_ptr<_cl_event, CLEventDeleter> cl_event_;
   size_t image_width_;
   size_t width_of_one_block_;
   size_t height_of_one_block_;

src/framework/executor.cpp

@@ -37,8 +37,6 @@ limitations under the License. */
 #include "framework/cl/cl_image.h"
 #endif
 
-int debug_to = 32;
-
 namespace paddle_mobile {
 namespace framework {
 
@@ -87,7 +85,7 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
   for (int i = 0; i < blocks.size(); ++i) {
     std::shared_ptr<framework::BlockDesc> block_desc = blocks[i];
     std::vector<std::shared_ptr<framework::OpDesc>> ops = block_desc->Ops();
-    for (int j = 0; j < debug_to; ++j) {
+    for (int j = 0; j < ops.size(); ++j) {
       std::shared_ptr<framework::OpDesc> op = ops[j];
       DLOG << "create op: " << j << "  " << op->Type();
       auto op_base = framework::OpRegistry<Dtype>::CreateOp(
@@ -416,7 +414,7 @@ std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
     }
   }
 #else
-  for (int i = 0; i < debug_to; i++) {
+  for (int i = 0; i < ops.size(); i++) {
 #ifdef PADDLE_MOBILE_PROFILE
     struct timespec ts;
     clock_gettime(CLOCK_MONOTONIC, &ts);
@@ -433,8 +431,6 @@ std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
 
   DLOG << " predict return nullptr";
 
-  return nullptr;
-
   auto last_op = ops.rbegin();
   auto output_map = (*last_op)->Outputs();
   std::vector<std::string> out_keys = (*last_op)->GetOutKeys();

src/operators/kernel/cl/conv_add_bn_relu_kernel.cpp

@@ -168,20 +168,20 @@ void ConvAddBNReluKernel<GPU_CL, float>::Compute(
   int output_width = param.Output()->WidthOfOneBlock();
   int output_height = param.Output()->HeightOfOneBlock();
 
-  DLOG << " c block " << c_block;
-  DLOG << " w " << w;
-  DLOG << " nh " << nh;
-  DLOG << " stride " << stride;
-  DLOG << " offset " << offset;
-  DLOG << " input_c " << input_c;
-  DLOG << " dilation " << dilation;
-  DLOG << " input width " << input_width;
-  DLOG << " input height " << input_height;
-  DLOG << " output width " << output_width;
-  DLOG << " output height " << output_height;
-  DLOG << " input dim " << param.Input()->dims();
-  DLOG << " output dim " << param.Output()->dims();
-  DLOG << " filter dim " << param.Filter()->dims();
+//  DLOG << " c block " << c_block;
+//  DLOG << " w " << w;
+//  DLOG << " nh " << nh;
+//  DLOG << " stride " << stride;
+//  DLOG << " offset " << offset;
+//  DLOG << " input_c " << input_c;
+//  DLOG << " dilation " << dilation;
+//  DLOG << " input width " << input_width;
+//  DLOG << " input height " << input_height;
+//  DLOG << " output width " << output_width;
+//  DLOG << " output height " << output_height;
+//  DLOG << " input dim " << param.Input()->dims();
+//  DLOG << " output dim " << param.Output()->dims();
+//  DLOG << " filter dim " << param.Filter()->dims();
 
   cl_int status;
 
@@ -236,9 +236,12 @@ void ConvAddBNReluKernel<GPU_CL, float>::Compute(
   status = clSetKernelArg(kernel, 16, sizeof(int), &output_height);
   CL_CHECK_ERRORS(status);
 
+  cl_event out_event = param.Output()->GetClEvent();
+  cl_event wait_event = param.Input()->GetClEvent();
+
   status =
       clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(), NULL,
-                             default_work_size.data(), NULL, 0, NULL, NULL);
+                             default_work_size.data(), NULL, 1, &wait_event, &out_event);
   CL_CHECK_ERRORS(status);
 }
 

src/operators/kernel/cl/conv_add_kernel.cpp

@@ -117,9 +117,12 @@ void ConvAddKernel<GPU_CL, float>::Compute(
   status = clSetKernelArg(kernel, 14, sizeof(int), &output_height);
   CL_CHECK_ERRORS(status);
 
+  cl_event out_event = param.Output()->GetClEvent();
+  cl_event wait_event = param.Input()->GetClEvent();
+
   status =
       clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(), NULL,
-                             default_work_size.data(), NULL, 0, NULL, NULL);
+                             default_work_size.data(), NULL, 1, &wait_event, &out_event);
   CL_CHECK_ERRORS(status);
 }
 

src/operators/kernel/cl/conv_kernel.cpp

@@ -62,27 +62,15 @@ bool ConvKernel<GPU_CL, float>::Init(ConvParam<GPU_CL> *param) {
 
 template <>
 void ConvKernel<GPU_CL, float>::Compute(const ConvParam<GPU_CL> &param) {
-  DLOG << " Compute helper: " << &cl_helper_;
-  DLOG << " begin compute ";
   auto kernel = this->cl_helper_.KernelAt(0);
-  DLOG << " get work size ";
   auto default_work_size = this->cl_helper_.DefaultWorkSize(*param.Output());
-  DLOG << " end work size ";
   int c_block = default_work_size[0];
   int w = default_work_size[1];
   int nh = default_work_size[2];
   auto input = param.Input()->GetCLImage();
-
-  DLOG << " get Input ";
-
   auto filter = param.Filter()->GetCLImage();
-
-  DLOG << " get Filter ";
-
   auto output = param.Output()->GetCLImage();
 
-  DLOG << " get Output ";
-
   int stride = param.Strides()[0];
   int offset = param.Offset();
   int input_c = param.Input()->CBlock();
@@ -109,56 +97,27 @@ void ConvKernel<GPU_CL, float>::Compute(const ConvParam<GPU_CL> &param) {
   DLOG << " output height " << output_height;
 
   status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 1, sizeof(int), &w);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 2, sizeof(int), &nh);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 5, sizeof(cl_mem), &output);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 6, sizeof(int), &stride);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 7, sizeof(int), &offset);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 8, sizeof(int), &input_c);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 9, sizeof(int), &dilation);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 10, sizeof(int), &input_width);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 11, sizeof(int), &input_height);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 12, sizeof(int), &output_width);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 13, sizeof(int), &output_height);
-  CL_CHECK_ERRORS(status);
-
-  DLOG << " end set kernel arg ";
 
-  DLOG << " begin enqueue ";
+  cl_event out_event = param.Output()->GetClEvent();
+  cl_event wait_event = param.Input()->GetClEvent();
 
   status =
       clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(), NULL,
-                             default_work_size.data(), NULL, 0, NULL, NULL);
+                             default_work_size.data(), NULL, 1, &wait_event, &out_event);
   CL_CHECK_ERRORS(status);
-  DLOG << " end enqueue ";
 }
 
 template class ConvKernel<GPU_CL, float>;

src/operators/kernel/cl/depthwise_conv_kernel.cpp

@@ -76,9 +76,12 @@ void DepthwiseConvKernel<GPU_CL, float>::Compute(
 
   CL_CHECK_ERRORS(status);
 
+  cl_event out_event = param.Output()->GetClEvent();
+  cl_event wait_event = param.Input()->GetClEvent();
+
   status =
       clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(), NULL,
-                             default_work_size.data(), NULL, 0, NULL, NULL);
+                             default_work_size.data(), NULL, 1, &wait_event, &out_event);
 
   CL_CHECK_ERRORS(status);
 }

src/operators/kernel/cl/softmax_kernel.cpp

@@ -34,21 +34,13 @@ void SoftmaxKernel<GPU_CL, float>::Compute(const SoftmaxParam<GPU_CL> &param) {
   auto inputImage = input->GetCLImage();
   auto outputImage = output->GetCLImage();
 
-  DLOG << " softmax - output image dim " << output->ImageDims();
-  DLOG << " softmax - output image tensor dim " << output->dims();
-
   int group = output->ImageWidth();
 
   cl_int status;
 
   status = clSetKernelArg(kernel, 0, sizeof(cl_mem), &inputImage);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 1, sizeof(cl_mem), &outputImage);
-  CL_CHECK_ERRORS(status);
-
   status = clSetKernelArg(kernel, 2, sizeof(int), &group);
-  CL_CHECK_ERRORS(status);
 
 //  const auto &inputDim = input->dims();
 //
@@ -62,7 +54,6 @@ void SoftmaxKernel<GPU_CL, float>::Compute(const SoftmaxParam<GPU_CL> &param) {
 //  clSetKernelArg(kernel, 3, sizeof(int), &dims[1]);
 //  clSetKernelArg(kernel, 4, sizeof(int), &dims[2]);
 //  clSetKernelArg(kernel, 5, sizeof(int), &dims[3]);
-  DLOG << "default_work_size:  " << default_work_size;
 
   status = clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(), NULL,
                          default_work_size.data(), NULL, 0, NULL, NULL);