opencl opt

src/operators/kernel/cl/conv_add_bn_relu_kernel.cpp

@@ -21,7 +21,7 @@ limitations under the License. */
 
 namespace paddle_mobile {
 namespace operators {
-
+bool optimise = true;
 template <>
 bool ConvAddBNReluKernel<GPU_CL, float>::Init(
     FusionConvAddBNReluParam<GPU_CL> *param) {
@@ -139,7 +139,12 @@ bool ConvAddBNReluKernel<GPU_CL, float>::Init(
   if (param->Filter()->dims()[2] == 1 && param->Filter()->dims()[3] == 1) {
     param->Filter()->InitNImage(cl_helper_.CLContext(),
                                 cl_helper_.CLCommandQueue());
+    if (optimise) {
+      this->cl_helper_.AddKernel("conv_1x1_spl", "conv_add_bn_relu_kernel.cl");
+    } else {
       this->cl_helper_.AddKernel("conv_1x1", "conv_add_bn_relu_kernel.cl");
+    }
+
     DLOG << " conv add bn relu conv 1x1";
   } else if (param->Filter()->dims()[1] == 1 &&
              param->Input()->dims()[1] == param->Output()->dims()[1] &&
@@ -205,10 +210,13 @@ void ConvAddBNReluKernel<GPU_CL, float>::Compute(
 
   cl_int status;
 
+  if (optimise) {
+    if (param.Filter()->dims()[2] == 1 && param.Filter()->dims()[3] == 1) {
       status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
       CL_CHECK_ERRORS(status);
 
-  status = clSetKernelArg(kernel, 1, sizeof(int), &w);
+      int maped_w = maptofactor(w, 4);
+      status = clSetKernelArg(kernel, 1, sizeof(int), &maped_w);
       CL_CHECK_ERRORS(status);
 
       status = clSetKernelArg(kernel, 2, sizeof(int), &nh);
@@ -256,30 +264,132 @@ 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 = clSetKernelArg(kernel, 17, sizeof(int), &w);
+      CL_CHECK_ERRORS(status);
 
-  /*
-  if (param.Filter()->dims()[2] == 1 &&
-      param.Filter()->dims()[3] == 1 &&
-      param.Filter()->dims()[0] % 16 == 0) {
-    DLOG << " before modifi work size: " << default_work_size;
+      const size_t work_size[3] = {
+          static_cast<const uint32_t>(default_work_size.data()[0]),
+          static_cast<const uint32_t>(maped_w),
+          static_cast<const uint32_t>(default_work_size.data()[2])};
 
-    default_work_size[0] = default_work_size[0] / 4;
+      status = clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel,
+                                      default_work_size.size(), NULL, work_size,
+                                      NULL, 0, NULL, NULL);
+      CL_CHECK_ERRORS(status);
+    } else {
+      status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
+      CL_CHECK_ERRORS(status);
 
-    DLOG << " modification work size: " << default_work_size;
-    DLOG << " input dims " << param.Input()->dims();
-    DLOG << " output dims " << param.Output()->dims();
-    DLOG << " filter dims: " << param.Filter()->dims();
-    DLOG << " biase dims : " << param.Bias()->dims();
+      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), &biase);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 6, sizeof(cl_mem), &new_scale);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 7, sizeof(cl_mem), &new_bias);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 8, sizeof(cl_mem), &output);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 9, sizeof(int), &stride);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 10, sizeof(int), &offset);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 11, sizeof(int), &input_c);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 12, sizeof(int), &dilation);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 13, sizeof(int), &input_width);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 14, sizeof(int), &input_height);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 15, sizeof(int), &output_width);
+      CL_CHECK_ERRORS(status);
+
+      status = clSetKernelArg(kernel, 16, sizeof(int), &output_height);
+      CL_CHECK_ERRORS(status);
+
+      status = clEnqueueNDRangeKernel(
+          this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(),
+          NULL, default_work_size.data(), NULL, 0, NULL, NULL);
+      CL_CHECK_ERRORS(status);
     }
-  */
 
+  } else {
+    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), &biase);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 6, sizeof(cl_mem), &new_scale);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 7, sizeof(cl_mem), &new_bias);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 8, sizeof(cl_mem), &output);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 9, sizeof(int), &stride);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 10, sizeof(int), &offset);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 11, sizeof(int), &input_c);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 12, sizeof(int), &dilation);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 13, sizeof(int), &input_width);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 14, sizeof(int), &input_height);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 15, sizeof(int), &output_width);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 16, sizeof(int), &output_height);
+    CL_CHECK_ERRORS(status);
     status = clEnqueueNDRangeKernel(
-      this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(), NULL,
-      default_work_size.data(), NULL, 0, NULL, NULL);
+        this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(),
+        NULL, default_work_size.data(), NULL, 0, NULL, NULL);
     CL_CHECK_ERRORS(status);
+  }
 }
 
 template class ConvAddBNReluKernel<GPU_CL, float>;

src/operators/kernel/cl/conv_add_kernel.cpp

@@ -18,6 +18,7 @@ limitations under the License. */
 
 namespace paddle_mobile {
 namespace operators {
+bool optimise_convadd = true;
 
 template <>
 bool ConvAddKernel<GPU_CL, float>::Init(FusionConvAddParam<GPU_CL> *param) {
@@ -35,8 +36,11 @@ bool ConvAddKernel<GPU_CL, float>::Init(FusionConvAddParam<GPU_CL> *param) {
   if (param->Filter()->dims()[2] == 1 && param->Filter()->dims()[3] == 1) {
     param->Filter()->InitNImage(cl_helper_.CLContext(),
                                 cl_helper_.CLCommandQueue());
-
+    if (optimise_convadd) {
+      this->cl_helper_.AddKernel("conv_1x1_spl", "conv_add_kernel.cl");
+    } else {
       this->cl_helper_.AddKernel("conv_1x1", "conv_add_kernel.cl");
+    }
   } else if (param->Filter()->dims()[1] == 1 &&
              param->Input()->dims()[1] == param->Output()->dims()[1] &&
              param->Filter()->dims()[2] == 3) {
@@ -95,10 +99,13 @@ void ConvAddKernel<GPU_CL, float>::Compute(
 
   cl_int status;
 
+  if (optimise_convadd && param.Filter()->dims()[2] == 1 &&
+      param.Filter()->dims()[3] == 1) {
     status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
     CL_CHECK_ERRORS(status);
 
-  status = clSetKernelArg(kernel, 1, sizeof(int), &w);
+    int maped_w = maptofactor(w, 4);
+    status = clSetKernelArg(kernel, 1, sizeof(int), &maped_w);
     CL_CHECK_ERRORS(status);
 
     status = clSetKernelArg(kernel, 2, sizeof(int), &nh);
@@ -140,13 +147,69 @@ 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 = clSetKernelArg(kernel, 15, sizeof(int), &w);
+    CL_CHECK_ERRORS(status);
+
+    const size_t work_size[3] = {
+        static_cast<const uint32_t>(default_work_size.data()[0]),
+        static_cast<const uint32_t>(maped_w),
+        static_cast<const uint32_t>(default_work_size.data()[2])};
+
+    status = clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel,
+                                    default_work_size.size(), NULL, work_size,
+                                    NULL, 0, NULL, NULL);
+    CL_CHECK_ERRORS(status);
+  } else {
+    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), &biase);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 6, sizeof(cl_mem), &output);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 7, sizeof(int), &stride);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 8, sizeof(int), &offset);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 9, sizeof(int), &input_c);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 10, sizeof(int), &dilation);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 11, sizeof(int), &input_width);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 12, sizeof(int), &input_height);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 13, sizeof(int), &output_width);
+    CL_CHECK_ERRORS(status);
+
+    status = clSetKernelArg(kernel, 14, sizeof(int), &output_height);
+    CL_CHECK_ERRORS(status);
 
     status = clEnqueueNDRangeKernel(
-      this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(), NULL,
-      default_work_size.data(), NULL, 0, NULL, NULL);
+        this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(),
+        NULL, default_work_size.data(), NULL, 0, NULL, NULL);
     CL_CHECK_ERRORS(status);
+  }
 }
 
 template class ConvAddKernel<GPU_CL, float>;

src/operators/kernel/conv_add_bn_relu_kernel.h

@@ -36,6 +36,9 @@ class ConvAddBNReluKernel
  public:
   void Compute(const FusionConvAddBNReluParam<DeviceType> &param);
   bool Init(FusionConvAddBNReluParam<DeviceType> *param);
+  inline int maptofactor(int i, int factor) {
+    return (i + factor - 1) / factor;
+  }
 };
 
 }  // namespace operators

src/operators/kernel/conv_add_kernel.h

@@ -41,6 +41,9 @@ class ConvAddKernel
  public:
   void Compute(const FusionConvAddParam<DeviceType> &param);
   bool Init(FusionConvAddParam<DeviceType> *param);
+  inline int maptofactor(int i, int factor) {
+    return (i + factor - 1) / factor;
+  }
 };
 
 }  // namespace operators