[mobile]Develop common deepwise & fix bug in element mul (#2687)

* [mobile][opencl]common deepwise conv,test=mobile

* [mobile][opencl]revert deepwise 3x3 for stable ,test = mobile

* [mobile][opencl]format convkernel.inc.cl with clang-format ,test = mobile

* [mobile][opencl] suite 1*X Y element_y ,test=mobile

* [mobile][opencl] add whole print method for cl_image ,test=mobile

mobile/src/framework/cl/cl_image.cpp

@@ -18,6 +18,37 @@ limitations under the License. */
 namespace paddle_mobile {
 namespace framework {
 
+void CLImage::PrintTensor(const CLImage &cl_image) const {
+  size_t width = cl_image.ImageDims()[0];
+  size_t height = cl_image.ImageDims()[1];
+
+  half_t *image_data = new half_t[height * width * 4];
+  cl_int err;
+  cl_mem image = cl_image.GetCLImage();
+  size_t origin[3] = {0, 0, 0};
+  size_t region[3] = {width, height, 1};
+  err = clEnqueueReadImage(cl_image.CommandQueue(), image, CL_TRUE, origin,
+                           region, 0, 0, image_data, 0, NULL, NULL);
+
+  CL_CHECK_ERRORS(err);
+
+  PADDLE_MOBILE_ENFORCE(cl_image.numel() != 0,
+                        "cl_image numel should not be 0 ");
+  float *tensor_data = new float[cl_image.numel()];
+  auto converter = cl_image.Converter();
+  converter->ImageToNCHW(image_data, tensor_data, cl_image.ImageDims(),
+                         cl_image.dims());
+  int stride = cl_image.numel() / 20;
+  stride = stride > 0 ? stride : 1;
+
+  for (int i = 0; i < cl_image.numel(); i++) {
+    printf("%f \n", tensor_data[i]);
+  }
+
+  delete[](tensor_data);
+  delete[](image_data);
+}
+
 void CLImageToTensor(CLImage *cl_image, Tensor *tensor, cl_context context,
                      cl_command_queue commandQueue, cl_kernel kernel) {
   tensor->mutable_data<float>();

mobile/src/framework/cl/cl_image.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <iostream>
 #include <memory>
 #include <vector>
 
@@ -285,6 +286,7 @@ class CLImage {
   cl_event GetClEvent() const { return cl_event_.get(); }
 
   CLImageConverterBase *Converter() const { return image_converter_; }
+  void PrintTensor(const CLImage &cl_image) const;
 
  private:
   void InitCLImage(cl_context context, size_t width, size_t height,

mobile/src/framework/cl/cl_tool.h

@@ -24,8 +24,9 @@ const char* opencl_error_to_str(cl_int error);
 #define CL_CHECK_ERRORS(ERR)                                                  \
   if (ERR != CL_SUCCESS) {                                                    \
     printf(                                                                   \
-        "OpenCL error with code %s happened in file %s at line %d. "  \
-        "Exiting.\n",                                                 \
+        "\033[1;31;40mOpenCL error with code %s happened in file %s at line " \
+        "%d. "                                                                \
+        "Exiting.\033[0m\n",                                                  \
         paddle_mobile::framework::opencl_error_to_str(ERR), __FILE__,         \
         __LINE__);                                                            \
   }

mobile/src/operators/kernel/cl/cl-kernel-func/conv_func.cpp

@@ -241,7 +241,9 @@ void ConvAddBnRelu(framework::CLHelper *cl_helper,
   cl_int status;
   int index = 0;
 
-  if (param.Filter()->dims()[2] == 1 && param.Filter()->dims()[3] == 1) {
+  const int filter_height = param.Filter()->dims()[2];
+  const int filter_width = param.Filter()->dims()[3];
+  if (filter_height == 1 && filter_width == 1) {
     status = clSetKernelArg(kernel, index++, sizeof(int), &c_block);
     CL_CHECK_ERRORS(status);
 
@@ -404,7 +406,7 @@ void ConvAddBnRelu(framework::CLHelper *cl_helper,
     status = clSetKernelArg(kernel, index++, sizeof(int), &output_height);
     CL_CHECK_ERRORS(status);
 
-    if (param.Filter()->dims()[2] == 3 && param.Filter()->dims()[3] == 3) {
+    if (filter_height == 3 && filter_width == 3) {
       // normal conv
       if (param.Filter()->dims()[0] == param.Output()->dims()[1] &&
           param.Filter()->dims()[1] == param.Input()->dims()[1]) {
@@ -425,6 +427,17 @@ void ConvAddBnRelu(framework::CLHelper *cl_helper,
         status = clSetKernelArg(kernel, index++, sizeof(int), &group);
         CL_CHECK_ERRORS(status);
       }
+    } else if (filter_height != 3 && filter_width != 3) {
+      // not 3x3
+      if (param.Filter()->dims()[1] == 1 &&
+          param.Input()->dims()[1] == param.Output()->dims()[1]) {
+        // deepwise basic use in not 3x3
+        status = clSetKernelArg(kernel, index++, sizeof(int), &filter_width);
+        CL_CHECK_ERRORS(status);
+
+        status = clSetKernelArg(kernel, index++, sizeof(int), &filter_height);
+        CL_CHECK_ERRORS(status);
+      }
     }
 
     status = clEnqueueNDRangeKernel(

mobile/src/operators/kernel/cl/cl_kernel/elementwise_mul_kernel.cl

@@ -13,33 +13,101 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma OPENCL EXTENSION cl_khr_fp16 : enable
-__kernel void elementwise_mul(__global image2d_t input, __global image2d_t bias,__write_only image2d_t outputImage) {
+__kernel void elementwise_mul(__global image2d_t input, __global image2d_t bias,
+                              __write_only image2d_t outputImage) {
   int x = get_global_id(0);
   int y = get_global_id(1);
-     const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+  const sampler_t sampler =
+      CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
   int2 coords;
   coords.x = x;
   coords.y = y;
   half4 in = read_imageh(input, sampler, coords);
   half4 biase = read_imageh(bias, sampler, coords);
   half4 output = in * biase;
-     write_imageh(outputImage,coords,output);
- }
-
+  write_imageh(outputImage, coords, output);
+}
 
-__kernel void channel_mul(__global image2d_t input, __global image2d_t bias,__write_only
-image2d_t outputImage, int w) {
+__kernel void channel_mul(__global image2d_t input, __global image2d_t bias,
+                          __write_only image2d_t outputImage, int w) {
   int x = get_global_id(0);
   int y = get_global_id(1);
-  const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+  const sampler_t sampler =
+      CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
   int2 coords;
   coords.x = x;
   coords.y = y;
   int2 coords_bias;
-  coords_bias.x = x/w;
+  coords_bias.x = x / w;
   coords_bias.y = 0;
   half4 in = read_imageh(input, sampler, coords);
   half4 biase = read_imageh(bias, sampler, coords_bias);
   half4 output = in * biase;
-  write_imageh(outputImage,coords,output);
+  write_imageh(outputImage, coords, output);
+}
+
+// etc : 1 1 1 72
+// run time Y  [value,0,0,0] * 72
+__kernel void channel_mul_d2(__global image2d_t input, __global image2d_t bias,
+                             __write_only image2d_t outputImage, int w) {
+  int x = get_global_id(0);
+  int y = get_global_id(1);
+  const sampler_t sampler =
+      CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+  int2 coords;
+  coords.x = x;
+  coords.y = y;
+
+  int2 coords_bias0;
+  int2 coords_bias1;
+  int2 coords_bias2;
+  int2 coords_bias3;
+
+  /*  if (x == 0 && y == 0) {
+      half4 b = (half4){0, 0, 0, 0};
+  #define PPI(j, k)                                                          \
+    b = read_imageh(bias, sampler, (int2){j, k});                            \
+    printf("bias(%d,%d)={ %f , %f , %f , %f }\n ", j, k, convert_float(b.x), \
+           convert_float(b.y), convert_float(b.z), convert_float(b.w));
+      for (int i = 0; i < 73; ++i) {
+        PPI(i, 0);
+      }
+  #undef PPI
+    }*/
+
+  coords_bias0.x = x / w * 4;
+  coords_bias0.y = 0;
+
+  coords_bias1.x = x / w * 4 + 1;
+  coords_bias1.y = 0;
+
+  coords_bias2.x = x / w * 4 + 2;
+  coords_bias2.y = 0;
+
+  coords_bias3.x = x / w * 4 + 3;
+  coords_bias3.y = 0;
+
+  half4 biase0 = read_imageh(bias, sampler, coords_bias0);
+  half4 biase1 = read_imageh(bias, sampler, coords_bias1);
+  half4 biase2 = read_imageh(bias, sampler, coords_bias2);
+  half4 biase3 = read_imageh(bias, sampler, coords_bias3);
+  /*  if (x == 0 && y == 0) {
+      printf("bias0={ %f , %f , %f , %f }\n ",
+             convert_float(biase0.x), convert_float(biase0.y),
+             convert_float(biase0.z), convert_float(biase0.w));
+
+      printf("bias1={ %f , %f , %f , %f }\n ",
+             convert_float(biase1.x), convert_float(biase1.y),
+             convert_float(biase1.z), convert_float(biase1.w));
+      printf("bias2={ %f , %f , %f , %f }\n ",
+             convert_float(biase2.x), convert_float(biase2.y),
+             convert_float(biase2.z), convert_float(biase2.w));
+      printf("bias3={ %f , %f , %f , %f }\n ",
+             convert_float(biase3.x), convert_float(biase3.y),
+             convert_float(biase3.z), convert_float(biase3.w));
+    }*/
+  half4 biase = {biase0.x, biase1.x, biase2.x, biase3.x};
+  half4 in = read_imageh(input, sampler, coords);
+  half4 output = mad(in, biase, 0);
+  write_imageh(outputImage, coords, output);
 }
\ No newline at end of file

mobile/src/operators/kernel/cl/conv_add_bn_relu_kernel.cpp

@@ -174,6 +174,16 @@ bool ConvAddBNReluKernel<GPU_CL, float>::Init(
                                  build_options);
     }
 
+  } else if (param->Filter()->dims()[1] == 1 &&
+             param->Input()->dims()[1] == param->Output()->dims()[1] &&
+             param->Filter()->dims()[2] != 3) {
+    param->Filter()->InitDWImage(cl_helper_.CLContext(),
+                                 cl_helper_.CLCommandQueue());
+    // other depthwise not with filter 3x3
+    DLOG << "depth_conv basic ";
+    param->ExecMode() = ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT;
+    this->cl_helper_.AddKernel("depth_conv", conv_kernel_file, build_options);
+
   } else if (param->Filter()->dims()[2] == 3 &&
              param->Filter()->dims()[3] == 3) {
     //    if (param->Strides()[0] == param->Strides()[1] &&
@@ -214,6 +224,7 @@ void ConvAddBNReluKernel<GPU_CL, float>::Compute(
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW1x1_FLOAT:
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW3x3_FLOAT:
     case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3_FLOAT:
+    case ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT:
       ConvAddBnRelu(&this->cl_helper_, param, true, param.Bias(),
                     param.NewScale(), param.NewBias());
       break;

mobile/src/operators/kernel/cl/conv_add_kernel.cpp

@@ -71,6 +71,14 @@ bool ConvAddKernel<GPU_CL, float>::Init(FusionConvAddParam<GPU_CL> *param) {
                                  build_options);
     }
 
+  } else if (param->Filter()->dims()[1] == 1 &&
+             param->Input()->dims()[1] == param->Output()->dims()[1] &&
+             param->Filter()->dims()[2] != 3) {
+    param->Filter()->InitDWImage(cl_helper_.CLContext(),
+                                 cl_helper_.CLCommandQueue());
+    param->ExecMode() = ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT;
+    this->cl_helper_.AddKernel("depth_conv", conv_kernel_file, build_options);
+
   } else if (param->Filter()->dims()[2] == 3 &&
              param->Filter()->dims()[3] == 3) {
     //    if (param->Strides()[0] == param->Strides()[1] &&
@@ -124,6 +132,7 @@ void ConvAddKernel<GPU_CL, float>::Compute(
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW1x1_FLOAT:
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW5x5_FLOAT:
     case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3_FLOAT:
+    case ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT:
       ConvAddBnRelu(&this->cl_helper_, param, false, param.Bias());
       break;
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW7x7_FLOAT:

mobile/src/operators/kernel/cl/conv_add_relu_kernel.cpp

@@ -72,6 +72,14 @@ bool ConvAddReluKernel<GPU_CL, float>::Init(
                                  build_options);
     }
 
+  } else if (param->Filter()->dims()[1] == 1 &&
+             param->Input()->dims()[1] == param->Output()->dims()[1] &&
+             param->Filter()->dims()[2] != 3) {
+    param->Filter()->InitDWImage(cl_helper_.CLContext(),
+                                 cl_helper_.CLCommandQueue());
+    DLOG << "init depwise conv basic";
+    param->ExecMode() = ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT;
+    this->cl_helper_.AddKernel("depth_conv", conv_kernel_file, build_options);
   } else if (param->Filter()->dims()[2] == 3 &&
              param->Filter()->dims()[3] == 3) {
     //    if (param->Strides()[0] == param->Strides()[1] &&
@@ -130,6 +138,7 @@ void ConvAddReluKernel<GPU_CL, float>::Compute(
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW5x5_FLOAT:
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW7x7_FLOAT:
     case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3_FLOAT:
+    case ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT:
       ConvAddBnRelu(&this->cl_helper_, param, true, param.Bias());
       break;
     case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3S1_FLOAT:

mobile/src/operators/kernel/cl/conv_bn_relu_kernel.cpp

@@ -129,6 +129,14 @@ bool ConvBNReluKernel<GPU_CL, float>::Init(
                                  build_options);
     }
 
+  } else if (param->Filter()->dims()[1] == 1 &&
+             param->Input()->dims()[1] == param->Output()->dims()[1] &&
+             param->Filter()->dims()[2] != 3) {
+    param->Filter()->InitDWImage(cl_helper_.CLContext(),
+                                 cl_helper_.CLCommandQueue());
+
+    param->ExecMode() = ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT;
+    this->cl_helper_.AddKernel("depth_conv", conv_kernel_file, build_options);
   } else if (param->Filter()->dims()[2] == 3 &&
              param->Filter()->dims()[3] == 3) {
     //    if (param->Strides()[0] == param->Strides()[1] &&
@@ -168,6 +176,7 @@ void ConvBNReluKernel<GPU_CL, float>::Compute(
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW1x1_FLOAT:
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW3x3_FLOAT:
     case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3_FLOAT:
+    case ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT:
       ConvAddBnRelu(&this->cl_helper_, param, true, nullptr, param.NewScale(),
                     param.NewBias());
       break;

mobile/src/operators/kernel/cl/conv_kernel.cpp

@@ -66,6 +66,14 @@ bool ConvKernel<GPU_CL, float>::Init(ConvParam<GPU_CL> *param) {
     }
     DLOG << "depth_conv 3x3";
 
+  } else if (param->Filter()->dims()[1] == 1 &&
+             param->Input()->dims()[1] == param->Output()->dims()[1] &&
+             param->Filter()->dims()[2] != 3) {
+    param->Filter()->InitDWImage(cl_helper_.CLContext(),
+                                 cl_helper_.CLCommandQueue());
+
+    param->ExecMode() = ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT;
+    this->cl_helper_.AddKernel("depth_conv", conv_kernel_file);
   } else if (param->Filter()->dims()[2] == 3 &&
              param->Filter()->dims()[3] == 3) {
     //    if (param->Strides()[0] == param->Strides()[1] &&
@@ -115,6 +123,7 @@ void ConvKernel<GPU_CL, float>::Compute(const ConvParam<GPU_CL> &param) {
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW3x3_FLOAT:
     case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3_FLOAT:
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW7x7_FLOAT:
+    case ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT:
       ConvAddBnRelu(&this->cl_helper_, param);
       break;
     case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3S1_FLOAT:

mobile/src/operators/kernel/cl/conv_relu_kernel.cpp

@@ -72,6 +72,14 @@ bool ConvReluKernel<GPU_CL, float>::Init(FusionConvReluParam<GPU_CL> *param) {
 
     DLOG << "depth_conv 3x3";
 
+  } else if (param->Filter()->dims()[1] == 1 &&
+             param->Input()->dims()[1] == param->Output()->dims()[1] &&
+             param->Filter()->dims()[2] != 3) {
+    param->Filter()->InitDWImage(cl_helper_.CLContext(),
+                                 cl_helper_.CLCommandQueue());
+
+    param->ExecMode() = ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT;
+    this->cl_helper_.AddKernel("depth_conv", conv_kernel_file, build_options);
   } else if (param->Filter()->dims()[2] == 3 &&
              param->Filter()->dims()[3] == 3) {
     //    if (param->Strides()[0] == param->Strides()[1] &&
@@ -120,6 +128,7 @@ void ConvReluKernel<GPU_CL, float>::Compute(
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW1x1_FLOAT:
     case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW3x3_FLOAT:
     case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3_FLOAT:
+    case ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT:
       ConvAddBnRelu(&this->cl_helper_, param, true);
       break;
     case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3S1_FLOAT:

mobile/src/operators/kernel/cl/elementwise_mul_kernel.cpp

@@ -15,6 +15,8 @@ limitations under the License. */
 #ifdef ELEMENTWISEMUL_OP
 
 #include "operators/kernel/elementwise_mul_kernel.h"
+#include <framework/cl/cl_half.h>
+#include <iostream>
 #include "framework/cl/cl_image.h"
 
 namespace paddle_mobile {
@@ -23,19 +25,24 @@ namespace operators {
 template <>
 bool ElementwiseMulKernel<GPU_CL, float>::Init(
     ElementwiseMulParam<GPU_CL> *param) {
-  DLOG << "-----init add-----";
   framework::CLImage *bias = reinterpret_cast<framework::CLImage *>(
       const_cast<framework::CLImage *>(param->InputY()));
   if (bias->dims() == param->InputX()->dims()) {
+    DLOG << "init element wise mul";
     this->cl_helper_.AddKernel("elementwise_mul", "elementwise_mul_kernel.cl");
-  } else if (bias->dims().size() == 4) {
+  } else if (bias->dims().size() == 1) {
+    DLOG << "init channel_mul";
     this->cl_helper_.AddKernel("channel_mul", "elementwise_mul_kernel.cl");
+  } else if (bias->dims().size() == 2) {
+    // etc. input  1 72 28 28
+    // filter 1 72
+    DLOG << "init channel_mul_d2";
+    this->cl_helper_.AddKernel("channel_mul_d2", "elementwise_mul_kernel.cl");
   } else {
-    DLOG << "error:bias dims is error";
+    PADDLE_MOBILE_ENFORCE(false, "element mul not supported yet");
   }
   return true;
 }
-
 template <>
 void ElementwiseMulKernel<GPU_CL, float>::Compute(
     const ElementwiseMulParam<GPU_CL> &param) {
@@ -64,8 +71,8 @@ void ElementwiseMulKernel<GPU_CL, float>::Compute(
         clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 2,
                                NULL, global_work_size, NULL, 0, NULL, NULL);
     CL_CHECK_ERRORS(status);
-  } else if (bias->dims().size() == 4) {
-    DLOG << "zp7 444";
+  } else if (bias->dims().size() == 1) {
+    DLOG << "channel mul";
     cl_mem input_image = input->GetCLImage();
     cl_mem bias_image = bias->GetCLImage();
     cl_mem output_image = output->GetCLImage();
@@ -84,14 +91,48 @@ void ElementwiseMulKernel<GPU_CL, float>::Compute(
     CL_CHECK_ERRORS(status);
     auto width = input->ImageWidth();
     auto height = input->ImageHeight();
-    DLOG << "dede:" << width << "," << height;
     size_t global_work_size[2] = {width, height};
     status =
         clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 2,
                                NULL, global_work_size, NULL, 0, NULL, NULL);
     CL_CHECK_ERRORS(status);
+  } else if (bias->dims().size() == 2) {
+    DLOG << "channel mul d2";
+
+    // etc. input  1 72 28 28
+    // filter 1 72   -->  1 1 1 72
+    DLOG << "input->ImageDims():  " << input->ImageDims();
+    DLOG << "bias->ImageDims():  " << bias->ImageDims();
+    DLOG << "out->ImageDims():  " << output->ImageDims();
+
+    DLOG << "channel mul d2";
+    cl_mem input_image = input->GetCLImage();
+    cl_mem bias_image = bias->GetCLImage();
+    cl_mem output_image = output->GetCLImage();
+    int tensor_w = input->dims()[input->dims().size() - 1];
+    status = clSetKernelArg(kernel, 0, sizeof(cl_mem),
+                            reinterpret_cast<void *>(&input_image));
+    CL_CHECK_ERRORS(status);
+    status = clSetKernelArg(kernel, 1, sizeof(cl_mem),
+                            reinterpret_cast<void *>(&bias_image));
+    CL_CHECK_ERRORS(status);
+    status = clSetKernelArg(kernel, 2, sizeof(cl_mem),
+                            reinterpret_cast<void *>(&output_image));
+    CL_CHECK_ERRORS(status);
+    status = clSetKernelArg(kernel, 3, sizeof(cl_int),
+                            reinterpret_cast<void *>(&tensor_w));
+    CL_CHECK_ERRORS(status);
+    auto width = input->ImageWidth();
+    auto height = input->ImageHeight();
+    size_t global_work_size[2] = {width, height};
+    status =
+        clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 2,
+                               NULL, global_work_size, NULL, 0, NULL, NULL);
+    CL_CHECK_ERRORS(status);
+
+    //    bias->PrintTensor(*bias);
   } else {
-    DLOG << "error:bias dims is error";
+    PADDLE_MOBILE_ENFORCE(false, "element mul not support this situation yet")
   }
 }
 

mobile/src/operators/op_param.h

@@ -489,6 +489,7 @@ class ConvParam : public OpParam {
     EXEC_SLIDINGWINDOW5x5_FLOAT,
     EXEC_SLIDINGWINDOW7x7_FLOAT,
     EXEC_GEMM1x1s1_FLOAT,
+    EXEC_DEPTHWISEBASIC_FLOAT,
   };
 
   ExecMode &ExecMode() const { return exec_mode_; }