[KERNEL][OPENCL] Fix concat; Enable conv3x3 with group (#4180)

* [KERNEL][OPENCL] support opencl concat 3inputs, 4inputs of cases; enable conv3x3 with group; fix buffer opencl concat; clean and clear cmake of opencl kernels. test=develop

lite/backends/opencl/cl_kernel/buffer/concat_kernel.cl

@@ -14,47 +14,62 @@ limitations under the License. */
 
 #include <cl_common.h>
 
-__kernel void concat2(__global const CL_DTYPE* x_data0, __global const CL_DTYPE* x_data1, __global CL_DTYPE* out_data, 
-    int size, int axis_size, int pre_size, int post_size, int total, int total0, int total1) {
-  const int index = get_global_id(0); 
-  if (index < size){
-    for (int i = 0; i < pre_size; i++){
-        int offset_out = index * post_size + i * total;
-        int offset_in = index * post_size + i * total0;
-        // memcpy(out_data + offset_out, x_data0 + offset_in, post_size);
-        CL_DTYPE* dst = out_data + offset_out;
-        CL_DTYPE* src = x_data0 + offset_in;
-        for (int k = 0; k < post_size; k++){
-           *dst++ = *src++;
-	}
-    }
-  }else if (index < axis_size){
-    for (int i = 0; i < pre_size; i++){
-        int offset_out = index * post_size + i * total;
-        int offset_in = index * post_size + i * total1;
-        // memcpy(out_data + offset_out, x_data1 + offset_in, post_size);
-        CL_DTYPE* dst = out_data + offset_out;
-        CL_DTYPE* src = x_data1 + offset_in;
-        for (int k = 0; k < post_size; k++){
-           *dst++ = *src++;
+__kernel void concat2(__global const CL_DTYPE* x_data0,
+                      __global const CL_DTYPE* x_data1,
+                      __global CL_DTYPE* out_data, 
+                      int size,
+                      int axis_size,
+                      int pre_size,
+                      int post_size,
+                      int total,
+                      int total0,
+                      int total1) {
+    const int index = get_global_id(0); 
+    if (index < size) {
+        for (int i = 0; i < pre_size; i++) {
+            int offset_out = index * post_size + i * total;
+            int offset_in = index * post_size + i * total0;
+            // memcpy(out_data + offset_out, x_data0 + offset_in, post_size);
+            __global CL_DTYPE* dst = (__global CL_DTYPE*)(out_data + offset_out);
+            __global CL_DTYPE* src = (__global CL_DTYPE*)(x_data0 + offset_in);
+            for (int k = 0; k < post_size; k++) {
+               *dst++ = *src++;
+            }
+        }
+    } else if (index < axis_size) {
+        for (int i = 0; i < pre_size; i++) {
+            int offset_out = index * post_size + i * total;
+            int offset_in = index * post_size + i * total1;
+            // memcpy(out_data + offset_out, x_data1 + offset_in, post_size);
+            __global CL_DTYPE* dst = (__global CL_DTYPE*)(out_data + offset_out);
+            __global CL_DTYPE* src = (__global CL_DTYPE*)(x_data1 + offset_in);
+            for (int k = 0; k < post_size; k++) {
+                *dst++ = *src++;
+            }
         }
     }
-  }
 }
 
-__kernel void concat_mul(__global const CL_DTYPE* x_data, __global CL_DTYPE* out_data, 
-    int axis_size, int pre_size, int post_size, int start, int total, int total0) {
-  const int index = get_global_id(0); 
-  if (index < axis_size){
-    for (int i = 0; i < pre_size; i++){
-        int offset_out = (start + index) * post_size + i * total;
-        int offset_in = index * post_size + i * total0;
-        // memcpy(out_data + offset_out, x_data + offset_in, post_size);
-        CL_DTYPE* dst = out_data + offset_out;
-        CL_DTYPE* src = x_data + offset_in;
-        for (int k = 0; k < post_size; k++){
-           *dst++ = *src++;
+__kernel void concat_mul_buffer(
+                     __global const CL_DTYPE* x_data,
+                     __global CL_DTYPE* out_data, 
+                     int axis_size,
+                     int pre_size,
+                     int post_size,
+                     int start,
+                     int total,
+                     int total0) {
+    const int index = get_global_id(0); // [0, axis_size)
+    if (index < axis_size) {
+        for (int i = 0; i < pre_size; i++) {
+            int offset_out = (start + index) * post_size + i * total;
+            int offset_in = index * post_size + i * total0;
+            // memcpy(out_data + offset_out, x_data + offset_in, post_size);
+            __global CL_DTYPE* dst = (__global CL_DTYPE*)(out_data + offset_out);
+            __global CL_DTYPE* src = (__global CL_DTYPE*)(x_data + offset_in);
+            for (int k = 0; k < post_size; k++) {
+                *dst++ = *src++;
+            }
         }
     }
-  }
 }

lite/backends/opencl/cl_kernel/image/activation_kernel.cl

@@ -77,7 +77,7 @@ __kernel void hard_sigmoid(__read_only image2d_t input,
       CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
 
   CL_DTYPE4 in = READ_IMG_TYPE(CL_DTYPE_CHAR, input, sampler, (int2)(x, y));
-  CL_DTYPE4 out = clamp(in * scale + value_offset, 0.0, 1.0);
+  CL_DTYPE4 out = clamp(in * (CL_DTYPE4)(scale) + (CL_DTYPE4)(value_offset), (CL_DTYPE4)(0.0), (CL_DTYPE4)(1.0));
 
   WRITE_IMG_TYPE(CL_DTYPE_CHAR, output, (int2)(x, y), out);
 }

lite/backends/opencl/cl_kernel/image/concat_kernel.cl

@@ -11,6 +11,286 @@ limitations under the License. */
 
 #include <cl_common.h>
 
+// deprecated
+__kernel void concatByCWith2Inputs(
+                    __write_only image2d_t output_image,
+                    __private const int output_tensor_c,
+                    __private const int output_tensor_w,
+                    __read_only image2d_t input0_image,
+                    __private const int input0_tensor_c,
+                    __read_only image2d_t input1_image,
+                    __private const int input1_tensor_c) {
+  const int out_c = get_global_id(0);   // [0, (output_tensor_c + 3) / 4)
+  const int out_w = get_global_id(1);   // [0, output_tensor_w)
+  const int out_nh = get_global_id(2);  // [0, output_tensor_n * output_tensor_h)
+
+  const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
+        CLK_ADDRESS_CLAMP |
+        CLK_FILTER_NEAREST;
+
+  int2 output_pos;
+  output_pos.x = out_c * output_tensor_w + out_w;
+  output_pos.y = out_nh;
+  CL_DTYPE4 output_data;
+
+  for (int i = 0; i < 4; i++) {
+    int c = out_c * 4 + i;
+    if (c >= output_tensor_c) {
+        break;
+    }
+    int c_in;
+    CL_DTYPE4 input_data;
+    if (c < input0_tensor_c) {
+      c_in = c;
+      int2 input_pos;
+      input_pos.x = (c_in / 4) * output_tensor_w + out_w;
+      input_pos.y = out_nh;
+      input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input0_image, sampler, input_pos);
+    } else {
+      c_in = c - input0_tensor_c;
+      int2 input_pos;
+      input_pos.x = (c_in / 4) * output_tensor_w + out_w;
+      input_pos.y = out_nh;
+      input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input1_image, sampler, input_pos);
+    }
+    int value_offset = c_in % 4;
+    float value;
+    if (value_offset == 0) {
+      value = input_data.x;
+    } else if (value_offset == 1) {
+      value = input_data.y;
+    } else if (value_offset == 2) {
+      value = input_data.z;
+    } else if (value_offset == 3) {
+      value = input_data.w;
+    }
+   if (i == 0) {
+      output_data.x = value;
+    } else if (i == 1) {
+      output_data.y = value;
+    } else if (i == 2) {
+      output_data.z = value;
+    } else if (i == 3) {
+      output_data.w = value;
+    }
+  }
+  WRITE_IMG_TYPE(CL_DTYPE_CHAR, output_image, output_pos, output_data);
+}
+
+
+__kernel void concatByCWith3Inputs(
+                    __write_only image2d_t output_image,
+                    __private const int output_tensor_c,
+                    __private const int output_tensor_w,
+                    __read_only image2d_t input0_image,
+                    __private const int input0_tensor_c,
+                    __read_only image2d_t input1_image,
+                    __private const int input1_tensor_c,
+                    __read_only image2d_t input2_image,
+                    __private const int input2_tensor_c) {
+  const int out_c = get_global_id(0);   // [0, (output_tensor_c + 3) / 4)
+  const int out_w = get_global_id(1);   // [0, output_tensor_w)
+  const int out_nh = get_global_id(2);  // [0, output_tensor_n * output_tensor_h)
+
+  const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
+        CLK_ADDRESS_CLAMP |
+        CLK_FILTER_NEAREST;
+
+  int2 output_pos;
+  output_pos.x = out_c * output_tensor_w + out_w;
+  output_pos.y = out_nh;
+  CL_DTYPE4 output_data;
+
+  for (int i = 0; i < 4; i++) {
+    int c = out_c * 4 + i;
+    if (c >= output_tensor_c) {
+        break;
+    }
+    int c_in;
+    CL_DTYPE4 input_data;
+    if (c < input0_tensor_c) {
+      c_in = c;
+      int2 input_pos;
+      input_pos.x = (c_in / 4) * output_tensor_w + out_w;
+      input_pos.y = out_nh;
+      input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input0_image, sampler, input_pos);
+    } else if (c < input0_tensor_c + input1_tensor_c) {
+      c_in = c - input0_tensor_c;
+      int2 input_pos;
+      input_pos.x = (c_in / 4) * output_tensor_w + out_w;
+      input_pos.y = out_nh;
+      input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input1_image, sampler, input_pos);
+    } else {
+      c_in = c - input0_tensor_c - input1_tensor_c;
+      int2 input_pos;
+      input_pos.x = (c_in / 4) * output_tensor_w + out_w;
+      input_pos.y = out_nh;
+      input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input2_image, sampler, input_pos);
+    }
+    int value_offset = c_in % 4;
+    float value;
+    if (value_offset == 0) {
+      value = input_data.x;
+    } else if (value_offset == 1) {
+      value = input_data.y;
+    } else if (value_offset == 2) {
+      value = input_data.z;
+    } else if (value_offset == 3) {
+      value = input_data.w;
+    }
+    if (i == 0) {
+      output_data.x = value;
+    } else if (i == 1) {
+      output_data.y = value;
+    } else if (i == 2) {
+      output_data.z = value;
+    } else if (i == 3) {
+      output_data.w = value;
+    }
+  }
+  WRITE_IMG_TYPE(CL_DTYPE_CHAR, output_image, output_pos, output_data);
+}
+
+
+__kernel void concatByCWith4Inputs(
+                    __write_only image2d_t output_image,
+                    __private const int output_tensor_c,
+                    __private const int output_tensor_w,
+                    __read_only image2d_t input0_image,
+                    __private const int input0_tensor_c,
+                    __read_only image2d_t input1_image,
+                    __private const int input1_tensor_c,
+                    __read_only image2d_t input2_image,
+                    __private const int input2_tensor_c,
+                    __read_only image2d_t input3_image,
+                    __private const int input3_tensor_c) {
+  const int out_c = get_global_id(0);   // [0, (output_tensor_c + 3) / 4)
+  const int out_w = get_global_id(1);   // [0, output_tensor_w)
+  const int out_nh = get_global_id(2);  // [0, output_tensor_n * output_tensor_h)
+
+  const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
+        CLK_ADDRESS_CLAMP |
+        CLK_FILTER_NEAREST;
+
+  int2 output_pos;
+  output_pos.x = out_c * output_tensor_w + out_w;
+  output_pos.y = out_nh;
+  CL_DTYPE4 output_data;
+
+  for (int i = 0; i < 4; i++) {
+    int c = out_c * 4 + i;
+    if (c >= output_tensor_c) {
+        break;
+    }
+    int c_in;
+    CL_DTYPE4 input_data;
+    if (c < input0_tensor_c) {
+      c_in = c;
+      int2 input_pos;
+      input_pos.x = (c_in / 4) * output_tensor_w + out_w;
+      input_pos.y = out_nh;
+      input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input0_image, sampler, input_pos);
+    } else if (c < input0_tensor_c + input1_tensor_c) {
+      c_in = c - input0_tensor_c;
+      int2 input_pos;
+      input_pos.x = (c_in / 4) * output_tensor_w + out_w;
+      input_pos.y = out_nh;
+      input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input1_image, sampler, input_pos);
+    } else if (c < input0_tensor_c + input1_tensor_c + input2_tensor_c) {
+      c_in = c - input0_tensor_c - input1_tensor_c;
+      int2 input_pos;
+      input_pos.x = (c_in / 4) * output_tensor_w + out_w;
+      input_pos.y = out_nh;
+      input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input2_image, sampler, input_pos);
+    }else if (c < input0_tensor_c + input1_tensor_c + input2_tensor_c + input3_tensor_c){
+      c_in = c - input0_tensor_c - input1_tensor_c - input2_tensor_c;
+      int2 input_pos;
+      input_pos.x = (c_in / 4) * output_tensor_w + out_w;
+      input_pos.y = out_nh;
+      input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input3_image, sampler, input_pos);
+    }
+    int value_offset = c_in % 4;
+    float value;
+    if (value_offset == 0) {
+      value = input_data.x;
+    } else if (value_offset == 1) {
+      value = input_data.y;
+    } else if (value_offset == 2) {
+      value = input_data.z;
+    } else if (value_offset == 3) {
+      value = input_data.w;
+    }
+    if (i == 0) {
+      output_data.x = value;
+    } else if (i == 1) {
+      output_data.y = value;
+    } else if (i == 2) {
+      output_data.z = value;
+    } else if (i == 3) {
+      output_data.w = value;
+    }
+  }
+  WRITE_IMG_TYPE(CL_DTYPE_CHAR, output_image, output_pos, output_data);
+}
+
+
+// deprecated
+__kernel void concatByH(__read_only image2d_t input_image,
+                        __write_only image2d_t output_image,
+                        __private const int out_W,
+                        __private const int out_H_Start) {
+
+  const int in_c = get_global_id(0);
+  const int in_w = get_global_id(1);
+  const int in_nh = get_global_id(2);
+
+  int2 input_pos;
+  input_pos.x = in_c * out_W + in_w;
+  input_pos.y = in_nh;
+
+  const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
+        CLK_ADDRESS_CLAMP |
+        CLK_FILTER_NEAREST;
+  CL_DTYPE4 input;
+  input = READ_IMG_TYPE(CL_DTYPE_CHAR, input_image, sampler,input_pos);
+
+  int2 output_pos;
+  output_pos.x = input_pos.x;
+  output_pos.y = out_H_Start + input_pos.y;
+
+  WRITE_IMG_TYPE(CL_DTYPE_CHAR, output_image, output_pos, input);
+
+}
+
+
+// deprecated
+__kernel void concatByW(__read_only image2d_t input_image,
+                        __write_only image2d_t output_image,
+                        __private const int in_W,
+                        __private const int pre_Width,
+                        __private const int out_Width) {
+
+  const int in_c = get_global_id(0);
+  const int in_w = get_global_id(1);
+  const int in_nh = get_global_id(2);
+
+  int2 input_pos;
+  input_pos.x = in_c * in_W + in_w;
+  input_pos.y = in_nh;
+
+  const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
+        CLK_ADDRESS_CLAMP |
+        CLK_FILTER_NEAREST;
+  CL_DTYPE4 input;
+  input = READ_IMG_TYPE(CL_DTYPE_CHAR, input_image, sampler,input_pos);
+
+  int2 output_pos;
+  output_pos.x = input_pos.x + pre_Width + out_Width * in_c;
+  output_pos.y = input_pos.y;
+  WRITE_IMG_TYPE(CL_DTYPE_CHAR, output_image, output_pos, input);
+}
+
+
 __kernel void concat2(__read_only image2d_t input0,
                       __read_only image2d_t input1,
                       __write_only image2d_t output,
@@ -103,62 +383,3 @@ __kernel void concat2(__read_only image2d_t input0,
     WRITE_IMG_TYPE(CL_DTYPE_CHAR, output, output_pos, input);
   }
 }
-
-__kernel void concat_mul(__read_only image2d_t input,
-                         __write_only image2d_t output,
-                         int flag, int C_0, int out_C, int out_W, int in_W, int width) {
-  const int in_w = get_global_id(0); // image_width cxw/4
-  const int in_c = get_global_id(1); // image_width cxw/4
-  const int in_nh = get_global_id(2); // image_height nxh
-
-  const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
-                            CLK_ADDRESS_CLAMP |
-                            CLK_FILTER_NEAREST;
-  int2 input_pos;
-  int2 output_pos;
-  input_pos.x = in_c * in_W + in_w;
-  input_pos.y = in_nh;
-  CL_DTYPE4 input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input, sampler, input_pos);
-  if (flag == 1){ // by channel
-    CL_DTYPE4 output_data;
-    for (int i = 0; i < 4; i++) {
-      int c_out = C_0 + in_c * 4 + i;
-      if (c_out >= out_C) {
-        break;
-      }
-      int2 output_pos;
-      output_pos.x = (c_out / 4) * in_W + in_w;
-      output_pos.y = in_nh;
-      CL_DTYPE val;
-      if (i == 0) {
-        val = input_data.x;
-      } else if (i == 1) {
-        val = input_data.y;
-      } else if (i == 2) {
-        val = input_data.z;
-      } else if (i == 3) {
-        val = input_data.w;
-      }
-      if (c_out % 4 == 0){
-        output_data.x = val;
-      }else if (c_out % 4 == 1){
-        output_data.y = val;
-      }else if (c_out % 4 == 2){
-        output_data.z = val;
-      }else if (c_out % 4 == 3){
-        output_data.w = val;
-      }
-      WRITE_IMG_TYPE(CL_DTYPE_CHAR, output, output_pos, output_data);
-    }
-  }else if (flag == 2){ // by height, width == n
-    int2 output_pos;
-    output_pos.x = in_c * in_W + in_w;
-    output_pos.y = in_nh + C_0 * width;
-    WRITE_IMG_TYPE(CL_DTYPE_CHAR, output, output_pos, input_data);
-  }else if (flag == 3){ // by width, width == C
-    int2 output_pos;
-    output_pos.y = in_nh;
-    output_pos.x = in_c * out_W + (in_w + C_0);
-    WRITE_IMG_TYPE(CL_DTYPE_CHAR, output, output_pos, input_data);
-  }
-}

lite/kernels/opencl/CMakeLists.txt

@@ -8,35 +8,35 @@ set(cl_kernel_deps op_params cl_runtime cl_context cl_wrapper cl_target_wrapper
 # image kernel      #
 #####################
 # basic
-add_kernel(elementwise_add_opencl OPENCL basic SRCS elementwise_add_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(elementwise_sub_opencl OPENCL basic SRCS elementwise_sub_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(elementwise_mul_opencl OPENCL basic SRCS elementwise_mul_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(fusion_elementwise_add_activation_opencl
+add_kernel(elementwise_add_opencl_image OPENCL basic SRCS elementwise_add_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(elementwise_sub_opencl_image OPENCL basic SRCS elementwise_sub_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(elementwise_mul_opencl_image OPENCL basic SRCS elementwise_mul_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(fusion_elementwise_add_activation_opencl_image
            OPENCL basic SRCS fusion_elementwise_add_activation_image_compute.cc
-           DEPS elementwise_add_opencl ${cl_kernel_deps})
-add_kernel(fusion_elementwise_sub_activation_opencl
+           DEPS elementwise_add_opencl_image ${cl_kernel_deps})
+add_kernel(fusion_elementwise_sub_activation_opencl_image
            OPENCL basic SRCS fusion_elementwise_sub_activation_image_compute.cc
-           DEPS elementwise_sub_opencl ${cl_kernel_deps})
-
-add_kernel(pool_opencl OPENCL basic SRCS pool_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(activation_opencl OPENCL basic SRCS activation_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(reshape_opencl OPENCL basic SRCS reshape_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(transpose_opencl OPENCL basic SRCS transpose_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(conv_opencl OPENCL basic SRCS conv_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(layout_opencl OPENCL basic SRCS layout_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(concat_opencl OPENCL basic SRCS concat_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(nearest_interp_opencl OPENCL basic SRCS nearest_interp_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(scale_opencl OPENCL basic SRCS scale_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(grid_sampler_opencl OPENCL basic SRCS grid_sampler_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(lrn_opencl OPENCL basic SRCS lrn_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(bilinear_interp_opencl OPENCL basic SRCS bilinear_interp_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(slice_opencl OPENCL basic SRCS slice_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(instance_norm_opencl OPENCL basic SRCS instance_norm_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(dropout_opencl OPENCL basic SRCS dropout_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(pad2d_opencl OPENCL basic SRCS pad2d_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(box_coder_opencl OPENCL basic SRCS box_coder_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(pixel_shuffle_opencl OPENCL basic SRCS pixel_shuffle_image_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(expand_opencl OPENCL basic SRCS expand_image_compute.cc DEPS ${cl_kernel_deps})
+           DEPS elementwise_sub_opencl_image ${cl_kernel_deps})
+
+add_kernel(pool_opencl_image OPENCL basic SRCS pool_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(activation_opencl_image OPENCL basic SRCS activation_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(reshape_opencl_image OPENCL basic SRCS reshape_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(transpose_opencl_image OPENCL basic SRCS transpose_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(conv_opencl_image OPENCL basic SRCS conv_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(layout_opencl_image OPENCL basic SRCS layout_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(concat_opencl_image OPENCL basic SRCS concat_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(nearest_interp_opencl_image OPENCL basic SRCS nearest_interp_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(scale_opencl_image OPENCL basic SRCS scale_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(grid_sampler_opencl_image OPENCL basic SRCS grid_sampler_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(lrn_opencl_image OPENCL basic SRCS lrn_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(bilinear_interp_opencl_image OPENCL basic SRCS bilinear_interp_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(slice_opencl_image OPENCL basic SRCS slice_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(instance_norm_opencl_image OPENCL basic SRCS instance_norm_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(dropout_opencl_image OPENCL basic SRCS dropout_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(pad2d_opencl_image OPENCL basic SRCS pad2d_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(box_coder_opencl_image OPENCL basic SRCS box_coder_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(pixel_shuffle_opencl_image OPENCL basic SRCS pixel_shuffle_image_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(expand_opencl_image OPENCL basic SRCS expand_image_compute.cc DEPS ${cl_kernel_deps})
 
 # extra
 # wait to add ...
@@ -48,89 +48,89 @@ add_kernel(expand_opencl OPENCL basic SRCS expand_image_compute.cc DEPS ${cl_ker
 # image kernel test  #
 ######################
 lite_cc_test(test_activation_image_opencl SRCS activation_image_compute_test.cc
-             DEPS activation_opencl layout_opencl op_registry program context)
+             DEPS activation_opencl_image layout_opencl_image op_registry program context)
 
 lite_cc_test(test_conv_image_opencl SRCS conv_image_compute_test.cc
-             DEPS conv_opencl op_registry program context)
+             DEPS conv_opencl_image op_registry program context)
 
 lite_cc_test(test_depthwise_conv2d_image_opencl SRCS depthwise_conv2d_image_compute_test.cc
-             DEPS conv_opencl op_registry program context)
+             DEPS conv_opencl_image op_registry program context)
 
 lite_cc_test(test_nearest_interp_image_opencl SRCS nearest_interp_image_compute_test.cc
-             DEPS nearest_interp_opencl layout_opencl op_registry program context)
+             DEPS nearest_interp_opencl_image layout_opencl_image op_registry program context)
 
 lite_cc_test(test_pool_image_opencl SRCS pool_image_compute_test.cc
-             DEPS pool_opencl op_registry program context)
+             DEPS pool_opencl_image op_registry program context)
 
 lite_cc_test(test_scale_image_opencl SRCS scale_image_compute_test.cc
-             DEPS scale_opencl op_registry program context)
+             DEPS scale_opencl_image op_registry program context)
 
 lite_cc_test(test_reshape_image_opencl SRCS reshape_image_compute_test.cc
-             DEPS reshape_opencl op_registry program context)
+             DEPS reshape_opencl_image op_registry program context)
 
 lite_cc_test(test_transpose_image_opencl SRCS transpose_image_compute_test.cc
-             DEPS transpose_opencl layout_opencl op_registry program context)
+             DEPS transpose_opencl_image layout_opencl_image op_registry program context)
              
 lite_cc_test(test_concat_image_opencl SRCS concat_image_compute_test.cc
-             DEPS concat_opencl layout_opencl op_registry program context)
+             DEPS concat_opencl_image layout_opencl_image op_registry program context)
 
 #lite_cc_test(test_elementwise_mul_image_opencl SRCS elementwise_mul_image_compute_test.cc
-#             DEPS elementwise_mul_opencl op_registry program context)
+#             DEPS elementwise_mul_opencl_image op_registry program context)
 
 lite_cc_test(test_layout_image_opencl SRCS layout_image_compute_test.cc
-             DEPS layout_opencl op_registry program context)
+             DEPS layout_opencl_image op_registry program context)
 
 lite_cc_test(test_pixel_shuffle_image_opencl SRCS pixel_shuffle_image_compute_test.cc
-             DEPS pixel_shuffle_opencl op_registry program context)
+             DEPS pixel_shuffle_opencl_image op_registry program context)
 
 lite_cc_test(test_expand_image_opencl SRCS expand_image_compute_test.cc
-             DEPS expand_opencl op_registry program context)
+             DEPS expand_opencl_image op_registry program context)
 
 lite_cc_test(test_elementwise_add_image_opencl SRCS elementwise_add_image_compute_test.cc
-             DEPS elementwise_add_opencl fusion_elementwise_add_activation_opencl op_registry program context)
+             DEPS elementwise_add_opencl_image fusion_elementwise_add_activation_opencl_image op_registry program context)
 lite_cc_test(test_elementwise_sub_image_opencl SRCS elementwise_sub_image_compute_test.cc
-             DEPS elementwise_sub_opencl fusion_elementwise_sub_activation_opencl op_registry program context)
+             DEPS elementwise_sub_opencl_image fusion_elementwise_sub_activation_opencl_image op_registry program context)
 
 lite_cc_test(test_grid_sampler_image_opencl SRCS grid_sampler_image_compute_test.cc
-             DEPS grid_sampler_opencl op_registry program context)
+             DEPS grid_sampler_opencl_image op_registry program context)
 
 lite_cc_test(test_lrn_image_opencl SRCS lrn_image_compute_test.cc
-             DEPS lrn_opencl op_registry program context)
+             DEPS lrn_opencl_image op_registry program context)
              
 lite_cc_test(test_bilinear_interp_image_opencl SRCS bilinear_interp_image_compute_test.cc
-	         DEPS bilinear_interp_opencl op_registry program context)
+             DEPS bilinear_interp_opencl_image op_registry program context)
 lite_cc_test(test_slice_image_opencl SRCS slice_image_compute_test.cc
-	         DEPS slice_opencl op_registry program context)
+             DEPS slice_opencl_image op_registry program context)
              
-	     #lite_cc_test(test_instance_norm_image_opencl SRCS instance_norm_image_compute_test.cc
-	     #             DEPS instance_norm_opencl op_registry program context)
+#lite_cc_test(test_instance_norm_image_opencl SRCS instance_norm_image_compute_test.cc
+#             DEPS instance_norm_opencl_image op_registry program context)
 
 lite_cc_test(test_dropout_image_opencl SRCS dropout_image_compute_test.cc
-                 DEPS dropout_opencl op_registry program context)  
+             DEPS dropout_opencl_image op_registry program context)
                  
 lite_cc_test(test_pad2d_image_opencl SRCS pad2d_image_compute_test.cc
-                 DEPS pad2d_opencl layout_opencl op_registry program context)
+             DEPS pad2d_opencl_image layout_opencl_image op_registry program context)
 
 lite_cc_test(test_box_coder_image_opencl SRCS box_coder_image_compute_test.cc
-                 DEPS box_coder_opencl op_registry program context)
+             DEPS box_coder_opencl_image op_registry program context)
 
 ######################
 # buffer kernel      #
 ######################
 # basic
-#add_kernel(activation_opencl OPENCL basic SRCS activation_buffer_compute.cc DEPS ${cl_kernel_deps})
-#add_kernel(conv_opencl OPENCL basic SRCS conv_buffer_compute.cc DEPS ${cl_kernel_deps})
+#add_kernel(activation_opencl_buffer OPENCL basic SRCS activation_buffer_compute.cc DEPS ${cl_kernel_deps})
+#add_kernel(conv_opencl_buffer OPENCL basic SRCS conv_buffer_compute.cc DEPS ${cl_kernel_deps})
 #add_kernel(depthwise_conv2d_opencl OPENCL basic SRCS depthwise_conv2d_buffer_compute.cc DEPS ${cl_kernel_deps})
-#add_kernel(pool_opencl OPENCL basic SRCS pool_buffer_compute.cc DEPS ${cl_kernel_deps})
-#add_kernel(concat_opencl OPENCL basic SRCS concat_buffer_compute.cc DEPS ${cl_kernel_deps})
-add_kernel(fc_opencl OPENCL basic SRCS fc_buffer_compute.cc DEPS ${cl_kernel_deps})
+#add_kernel(pool_opencl_buffer OPENCL basic SRCS pool_buffer_compute.cc DEPS ${cl_kernel_deps})
+#add_kernel(concat_opencl_buffer OPENCL basic SRCS concat_buffer_compute.cc DEPS ${cl_kernel_deps})
+add_kernel(fc_opencl_buffer OPENCL basic SRCS fc_buffer_compute.cc DEPS ${cl_kernel_deps})
 # NOTE(ysh329): use fc as `mul`, and mul is not used.
-#add_kernel(mul_opencl OPENCL basic SRCS mul_buffer_compute.cc DEPS ${cl_kernel_deps})
-#add_kernel(elementwise_add_opencl OPENCL basic SRCS elementwise_add_buffer_compute.cc DEPS ${cl_kernel_deps})
-#add_kernel(fusion_elementwise_add_activation_opencl
+#add_kernel(mul_opencl_buffer OPENCL basic SRCS mul_buffer_compute.cc DEPS ${cl_kernel_deps})
+#add_kernel(elementwise_add_opencl_buffer OPENCL basic SRCS elementwise_add_buffer_compute.cc DEPS ${cl_kernel_deps})
+#add_kernel(fusion_elementwise_add_activation_opencl_buffer
 #           OPENCL basic SRCS fusion_elementwise_add_activation_buffer_compute.cc
 #           DEPS elementwise_add_opencl ${cl_kernel_deps})
-add_kernel(io_copy_opencl OPENCL basic SRCS io_copy_buffer_compute.cc DEPS ${tensor_lite} ${cl_kernel_deps})
+add_kernel(io_copy_opencl_buffer OPENCL basic SRCS io_copy_buffer_compute.cc DEPS ${tensor_lite} ${cl_kernel_deps})
 
 # extra
 # wait to add ...
@@ -156,10 +156,10 @@ add_kernel(io_copy_opencl OPENCL basic SRCS io_copy_buffer_compute.cc DEPS ${ten
 #             DEPS pool_opencl op_registry program context)
 
 #lite_cc_test(test_concat_buffer_opencl SRCS concat_buffer_compute_test.cc
-#             DEPS concat_opencl op_registry program context)
+#             DEPS concat_opencl_buffer op_registry program context)
 
 lite_cc_test(test_fc_buffer_opencl SRCS fc_buffer_compute_test.cc
-             DEPS fc_opencl op_registry program context)
+             DEPS fc_opencl_buffer op_registry program context)
 
 #lite_cc_test(test_mul_buffer_opencl SRCS mul_buffer_compute_test.cc
 #             DEPS mul_opencl op_registry program context)
@@ -168,4 +168,4 @@ lite_cc_test(test_fc_buffer_opencl SRCS fc_buffer_compute_test.cc
 #             DEPS elementwise_add_opencl op_registry program context)
 
 lite_cc_test(test_io_copy_buffer_opencl SRCS io_copy_buffer_compute_test.cc
-             DEPS io_copy_opencl op_registry program context)
+             DEPS io_copy_opencl_buffer op_registry program context)

lite/kernels/opencl/activation_image_compute.cc

@@ -77,7 +77,7 @@ class ActivationComputeImageDefault
         scale_ = act_param_->hard_sigmoid_slope;
         threshold_ = act_param_->hard_sigmoid_offset;
         break;
-      defauln:
+      default:
         LOG(FATAL) << "This act type:" << act_type << " doesn't support.";
         return;
     }

lite/kernels/opencl/concat_buffer_compute.cc

@@ -40,7 +40,7 @@ class ConcatCompute : public KernelLite<TARGET(kOpenCL),
     if (concat_param_->x.size() == 2) {
       kernel_func_name_ = "concat2";
     } else {
-      kernel_func_name_ = "concat_mul";
+      kernel_func_name_ = "concat_mul_buffer";
     }
     context.cl_context()->AddKernel(kernel_func_name_,
                                     "buffer/concat_kernel.cl",
@@ -86,7 +86,6 @@ class ConcatCompute : public KernelLite<TARGET(kOpenCL),
   void Run() override {
     auto& param = *param_.get_mutable<param_t>();
     const auto& x_dims = param.output->dims();
-    auto image_shape = InitImageDimInfoWith(x_dims);
     auto* out_buf =
         param.output->mutable_data<float, cl::Buffer>(TARGET(kOpenCL));
     const auto& y_dims = param.output->dims();  // useless: check dim only
@@ -98,8 +97,9 @@ class ConcatCompute : public KernelLite<TARGET(kOpenCL),
 
     auto inputs = param.x;
     int arg_idx = 0;
-    auto global_work_size = cl::NDRange{axis_size_};
+    auto global_work_size = cl::NDRange{static_cast<cl::size_type>(axis_size_)};
     int total = axis_size_ * post_size_;
+
     auto kernel = context.cl_context()->GetKernel(kernel_key.str());
     if (inputs.size() == 2) {
       auto* x_buf0 = inputs[0]->data<float, cl::Buffer>();
@@ -144,6 +144,15 @@ class ConcatCompute : public KernelLite<TARGET(kOpenCL),
         auto* x_buf = inputs[i]->data<float, cl::Buffer>();
         global_work_size = cl::NDRange{static_cast<size_t>(size)};
         int total0 = size * post_size_;
+#ifdef LITE_WITH_LOG
+        LOG(INFO) << "------------- i=" << i << " -------------";
+        LOG(INFO) << "pre_size:" << pre_size_;
+        LOG(INFO) << "post_size:" << post_size_;
+        LOG(INFO) << "size:" << size;
+        LOG(INFO) << "start:" << start;
+        LOG(INFO) << "total:" << total;
+        LOG(INFO) << "total0:" << total0;
+#endif
         cl_int status = kernel.setArg(arg_idx, *x_buf);
         CL_CHECK_FATAL(status);
         status = kernel.setArg(++arg_idx, *out_buf);

lite/kernels/opencl/concat_buffer_compute_test.cc

@@ -99,13 +99,14 @@ TEST(opencl_concat_buffer, compute) {
   auto *mapped_x2 = static_cast<float *>(
       TargetWrapperCL::Map(x2_data, 0, sizeof(float) * x2_dim.production()));
   for (int i = 0; i < x0_dim.production(); i++) {
-    mapped_x0[i] = dist(engine);
+    mapped_x0[i] = i + 1;  // dist(engine);
   }
   for (int i = 0; i < x1_dim.production(); i++) {
-    mapped_x1[i] = dist(engine);
+    mapped_x1[i] = x0_dim.production() + i + 1;  // dist(engine);
   }
   for (int i = 0; i < x2_dim.production(); i++) {
-    mapped_x2[i] = dist(engine);
+    mapped_x2[i] =
+        x0_dim.production() + x1_dim.production() + i + 1;  // dist(engine);
   }
 
   // set param and kernel, then run
@@ -151,9 +152,13 @@ TEST(opencl_concat_buffer, compute) {
   auto *out_data = out.mutable_data<float, cl::Buffer>();
   auto *mapped_out = static_cast<float *>(
       TargetWrapperCL::Map(out_data, 0, sizeof(float) * out_dim.production()));
+#ifdef PRINT_RESULT_CONCAT_BUFFER
   for (int i = 0; i < out_dim.production(); i++) {
-    EXPECT_NEAR(mapped_out[i], out_ref_data[i], 1e-6);
+    LOG(INFO) << "i:" << i << ", out[" << i << "]:" << mapped_out[i]
+              << ", out_ref_data[" << i << "]:" << out_ref_data[i];
   }
+#endif
+  EXPECT_NEAR(mapped_out[i], out_ref_data[i], 1e-6);
   TargetWrapperCL::Unmap(out_data, mapped_out);
   TargetWrapperCL::Unmap(x0_data, mapped_x0);
   TargetWrapperCL::Unmap(x1_data, mapped_x1);

lite/kernels/opencl/conv_image_compute.cc

@@ -185,47 +185,29 @@ void ConvImageCompute::PrepareForRun() {
 
     impl_ = &ConvImageCompute::DepthwiseConv2d;
   } else if (filter_tensor_h_ == 3 && filter_tensor_w_ == 3) {
-// #define CONV3x3OPT_FALL_BACK
-#ifndef CONV3x3OPT_FALL_BACK
     // conv2d_3x3
-    kernel_func_names_.push_back(input_tensor_n_ > 1 ? "conv2d_3x3_multi_batch"
-                                                     : "conv2d_3x3_opt");
-    kernel_func_paths_.push_back("image/conv2d_3x3_opt_kernel.cl");
-
-    CLImageConverterFolder converter;
-    const DDim& filter_image_dims = converter.InitImageDimInfoWith(filter_dims);
-    filter_image_h_ = filter_image_dims[1];
-    filter_image_w_ = filter_image_dims[0];
-    tensor_hold_filter_image_->Resize({1, filter_image_w_, filter_image_h_, 4});
-
-    half_t* filter_image_data =
-        tensor_hold_filter_image_->mutable_data<half_t>();
-
-    converter.NCHWToImage(filter_cpu, filter_image_data, filter_dims);
-    filter_gpu_image_->mutable_data<half_t, cl::Image2D>(
-        filter_image_w_, filter_image_h_, filter_image_data);
-
-    impl_ = &ConvImageCompute::Conv2d3x3opt;
-#else
-    kernel_func_names_.push_back("conv2d_3x3");
-    kernel_func_paths_.push_back("image/conv2d_3x3_kernel.cl");
+    if (groups_ == 1) {
+      kernel_func_names_.push_back(
+          input_tensor_n_ > 1 ? "conv2d_3x3_multi_batch" : "conv2d_3x3_opt");
+      kernel_func_paths_.push_back("image/conv2d_3x3_opt_kernel.cl");
+      impl_ = &ConvImageCompute::Conv2d3x3opt;
+    } else {  // groups_ > 1
+      kernel_func_names_.push_back("conv2d_3x3");
+      kernel_func_paths_.push_back("image/conv2d_3x3_kernel.cl");
+      impl_ = &ConvImageCompute::Conv2d3x3;
+    }
 
     CLImageConverterFolder converter;
     const DDim& filter_image_dims = converter.InitImageDimInfoWith(filter_dims);
     filter_image_h_ = filter_image_dims[1];
     filter_image_w_ = filter_image_dims[0];
     tensor_hold_filter_image_->Resize({1, filter_image_w_, filter_image_h_, 4});
-
     half_t* filter_image_data =
         tensor_hold_filter_image_->mutable_data<half_t>();
 
     converter.NCHWToImage(filter_cpu, filter_image_data, filter_dims);
     filter_gpu_image_->mutable_data<half_t, cl::Image2D>(
         filter_image_w_, filter_image_h_, filter_image_data);
-
-    impl_ = &ConvImageCompute::Conv2d3x3;
-#endif
-#undef CONV3x3OPT_FALL_BACK
   } else if (filter_tensor_h_ == 5 && filter_tensor_w_ == 5) {
 #define CONV_5x5_OPT
 #ifndef CONV_5x5_OPT
@@ -584,6 +566,11 @@ void ConvImageCompute::GetGlobalWorkSize() {
                                     static_cast<size_t>(w_blk_),
                                     static_cast<size_t>(nh_blk_)};
     input_c_block_ = static_cast<const int>((input_tensor_c_ + 3) / 4);
+  } else if (kernel_func_names_[0] == "conv2d_3x3") {
+    global_work_size_ = cl::NDRange{static_cast<size_t>(c_blk_),
+                                    static_cast<size_t>(w_blk_),
+                                    static_cast<size_t>(nh_blk_)};
+
   } else if (kernel_func_names_[0] == "conv2d_3x3_multi_batch" ||
              kernel_func_names_[0] == "conv2d_3x3_opt") {
     int w_blk_size = 5;
@@ -1185,6 +1172,56 @@ void ConvImageCompute::PrintConvInfo() {
   VLOG(4) << "dilations: " << dilation_h_ << ", " << dilation_w_;
   VLOG(4) << "global_work_size_[3D]: {" << global_work_size_[0] << ","
           << global_work_size_[1] << "," << global_work_size_[2] << "}";
+  VLOG(4) << "groups_:" << groups_;
+
+  LOG(INFO) << "================================";
+  LOG(INFO) << "c_blk_=" << c_blk_ << ", w_blk_=" << w_blk_
+            << ",nh_blk_=" << nh_blk_;
+  LOG(INFO) << "input_image_p_:" << input_image_p_;
+  LOG(INFO) << "filter_image_p_:" << filter_image_p_;
+  LOG(INFO) << "bias_image_p_:" << bias_image_p_;
+  LOG(INFO) << "output_image_p_:" << output_image_p_;
+
+  LOG(INFO) << "stride_h_:" << stride_h_;
+  LOG(INFO) << "stride_w_:" << stride_w_;
+
+  LOG(INFO) << "dilation_h_:" << dilation_h_;
+  LOG(INFO) << "dilation_w_:" << dilation_w_;
+
+  LOG(INFO) << "pad_up_:" << pad_up_;
+  LOG(INFO) << "pad_down_:" << pad_down_;
+  LOG(INFO) << "pad_left_:" << pad_left_;
+  LOG(INFO) << "pad_right_:" << pad_right_;
+
+  LOG(INFO) << "offset_:" << offset_;
+  LOG(INFO) << "groups_:" << groups_;
+  LOG(INFO) << "relu_fused_:" << relu_fused_;
+  LOG(INFO) << "has_bias_:" << has_bias_;
+
+  LOG(INFO) << "input_tensor_n_:" << input_tensor_n_;
+  LOG(INFO) << "input_tensor_c_:" << input_tensor_c_;
+  LOG(INFO) << "input_tensor_h_:" << input_tensor_h_;
+  LOG(INFO) << "input_tensor_w_:" << input_tensor_w_;
+  LOG(INFO) << "input_image_h_:" << input_image_h_;
+  LOG(INFO) << "input_image_w_:" << input_image_w_;
+  LOG(INFO) << "input_c_block_:" << input_c_block_;
+
+  LOG(INFO) << "output_tensor_n_:" << output_tensor_n_;
+  LOG(INFO) << "output_tensor_c_:" << output_tensor_c_;
+  LOG(INFO) << "output_tensor_h_:" << output_tensor_h_;
+  LOG(INFO) << "output_tensor_w_:" << output_tensor_w_;
+  LOG(INFO) << "output_image_h_:" << output_image_h_;
+  LOG(INFO) << "output_image_w_:" << output_image_w_;
+
+  LOG(INFO) << "filter_tensor_n_:" << filter_tensor_n_;
+  LOG(INFO) << "filter_tensor_c_:" << filter_tensor_c_;
+  LOG(INFO) << "filter_tensor_h_:" << filter_tensor_h_;
+  LOG(INFO) << "filter_tensor_w_:" << filter_tensor_w_;
+  LOG(INFO) << "filter_image_h_:" << filter_image_h_;
+  LOG(INFO) << "filter_image_w_:" << filter_image_w_;
+
+  LOG(INFO) << "bias_image_h_" << bias_image_h_;
+  LOG(INFO) << "bias_image_w_" << bias_image_w_;
 }
 
 double ConvImageCompute::Tune(int times) {