!5876 [MS][LITE][Develop] add new ops named arithmetic_self

Merge pull request !5876 from pengyongrong/arithmetic_self

mindspore/lite/src/runtime/kernel/opencl/cl/arithmeticself.cl

+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
+
+__kernel void ArithmeticSelf_ElementAbs_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                              int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = result.x >= 0 ? result.x : -result.x;
+  result.y = result.y >= 0 ? result.y : -result.y;
+  result.z = result.z >= 0 ? result.z : -result.z;
+  result.w = result.w >= 0 ? result.w : -result.w;
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementAbs_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                               int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = result.x >= 0 ? result.x : -result.x;
+  result.y = result.y >= 0 ? result.y : -result.y;
+  result.z = result.z >= 0 ? result.z : -result.z;
+  result.w = result.w >= 0 ? result.w : -result.w;
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementCos_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                              int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = cos(result.x);
+  result.y = cos(result.y);
+  result.z = cos(result.z);
+  result.w = cos(result.w);
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementCos_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                               int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = cos(result.x);
+  result.y = cos(result.y);
+  result.z = cos(result.z);
+  result.w = cos(result.w);
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementSin_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                              int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = sin(result.x);
+  result.y = sin(result.y);
+  result.z = sin(result.z);
+  result.w = sin(result.w);
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementSin_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                               int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = sin(result.x);
+  result.y = sin(result.y);
+  result.z = sin(result.z);
+  result.w = sin(result.w);
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementTanh_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                               int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = tanh(result.x);
+  result.y = tanh(result.y);
+  result.z = tanh(result.z);
+  result.w = tanh(result.w);
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementTanh_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = tanh(result.x);
+  result.y = tanh(result.y);
+  result.z = tanh(result.z);
+  result.w = tanh(result.w);
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementNeg_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                              int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = -result.x;
+  result.y = -result.y;
+  result.z = -result.z;
+  result.w = -result.w;
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementNeg_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                               int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = -result.x;
+  result.y = -result.y;
+  result.z = -result.z;
+  result.w = -result.w;
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementExp_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                              int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = exp(result.x);
+  result.y = exp(result.y);
+  result.z = exp(result.z);
+  result.w = exp(result.w);
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementExp_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                               int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = exp(result.x);
+  result.y = exp(result.y);
+  result.z = exp(result.z);
+  result.w = exp(result.w);
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementLog_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                              int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = result.x > 0 ? log(result.x) : HUGE_VALF;
+  result.y = result.y > 0 ? log(result.y) : HUGE_VALF;
+  result.z = result.z > 0 ? log(result.z) : HUGE_VALF;
+  result.w = result.w > 0 ? log(result.w) : HUGE_VALF;
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementLog_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                               int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = result.x > 0 ? log(result.x) : HUGE_VALF;
+  result.y = result.y > 0 ? log(result.y) : HUGE_VALF;
+  result.z = result.z > 0 ? log(result.z) : HUGE_VALF;
+  result.w = result.w > 0 ? log(result.w) : HUGE_VALF;
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementSquare_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                 int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = result.x * result.x;
+  result.y = result.y * result.y;
+  result.z = result.z * result.z;
+  result.w = result.w * result.w;
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementSquare_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                  int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = result.x * result.x;
+  result.y = result.y * result.y;
+  result.z = result.z * result.z;
+  result.w = result.w * result.w;
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementSqrt_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                               int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = result.x > 0 ? sqrt(result.x) : HUGE_VALF;
+  result.y = result.y > 0 ? sqrt(result.y) : HUGE_VALF;
+  result.z = result.z > 0 ? sqrt(result.z) : HUGE_VALF;
+  result.w = result.w > 0 ? sqrt(result.w) : HUGE_VALF;
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementSqrt_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = result.x > 0 ? sqrt(result.x) : HUGE_VALF;
+  result.y = result.y > 0 ? sqrt(result.y) : HUGE_VALF;
+  result.z = result.z > 0 ? sqrt(result.z) : HUGE_VALF;
+  result.w = result.w > 0 ? sqrt(result.w) : HUGE_VALF;
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementRsqrt_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = result.x > 0 ? 1.0f / sqrt(result.x) : HUGE_VALF;
+  result.y = result.y > 0 ? 1.0f / sqrt(result.y) : HUGE_VALF;
+  result.z = result.z > 0 ? 1.0f / sqrt(result.z) : HUGE_VALF;
+  result.w = result.w > 0 ? 1.0f / sqrt(result.w) : HUGE_VALF;
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementRsqrt_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                 int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = result.x > 0 ? 1.0f / sqrt(result.x) : HUGE_VALF;
+  result.y = result.y > 0 ? 1.0f / sqrt(result.y) : HUGE_VALF;
+  result.z = result.z > 0 ? 1.0f / sqrt(result.z) : HUGE_VALF;
+  result.w = result.w > 0 ? 1.0f / sqrt(result.w) : HUGE_VALF;
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementLogicalNot_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                     int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = result.x > 0 || result.x < 0 ? false : true;
+  result.y = result.y > 0 || result.y < 0 ? false : true;
+  result.z = result.z > 0 || result.z < 0 ? false : true;
+  result.w = result.w > 0 || result.w < 0 ? false : true;
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementLogicalNot_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                      int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = result.x > 0 || result.x < 0 ? false : true;
+  result.y = result.y > 0 || result.y < 0 ? false : true;
+  result.z = result.z > 0 || result.z < 0 ? false : true;
+  result.w = result.w > 0 || result.w < 0 ? false : true;
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementFloor_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = floor(result.x);
+  result.y = floor(result.y);
+  result.z = floor(result.z);
+  result.w = floor(result.w);
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementFloor_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                 int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = floor(result.x);
+  result.y = floor(result.y);
+  result.z = floor(result.z);
+  result.w = floor(result.w);
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementCeil_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                               int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = ceil(result.x);
+  result.y = ceil(result.y);
+  result.z = ceil(result.z);
+  result.w = ceil(result.w);
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementCeil_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = ceil(result.x);
+  result.y = ceil(result.y);
+  result.z = ceil(result.z);
+  result.w = ceil(result.w);
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementRound_NHWC4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*output_shape.w + Z, (X)));
+  result.x = round(result.x);
+  result.y = round(result.y);
+  result.z = round(result.z);
+  result.w = round(result.w);
+  WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
+}
+
+__kernel void ArithmeticSelf_ElementRound_NC4HW4(__read_only image2d_t input0, __write_only image2d_t output,
+                                                 int4 output_shape) {
+  int X = get_global_id(0);  // N*H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // c/4
+  if (X >= output_shape.x * output_shape.y || Y >= output_shape.z || Z >= output_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y), (Z * output_shape.y + X)));
+  result.x = round(result.x);
+  result.y = round(result.y);
+  result.z = round(result.z);
+  result.w = round(result.w);
+  WRITE_IMAGE(output, (int2)((Y), (Z * output_shape.y + X)), result);
+}

mindspore/lite/src/runtime/kernel/opencl/kernel/arithmetic_self.cc

+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * 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 <cstring>
+#include <algorithm>
+#include <set>
+#include<string>
+#include "src/kernel_registry.h"
+#include "src/runtime/opencl/opencl_runtime.h"
+#include "src/runtime/kernel/opencl/kernel/arithmetic_self.h"
+#include "src/runtime/kernel/opencl/cl/arithmeticself.cl.inc"
+
+using mindspore::kernel::KERNEL_ARCH::kGPU;
+using mindspore::lite::KernelRegistrar;
+using mindspore::schema::PrimitiveType_Abs;
+using mindspore::schema::PrimitiveType_Ceil;
+using mindspore::schema::PrimitiveType_Cos;
+using mindspore::schema::PrimitiveType_Exp;
+using mindspore::schema::PrimitiveType_Floor;
+using mindspore::schema::PrimitiveType_Log;
+using mindspore::schema::PrimitiveType_LogicalNot;
+using mindspore::schema::PrimitiveType_Neg;
+using mindspore::schema::PrimitiveType_Round;
+using mindspore::schema::PrimitiveType_Rsqrt;
+using mindspore::schema::PrimitiveType_Sin;
+using mindspore::schema::PrimitiveType_Sqrt;
+using mindspore::schema::PrimitiveType_Square;
+
+namespace mindspore::kernel {
+
+int ArithmeticSelfOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size) {
+  size_t CO4 = UP_DIV(out_tensors_[0]->Channel(), C4NUM);
+  size_t im_dst_x, im_dst_y;
+  if (in_tensors_[0]->GetFormat() == schema::Format_NHWC4) {
+    im_dst_x = out_tensors_[0]->Width() * CO4;
+    im_dst_y = out_tensors_[0]->Height() * out_tensors_[0]->Batch();
+  } else {
+    im_dst_y = out_tensors_[0]->Batch() * out_tensors_[0]->Height() * CO4;
+    im_dst_x = out_tensors_[0]->Width();
+  }
+  size_t img_dtype = CL_FLOAT;
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  auto enable_fp16_ = ocl_runtime->GetFp16Enable();
+  if (enable_fp16_) {
+    img_dtype = CL_HALF_FLOAT;
+  }
+  img_size->clear();
+  std::vector<size_t> vec{im_dst_x, im_dst_y, img_dtype};
+  *img_size = vec;
+  return RET_OK;
+}
+
+void ArithmeticSelfOpenCLKernel::GetKernelName(std::string *kernel_name, ArithmeticSelfParameter *param) {
+  switch (param->op_parameter_.type_) {
+    case PrimitiveType_Abs:
+      kernel_name[0] += "_ElementAbs";
+      break;
+    case PrimitiveType_Cos:
+      kernel_name[0] += "_ElementCos";
+      break;
+    case PrimitiveType_Exp:
+      kernel_name[0] += "_ElementExp";
+      break;
+    case PrimitiveType_Log:
+      kernel_name[0] += "_ElementLog";
+      break;
+    case PrimitiveType_Square:
+      kernel_name[0] += "_ElementSquare";
+      break;
+    case PrimitiveType_Sqrt:
+      kernel_name[0] += "_ElementSqrt";
+      break;
+    case PrimitiveType_Rsqrt:
+      kernel_name[0] += "_ElementRsqrt";
+      break;
+    case PrimitiveType_Sin:
+      kernel_name[0] += "_ElementSin";
+      break;
+    case PrimitiveType_LogicalNot:
+      kernel_name[0] += "_ElementLogicalNot";
+      break;
+    case PrimitiveType_Floor:
+      kernel_name[0] += "_ElementFloor";
+      break;
+    case PrimitiveType_Ceil:
+      kernel_name[0] += "_ElementCeil";
+      break;
+    case PrimitiveType_Round:
+      kernel_name[0] += "_ElementRound";
+    case PrimitiveType_Neg:
+      kernel_name[0] += "_ElementNeg";
+      break;
+    default:
+      break;
+  }
+}
+
+int ArithmeticSelfOpenCLKernel::Init() {
+  if (in_tensors_[0]->shape().size() != 4) {
+    MS_LOG(ERROR) << " only support dim = 4 ";
+    return RET_ERROR;
+  }
+  auto param = reinterpret_cast<ArithmeticSelfParameter *>(this->op_parameter_);
+
+  auto in_format = op_format_;
+  if (in_format != schema::Format_NHWC4 && in_format != schema::Format_NC4HW4) {
+    MS_LOG(ERROR) << "input format(" << in_format << ") "
+                  << "format not support!";
+    return RET_ERROR;
+  }
+  in_ori_format_ = in_tensors_[0]->GetFormat();
+  in_tensors_[0]->SetFormat(op_format_);
+  out_ori_format_ = out_tensors_[0]->GetFormat();
+  out_tensors_[0]->SetFormat(op_format_);
+
+  std::string kernel_name = "ArithmeticSelf";
+  GetKernelName(&kernel_name, param);
+  if (in_format == schema::Format_NC4HW4) {
+    kernel_name += "_NC4HW4";
+  } else if (in_format == schema::Format_NHWC4) {
+    kernel_name += "_NHWC4";
+  }
+  MS_LOG(DEBUG) << "execute kernel name : " << kernel_name;
+  std::set<std::string> build_options;
+  std::string source = arithmeticself_source;
+  std::string program_name = "ArithmeticSelf";
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  ocl_runtime->LoadSource(program_name, source);
+  ocl_runtime->BuildKernel(kernel_, program_name, kernel_name, build_options);
+
+  return RET_OK;
+}
+
+int ArithmeticSelfOpenCLKernel::ReSize() { return RET_OK; }
+
+int ArithmeticSelfGetBiggestDividerWithPriority(int number, int max_divider) {
+  if (number % 8 == 0 && max_divider >= 8) {
+    return number / 8;
+  }
+  if (number % 4 == 0 && 4 <= max_divider) {
+    return number / 4;
+  }
+  if (number % 2 == 0 && 2 <= max_divider) {
+    return number / 2;
+  }
+
+  for (int i = max_divider; i != 0; i--) {
+    if (number % i == 0) {
+      return i;
+    }
+  }
+  return RET_OK;
+}
+
+void ArithmeticSelfGetWorkGroup(const std::vector<size_t> &global, std::vector<size_t> *local, int max_size) {
+  const int max_divider = 8;
+  const int max_x = 4, max_y = 8;
+  int x = std::min(ArithmeticSelfGetBiggestDividerWithPriority(global[0], max_divider), max_x);
+  int yz = max_size / x;
+  int y = std::min(std::min(ArithmeticSelfGetBiggestDividerWithPriority(global[1], max_divider), yz), max_y);
+  int z = std::min(yz / y, static_cast<int>(UP_DIV(global[2], 2)));
+
+  local->clear();
+  local->push_back(x);
+  local->push_back(y);
+  local->push_back(z);
+}
+
+int ArithmeticSelfOpenCLKernel::Run() {
+  MS_LOG(DEBUG) << this->name() << " Running! ";
+
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  auto output_shape = out_tensors_[0]->shape();
+  cl_int4 output_shape_ = {output_shape[0], output_shape[1], output_shape[2], UP_DIV(output_shape[3], C4NUM)};
+
+  uint32_t OH = output_shape[0] * output_shape[1];  // N*H
+  uint32_t OW = output_shape[2];
+  uint32_t OC = UP_DIV(output_shape[3], C4NUM);
+
+  const std::vector<size_t> &max_global = ocl_runtime->GetWorkItemSize();
+  std::vector<size_t> local = {1, 1, 1};  // init local
+  std::vector<size_t> global = {OH, OW, OC};
+  ArithmeticSelfGetWorkGroup(global, &local, max_global[0]);
+
+  int arg_cn = 0;
+  ocl_runtime->SetKernelArg(kernel_, arg_cn++, in_tensors_[0]->Data());
+  ocl_runtime->SetKernelArg(kernel_, arg_cn++, out_tensors_[0]->Data());
+  ocl_runtime->SetKernelArg(kernel_, arg_cn++, output_shape_);
+
+  ocl_runtime->RunKernel(kernel_, global, local, nullptr);
+
+  return RET_OK;
+}
+
+kernel::LiteKernel *OpenCLArithmeticSelfKernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
+                                                      const std::vector<lite::tensor::Tensor *> &outputs,
+                                                      OpParameter *opParameter, const lite::Context *ctx,
+                                                      const kernel::KernelKey &desc,
+                                                      const mindspore::lite::PrimitiveC *primitive) {
+  auto *kernel = new (std::nothrow) ArithmeticSelfOpenCLKernel(opParameter, inputs, outputs);
+  if (kernel == nullptr) {
+    MS_LOG(ERROR) << " new ArithmeticSelfOpenCLKernel failed ";
+    return nullptr;
+  }
+  auto ret = kernel->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << " Init kernel failed, name: ArithmeticSelf ";
+    delete kernel;
+    return nullptr;
+  }
+  return kernel;
+}
+
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Abs, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Ceil, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Cos, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Exp, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Floor, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Log, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_LogicalNot, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Round, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Rsqrt, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Sin, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Neg, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Sqrt, OpenCLArithmeticSelfKernelCreator)
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Square, OpenCLArithmeticSelfKernelCreator)
+
+}  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/opencl/kernel/arithmetic_self.h

+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_ARITHMETIC_SELF_PARAMETER_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_ARITHMETIC_SELF_PARAMETER_H_
+
+#include <vector>
+#include<string>
+#include "ir/anf.h"
+#include "src/runtime/kernel/opencl/opencl_kernel.h"
+#include "src/runtime/opencl/opencl_runtime.h"
+#include "nnacl/arithmetic_self_parameter.h"
+
+namespace mindspore::kernel {
+
+class ArithmeticSelfOpenCLKernel : public OpenCLKernel {
+ public:
+  explicit ArithmeticSelfOpenCLKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
+                                      const std::vector<lite::tensor::Tensor *> &outputs)
+      : OpenCLKernel(parameter, inputs, outputs) {}
+
+  ~ArithmeticSelfOpenCLKernel() override{};
+
+  int Init() override;
+
+  int ReSize() override;
+
+  int Run() override;
+
+  int GetImageSize(size_t idx, std::vector<size_t> *img_size) override;
+
+  void GetKernelName(std::string *kernel_name, ArithmeticSelfParameter *param);
+
+ private:
+  cl::Kernel kernel_;
+};
+
+}  // namespace mindspore::kernel
+#endif

mindspore/lite/src/runtime/kernel/opencl/kernel/transpose.cc

@@ -114,7 +114,11 @@ int TransposeOpenCLKernel::Run() {
   cl_int2 C = {c, c4};
   int arg_idx = 0;
   ocl_runtime->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->Data());
-  ocl_runtime->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
+  if (out_mem_type_ == OpenCLMemType::BUF) {
+    ocl_runtime->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data(), lite::opencl::MemType::BUF);
+  } else {
+    ocl_runtime->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
+  }
   ocl_runtime->SetKernelArg(kernel_, arg_idx++, HW);
   ocl_runtime->SetKernelArg(kernel_, arg_idx++, C);
   ocl_runtime->SetKernelArg(kernel_, arg_idx++, w);

mindspore/lite/test/CMakeLists.txt

@@ -150,6 +150,7 @@ if (SUPPORT_GPU)
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/matmul.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/softmax.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/concat.cc
+            ${LITE_DIR}/src/runtime/kernel/opencl/kernel/arithmetic_self.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/batchnorm.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/slice.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/activation.cc
@@ -334,6 +335,7 @@ if (SUPPORT_GPU)
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/concat_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/batchnorm_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/slice_tests.cc
+            ${TEST_DIR}/ut/src/runtime/kernel/opencl/arithmetic_self_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/softmax_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/arithmetic_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/avg_pooling_tests.cc

mindspore/lite/test/ut/src/runtime/kernel/opencl/arithmetic_self_tests.cc

+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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 <iostream>
+#include <memory>
+#include "utils/log_adapter.h"
+#include "common/common_test.h"
+#include "mindspore/lite/src/runtime/opencl/opencl_runtime.h"
+#include "mindspore/lite/src/common/file_utils.h"
+#include "mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h"
+#include "mindspore/lite/src/runtime/kernel/opencl/kernel/arithmetic_self.h"
+
+namespace mindspore {
+class TestArithmeticSelfOpenCLfp16 : public mindspore::CommonTest {
+ public:
+  TestArithmeticSelfOpenCLfp16() {}
+};
+
+template <typename T>
+void CompareOutputData1(T *input_data1, T *output_data, T *correct_data, int size, float err_bound) {
+  for (size_t i = 0; i < 100; i++) {
+    T abs = fabs(output_data[i] - correct_data[i]);
+    ASSERT_LE(abs, err_bound);
+  }
+}
+
+TEST_F(TestArithmeticSelfOpenCLfp16, ArithmeticSelfOpenCLFp16) {
+  MS_LOG(INFO) << " begin test ";
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  ocl_runtime->SetFp16Enable(true);
+  ocl_runtime->Init();
+  auto allocator = ocl_runtime->GetAllocator();
+
+  // get the input from .bin
+  size_t input1_size, output_size;
+  std::string input1Ppath = "./test_data/in_arithmetic_selffp16.bin";
+  std::string correctOutputPath = "./test_data/out_arithmetic_selffp16.bin";
+  auto input_data1 = reinterpret_cast<float16_t *>(mindspore::lite::ReadFile(input1Ppath.c_str(), &input1_size));
+  auto correctOutput =
+    reinterpret_cast<float16_t *>(mindspore::lite::ReadFile(correctOutputPath.c_str(), &output_size));
+
+  MS_LOG(INFO) << " init tensors ";
+
+  std::vector<int> shape = {1, 19, 19, 96};
+  auto data_type = kNumberTypeFloat16;
+  auto tensor_type = schema::NodeType_ValueNode;
+  auto *input_tensor = new (std::nothrow) lite::tensor::Tensor(data_type, shape, schema::Format_NHWC, tensor_type);
+  auto *output_tensor = new (std::nothrow) lite::tensor::Tensor(data_type, shape, schema::Format_NHWC, tensor_type);
+  if (input_tensor == nullptr || output_tensor == nullptr) {
+    MS_LOG(INFO) << " new input_tensor or output_tensor failed ";
+    return;
+  }
+  std::vector<lite::tensor::Tensor *> inputs{input_tensor};
+  std::vector<lite::tensor::Tensor *> outputs{output_tensor};
+
+  MS_LOG(INFO) << " initialize param ";
+  auto param = new (std::nothrow) ArithmeticSelfParameter();
+  if (param == nullptr) {
+    MS_LOG(INFO) << " new ConcatParameter failed ";
+    for (auto tensor : inputs) {
+      delete tensor;
+    }
+    for (auto tensor : outputs) {
+      delete tensor;
+    }
+    return;
+  }
+  param->op_parameter_.type_ = schema::PrimitiveType_Neg;
+  auto *arithmeticself_kernel =
+    new (std::nothrow) kernel::ArithmeticSelfOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  if (arithmeticself_kernel == nullptr) {
+    MS_LOG(INFO) << " new kernel::ArithmeticSelfOpenCLKernel failed ";
+    for (auto tensor : inputs) {
+      delete tensor;
+    }
+    for (auto tensor : outputs) {
+      delete tensor;
+    }
+    delete param;
+    return;
+  }
+  arithmeticself_kernel->SetFormatType(schema::Format_NC4HW4);
+  arithmeticself_kernel->Init();
+  // to do allocate memory for inputs and outputs
+  for (auto &input_tensor : inputs) {
+    input_tensor->MallocData(allocator);
+  }
+  MS_LOG(INFO) << " initialize sub_graph ";
+  std::vector<kernel::LiteKernel *> kernels{arithmeticself_kernel};
+  auto *sub_graph = new (std::nothrow) kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  if (sub_graph == nullptr) {
+    MS_LOG(INFO) << " new kernel::SubGraphOpenCLKernel failed ";
+    for (auto tensor : inputs) {
+      delete tensor;
+    }
+    for (auto tensor : outputs) {
+      delete tensor;
+    }
+    delete param;
+    delete arithmeticself_kernel;
+    return;
+  }
+  sub_graph->Init();
+  MS_LOG(INFO) << " initialize input data ";
+  memcpy(inputs[0]->Data(), input_data1, input1_size);
+
+  std::cout << "==================output data================" << std::endl;
+  sub_graph->Run();
+  auto *output_data_gpu = reinterpret_cast<float16_t *>(output_tensor->Data());
+  CompareOutputData1(input_data1, output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.000001);
+  for (auto tensor : inputs) {
+    delete tensor;
+  }
+  for (auto tensor : outputs) {
+    delete tensor;
+  }
+  delete param;
+  delete arithmeticself_kernel;
+  delete sub_graph;
+}
+}  // namespace mindspore