codeclean for layernorm and conv

mindspore/lite/src/runtime/kernel/opencl/cl/conv2d.cl

@@ -39,9 +39,9 @@ __constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP
 __kernel void Conv2D_H1W1C1(__read_only image2d_t input, __write_only image2d_t output, __global FLT4 *weight,
                             __global FLT4 *bias, int4 input_shape, int4 output_shape, int4 kernel_stride, int4 pad,
                             int2 dilation, int act_type, float alpha) {
-  int BlockH = 1;
-  int BlockW = 1;
-  int BlockC = 1;
+  const int BlockH = 1;
+  const int BlockW = 1;
+  const int BlockC = 1;
   DEFINE_ARGS;
 
   int oh0 = oh + 0;
@@ -102,9 +102,9 @@ __kernel void Conv2D_H1W1C1(__read_only image2d_t input, __write_only image2d_t
 __kernel void Conv2D_H2W1C1(__read_only image2d_t input, __write_only image2d_t output, __global FLT4 *weight,
                             __global FLT4 *bias, int4 input_shape, int4 output_shape, int4 kernel_stride, int4 pad,
                             int2 dilation, int act_type, float alpha) {
-  int BlockH = 2;
-  int BlockW = 1;
-  int BlockC = 1;
+  const int BlockH = 2;
+  const int BlockW = 1;
+  const int BlockC = 1;
   DEFINE_ARGS;
 
   int oh0 = oh + 0;
@@ -189,9 +189,9 @@ __kernel void Conv2D_H2W1C1(__read_only image2d_t input, __write_only image2d_t
 __kernel void Conv2D_H2W1C2(__read_only image2d_t input, __write_only image2d_t output, __global FLT4 *weight,
                             __global FLT4 *bias, int4 input_shape, int4 output_shape, int4 kernel_stride, int4 pad,
                             int2 dilation, int act_type, float alpha) {
-  int BlockH = 2;
-  int BlockW = 1;
-  int BlockC = 2;
+  const int BlockH = 2;
+  const int BlockW = 1;
+  const int BlockC = 2;
   DEFINE_ARGS;
 
   int oh0 = oh + 0;
@@ -312,9 +312,9 @@ __kernel void Conv2D_H2W1C2(__read_only image2d_t input, __write_only image2d_t
 __kernel void Conv2D_H2W2C2(__read_only image2d_t input, __write_only image2d_t output, __global FLT4 *weight,
                             __global FLT4 *bias, int4 input_shape, int4 output_shape, int4 kernel_stride, int4 pad,
                             int2 dilation, int act_type, float alpha) {
-  int BlockH = 2;
-  int BlockW = 2;
-  int BlockC = 2;
+  const int BlockH = 2;
+  const int BlockW = 2;
+  const int BlockC = 2;
   DEFINE_ARGS;
 
   int oh0 = oh + 0;

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

@@ -7,7 +7,7 @@ __kernel void ComputeMeanVarDim1NHWC4(__read_only image2d_t src_data, __global F
                                       int4 in_shape, int normalized_shape_size) {
   int X = get_global_id(0);  // n*h
   int Y = get_global_id(1);  // w
-  if (X > in_shape.x * in_shape.y || Y > in_shape.z || in_shape.y == 0) {
+  if (X > in_shape.x * in_shape.y || Y > in_shape.z || in_shape.y == 0 || normalized_shape_size == 0) {
     return;
   }
   int n = X / in_shape.y;

mindspore/lite/src/runtime/kernel/opencl/kernel/conv2d.cc

@@ -41,8 +41,8 @@ using mindspore::schema::PrimitiveType_FullConnection;
 
 namespace mindspore::kernel {
 
-constexpr size_t CI_TILE = C4NUM;
-constexpr size_t CO_TILE = C4NUM;
+const size_t CI_TILE = C4NUM;
+const size_t CO_TILE = C4NUM;
 
 int Conv2DOpenCLKernel::CheckSpecs() {
   if (in_tensors_.size() != 2 && in_tensors_.size() != 3) {
@@ -164,12 +164,12 @@ int Conv2DOpenCLKernel::Prepare() {
 }
 
 int Conv2DOpenCLKernel::GenerateWinogradFilter() {
-  constexpr float Gt[] = {1.0000000000, 1.0000000000, 1.0000000000,  1.0000000000, 1.0000000000,  0.0000000000,
-                          0.0000000000, 0.7071067691, -0.7071067691, 1.4142135382, -1.4142135382, 0.0000000000,
-                          0.0000000000, 0.4999999702, 0.4999999702,  1.9999998808, 1.9999998808,  1.0000000000};
-  constexpr float G[] = {1.0000000000, 0.0000000000,  0.0000000000, 1.0000000000, 0.7071067691, 0.4999999702,
-                         1.0000000000, -0.7071067691, 0.4999999702, 1.0000000000, 1.4142135382, 1.9999998808,
-                         1.0000000000, -1.4142135382, 1.9999998808, 0.0000000000, 0.0000000000, 1.0000000000};
+  const float Gt[] = {1.0000000000, 1.0000000000, 1.0000000000,  1.0000000000, 1.0000000000,  0.0000000000,
+                      0.0000000000, 0.7071067691, -0.7071067691, 1.4142135382, -1.4142135382, 0.0000000000,
+                      0.0000000000, 0.4999999702, 0.4999999702,  1.9999998808, 1.9999998808,  1.0000000000};
+  const float G[] = {1.0000000000, 0.0000000000,  0.0000000000, 1.0000000000, 0.7071067691, 0.4999999702,
+                     1.0000000000, -0.7071067691, 0.4999999702, 1.0000000000, 1.4142135382, 1.9999998808,
+                     1.0000000000, -1.4142135382, 1.9999998808, 0.0000000000, 0.0000000000, 1.0000000000};
 
   auto weight_tensor = in_tensors_.at(1);
   auto origin_weight_fp32 = reinterpret_cast<float *>(weight_tensor->data_c());
@@ -366,11 +366,11 @@ void Conv2DOpenCLKernel::SetGlobalLocal() {
     size_t global_h = batch_size_ * UP_DIV(OH_, block_size_.H);
     size_t global_w = UP_DIV(OW_, block_size_.W);
     size_t global_c = UP_DIV(CO_SLICES_, block_size_.C);
-    constexpr int local_c_max = 16;
-    constexpr int local_hw_max = 256;
-    constexpr int OH_threshold = 100;
-    constexpr int OW_threshold = 100;
-    constexpr int OC_threshold = 64;
+    const int local_c_max = 16;
+    const int local_hw_max = 256;
+    const int OH_threshold = 100;
+    const int OW_threshold = 100;
+    const int OC_threshold = 64;
     size_t local_c = GetMaxDivisor(global_c, local_c_max);
     local_c = std::max<size_t>(local_c, 1);
     size_t local_hw = local_hw_max / local_c;

mindspore/lite/test/models_npu.cfg

@@ -1,3 +1,3 @@
-mobilenet_v1_1.0_224.tflite 2.5
+mobilenet_v2_1.0_224.tflite 2.5
 squeezenet.tflite 2.5
 inception_v3.tflite 1