!10405 [MS][LITE][Develop] add new ops named layer_norm for gpu

From: @pengyongrong
Reviewed-by: @ddwsky,@zhanghaibo5
Signed-off-by: @ddwsky

cmake/external_libs/opencl.cmake

@@ -1,9 +1,9 @@
 if (ENABLE_GITEE)
-    set(REQ_URL "https://gitee.com/mirrors/flatbuffers/repository/archive/v2020.06.16.tar.gz")
+    set(REQ_URL "https://gitee.com/mirrors/OpenCL-Headers/repository/archive/v2020.06.16.tar.gz")
     set(MD5 "fc7627b5a8a95ecbe3d5df43bc88aa44")
     set(PKG_GIT_TAG "")
     __download_pkg_with_git(OpenCL-Headers ${REQ_URL} ${PKG_GIT_TAG} ${MD5})
-    set(REQ_URL "https://gitee.com/mirrors/flatbuffers/repository/archive/v2.0.12.tar.gz")
+    set(REQ_URL "https://gitee.com/mirrors/OpenCL-CLHPP/repository/archive/v2.0.12.tar.gz")
     set(MD5 "bd00fca8f861b3b65660d719f00a58dd")
     set(PKG_GIT_TAG "")
     __download_pkg_with_git(OpenCL-CLHPP ${REQ_URL} ${PKG_GIT_TAG} ${MD5})

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

@@ -0,0 +1,103 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+#define UP_DIV(x, y) (((x) + (y) - (1)) / (y))
+#define C4NUM 4
+
+__kernel void ComputeMeanVarDim1NHWC4(__read_only image2d_t src_data, __global FLT *mean_, __global FLT *variance_,
+                                      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) {
+    return;
+  }
+  int n = X / in_shape.y;
+  int h = X % in_shape.y;
+  int w = Y;
+  int ci4 = UP_DIV(in_shape.w, C4NUM);
+  int remainder = in_shape.w % C4NUM;
+  FLT4 mean_temp = {0.0f, 0.0f, 0.0f, 0.0f};
+  FLT4 var_temp = {0.0f, 0.0f, 0.0f, 0.0f};
+  FLT mean = 0.0f;
+  FLT var = 0.0f;
+
+  // compute mean
+  for (int i = 0; i < ci4; ++i) {
+    FLT4 result_temp = READ_IMAGE(src_data, smp_none, (int2)(w * ci4 + i, n * in_shape.y + h));
+    mean_temp += result_temp;
+  }
+  mean = (mean_temp.x + mean_temp.y + mean_temp.z + mean_temp.w) / normalized_shape_size;
+  mean_temp.x = mean_temp.y = mean_temp.z = mean_temp.w = mean;
+
+  // compute var
+  for (int i = 0; i < ci4; ++i) {
+    FLT4 result_temp = READ_IMAGE(src_data, smp_none, (int2)(w * ci4 + i, n * in_shape.y + h));
+    if ((i + 1) * C4NUM <= in_shape.w) {
+      var_temp += (result_temp - mean_temp) * (result_temp - mean_temp);
+    } else {
+      if (remainder == 1) {
+        mean_temp.x = mean;
+        mean_temp.y = mean_temp.z = mean_temp.w = 0.0f;
+      } else if (remainder == 2) {
+        mean_temp.x = mean_temp.y = mean;
+        mean_temp.z = mean_temp.w = 0.0f;
+      } else {
+        mean_temp.x = mean_temp.y = mean_temp.z = mean;
+        mean_temp.w = 0.0f;
+      }
+      var_temp += (result_temp - mean_temp) * (result_temp - mean_temp);
+    }
+  }
+  var = (var_temp.x + var_temp.y + var_temp.z + var_temp.w) / normalized_shape_size;
+
+  // write result to dst
+  int postion = (n * in_shape.y + h) * in_shape.z + w;
+  mean_[postion] = mean;
+  variance_[postion] = var;
+}
+
+__kernel void LayerNormalization_NHWC4(__read_only image2d_t src_data, __write_only image2d_t dst_data,
+                                       __global FLT *mean_, __global FLT *variance_, __global FLT *gamma_,
+                                       __global FLT *beta_, int4 in_shape, float epsilon_, int normalized_dims_,
+                                       int elementwise_affine_) {
+  int X = get_global_id(0);  // n*h
+  int Y = get_global_id(1);  // w
+  int Z = get_global_id(2);  // c4
+  if (X >= in_shape.x * in_shape.y || Y >= in_shape.z || Z >= in_shape.w || in_shape.y == 0) {
+    return;
+  }
+  int n = X / in_shape.y;
+  int h = X % in_shape.y;
+  int w = Y;
+  int c = Z;
+  int ci4 = UP_DIV(in_shape.w, C4NUM);
+  int postion_mv = 0;
+  int postion_gb = 0;
+  if (normalized_dims_ == 1) {
+    postion_mv = (n * in_shape.y + h) * in_shape.z + w;
+    postion_gb = c * C4NUM;
+  } else if (normalized_dims_ == 2) {
+    postion_mv = n * in_shape.y + h;
+    postion_gb = w * ci4 * C4NUM + c * C4NUM;
+  } else if (normalized_dims_ == 3) {
+    postion_mv = n;
+    postion_gb = (h * in_shape.z + w) * ci4 * C4NUM + c * C4NUM;
+  }
+  FLT4 result = {0.0f, 0.0f, 0.0f, 0.0f};
+  FLT4 result_in = READ_IMAGE(src_data, smp_none, (int2)(w * ci4 + c, n * in_shape.y + h));
+  if (elementwise_affine_) {
+    result.x = ((result_in.x - mean_[postion_mv]) / sqrt(variance_[postion_mv] + epsilon_)) * gamma_[postion_gb] +
+               beta_[postion_gb];
+    result.y = ((result_in.y - mean_[postion_mv]) / sqrt(variance_[postion_mv] + epsilon_)) * gamma_[postion_gb + 1] +
+               beta_[postion_gb + 1];
+    result.z = ((result_in.z - mean_[postion_mv]) / sqrt(variance_[postion_mv] + epsilon_)) * gamma_[postion_gb + 2] +
+               beta_[postion_gb + 2];
+    result.w = ((result_in.w - mean_[postion_mv]) / sqrt(variance_[postion_mv] + epsilon_)) * gamma_[postion_gb + 3] +
+               beta_[postion_gb + 3];
+  } else {
+    result.x = ((result_in.x - mean_[postion_mv]) / sqrt(variance_[postion_mv] + epsilon_));
+    result.y = ((result_in.y - mean_[postion_mv]) / sqrt(variance_[postion_mv] + epsilon_));
+    result.z = ((result_in.z - mean_[postion_mv]) / sqrt(variance_[postion_mv] + epsilon_));
+    result.w = ((result_in.w - mean_[postion_mv]) / sqrt(variance_[postion_mv] + epsilon_));
+  }
+  WRITE_IMAGE(dst_data, (int2)((w * ci4 + c), (n * in_shape.y + h)), result);
+}

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

@@ -0,0 +1,250 @@
+/**
+ * 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/kernel/opencl/kernel/layer_norm.h"
+#include "nnacl/layer_norm_parameter.h"
+#include "src/runtime/kernel/opencl/utils.h"
+#include "src/runtime/kernel/opencl/cl/layer_norm.cl.inc"
+
+using mindspore::kernel::KERNEL_ARCH::kGPU;
+using mindspore::lite::KernelRegistrar;
+using mindspore::lite::RET_ERROR;
+using mindspore::lite::RET_OK;
+using mindspore::schema::PrimitiveType_LayerNorm;
+
+namespace mindspore::kernel {
+
+int LayerNormOpenCLKernel::CheckSpecs() {
+  auto param = reinterpret_cast<LayerNormParameter *>(this->op_parameter_);
+  if (param->elementwise_mode_ == ELEMENTWISE_PER_CHANNEL) {
+    if (in_tensors_.size() != 3) {
+      MS_LOG(ERROR) << " invalid in_tensors_ size" << in_tensors_.size() << std::endl;
+      return RET_ERROR;
+    }
+    if (param->normalized_dims_ > in_tensors_.at(0)->shape().size()) {
+      MS_LOG(ERROR) << " invalid normalized_shape_ size" << param->normalized_dims_ << std::endl;
+      return RET_ERROR;
+    }
+  } else if (param->elementwise_mode_ == ELEMENTWISE_NOT) {
+    if (in_tensors_.size() != 1) {
+      MS_LOG(ERROR) << " invalid in_tensors_ size" << in_tensors_.size() << std::endl;
+      return RET_ERROR;
+    }
+  } else {
+    MS_LOG(ERROR) << "Unsupported elementwise_mode_" << param->elementwise_mode_;
+    return RET_ERROR;
+  }
+  if (in_tensors_.at(0)->shape().size() != 4 || out_tensors_.size() != 1) {
+    MS_LOG(ERROR) << "UnSupported in_tensors_.shape.size: " << in_tensors_.at(0)->shape().size()
+                  << " out_tensors_.size(): " << out_tensors_.size();
+    return RET_ERROR;
+  }
+  if (param->normalized_dims_ != 1) {
+    MS_LOG(ERROR) << "UnSupported normalized_shape_ size: " << param->normalized_dims_;
+    return RET_ERROR;
+  }
+  return RET_OK;
+}
+
+void LayerNormGetWorkGroup(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(GetMaxDivisorStrategy1(global[0], max_divider), max_x);
+  int yz = max_size / x;
+  int y = std::min(std::min(GetMaxDivisorStrategy1(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);
+}
+
+void LayerNormOpenCLKernel::SetConstArgs() {
+  int arg_cn = 6;
+  ocl_runtime_->SetKernelArg(kernel_, arg_cn++, in_shape_);
+  ocl_runtime_->SetKernelArg(kernel_, arg_cn++, epsilon_);
+  ocl_runtime_->SetKernelArg(kernel_, arg_cn++, normalized_dims_);
+  if (elementwise_affine_) {
+    ocl_runtime_->SetKernelArg(kernel_, arg_cn++, 1);
+  } else {
+    ocl_runtime_->SetKernelArg(kernel_, arg_cn++, 0);
+  }
+
+  ocl_runtime_->SetKernelArg(kernel_mean_var_, 3, in_shape_);
+  ocl_runtime_->SetKernelArg(kernel_mean_var_, 4, normalized_shape_size_);
+}
+
+void AlignMeanVarGlobalLocal(const std::vector<int> &global, const std::vector<int> &local, cl::NDRange *global_range,
+                             cl::NDRange *local_range) {
+  *local_range = cl::NDRange(local[0], local[1], local[2]);
+  *global_range =
+    cl::NDRange(UP_ROUND(global[0], local[0]), UP_ROUND(global[1], local[1]), UP_ROUND(global[2], local[2]));
+}
+
+void LayerNormOpenCLKernel::SetGlobalLocal() {
+  size_t OH = 1, OW = 1, OC = 1;
+  OH = in_shape_.s[0] * in_shape_.s[1];
+  OW = in_shape_.s[2];
+  OC = UP_DIV(in_shape_.s[3], C4NUM);
+  local_size_ = {1, 1, 1};  // init local
+  global_size_ = {OH, OW, OC};
+  const std::vector<size_t> &max_global = ocl_runtime_->GetWorkItemSize();
+  LayerNormGetWorkGroup(global_size_, &local_size_, max_global[0]);
+  OpenCLKernel::AlignGlobalLocal(global_size_, local_size_);
+  if (normalized_dims_ != in_tensors_.at(0)->shape().size()) {
+    if (normalized_dims_ == 1) {
+      OH = in_shape_.s[0] * in_shape_.s[1];
+      OW = in_shape_.s[2];
+      OC = 1;
+    } else if (normalized_dims_ == 2) {
+      OH = in_shape_.s[0] * in_shape_.s[1];
+      OW = 1;
+      OC = 1;
+    } else {
+      OH = in_shape_.s[0];
+      OW = 1;
+      OC = 1;
+    }
+  } else {
+    OH = 1;
+    OW = 1;
+    OC = 1;
+  }
+  AlignMeanVarGlobalLocal({static_cast<int>(OH), static_cast<int>(OW), static_cast<int>(OC)}, {1, 1, 1},
+                          &global_mean_var_, &local_mean_var_);
+}
+
+int LayerNormOpenCLKernel::Initweight() {
+  auto allocator = ocl_runtime_->GetAllocator();
+  GpuTensorInfo img_info(in_tensors_.at(1));  // gamma
+  auto weight_tensor = in_tensors_.at(1);
+  size_t weight_size = img_info.Image2DSize;
+  // allocated memory for weight and init value
+  gamma_ = allocator->Malloc(weight_size);
+  beta_ = allocator->Malloc(weight_size);
+  allocator->MapBuffer(gamma_, CL_MAP_WRITE, nullptr, true);
+  allocator->MapBuffer(beta_, CL_MAP_WRITE, nullptr, true);
+  memset(gamma_, 0x01, weight_size);
+  memset(beta_, 0x00, weight_size);
+
+  if (weight_tensor->data_type() == kNumberTypeFloat16) {
+    if (use_fp16_enable_) {
+      memcpy(gamma_, in_tensors_.at(1)->data_c(), weight_size);
+      memcpy(beta_, in_tensors_.at(2)->data_c(), weight_size);
+    } else {
+      auto gamma_fp32 = reinterpret_cast<float *>(gamma_);
+      auto beta_fp32 = reinterpret_cast<float *>(beta_);
+      auto origin_gamma_fp16 = reinterpret_cast<float16_t *>(in_tensors_.at(1)->data_c());
+      auto origin_beta_fp16 = reinterpret_cast<float16_t *>(in_tensors_.at(2)->data_c());
+
+      for (int i = 0; i < img_info.ElementsNum; ++i) {
+        gamma_fp32[i] = static_cast<float>(origin_gamma_fp16[i]);
+        beta_fp32[i] = static_cast<float>(origin_beta_fp16[i]);
+      }
+    }
+  } else {
+    if (use_fp16_enable_) {
+      auto gamma_fp16 = reinterpret_cast<float16_t *>(gamma_);
+      auto beta_fp16 = reinterpret_cast<float16_t *>(beta_);
+      auto origin_gamma_fp32 = reinterpret_cast<float *>(in_tensors_.at(1)->data_c());
+      auto origin_beta_fp32 = reinterpret_cast<float *>(in_tensors_.at(2)->data_c());
+
+      for (int i = 0; i < img_info.ElementsNum; ++i) {
+        gamma_fp16[i] = static_cast<float16_t>(origin_gamma_fp32[i]);
+        beta_fp16[i] = static_cast<float16_t>(origin_beta_fp32[i]);
+      }
+    } else {
+      memcpy(gamma_, in_tensors_.at(1)->data_c(), weight_size);
+      memcpy(beta_, in_tensors_.at(2)->data_c(), weight_size);
+    }
+  }
+  allocator->UnmapBuffer(gamma_);
+  allocator->UnmapBuffer(beta_);
+  return RET_OK;
+}
+
+int LayerNormOpenCLKernel::Prepare() {
+  use_fp16_enable_ = ocl_runtime_->GetFp16Enable();
+  auto param = reinterpret_cast<LayerNormParameter *>(this->op_parameter_);
+  elementwise_affine_ = param->elementwise_mode_;
+  normalized_dims_ = param->normalized_dims_;
+  epsilon_ = param->epsilon_;
+  if (elementwise_affine_) {
+    int ret = Initweight();
+    if (ret) {
+      MS_LOG(ERROR) << "Initweight failed ";
+      return RET_ERROR;
+    }
+  }
+  auto allocator = ocl_runtime_->GetAllocator();
+  size_t mean_size = 1;
+  size_t size = in_tensors_.at(0)->shape().size() - normalized_dims_;
+  for (int i = 0; i < size; ++i) {
+    mean_size *= in_tensors_.at(0)->shape()[i];
+  }
+  size_t size_dtype = use_fp16_enable_ ? sizeof(float16_t) : sizeof(float);
+  mean_size *= size_dtype;
+  mean_ = allocator->Malloc(mean_size);
+  var_ = allocator->Malloc(mean_size);
+  GpuTensorInfo img_info(in_tensors_.at(0));
+  in_shape_.s[0] = img_info.N, in_shape_.s[1] = img_info.H, in_shape_.s[2] = img_info.W, in_shape_.s[3] = img_info.C;
+
+  for (int i = 0; i < normalized_dims_; ++i) {
+    normalized_shape_size_ *= param->normalized_shape_[i];
+  }
+  std::string kernel_name = "LayerNormalization_NHWC4";
+  std::string kernel_name_mean_var = "ComputeMeanVar";
+  std::set<std::string> build_options;
+  std::string source = layer_norm_source;
+  std::string program_name = "LayerNormalization";
+  ocl_runtime_->LoadSource(program_name, source);
+  ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name, build_options);
+  kernel_name_mean_var += "Dim" + std::to_string(normalized_dims_) + "NHWC4";
+  ocl_runtime_->BuildKernel(kernel_mean_var_, program_name, kernel_name_mean_var, build_options);
+  MS_LOG(DEBUG) << kernel_name << " Init Done!";
+  SetConstArgs();
+  SetGlobalLocal();
+
+  return RET_OK;
+}
+
+int LayerNormOpenCLKernel::Run() {
+  MS_LOG(DEBUG) << this->name() << " Running! ";
+  int arg1_cn = 0;
+  ocl_runtime_->SetKernelArg(kernel_mean_var_, arg1_cn++, in_tensors_.at(0)->data_c());        // input tensor
+  ocl_runtime_->SetKernelArg(kernel_mean_var_, arg1_cn++, mean_, lite::opencl::MemType::BUF);  // mean_
+  ocl_runtime_->SetKernelArg(kernel_mean_var_, arg1_cn++, var_, lite::opencl::MemType::BUF);   // var_  return RET_OK;
+  ocl_runtime_->RunKernel(kernel_mean_var_, global_mean_var_, local_mean_var_, nullptr, &event_);
+
+  int arg_cn = 0;
+  ocl_runtime_->SetKernelArg(kernel_, arg_cn++, in_tensors_.at(0)->data_c());         // input tensor
+  ocl_runtime_->SetKernelArg(kernel_, arg_cn++, out_tensors_.at(0)->data_c());        // out tensor
+  ocl_runtime_->SetKernelArg(kernel_, arg_cn++, mean_, lite::opencl::MemType::BUF);   // mean_
+  ocl_runtime_->SetKernelArg(kernel_, arg_cn++, var_, lite::opencl::MemType::BUF);    // var_
+  ocl_runtime_->SetKernelArg(kernel_, arg_cn++, gamma_, lite::opencl::MemType::BUF);  // gamma_
+  ocl_runtime_->SetKernelArg(kernel_, arg_cn++, beta_, lite::opencl::MemType::BUF);   // beta_
+  ocl_runtime_->RunKernel(kernel_, global_range_, local_range_, nullptr, &event_);
+  return RET_OK;
+}
+
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_LayerNorm, OpenCLKernelCreator<LayerNormOpenCLKernel>)
+REG_KERNEL(kGPU, kNumberTypeFloat16, PrimitiveType_LayerNorm, OpenCLKernelCreator<LayerNormOpenCLKernel>)
+}  // namespace mindspore::kernel

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

@@ -0,0 +1,61 @@
+/**
+ * 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_LAYER_NORM_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_LAYER_NORM_H_
+
+#include <vector>
+#include "src/runtime/kernel/opencl/opencl_kernel.h"
+
+namespace mindspore::kernel {
+
+class LayerNormOpenCLKernel : public OpenCLKernel {
+ public:
+  LayerNormOpenCLKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
+                        const std::vector<lite::Tensor *> &outputs)
+      : OpenCLKernel(parameter, inputs, outputs) {}
+
+  ~LayerNormOpenCLKernel() override = default;
+
+  int Run() override;
+  int Prepare() override;
+
+  int CheckSpecs() override;
+  void SetConstArgs() override;
+  void SetGlobalLocal() override;
+
+ private:
+  int Initweight();
+  void GetMeanVar();
+
+ private:
+  cl::Kernel kernel_mean_var_;
+  cl::NDRange global_mean_var_, local_mean_var_;
+  bool use_fp16_enable_{false};
+  void *gamma_{nullptr};
+  void *mean_{nullptr};
+  void *var_{nullptr};
+  void *beta_{nullptr};
+  cl_int4 in_shape_{};
+  int elementwise_affine_;
+  int32_t normalized_dims_{1};
+  int normalized_shape_size_{1};
+  float epsilon_{0.0f};
+  cl::Kernel kernel_;
+};
+
+}  // namespace mindspore::kernel
+#endif

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

@@ -104,6 +104,10 @@ void TestMain(const std::vector<ArgsTupleWithDtype> &input_infos, std::tuple<std
     return;
   }
 
+  // call sub_graph->Init() after construct subgraph like scheduler.cc
+  MS_LOG(DEBUG) << "call sub_graph->Init()";
+  EXPECT_TRUE(sub_graph->Init() == RET_OK);
+
   // simulating benchmark:  session_->CompileGraph() -> PrepareKernels() -> OpenCLSubGraph.Prepare()
   MS_LOG(DEBUG) << "call sub_graph->Prepare()";
   EXPECT_TRUE(sub_graph->Prepare() == RET_OK);  // will set Tensor's allocator be OpenCLAllocator

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

@@ -0,0 +1,62 @@
+/**
+ * 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 "ut/src/runtime/kernel/opencl/common.h"
+#include "nnacl/layer_norm_parameter.h"
+
+namespace mindspore::lite::opencl::test {
+
+class TestOpenCL_LayerNorm : public CommonTest {};
+
+namespace {
+// PrimitiveType_Stack: src/ops/populate/stack_populate.cc
+OpParameter *CreateParameter(float epsilon, int normalized_dims_, std::vector<int> normalizedShape) {
+  auto *param = test::CreateParameter<LayerNormParameter>(schema::PrimitiveType_LayerNorm);
+  param->elementwise_mode_ = ELEMENTWISE_PER_CHANNEL;
+  param->epsilon_ = epsilon;
+  param->normalized_dims_ = normalized_dims_;
+  for (int i = 0; i < normalizedShape.size() && i < normalized_dims_; ++i) {
+    param->normalized_shape_[i] = normalizedShape[i];
+  }
+  return reinterpret_cast<OpParameter *>(param);
+}
+}  // namespace
+
+TEST_F(TestOpenCL_LayerNorm, test1) {
+  float epsilon = 1e-5;
+  int normalized_dims_ = 1;
+  std::vector<int> normalizedShape = {5};
+  std::vector<int> input_shape = {2, 3, 4, 5};
+  std::vector<int> gamma_shape = {1, 1, 1, 5};
+  std::vector<int> beta_shape = {1, 1, 1, 5};
+  std::vector<int> output_shape = {2, 3, 4, 5};
+  size_t input_size, gamma_size, beta_size, output_size;
+  std::string inputPpath = "./test_data/layernormfp32_input.bin";
+  std::string gammaPpath = "./test_data/gammafp32_input.bin";
+  std::string betaPpath = "./test_data/betafp32_input.bin";
+  std::string correctOutputPath = "./test_data/layernormfp32_output.bin";
+  auto input_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(inputPpath.c_str(), &input_size));
+  auto gamma_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(gammaPpath.c_str(), &gamma_size));
+  auto beta_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(betaPpath.c_str(), &beta_size));
+  auto output_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(correctOutputPath.c_str(), &output_size));
+  for (auto fp16_enable : {false}) {
+    auto *param = CreateParameter(epsilon, normalized_dims_, normalizedShape);
+
+    TestMain(
+      {{input_shape, input_data, VAR}, {gamma_shape, gamma_data, CONST_TENSOR}, {beta_shape, beta_data, CONST_TENSOR}},
+      {output_shape, output_data}, param, fp16_enable, fp16_enable ? 1e-3 : 1e-6);
+  }
+}
+}  // namespace mindspore::lite::opencl::test

mindspore/lite/test/ut/src/runtime/kernel/opencl/test_data/layer_norm/test1/layernormfp32_input.bin

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