add new op instancenorm2d

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/instance_norm_impl.cu

@@ -0,0 +1,90 @@
+/**
+ * Copyright 2021 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 "backend/kernel_compiler/gpu/cuda_impl/instance_norm_impl.cuh"
+#include "backend/kernel_compiler/gpu/cuda_impl/util.cuh"
+
+__global__ void CopyMemKernel(const size_t thread_num, const size_t N, const size_t C,
+                              float *gamma_addr, float *beta_addr,
+                              float *runing_mean_addr, float *runnig_variance_addr,
+                              float *ws_gamma, float *ws_beta, float *ws_mean, float *ws_var) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < thread_num; pos += gridDim.x * blockDim.x) {
+    size_t cur_addr = pos / (N * C);
+    size_t cur_local_index = pos % (N * C);
+    size_t local_index = 0;
+    switch (cur_addr) {
+      case 0:
+        if (!(gamma_addr && ws_gamma)) break;
+        local_index = cur_local_index % C;
+        ws_gamma[cur_local_index] = gamma_addr[local_index];
+        break;
+      case 1:
+        if (!(beta_addr && ws_beta)) break;
+        local_index = cur_local_index % C;
+        ws_beta[cur_local_index] = beta_addr[local_index];
+        break;
+      case 2:
+        if (!(runing_mean_addr && ws_mean)) break;
+        local_index = cur_local_index % C;
+        ws_mean[cur_local_index] = runing_mean_addr[local_index];
+        break;
+      default:
+        if (!(runnig_variance_addr && ws_var)) break;
+        local_index = cur_local_index % C;
+        ws_var[cur_local_index] = runnig_variance_addr[local_index];
+    }
+  }
+  return;
+}
+
+void CopyMemDevice2Device(const size_t N, const size_t C, float *gamma_addr, float *beta_addr,
+                          float *runing_mean_addr, float *runnig_variance_addr,
+                          float *ws_gamma, float *ws_beta, float *ws_mean, float *ws_var,
+                          cudaStream_t cuda_stream) {
+  size_t thread_num = N * C * 4;
+  CopyMemKernel<<<GET_BLOCKS(thread_num), GET_THREADS, 0, cuda_stream>>>(
+          thread_num, N, C, gamma_addr, beta_addr, runing_mean_addr, runnig_variance_addr,
+          ws_gamma, ws_beta, ws_mean, ws_var);
+}
+
+__global__ void ComputeMeanKernel(const size_t thread_num, const size_t N, const size_t C,
+                              float *save_mean_addr, float *save_var_addr) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < thread_num; pos += gridDim.x * blockDim.x) {
+    size_t cur_addr = pos / C;
+    size_t cur_local_index = pos % C;
+    float tmp = 0;
+    if (cur_addr) {
+      for (size_t i = 0; i < N; i++) {
+        tmp += save_var_addr[i * C + cur_local_index];
+      }
+      save_var_addr[cur_local_index] = tmp / N;
+    } else {
+      for (size_t i = 0; i < N; i++) {
+        tmp += save_mean_addr[i * C + cur_local_index];
+      }
+      save_mean_addr[cur_local_index] = tmp / N;
+    }
+  }
+  return;
+}
+
+void ComputeMean(const size_t N, const size_t C,
+                 float *save_mean_addr, float *save_var_addr,
+                 cudaStream_t cuda_stream) {
+  size_t thread_num = C * 2;
+  ComputeMeanKernel<<<GET_BLOCKS(thread_num), GET_THREADS, 0, cuda_stream>>>(
+          thread_num, N, C, save_mean_addr, save_var_addr);
+}

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/instance_norm_impl.cuh

@@ -0,0 +1,27 @@
+/**
+ * Copyright 2021 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_CCSRC_KERNEL_GPU_CUDA_IMP_INSTANCE_NORM_IMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_INSTANCE_NORM_IMPL_H_
+
+#include "runtime/device/gpu/cuda_common.h"
+void CopyMemDevice2Device(const size_t N, const size_t C,
+                          float *gamma_addr, float *beta_addr, float *runing_mean_addr, float *runnig_variance_addr,
+                          float *ws_gamma, float *ws_beta, float *ws_mean, float *ws_var,
+                          cudaStream_t cuda_stream);
+void ComputeMean(const size_t N, const size_t C, float *save_mean_addr, float *save_var_addr,
+                 cudaStream_t cuda_stream);
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_INSTANCE_NORM_IMPL_H_

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/instance_norm_gpu_kernel.cc

@@ -0,0 +1,44 @@
+/**
+ * Copyright 2021 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 "backend/kernel_compiler/gpu/nn/instance_norm_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(InstanceNorm,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      InstanceNormGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(InstanceNorm,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      InstanceNormGpuKernel, half)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/instance_norm_gpu_kernel.h

@@ -0,0 +1,240 @@
+/**
+ * Copyright 2021 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_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_INSTANCE_NORM_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_INSTANCE_NORM_GPU_KERNEL_H_
+
+#include <string>
+#include <vector>
+#include "backend/kernel_compiler/gpu/gpu_kernel.h"
+#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
+#include "backend/kernel_compiler/gpu/kernel_constants.h"
+#include "utils/utils.h"
+#include "backend/kernel_compiler/gpu/cuda_impl/instance_norm_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class InstanceNormGpuKernel : public GpuKernel {
+ public:
+  InstanceNormGpuKernel()
+      : input_x_size_(0),
+        input_z_size_(0),
+        para_size_(0),
+        output_size_(0),
+        workspace_size_(0),
+        mode_(CUDNN_BATCHNORM_SPATIAL),
+        bn_ops_(CUDNN_BATCHNORM_OPS_BN),
+        is_training_(true),
+        epsilon_(10e-5),
+        exp_avg_factor_(0.1),
+        is_null_input_(false),
+        x_desc_(nullptr),
+        y_desc_(nullptr),
+        z_desc_(nullptr),
+        scale_bias_mean_var_desc_(nullptr),
+        handle_(nullptr),
+        cudnn_data_type_(CUDNN_DATA_FLOAT) {}
+  ~InstanceNormGpuKernel() override { DestroyResource(); }
+
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
+    VARIABLE_NOT_USED(workspace);
+    VARIABLE_NOT_USED(stream_ptr);
+    if (is_null_input_) {
+      return true;
+    }
+    auto x_addr = GetDeviceAddress<T>(inputs, 0);
+    auto gamma_addr = GetDeviceAddress<float>(inputs, 1);
+    auto beta_addr = GetDeviceAddress<float>(inputs, 2);
+    auto runing_mean_addr = GetDeviceAddress<float>(inputs, 3);
+    auto runnig_variance_addr = GetDeviceAddress<float>(inputs, 4);
+    T *z = nullptr;
+
+    auto y_addr = GetDeviceAddress<T>(outputs, 0);
+    auto save_mean_addr = GetDeviceAddress<float>(outputs, 1);
+    auto save_variance_addr = GetDeviceAddress<float>(outputs, 2);
+
+    float *ws_gamma = GetDeviceAddress<float>(workspace, 0);
+    float *ws_beta = GetDeviceAddress<float>(workspace, 1);
+    float *ws_mean = GetDeviceAddress<float>(workspace, 2);
+    float *ws_var = GetDeviceAddress<float>(workspace, 3);
+    T *workspace_addr = nullptr;
+    if (workspace_size_ != 0) {
+      workspace_addr = GetDeviceAddress<T>(workspace, 4);
+    }
+
+    size_t N = input_shape_[0];
+    size_t C = input_shape_[1];
+    CopyMemDevice2Device(N, C, gamma_addr, beta_addr, runing_mean_addr, runnig_variance_addr, ws_gamma, ws_beta,
+                         ws_mean, ws_var, reinterpret_cast<cudaStream_t>(stream_ptr));
+
+    const float alpha = 1;
+    const float beta = 0;
+    float *reserve_addr = nullptr;
+    if (is_training_) {
+      CHECK_CUDNN_RET_WITH_EXCEPT(
+        kernel_node_,
+        cudnnBatchNormalizationForwardTrainingEx(
+          handle_, mode_, bn_ops_, &alpha, &beta, x_desc_, x_addr, z_desc_, z, y_desc_, y_addr,
+          scale_bias_mean_var_desc_, ws_gamma, ws_beta, exp_avg_factor_, ws_mean, ws_var, epsilon_, save_mean_addr,
+          save_variance_addr, nullptr, workspace_addr, workspace_size_, reserve_addr, 0),
+        "Kernel launch failed");
+    } else {
+      CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_,
+                                  cudnnBatchNormalizationForwardInference(
+                                    handle_, mode_, &alpha, &beta, x_desc_, x_addr, y_desc_, y_addr,
+                                    scale_bias_mean_var_desc_, ws_gamma, ws_beta, ws_mean, ws_var, epsilon_),
+                                  "Kernel launch failed");
+    }
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    kernel_node_ = kernel_node;
+    MS_EXCEPTION_IF_NULL(kernel_node);
+    std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
+    bn_ops_ = CUDNN_BATCHNORM_OPS_BN;
+
+    InitResource();
+    is_training_ = GetAttr<bool>(kernel_node, "is_training");
+    mode_ = is_training_ ? CUDNN_BATCHNORM_SPATIAL_PERSISTENT : CUDNN_BATCHNORM_SPATIAL;
+    epsilon_ = GetAttr<float>(kernel_node, "epsilon");
+    exp_avg_factor_ = GetAttr<float>(kernel_node, "momentum");
+
+    cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 5) {
+      MS_LOG(EXCEPTION) << "input tensor size is " << input_num << ", " << kernel_name << "  should be 5";
+    }
+    input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
+    if (input_shape_.size() != 4) {
+      MS_LOG(EXCEPTION) << "tensor shape is " << input_shape_.size() << ", InstanceNormGpuKernel should be 4";
+    }
+    is_null_input_ = CHECK_NULL_INPUT(input_shape_);
+    if (is_null_input_) {
+      MS_LOG(WARNING) << "InstanceNormGpuKernel input is null";
+      InitSizeLists();
+      return true;
+    }
+    SetTensorDescriptor();
+    InitSizeLists();
+    return true;
+  }
+
+  void DestroyResource() noexcept override {
+    CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
+    CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(y_desc_), "Destroy y desc failed");
+    CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(scale_bias_mean_var_desc_),
+                               "Destroy para desc failed");
+  }
+
+ protected:
+  void InitResource() override {
+    handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
+    CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&x_desc_), "Create x desc failed");
+    CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&y_desc_), "Create y desc failed");
+    CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&scale_bias_mean_var_desc_),
+                                "Create para desc failed");
+  }
+
+  void InitSizeLists() override {
+    if (!is_null_input_) {
+      CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(x_desc_, &input_x_size_),
+                                  "Get input x size failed");
+      CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(scale_bias_mean_var_desc_, &para_size_),
+                                  "Get para size failed");
+      CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(y_desc_, &output_size_),
+                                  "Get output size failed");
+
+      CHECK_CUDNN_RET_WITH_EXCEPT(
+        kernel_node_,
+        cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(handle_, mode_, bn_ops_, x_desc_, z_desc_, y_desc_,
+                                                                 scale_bias_mean_var_desc_, nullptr, &workspace_size_),
+        "cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize failed");
+    }
+
+    input_size_list_.push_back(input_x_size_);    // input x
+    input_size_list_.push_back(input_shape_[1]);  // gamma
+    input_size_list_.push_back(input_shape_[1]);  // beta
+    input_size_list_.push_back(input_shape_[1]);  // mean
+    input_size_list_.push_back(input_shape_[1]);  // variance
+
+    output_size_list_.push_back(output_size_);  // output
+    output_size_list_.push_back(para_size_);    // save mean
+    output_size_list_.push_back(para_size_);    // save variance
+
+    workspace_size_list_.push_back(para_size_);  // ws gamma
+    workspace_size_list_.push_back(para_size_);  // ws beta
+    workspace_size_list_.push_back(para_size_);  // ws mean
+    workspace_size_list_.push_back(para_size_);  // ws variance
+    workspace_size_list_.push_back(workspace_size_);
+  }
+
+ private:
+  void SetTensorDescriptor() {
+    cudnnTensorFormat_t cudnn_format;
+    int batch, channel, height, width;
+    batch = 1;
+    channel = SizeToInt(input_shape_[0]) * SizeToInt(input_shape_[1]);
+    height = SizeToInt(input_shape_[2]);
+    width = SizeToInt(input_shape_[3]);
+    cudnn_format = CUDNN_TENSOR_NCHW;
+
+    CHECK_CUDNN_RET_WITH_EXCEPT(
+      kernel_node_, cudnnSetTensor4dDescriptor(x_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
+      "Set x desc failed");
+
+    CHECK_CUDNN_RET_WITH_EXCEPT(
+      kernel_node_, cudnnSetTensor4dDescriptor(y_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
+      "Set y desc failed");
+
+    CHECK_CUDNN_RET_WITH_EXCEPT(
+      kernel_node_,
+      cudnnSetTensor4dDescriptor(scale_bias_mean_var_desc_, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, channel, 1, 1),
+      "Set para desc failed");
+  }
+
+  size_t input_x_size_;
+  size_t input_z_size_;
+  size_t para_size_;
+  size_t output_size_;
+  size_t workspace_size_;
+  cudnnBatchNormMode_t mode_;
+  cudnnBatchNormOps_t bn_ops_;
+  bool is_training_;
+  double epsilon_;
+  double exp_avg_factor_;
+  bool is_null_input_;
+  cudnnTensorDescriptor_t x_desc_;
+  cudnnTensorDescriptor_t y_desc_;
+  cudnnTensorDescriptor_t z_desc_;
+  cudnnTensorDescriptor_t scale_bias_mean_var_desc_;
+
+  cudnnHandle_t handle_;
+  cudnnDataType_t cudnn_data_type_;
+  std::vector<size_t> input_shape_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_INSTANCE_NORM_GPU_KERNEL_H_

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/instance_norm_grad_gpu_kernel.cc

@@ -0,0 +1,44 @@
+/**
+ * Copyright 2021 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 "backend/kernel_compiler/gpu/nn/instance_norm_grad_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(InstanceNormGrad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)    // dy
+                        .AddInputAttr(kNumberTypeFloat32)    // x
+                        .AddInputAttr(kNumberTypeFloat32)    // scale
+                        .AddInputAttr(kNumberTypeFloat32)    // save_mean
+                        .AddInputAttr(kNumberTypeFloat32)    // save_variance
+                        .AddOutputAttr(kNumberTypeFloat32)   // dx
+                        .AddOutputAttr(kNumberTypeFloat32)   // dscale
+                        .AddOutputAttr(kNumberTypeFloat32),  // dbias
+                      InstanceNormGradGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(InstanceNormGrad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat16)    // dy
+                        .AddInputAttr(kNumberTypeFloat16)    // x
+                        .AddInputAttr(kNumberTypeFloat32)    // scale
+                        .AddInputAttr(kNumberTypeFloat32)    // save_mean
+                        .AddInputAttr(kNumberTypeFloat32)    // save_variance
+                        .AddOutputAttr(kNumberTypeFloat16)   // dx
+                        .AddOutputAttr(kNumberTypeFloat32)   // dscale
+                        .AddOutputAttr(kNumberTypeFloat32),  // dbias
+                      InstanceNormGradGpuKernel, half)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/instance_norm_grad_gpu_kernel.h

@@ -0,0 +1,238 @@
+/**
+ * Copyright 2021 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_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_INSTANCE_NORM_GRAD_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_INSTANCE_NORM_GRAD_GPU_KERNEL_H_
+
+#include <string>
+#include <vector>
+#include "utils/utils.h"
+
+#include "backend/kernel_compiler/gpu/gpu_kernel.h"
+#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
+#include "backend/kernel_compiler/gpu/kernel_constants.h"
+#include "backend/kernel_compiler/gpu/cuda_impl/instance_norm_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class InstanceNormGradGpuKernel : public GpuKernel {
+ public:
+  InstanceNormGradGpuKernel()
+      : x_size_(0),
+        para_size_(0),
+        workspace_size_(0),
+        mode_(CUDNN_BATCHNORM_SPATIAL),
+        bn_ops_(CUDNN_BATCHNORM_OPS_BN),
+        epsilon_(10e-5),
+        is_training_(true),
+        is_null_input_(false),
+        x_desc_(nullptr),
+        y_desc_(nullptr),
+        dy_desc_(nullptr),
+        dx_desc_(nullptr),
+        dz_desc_(nullptr),
+        scale_bias_diff_desc_(nullptr),
+        activation_desc_(nullptr),
+        handle_(nullptr),
+        cudnn_data_type_(CUDNN_DATA_FLOAT),
+        beta_data_diff_(0) {}
+  ~InstanceNormGradGpuKernel() override { DestroyResource(); }
+
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
+    VARIABLE_NOT_USED(workspace);
+    VARIABLE_NOT_USED(stream_ptr);
+    if (is_null_input_) {
+      return true;
+    }
+    auto dy = GetDeviceAddress<T>(inputs, 0);
+    auto x = GetDeviceAddress<T>(inputs, 1);
+    auto gamma = GetDeviceAddress<float>(inputs, 2);
+    auto save_mean = GetDeviceAddress<float>(inputs, 3);
+    auto save_variance = GetDeviceAddress<float>(inputs, 4);
+    void *beta = nullptr;
+    T *y = nullptr;
+
+    auto dx = GetDeviceAddress<T>(outputs, 0);
+    auto dgamma = GetDeviceAddress<float>(outputs, 1);
+    auto dbeta = GetDeviceAddress<float>(outputs, 2);
+    T *dz = nullptr;
+
+    float *ws_gamma = GetDeviceAddress<float>(workspace, 0);
+    void *workspace_addr = nullptr;
+    if (workspace_size_ != 0) {
+      workspace_addr = GetDeviceAddress<T>(workspace, 3);
+    }
+
+    size_t N = input_shape_[0];
+    size_t C = input_shape_[1];
+    CopyMemDevice2Device(N, C, gamma, nullptr, nullptr, nullptr, ws_gamma, nullptr, nullptr, nullptr,
+                         reinterpret_cast<cudaStream_t>(stream_ptr));
+    CHECK_CUDA_RET_WITH_EXCEPT(kernel_node_, cudaStreamSynchronize(reinterpret_cast<cudaStream_t>(stream_ptr)),
+                               "cudaStreamSynchronized failed");
+
+    const float alpha_data_diff = 1;
+    const float alpha_param_diff = 1;
+    const float beta_param_diff = 0;
+    float *reserve_addr = nullptr;
+    if (is_training_) {
+      CHECK_CUDNN_RET_WITH_EXCEPT(
+        kernel_node_,
+        cudnnBatchNormalizationBackwardEx(
+          handle_, mode_, bn_ops_, &alpha_data_diff, &beta_data_diff_, &alpha_param_diff, &beta_param_diff, x_desc_, x,
+          y_desc_, y, dy_desc_, dy, dz_desc_, dz, dx_desc_, dx, scale_bias_diff_desc_, ws_gamma, beta, dgamma, dbeta,
+          epsilon_, save_mean, save_variance, activation_desc_, workspace_addr, workspace_size_, reserve_addr, 0),
+        "Kernel launch failed");
+      ComputeMean(N, C, dgamma, dbeta, reinterpret_cast<cudaStream_t>(stream_ptr));
+    } else {
+      MS_LOG(EXCEPTION) << "The backward of InstanceNorm operator in evaluation mode is not implemented yet.";
+    }
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    kernel_node_ = kernel_node;
+    MS_EXCEPTION_IF_NULL(kernel_node);
+    std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
+    bn_ops_ = CUDNN_BATCHNORM_OPS_BN;
+
+    InitResource();
+    is_training_ = GetAttr<bool>(kernel_node, "is_training");
+    mode_ = is_training_ ? CUDNN_BATCHNORM_SPATIAL_PERSISTENT : CUDNN_BATCHNORM_SPATIAL;
+    epsilon_ = GetAttr<float>(kernel_node, "epsilon");
+
+    cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 5) {
+      MS_LOG(EXCEPTION) << "input tensor size is " << input_num << ", " << kernel_name << " should be 5";
+    }
+
+    input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
+    if (input_shape_.size() != 4) {
+      MS_LOG(EXCEPTION) << "tensor shape is " << input_shape_.size() << ", InstanceNormGradGpuKernel should be 4";
+    }
+    is_null_input_ = CHECK_NULL_INPUT(input_shape_);
+    if (is_null_input_) {
+      MS_LOG(WARNING) << "InstanceNormGradGpuKernel input is null";
+      InitSizeLists();
+      return true;
+    }
+    beta_data_diff_ = GetAttrWithDefault(kernel_node, "inplace_algo", std::string("cover")) == "cover" ? 0 : 1;
+    SetTensorDescriptor();
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitResource() override {
+    handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
+    CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&x_desc_), "Create x desc failed");
+    CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&dy_desc_), "Create dy desc failed");
+    CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&dx_desc_), "Create dx desc failed");
+    CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&scale_bias_diff_desc_),
+                                "Create para desc failed");
+  }
+
+  void InitSizeLists() override {
+    if (!is_null_input_) {
+      CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(x_desc_, &x_size_), "Get x size failed");
+      CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(scale_bias_diff_desc_, &para_size_),
+                                  "Get para size failed");
+      CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_,
+                                  cudnnGetBatchNormalizationBackwardExWorkspaceSize(
+                                    handle_, mode_, bn_ops_, x_desc_, y_desc_, dy_desc_, dz_desc_, dx_desc_,
+                                    scale_bias_diff_desc_, activation_desc_, &workspace_size_),
+                                  "cudnnGetBatchNormalizationBackwardExWorkspaceSize failed");
+    }
+
+    input_size_list_.push_back(x_size_);
+    input_size_list_.push_back(x_size_);
+    input_size_list_.push_back(input_shape_[1]);
+    input_size_list_.push_back(para_size_);
+    input_size_list_.push_back(para_size_);
+
+    output_size_list_.push_back(x_size_);
+    output_size_list_.push_back(para_size_);
+    output_size_list_.push_back(para_size_);
+
+    workspace_size_list_.push_back(para_size_);  // ws gamma
+    workspace_size_list_.push_back(workspace_size_);
+  }
+  void DestroyResource() noexcept override {
+    CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
+    CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(dy_desc_), "Destroy dy desc failed");
+    CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(dx_desc_), "Destroy dx desc failed");
+    CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(scale_bias_diff_desc_),
+                               "Destroy para desc failed");
+  }
+
+ private:
+  void SetTensorDescriptor() {
+    int batch, channel, height, width;
+    batch = 1;
+    channel = SizeToInt(input_shape_[0]) * SizeToInt(input_shape_[1]);
+    height = SizeToInt(input_shape_[2]);
+    width = SizeToInt(input_shape_[3]);
+    cudnnTensorFormat_t cudnn_format = CUDNN_TENSOR_NCHW;
+
+    CHECK_CUDNN_RET_WITH_EXCEPT(
+      kernel_node_, cudnnSetTensor4dDescriptor(x_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
+      "Set x desc failed");
+    CHECK_CUDNN_RET_WITH_EXCEPT(
+      kernel_node_, cudnnSetTensor4dDescriptor(dy_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
+      "Set dy desc failed");
+    CHECK_CUDNN_RET_WITH_EXCEPT(
+      kernel_node_, cudnnSetTensor4dDescriptor(dx_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
+      "Set dx desc failed");
+    CHECK_CUDNN_RET_WITH_EXCEPT(
+      kernel_node_,
+      cudnnSetTensor4dDescriptor(scale_bias_diff_desc_, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, channel, 1, 1),
+      "Set para desc failed");
+  }
+
+  size_t x_size_;
+  size_t para_size_;
+  size_t workspace_size_;
+  cudnnBatchNormMode_t mode_;
+  cudnnBatchNormOps_t bn_ops_;
+  double epsilon_;
+  bool is_training_;
+  bool is_null_input_;
+
+  cudnnTensorDescriptor_t x_desc_;
+  cudnnTensorDescriptor_t y_desc_;
+  cudnnTensorDescriptor_t dy_desc_;
+  cudnnTensorDescriptor_t dx_desc_;
+  cudnnTensorDescriptor_t dz_desc_;
+  cudnnTensorDescriptor_t scale_bias_diff_desc_;
+  cudnnActivationDescriptor_t activation_desc_;
+
+  cudnnHandle_t handle_;
+  cudnnDataType_t cudnn_data_type_;
+  float beta_data_diff_;
+  std::vector<size_t> input_shape_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_INSTANCE_NORM_GRAD_GPU_KERNEL_H_

mindspore/ccsrc/utils/utils.h

@@ -47,6 +47,7 @@ constexpr auto kBNGrad1OpName = "BNGrad1";
 constexpr auto kBNGrad2OpName = "BNGrad2";
 constexpr auto kBNGrad3OpName = "BNGrad3";
 constexpr auto kFusedBatchNormEx = "FusedBatchNormEx";
+constexpr auto kInstanceNorm = "InstanceNorm";
 constexpr auto kFusedBatchNormExWithActivation = "FusedBatchNormExWithActivation";
 constexpr auto kFusedBatchNormExWithAddAndActivation = "FusedBatchNormExWithAddAndActivation";
 constexpr auto kFusedBatchNormGradEx = "FusedBatchNormGradEx";

mindspore/nn/layer/normalization.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -26,7 +26,7 @@ from mindspore.communication import management
 from mindspore.ops import _selected_ops
 from ..cell import Cell
 
-__all__ = ['BatchNorm1d', 'BatchNorm2d', 'LayerNorm', 'GroupNorm', 'GlobalBatchNorm']
+__all__ = ['BatchNorm1d', 'BatchNorm2d', 'LayerNorm', 'GroupNorm', 'GlobalBatchNorm', 'InstanceNorm2d']
 
 
 class _BatchNorm(Cell):
@@ -705,6 +705,119 @@ class LayerNorm(Cell):
             self.normalized_shape, self.begin_norm_axis, self.begin_params_axis, self.gamma, self.beta)
 
 
+class InstanceNorm2d(Cell):
+    r"""
+    Instance normalization layer over a 4D input.
+
+    This layer applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with
+    additional channel dimension) as described in the paper `Instance Normalization: The Missing Ingredient for
+    Fast Stylization <https://arxiv.org/abs/1607.08022>`_. It rescales and recenters the feature using a mini-batch
+    of data and the learned parameters which can be described in the following formula.
+
+    .. math::
+        y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+
+    Note:
+        Note that the formula for updating the running_mean and running_var is
+        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times x_t + \text{momentum} \times \hat{x}`,
+        where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value.
+
+    Args:
+        num_features (int): `C` from an expected input of size (N, C, H, W).
+        eps (float): A value added to the denominator for numerical stability. Default: 1e-5.
+        momentum (float): A floating hyperparameter of the momentum for the
+            running_mean and running_var computation. Default: 0.1.
+        affine (bool): A bool value. When set to True, gamma and beta can be learned. Default: True.
+        gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'ones'.
+        beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'zeros'.
+        moving_mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving mean.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'zeros'.
+        moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'ones'.
+        use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false,
+            use the mean value and variance value of specified value. Default: True.
+
+    Inputs:
+        - **input** (Tensor) - Tensor of shape :math:`(N, C, H, W)`. Data type: float16 or float32.
+
+    Outputs:
+        Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C, H, W)`. Same type and
+        shape as the `input_x`.
+
+    Supported Platforms:
+        ``GPU``
+
+    Raise:
+        ValueError: If num_features is less than 1 or momentum not in (0, 1).
+
+    Examples:
+        >>> net = nn.InstanceNorm2d(3)
+        >>> np.random.seed(0)
+        >>> input = Tensor(np.random.randint(0, 255, [2, 3, 2, 2]), mindspore.float32)
+        >>> output = net(input)
+        >>> print(output.shape)
+        (2, 3, 2, 2)
+    """
+
+    @cell_attr_register
+    def __init__(self,
+                 num_features,
+                 eps=1e-5,
+                 momentum=0.1,
+                 affine=True,
+                 gamma_init='ones',
+                 beta_init='zeros',
+                 moving_mean_init='zeros',
+                 moving_var_init='ones',
+                 use_batch_statistics=True,
+                 input_dims='2d'):
+        super(InstanceNorm2d, self).__init__()
+        if num_features < 1:
+            raise ValueError("num_features must be at least 1")
+
+        if momentum < 0 or momentum > 1:
+            raise ValueError("momentum should be a number in range [0, 1], but got {}".format(momentum))
+        self.use_batch_statistics = use_batch_statistics
+        self.num_features = num_features
+        self.eps = eps
+        self.input_dims = input_dims
+        self.moving_mean = Parameter(initializer(
+            moving_mean_init, num_features), name="mean", requires_grad=False)
+        self.moving_variance = Parameter(initializer(
+            moving_var_init, num_features), name="variance", requires_grad=False)
+        self.gamma = Parameter(initializer(
+            gamma_init, num_features), name="gamma", requires_grad=affine)
+        self.beta = Parameter(initializer(
+            beta_init, num_features), name="beta", requires_grad=affine)
+
+        self.shape = P.Shape()
+        self.momentum = momentum
+        self.instance_bn = P.InstanceNorm(is_training=self.use_batch_statistics,
+                                          epsilon=self.eps,
+                                          momentum=self.momentum)
+
+    def _check_data_dim(self, x):
+        raise NotImplementedError
+
+    def construct(self, x):
+        _shape_check_bn(self.shape(x), self.input_dims)
+        return self.instance_bn(x,
+                                self.gamma,
+                                self.beta,
+                                self.moving_mean,
+                                self.moving_variance)[0]
+
+    def extend_repr(self):
+        return 'num_features={}, eps={}, momentum={}, gamma={}, beta={}, moving_mean={}, moving_variance={}'.format(
+            self.num_features, self.eps, self.momentum, self.gamma, self.beta, self.moving_mean, self.moving_variance)
+
+
 class GroupNorm(Cell):
     r"""
     Group Normalization over a mini-batch of inputs.

mindspore/ops/_grad/grad_nn_ops.py

@@ -688,6 +688,24 @@ def get_bprop_fused_batch_norm_ex(self):
     return bprop
 
 
+@bprop_getters.register(P.InstanceNorm)
+def get_bprop_instance_norm(self):
+    """Grad definition for `InstanceNorm` operation."""
+    is_training = self.is_training
+    input_grad = G.InstanceNormGrad(is_training, self.epsilon, self.momentum)
+
+    def bprop(x, gamma, beta, mean, variance, out, dout):
+        saved_mean = out[1]
+        saved_variance = out[2]
+        out = input_grad(dout[0], x, gamma, saved_mean, saved_variance)
+        dx = out[0]
+        dgamma = out[1]
+        dbeta = out[2]
+        return dx, dgamma, dbeta, zeros_like(mean), zeros_like(variance)
+
+    return bprop
+
+
 @bprop_getters.register(P.BatchNorm)
 def get_bprop_batch_norm(self):
     """Grad definition for `BatchNorm` operation."""

mindspore/ops/operations/__init__.py

@@ -63,8 +63,8 @@ from .random_ops import (RandomChoiceWithMask, StandardNormal, Gamma, Poisson, U
 from .nn_ops import (LSTM, SGD, Adam, FusedSparseAdam, FusedSparseLazyAdam, AdamNoUpdateParam, ApplyMomentum, BatchNorm,
                      BiasAdd, Conv2D,
                      DepthwiseConv2dNative,
-                     DropoutDoMask, Dropout,
-                     DropoutGenMask, Flatten, FusedBatchNorm, FusedBatchNormEx, BNTrainingReduce, BNTrainingUpdate,
+                     DropoutDoMask, Dropout, DropoutGenMask, Flatten,
+                     FusedBatchNorm, FusedBatchNormEx, InstanceNorm, BNTrainingReduce, BNTrainingUpdate,
                      Gelu, FastGelu, Elu,
                      GetNext, L2Normalize, LayerNorm, L2Loss, CTCLoss, CTCGreedyDecoder,
                      LogSoftmax,
@@ -130,6 +130,7 @@ __all__ = [
     'MaxPoolWithArgmax',
     'FusedBatchNorm',
     'FusedBatchNormEx',
+    'InstanceNorm',
     'BNTrainingReduce',
     'BNTrainingUpdate',
     'BatchNorm',

mindspore/ops/operations/_grad_ops.py

@@ -714,6 +714,21 @@ class FusedBatchNormGradEx(PrimitiveWithInfer):
         return (x_type, scale_type, scale_type)
 
 
+class InstanceNormGrad(PrimitiveWithInfer):
+    """Gradients of InstanceNorm operation."""
+
+    @prim_attr_register
+    def __init__(self, is_training=True, epsilon=0.0, momentum=0.1):
+        self.init_prim_io_names(inputs=['dy', 'x', 'gamma', 'save_mean', 'save_variance'],
+                                outputs=['dx', 'bn_gamma', 'bn_beta'])
+
+    def infer_shape(self, y_backprop_shape, x_shape, gamma_shape, save_mean_shape, save_variance_shape):
+        return (x_shape, gamma_shape, gamma_shape)
+
+    def infer_dtype(self, y_backprop_type, x_type, gamma_type, save_mean_type, save_variance_type):
+        return (x_type, gamma_type, gamma_type)
+
+
 class UniqueGrad(Primitive):
     """Gradients of Unique operation."""
 

mindspore/ops/operations/nn_ops.py

@@ -859,6 +859,119 @@ class FusedBatchNormEx(PrimitiveWithInfer):
         return (input_x, scale, scale, scale, scale, scale)
 
 
+class InstanceNorm(PrimitiveWithInfer):
+    r"""
+    Instance normalization over a 4D input.
+
+    This operator applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with
+    additional channel dimension) as described in the paper `Instance Normalization: The Missing Ingredient for
+    Fast Stylization <https://arxiv.org/abs/1607.08022>`_. It rescales and recenters the feature using a mini-batch
+    of data and the learned parameters which can be described in the following formula.
+
+    .. math::
+        y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta
+
+    where :math:`\gamma` is scale, :math:`\beta` is bias, :math:`\epsilon` is epsilon.
+
+    Args:
+        is_training (bool): Is training or inference. Default: True.
+        epsilon (float): A small value added for numerical stability. Default: 1e-5.
+        momentum (float): The hyper parameter to compute moving average for running_mean and running_var
+            (e.g. :math:`new\_running\_mean = momentum * running\_mean + (1 - momentum) * current\_mean`).
+            Momentum value must be [0, 1]. Default: 0.1.
+        data_format (str): The optional value for data format, is 'NCHW'. Default: "NCHW".
+
+    Inputs:
+        - **input_x** (Tensor) - The input of InstanceNorm, Tensor of shape :math:`(N, C)`,
+          data type: float16 or float32.
+        - **gamma** (Parameter) - scale, Tensor of shape :math:`(C,)`,
+          data type: float32.
+        - **beta** (Parameter) - bias, Tensor of shape :math:`(C,)`,
+          data type: float32.
+        - **mean** (Parameter) - mean value, Tensor of shape :math:`(C,)`, data type: float32.
+        - **variance** (Parameter) - variance value, Tensor of shape :math:`(C,)`, data type: float32.
+
+    Outputs:
+        Tuple of 3 Tensors, the normalized input, the updated parameters.
+
+        - **output_x** (Tensor) - The output of InstanceNorm, same type and shape as the `input_x`.
+        - **updated_moving_mean** (Tensor) - Updated mean value, Tensor of shape :math:`(NC,)`, data type: float32.
+        - **updated_moving_variance** (Tensor) - Updated variance value, Tensor of shape :math:`(NC,)`,
+          data type: float32.
+
+    Supported Platforms:
+        ``GPU``
+
+    Raise:
+        TypeError: If any validator check fails.
+
+    Examples:
+        >>> import mindspore
+        >>> import mindspore.nn as nn
+        >>> import numpy as np
+        >>> from mindspore import Parameter
+        >>> from mindspore import Tensor
+        >>> from mindspore.ops import operations as ops
+        >>> class InstanceNormNet(nn.Cell):
+        >>>     def __init__(self):
+        >>>         super(InstanceNormNet, self).__init__()
+        >>>         self.instance_norm = ops.InstanceNorm()
+        >>>         self.gamma = Parameter(Tensor(np.ones([64]), mindspore.float32), name="gamma")
+        >>>         self.beta = Parameter(Tensor(np.ones([64]), mindspore.float32), name="beta")
+        >>>         self.mean = Parameter(Tensor(np.ones([64]), mindspore.float32), name="mean")
+        >>>         self.variance = Parameter(Tensor(np.ones([64]), mindspore.float32), name="variance")
+        >>>
+        >>>     def construct(self, input_x):
+        >>>         out = self.instance_norm(input_x, self.gamma, self.beta, self.mean, self.variance)
+        >>>         return out
+        >>>
+        >>> input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32)
+        >>> net = InstanceNormNet()
+        >>> output = net(input_x)
+        >>> result = output[0].shape
+        >>> print(result)
+        (128, 64, 32, 64)
+    """
+    __mindspore_signature__ = (
+        sig.make_sig('input_x', dtype=sig.sig_dtype.T2),
+        sig.make_sig('gamma', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
+        sig.make_sig('beta', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
+        sig.make_sig('mean', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
+        sig.make_sig('variance', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
+    )
+
+    @prim_attr_register
+    def __init__(self, is_training=True, epsilon=1e-5, momentum=0.1):
+        self.init_prim_io_names(inputs=['x', 'gamma', 'beta', 'mean', 'variance'],
+                                outputs=['y', 'save_mean', 'save_variance'])
+        self.is_training = validator.check_bool(is_training, self.name)
+        self.epsilon = validator.check_float_range(epsilon, 0, 1, Rel.INC_RIGHT, 'epsilon', self.name)
+        self.momentum = validator.check_float_range(momentum, 0, 1, Rel.INC_BOTH, 'momentum', self.name)
+        self._update_parameter = True
+
+    def infer_shape(self, input_x, gamma, beta, mean, variance):
+        input_shape_norm = input_x
+        validator.check_equal_int(len(gamma), 1, "gamma rank", self.name)
+        validator.check("gamma shape", gamma, "beta shape", beta, Rel.EQ, self.name)
+        validator.check("gamma shape[0]", gamma[0], "input channel", input_shape_norm[1], Rel.EQ, self.name)
+        validator.check_equal_int(len(mean), 1, "mean rank", self.name)
+
+        validator.check("mean shape", mean, "variance shape", variance, Rel.EQ, self.name)
+        validator.check("mean shape", mean, "gamma shape", gamma, Rel.EQ, self.name)
+        save_mean_shape = gamma
+        save_mean_shape[0] = save_mean_shape[0] * input_shape_norm[0]
+        return (input_x, save_mean_shape, save_mean_shape)
+
+    def infer_dtype(self, input_x, gamma, beta, mean, variance):
+        validator.check_tensor_dtype_valid("input_x", input_x, [mstype.float16, mstype.float32], self.name)
+        args = {"gamma": gamma, "beta": beta}
+        validator.check_tensors_dtypes_same_and_valid(args, [mstype.float32], self.name)
+        args_moving = {"mean": mean, "variance": variance}
+        valid_dtypes = [mstype.tensor_type(mstype.float32)]
+        validator.check_types_same_and_valid(args_moving, valid_dtypes, self.name)
+        return (input_x, gamma, gamma)
+
+
 class BNTrainingReduce(PrimitiveWithInfer):
     """
     For the BatchNorm operation this operator update the moving averages for training and is used in conjunction with

tests/st/ops/gpu/test_instancenorm2d.py

@@ -0,0 +1,62 @@
+# Copyright 2021 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.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.common.api import ms_function
+from mindspore.ops import functional as F
+from mindspore.ops.composite import GradOperation
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+class Grad(nn.Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = GradOperation(get_all=True, sens_param=True)
+        self.network = network
+
+    @ms_function
+    def construct(self, input_x, grad):
+        return self.grad(self.network)(input_x, grad)
+
+class Net(nn.Cell):
+    def __init__(self, n):
+        super(Net, self).__init__()
+        self.ops = nn.BatchNorm2d(n, use_batch_statistics=True, gamma_init=0.5, beta_init=0.5)
+
+    def construct(self, x):
+        shape = F.shape(x)
+        return F.reshape(self.ops(F.reshape(x, (1, -1, shape[2], shape[3]))), shape)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_InstanceNorm2d_fp32():
+    x_np = np.random.randn(3, 3, 2, 2).astype(np.float32)
+    bn_instance_comp = Net(3 * 3)
+    bn_instance_op = nn.InstanceNorm2d(3, use_batch_statistics=True, gamma_init=0.5, beta_init=0.5)
+    comp_out = bn_instance_comp(Tensor(x_np))
+    op_out = bn_instance_op(Tensor(x_np))
+    assert np.allclose(comp_out.asnumpy(), op_out.asnumpy())
+
+    sens = np.random.randn(3, 3, 2, 2).astype(np.float32)
+    bn_comp_backward_net = Grad(bn_instance_comp)
+    bn_op_backward_net = Grad(bn_instance_op)
+    output1 = bn_comp_backward_net(Tensor(x_np), Tensor(sens))
+    output2 = bn_op_backward_net(Tensor(x_np), Tensor(sens))
+    assert np.allclose(output1[0].asnumpy(), output2[0].asnumpy())