text: # 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.
  # ============================================================================
  
  """_BatchNormFold2Grad op"""
  import te.lang.cce
  from te import tvm
  from te.platform.fusion_manager import fusion_manager
  from topi import generic
  from topi.cce import util
  from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
  
  SHAPE_SIZE_LIMIT = 2147483648
  
  batchnorm_fold2_grad_op_info = TBERegOp("BatchNormFold2GradD") \
      .fusion_type("OPAQUE") \
      .async_flag(False) \
      .binfile_name("batchnorm_fold2_grad.so") \
      .compute_cost(10) \
      .kernel_name("batchnorm_fold2_grad") \
      .partial_flag(True) \
      .input(0, "dout", None, "required", None) \
      .input(1, "dout_reduce", None, "required", None) \
      .input(2, "dout_x_reduce", None, "required", None) \
      .input(3, "gamma", None, "required", None) \
      .input(4, "batch_std", None, "required", None) \
      .input(5, "batch_mean", None, "required", None) \
      .input(6, "running_std", None, "required", None) \
      .output(0, "d_batch_std", True, "required", "all") \
      .output(1, "d_batch_mean", True, "required", "all") \
      .output(2, "d_gamma", True, "required", "all") \
      .output(3, "dx", True, "required", "all") \
      .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
                    DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
                    DataType.F32_5HD) \
      .get_op_info()
  
  
  @op_info_register(batchnorm_fold2_grad_op_info)
  def _batchnorm_fold2_grad_tbe():
      """_BatchNormFold2Grad TBE register"""
      return
  
  
  @fusion_manager.register("batchnorm_fold2_grad")
  def batchnorm_fold2_grad_compute(dout, dout_reduce, dout_x_reduce, gamma, batch_std, batch_mean, running_std,
                                   kernel_name="batchnorm_fold2_grad"):
      """_BatchNormFold2Grad"""
      shape_x = te.lang.cce.util.shape_to_list(dout.shape)
  
      d_batch_std_1 = te.lang.cce.vmul(dout_reduce, batch_mean)
      d_batch_std_1 = te.lang.cce.vmul(d_batch_std_1, gamma)
      d_batch_std_2 = te.lang.cce.vmul(dout_x_reduce, running_std)
      d_batch_std = te.lang.cce.vsub(d_batch_std_1, d_batch_std_2)
      d_batch_std = te.lang.cce.vdiv(d_batch_std, batch_std)
      d_batch_std = te.lang.cce.vdiv(d_batch_std, batch_std)
  
      d_batch_mean = te.lang.cce.vmul(dout_reduce, gamma)
      d_batch_mean = te.lang.cce.vdiv(d_batch_mean, batch_std)
      d_batch_mean = te.lang.cce.vmuls(d_batch_mean, -1.)
  
      d_gamma = te.lang.cce.vmul(dout_reduce, batch_mean)
      d_gamma = te.lang.cce.vdiv(d_gamma, batch_std)
      d_gamma = te.lang.cce.vmuls(d_gamma, -1.)
  
      dx = te.lang.cce.vdiv(running_std, batch_std)
      dx = te.lang.cce.broadcast(dx, shape_x)
      dx = te.lang.cce.vmul(dx, dout)
      return [d_batch_std, d_batch_mean, d_gamma, dx]
  
  
  @util.check_input_type(dict, dict, dict, dict, dict, dict, dict, dict, dict, dict, dict, str)
  def batchnorm_fold2_grad(dout, dout_reduce, dout_x_reduce, gamma, batch_std, batch_mean, running_std, d_batch_std,
                           d_batch_mean, d_gamma, dx, kernel_name="batchnorm_fold2_grad"):
      """_BatchNormFold2Grad op """
      shape = dout.get("shape")
      util.check_kernel_name(kernel_name)
      util.check_shape_rule(shape)
      util.check_shape_size(shape, SHAPE_SIZE_LIMIT)
      check_list = ["float16", "float32"]
      inp_dtype = dout.get("dtype").lower()
      if not inp_dtype in check_list:
          raise RuntimeError("Dtype of input only support float16, float32")
      data_format = dout.get("format")
      ori_format = dout.get("ori_format")
      if data_format.upper() not in ("NC1HWC0", "NCHW"):
          raise RuntimeError("Un supported data format {}".format(data_format))
      if data_format.upper() == "NCHW" and ori_format != "NCHW":
          raise RuntimeError("data_format(NCHW) must same as ori_format")
      shape_c = gamma.get("shape")
      if gamma.get("format").upper() == "NCHW":
          shape_c = 1, gamma.get("shape")[0], 1, 1
  
      dout_t = tvm.placeholder(shape, name="dout", dtype=inp_dtype)
      dout_reduce_t = tvm.placeholder(shape_c, name="dout_reduce", dtype=inp_dtype)
      dout_x_reduce_t = tvm.placeholder(shape_c, name="dout_x_reduce", dtype=inp_dtype)
      gamma_t = tvm.placeholder(shape_c, name="gamma", dtype=inp_dtype)
      batch_std_t = tvm.placeholder(shape_c, name="batch_std", dtype=inp_dtype)
      batch_mean_t = tvm.placeholder(shape_c, name="batch_mean", dtype=inp_dtype)
      running_std_t = tvm.placeholder(shape_c, name="running_std", dtype=inp_dtype)
  
      res_list = batchnorm_fold2_grad_compute(dout_t, dout_reduce_t, dout_x_reduce_t, gamma_t, batch_std_t, batch_mean_t,
                                              running_std_t, kernel_name)
  
      with tvm.target.cce():
          sch = generic.auto_schedule(res_list)
  
      tensor_list = [dout_t, dout_reduce_t, dout_x_reduce_t, gamma_t, batch_std_t, batch_mean_t, running_std_t] + list(
          res_list)
      config = {"print_ir": False,
                "name": kernel_name,
                "tensor_list": tensor_list}
  
      te.lang.cce.cce_build_code(sch, config)
  