text: # Copyright 2019-2021 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.
  
  """run function: splited bn"""
  import time
  
  import numpy as np
  from tests.common.tensorio import compare_tensor
  from akg.utils import kernel_exec as utils
  from akg.ops.nn.ascend import FusedBn1, FusedBn2, FusedBn3
  from tests.common.base import get_rtol_atol
  from tests.common.gen_random import random_gaussian
  
  def bn_benchmark(data, gamma, beta, running_mean, running_var,
                   momentum, eps, part_num: int = 0):
      """Benchmark function for bn split."""
      mean = np.mean(data.astype(np.float64), axis=(0, 2, 3),
                     keepdims=True).astype(np.float32)
      if part_num == 1:
          var_part = np.mean(np.power(data, 2).astype(np.float64),
                             axis=(0, 2, 3), keepdims=True).astype(np.float32)
          return (mean, var_part)
  
      var = np.var(data.astype(np.float64), axis=(0, 2, 3),
                   keepdims=True).astype(np.float32)
      mean_new = momentum * running_mean + (1 - momentum) * mean
      var_new = momentum * running_var + (1 - momentum) * var
  
      if part_num == 2:
          return (var, mean_new, var_new)
  
      rsd = (1.0 / np.sqrt(var + eps)).astype("float32")
      hat_gamma = gamma * rsd
      hat_beta = beta - gamma * mean * rsd
  
      data_cast = data.astype("float32")
  
      res = (hat_gamma * data_cast + hat_beta).astype(data.dtype)
      if part_num == 3:
          return (res,)
  
      if part_num == 0:
          # Whole BN outputs
          return (res, mean_new, var_new, mean, var)
  
      return None
  
  def get_compile_param(shape, dtype, part_num: int = 1):
      """get parameters for compiling module"""
      assert 0 <= part_num <= 3, \
          "parameter part_num({}) is invalid".format(part_num)
  
      mid_shape = (1, shape[1], 1, 1, shape[4])
      mid_dtype = "float32"
  
      if part_num == 1:
          in_shapes = [shape]
          in_dtypes = [dtype]
      elif part_num == 2:
          in_shapes = [mid_shape] * 4
          in_dtypes = [mid_dtype] * 4
      elif part_num == 3:
          in_shapes = [shape] + [mid_shape] * 4
          in_dtypes = [dtype] + [mid_dtype] * 4
      else:
          in_shapes = []
          in_dtypes = []
  
      return in_shapes, in_dtypes
  
  def malloc_out_buffer(expects, full_value=0):
      """malloc buffer by expects for launch"""
      return tuple([np.full(e.shape, full_value, e.dtype) for e in expects])
  
  def gen_data(shape, dtype, momentum, eps, part_num=0):
      """Generate datas.
  
      Generate input datas, calculate expect results,
      and generate output_buffers for splited fused batch norm.
  
      Args:
          shape: Shape of data that will be normalized.
          dtype: Data's type.
          momentum: Momentum for moving average.
          eps: A small value for avoiding divide zero.
  
      Returns:
          inputs: A tuple contain all generated input data.
          output_buffers: A tuple contain all generated output buffer.
          expects: A tuple contain expect results.
      """
  
      mid_shape = (1, shape[1], 1, 1, shape[4])
      mid_dtype = "float32"
  
      seed_tmp = int(time.time())
      data = random_gaussian(shape, miu=1, sigma=0.3,
                             seed=seed_tmp).astype(dtype)
  
      inputs1 = (data,)
      expects1 = bn_benchmark(data, None, None, None, None, momentum, eps, 1)
      out_buffer1 = malloc_out_buffer(expects1)
  
      if part_num == 1:
          return inputs1, out_buffer1, expects1
  
      running_mean = random_gaussian(mid_shape, miu=1, sigma=0.3,
                                           seed=seed_tmp + 3).astype(mid_dtype)
      running_var = abs(random_gaussian(mid_shape, miu=1, sigma=0.3,
                                              seed=seed_tmp + 4)).astype(mid_dtype)
  
      inputs2 = (*expects1, running_mean, running_var)
      expects2 = bn_benchmark(data, None, None, running_mean, running_var,
                              momentum, eps, 2)
      out_buffer2 = malloc_out_buffer(expects2)
      if part_num == 2:
          return inputs2, out_buffer2, expects2
  
      gamma = random_gaussian(mid_shape, miu=1, sigma=0.3,
                              seed=seed_tmp + 1).astype(mid_dtype)
      beta = random_gaussian(mid_shape, miu=1, sigma=0.3,
                             seed=seed_tmp + 2).astype(mid_dtype)
  
      inputs3 = (data, expects1[0], expects2[0], gamma, beta)
      expects3 = bn_benchmark(data, gamma, beta, running_mean, running_var,
                              momentum, eps, 3)
      out_buffer3 = malloc_out_buffer(expects3)
  
      if part_num == 3:
          return inputs3, out_buffer3, expects3
  
      # Whole BN inputs
      inputs = [inputs1, inputs2, inputs3]
      inputs.append((data, gamma, beta, running_mean, running_var))
      out_buffers = [out_buffer1, out_buffer2, out_buffer3]
  
      expects = (expects3[0], expects2[1], expects2[2], expects1[0], expects2[0])
      print("INFO data seed: ", seed_tmp)
  
      return inputs, out_buffers, expects
  
  def bn_1_run(shape, dtype, momentum, eps, kernel_name, attrs):
      """Test run function for first part of splited bn"""
      in_shapes, in_dtypes = get_compile_param(shape, dtype, 1)
  
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
  
          mod = utils.op_build_test(FusedBn1,
                                    in_shapes, in_dtypes,
                                    kernel_name=kernel_name,
                                    attrs=attrs, tuning=t)
          if t:
              inputs, output_buffers, expects = gen_data(shape, dtype,
                                                         momentum, eps, 1)
              output_places = list(range(-len(output_buffers), 0))
              return mod, expects, {
                  "args": (*inputs, *output_buffers),
                  'outputs': output_places,
                  'tuning': False}
          return mod
  
      mod_1 = utils.op_build_test(FusedBn1,
                                  in_shapes, in_dtypes,
                                  kernel_name="fusedbn1_"+kernel_name,
                                  attrs=attrs)
  
      inputs, output_buffers, expects = gen_data(shape, dtype, momentum, eps, 1)
      output_places1 = list(range(-len(output_buffers), 0))
      res_1 = utils.mod_launch(mod_1, [*inputs, *output_buffers],
                               outputs=output_places1, expect=expects)
  
      rtol, atol = get_rtol_atol("bn_split", dtype)
      cmp_res = list(map(lambda x, y:
                         compare_tensor(x, y, rtol=rtol, atol=atol),
                         res_1, expects))
  
      return inputs, res_1, expects, all(cmp_res)
  
  def bn_2_run(shape, dtype, momentum, eps, kernel_name, attrs):
      """Test run function for second part of splited bn"""
      in_shapes, in_dtypes = get_compile_param(shape, dtype, 2)
  
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(FusedBn2,
                                    in_shapes, in_dtypes,
                                    op_attrs=[momentum],
                                    kernel_name=kernel_name,
                                    attrs=attrs, tuning=t)
          if t:
              inputs, output_buffers, expects = gen_data(shape, dtype, momentum, eps, 2)
              inplace_binds = ((2, 1), (3, 2))
              output_places2 = list(range(-len(output_buffers), 0))
              if inplace_binds is not None:
                  for bind in inplace_binds:
                      output_places2[bind[1]] = bind[0]
              return mod, expects, {
                  "args": (*inputs, *output_buffers),
                  'outputs': output_places2,
                  'tuning': False}
          return mod
  
      mod_2 = utils.op_build_test(FusedBn2,
                                  in_shapes, in_dtypes,
                                  op_attrs=[momentum],
                                  kernel_name="fusedbn2_"+kernel_name,
                                  attrs=attrs)
  
      inputs, output_buffers, expects = gen_data(shape, dtype, momentum, eps, 2)
      inplace_binds = ((2, 1), (3, 2))
      output_places2 = list(range(-len(output_buffers), 0))
      if inplace_binds is not None:
          for bind in inplace_binds:
              output_places2[bind[1]] = bind[0]
      res_2 = utils.mod_launch(mod_2, [*inputs, *output_buffers],
                               outputs=output_places2, expect=expects)
  
      rtol, atol = get_rtol_atol("bn_split", dtype)
      cmp_res = list(map(lambda x, y:
                         compare_tensor(x, y, rtol=rtol, atol=atol),
                         res_2, expects))
      return inputs, res_2, expects, all(cmp_res)
  
  def bn_3_run(shape, dtype, momentum, eps, kernel_name, attrs):
      """Test run function for third part of splited bn"""
      in_shapes, in_dtypes = get_compile_param(shape, dtype, 3)
  
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(FusedBn3,
                                    in_shapes, in_dtypes,
                                    op_attrs=[eps],
                                    kernel_name=kernel_name,
                                    attrs=attrs, tuning=t)
          if t:
              inputs, output_buffers, expects = gen_data(shape, dtype,
                                                         momentum, eps, 3)
              output_places3 = list(range(-len(output_buffers), 0))
              return mod, expects[0], (*inputs, *output_buffers)
  
          return mod
  
      mod_3 = utils.op_build_test(FusedBn3,
                                  in_shapes, in_dtypes,
                                  op_attrs=[eps],
                                  kernel_name="fusedbn3_"+kernel_name,
                                  attrs=attrs)
  
      inputs, output_buffers, expects = gen_data(shape, dtype, momentum, eps, 3)
      output_places3 = list(range(-len(output_buffers), 0))
      res_3 = utils.mod_launch(mod_3, [*inputs, *output_buffers],
                               outputs=output_places3, expect=expects)
  
      if not isinstance(res_3, tuple):
          res_3 = (res_3,)
  
      rtol, atol = get_rtol_atol("bn_split", dtype)
      cmp_res = list(map(lambda x, y:
                         compare_tensor(x, y, rtol=rtol, atol=atol),
                         res_3, expects))
      return inputs, res_3, expects, all(cmp_res)
  
  def bn_split_run(shape, dtype, momentum, eps, kernel_name, attrs):
      """Test run function for whole splited bn"""
      in_shapes1, in_dtypes1 = get_compile_param(shape, dtype, 1)
      mod_1 = utils.op_build_test(FusedBn1,
                                  in_shapes1, in_dtypes1,
                                  kernel_name="fused_bn1_"+kernel_name,
                                  attrs=attrs.copy())
  
  
      in_shapes2, in_dtypes2 = get_compile_param(shape, dtype, 2)
      mod_2 = utils.op_build_test(FusedBn2,
                                  in_shapes2, in_dtypes2,
                                  op_attrs=[momentum],
                                  kernel_name="fused_bn2_"+kernel_name,
                                  attrs=attrs.copy())
  
      in_shapes3, in_dtypes3 = get_compile_param(shape, dtype, 3)
      mod_3 = utils.op_build_test(FusedBn3,
                                  in_shapes3, in_dtypes3,
                                  op_attrs=[eps],
                                  kernel_name="fused_bn3_"+kernel_name,
                                  attrs=attrs.copy())
  
      inputs, output_buffers, expects = gen_data(shape, dtype, momentum, eps, 0)
      output_places1 = list(range(-len(output_buffers[0]), 0))
      res_1_tmp = utils.mod_launch(mod_1, [inputs[-1][0], *output_buffers[0]],
                                   outputs=output_places1, expect=expects)
  
      inplace_binds = ((2, 1), (3, 2))
      output_places2 = list(range(-len(output_buffers[1]), 0))
      if inplace_binds is not None:
          for bind in inplace_binds:
              output_places2[bind[1]] = bind[0]
      res_2_tmp = utils.mod_launch(mod_2,
                                   [res_1_tmp[0], res_1_tmp[1],
                                    inputs[-1][3], inputs[-1][4],
                                    *output_buffers[1]],
                                   outputs=output_places2)
  
      output_places3 = list(range(-len(output_buffers[2]), 0))
      res_3_tmp = utils.mod_launch(mod_3,
                                   [inputs[-1][0], res_1_tmp[0], res_2_tmp[0],
                                    inputs[-1][1], inputs[-1][2],
                                    *output_buffers[2]],
                                   outputs=output_places3, expect=expects)
  
      results = (res_3_tmp, res_2_tmp[1], res_2_tmp[2],
                 res_1_tmp[0], res_2_tmp[0])
  
      rtol, atol = get_rtol_atol("bn_split", dtype)
      cmp_res = list(map(lambda x, y:
                         compare_tensor(x, y, rtol=rtol, atol=atol),
                         results, expects))
  
      return inputs[-1], results, expects, all(cmp_res)
  