text: """
  Copyright 2020 The OneFlow Authors. All rights reserved.
  
  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 unittest
  from collections import OrderedDict
  import tempfile
  import os
  import numpy as np
  from oneflow.test_utils.test_util import GenArgDict
  from optimizer_test_util import clip_grad_norm_np
  
  import oneflow as flow
  from oneflow.nn.parameter import Parameter
  
  
  def compare_with_numpy_adam(
      test_case,
      weight_decay,
      scale,
      learning_rate,
      train_iters,
      do_bias_correction,
      beta1,
      beta2,
  ):
  
      num_rows = 500
      embedding_size = 128
      model_shape = (num_rows, embedding_size)
      line_size = embedding_size * 3
  
      num_valid_seq = np.random.randint(1, num_rows, (train_iters))
      skip_if_seq = [np.random.randint(2) for i in range(train_iters)]
  
      random_grad_seq = []
      for _ in range(train_iters):
          random_grad_seq.append(np.random.uniform(size=model_shape).astype(np.float32))
  
      init_value = np.random.uniform(size=(num_rows, line_size)).astype(np.float32)
  
      down_scale_by = 10
      epsilon = 1e-5
  
      def adam_by_oneflow():
          unique_embeddings_tensor = flow.tensor(init_value, requires_grad=False).to(
              "cuda"
          )
          lr_tensor = flow.tensor(
              np.array(learning_rate).reshape(1,).astype(np.float32)
          ).to("cuda")
          down_scale_by_tensor = flow.tensor(
              np.array(down_scale_by).astype(np.float32)
          ).to("cuda")
  
          def train_one_iter(
              num_valid,
              unique_embeddings,
              embedding_grad,
              skip_if,
              bias_correction1,
              bias_correction2,
          ):
              return flow._C.one_embedding_adam_update(
                  num_valid,
                  unique_embeddings,
                  embedding_grad,
                  lr_tensor,
                  down_scale_by_tensor,
                  skip_if,
                  bias_correction1,
                  bias_correction2,
                  scale,
                  weight_decay,
                  beta1,
                  beta2,
                  epsilon,
                  do_bias_correction,
              )
  
          for i in range(1, train_iters):
              num_valid_tensor = flow.tensor(
                  np.array(num_valid_seq[i]).reshape(1,).astype(np.int32)
              ).to("cuda")
              grad_tensor = flow.tensor(random_grad_seq[i]).to("cuda")
              skip_if_tensor = flow.tensor(
                  np.array(skip_if_seq[i]).reshape(1,).astype(np.int64)
              ).to("cuda")
              if do_bias_correction:
                  bias_correction1 = 1.0 - np.power(beta1, i)
                  bias_correction2 = 1.0 - np.power(beta2, i)
                  bias_correction1_tensor = flow.tensor(
                      np.array(bias_correction1).reshape(1,).astype(np.float32)
                  ).to("cuda")
                  bias_correction2_tensor = flow.tensor(
                      np.array(bias_correction2).reshape(1,).astype(np.float32)
                  ).to("cuda")
              else:
                  bias_correction1_tensor = None
                  bias_correction2_tensor = None
              updated_tensor = train_one_iter(
                  num_valid_tensor,
                  unique_embeddings_tensor,
                  grad_tensor,
                  skip_if_tensor,
                  bias_correction1_tensor,
                  bias_correction2_tensor,
              )
              unique_embeddings_tensor[0 : num_valid_seq[i]] = updated_tensor[
                  0 : num_valid_seq[i]
              ]
          return unique_embeddings_tensor
  
      def adam_by_numpy():
          x = init_value[:, 0:embedding_size]
          m = init_value[:, embedding_size : 2 * embedding_size]
          v = init_value[:, 2 * embedding_size : 3 * embedding_size]
  
          def np_train_one_iter(step, num_valid, grad, model, state_m, state_v):
              grad[0:num_valid] = grad[0:num_valid] * (scale / down_scale_by)
  
              bias_correction1 = 1.0
              bias_correction2 = 1.0
  
              if do_bias_correction:
                  bias_correction1 = 1.0 - np.power(beta1, step)
                  bias_correction2 = 1.0 - np.power(beta2, step)
  
              state_m[0:num_valid] = (
                  beta1 * state_m[0:num_valid] + (1 - beta1) * grad[0:num_valid]
              )
              state_v[0:num_valid] = (
                  beta2 * state_v[0:num_valid]
                  + (1 - beta2) * grad[0:num_valid] * grad[0:num_valid]
              )
              denom = np.sqrt(state_v[0:num_valid]) / np.sqrt(bias_correction2) + epsilon
  
              model[0:num_valid] = (
                  model[0:num_valid]
                  - ((learning_rate / bias_correction1) * state_m[0:num_valid] / denom)
                  - learning_rate * weight_decay * model[0:num_valid]
              )
              return (model, state_m, state_v)
  
          for i in range(1, train_iters):  # if step = 0, bias_correction2 is 0
              if skip_if_seq[i] > 0:
                  pass
              else:
                  (x, m, v) = np_train_one_iter(
                      i, int(num_valid_seq[i]), random_grad_seq[i], x, m, v
                  )
          return x, m, v
  
      oneflow_res = adam_by_oneflow().numpy()
      of_model = oneflow_res[:, 0:embedding_size]
      of_m = oneflow_res[:, embedding_size : 2 * embedding_size]
      of_v = oneflow_res[:, 2 * embedding_size : 3 * embedding_size]
      np_model, np_m, np_v = adam_by_numpy()
      test_case.assertTrue(
          np.allclose(of_model.flatten(), np_model.flatten(), rtol=0.001, atol=0.001)
      )
      test_case.assertTrue(
          np.allclose(of_m.flatten(), np_m.flatten(), rtol=0.001, atol=0.001)
      )
      test_case.assertTrue(
          np.allclose(of_v.flatten(), np_v.flatten(), rtol=0.001, atol=0.001)
      )
  
  
  @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases")
  @flow.unittest.skip_unless_1n1d()
  class TestOptimizers(flow.unittest.TestCase):
      def test_one_embedding_adam(test_case):
          arg_dict = OrderedDict()
          arg_dict["weight_decay"] = [0, 0.1]
          arg_dict["scale"] = [1, 0.1]
          arg_dict["learning_rate"] = [1, 1.5]
          arg_dict["train_iters"] = [10]
          arg_dict["do_bias_correction"] = [True, False]
          arg_dict["beta1"] = [0.9, 0.8]
          arg_dict["beta2"] = [0.9, 0.8]
  
          for arg in GenArgDict(arg_dict):
              compare_with_numpy_adam(test_case, **arg)
  
  
  if __name__ == "__main__":
      unittest.main()
  