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 tempfile
  import unittest
  from collections import OrderedDict
  
  import numpy as np
  from test_util import GenArgList
  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,
      device,
      x_shape,
      learning_rate,
      train_iters,
      betas,
      weight_decay,
      eps,
      do_bias_correction,
      amsgrad,
      reload_state_step,
      save_load_by_pickle,
  ):
      random_grad_seq = []
      for _ in range(train_iters):
          random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32))
      init_value = np.random.uniform(size=x_shape).astype(np.float32)
  
      def train_by_oneflow():
          x = Parameter(flow.Tensor(init_value, device=flow.device(device)))
          adam = flow.optim.Adam(
              [
                  {
                      "params": [x],
                      "lr": learning_rate,
                      "betas": betas,
                      "eps": eps,
                      "weight_decay": weight_decay,
                  }
              ],
              do_bias_correction=do_bias_correction,
              amsgrad=amsgrad,
          )
  
          def train_one_iter(grad):
              grad_tensor = flow.tensor(
                  grad,
                  dtype=flow.float32,
                  requires_grad=False,
                  device=flow.device(device),
              )
              loss = flow.sum(x * grad_tensor)
              loss.backward()
              adam.step()
              adam.zero_grad()
  
          for i in range(train_iters):
              train_one_iter(random_grad_seq[i])
              if i == reload_state_step:
                  state_dict = adam.state_dict()
                  adam = flow.optim.Adam(
                      [{"params": [x],}], do_bias_correction=do_bias_correction,
                  )
                  if save_load_by_pickle:
                      with tempfile.TemporaryDirectory() as save_dir:
                          flow.save(state_dict, save_dir)
                          state_dict = flow.load(save_dir)
                  adam.load_state_dict(state_dict)
          return x
  
      def train_by_numpy():
          x = init_value
          vt = np.zeros_like(x)
          st = np.zeros_like(x)
          max_st = np.zeros_like(x)
          beta1 = betas[0]
          beta2 = betas[1]
  
          def np_train_one_iter(step, grad):
              grad = grad + weight_decay * x
  
              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)
  
              v = beta1 * vt + (1 - beta1) * grad
              s = beta2 * st + (1 - beta2) * grad * grad
              max_s = np.zeros_like(x)
  
              if amsgrad:
                  max_s = np.maximum(s, max_st)
                  denom = np.sqrt(max_s) / np.sqrt(bias_correction2) + eps
              else:
                  denom = np.sqrt(s) / np.sqrt(bias_correction2) + eps
  
              param = x - ((learning_rate / bias_correction1) * v / denom)
              return (param, v, s, max_s)
  
          for i in range(1, train_iters + 1):
              (x, vt, st, max_st) = np_train_one_iter(i, random_grad_seq[i - 1])
          return x
  
      oneflow_res = train_by_oneflow().numpy()
      numpy_res = train_by_numpy()
      test_case.assertTrue(
          np.allclose(oneflow_res.flatten(), numpy_res.flatten(), rtol=1e-3, atol=1e-3)
      )
  
  
  def compare_with_numpy_adam_clip_grad(
      test_case,
      device,
      x_shape,
      learning_rate,
      train_iters,
      betas,
      weight_decay,
      eps,
      do_bias_correction,
      amsgrad,
      clip_grad_max_norm,
      clip_grad_norm_type,
      reload_state_step,
      save_load_by_pickle,
  ):
      random_grad_seq = []
      for _ in range(train_iters):
          random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32))
      init_value = np.random.uniform(size=x_shape).astype(np.float32)
  
      def train_by_oneflow():
          x = Parameter(flow.Tensor(init_value, device=flow.device(device)))
          adam = flow.optim.Adam(
              [
                  {
                      "params": [x],
                      "lr": learning_rate,
                      "betas": betas,
                      "eps": eps,
                      "weight_decay": weight_decay,
                      "clip_grad_max_norm": clip_grad_max_norm,
                      "clip_grad_norm_type": clip_grad_norm_type,
                  }
              ],
              do_bias_correction=do_bias_correction,
              amsgrad=amsgrad,
          )
  
          def train_one_iter(grad):
              grad_tensor = flow.tensor(
                  grad,
                  dtype=flow.float32,
                  requires_grad=False,
                  device=flow.device(device),
              )
              loss = flow.sum(x * grad_tensor)
              loss.backward()
              adam.clip_grad()
              adam.step()
              adam.zero_grad()
  
          for i in range(train_iters):
              train_one_iter(random_grad_seq[i])
              if i == reload_state_step:
                  state_dict = adam.state_dict()
                  adam = flow.optim.Adam(
                      [{"params": [x],}], do_bias_correction=do_bias_correction,
                  )
                  if save_load_by_pickle:
                      with tempfile.TemporaryDirectory() as save_dir:
                          flow.save(state_dict, save_dir)
                          state_dict = flow.load(save_dir)
                  adam.load_state_dict(state_dict)
          return x
  
      def train_by_numpy():
          x = init_value
          vt = np.zeros_like(x)
          st = np.zeros_like(x)
          max_st = np.zeros_like(x)
          beta1 = betas[0]
          beta2 = betas[1]
  
          def train_one_iter(step, grad):
              total_norm, grad = clip_grad_norm_np(
                  grad, clip_grad_max_norm, clip_grad_norm_type
              )
              grad = grad + weight_decay * x
  
              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)
  
              v = beta1 * vt + (1 - beta1) * grad
              s = beta2 * st + (1 - beta2) * grad * grad
              max_s = np.zeros_like(x)
  
              if amsgrad:
                  max_s = np.maximum(s, max_st)
                  denom = np.sqrt(max_s) / np.sqrt(bias_correction2) + eps
              else:
                  denom = np.sqrt(s) / np.sqrt(bias_correction2) + eps
  
              param = x - ((learning_rate / bias_correction1) * v / denom)
              return (param, v, s, max_s)
  
          for i in range(1, train_iters + 1):
              (x, vt, st, max_st) = train_one_iter(i, random_grad_seq[i - 1])
          return x
  
      oneflow_res = train_by_oneflow().numpy()
      numpy_res = train_by_numpy()
      test_case.assertTrue(
          np.allclose(oneflow_res.flatten(), numpy_res.flatten(), rtol=1e-3, atol=1e-3)
      )
  
  
  @flow.unittest.skip_unless_1n1d()
  class TestAdam(flow.unittest.TestCase):
      def test_adam(test_case):
          arg_dict = OrderedDict()
          arg_dict["device"] = ["cuda", "cpu"]
          arg_dict["x_shape"] = [(10,)]
          arg_dict["learning_rate"] = [1, 1e-3]
          arg_dict["train_iters"] = [10]
          arg_dict["betas"] = [(0.99, 0.9)]
          arg_dict["weight_decay"] = [0.9, 0.000]
          arg_dict["eps"] = [1e-08]
          arg_dict["do_bias_correction"] = [True, False]
          arg_dict["amsgrad"] = [True, False]
          arg_dict["reload_state_step"] = [5]  # save and load optim state
          arg_dict["save_load_by_pickle"] = [False, True]
  
          for arg in GenArgList(arg_dict):
              compare_with_numpy_adam(test_case, *arg)
  
      def test_adam_clip_grad(test_case):
          arg_dict = OrderedDict()
          arg_dict["device"] = ["cpu", "cuda"]
          arg_dict["x_shape"] = [(10,)]
          arg_dict["learning_rate"] = [1e-3]
          arg_dict["train_iters"] = [10]
          arg_dict["betas"] = [(0.99, 0.9)]
          arg_dict["weight_decay"] = [0.1, 0.000]
          arg_dict["eps"] = [1e-08]
          arg_dict["do_bias_correction"] = [True, False]
          arg_dict["amsgrad"] = [True, False]
          arg_dict["clip_grad_max_norm"] = [0, 0.5, 1.0]
          arg_dict["clip_grad_norm_type"] = ["inf", "-inf", 0.0, 1.0, 2.0, 3.5]
          arg_dict["reload_state_step"] = [5]  # save and load optim state
          arg_dict["save_load_by_pickle"] = [False, True]
  
          for arg in GenArgList(arg_dict):
              compare_with_numpy_adam_clip_grad(test_case, *arg)
  
  
  if __name__ == "__main__":
      unittest.main()
  