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 os
  import unittest
  from collections import OrderedDict
  import numpy as np
  import copy
  
  from test_util import GenArgList
  from optimizer_test_util import clip_grad_norm_np
  
  import oneflow as flow
  from oneflow.one_embedding import Ftrl
  
  
  def compare_with_numpy_ftrl(
      test_case,
      device,
      x_shape,
      learning_rate,
      train_iters,
      weight_decay,
      lr_power,
      initial_accumulator_value,
      lambda1,
      lambda2,
      beta,
  ):
      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)
  
      class CustomModule(flow.nn.Module):
          def __init__(self):
              super().__init__()
              self.para0 = flow.nn.Parameter(
                  flow.Tensor(init_value, device=flow.device(device))
              )
  
          def forward(self, mask):
              return self.para0 * mask
  
      simp_module = CustomModule()
      simp_module.to(device)
      simp_module.train()
  
      ftrl = Ftrl(
          [
              {
                  "params": simp_module.parameters(),
                  "lr": learning_rate,
                  "weight_decay": weight_decay,
                  "lr_power": lr_power,
                  "initial_accumulator_value": initial_accumulator_value,
                  "lambda1": lambda1,
                  "lambda2": lambda2,
                  "beta": beta,
              }
          ]
      )
  
      class CustomftrlGraph(flow.nn.Graph):
          def __init__(self):
              super().__init__()
              self.m = simp_module
              self.add_optimizer(ftrl)
  
          def build(self, mask_tensor):
              loss = flow.sum(self.m(mask_tensor))
              loss.backward()
              return loss
  
      of_res_list = []
      ftrl_graph = CustomftrlGraph()
  
      for i in range(train_iters):
          mask_tensor = flow.tensor(
              random_grad_seq[i], requires_grad=False, device=flow.device(device)
          )
          ftrl_x = ftrl_graph(mask_tensor)
  
          of_res_list.append(copy.copy(simp_module.para0.numpy()))
  
      np_res_list = []
  
      def train_by_numpy():
          x = init_value
          accum = np.zeros_like(x)
          accum.fill(initial_accumulator_value)
          z_arr = np.zeros_like(x)
  
          def np_train_one_iter(grad):
              grad = grad + weight_decay * x
  
              new_accum = accum + grad * grad
              sigma = (
                  np.power(new_accum, lr_power) - np.power(accum, lr_power)
              ) / learning_rate
              new_z_val = z_arr + grad - sigma * x
  
              update_val = (np.sign(new_z_val) * lambda1 - new_z_val) / (
                  (beta + np.power(new_accum, lr_power)) / learning_rate + lambda2
              )
              param = np.where(np.abs(new_z_val) < lambda1, 0.0, update_val)
              return (param, new_accum, new_z_val)
  
          for i in range(1, train_iters + 1):
              (x, accum, z_arr) = np_train_one_iter(random_grad_seq[i - 1])
              np_res_list.append(x)
          return x
  
      train_by_numpy()
      test_case.assertTrue(np.allclose(of_res_list, np_res_list, rtol=1e-4, atol=1e-4))
  
  
  def compare_with_numpy_ftrl_clip_grad(
      test_case,
      device,
      x_shape,
      learning_rate,
      train_iters,
      weight_decay,
      lr_power,
      initial_accumulator_value,
      lambda1,
      lambda2,
      beta,
      clip_grad_max_norm,
      clip_grad_norm_type,
  ):
      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)
  
      class CustomModule(flow.nn.Module):
          def __init__(self):
              super().__init__()
              self.para0 = flow.nn.Parameter(
                  flow.Tensor(init_value, device=flow.device(device))
              )
  
          def forward(self, mask):
              return self.para0 * mask
  
      simp_module = CustomModule()
      simp_module.to(device)
      simp_module.train()
  
      ftrl = Ftrl(
          [
              {
                  "params": simp_module.parameters(),
                  "lr": learning_rate,
                  "weight_decay": weight_decay,
                  "lr_power": lr_power,
                  "initial_accumulator_value": initial_accumulator_value,
                  "lambda1": lambda1,
                  "lambda2": lambda2,
                  "beta": beta,
                  "clip_grad_max_norm": clip_grad_max_norm,
                  "clip_grad_norm_type": clip_grad_norm_type,
              }
          ]
      )
  
      class CustomftrlGraph(flow.nn.Graph):
          def __init__(self):
              super().__init__()
              self.m = simp_module
              self.add_optimizer(ftrl)
  
          def build(self, mask_tensor):
              loss = flow.sum(self.m(mask_tensor))
              loss.backward()
              return loss
  
      of_res_list = []
      ftrl_graph = CustomftrlGraph()
  
      for i in range(train_iters):
          mask_tensor = flow.tensor(
              random_grad_seq[i], requires_grad=False, device=flow.device(device)
          )
          ftrl_x = ftrl_graph(mask_tensor)
  
          of_res_list.append(copy.copy(simp_module.para0.numpy()))
  
      np_res_list = []
  
      def train_by_numpy():
          x = init_value
          accum = np.zeros_like(x)
          accum.fill(initial_accumulator_value)
          z_arr = np.zeros_like(x)
  
          def np_train_one_iter(grad):
              norm, grad = clip_grad_norm_np(
                  grad, clip_grad_max_norm, clip_grad_norm_type
              )
              grad = grad + weight_decay * x
  
              new_accum = accum + grad * grad
              sigma = (
                  np.power(new_accum, lr_power) - np.power(accum, lr_power)
              ) / learning_rate
              new_z_val = z_arr + grad - sigma * x
  
              update_val = (np.sign(new_z_val) * lambda1 - new_z_val) / (
                  (beta + np.power(new_accum, lr_power)) / learning_rate + lambda2
              )
              param = np.where(np.abs(new_z_val) < lambda1, 0.0, update_val)
              return (param, new_accum, new_z_val)
  
          for i in range(1, train_iters + 1):
              (x, accum, z_arr) = np_train_one_iter(random_grad_seq[i - 1])
              np_res_list.append(x)
          return x
  
      train_by_numpy()
  
      test_case.assertTrue(np.allclose(of_res_list, np_res_list, rtol=1e-4, atol=1e-4))
  
  
  @flow.unittest.skip_unless_1n1d()
  class Testftrl(flow.unittest.TestCase):
      def test_ftrl(test_case):
          arg_dict = OrderedDict()
          arg_dict["device"] = ["cuda"]
          if os.getenv("ONEFLOW_TEST_CPU_ONLY"):
              arg_dict["device"] = ["cpu"]
          arg_dict["x_shape"] = [(10,)]
          arg_dict["learning_rate"] = [1, 1e-3]
          arg_dict["train_iters"] = [10]
          arg_dict["weight_decay"] = [0.9, 0.000]
          arg_dict["lr_power"] = [-0.5, 0.5]
          arg_dict["initial_accumulator_value"] = [0.1, 0.05]
          arg_dict["lambda1"] = [0.01]
          arg_dict["lambda2"] = [0.0, 0.01]
          arg_dict["beta"] = [1.0]
  
          for arg in GenArgList(arg_dict):
              compare_with_numpy_ftrl(test_case, *arg)
  
      def test_ftrl_clip_grad(test_case):
          arg_dict = OrderedDict()
          arg_dict["device"] = ["cuda"]
          if os.getenv("ONEFLOW_TEST_CPU_ONLY"):
              arg_dict["device"] = ["cpu"]
          arg_dict["x_shape"] = [(10,)]
          arg_dict["learning_rate"] = [1, 1e-3]
          arg_dict["train_iters"] = [10]
          arg_dict["weight_decay"] = [0.9, 0.000]
          arg_dict["lr_power"] = [-0.5, 0.5]
          arg_dict["initial_accumulator_value"] = [0.1, 0.05]
          arg_dict["lambda1"] = [0.01]
          arg_dict["lambda2"] = [0.0, 0.01]
          arg_dict["beta"] = [1.0]
          arg_dict["clip_grad_max_norm"] = [1.0]
          arg_dict["clip_grad_norm_type"] = [2.0]
          for arg in GenArgList(arg_dict):
              compare_with_numpy_ftrl_clip_grad(test_case, *arg)
  
  
  if __name__ == "__main__":
      unittest.main()
  