docstr: Check the robustness of the ZeroRedundancyOptimizer constructor by
  passing different values for the ``params`` argument.
docstr_tok: ["Check","the","robustness","of","the","ZeroRedundancyOptimizer","constructor","by","passing","different","values","for","the","``","params","``","argument","."]
code: def test_constructor(self):
      """Check the robustness of the ZeroRedundancyOptimizer constructor by
      passing different values for the ``params`` argument."""
      self.dist_init(self.rank)
      LR = 0.01
      m = torch.nn.Sequential(
          torch.nn.Linear(5, 10),
          torch.nn.Linear(10, 10),
          torch.nn.Linear(10, 10),
      )
      # Test various constructor inputs in the form: (input, expected error)
      ctor_inputs = [
          ([], ValueError),  # empty parameter list
          (torch.randn(1), TypeError),  # non-iterable: `torch.Tensor`
          (1.2, TypeError),  # non-iterable: `float`
          (
              [
                  {"params": [l.weight for l in m]},
                  {"params": [l.bias for l in m]},
              ],
              None,
          ),  # iterable of dict
          (
              list(m.parameters()) + [42],
              TypeError,
          ),  # iterable containing invalid type
          (m.parameters(), None),  # `params` as a generator
          (list(m.parameters()), None),  # `params` as a list
      ]
      for ctor_input, error in ctor_inputs:
          context = self.assertRaises(error) if error else nullcontext()
          with context:
              ZeroRedundancyOptimizer(
                  ctor_input,
                  optimizer_class=SGD,
                  lr=LR,
              )
  
      # Test constructing with multiple parameter groups more thoroughly
      WD = 0.01
      BETAS = (0.9, 0.999)
      EPS = 1e-8
      params = [
          {"params": [l.weight for l in m], "weight_decay": 0.0},
          {"params": [l.bias for l in m], "weight_decay": WD},
      ]
      o = ZeroRedundancyOptimizer(
          params,
          optimizer_class=AdamW,
          lr=LR,
          betas=BETAS,
          eps=EPS,
      )
      assert (
          len(o.param_groups) == 2
      ), f"Expected 2 ZeRO param groups, but got {len(o.param_groups)}"
      assert len(o.optim.param_groups) == 2, (
          "Expected 2 local optimizer param groups, but got "
          f"{len(o.optim.param_groups)}"
      )
  
imports: import copy
  import os
  import sys
  import unittest
  from contextlib import nullcontext
  from typing import Any, cast, List
  import numpy
  import torch
  import torch.distributed
  from torch.distributed.algorithms.ddp_comm_hooks.ddp_zero_hook import hook_with_zero_step, hook_with_zero_step_interleaved
  from torch.distributed.algorithms.ddp_comm_hooks.default_hooks import allreduce_hook
  from torch.distributed.algorithms.join import Join, Joinable, JoinHook
  from torch.distributed.optim import ZeroRedundancyOptimizer
  from torch.distributed.optim.zero_redundancy_optimizer import _broadcast_object
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import AdamW, SGD
  from torch.testing._internal import common_distributed
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, IS_WINDOWS, parametrize, run_tests, TEST_WITH_ASAN, TEST_WITH_DEV_DBG_ASAN
label_desc: Use image node_id 7 for calling the TestZeroRedundancyOptimizerSingleRank obj's underlying member method code with example usage: obj.test_constructor() without return types