text: import contextlib
  import io
  import unittest
  from copy import deepcopy
  from collections import OrderedDict
  from itertools import product
  
  import torch
  from torch import nn
  from torch.cuda.amp import autocast
  import torch.nn.parallel as dp
  from torch.testing._internal.common_cuda import TEST_MULTIGPU, TEST_CUDA
  from torch.testing._internal.common_utils import run_tests, TestCase, repeat_test_for_types, ALL_TENSORTYPES
  from torch.testing._internal.common_utils import _assertGradAndGradgradChecks
  from torch.testing._internal.common_utils import dtype2prec_DONTUSE
  from torch.testing._internal.common_utils import skipIfRocm
  import torch.nn.functional as F
  
  torch.set_default_dtype(torch.double)
  
  
  class TestDataParallel(TestCase):
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_buffers_requiring_grad(self):
          class TestModule(nn.Module):
              def __init__(self, t):
                  super(TestModule, self).__init__()
                  self.register_buffer('t_rg', t)
                  self.register_buffer('t_not_rg', t.clone().detach())
  
              def forward(self, x):
                  return x * self.t_rg + self.t_not_rg
  
          m = TestModule(torch.randn(100, device='cuda', requires_grad=True))
          self.assertTrue(m.t_rg.requires_grad)
  
          dpm = nn.DataParallel(m, [0, 1])
          inp = torch.randn(2, 100, device='cuda')
  
          def fn(t):
              return dpm(inp)
  
          torch.autograd.gradcheck(fn, (m.t_rg,))
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_rnn(self):
  
          class TestModule(torch.nn.Module):
  
              def __init__(self):
                  super(TestModule, self).__init__()
                  self.rnn = torch.nn.LSTM(300, 1024, 1, batch_first=True, bidirectional=True)
  
              def forward(self, x):
                  self.rnn.flatten_parameters()
                  return self.rnn(x)
  
          def step(model):
              opt = torch.optim.SGD(model.parameters(), lr=10)
              input = torch.ones(4, 4, 300).to(0)
              output = model(input)
              loss = F.mse_loss(output[0], torch.zeros_like(output[0]))
              loss.backward()
              opt.step()
  
          with torch.no_grad():
              model = TestModule().to(0)
              model_dp = torch.nn.DataParallel(deepcopy(model))
  
              # make sure DP does not crash when grad is disabled.
              # See #21108
              model_dp(torch.rand(2, 4, 300).to(0))
  
          step(model)
          step(model_dp)
  
          for p1, p2 in zip(model.parameters(), model_dp.parameters()):
              self.assertTrue(p1.allclose(p2))
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_parallel_apply(self):
          l1 = nn.Linear(10, 5).to("cuda:0", torch.float)
          l2 = nn.Linear(10, 5).to("cuda:1", torch.float)
          i1 = torch.randn(2, 10, device="cuda:0", dtype=torch.float)
          i2 = torch.randn(2, 10, device="cuda:1", dtype=torch.float)
          expected1 = l1(i1)
          expected2 = l2(i2)
          modules = (l1, l2)
          expected_outputs = (expected1, expected2)
  
          # each input can be either a collection of positional arguments
          #                       or an object representing the single argument
          for inputs in [((i1,), (i2,)), (i1, i2)]:
              outputs = dp.parallel_apply(modules, inputs, None)
              for out, expected in zip(outputs, expected_outputs):
                  self.assertEqual(out, expected)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_parallel_apply_autocast(self):
          l1 = nn.Linear(10, 5).to("cuda:0", torch.float)
          l2 = nn.Linear(10, 5).to("cuda:1", torch.float)
          i1 = torch.randn(2, 10, device="cuda:0", dtype=torch.float)
          i2 = torch.randn(2, 10, device="cuda:1", dtype=torch.float)
          with autocast():
              expected1 = l1(i1)
              expected2 = l2(i2)
          modules = (l1, l2)
          expected_outputs = (expected1, expected2)
  
          # each input can be either a collection of positional arguments
          #                       or an object representing the single argument
          for inputs in [((i1,), (i2,)), (i1, i2)]:
              with autocast():
                  outputs = dp.parallel_apply(modules, inputs, None)
              for out, expected in zip(outputs, expected_outputs):
                  self.assertEqual(out, expected)
  
      @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
      def test_parallel_apply_passes_exception(self):
          # we define and instantiate a module that will throw a KeyError
          class TestModule(nn.Module):
  
              def forward(self, *args):
                  return {}['wonderful']
  
          l1 = TestModule().to("cuda", torch.float)
          # and check that parallel_apply passes on the exception
          # (we can use a single device twice for this test)
          with self.assertRaisesRegex(KeyError,
                                      'Caught KeyError in replica \\d '
                                      'on device 0.\nOriginal Traceback'
                                      '[\\s\\S]+wonderful'):
              dp.parallel_apply(modules=(l1, l1), inputs=(None, None))
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_multiple_input(self):
          class TestModule(nn.Module):
  
              def forward(self, var1, var2, float1, var3=None):
                  if var3 is None:
                      return float1 * (var1 * var2)
                  else:
                      return float1 * (var1 * var2 + var3)
  
          m = TestModule()
          var1 = torch.randn(5, 5, dtype=torch.float, requires_grad=True)
          var2 = torch.randn(5, 5, dtype=torch.float, requires_grad=True)
          var3 = torch.randn(5, 5, dtype=torch.float, requires_grad=False)
  
          float1 = torch.randn(1).item()
  
          expected = m(var1, var2, float1)
          loss = expected.sum()
          loss.backward()
          gvar1_exp = var1.grad.clone()
          gvar2_exp = var2.grad.clone()
  
          def local_test(out):
              with torch.no_grad():
                  var1.grad.fill_(0.0)
                  var2.grad.fill_(0.0)
              loss = out.sum()
              loss.backward()
              self.assertEqual(out, expected)
              self.assertEqual(gvar1_exp, var1.grad)
              self.assertEqual(gvar2_exp, var2.grad)
  
          out = dp.data_parallel(m, (var1, var2, float1), (0, 1))
          local_test(out)
  
          out = dp.data_parallel(m, (var1, var2, float1), (1, 0))
          local_test(out)
  
          out = dp.data_parallel(m, (var1, var2, float1), (0,))
          local_test(out)
  
          with torch.no_grad():
              var1.grad.fill_(0.0)
              var2.grad.fill_(0.0)
          expected = m(var1, var2, float1, var3=var3)
          loss = expected.sum()
          loss.backward()
          gvar1_exp = var1.grad.clone()
          gvar2_exp = var2.grad.clone()
  
          dpm = nn.DataParallel(TestModule())
          out = dpm(var1, var2, float1, var3=var3)
          local_test(out)
  
          dpm = nn.DataParallel(TestModule(), device_ids=[0])
          out = dpm(var1, var2, float1, var3=var3)
          local_test(out)
  
          kwarg_wrap = {'var3': var3}
          out = dp.data_parallel(
              m, (var1, var2, float1), (0, 1), module_kwargs=kwarg_wrap)
          local_test(out)
  
          out = dp.data_parallel(
              m, (var1, var2, float1), (0,), module_kwargs=kwarg_wrap)
          local_test(out)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_small_back(self):
          l = nn.Linear(10, 5).float().cuda()
          i = torch.randn(20, 10, dtype=torch.float, device="cuda")
          out = dp.data_parallel(l, i, (0, 1))
          self.assertEqual(out, l(i))
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_model_device(self):
          r"""Test device[0] check at forward time.
          """
          l = nn.Linear(2, 2)
          inp = torch.randn(2, 2)
          inp_cuda0 = inp.cuda(0)
          inp_cuda1 = inp.cuda(1)
  
          error_msg = "module must have its parameters and buffers on device {}"
  
          @contextlib.contextmanager
          def dummy_ctx_manager():
              yield
  
          def test(inner_m, dp_device, inp, device_ids, should_fail):
              if device_ids is None:
                  device_ids = list(range(torch.cuda.device_count()))
  
              if isinstance(device_ids[0], torch.device):
                  expect_device = device_ids[0]
              else:
                  expect_device = torch.device("cuda:{}".format(device_ids[0]))
  
              if should_fail:
                  def assert_correct():
                      return self.assertRaisesRegex(RuntimeError, error_msg.format(expect_device))
              else:
                  assert_correct = dummy_ctx_manager
  
              # test DataParallel module
              dpm = nn.DataParallel(inner_m, device_ids)
              if dp_device is not None:
                  dpm = dpm.to(dp_device)
  
              with assert_correct():
                  dpm(inp)
  
              # test functional
              with assert_correct():
                  nn.parallel.data_parallel(inner_m.to(dp_device), inp, device_ids)
  
          test(l.to('cpu'), None, inp, None, should_fail=True)
          test(l.cuda(1), None, inp_cuda0, None, should_fail=True)
          test(l.cuda(), None, inp_cuda0, [1, 0], should_fail=True)
  
          test(l.cuda(), None, inp_cuda0, None, should_fail=False)
          test(l.cpu(), 'cuda', inp_cuda0, None, should_fail=False)
          test(l.cuda(1), None, inp_cuda1, [1, 0], should_fail=False)
          test(l.cpu(), 'cuda:1', inp_cuda1, [1, 0], should_fail=False)
  
          s = nn.Sequential(l.cpu())
          test(s, None, inp, None, should_fail=True)
          test(s, None, inp, [0, 1], should_fail=True)
          test(s, None, inp, [1, 0], should_fail=True)
  
          s = nn.Sequential(deepcopy(l).cpu(), l.cuda())
          test(s, None, inp, None, should_fail=True)
          test(s, None, inp, [0, 1], should_fail=True)
          test(s, None, inp, [1, 0], should_fail=True)
  
          s = nn.Sequential(l.cuda(), deepcopy(l).cuda(1))
          test(s, None, inp, None, should_fail=True)
          test(s, None, inp, [0, 1], should_fail=True)
          test(s, None, inp, [1, 0], should_fail=True)
  
          s = nn.Sequential(l.cuda(), deepcopy(l).cuda())
          test(s, None, inp, None, should_fail=False)
          test(s, None, inp, [0, 1], should_fail=False)
          test(s, None, inp, [1, 0], should_fail=True)
          test(s.cpu(), None, inp, [1, 0], should_fail=True)
          test(s.cuda(1), None, inp, [1, 0], should_fail=False)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_model_no_refcycles(self):
          # Python 2.7 will create reference cycles with the following
          # Module on multiple GPUs, but Python 3 shouldn't unless
          # there are refcycles on the PyTorch side (or the defined module)
          import gc
  
          class Model(nn.Module):
              def __init__(self):
                  super(Model, self).__init__()
                  self.linear = nn.Linear(1, 1)
  
              def forward(self, x):
                  return self.linear(x)
  
          gc.collect()
          model = nn.DataParallel(Model().cuda())
          data = torch.randn(1, device="cuda")
          model(data)
  
          refcycles = gc.collect()
          self.assertEqual(refcycles, 0)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_no_grad(self):
          test = self
  
          class Layer(nn.Module):
              def forward(self, x):
                  test.assertFalse(torch.is_grad_enabled())
                  return x
  
          l = Layer()
          i = torch.randn(20, 10, dtype=torch.float, device="cuda")
          with torch.no_grad():
              dp.data_parallel(l, i, (0, 1))
          self.assertRaises(AssertionError, lambda: dp.data_parallel(l, i, (0, 1)))
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel(self):
          l = nn.Linear(10, 5).float().cuda()
          i = torch.randn(20, 10, dtype=torch.float, device="cuda:1")
          l.cuda(1)
          expected_out = l(i)
          loss = expected_out.sum()
          loss.backward()
          expected_grads = []
          for param in l.parameters():
              expected_grads.append(param.grad.clone())
          dev_ids_list = [(0, 1), (1, 0)]
          for dev_id in dev_ids_list:
              with torch.cuda.device(dev_id[0]):
                  l.cuda()
                  l.zero_grad()
                  out = dp.data_parallel(l, i, dev_id)
                  loss = out.sum()
                  loss.backward()
                  self.assertEqual(out.get_device(), dev_id[0])
                  self.assertEqual(out, expected_out)
                  for expected, param in zip(expected_grads, l.parameters()):
                      self.assertEqual(param.grad, expected)
  
          # Check for None device_ids
          l = l.cuda()
          out = dp.data_parallel(l, i)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_sparse(self):
          l = nn.Embedding(10, 5, sparse=True).to("cuda:1")
          i = torch.randint(10, (20, 5), device="cuda:1", dtype=torch.long)
          expected_out = l(i)
          loss = expected_out.sum()
          loss.backward()
          expected_grads = []
          for param in l.parameters():
              expected_grads.append(param.grad.clone())
          dev_ids_list = [(0, 1), (1, 0)]
          for dev_id in dev_ids_list:
              with torch.cuda.device(dev_id[0]):
                  l.cuda()
                  l.zero_grad()
                  out = dp.data_parallel(l, i, dev_id)
                  loss = out.sum()
                  loss.backward()
                  self.assertEqual(out.get_device(), dev_id[0])
                  self.assertEqual(out, expected_out)
                  for expected, param in zip(expected_grads, l.parameters()):
                      self.assertEqual(param.grad, expected)
  
          # Check for None device_ids
          l = l.cuda()
          out = dp.data_parallel(l, i)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_nested_output(self):
          def fn(input):
              return [
                  input, (input.sin(), input.cos(), [input.add(1)]), input,
                  OrderedDict(a=input, b=[input.sin()])
              ]
  
          class Net(nn.Module):
              def forward(self, input):
                  return fn(input)
  
          i = torch.randn(2, 2).float().cuda(1)
          gpus = range(torch.cuda.device_count())
          output = dp.data_parallel(Net(), i, gpus)
          self.assertEqual(output, fn(i))
          self.assertIsInstance(output[0], torch.Tensor)
          self.assertIsInstance(output[1], tuple)
          self.assertIsInstance(output[1][0], torch.Tensor)
          self.assertIsInstance(output[1][1], torch.Tensor)
          self.assertIsInstance(output[1][2], list)
          self.assertIsInstance(output[1][2][0], torch.Tensor)
          self.assertIsInstance(output[2], torch.Tensor)
          self.assertIsInstance(output[3], dict)
          self.assertEqual(len(output[3]), 2)
          self.assertIn('a', output[3])
          self.assertIn('b', output[3])
          self.assertIsInstance(output[3]['a'], torch.Tensor)
          self.assertIsInstance(output[3]['b'], list)
          self.assertIsInstance(output[3]['b'][0], torch.Tensor)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_nested_input(self):
          def fn(input):
              return input[1][0]
  
          class Net(nn.Module):
              def forward(self, *input):
                  return fn(input)
  
          i = torch.randn(20, 3, dtype=torch.float, device="cuda:1")
          input = (i.cos(), (i.sin(), i), i.sin())
          gpus = range(torch.cuda.device_count())
          output = dp.data_parallel(Net(), input, gpus)
          self.assertEqual(output, fn(input))
  
      @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
      @repeat_test_for_types(ALL_TENSORTYPES)
      def test_data_parallel_module(self, dtype=torch.float):
          l = nn.Linear(10, 5).to("cuda", dtype)
          i = torch.randn(20, 10, device="cuda", dtype=dtype)
          expected_out = l(i)
          net = nn.DataParallel(l)
          out = net(i)
          self.assertEqual(out.get_device(), 0)
          self.assertEqual(out, expected_out, atol=dtype2prec_DONTUSE[dtype], rtol=0)
  
      @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
      @repeat_test_for_types(ALL_TENSORTYPES)
      def test_data_parallel_module_kwargs_only(self, dtype=torch.float):
          class Net(nn.Module):
              def __init__(self):
                  super(Net, self).__init__()
                  self.l = l
  
              def forward(self, input):
                  return self.l(input)
  
          l = nn.Linear(10, 5).to("cuda", dtype)
          i = torch.randn(20, 10, device="cuda", dtype=dtype)
          expected_out = l(i)
          n = nn.DataParallel(Net())
          out = n(input=i)
          self.assertEqual(out.get_device(), 0)
          self.assertEqual(out, expected_out, atol=dtype2prec_DONTUSE[dtype], rtol=0)
  
      @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
      @repeat_test_for_types(ALL_TENSORTYPES)
      def test_data_parallel_module_kwargs_only_empty_list(self, dtype=torch.float):
          class Net(nn.Module):
              def __init__(self):
                  super(Net, self).__init__()
                  self.l = l
  
              def forward(self, input):
                  return self.l(input['data'])
  
          l = nn.Linear(10, 5).to("cuda", dtype)
          i = torch.randn(20, 10, device="cuda", dtype=dtype)
          expected_out = l(i)
          n = nn.DataParallel(Net())
          out = n(input={'data': i, 'unused': []})
          self.assertEqual(out.get_device(), 0)
          self.assertEqual(out, expected_out, atol=dtype2prec_DONTUSE[dtype], rtol=0)
  
      @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
      @repeat_test_for_types(ALL_TENSORTYPES)
      def test_data_parallel_module_kwargs_only_empty_dict(self, dtype=torch.float):
          class Net(nn.Module):
              def __init__(self):
                  super(Net, self).__init__()
                  self.l = l
  
              def forward(self, input):
                  return self.l(input['data'])
  
          l = nn.Linear(10, 5).to("cuda", dtype)
          i = torch.randn(20, 10, device="cuda", dtype=dtype)
          expected_out = l(i)
          n = nn.DataParallel(Net())
          out = n(input={'data': i, 'unused': {}})
          self.assertEqual(out.get_device(), 0)
          self.assertEqual(out, expected_out, atol=dtype2prec_DONTUSE[dtype], rtol=0)
  
      @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
      @repeat_test_for_types(ALL_TENSORTYPES)
      def test_data_parallel_module_kwargs_only_empty_tuple(self, dtype=torch.float):
          class Net(nn.Module):
              def __init__(self):
                  super(Net, self).__init__()
                  self.l = l
  
              def forward(self, input):
                  return self.l(input['data'])
  
          l = nn.Linear(10, 5).to("cuda", dtype)
          i = torch.randn(20, 10, device="cuda", dtype=dtype)
          expected_out = l(i)
          n = nn.DataParallel(Net())
          out = n(input={'data': i, 'unused': ()})
          self.assertEqual(out.get_device(), 0)
          self.assertEqual(out, expected_out, atol=dtype2prec_DONTUSE[dtype], rtol=0)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_device_args(self):
          cuda0 = torch.device('cuda:0')
          cuda1 = torch.device('cuda:1')
  
          # test output_device
          l = nn.Linear(10, 5).to(cuda0, torch.float)
          i = torch.randn(20, 10, dtype=torch.float, device=cuda0, requires_grad=True)
          out = dp.data_parallel(l, i, device_ids=(0, 1), output_device=cuda0)
          self.assertEqual(out, l(i))
  
          # test device_ids
          l = nn.Linear(10, 5).to(cuda0, torch.float)
          i = torch.randn(20, 10, dtype=torch.float, device=cuda0, requires_grad=True)
          out = dp.data_parallel(l, i, device_ids=(cuda0, cuda1), output_device=cuda0)
          self.assertEqual(out, l(i))
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_data_parallel_function_deletion(self):
          # this test case is originated from #16532
          def gradient_penalty(net, x):
              output = net(x)
              loss = torch.autograd.grad(
                  outputs=output, inputs=x,
                  grad_outputs=x.new_ones(output.size()),
                  create_graph=True, retain_graph=True)[0].mean()
              return loss
  
          net = nn.Linear(4, 1).cuda()
          dpn = nn.DataParallel(net, [0, 1])
          x = torch.ones(2, 4, requires_grad=True).cuda()
  
          dpn.zero_grad()
          loss = gradient_penalty(dpn, x)
          loss.backward()
          grads = [p.grad for p in net.parameters()]
          self.assertEqual(2, len(grads))
          self.assertEqual(
              torch.tensor([[0.25, 0.25, 0.25, 0.25]], device='cuda:0'),
              grads[0])
          self.assertEqual(torch.tensor([0.0], device='cuda:0'), grads[1])
  
      def _test_scatter(self, tensor):
          x = tensor.detach().requires_grad_()
          result = dp.scatter(x, (0, 1))
          self.assertEqual(len(result), 2)
          self.assertEqual(result[0], x[:2])
          self.assertEqual(result[0].get_device(), 0)
          self.assertEqual(result[1], x[2:])
          self.assertEqual(result[1].get_device(), 1)
          grad = result[0].detach().clone().fill_(2)
          result[0].backward(grad)
          self.assertEqual(x.grad[:2], grad)
          self.assertEqual(x.grad[2:], grad.clone().zero_())
          _assertGradAndGradgradChecks(self, lambda y: dp.scatter(y, (0, 1)), (x,))
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_scatter_cpu(self):
          self._test_scatter(torch.randn((4, 4)))
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_scatter_gpu(self):
          self._test_scatter(torch.randn((4, 4)).cuda())
  
      def _test_gather(self, output_device):
          inputs = (
              torch.randn(2, 4, device='cuda:0', requires_grad=True),
              torch.randn(2, 4, device='cuda:1', requires_grad=True),
          )
          result = dp.gather(inputs, output_device)
          self.assertEqual(result.size(), torch.Size([4, 4]))
          self.assertEqual(result[:2], inputs[0])
          self.assertEqual(result[2:], inputs[1])
          if output_device != -1:
              self.assertEqual(result.get_device(), output_device)
          else:
              self.assertFalse(result.is_cuda)
          grad = torch.randn((4, 4))
          if output_device != -1:
              grad = grad.cuda(output_device)
          result.backward(grad)
          self.assertEqual(inputs[0].grad, grad[:2])
          self.assertEqual(inputs[1].grad, grad[2:])
          _assertGradAndGradgradChecks(self, lambda x, y: dp.gather((x, y), output_device), inputs)
  
          # test scalar inputs, should stack into a vector in this case
          inputs = (
              torch.randn((), device='cuda:0', requires_grad=True),
              torch.randn((), device='cuda:1', requires_grad=True),
          )
          result = dp.gather(inputs, output_device)
          self.assertEqual(result.size(), torch.Size([2]))
          self.assertEqual(result[0], inputs[0])
          self.assertEqual(result[1], inputs[1])
          if output_device != -1:
              self.assertEqual(result.get_device(), output_device)
          else:
              self.assertFalse(result.is_cuda)
          grad = torch.randn(2)
          if output_device != -1:
              grad = grad.cuda(output_device)
          result.backward(grad)
          self.assertEqual(inputs[0].grad, grad[0])
          self.assertEqual(inputs[1].grad, grad[1])
          _assertGradAndGradgradChecks(self, lambda x, y: dp.gather((x, y), output_device), inputs)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_gather_cpu(self):
          self._test_gather(-1)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_gather_gpu(self):
          self._test_gather(0)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_gather_different_len_dicts(self):
          inputs = (
              {'a': torch.randn(1, 2, requires_grad=True, device="cuda:0")},
              {
                  'b': torch.randn(1, 2, requires_grad=True, device="cuda:1"),
                  'a': torch.randn(1, 2, requires_grad=True, device="cuda:1"),
              }
          )
          with self.assertRaises(ValueError):
              _ = dp.gather(inputs, target_device=0)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_replicate(self):
          module = nn.Linear(10, 5).float().cuda()
          input = torch.randn(2, 10, dtype=torch.float, device="cuda")
          expected_output = module(input)
          for devices in [(0, 1), [0, 1]]:
              replicas = dp.replicate(module, devices)
              for i, replica in enumerate(replicas):
                  for p in replica.parameters():
                      self.assertEqual(p.get_device(), i)
                  replica_input = input.cuda(i)
                  self.assertEqual(replica(replica_input), expected_output)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_replicate_buffers(self):
          net = nn.Module()
          net.bn = nn.BatchNorm2d(10)
          net.cuda()
          for devices in [(0, 1), [0, 1]]:
              replicas = dp.replicate(net, devices)
              for i, replica in enumerate(replicas):
                  self.assertEqual(replica.bn.running_mean.get_device(), i, msg='buffer on wrong device')
                  self.assertEqual(replica.bn.running_var.get_device(), i, msg='buffer on wrong device')
                  self.assertEqual(replica.bn.num_batches_tracked.get_device(), i, msg='buffer on wrong device')
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_zero_grad(self):
          # zero_grad should warn about using gradients inside forward
  
          class Net(torch.nn.Module):
              def __init__(self, testcase):
                  super(Net, self).__init__()
                  self._testcase = testcase
  
              def forward(self, x):
                  with self._testcase.assertWarnsRegex(
                          UserWarning,
                          r"Calling \.zero_grad\(\) from a module created with nn\.DataParallel\(\) has no effect."):
                      self.zero_grad()
                  return x
  
          module = Net(self).cuda()
          dpm = dp.DataParallel(module)
          dpm(torch.rand(4, 3, 6, 5))
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      @skipIfRocm
      def test_autocast(self):
          class Model(torch.nn.Linear):
              def __init__(self):
                  super(Model, self).__init__(8, 8)
  
              @torch.cuda.amp.autocast()
              def forward(self, input):
                  return super(Model, self).forward(input)
  
          model = dp.DataParallel(Model().cuda().to(dtype=torch.float32))
          input = torch.randn((8, 8), dtype=torch.float32, device="cuda")
          self.assertTrue(model(input).dtype is torch.float16)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      @skipIfRocm
      def test_save_replica_module(self):
          # DataParallel replicas can be saved (gh-37182)
          module = torch.nn.Linear(8, 8).cuda()
          dpm = torch.nn.parallel.replicate(module, devices=[0, 1], detach=False)
          data = io.BytesIO()
          torch.save(dpm, data)
          dpm = torch.nn.parallel.replicate(module, devices=[0, 1], detach=True)
          torch.save(dpm, data)
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      @skipIfRocm
      def test_strided_grad_layout(self):
          class ConvNet(nn.Module):
              def __init__(self, layouts, dtypes):
                  super(ConvNet, self).__init__()
                  self.dtypes = dtypes
                  self.conv0 = torch.nn.Conv2d(8, 16, (2, 2)).to(memory_format=layouts[0], dtype=dtypes[0])
                  self.conv1 = torch.nn.Conv2d(16, 32, (2, 2)).to(memory_format=layouts[1], dtype=dtypes[1])
                  self.conv2 = torch.nn.Conv2d(32, 16, (2, 2)).to(memory_format=layouts[2], dtype=dtypes[2])
                  self.conv3 = torch.nn.Conv2d(16, 8, (2, 2)).to(memory_format=layouts[3], dtype=dtypes[3])
  
              def forward(self, x):
                  x = x.to(self.dtypes[0])
                  x = self.conv0(x).to(self.dtypes[1])
                  x = self.conv1(x).to(self.dtypes[2])
                  x = self.conv2(x).to(self.dtypes[3])
                  x = self.conv3(x)
                  return x
  
          layer_formats = ([torch.contiguous_format] * 4,
                           [torch.channels_last] * 2 + [torch.contiguous_format] * 2,
                           [torch.channels_last] * 4,)
          layer_dtypes = ([torch.float] * 4,
                          [torch.float] * 2 + [torch.half] * 2,
                          [torch.half] * 4,)
  
          ndevs = torch.cuda.device_count()
          input = torch.randn(ndevs * 8, 8, 8, 8, device="cuda:0", dtype=torch.float)
          target = torch.randn(ndevs * 8, 8, 4, 4, device="cuda:0", dtype=torch.float)
          device_ids = list(range(ndevs))
  
          with torch.backends.cudnn.flags(enabled=True, deterministic=True, benchmark=False):
              for formats, dtypes in product(layer_formats, layer_dtypes):
                  model_msg = "formats = {} dtypes = {}".format(formats, dtypes)
                  try:
                      m = ConvNet(formats, dtypes).cuda(device="cuda:0")
                      m_dp = dp.DataParallel(deepcopy(m), device_ids=device_ids)
                      opt = torch.optim.SGD(m.parameters(), lr=0.1)
                      opt_dp = torch.optim.SGD(m_dp.parameters(), lr=0.1)
                      has_half = any(p.dtype is torch.half for p in m.parameters())
                      tol = 1.e-3 if has_half else 1.e-5
                  except BaseException:
                      # Prints case-specific debugging info to narrow down failing case.
                      print("Caught exception during model creation for " + model_msg, flush=True)
                      raise
                  # 2 iters:  First iter creates grads, second iter tries zeroed grads.
                  for it in range(2):
                      iter_msg = "iter = {} ".format(it) + model_msg
                      named_msg = iter_msg
                      try:
                          F.mse_loss(m(input).float(), target).backward()
                          F.mse_loss(m_dp(input).float(), target).backward()
                          for i, ((layer_name, m_child), m_dp_child) in enumerate(zip(m.named_children(),
                                                                                      m_dp.module.children())):
                              named_msg = layer_name + ".weight " + iter_msg
                              self.assertTrue(m_child.weight.grad.is_contiguous(memory_format=formats[i]), named_msg)
                              self.assertTrue(m_dp_child.weight.grad.is_contiguous(memory_format=formats[i]), named_msg)
                              for j, ((param_name, p), p_dp) in enumerate(zip(m_child.named_parameters(),
                                                                              m_dp_child.parameters())):
                                  named_msg = layer_name + "." + param_name + " " + iter_msg
                                  self.assertEqual(p.grad, p_dp.grad, rtol=tol, atol=tol)
                          opt.step()
                          opt_dp.step()
                          opt.zero_grad()
                          opt_dp.zero_grad()
                      except BaseException:
                          # Makes sure we still get info if an error occurred somewhere other than the asserts.
                          print("Caught exception during iterations at " + named_msg, flush=True)
                          raise
  
      @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
      def test_parameter_list_dict_replica(self):
          class MyMod(torch.nn.Module):
              def __init__(self, data):
                  super(MyMod, self).__init__()
                  self.data = data
  
              def forward(self, inp):
                  return inp
  
          p1 = torch.nn.Parameter(torch.rand(10))
          p2 = torch.nn.Parameter(torch.rand(10))
          module = MyMod(torch.nn.ParameterList([p1, p2])).cuda()
          model = dp.DataParallel(module)
          input = torch.randn((8, 8), device="cuda")
  
          with self.assertWarnsRegex(
                  UserWarning,
                  r"nn\.ParameterList is being used with DataParallel but this"):
              model(input)
  
          module = MyMod(torch.nn.ParameterDict({"0": p1, "1": p2})).cuda()
          model = dp.DataParallel(module)
          input = torch.randn((8, 8), device="cuda")
  
          with self.assertWarnsRegex(
                  UserWarning,
                  r"nn\.ParameterDict is being used with DataParallel but this"):
              model(input)
  
  
  if __name__ == '__main__':
      run_tests()
  