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pytorch/test/distributed/test_data_parallel.py
Emilio Castillo d38a71d579 torch.nn.modules.LazyModuleMixin and torch.nn.LazyLinear (Shape Inference II) (#44538) 
Summary:
Retake on https://github.com/pytorch/pytorch/issues/40493 after all the feedback from albanD

This PR implements the generic Lazy mechanism and a sample `LazyLinear` layer with the `UninitializedParameter`.

The main differences with the previous PR are two;
Now `torch.nn.Module` remains untouched.
We don't require an explicit initialization or a dummy forward pass before starting the training or inference of the actual module. Making this much simpler to use from the user side.

As we discussed offline, there was the suggestion of not using a mixin, but changing the `__class__` attribute of `LazyLinear` to become `Linear` once it's completely initialized. While this can be useful, by the time being we need `LazyLinear` to be a `torch.nn.Module` subclass since there are many checks that rely on the modules being instances of `torch.nn.Module`.
This can cause problems when we create complex modules such as
```
class MyNetwork(torch.nn.Module):
    def __init__(self):
        super(MyNetwork, self).__init__()
        self.conv = torch.nn.Conv2d(20, 4, 2)
        self.linear = torch.nn.LazyLinear(10)
    def forward(self, x):
        y = self.conv(x).clamp(min=0)
        return self.linear(y)
```
Here, when the __setattr__ function is called at the time LazyLinear is registered, it won't be added to the child modules of `MyNetwork`, so we have to manually do it later, but currently there is no way to do such thing as we can't access the parent module from LazyLinear once it becomes the Linear module. (We can add a workaround to this if needed).

TODO:

Add convolutions once the design is OK
Fix docstrings

Pull Request resolved: https://github.com/pytorch/pytorch/pull/44538

Reviewed By: ngimel

Differential Revision: D24162854

Pulled By: albanD

fbshipit-source-id: 6d58dfe5d43bfb05b6ee506e266db3cf4b885f0c
2020-10-19 13:13:54 -07:00

817 lines
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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_data_parallel_lazy_linear(self):
with self.assertRaisesRegex(RuntimeError, 'Modules with uninitialized parameters'):
model_dp = torch.nn.DataParallel(torch.nn.LazyLinear(10).to(0))
model_dp(torch.rand(10, 10).to(0))
@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()
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