mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
99dbc5b0e2
See #145101 for details. Pull Request resolved: https://github.com/pytorch/pytorch/pull/145176 Approved by: https://github.com/bobrenjc93
247 lines
8.5 KiB
Python
247 lines
8.5 KiB
Python
# Owner(s): ["oncall: distributed"]
|
|
|
|
import copy
|
|
import os
|
|
import tempfile
|
|
|
|
from test_c10d_spawn import _torch_dist_nn_available, TestDistributedNNFunctions
|
|
|
|
import torch
|
|
import torch.distributed as c10d
|
|
import torch.nn as nn
|
|
from torch.testing._internal.common_cuda import TEST_CUDA
|
|
from torch.testing._internal.common_distributed import requires_gloo, skip_if_lt_x_gpu
|
|
from torch.testing._internal.common_utils import (
|
|
run_tests,
|
|
skip_but_pass_in_sandcastle_if,
|
|
TEST_WITH_DEV_DBG_ASAN,
|
|
TestCase,
|
|
)
|
|
|
|
|
|
# Fails on Python-3.9, see https://github.com/pytorch/pytorch/issues/51619
|
|
|
|
|
|
class DistributedDataParallelSingleProcessTest(TestCase):
|
|
def setUp(self):
|
|
self.rank = 0
|
|
self.world_size = 1
|
|
self.file = tempfile.NamedTemporaryFile(delete=False) # noqa: P201
|
|
|
|
def tearDown(self):
|
|
try:
|
|
os.remove(self.file.name)
|
|
except OSError:
|
|
pass
|
|
|
|
def _test_base(self, net, inp, check_allclose=True):
|
|
store = c10d.FileStore(self.file.name, self.world_size)
|
|
c10d.init_process_group(
|
|
backend="gloo", store=store, rank=self.rank, world_size=self.world_size
|
|
)
|
|
process_group = c10d.distributed_c10d._get_default_group()
|
|
if inp[0].is_cuda:
|
|
device_ids = [torch.cuda.current_device()]
|
|
else:
|
|
device_ids = None
|
|
|
|
ddp = nn.parallel.DistributedDataParallel(
|
|
copy.deepcopy(net), device_ids=device_ids, process_group=process_group
|
|
)
|
|
|
|
net_opt = torch.optim.Adam(net.parameters(), lr=0.001)
|
|
ddp_opt = torch.optim.Adam(ddp.parameters(), lr=0.001)
|
|
|
|
for i, j in zip(ddp.parameters(), net.parameters()):
|
|
self.assertTrue(i.allclose(j))
|
|
|
|
for _ in range(10):
|
|
net_out = net(*inp)
|
|
ddp_out = ddp(*inp)
|
|
|
|
net_out.sum().backward()
|
|
ddp_out.sum().backward()
|
|
|
|
net_opt.step()
|
|
ddp_opt.step()
|
|
|
|
if check_allclose:
|
|
for i, j in zip(ddp.parameters(), net.parameters()):
|
|
self.assertTrue(i.allclose(j))
|
|
|
|
@requires_gloo()
|
|
def test_cpu(self):
|
|
self._test_base(nn.Linear(2, 2), [torch.randn(30, 2)])
|
|
|
|
@requires_gloo()
|
|
@skip_but_pass_in_sandcastle_if(not TEST_CUDA, "At least 1 CUDA GPUS needed")
|
|
def test_cuda(self):
|
|
self._test_base(nn.Linear(2, 2).to(0), [torch.randn(30, 2).to(0)])
|
|
|
|
@requires_gloo()
|
|
@skip_but_pass_in_sandcastle_if(not TEST_CUDA, "At least 1 CUDA GPUS needed")
|
|
def test_rnn(self):
|
|
# This test is inspired by the bug reported in
|
|
# https://github.com/pytorch/pytorch/issues/36268
|
|
BATCH_SIZE = 12 # Divisible by 2, 3, 4
|
|
INPUT_DIM = 256
|
|
OUTPUT_DIM = 256
|
|
HIDDEN_DIM = 256
|
|
N_LAYERS = 3
|
|
SEQ_LEN = 100
|
|
|
|
class Net(nn.Module):
|
|
def __init__(self, input_dim, hidden_dim, output_dim, hidden_layers):
|
|
super().__init__()
|
|
self.input_dim = input_dim
|
|
self.hidden_dim = hidden_dim
|
|
self.output_dim = output_dim
|
|
self.hidden_layers = hidden_layers
|
|
|
|
self.lstm = nn.LSTM(
|
|
input_dim, hidden_dim, hidden_layers, batch_first=True
|
|
)
|
|
self.h2o = nn.Linear(hidden_dim, output_dim)
|
|
|
|
def forward(self, x, y):
|
|
self.lstm.flatten_parameters()
|
|
h_t, _ = self.lstm(x)
|
|
output = self.h2o(h_t)
|
|
loss = nn.functional.mse_loss(output, y)
|
|
return loss
|
|
|
|
net = Net(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, N_LAYERS).to(0)
|
|
inp = [
|
|
torch.randn((BATCH_SIZE, SEQ_LEN, INPUT_DIM)).to(0),
|
|
torch.rand((BATCH_SIZE, SEQ_LEN, OUTPUT_DIM)).to(0),
|
|
]
|
|
|
|
# Not checking result allclose as the parameter inconsistency exist
|
|
# prior to this change. See #37079
|
|
self._test_base(net, inp, check_allclose=False)
|
|
|
|
|
|
# Skip dev-asan as torch + multiprocessing spawn have known issues
|
|
if not TEST_WITH_DEV_DBG_ASAN:
|
|
|
|
class TestDistributedNNFunctionsGloo(TestDistributedNNFunctions):
|
|
# Test Common Ops First.
|
|
@requires_gloo()
|
|
@skip_if_lt_x_gpu(2)
|
|
@skip_but_pass_in_sandcastle_if(
|
|
not _torch_dist_nn_available, "torch.distributed.nn is not available"
|
|
)
|
|
def test_broadcast(self):
|
|
self._test_broadcast("gloo")
|
|
|
|
@requires_gloo()
|
|
@skip_if_lt_x_gpu(2)
|
|
@skip_but_pass_in_sandcastle_if(
|
|
not _torch_dist_nn_available, "torch.distributed.nn is not available"
|
|
)
|
|
def test_reduce(self):
|
|
self._test_reduce("gloo")
|
|
|
|
@requires_gloo()
|
|
@skip_if_lt_x_gpu(2)
|
|
@skip_but_pass_in_sandcastle_if(
|
|
not _torch_dist_nn_available, "torch.distributed.nn is not available"
|
|
)
|
|
def test_allreduce(self):
|
|
self._test_allreduce("gloo")
|
|
|
|
@requires_gloo()
|
|
@skip_if_lt_x_gpu(2)
|
|
@skip_but_pass_in_sandcastle_if(
|
|
not _torch_dist_nn_available, "torch.distributed.nn is not available"
|
|
)
|
|
def test_all_gather(self):
|
|
self._test_all_gather("gloo")
|
|
|
|
@requires_gloo()
|
|
@skip_if_lt_x_gpu(2)
|
|
@skip_but_pass_in_sandcastle_if(
|
|
not _torch_dist_nn_available, "torch.distributed.nn is not available"
|
|
)
|
|
def test_all_to_all(self):
|
|
self._test_all_to_all("gloo")
|
|
|
|
@requires_gloo()
|
|
@skip_if_lt_x_gpu(2)
|
|
@skip_but_pass_in_sandcastle_if(
|
|
not _torch_dist_nn_available, "torch.distributed.nn is not available"
|
|
)
|
|
def test_all_to_all_single(self):
|
|
self._test_all_to_all_single("gloo")
|
|
|
|
# Test Ops only supported in GLOO.
|
|
@requires_gloo()
|
|
@skip_if_lt_x_gpu(2)
|
|
@skip_but_pass_in_sandcastle_if(
|
|
not _torch_dist_nn_available, "torch.distributed.nn is not available"
|
|
)
|
|
def test_gather(self):
|
|
store = c10d.FileStore(self.file_name, self.world_size)
|
|
# This is required because these functions calls directly to the .dist and needs
|
|
# the world to be initialized
|
|
c10d.init_process_group(
|
|
store=store, rank=self.rank, world_size=self.world_size, backend="gloo"
|
|
)
|
|
device = torch.device(f"cuda:{self.rank}")
|
|
x = torch.ones(5, 5, device=device) + self.rank
|
|
x.requires_grad = True
|
|
tensors = torch.distributed.nn.gather(x, 1)
|
|
if self.rank == 1:
|
|
for i, t in enumerate(tensors):
|
|
self.assertEqual(t, torch.ones(5, 5, device=device) + i)
|
|
elif self.rank == 0:
|
|
for i, t in enumerate(tensors):
|
|
zeros = torch.zeros(5, 5, device=device)
|
|
self.assertEqual(t, zeros)
|
|
y = torch.sum(torch.stack(tensors), axis=0)
|
|
z = y.sin().sum()
|
|
z.backward()
|
|
|
|
# Test gradient
|
|
x_s = 3 * torch.ones(5, 5, device=device)
|
|
self.assertEqual(x.grad, x_s.cos())
|
|
|
|
@requires_gloo()
|
|
@skip_if_lt_x_gpu(2)
|
|
@skip_but_pass_in_sandcastle_if(
|
|
not _torch_dist_nn_available, "torch.distributed.nn is not available"
|
|
)
|
|
def test_scatter(self):
|
|
store = c10d.FileStore(self.file_name, self.world_size)
|
|
# This is required because these functions calls directly to the .dist and needs
|
|
# the world to be initialized
|
|
c10d.init_process_group(
|
|
store=store, rank=self.rank, world_size=self.world_size, backend="gloo"
|
|
)
|
|
device = torch.device(f"cuda:{self.rank}")
|
|
x0 = torch.ones(5, 5, device=device)
|
|
x1 = torch.ones(5, 5, device=device) + 1
|
|
x0.requires_grad = True
|
|
x1.requires_grad = True
|
|
|
|
y = torch.distributed.nn.scatter([x0, x1], 1)
|
|
if self.rank == 1:
|
|
self.assertEqual(y, 1 + torch.ones(5, 5, device=device))
|
|
elif self.rank == 0:
|
|
self.assertEqual(y, torch.ones(5, 5, device=device))
|
|
z = y.sin().sum()
|
|
z.backward()
|
|
|
|
# Test gradient
|
|
if self.rank == 1:
|
|
x0_s = torch.ones(5, 5, device=device).cos()
|
|
x1_s = (2 * torch.ones(5, 5, device=device)).cos()
|
|
self.assertEqual(x0.grad, x0_s)
|
|
self.assertEqual(x1.grad, x1_s)
|
|
if self.rank == 0:
|
|
self.assertEqual(x0.grad, torch.zeros(5, 5, device=device))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_tests()
|