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# Owner(s): ["oncall: distributed"]
import copy
import os
import sys
import tempfile
import test_c10d_spawn
import torch
import torch.distributed as c10d
import torch.nn as nn
from test_c10d_spawn import _torch_dist_nn_available, TestDistributedNNFunctions
from torch.testing._internal.common_cuda import TEST_CUDA, TEST_MULTIGPU
from torch.testing._internal.common_distributed import requires_gloo, \
create_device, skip_if_lt_x_gpu
from torch.testing._internal.common_utils import TestCase, run_tests, sandcastle_skip_if, TEST_WITH_DEV_DBG_ASAN
# Fails on Python-3.9, see https://github.com/pytorch/pytorch/issues/51619
if sys.version_info < (3, 9):
class ProcessGroupShareTensorTest(test_c10d_spawn.AbstractProcessGroupShareTensorTest, TestCase):
@classmethod
def opts(cls, threads=2):
opts = c10d.ProcessGroupGloo._Options()
opts._timeout = 5.0
opts._devices = [create_device(interface='lo')]
opts._threads = threads
return opts
@classmethod
def _init_pg_gloo(cls, rank, filename, world_size):
store = c10d.FileStore(filename, world_size)
return c10d.ProcessGroupGloo(
store, rank, world_size, ProcessGroupShareTensorTest.opts())
@sandcastle_skip_if(not TEST_MULTIGPU, "At least 2 CUDA GPUS needed")
def test_shared_broadcast_gloo(self):
self._test_multiprocess(
ProcessGroupShareTensorTest._test_broadcast_process,
[torch.ones(2, 2).to(i) * i for i in range(self.world_size)],
ProcessGroupShareTensorTest._init_pg_gloo,
1)
@sandcastle_skip_if(not TEST_MULTIGPU, "At least 2 CUDA GPUS needed")
def test_shared_allreduce_gloo(self):
self._test_multiprocess(
ProcessGroupShareTensorTest._test_allreduce_process,
[torch.ones(2, 2).to(i) for i in range(self.world_size)],
ProcessGroupShareTensorTest._init_pg_gloo,
1)
@sandcastle_skip_if(not TEST_MULTIGPU, "At least 2 CUDA GPUS needed")
def test_shared_allgather_gloo(self):
self._test_multiprocess(
ProcessGroupShareTensorTest._test_allgather_process,
[torch.ones(2, 2).to(i) * i for i in range(self.world_size)],
ProcessGroupShareTensorTest._init_pg_gloo,
self.world_size)
@classmethod
def _test_allgather_chunk_process(
cls, rank, filename, shared_tensor, world_size, init_pg, c2p, p2c):
pg = init_pg(rank, filename, world_size)
chunks = torch.chunk(shared_tensor, world_size, dim=0)
x = chunks[rank]
ys = [torch.zeros_like(x) for _ in range(world_size)]
pg.allgather(ys, x).wait()
c2p.put((rank, chunks[0].to("cpu"), ys[0].to("cpu")))
c2p.put((rank, chunks[1].to("cpu"), ys[1].to("cpu")))
p2c.get()
@sandcastle_skip_if(not TEST_MULTIGPU, "At least 2 CUDA GPUS needed")
def test_shared_allgather_chunk_gloo(self):
self._test_multiprocess(
ProcessGroupShareTensorTest._test_allgather_chunk_process,
torch.tensor(range(4)).reshape(2, 2),
ProcessGroupShareTensorTest._init_pg_gloo,
self.world_size)
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)
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size)
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()
@sandcastle_skip_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()
@sandcastle_skip_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(Net, self).__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)
@sandcastle_skip_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)
@sandcastle_skip_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)
@sandcastle_skip_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)
@sandcastle_skip_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)
@sandcastle_skip_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)
@sandcastle_skip_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)
@sandcastle_skip_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)
@sandcastle_skip_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()
|
