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# Owner(s): ["oncall: distributed"]
import os
import sys
import tempfile
import torch
import torch.distributed as c10d
import torch.multiprocessing as mp
from torch.testing._internal.common_distributed import \
MultiProcessTestCase
from torch.testing._internal.common_utils import load_tests,\
NO_MULTIPROCESSING_SPAWN
# Torch distributed.nn is not available in windows
# check #42095, it errors on import.
_torch_dist_nn_available = True
try:
import torch.distributed.nn
except ImportError:
_torch_dist_nn_available = False
# load_tests from common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
if not c10d.is_available():
print('c10d not available, skipping tests', file=sys.stderr)
sys.exit(0)
if NO_MULTIPROCESSING_SPAWN:
print('spawn not available, skipping tests', file=sys.stderr)
sys.exit(0)
class AbstractProcessGroupShareTensorTest(object):
world_size = 2
def _test_multiprocess(self, f, shared_tensors, init_pg, n_output):
ws = self.world_size
# file store will delete the test file on destruction
file = tempfile.NamedTemporaryFile(delete=False)
ctx = mp.get_context('spawn')
c2p = ctx.Queue(2)
p2c = ctx.Queue(2)
ps = []
for i in range(ws):
p = ctx.Process(
target=f,
args=(i, file.name, shared_tensors, ws, init_pg, c2p, p2c))
p.start()
ps.append(p)
for _ in range(ws * n_output):
pid, expected, result = c2p.get()
self.assertEqual(
expected,
result,
msg=(
"Expect rank {} to receive tensor {} but got {}."
).format(pid, expected, result)
)
for _ in range(ws):
p2c.put(0)
for p in ps:
p.join(2)
# Why classmethod? multiprocessing cannot pickle TestCase subclass when in
# spawn mode. See https://bugs.python.org/issue33884.
@classmethod
def _test_broadcast_process(
cls, rank, filename, shared_tensors, world_size, init_pg, c2p, p2c):
pg = init_pg(rank, filename, world_size)
xs = [shared_tensors[rank]]
pg.broadcast(xs).wait()
c2p.put((rank, torch.zeros(2, 2), xs[0].to("cpu")))
p2c.get()
@classmethod
def _test_allreduce_process(
cls, rank, filename, shared_tensors, world_size, init_pg, c2p, p2c):
pg = init_pg(rank, filename, world_size)
xs = [shared_tensors[rank]]
pg.allreduce(xs, op=c10d.ReduceOp.SUM).wait()
c2p.put((rank, torch.ones(2, 2) * 2, xs[0].to("cpu")))
p2c.get()
@classmethod
def _test_allgather_process(
cls, rank, filename, shared_tensors, world_size, init_pg, c2p, p2c):
pg = init_pg(rank, filename, world_size)
xs = [shared_tensors[rank]]
ys = [[torch.zeros_like(xs[0]) for i in range(world_size)]]
pg.allgather(ys, xs).wait()
for i in range(world_size):
c2p.put((rank, torch.ones(2, 2) * i, ys[0][i].to("cpu")))
p2c.get()
class TestDistributedNNFunctions(MultiProcessTestCase):
def setUp(self):
super(TestDistributedNNFunctions, self).setUp()
self._spawn_processes()
def tearDown(self):
super(TestDistributedNNFunctions, self).tearDown()
try:
os.remove(self.file_name)
except OSError:
pass
@property
def op_timeout_sec(self):
return 1
@property
def world_size(self):
return 2
def _test_broadcast(self, backend):
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=backend
)
device = torch.device(f"cuda:{self.rank}")
x = torch.ones(5, 5, device=device) + self.rank
x.requires_grad = True
y = torch.distributed.nn.broadcast(x, 1)
self.assertEqual(y, 1 + torch.ones(5, 5))
z = y.sin().sum()
z.backward()
# We can't check the gradient of communications numerically so we have to do some calculations
if self.rank == 1:
self.assertEqual(x.grad, 2 * torch.cos(x))
elif self.rank == 0:
self.assertEqual(x.grad, torch.zeros(5, 5, device=device))
def _test_reduce(self, backend):
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=backend
)
device = torch.device(f"cuda:{self.rank}")
x = torch.ones(5, 5, device=device) + self.rank
x.requires_grad = True
y = torch.distributed.nn.reduce(x, 1, op=c10d.ReduceOp.SUM)
if self.rank == 1:
self.assertEqual(y, 3 * torch.ones(5, 5, device=device))
z = y.sin().sum()
z.backward()
# Gradients are broadcasted to both ranks
x_g = (3 * torch.ones(5, 5, device=device)).cos()
self.assertEqual(x.grad, x_g)
def _test_allreduce(self, backend):
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=backend
)
device = torch.device(f"cuda:{self.rank}")
x = torch.ones(5, 5, device=device) + self.rank
x.requires_grad = True
y = torch.distributed.nn.all_reduce(x, op=c10d.ReduceOp.SUM)
self.assertEqual(y, 3 * torch.ones(5, 5, device=device))
z = y.sin().sum()
z.backward()
x_g = 2 * (3 * torch.ones(5, 5, device=device)).cos()
self.assertEqual(x.grad, x_g)
def _test_all_gather(self, backend):
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=backend
)
device = torch.device(f"cuda:{self.rank}")
x = torch.ones(5, 5, device=device) + self.rank
x.requires_grad = True
tensors = torch.distributed.nn.all_gather(x)
for i, t in enumerate(tensors):
self.assertEqual(t, torch.ones(5, 5, device=device) + i)
y = torch.sum(torch.stack(tensors), axis=0)
z = y.sin().sum()
z.backward()
x_s = 2 * (3 * torch.ones(5, 5, device=device)).cos()
self.assertEqual(x.grad, x_s)
def _test_all_to_all(self, backend):
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=backend
)
device = torch.device(f"cuda:{self.rank}")
x0 = torch.ones(5, 5, device=device) + 2 * self.rank
x1 = torch.ones(5, 5, device=device) + 2 * self.rank
x0.requires_grad = True
x1.requires_grad = True
y0 = torch.empty_like(x0)
y1 = torch.empty_like(x1)
tensors = torch.distributed.nn.all_to_all([y0, y1], [x0, x1])
for i, t in enumerate(tensors):
self.assertEqual(t, torch.ones(5, 5, device=device) + 2 * i)
y = torch.sum(torch.stack(tensors), axis=0)
z = y.sin().sum()
z.backward()
x_s = (4 * torch.ones(5, 5, device=device)).cos()
self.assertEqual(x0.grad, x_s)
self.assertEqual(x1.grad, x_s)
def _test_all_to_all_single(self, backend):
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=backend
)
device = torch.device(f"cuda:{self.rank}")
row = self.world_size * (self.rank + 1) * (self.world_size + 1) / 2
x = torch.ones(int(row), 5, device=device) * (self.rank + 1)
x.requires_grad = True
y = torch.empty_like(x)
split_sizes = [(i + 1) * (self.rank + 1) for i in range(self.world_size)]
y = torch.distributed.nn.all_to_all_single(
y, x, output_split_sizes=split_sizes, input_split_sizes=split_sizes
)
expected = []
for idx, tensor in enumerate(torch.split(x, split_sizes)):
expected.append(torch.full_like(tensor, (idx + 1)))
expected = torch.cat(expected)
self.assertEqual(y, expected)
z = y.sin().sum()
z.backward()
x_s = ((self.rank + 1) * torch.ones(int(row), 5, device=device)).cos()
self.assertEqual(x.grad, x_s)
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