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android / platform / external / pytorch / 8746f90cf6 / . / test / test_c10d.py
blob: 6320da915b90b874165aa80bfef42b462ae47ec2 [file] [log] [blame]
from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import math
import os
import random
import signal
import sys
import tempfile
import threading
import time
import unittest
from datetime import timedelta
from sys import platform
from itertools import groupby
from functools import partial, reduce
import operator
import torch
from torch._six import string_classes
import common_utils as common
from torch import nn
import torch.nn.functional as F
import torch.distributed as c10d
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel
from common_distributed import MultiProcessTestCase, \
requires_gloo, requires_nccl, requires_nccl_version, \
skip_if_not_multigpu, skip_if_lt_x_gpu, skip_for_known_issues, get_timeout, skip_if_rocm
from common_utils import TestCase, load_tests, run_tests, retry_on_address_already_in_use_error, TEST_WITH_TSAN
# 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')
sys.exit(0)
if platform == 'darwin':
LOOPBACK = 'lo0'
else:
LOOPBACK = 'lo'
def gpus_for_rank(world_size):
"""Multigpu tests are designed to simulate the multi nodes with multi
GPUs on each node. Nccl backend requires equal #GPUs in each process.
On a single node, all visible GPUs are evenly
divided to subsets, each process only uses a subset.
"""
visible_devices = list(range(torch.cuda.device_count()))
gpus_per_process = torch.cuda.device_count() // world_size
gpus_for_rank = []
for rank in range(world_size):
gpus_for_rank.append(visible_devices[rank * gpus_per_process: (rank + 1) * gpus_per_process])
return gpus_for_rank
def simple_reduce_tests(rank, world_size):
tests = [
(
c10d.ReduceOp.SUM,
torch.tensor([rank + 1.0]),
torch.tensor([float(world_size * (world_size + 1) / 2)]),
),
(
c10d.ReduceOp.PRODUCT,
torch.tensor([rank + 1.0]),
torch.tensor([float(math.factorial(world_size))]),
),
(
c10d.ReduceOp.MIN,
torch.tensor([rank + 1.0]),
torch.tensor([1.0]),
),
(
c10d.ReduceOp.MAX,
torch.tensor([rank + 1.0]),
torch.tensor([world_size]),
),
]
# Generate tests for BAND.
# The bit that is set changes in every iteration to check
# that the output changes accordingly.
for i in range(4):
vin = rank | (1 << i)
vout = (1 << i)
tests.append(
(
c10d.ReduceOp.BAND,
torch.tensor([vin], dtype=torch.int32),
torch.tensor([vout], dtype=torch.int32),
),
)
# Generate tests for BOR.
# These emulate a larger world size per iteration by having every
# rank contribute multiple values that are pre-OR'ed.
for i in range(1, 5):
vin = reduce(operator.or_, [rank * i + j for j in range(i)])
vout = reduce(operator.or_, range(world_size * i))
tests.append(
(
c10d.ReduceOp.BOR,
torch.tensor([vin], dtype=torch.int32),
torch.tensor([vout], dtype=torch.int32),
),
)
# Generate tests for XOR.
# These emulate a larger world size per iteration by having every
# rank contribute multiple values that are pre-XOR'ed.
for i in range(1, 5):
vin = reduce(operator.xor, [rank * i + j for j in range(i)])
vout = reduce(operator.xor, range(world_size * i))
tests.append(
(
c10d.ReduceOp.BXOR,
torch.tensor([vin], dtype=torch.int32),
torch.tensor([vout], dtype=torch.int32),
),
)
return tests
def simple_coalesced_reduce_tests(rank, world_size):
return [
(
c10d.ReduceOp.SUM,
[torch.tensor([rank + 1]), torch.tensor([(rank + 1) ** 2])],
[
torch.tensor([float(world_size * (world_size + 1) / 2)]),
torch.tensor([float(world_size * (world_size + 1) * (2 * world_size + 1) / 6)])
]
),
(
c10d.ReduceOp.PRODUCT,
[torch.tensor([rank + 1.0]), torch.tensor([rank + 2.0])],
[
torch.tensor([float(math.factorial(world_size))]),
torch.tensor([float(math.factorial(world_size + 1))])
]
),
(
c10d.ReduceOp.MIN,
[torch.tensor([rank + x]) for x in [0.0, 1.0]],
[torch.tensor([0.0]), torch.tensor([1.0])]
),
(
c10d.ReduceOp.MAX,
[torch.tensor([rank + x]) for x in [1.0, 2.0]],
[torch.tensor([world_size]), torch.tensor([world_size + 1.0])]
)
]
def simple_multi_input_reduce_tests(rank, world_size):
return [
(
c10d.ReduceOp.SUM,
[torch.tensor([2 * rank + 0.0]), torch.tensor([2 * rank + 1.0])],
torch.tensor([float(world_size * (2 * world_size - 1))]),
),
(
c10d.ReduceOp.PRODUCT,
[torch.tensor([2 * rank + 1.0]), torch.tensor([2 * rank + 2.0])],
torch.tensor([float(math.factorial(2 * world_size))]),
),
(
c10d.ReduceOp.MIN,
[torch.tensor([2 * rank + 1.0]), torch.tensor([2 * rank + 2.0])],
torch.tensor([1.0]),
),
(
c10d.ReduceOp.MAX,
[torch.tensor([2 * rank + 1.0]), torch.tensor([2 * rank + 2.0])],
torch.tensor([2 * world_size]),
),
]
def simple_sparse_reduce_tests(rank, world_size, num_inputs=1):
"""
Generate a number of basic test cases for sparse reduction.
These cover tensors with a varying number of sparse dimensions and a varying
number of dense dimensions. The only reduction operation we support is sum.
"""
def generate(rank, world_size, sparse_dims=1, dense_dims=0):
# First sparse dimension is [0..rank].
# Subsequent dimensions are always 0, so we know there is
# a non-empty intersection between any two sparse tensors.
indices = [range(rank + 1)]
shape = [world_size] + [2 for _ in range(dense_dims)]
for _ in range(sparse_dims - 1):
indices.append([0] * (rank + 1))
shape.append(world_size)
values = torch.ones([rank + 1] + [2 for _ in range(dense_dims)])
return torch.sparse_coo_tensor(indices, values, shape)
def compute_sum(fn, world_size):
return reduce(lambda a, b: a + b, [fn(rank, world_size) for rank in range(world_size)])
return [
(
[
fn(num_inputs * rank + i, num_inputs * world_size)
for i in range(num_inputs)
],
[
compute_sum(fn, num_inputs * world_size)
for i in range(num_inputs)
],
)
for fn in [
partial(generate, sparse_dims=1),
partial(generate, sparse_dims=2),
partial(generate, sparse_dims=3),
partial(generate, dense_dims=1),
partial(generate, dense_dims=2),
partial(generate, dense_dims=3),
]
]
class StoreTestBase(object):
def _create_store(self, i):
raise RuntimeError("not implemented")
def _test_set_get(self, fs):
fs.add("key", 1)
fs.add("key", 2)
fs.add("key", 3)
fs.set("key0", "value0")
fs.add("key3", 1)
fs.set("key1", "value1")
fs.add("key3", 2)
fs.set("key2", "value2")
fs.add("key3", 3)
fs.add("key3", 4)
fs.add("key3", 5)
fs.add("key3", 6)
self.assertEqual(b"6", fs.get("key"))
self.assertEqual(b"value0", fs.get("key0"))
self.assertEqual(b"value1", fs.get("key1"))
self.assertEqual(b"value2", fs.get("key2"))
self.assertEqual(b"21", fs.get("key3"))
def test_set_get(self):
self._test_set_get(self._create_store())
class FileStoreTest(TestCase, StoreTestBase):
def setUp(self):
super(FileStoreTest, self).setUp()
self.file = tempfile.NamedTemporaryFile(delete=False)
def _create_store(self):
store = c10d.FileStore(self.file.name, 1)
store.set_timeout(timedelta(seconds=300))
return store
class PrefixFileStoreTest(TestCase, StoreTestBase):
def setUp(self):
super(PrefixFileStoreTest, self).setUp()
self.file = tempfile.NamedTemporaryFile(delete=False)
self.filestore = c10d.FileStore(self.file.name, 1)
self.prefix = "test_prefix"
self.filestore.set_timeout(timedelta(seconds=300))
def _create_store(self):
return c10d.PrefixStore(self.prefix, self.filestore)
def create_tcp_store(addr):
"""
Creates a TCP store. Retries if the chosen port is already in use.
"""
ports = []
for _ in range(10):
try:
port = common.find_free_port()
ports.append(port)
return c10d.TCPStore(addr, port, 1, True)
except RuntimeError as error:
if str(error) == "Address already in use":
continue
raise
raise RuntimeError("Unable to find free port (tried %s)" % ", ".join(ports))
class TCPStoreTest(TestCase, StoreTestBase):
def _create_store(self):
store = create_tcp_store('localhost')
store.set_timeout(timedelta(seconds=300))
return store
def test_address_already_in_use(self):
with self.assertRaisesRegex(RuntimeError, "^Address already in use$"):
addr = 'localhost'
port = common.find_free_port()
# Use noqa to silence flake8.
# Need to store in an unused variable here to ensure the first
# object is not destroyed before the second object is created.
store1 = c10d.TCPStore(addr, port, 1, True) # noqa: F841
store2 = c10d.TCPStore(addr, port, 1, True) # noqa: F841
class PrefixTCPStoreTest(TestCase, StoreTestBase):
def setUp(self):
super(PrefixTCPStoreTest, self).setUp()
self.tcpstore = create_tcp_store('localhost')
self.prefix = "test_prefix"
self.tcpstore.set_timeout(timedelta(seconds=300))
def _create_store(self):
return c10d.PrefixStore(self.prefix, self.tcpstore)
class MyPythonStore(c10d.Store):
def __init__(self):
super(MyPythonStore, self).__init__()
self.store = dict()
def set(self, key, value):
if not isinstance(key, string_classes):
raise AssertionError("Expected set to be called with string key")
if type(value) is not bytes:
raise AssertionError("Expected set to be called with bytes value")
self.store[key] = value
def get(self, key):
value = self.store.get(key, b"")
if type(value) is not bytes:
raise AssertionError("Expected get to return bytes value")
return value
def add(self, key, value):
new = int(self.store.get(key, 0)) + value
self.set(key, bytes(str(new).encode("utf-8")))
return new
class PythonStoreTest(TestCase):
def setUp(self):
super(PythonStoreTest, self).setUp()
def test_set_get(self):
# If we were to inherit from StoreTestBase and try to use
# its test_set_get function, we would exercise the Python
# API directly, instead of going through the C++ trampoline.
# We care about testing the C++ trampoline, so run the
# equivalent of StoreTestBase.test_set_get from C++.
# See `torch/csrc/distributed/c10d/init.cpp` for the definition
# of this test function.
c10d._test_python_store(MyPythonStore())
class RendezvousTest(TestCase):
def test_unknown_handler(self):
with self.assertRaisesRegex(RuntimeError, "^No rendezvous handler"):
c10d.rendezvous('invalid://')
class RendezvousEnvTest(TestCase):
@retry_on_address_already_in_use_error
def test_common_errors(self):
# TODO remove this hack
if not hasattr(c10d, "ProcessGroupNCCL"):
raise unittest.SkipTest("C10D is not built with NCCL process group,"
" skipping test")
vars = {
"WORLD_SIZE": "1",
"RANK": "0",
"MASTER_ADDR": "127.0.0.1",
"MASTER_PORT": common.find_free_port(),
}
class Env(object):
def __init__(self, vars):
self.vars = vars
def __enter__(self):
for key, value in self.vars.items():
os.environ[key] = str(value)
def __exit__(self, type, value, traceback):
for key in self.vars.keys():
del os.environ[key]
def without(d, key):
d = d.copy()
d.pop(key)
return d
def withouts(d, keys):
d = d.copy()
for key in keys:
d.pop(key)
return d
with Env(without(vars, 'WORLD_SIZE')):
with self.assertRaisesRegex(ValueError, 'WORLD_SIZE expected'):
gen = c10d.rendezvous('env://')
next(gen)
c10d.init_process_group(backend='nccl', world_size=1)
self.assertEqual(c10d.get_rank(), 0)
self.assertEqual(c10d.get_world_size(), 1)
c10d.destroy_process_group()
with Env(without(vars, 'RANK')):
with self.assertRaisesRegex(ValueError, 'RANK expected'):
gen = c10d.rendezvous('env://')
next(gen)
c10d.init_process_group(backend='nccl', rank=0)
self.assertEqual(c10d.get_rank(), 0)
self.assertEqual(c10d.get_world_size(), 1)
c10d.destroy_process_group()
with Env(withouts(vars, ['RANK', 'WORLD_SIZE'])):
c10d.init_process_group(backend='nccl', rank=0, world_size=1)
self.assertEqual(c10d.get_rank(), 0)
self.assertEqual(c10d.get_world_size(), 1)
c10d.destroy_process_group()
with Env(vars):
c10d.init_process_group(backend='nccl')
self.assertEqual(c10d.get_rank(), 0)
self.assertEqual(c10d.get_world_size(), 1)
c10d.destroy_process_group()
with Env(without(vars, 'MASTER_ADDR')):
with self.assertRaisesRegex(ValueError, 'MASTER_ADDR expected'):
gen = c10d.rendezvous('env://')
next(gen)
with Env(without(vars, 'MASTER_PORT')):
with self.assertRaisesRegex(ValueError, 'MASTER_PORT expected'):
gen = c10d.rendezvous('env://')
next(gen)
with Env(without(vars, 'WORLD_SIZE')):
gen = c10d.rendezvous('env://?world_size={}'.format(1))
_, _, size = next(gen)
self.assertEqual(size, 1)
with Env(without(vars, 'RANK')):
gen = c10d.rendezvous('env://?rank={}'.format(0))
_, rank, _ = next(gen)
self.assertEqual(rank, 0)
with Env(withouts(vars, ['RANK', 'WORLD_SIZE'])):
gen = c10d.rendezvous('env://?rank={}&world_size={}'.format(0, 1))
_, rank, size = next(gen)
self.assertEqual(rank, 0)
self.assertEqual(size, 1)
@retry_on_address_already_in_use_error
def test_nominal(self):
os.environ['WORLD_SIZE'] = '1'
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = str(common.find_free_port())
# Single rank
os.environ['RANK'] = '0'
gen0 = c10d.rendezvous('env://')
store0, rank0, size0 = next(gen0)
self.assertEqual(0, rank0)
self.assertEqual(1, size0)
store0.set("key0", "value0")
# check with get
self.assertEqual(b"value0", store0.get("key0"))
class RendezvousFileTest(TestCase):
def test_common_errors(self):
with self.assertRaisesRegex(ValueError, 'path missing'):
gen = c10d.rendezvous('file://?rank=0&world_size=1')
next(gen)
with self.assertRaisesRegex(ValueError, 'rank parameter missing'):
gen = c10d.rendezvous('file:///tmp/foo?world_size=1')
next(gen)
with self.assertRaisesRegex(ValueError, 'size parameter missing'):
gen = c10d.rendezvous('file:///tmp/foo?rank=0')
next(gen)
def test_nominal(self):
with tempfile.NamedTemporaryFile(delete=False) as file:
url = 'file://%s?world_size=%d' % (file.name, 2)
gen0 = c10d.rendezvous(url + "&rank=0")
store0, rank0, size0 = next(gen0)
self.assertEqual(0, rank0)
self.assertEqual(2, size0)
gen1 = c10d.rendezvous(url + "&rank=1")
store1, rank1, size1 = next(gen1)
self.assertEqual(1, rank1)
self.assertEqual(2, size1)
# Set value on both stores
store0.set("key0", "value0")
store1.set("key1", "value1")
# Cross check with get
self.assertEqual(b"value0", store1.get("key0"))
self.assertEqual(b"value1", store0.get("key1"))
class RendezvousTCPTest(TestCase):
def test_common_errors(self):
with self.assertRaisesRegex(ValueError, 'port number missing'):
gen = c10d.rendezvous('tcp://127.0.0.1?rank=0&world_size=1')
next(gen)
with self.assertRaisesRegex(ValueError, 'rank parameter missing'):
gen = c10d.rendezvous('tcp://127.0.0.1:23456?world_size=1')
next(gen)
with self.assertRaisesRegex(ValueError, 'size parameter missing'):
gen = c10d.rendezvous('tcp://127.0.0.1:23456?rank=0')
next(gen)
@retry_on_address_already_in_use_error
def test_nominal(self):
addr = 'localhost'
port = common.find_free_port()
url = 'tcp://%s:%d?world_size=%d' % (addr, port, 1)
gen0 = c10d.rendezvous(url + "&rank=0")
store0, rank0, size0 = next(gen0)
self.assertEqual(0, rank0)
self.assertEqual(1, size0)
# Set value on the single store
store0.set("key0", "value0")
# check with get
self.assertEqual(b"value0", store0.get("key0"))
class TimeoutTest(TestCase):
def _test_store_timeout(self, backend, init_method, c2p):
try:
c10d.distributed_c10d.init_process_group(
backend=backend, init_method=init_method, world_size=1, rank=0,
timeout=timedelta(seconds=1))
default_store = c10d.distributed_c10d._get_default_store()
tik = time.time()
with self.assertRaisesRegex(RuntimeError, "Timeout"):
default_store.get("nonexistent key")
tok = time.time()
c10d.destroy_process_group()
c2p.append(float(tok - tik))
except RuntimeError as e:
# catch "Address already in use" error and report it to the main
# thread
c2p.append(e)
def _init_methods(self):
f = tempfile.NamedTemporaryFile(delete=False)
yield "file://%s" % f.name
f.close()
yield "tcp://127.0.0.1:%d" % common.find_free_port()
def _test_default_store_timeout(self, backend):
for init_method in self._init_methods():
c2p = []
t = threading.Thread(
target=self._test_store_timeout,
args=(backend, init_method, c2p))
t.daemon = True
t.start()
t.join(5)
self.assertEqual(1, len(c2p))
if isinstance(c2p[0], float):
# waiting time should be 1s, use 3s to rule out false alarm
self.assertGreater(3, c2p[0])
elif isinstance(c2p[0], RuntimeError):
# let @retry_on_address_already_in_use_error handle the error
raise c2p[0]
else:
raise RuntimeError("Unexpected type {}".format(type(c2p[0])))
@requires_nccl()
@retry_on_address_already_in_use_error
def test_default_store_timeout_nccl(self):
self._test_default_store_timeout('nccl')
@requires_gloo()
@retry_on_address_already_in_use_error
def test_default_store_timeout_gloo(self):
self._test_default_store_timeout('gloo')
@requires_gloo()
@unittest.skipIf(TEST_WITH_TSAN, "TSAN is not fork-safe since we're forking in a multi-threaded environment")
class ProcessGroupGlooTest(MultiProcessTestCase):
def setUp(self):
super(ProcessGroupGlooTest, self).setUp()
self._fork_processes()
def opts(self, threads=2):
opts = c10d.ProcessGroupGloo.Options()
opts.devices = [c10d.ProcessGroupGloo.create_device(interface=LOOPBACK)]
opts.timeout = 5.0
opts.threads = threads
return opts
def test_multi_device_constructor(self):
store = c10d.FileStore(self.file_name, self.world_size)
opts = c10d.ProcessGroupGloo.Options()
opts.timeout = 5.0
opts.devices = [
c10d.ProcessGroupGloo.create_device(interface=LOOPBACK),
c10d.ProcessGroupGloo.create_device(interface=LOOPBACK),
]
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, opts)
# Execute 2x the number of operations to ensure we use every device.
for work in [pg.allreduce(torch.ones(i + 1)) for i in range(4)]:
work.wait()
def test_empty_tensors(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
xs = [torch.FloatTensor([])]
pg.broadcast(xs).wait()
self.assertEqual(0, xs[0].numel())
def test_broadcast_checks(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
t1 = torch.zeros([1], dtype=torch.float32)
t2 = torch.zeros([1], dtype=torch.float64)
t3 = torch.zeros([2], dtype=torch.float32)
with self.assertRaisesRegex(ValueError, "invalid root rank"):
opts = c10d.BroadcastOptions()
opts.rootRank = -1
opts.rootTensor = 0
pg.broadcast([t1], opts)
with self.assertRaisesRegex(ValueError, "invalid root rank"):
opts = c10d.BroadcastOptions()
opts.rootRank = self.world_size
opts.rootTensor = 0
pg.broadcast([t1], opts)
with self.assertRaisesRegex(ValueError, "invalid root tensor"):
opts = c10d.BroadcastOptions()
opts.rootRank = self.rank
opts.rootTensor = -1
pg.broadcast([t1], opts)
with self.assertRaisesRegex(ValueError, "invalid root tensor"):
opts = c10d.BroadcastOptions()
opts.rootRank = self.rank
opts.rootTensor = 1
pg.broadcast([t1], opts)
with self.assertRaisesRegex(ValueError, "invalid root tensor"):
opts = c10d.BroadcastOptions()
opts.rootRank = self.rank
opts.rootTensor = 0
pg.broadcast([], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor type"):
opts = c10d.BroadcastOptions()
opts.rootRank = self.rank
opts.rootTensor = 0
pg.broadcast([t1, t2], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor size"):
opts = c10d.BroadcastOptions()
opts.rootRank = self.rank
opts.rootTensor = 0
pg.broadcast([t1, t3], opts)
def _test_broadcast_basics(self, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
def broadcast(xs, rootRank, rootTensor):
opts = c10d.BroadcastOptions()
opts.rootRank = rootRank
opts.rootTensor = rootTensor
work = pg.broadcast(xs, opts)
work.wait()
# Every rank is root once
for i in range(self.world_size):
# Run with 1 input tensor
x = fn(torch.tensor([self.rank]))
broadcast([x], i, 0)
self.assertEqual(torch.tensor([i]), x)
# Run with 2 input tensors
num = 2
for j in range(num):
xs = [
fn(torch.tensor([self.rank * num + 0.0])),
fn(torch.tensor([self.rank * num + 1.0])),
]
broadcast(xs, i, j)
self.assertEqual(torch.tensor([i * num + j]), xs[0])
self.assertEqual(torch.tensor([i * num + j]), xs[1])
# Test overloaded convenience function
x = torch.tensor([self.rank + 1.0])
work = pg.broadcast(x, root=0)
work.wait()
self.assertEqual(torch.tensor([1.0]), x)
def test_broadcast_basics(self):
self._test_broadcast_basics(lambda t: t.clone())
@skip_if_not_multigpu
@skip_if_rocm
def test_broadcast_basics_cuda(self):
self._test_broadcast_basics(lambda t: t.clone().cuda())
def _test_broadcast_stress(self, inputs):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts(threads=8))
work_handles = [
pg.broadcast(inputs[i], root=(i % self.world_size))
for i in range(len(inputs))
]
for i, work_handle in enumerate(work_handles):
work_handle.wait()
self.assertEqual(
torch.tensor([
(i * self.world_size) + (i % self.world_size)
]),
inputs[i],
message=("Mismatch in iteration %d" % i),
)
def test_broadcast_stress(self):
inputs = [torch.tensor([i * self.world_size + self.rank]) for i in range(1000)]
self._test_broadcast_stress(inputs)
@skip_if_not_multigpu
@skip_if_rocm
def test_broadcast_stress_cuda(self):
inputs = [torch.tensor([i * self.world_size + self.rank]).cuda() for i in range(1000)]
self._test_broadcast_stress(inputs)
def test_allreduce_checks(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
t1 = torch.zeros([1], dtype=torch.float32)
t2 = torch.zeros([1], dtype=torch.float64)
t3 = torch.zeros([2], dtype=torch.float32)
with self.assertRaisesRegex(ValueError, "requires non-empty tensor list"):
opts = c10d.AllreduceOptions()
pg.allreduce([], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor type"):
opts = c10d.AllreduceOptions()
pg.allreduce([t1, t2], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor size"):
opts = c10d.AllreduceOptions()
pg.allreduce([t1, t3], opts)
def _test_allreduce_basics(self, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
# Single input tests
tests = simple_reduce_tests(self.rank, self.world_size)
for (op, input, output) in tests:
opts = c10d.AllreduceOptions()
opts.reduceOp = op
tensor = fn(input)
work = pg.allreduce([tensor], opts)
work.wait()
self.assertEqual(output, tensor)
# Multi input tests
tests = simple_multi_input_reduce_tests(self.rank, self.world_size)
for (op, inputs, output) in tests:
opts = c10d.AllreduceOptions()
opts.reduceOp = op
tensors = [fn(input) for input in inputs]
work = pg.allreduce(tensors, opts)
work.wait()
for tensor in tensors:
self.assertEqual(output, tensor)
# Test overloaded convenience function (defaults to using sum)
x = fn(torch.tensor([self.rank + 1.0]))
work = pg.allreduce(x)
work.wait()
self.assertEqual(torch.tensor([float(self.world_size * (self.world_size + 1) / 2)]), x)
def test_allreduce_basics(self):
self._test_allreduce_basics(lambda t: t.clone())
@skip_if_not_multigpu
@skip_if_rocm
def test_allreduce_basics_cuda(self):
self._test_allreduce_basics(lambda t: t.clone().cuda())
def _test_allreduce_stress(self, inputs):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts(threads=8))
work_handles = [pg.allreduce(inputs[i]) for i in range(len(inputs))]
for i, work_handle in enumerate(work_handles):
work_handle.wait()
self.assertEqual(
torch.tensor([
(i * self.world_size) +
(self.world_size * (self.world_size - 1) / 2)
]),
inputs[i],
message=("Mismatch in iteration %d" % i),
)
def test_allreduce_stress(self):
inputs = [torch.tensor([i + self.rank]) for i in range(1000)]
self._test_allreduce_stress(inputs)
@skip_if_not_multigpu
@skip_if_rocm
def test_allreduce_stress_cuda(self):
inputs = [torch.tensor([i + self.rank]).cuda() for i in range(1000)]
self._test_allreduce_stress(inputs)
def test_allreduce_coalesced_checks(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
t1 = torch.zeros(1, dtype=torch.float32)
t2 = torch.zeros(1, dtype=torch.float64)
t3 = torch.sparse_coo_tensor([[0]], [1], size=(1,))
with self.assertRaisesRegex(ValueError, "requires non-empty tensor list"):
opts = c10d.AllreduceCoalescedOptions()
pg.allreduce_coalesced([], opts)
with self.assertRaisesRegex(ValueError, "tensors must all have the same type"):
opts = c10d.AllreduceCoalescedOptions()
pg.allreduce_coalesced([t1, t2], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor layout at index"):
opts = c10d.AllreduceCoalescedOptions()
pg.allreduce_coalesced([t1, t3], opts)
with self.assertRaisesRegex(ValueError, "unsupported layout"):
opts = c10d.AllreduceCoalescedOptions()
pg.allreduce_coalesced([t3, t3.clone()], opts)
@skip_if_lt_x_gpu(1)
def test_allreduce_coalesced_checks_cuda(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
t1 = torch.zeros(1, dtype=torch.float32)
with self.assertRaisesRegex(ValueError, "unsupported device type"):
opts = c10d.AllreduceCoalescedOptions()
pg.allreduce_coalesced([t1.cuda(), t1.cuda()], opts)
def _test_allreduce_coalesced_basics(self, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
test_cases = simple_coalesced_reduce_tests(self.rank, self.world_size)
for op, inputs, outputs in test_cases:
opts = c10d.AllreduceCoalescedOptions()
opts.reduceOp = op
tensors = [fn(x) for x in inputs]
work = pg.allreduce_coalesced(tensors, opts)
work.wait()
for result_tensor, expected in zip(tensors, outputs):
self.assertEqual(result_tensor, expected)
def test_allreduce_coalesced_basics(self):
self._test_allreduce_coalesced_basics(lambda t: t.clone())
def _test_allreduce_coalesced_stress(self, inputs):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts(threads=8))
work_handles = [pg.allreduce_coalesced(input) for input in inputs]
for i, work_handle in enumerate(work_handles):
work_handle.wait()
self.assertEqual(
2 * [torch.tensor([(i * self.world_size) + (self.world_size * (self.world_size - 1) / 2)])],
inputs[i],
message="Mismatch in interation {}".format(i)
)
def test_allreduce_coalesced_stress(self):
inputs = [2 * [torch.tensor([i + self.rank])] for i in range(1000)]
self._test_allreduce_coalesced_stress(inputs)
def test_sparse_allreduce_checks(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
t1 = torch.zeros([1])
t2 = torch.sparse_coo_tensor([[0]], [1], size=(2,))
t3 = torch.sparse_coo_tensor([[0]], [1], size=(4,))
with self.assertRaisesRegex(ValueError, "requires non-empty tensor list"):
opts = c10d.AllreduceOptions()
pg.allreduce([], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor layout"):
opts = c10d.AllreduceOptions()
pg.allreduce([t1, t2], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor size"):
opts = c10d.AllreduceOptions()
pg.allreduce([t2, t3], opts)
# Sparse allreduce only works with c10d.ReduceOp.SUM.
for op in [c10d.ReduceOp.PRODUCT, c10d.ReduceOp.MIN, c10d.ReduceOp.MAX]:
with self.assertRaisesRegex(ValueError, "unsupported reduction operation"):
opts = c10d.AllreduceOptions()
opts.reduceOp = op
pg.allreduce([t3], opts)
def _test_sparse_allreduce_basics(self, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
for num_inputs_per_rank in [1, 2]:
tests = simple_sparse_reduce_tests(
self.rank,
self.world_size,
num_inputs=num_inputs_per_rank)
for (inputs, outputs) in tests:
work = pg.allreduce([fn(input) for input in inputs])
work.wait()
self.assertEqual(work.result(), outputs)
def test_sparse_allreduce_basics(self):
self._test_sparse_allreduce_basics(lambda t: t)
@skip_if_not_multigpu
@skip_if_rocm
def test_sparse_allreduce_basics_cuda(self):
self._test_sparse_allreduce_basics(lambda t: t.clone().cuda())
def test_scatter_checks(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
t1 = torch.zeros([1], dtype=torch.float32)
t2 = torch.zeros([1], dtype=torch.float64)
t3 = torch.zeros([2], dtype=torch.float32)
with self.assertRaisesRegex(ValueError, "invalid root rank"):
opts = c10d.ScatterOptions()
opts.rootRank = -1
pg.scatter([t1], [], opts)
with self.assertRaisesRegex(ValueError, "invalid root rank"):
opts = c10d.ScatterOptions()
opts.rootRank = self.world_size
pg.scatter([t1], [], opts)
with self.assertRaisesRegex(ValueError, "requires a single-element output tensor list"):
opts = c10d.ScatterOptions()
opts.rootRank = 0
pg.scatter([], [], opts)
with self.assertRaisesRegex(ValueError, "requires a single-element output tensor list"):
opts = c10d.ScatterOptions()
opts.rootRank = 0
pg.scatter([t1, t1], [], opts)
with self.assertRaisesRegex(ValueError, "requires a single-element input list"):
opts = c10d.ScatterOptions()
opts.rootRank = self.rank
pg.scatter([t1], [], opts)
with self.assertRaisesRegex(ValueError, "requires a single-element input list"):
opts = c10d.ScatterOptions()
opts.rootRank = self.rank
pg.scatter([t1], [[t1] * self.world_size, [t1] * self.world_size], opts)
desired_list_size = self.world_size
incorrect_list_size = self.world_size - 1
err_str = "Incorrect input list size {}. Input list size should be {}"
with self.assertRaisesRegex(ValueError, err_str.format(incorrect_list_size, desired_list_size)):
opts = c10d.ScatterOptions()
opts.rootRank = self.rank
pg.scatter([t1], [[t1] * incorrect_list_size], opts)
incorrect_list_size = self.world_size + 1
with self.assertRaisesRegex(ValueError, err_str.format(incorrect_list_size, desired_list_size)):
opts = c10d.ScatterOptions()
opts.rootRank = self.rank
pg.scatter([t1], [[t1] * incorrect_list_size], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor type"):
opts = c10d.ScatterOptions()
opts.rootRank = self.rank
pg.scatter([t1], [[t2] * self.world_size], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor size"):
opts = c10d.ScatterOptions()
opts.rootRank = self.rank
pg.scatter([t1], [[t3] * self.world_size], opts)
with self.assertRaisesRegex(ValueError, "requires empty input on non-root"):
opts = c10d.ScatterOptions()
opts.rootRank = (self.rank + 1) % self.world_size
pg.scatter([t1], [[t1] * self.world_size], opts)
def _test_scatter_basics(self, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
# Preallocate tensors for input/output
input = [fn(torch.tensor([self.rank])) for _ in range(self.world_size)]
outputs = [fn(torch.tensor([-1])) for _ in range(self.world_size)]
# Take turns being the scatter root and accumulate work items
work = []
for i in range(self.world_size):
opts = c10d.ScatterOptions()
opts.rootRank = i
if i == self.rank:
work.append(pg.scatter([outputs[i]], [input], opts))
else:
work.append(pg.scatter([outputs[i]], [], opts))
# Wait for work to complete
for i in range(self.world_size):
work[i].wait()
self.assertEqual(torch.tensor([i]), outputs[i])
def test_scatter_basics(self):
self._test_scatter_basics(lambda t: t.clone())
@skip_if_not_multigpu
@skip_if_rocm
def test_scatter_basics_cuda(self):
self._test_scatter_basics(lambda t: t.clone().cuda())
def _test_scatter_stress(self, inputs, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts(threads=8))
outputs = [
[fn(torch.tensor([-1])) for _ in range(self.world_size)]
for _ in range(len(inputs))
]
work_handles = []
for i in range(len(inputs)):
for root in range(self.world_size):
opts = c10d.ScatterOptions()
opts.rootRank = root
if root == self.rank:
work = pg.scatter([outputs[i][root]], [[fn(e) for e in inputs[i]]], opts)
else:
work = pg.scatter([outputs[i][root]], [], opts)
work_handles.append(work)
for i, work_handle in enumerate(work_handles):
work_handle.wait()
iter = i // self.world_size
root = i % self.world_size
self.assertEqual(
torch.tensor([iter + root]),
outputs[iter][root],
message=("Mismatch in iteration %d for rank %d" % (iter, root)),
)
def test_scatter_stress(self):
inputs = [
[torch.tensor([i + self.rank]) for _ in range(self.world_size)]
for i in range(1000)
]
self._test_scatter_stress(inputs, lambda t: t.clone())
@unittest.skip("Test is flaky, see https://github.com/pytorch/pytorch/issues/15963")
@skip_if_not_multigpu
def test_scatter_stress_cuda(self):
inputs = [
[torch.tensor([i + self.rank]) for _ in range(self.world_size)]
for i in range(1000)
]
self._test_scatter_stress(inputs, lambda t: t.clone().cuda())
def test_gather_checks(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
t1 = torch.zeros([1], dtype=torch.float32)
t2 = torch.zeros([1], dtype=torch.float64)
t3 = torch.zeros([2], dtype=torch.float32)
with self.assertRaisesRegex(ValueError, "invalid root rank"):
opts = c10d.GatherOptions()
opts.rootRank = -1
pg.gather([], [t1], opts)
with self.assertRaisesRegex(ValueError, "invalid root rank"):
opts = c10d.GatherOptions()
opts.rootRank = self.world_size
pg.gather([], [t1], opts)
with self.assertRaisesRegex(ValueError, "requires a single-element input tensor list"):
opts = c10d.GatherOptions()
opts.rootRank = 0
pg.gather([], [], opts)
with self.assertRaisesRegex(ValueError, "requires a single-element input tensor list"):
opts = c10d.GatherOptions()
opts.rootRank = 0
pg.gather([], [t1, t1], opts)
with self.assertRaisesRegex(ValueError, "requires a single-element output list"):
opts = c10d.GatherOptions()
opts.rootRank = self.rank
pg.gather([], [t1], opts)
with self.assertRaisesRegex(ValueError, "requires a single-element output list"):
opts = c10d.GatherOptions()
opts.rootRank = self.rank
pg.gather([[t1] * self.world_size, [t1] * self.world_size], [t1], opts)
desired_list_size = self.world_size
incorrect_list_size = self.world_size - 1
err_str = "Incorrect output list size {}. Output list size should be {}"
with self.assertRaisesRegex(ValueError, err_str.format(incorrect_list_size, desired_list_size)):
opts = c10d.GatherOptions()
opts.rootRank = self.rank
pg.gather([[t1] * incorrect_list_size], [t1], opts)
incorrect_list_size = self.world_size + 1
with self.assertRaisesRegex(ValueError, err_str.format(incorrect_list_size, desired_list_size)):
opts = c10d.GatherOptions()
opts.rootRank = self.rank
pg.gather([[t1] * incorrect_list_size], [t1], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor type"):
opts = c10d.GatherOptions()
opts.rootRank = self.rank
pg.gather([[t2] * self.world_size], [t1], opts)
with self.assertRaisesRegex(ValueError, "invalid tensor size"):
opts = c10d.GatherOptions()
opts.rootRank = self.rank
pg.gather([[t3] * self.world_size], [t1], opts)
with self.assertRaisesRegex(ValueError, "requires empty output on non-root"):
opts = c10d.GatherOptions()
opts.rootRank = (self.rank + 1) % self.world_size
pg.gather([[t1] * self.world_size], [t1], opts)
def _test_gather_basics(self, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
# Preallocate tensors for input/output
input = [fn(torch.tensor([self.rank]))]
outputs = [fn(torch.tensor([-1])) for _ in range(self.world_size)]
# Take turns being the gather root and accumulate work items
work = []
for i in range(self.world_size):
opts = c10d.GatherOptions()
opts.rootRank = i
if i == self.rank:
work.append(pg.gather([outputs], input, opts))
else:
work.append(pg.gather([], input, opts))
# Wait for work to complete
expected = [torch.tensor([rank]) for rank in range(self.world_size)]
for i in range(self.world_size):
work[i].wait()
if i == self.rank:
self.assertEqual(expected, outputs)
def test_gather_basics(self):
self._test_gather_basics(lambda t: t.clone())
@skip_if_not_multigpu
@skip_if_rocm
def test_gather_basics_cuda(self):
self._test_gather_basics(lambda t: t.clone().cuda())
def _test_gather_stress(self, inputs, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts(threads=8))
work_handles = []
outputs = [
[
[fn(torch.tensor([-1])) for _ in range(self.world_size)]
] for _ in range(len(inputs))
]
expected_outputs = [
[
[torch.tensor([i + j]) for j in range(self.world_size)]
] for i in range(len(inputs))
]
for i in range(len(inputs)):
for root in range(self.world_size):
opts = c10d.GatherOptions()
opts.rootRank = root
if root == self.rank:
work = pg.gather(outputs[i], [fn(inputs[i])], opts)
else:
work = pg.gather([], [fn(inputs[i])], opts)
work_handles.append(work)
for i, work_handle in enumerate(work_handles):
work_handle.wait()
iter = i // self.world_size
root = i % self.world_size
if root == self.rank:
self.assertEqual(
expected_outputs[iter],
outputs[iter],
message=("Mismatch in iteration %d for root %d" % (iter, root))
)
def test_gather_stress(self):
inputs = [torch.tensor([i + self.rank]) for i in range(1000)]
self._test_gather_stress(inputs, lambda t: t.clone())
@skip_if_not_multigpu
@skip_if_rocm
def test_gather_stress_cuda(self):
inputs = [torch.tensor([i + self.rank]).cuda() for i in range(1000)]
self._test_gather_stress(inputs, lambda t: t.clone().cuda())
def test_allgather_checks(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
t1 = torch.zeros([1], dtype=torch.float32)
t2 = torch.zeros([1], dtype=torch.float64)
t3 = torch.zeros([2], dtype=torch.float32)
with self.assertRaisesRegex(ValueError, "requires non-empty input tensor list"):
pg.allgather([], [])
with self.assertRaisesRegex(ValueError, "requires input/output tensor lists to have the same length"):
pg.allgather([], [t1])
with self.assertRaisesRegex(ValueError, "requires input/output tensor lists to have the same length"):
pg.allgather([[t1] * self.world_size, [t1] * self.world_size], [t1])
with self.assertRaisesRegex(ValueError, "invalid output tensor list"):
pg.allgather([[t1] * (self.world_size - 1)], [t1])
with self.assertRaisesRegex(ValueError, "invalid output tensor list"):
pg.allgather([[t1] * (self.world_size + 1)], [t1])
with self.assertRaisesRegex(ValueError, "invalid tensor type"):
pg.allgather([[t1, t1] * (self.world_size), [t1, t1] * (self.world_size)], [t1, t2])
with self.assertRaisesRegex(ValueError, "invalid tensor size"):
pg.allgather([[t1, t1] * (self.world_size), [t1, t1] * (self.world_size)], [t1, t3])
with self.assertRaisesRegex(ValueError, "invalid tensor type"):
pg.allgather([([t1, t2] * (self.world_size))[:self.world_size]], [t1])
with self.assertRaisesRegex(ValueError, "invalid tensor size"):
pg.allgather([([t1, t3] * (self.world_size))[:self.world_size]], [t1])
def _test_allgather_basics(self, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
# Run with N input tensor per rank
for n in [1, 2, 3]:
input = [
fn(torch.tensor([n * self.rank + i])) for i in range(n)
]
output = [
[
fn(torch.tensor([-1])) for _ in range(n * self.world_size)
] for _ in range(n)
]
expected_output = [
[
torch.tensor([i]) for i in range(n * self.world_size)
] for _ in range(n)
]
work = pg.allgather(output, input)
work.wait()
self.assertEqual(expected_output, output)
def test_allgather_basics(self):
self._test_allgather_basics(lambda t: t.clone())
@skip_if_not_multigpu
@skip_if_rocm
def test_allgather_basics_cuda(self):
self._test_allgather_basics(lambda t: t.clone().cuda())
def _test_allgather_stress(self, inputs, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts(threads=8))
work_handles = []
outputs = [
[
[fn(torch.tensor([-1])) for _ in range(self.world_size)]
] for _ in range(len(inputs))
]
expected_outputs = [
[
[torch.tensor([i + j]) for j in range(self.world_size)]
] for i in range(len(inputs))
]
for i in range(len(inputs)):
work = pg.allgather(outputs[i], [fn(inputs[i])])
work_handles.append(work)
for i, work_handle in enumerate(work_handles):
work_handle.wait()
self.assertEqual(
expected_outputs[i],
outputs[i],
message=("Mismatch in iteration %d" % i),
)
def test_allgather_stress(self):
inputs = [torch.tensor([i + self.rank]) for i in range(1000)]
self._test_allgather_stress(inputs, lambda t: t.clone())
@skip_if_not_multigpu
@skip_if_rocm
def test_allgather_stress_cuda(self):
inputs = [torch.tensor([i + self.rank]).cuda() for i in range(1000)]
self._test_allgather_stress(inputs, lambda t: t.clone().cuda())
def test_allgather_coalesced_checks(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
dummy_input = [torch.zeros([1], dtype=torch.float32)]
dummy_output_lists = [
[torch.zeros([1], dtype=torch.float32)] for _ in range(self.world_size)
]
# One of output tensors does not match input list.
dummy_output_lists[0] = [torch.zeros([0], dtype=torch.float32)]
with self.assertRaisesRegex(ValueError,
"invalid size of output tensor at index 0"):
c10d.all_gather_coalesced(dummy_output_lists, dummy_input, pg)
# One of output tensors does not match input list.
dummy_output_lists[0] = [torch.zeros([1], dtype=torch.float64)]
with self.assertRaisesRegex(ValueError,
"invalid tensor type at index 0"):
c10d.all_gather_coalesced(dummy_output_lists, dummy_input, pg)
# Output lists have too many elements
dummy_output_lists = [
[
torch.zeros([1], dtype=torch.float32)
] for _ in range(self.world_size + 1)
]
with self.assertRaisesRegex(ValueError,
"output lists should be equal to world size"):
c10d.all_gather_coalesced(dummy_output_lists, dummy_input, pg)
# Output is not a list of lists.
dummy_output_lists = [torch.zeros([0], dtype=torch.float32)]
with self.assertRaisesRegex(RuntimeError,
"Invalid function argument.*output_tensor_lists"):
c10d.all_gather_coalesced(dummy_output_lists, dummy_input, pg)
def test_reduce_checks(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
t1 = torch.zeros([1], dtype=torch.float32)
with self.assertRaisesRegex(ValueError, "invalid root rank"):
opts = c10d.ReduceOptions()
opts.rootRank = -1
opts.rootTensor = 0
pg.reduce([t1], opts)
with self.assertRaisesRegex(ValueError, "invalid root rank"):
opts = c10d.ReduceOptions()
opts.rootRank = self.world_size
opts.rootTensor = 0
pg.reduce([t1], opts)
with self.assertRaisesRegex(ValueError, "invalid root tensor"):
opts = c10d.ReduceOptions()
opts.rootRank = self.rank
opts.rootTensor = 1
pg.reduce([t1], opts)
with self.assertRaisesRegex(ValueError, "requires a single-element tensor list"):
opts = c10d.ReduceOptions()
opts.rootRank = self.rank
opts.rootTensor = 0
pg.reduce([t1, t1], opts)
def _test_reduce_basics(self, fn):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
for (op, input, output) in simple_reduce_tests(self.rank, self.world_size):
for root in range(self.world_size):
opts = c10d.ReduceOptions()
opts.reduceOp = op
opts.rootRank = root
tmp = fn(input)
work = pg.reduce([tmp], opts)
work.wait()
if root == self.rank:
self.assertEqual(output, tmp)
def test_reduce_basics(self):
self._test_reduce_basics(lambda t: t.clone())
@skip_if_not_multigpu
@skip_if_rocm
def test_reduce_basics_cuda(self):
self._test_reduce_basics(lambda t: t.clone().cuda())
def _test_reduce_stress(self, inputs):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts(threads=8))
work_handles = []
outputs = []
for i in range(len(inputs)):
for root in range(self.world_size):
opts = c10d.ReduceOptions()
opts.rootRank = root
tmp = inputs[i].clone()
outputs.append(tmp)
work = pg.reduce([tmp], opts)
work_handles.append(work)
for i, work_handle in enumerate(work_handles):
work_handle.wait()
iter = i // self.world_size
root = i % self.world_size
if root == self.rank:
self.assertEqual(
torch.tensor([
(iter * self.world_size) +
(self.world_size * (self.world_size - 1) / 2)
]),
outputs[i],
message=("Mismatch in iteration %d with root rank %d" % (iter, root)),
)
def test_reduce_stress(self):
inputs = [torch.tensor([i + self.rank]) for i in range(1000)]
self._test_reduce_stress(inputs)
@skip_if_not_multigpu
@skip_if_rocm
def test_reduce_stress_cuda(self):
inputs = [torch.tensor([i + self.rank]).cuda() for i in range(1000)]
self._test_reduce_stress(inputs)
def test_send_recv_all_to_all(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
# Preallocate tensors for input/output
inputs = [torch.tensor([self.rank]) for _ in range(self.world_size)]
outputs = [torch.tensor([-1]) for _ in range(self.world_size)]
# Issue sends
send_work = []
for i in range(self.world_size):
if i == self.rank:
continue
send_work.append(pg.send([inputs[i]], i, 0))
# Issue recvs
recv_work = []
for i in range(self.world_size):
if i == self.rank:
continue
recv_work.append(pg.recv([outputs[i]], i, 0))
# Wait for sends to complete
for work in send_work:
work.wait()
self.assertTrue(work.is_completed())
# Wait for recvs to complete
for work in recv_work:
work.wait()
self.assertTrue(work.is_completed())
# Test that every output other than our own contains the respective rank
for i in range(self.world_size):
if i == self.rank:
continue
self.assertEqual(torch.tensor([i]), outputs[i])
@unittest.skipIf(platform == 'darwin', 'ProcessGroup timeout not yet supported on macOS')
def test_timeout_kwarg(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(
store,
self.rank,
self.world_size,
timeout=timedelta(seconds=0.5))
# Wait on barrier
pg.barrier().wait()
# Sleep on one of the processes to trigger barrier timeout
if self.rank == 0:
time.sleep(1.0)
# The barrier will now time out
with self.assertRaisesRegex(RuntimeError, " (Timed out|closed) "):
pg.barrier().wait()
def test_barrier_implies_wait(self):
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size)
# Kick off allreduce operations
size = (100, 100)
num = 16
tensors = [torch.full(size, float(i)) for i in range(num)]
for tensor in tensors:
# Note: leak the returned work handle
pg.allreduce(tensor)
# Barrier should ensure all previous work has completed
pg.barrier().wait()
for i, tensor in enumerate(tensors):
self.assertEqual(torch.full(size, float(i * self.world_size)), tensor)
def test_round_robin(self):
num_process_groups = 2
store = c10d.FileStore(self.file_name, self.world_size)
pg = c10d._round_robin_process_groups([
c10d.ProcessGroupGloo(
c10d.PrefixStore(str(i), store),
self.rank,
self.world_size)
for i in range(num_process_groups)
])
# Run a few collectives so that we have called each process group
for _ in range(num_process_groups + 1):
tensor = torch.full([100, 100], self.rank)
pg.broadcast(tensor, root=0).wait()
self.assertEqual(torch.full([100, 100], 0), tensor)
def test_round_robin_create_destroy(self):
store = c10d.FileStore(self.file_name, self.world_size)
def create(num, prefix):
return c10d._round_robin_process_groups([
c10d.ProcessGroupGloo(
c10d.PrefixStore("%s/%d" % (prefix, i), store),
self.rank,
self.world_size)
for i in range(num)
])
# Run create/use/destroy twice
for i in range(2):
num_process_groups = 2
pg = create(num=num_process_groups, prefix=i)
for _ in range(3):
tensor = torch.ones([10, 10])
pg.allreduce(tensor).wait()
self.assertEqual(torch.full([10, 10], self.world_size), tensor)
del pg
@requires_nccl()
class ProcessGroupNCCLTest(TestCase):
MAIN_PROCESS_RANK = 0
def setUp(self):
self.rank = self.MAIN_PROCESS_RANK
self.world_size = 1
self.file = tempfile.NamedTemporaryFile(delete=False)
self.num_gpus = torch.cuda.device_count()
if self.num_gpus < 2:
raise unittest.SkipTest("NCCL test requires 2+ GPUs")
def tearDown(self):
pass
def test_empty_tensors(self):
store = c10d.FileStore(self.file.name, self.world_size)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
xs = [torch.cuda.FloatTensor([])]
pg.broadcast(xs).wait()
self.assertEqual(0, xs[0].numel())
pg.allreduce(xs).wait()
self.assertEqual(0, xs[0].numel())
pg.reduce(xs).wait()
self.assertEqual(0, xs[0].numel())
ys = [[torch.cuda.FloatTensor([]) for _ in range(self.world_size)]]
pg.allgather(ys, xs).wait()
for y in ys[0]:
self.assertEqual(0, y.numel())
ys = [torch.cuda.FloatTensor([])]
xs = [[torch.cuda.FloatTensor([]) for _ in range(self.world_size)]]
pg.reduce_scatter(ys, xs).wait()
self.assertEqual(0, ys[0].numel())
def test_broadcast_ops(self):
store = c10d.FileStore(self.file.name, self.world_size)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
def broadcast(xs, rootRank, rootTensor):
opts = c10d.BroadcastOptions()
opts.rootRank = rootRank
opts.rootTensor = rootTensor
work = pg.broadcast(xs, opts)
work.wait()
# for every root tensor
for rt in range(self.num_gpus):
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.tensor([i]).cuda(i))
broadcast(tensors, self.rank, rt)
for i in range(self.num_gpus):
self.assertEqual(tensors[i], tensors[rt])
def test_allreduce_ops(self):
store = c10d.FileStore(self.file.name, self.world_size)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
def allreduce(tensors, op):
opts = c10d.AllreduceOptions()
opts.reduceOp = op
work = pg.allreduce(tensors, opts)
work.wait()
# Sum
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.tensor([i + 1]).cuda(i))
allreduce(tensors, c10d.ReduceOp.SUM)
for i in range(self.num_gpus):
self.assertEqual(
torch.tensor([float(self.num_gpus * (self.num_gpus + 1) / 2)]),
tensors[i])
# Product
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.tensor([i + 1]).cuda(i))
allreduce(tensors, c10d.ReduceOp.PRODUCT)
for i in range(self.num_gpus):
self.assertEqual(
torch.tensor([float(math.factorial(self.num_gpus))]),
tensors[i])
# Min
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.tensor([i + 1]).cuda(i))
allreduce(tensors, c10d.ReduceOp.MIN)
for i in range(self.num_gpus):
self.assertEqual(torch.tensor([1.0]), tensors[i])
# Max
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.tensor([i + 1]).cuda(i))
allreduce(tensors, c10d.ReduceOp.MAX)
for i in range(self.num_gpus):
self.assertEqual(torch.tensor([self.num_gpus]), tensors[i])
def test_reduce_ops(self):
store = c10d.FileStore(self.file.name, self.world_size)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
def reduce(xs, rootRank, rootTensor):
opts = c10d.ReduceOptions()
opts.rootRank = rootRank
opts.rootTensor = rootTensor
work = pg.reduce(xs, opts)
work.wait()
# for every root tensor
for rt in range(self.num_gpus):
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.tensor([i + 1]).cuda(i))
reduce(tensors, self.rank, rt)
self.assertEqual(
torch.tensor([float(self.num_gpus * (self.num_gpus + 1) / 2)]),
tensors[rt])
def test_allgather_ops(self):
store = c10d.FileStore(self.file.name, self.world_size)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
def allgather(output_ts, input_ts):
work = pg.allgather(output_ts, input_ts)
work.wait()
tensors = []
output_ts = [[] for _ in range(self.num_gpus)]
for idx, ls in enumerate(output_ts):
for _ in range(self.world_size * self.num_gpus):
ls.append(torch.tensor([0]).cuda(idx))
for i in range(self.num_gpus):
tensors.append(torch.tensor([i]).cuda(i))
allgather(output_ts, tensors)
# Verification
for device_ts in output_ts:
for s_idx, t in enumerate(device_ts):
self.assertEqual(torch.tensor([s_idx]), t)
def test_reduce_scatter_ops(self):
store = c10d.FileStore(self.file.name, self.world_size)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
def reduce_scatter(outputs, input_lists, op):
opts = c10d.ReduceScatterOptions()
opts.reduceOp = op
work = pg.reduce_scatter(outputs, input_lists, opts)
work.wait()
virtual_rank = self.rank * self.world_size
virtual_world_size = self.num_gpus * self.world_size
output = [
torch.tensor([0]).cuda(i)
for i in range(self.num_gpus)
]
# 0 1 2
# 0 [0..11] [1..12]
# 1 [3..14]
# 2
# 3
# Sum
tensor_lists = [
[
torch.tensor([self.rank * self.num_gpus + i + j]).cuda(i)
for j in range(virtual_world_size)
]
for i in range(self.num_gpus)
]
reduce_scatter(output, tensor_lists, c10d.ReduceOp.SUM)
for i in range(self.num_gpus):
expected = torch.tensor([
float(self.num_gpus * (self.num_gpus - 1) / 2) +
(virtual_rank + i) * virtual_world_size
])
self.assertEqual(expected, output[i])
# Min
reduce_scatter(output, tensor_lists, c10d.ReduceOp.MIN)
for i in range(self.num_gpus):
expected = torch.tensor([self.rank * self.world_size + i])
self.assertEqual(expected, output[i])
# Max
reduce_scatter(output, tensor_lists, c10d.ReduceOp.MAX)
for i in range(self.num_gpus):
expected = torch.tensor(
[self.rank * self.world_size + i + virtual_world_size - 1]
)
self.assertEqual(expected, output[i])
# Product
tensor_lists = [
[
torch.tensor([
(self.rank * self.num_gpus + i + j) % virtual_world_size + 1
]).cuda(i)
for j in range(virtual_world_size)
]
for i in range(self.num_gpus)
]
reduce_scatter(output, tensor_lists, c10d.ReduceOp.PRODUCT)
for i in range(self.num_gpus):
expected = torch.tensor([float(math.factorial(virtual_world_size))])
self.assertEqual(expected, output[i])
def test_barrier(self):
store = c10d.FileStore(self.file.name, self.world_size)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
def allreduce(tensors):
opts = c10d.AllreduceOptions()
work = pg.allreduce(tensors, opts)
return work
# Making the collective to operate on
# 1, 2, 3, 4, .... self.num_gpus GPUs
tensors_list = [[] for _ in range(2, self.num_gpus + 1)]
for i in range(2, self.num_gpus + 1):
for j in range(i):
tensors_list[i - 2].append(torch.tensor([j + 1]).cuda(j))
works = []
for tensors in tensors_list:
work = allreduce(tensors)
works.append(work)
# Barrier will ensure that all previous work is completed
pg.barrier().wait()
for i in range(2, self.num_gpus + 1):
for j in range(i):
self.assertEqual(
torch.tensor([float(i * (i + 1) / 2)]),
tensors_list[i - 2][j])
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(2, 10, bias=False)
self.fc2 = nn.Linear(10, 50, bias=False)
self.fc3 = nn.Linear(50, 4, bias=False)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return F.softmax(x, dim=1)
class DoubleGpuNet(nn.Module):
def __init__(self, gpus):
super(DoubleGpuNet, self).__init__()
self.fc1 = nn.Linear(2, 10, bias=False).to(gpus[0])
self.fc2 = nn.Linear(10, 50, bias=False).to(gpus[1])
self.fc3 = nn.Linear(50, 4, bias=False).to(gpus[1])
self.relu = nn.ReLU()
self.no_grad_param = nn.Parameter(torch.tensor([2, 2]).long(),
requires_grad=False).to(gpus[0])
def forward(self, x):
dev0 = self.fc1.weight.device
dev1 = self.fc2.weight.device
x = self.relu(self.fc1(x.to(dev0)))
x = self.relu(self.fc2(x.to(dev1)))
x = self.fc3(x)
return F.softmax(x, dim=1).to(dev0)
class QuadraGpuNet(nn.Module):
def __init__(self, gpus):
super(QuadraGpuNet, self).__init__()
self.fc1 = nn.Linear(2, 10, bias=False).to(gpus[0])
self.fc2 = nn.Linear(10, 50, bias=False).to(gpus[1])
self.fc3 = nn.Linear(50, 4, bias=False).to(gpus[2])
self.fc4 = nn.Linear(4, 4, bias=False).to(gpus[3])
self.relu = nn.ReLU()
self.no_grad_param = nn.Parameter(torch.tensor([2, 2]).long(),
requires_grad=False).to(gpus[0])
def forward(self, x):
dev0 = self.fc1.weight.device
dev1 = self.fc2.weight.device
dev2 = self.fc3.weight.device
dev3 = self.fc4.weight.device
x = self.relu(self.fc1(x.to(dev0)))
x = self.relu(self.fc2(x.to(dev1)))
x = self.relu(self.fc3(x.to(dev2)))
x = self.fc4(x.to(dev3))
return F.softmax(x, dim=1).to(dev0)
@unittest.skipIf(TEST_WITH_TSAN, "TSAN is not fork-safe since we're forking in a multi-threaded environment")
class DistributedDataParallelTest(MultiProcessTestCase):
def setUp(self):
super(DistributedDataParallelTest, self).setUp()
self._fork_processes()
def tearDown(self):
# DistributedDataParallel test doesn't seem to call FileStore destructor
# TODO: investigate this test and the test is known to have issues
# Use this hack to remove files for that test
try:
os.remove(self.file_name)
except OSError:
pass
@property
def world_size(self):
return 2
def _prepare_single_device_module(self, process_group, devices, device_ids, global_batch_size):
model = Net()
ddp_model = DistributedDataParallel(
copy.deepcopy(model).to(devices[0]),
device_ids=device_ids,
process_group=process_group,
bucket_cap_mb=0.001)
model.to(devices[0])
input = torch.randn(global_batch_size, 2).to(devices[0])
target = torch.randn(global_batch_size, 4).to(devices[0])
return model, ddp_model, input, target
def _prepare_multi_device_module(self, process_group, devices, device_ids, global_batch_size):
self.assertTrue(
len(devices) == 2 or len(devices) == 4,
"unexpected devices for ddp tests {}".format(devices))
if len(devices) == 2:
model = DoubleGpuNet(devices)
elif len(devices) == 4:
model = QuadraGpuNet(devices)
ddp_model = DistributedDataParallel(
copy.deepcopy(model),
device_ids=device_ids,
process_group=process_group,
bucket_cap_mb=0.001)
input = torch.randn(global_batch_size, 2).cuda(devices[0])
target = torch.randn(global_batch_size, 4)
return model, ddp_model, input, target
def _test_ddp_with_process_group(self, process_group, devices, device_ids, multi_device=False):
"""
Note: we pass down `device_ids` all the way to DistributedDataParallel
as part of the test. Below you find tests that either use a list of
integers, a list of `torch.Device` instances, or an empty list.
The `devices` argument is used to control placement of the model and
must always be specified as list of `torch.Device` instances.
"""
local_batch_size = len(devices)
global_batch_size = self.world_size * local_batch_size
if multi_device:
model, ddp_model, input, target = \
self._prepare_multi_device_module(
process_group, devices, device_ids, global_batch_size)
else:
model, ddp_model, input, target = \
self._prepare_single_device_module(
process_group, devices, device_ids, global_batch_size)
def step_model(model, input, target):
model.train()
output = model(input)
loss = F.mse_loss(output, target.to(output.device))
loss.backward()
def update_parameters(model):
for param in model.parameters():
param.data -= param.grad
param.grad = None
# check two model parameters over 2 iterations
for iteration in range(2):
# single cpu/gpu training
step_model(model, input, target)
# DDP training, DDP scatters subsets of input_cpu to nodes/GPUs
step_model(ddp_model,
input[self.rank * local_batch_size: (self.rank + 1) * local_batch_size],
target[self.rank * local_batch_size: (self.rank + 1) * local_batch_size])
# Update weights and run a second iteration to shake out errors
update_parameters(model)
update_parameters(ddp_model)
self.assertEqual(len(list(model.parameters())), len(list(ddp_model.parameters())))
for i, j in zip(model.parameters(), ddp_model.parameters()):
self.assertEqual(i, j)
# Shuffle the input so that DDP input is different
torch.manual_seed(1337 + iteration)
input = input[torch.randperm(global_batch_size)]
def _test_gloo_backend(self, devices, device_ids, multi_device=False):
store = c10d.FileStore(self.file_name, self.world_size)
options = c10d.ProcessGroupGloo.Options()
options.devices = [c10d.ProcessGroupGloo.create_device(interface=LOOPBACK)]
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size, options)
self._test_ddp_with_process_group(process_group, devices, device_ids, multi_device)
@requires_gloo()
def test_gloo_backend_cpu_module(self):
self._test_gloo_backend([torch.device('cpu')], [])
@requires_gloo()
@skip_if_not_multigpu
def test_gloo_backend_1gpu_module_device_ids_integer_list(self):
int_devices = gpus_for_rank(self.world_size)[self.rank][:1]
devices = list([torch.device('cuda:' + str(i)) for i in int_devices])
self._test_gloo_backend(devices, int_devices)
@requires_gloo()
@skip_if_not_multigpu
def test_gloo_backend_1gpu_module_device_ids_torch_device_list(self):
int_devices = gpus_for_rank(self.world_size)[self.rank][:1]
devices = list([torch.device('cuda:' + str(i)) for i in int_devices])
self._test_gloo_backend(devices, devices)
@requires_gloo()
@skip_if_lt_x_gpu(4)
def test_gloo_backend_2gpu_module(self):
int_devices = gpus_for_rank(self.world_size)[self.rank][:2]
devices = list([torch.device('cuda:' + str(i)) for i in int_devices])
self._test_gloo_backend(devices, [], multi_device=True)
@requires_gloo()
@skip_if_lt_x_gpu(8)
def test_gloo_backend_4gpu_module(self):
int_devices = gpus_for_rank(self.world_size)[self.rank][:4]
devices = list([torch.device('cuda:' + str(i)) for i in int_devices])
self._test_gloo_backend(devices, [], multi_device=True)
def _test_nccl_backend(self, devices, device_ids, multi_device=False):
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
self._test_ddp_with_process_group(process_group, devices, device_ids, multi_device)
@requires_nccl()
@skip_if_not_multigpu
def test_nccl_backend_1gpu_module_device_ids_integer_list(self):
int_devices = gpus_for_rank(self.world_size)[self.rank][:1]
devices = list([torch.device('cuda:' + str(i)) for i in int_devices])
self._test_nccl_backend(devices, int_devices)
@requires_nccl()
@skip_if_not_multigpu
def test_nccl_backend_1gpu_module_device_ids_torch_device_list(self):
int_devices = gpus_for_rank(self.world_size)[self.rank][:1]
devices = list([torch.device('cuda:' + str(i)) for i in int_devices])
self._test_nccl_backend(devices, devices)
@requires_nccl()
@skip_if_lt_x_gpu(4)
@skip_if_rocm
def test_nccl_backend_2gpu_module(self):
int_devices = gpus_for_rank(self.world_size)[self.rank][:2]
devices = list([torch.device('cuda:' + str(i)) for i in int_devices])
self._test_nccl_backend(devices, [], multi_device=True)
@requires_nccl()
@skip_if_lt_x_gpu(8)
@skip_if_rocm
def test_nccl_backend_4gpu_module(self):
int_devices = gpus_for_rank(self.world_size)[self.rank][:4]
devices = list([torch.device('cuda:' + str(i)) for i in int_devices])
self._test_nccl_backend(devices, [], multi_device=True)
@requires_nccl()
@skip_if_lt_x_gpu(4)
def test_ddp_multi_device_module_config(self):
gpus = gpus_for_rank(self.world_size)[self.rank]
self.assertTrue(len(gpus) >= 2, "expecting at least 2 gpus per process")
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
gpus = gpus[:2]
model = DoubleGpuNet(gpus)
with self.assertRaisesRegex(AssertionError, "output_device .* single-device CUDA"):
ddp_model = DistributedDataParallel(
model, output_device=gpus[1], process_group=process_group)
with self.assertRaisesRegex(AssertionError, "device_ids .* single-device CUDA"):
ddp_model = DistributedDataParallel(
model, device_ids=gpus, process_group=process_group)
with self.assertRaisesRegex(AssertionError, "only works with CUDA devices"):
model.fc1 = model.fc1.cpu()
ddp_model = DistributedDataParallel(model, process_group=process_group)
model = model.cpu()
with self.assertRaisesRegex(AssertionError, "device_ids .* single-device CUDA"):
ddp_model = DistributedDataParallel(
model, device_ids=gpus, process_group=process_group)
@requires_nccl()
@skip_if_not_multigpu
@skip_for_known_issues
def test_dist_broadcast_coalesced_nccl(self):
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
device = torch.device('cuda')
for fine_grained in [False, True]:
target = torch.arange(60, dtype=torch.float16, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float32, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float16, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float64, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float16, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float32, device=device).chunk(5)
if self.is_master:
# All processes should have these tensors in the end.
tensors = target
else:
# Non-master processes start with empty tensors and should be
# filled with the tensors from the master.
tensors = torch.zeros(60, dtype=torch.float16, device=device).chunk(5)
tensors += torch.zeros(60, dtype=torch.float32, device=device).chunk(5)
tensors += torch.zeros(60, dtype=torch.float16, device=device).chunk(5)
tensors += torch.zeros(60, dtype=torch.float64, device=device).chunk(5)
tensors += torch.zeros(60, dtype=torch.float16, device=device).chunk(5)
tensors += torch.zeros(60, dtype=torch.float32, device=device).chunk(5)
c10d._dist_broadcast_coalesced(
process_group,
tensors,
buffer_size=256,
fine_grained=fine_grained)
self.assertEqual(tensors, target)
@requires_gloo()
@skip_if_not_multigpu
@skip_if_rocm
def test_dist_broadcast_coalesced_gloo(self):
store = c10d.FileStore(self.file_name, self.world_size)
options = c10d.ProcessGroupGloo.Options()
options.devices = [c10d.ProcessGroupGloo.create_device(interface=LOOPBACK)]
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size, options)
device = torch.device('cuda')
for fine_grained in [False, True]:
target = torch.arange(60, dtype=torch.float16, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float32, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float16, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float64, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float16, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float32, device=device).chunk(5)
if self.is_master:
# All processes should have these tensors in the end.
tensors = target
else:
# Non-master processes start with empty tensors and should be
# filled with the tensors from the master.
tensors = torch.zeros(60, dtype=torch.float16, device=device).chunk(5)
tensors += torch.zeros(60, dtype=torch.float32, device=device).chunk(5)
tensors += torch.zeros(60, dtype=torch.float16, device=device).chunk(5)
tensors += torch.zeros(60, dtype=torch.float64, device=device).chunk(5)
tensors += torch.zeros(60, dtype=torch.float16, device=device).chunk(5)
tensors += torch.zeros(60, dtype=torch.float32, device=device).chunk(5)
c10d._dist_broadcast_coalesced(
process_group,
tensors,
buffer_size=128,
fine_grained=fine_grained)
self.assertEqual(tensors, target)
@requires_gloo()
@skip_if_not_multigpu
def test_sync_params_no_buffers(self, dtype=torch.double):
store = c10d.FileStore(self.file_name, self.world_size)
options = c10d.ProcessGroupGloo.Options()
options.devices = [c10d.ProcessGroupGloo.create_device(interface=LOOPBACK)]
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size, options)
# Use all available devices on every process here (data is small, so should be fine).
devices = gpus_for_rank(self.world_size)[self.rank]
target = torch.arange(10, dtype=dtype, device='cuda:{}'.format(devices[0])).chunk(5)
parameter_data = [target]
parameter_data += [torch.zeros(10, dtype=dtype, device=torch.device('cuda', d)).chunk(5) for d in devices[1:]]
buffer_data = [[]] * len(parameter_data)
c10d._sync_params(
process_group,
parameter_data=parameter_data,
buffer_data=buffer_data,
devices=devices,
broadcast_bucket_size=10,
broadcast_buffers=False)
for device_data in parameter_data:
for i, parameter in enumerate(device_data):
self.assertEqual(parameter, target[i])
@requires_gloo()
@skip_if_not_multigpu
@skip_if_rocm
def test_sync_params_with_buffers(self, dtype=torch.double):
store = c10d.FileStore(self.file_name, self.world_size)
options = c10d.ProcessGroupGloo.Options()
options.devices = [c10d.ProcessGroupGloo.create_device(interface=LOOPBACK)]
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size, options)
devices = gpus_for_rank(self.world_size)[self.rank]
target = torch.arange(10, dtype=dtype, device='cuda:{}'.format(devices[0])).chunk(5)
parameter_data = [target]
parameter_data += [torch.zeros(10, dtype=dtype, device=torch.device('cuda', d)).chunk(5) for d in devices[1:]]
# sync_params should do a dist_broadcast for buffers, so we only populate the master buffers and
# then check that other processes' tensors end up matching.
if self.is_master:
buffer_data = [target]
buffer_data += [torch.zeros(10, dtype=dtype, device=torch.device('cuda', d)).chunk(5) for d in devices[1:]]
else:
buffer_data = [torch.zeros(10, dtype=dtype, device=torch.device('cuda', d)).chunk(5) for d in devices]
c10d._sync_params(
process_group,
parameter_data=parameter_data,
buffer_data=buffer_data,
devices=devices,
broadcast_bucket_size=10,
broadcast_buffers=True)
for device_data in parameter_data:
for i, parameter in enumerate(device_data):
self.assertEqual(parameter, target[i])
for device_data in buffer_data:
for i, buffer in enumerate(device_data):
self.assertEqual(buffer, target[i])
@requires_nccl()
@skip_if_not_multigpu
@skip_if_rocm
def test_fp16(self):
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
gpus = gpus_for_rank(self.world_size)[self.rank]
model = nn.Linear(1, 1, bias=False).cuda(gpus[0]).half()
nn.init.constant_(model.weight, 1)
ddp_model = DistributedDataParallel(
model,
device_ids=[gpus[0]],
process_group=process_group,
bucket_cap_mb=0.001,
)
# Input 2**15, so that the gradients will overflow with a
# world_size of 2, unless we normalize the gradient by the
# world_size before the reduction
input = torch.tensor([[2**15]]).cuda(gpus[0]).half()
# Step model
ddp_model.train()
output = ddp_model(input)
loss = output.sum()
loss.backward()
self.assertFalse(
any(torch.isinf(p.grad).any() for p in ddp_model.parameters())
)
@requires_nccl()
@skip_if_not_multigpu
@skip_if_rocm
def test_queue_reduction(self):
# Set up process group.
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
# Get this process' split of devices.
devices = gpus_for_rank(self.world_size)[self.rank]
grads_batch = [(torch.ones(10, device=torch.device('cuda', d)) *
(self.rank + 1)).chunk(5)
for d in devices]
work, local_grad_sum = c10d._queue_reduction(process_group,
grads_batch,
devices)
# The first return value should be the allreduce work item.
self.assertTrue(isinstance(work, c10d.Work))
# The second return value will be the finished allreduced gradients.
self.assertTrue(isinstance(local_grad_sum, torch.Tensor))
# Wait for the allreduce to finish.
work.wait()
# The expected result of the allreduce should be the average
self.assertEqual(local_grad_sum,
torch.ones(10) * (self.world_size + 1) * len(devices) / 2.0)
@requires_nccl()
@skip_if_not_multigpu
@skip_if_rocm
def test_sync_reduction(self):
# Set up process group.
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
# Get this process' split of devices.
devices = gpus_for_rank(self.world_size)[self.rank]
grads_batch = [(torch.ones(10, device=torch.device('cuda', d)) *
(self.rank + 1)).chunk(5)
for d in devices]
work, local_grad_sum = c10d._queue_reduction(process_group,
grads_batch,
devices)
c10d._sync_reduction(work, grads_batch[0], local_grad_sum)
# The expected result of the allreduce should be the average
self.assertEqual(grads_batch[0], (torch.ones(10) * (self.world_size + 1) * len(devices) / 2.0).chunk(5))
@requires_nccl()
@skip_if_not_multigpu
@skip_if_rocm
def test_arbitrary_forward_return_value(self):
"""
Note: this test can be sped up by only running it on a CPU module
once DistributedDataParallel supports them.
"""
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
class ForwardReturnValueModule(nn.Module):
def __init__(self):
super(ForwardReturnValueModule, self).__init__()
self.fc1 = nn.Linear(2, 10, bias=False)
self.fc2 = nn.Linear(10, 4, bias=False)
self.fc3 = nn.Linear(4, 4, bias=False)
self.relu = nn.ReLU()
def forward(self, x, fn):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
# The first softmax does NOT include fc3 in its autograd graph
# whereas the second softmax DOES. If we pass only the first
# tensor we see in the output to the reducer, it marks the
# gradient for fc3 as ready (because it doesn't show up). If
# downstream uses of this return value choose to differentiate
# against the second output tensor, it would still receive a
# gradient and a callback for this tensor, resulting in a crash.
return fn(
F.softmax(x, dim=1),
F.softmax(self.fc3(x), dim=1),
)
device_id = gpus_for_rank(self.world_size)[self.rank][0]
model = DistributedDataParallel(
ForwardReturnValueModule().float().to(device_id),
device_ids=[device_id],
process_group=process_group,
)
batch_size = 4
criterion = nn.CrossEntropyLoss()
input = torch.rand([batch_size, 2], dtype=torch.float)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)]).to(device_id)
# Always run "backward" to ensure the reducer is called by autograd.
# If we don't correctly capture the output tensors from the return value,
# the reducer won't see a hook for the unused parameter, and throw an error.
# The correct capture is what we're testing in this function.
def test(box, unbox):
output = model(input, fn=box)
loss = criterion(unbox(output), target)
loss.backward()
# Test with identity return value
test(
box=lambda x, y: (x, y),
unbox=lambda obj: obj[1],
)
# Test with list return value
test(
box=lambda x, y: ["foo", x, "bar", y],
unbox=lambda obj: obj[3],
)
# Test with tuple return value
test(
box=lambda x, y: ("foo", x, "bar", y),
unbox=lambda obj: obj[3],
)
# Test with dict return value
test(
box=lambda x, y: {"foo": "bar", "a": x, "b": y},
unbox=lambda obj: obj["b"],
)
# Test with list with dict return value
test(
box=lambda x, y: ["foo", "bar", {"a": x, "b": y}],
unbox=lambda obj: obj[2]["b"],
)
# Test with dict with list return value
test(
box=lambda x, y: {"foo": "bar", "list": [0, x, 1, y]},
unbox=lambda obj: obj["list"][3],
)
@requires_nccl()
@skip_if_not_multigpu
@skip_if_rocm
def test_find_unused_parameters_kwarg(self):
"""
Note: this test can be sped up by only running it on a CPU module
once DistributedDataParallel supports them.
"""
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
class FindUnusedParametersModule(nn.Module):
def __init__(self):
super(FindUnusedParametersModule, self).__init__()
self.fc1 = nn.Linear(2, 10, bias=False)
self.fc2 = nn.Linear(10, 4, bias=False)
self.fc3 = nn.Linear(4, 4, bias=False)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
# Return the fc3 module so that the caller can invoke it
# outside of the forward function. While this is bad practice,
# we can use it to trigger a reducer error.
return (F.softmax(x, dim=1), self.fc3)
device_id = gpus_for_rank(self.world_size)[self.rank][0]
batch_size = 4
criterion = nn.CrossEntropyLoss()
input = torch.rand([batch_size, 2], dtype=torch.float)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)]).to(device_id)
def test_find_unused_parameters(find_unused_parameters, test_default=False):
if test_default:
model = DistributedDataParallel(
FindUnusedParametersModule().float().to(device_id),
device_ids=[device_id],
process_group=process_group,
)
else:
model = DistributedDataParallel(
FindUnusedParametersModule().float().to(device_id),
device_ids=[device_id],
process_group=process_group,
find_unused_parameters=find_unused_parameters,
)
output, fc3 = model(input)
output = fc3(output)
loss = criterion(output, target)
loss.backward()
# First test that finding unused params under these conditions is to
# trigger an error when `backward` is called (because fc3 is an unused
# parameter and will therefore be marked ready twice).
try:
test_find_unused_parameters(True)
except Exception as ex:
self.assertTrue(
str(ex).startswith("Expected to mark a variable ready only once."))
else:
self.fail("Expected exception")
# Then test that the default behavior can be overridden by setting
# `find_unused_parameters=False`.
try:
test_find_unused_parameters(False)
except Exception as ex:
self.fail("Unexpected exception: %s" % ex)
# Test find_unused_parameters defaults to False
try:
test_find_unused_parameters(True, test_default=True)
except Exception as ex:
self.fail("Unexpected exception: %s" % ex)
@requires_gloo()
@skip_if_lt_x_gpu(2)
def test_global_local_unused_params_grad(self):
"""
By simulating a multi-task training, this test is to make sure:
1) DDP does not touch the grad of globally unused parameters.
2) DDP does update the grad of locally unused parameters.
"""
class GlobalLocalUnusedParamModule(nn.Module):
class Task(nn.Module):
def __init__(self):
super(GlobalLocalUnusedParamModule.Task, self).__init__()
self.p = nn.Parameter(torch.ones(2, 2))
def forward(self, x):
return self.p + x
def __init__(self):
super(GlobalLocalUnusedParamModule, self).__init__()
self.t0 = self.Task()
self.t1 = self.Task()
self.task_unused = self.Task()
def task_parameters(self):
return (self.t0.p, self.t1.p, self.task_unused.p)
def forward(self, x, rank):
return self.t0(x) if rank == 0 else self.t1(x)
def run_and_verify_grad(model):
# Run forward
output = model(8, self.rank)
# The grads of all parameters should be None at this point.
t0_p, t1_p, task_unused_p = model.module.task_parameters()
self.assertIsNone(t0_p.grad)
self.assertIsNone(t1_p.grad)
self.assertIsNone(task_unused_p.grad)
# Run backward
output.mean().backward()
# Now locally unused parameter should have grad updated on all ranks.
# However the globally unused parameter should still have None grad.
self.assertIsNotNone(t0_p.grad)
self.assertIsNotNone(t1_p.grad)
self.assertIsNone(task_unused_p.grad)
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size)
# Test on CPU
cpu_model = DistributedDataParallel(
GlobalLocalUnusedParamModule().cpu(),
process_group=process_group,
find_unused_parameters=True,
)
run_and_verify_grad(cpu_model)
# Test on GPU
device_id = gpus_for_rank(self.world_size)[self.rank][0]
gpu_model = DistributedDataParallel(
GlobalLocalUnusedParamModule().to(device_id),
device_ids=[device_id],
process_group=process_group,
find_unused_parameters=True,
)
run_and_verify_grad(gpu_model)
@requires_nccl()
@skip_if_not_multigpu
@skip_if_rocm
def test_multiple_outputs_multiple_backward(self):
"""
Note: this test can be sped up by only running it on a CPU module
once DistributedDataParallel supports them.
"""
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
class MultipleOutputModule(nn.Module):
def __init__(self):
super(MultipleOutputModule, self).__init__()
def define_module():
return nn.Sequential(
nn.Linear(2, 10, bias=False),
nn.ReLU(),
nn.Linear(10, 4, bias=False),
nn.ReLU(),
)
self.module0 = define_module()
self.module1 = define_module()
def forward(self, x):
return (
F.softmax(self.module0(x), dim=1),
F.softmax(self.module1(x), dim=1),
)
device_id = gpus_for_rank(self.world_size)[self.rank][0]
model = DistributedDataParallel(
MultipleOutputModule().float().to(device_id),
device_ids=[device_id],
process_group=process_group,
)
batch_size = 4
criterion = nn.CrossEntropyLoss()
input = torch.rand([batch_size, 2], dtype=torch.float)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)]).to(device_id)
# Compute loss and gradients for both outputs
output1, output2 = model(input)
loss1 = criterion(output1, target)
loss1.backward()
loss2 = criterion(output2, target)
loss2.backward()
@requires_nccl()
@skip_if_not_multigpu
@skip_if_rocm
def test_no_grad(self):
"""
Note: this test can be sped up by only running it on a CPU module
once DistributedDataParallel supports them.
"""
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
class NoGradModule(nn.Module):
def __init__(self):
super(NoGradModule, self).__init__()
self.fc1 = nn.Linear(2, 10, bias=False)
self.fc2 = nn.Linear(10, 4, bias=False)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
return F.softmax(x, dim=1)
device_id = gpus_for_rank(self.world_size)[self.rank][0]
model = DistributedDataParallel(
NoGradModule().float().to(device_id),
device_ids=[device_id],
process_group=process_group,
)
batch_size = 4
input = torch.rand([batch_size, 2], dtype=torch.float)
def check_no_grads():
for p in model.parameters():
self.assertTrue(p.requires_grad)
self.assertIsNone(p.grad)
# After initialization, no parameter has their gradient set.
check_no_grads()
# Run `forward` function with torch.no_grad()
with torch.no_grad():
output = model(input)
self.assertTrue(torch.is_tensor(output))
# No parameter should have their gradient set.
check_no_grads()
@requires_nccl()
@skip_if_not_multigpu
def test_accumulate_gradients_no_sync(self):
# This is the recommended way to implement accumulate grads
int_devices = gpus_for_rank(self.world_size)[self.rank][:1]
devices = list([torch.device('cuda:' + str(i)) for i in int_devices])
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
global_batch_size = self.world_size
local_batch_size = len(devices)
model, ddp_model, input, target = \
self._prepare_single_device_module(
process_group, devices, devices, global_batch_size)
def step_model(model, input, target):
model.train()
output = model(input)
loss = F.mse_loss(output, target.to(output.device))
loss.backward()
# ensure accumulate grads works with no_grad
with torch.no_grad():
with ddp_model.no_sync():
ddp_model.train()
ddp_model(input)
# check two model parameters over 2 iterations
for iteration in range(2):
# single cpu/gpu training
step_model(model, input, target)
ddp_input = input[self.rank * local_batch_size: (self.rank + 1) * local_batch_size]
ddp_target = target[self.rank * local_batch_size: (self.rank + 1) * local_batch_size]
if iteration % 2 == 0:
# accumulate grads locally when iteration == 0
with ddp_model.no_sync():
step_model(ddp_model, ddp_input, ddp_target)
else:
# sync grads when iteration == 1
step_model(ddp_model, ddp_input, ddp_target)
for i, j in zip(model.parameters(), ddp_model.parameters()):
if iteration % 2 == 0:
self.assertNotEqual(i.grad, j.grad)
else:
self.assertEqual(i.grad, j.grad)
# Shuffle the input so that DDP input is different
torch.manual_seed(1337 + iteration)
input = input[torch.randperm(global_batch_size)]
@requires_nccl()
@skip_if_not_multigpu
def test_accumulate_gradients_module(self):
# This is NOT the recommended way to implement accumulating grads, but
# we would like to make sure DDP does not mess up with the underlying
# module.
int_devices = gpus_for_rank(self.world_size)[self.rank][:1]
devices = list([torch.device('cuda:' + str(i)) for i in int_devices])
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
global_batch_size = self.world_size
model, ddp_model, input, target = \
self._prepare_single_device_module(
process_group, devices, devices, global_batch_size)
def step_model(model, input, target):
model.train()
output = model(input)
loss = F.mse_loss(output, target.to(output.device))
loss.backward()
# ensure accumulate grads works with no_grad
with torch.no_grad():
ddp_model.train()
ddp_model.module(input)
# Check two model parameters over 4 iterations.
# Use 4 iterations because we alternate between reducing and
# not reducing and want to make sure we switch both ways.
for iteration in range(4):
step_model(model, input, target)
if iteration % 2 == 0:
# Skip gradients sync without calling prepare_for_backward
step_model(
ddp_model.module,
input[self.rank : (self.rank + 1)],
target[self.rank : (self.rank + 1)])
for i, j in zip(model.parameters(), ddp_model.parameters()):
self.assertNotEqual(i.grad, j.grad)
else:
step_model(
ddp_model,
input[self.rank : (self.rank + 1)],
target[self.rank : (self.rank + 1)])
for i, j in zip(model.parameters(), ddp_model.parameters()):
self.assertEqual(i.grad, j.grad)
# Shuffle the input so that DDP input is different
torch.manual_seed(1337 + iteration)
input = input[torch.randperm(global_batch_size)]
def test_ignored_output(self):
"""
Test that the output of a model can be ignored and that there is no
implicit requirement that `backward` gets called.
"""
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size)
class IgnoredOutput(nn.Module):
def __init__(self):
super(IgnoredOutput, self).__init__()
self.fc1 = nn.Linear(2, 10, bias=False)
self.fc2 = nn.Linear(10, 4, bias=False)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
return F.softmax(x, dim=1)
model = DistributedDataParallel(
IgnoredOutput().float(),
process_group=process_group,
)
batch_size = 4
criterion = nn.CrossEntropyLoss()
input = torch.rand([batch_size, 2], dtype=torch.float)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)])
# Run a few iterations where we ignore the output.
for _ in range(4):
output = model(input)
del output
# Run a few iterations where we use the output.
for _ in range(4):
output = model(input)
loss = criterion(output, target)
loss.backward()
def test_ignored_output_with_unused_parameters(self):
"""
Test that the output of a model can be ignored and that there is no
implicit requirement that `backward` gets called, if not all model
parameters participated in computing the model output.
"""
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size)
class IgnoredOutputWithUnusedParameters(nn.Module):
def __init__(self):
super(IgnoredOutputWithUnusedParameters, self).__init__()
self.fc1 = nn.Linear(2, 10, bias=False)
self.fc2 = nn.Linear(10, 4, bias=False)
self.fc3 = nn.Linear(4, 4, bias=False)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
return F.softmax(x, dim=1)
model = DistributedDataParallel(
IgnoredOutputWithUnusedParameters().float(),
process_group=process_group,
find_unused_parameters=True,
)
batch_size = 4
criterion = nn.CrossEntropyLoss()
input = torch.rand([batch_size, 2], dtype=torch.float)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)])
# Run a few iterations where we ignore the output.
for _ in range(4):
output = model(input)
del output
# Run a few iterations where we use the output.
for _ in range(4):
output = model(input)
loss = criterion(output, target)
loss.backward()
@requires_nccl()
@skip_if_not_multigpu
@skip_if_rocm
def test_failure_recovery(self):
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
# need to create a separate file for the recovered FileStore, because
# the original one will be deleted when destructing the first FileStore.
recovery_filename = self.file_name + "_recovery"
if self.rank == 0:
# the file will be deleted by the recovered FileStore
open(recovery_filename, "w").close()
# not necessary to run barrier here, as DDP will synchronize
class TestModel(nn.Module):
def __init__(self):
super(TestModel, self).__init__()
self.fc1 = nn.Linear(2, 10, bias=False)
self.fc2 = nn.Linear(10, 4, bias=False)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
return F.softmax(x, dim=1)
device_id = gpus_for_rank(self.world_size)[self.rank][0]
model = TestModel().float().to(device_id)
ddp = DistributedDataParallel(
model,
device_ids=[device_id],
process_group=process_group,
)
batch_size = 4
criterion = nn.CrossEntropyLoss()
input = torch.rand([batch_size, 2], dtype=torch.float)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)]).to(device_id)
for _ in range(6):
output = ddp(input)
loss = criterion(output, target)
loss.backward()
del ddp
del process_group
del store # this will delete self.file_name
store = c10d.FileStore(recovery_filename, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
ddp = DistributedDataParallel(
model,
device_ids=[device_id],
process_group=process_group,
)
input = torch.rand([batch_size, 2], dtype=torch.float)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)]).to(device_id)
for _ in range(6):
output = ddp(input)
loss = criterion(output, target)
loss.backward()
@requires_gloo()
def test_sparse_gradients(self):
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size)
class SparseGradientModule(nn.Module):
def __init__(self):
super(SparseGradientModule, self).__init__()
self.embedding = nn.EmbeddingBag(10, 10, sparse=True)
def forward(self, x):
return F.softmax(self.embedding(x), dim=1)
# Ensure initialized weights and inputs are identical across processes
torch.manual_seed(1337)
vanilla_model = SparseGradientModule()
ddp_model = DistributedDataParallel(
copy.deepcopy(vanilla_model),
process_group=process_group,
)
mult = 2
batch_size = mult * self.world_size
criterion = nn.CrossEntropyLoss()
input = torch.randint(0, 10, [batch_size, 2])
target = torch.randint(0, 10, [batch_size])
# Run with entire batch against single process version
criterion(vanilla_model(input), target).backward()
# Run with partial batch against multi process version
partial_input = input.split(mult)[self.rank]
partial_target = target.split(mult)[self.rank]
criterion(ddp_model(partial_input), partial_target).backward()
# Check that the gradients are sparse and identical
vanilla_parameter = next(vanilla_model.parameters())
ddp_parameter = next(ddp_model.parameters())
self.assertEqual(vanilla_parameter.grad, ddp_parameter.grad)
class ReducerModule(nn.Module):
def __init__(self):
super(ReducerModule, self).__init__()
self.fc1 = nn.Linear(2, 10, bias=False)
self.fc2 = nn.Linear(10, 4, bias=False)
self.fc3 = nn.Linear(4, 4, bias=False)
self.relu = nn.ReLU()
def forward(self, x, use_fc3=True):
x = self.relu(self.fc1(x)).float()
x = self.relu(self.fc2(x)).float()
if use_fc3:
x = self.fc3(x).float()
return F.softmax(x, dim=1)
@requires_gloo()
class ReducerTest(TestCase):
def setUp(self):
self.store = c10d.FileStore("/dev/null", 1)
self.process_group = c10d.ProcessGroupGloo(self.store, 0, 1)
def test_single_dtype_single_bucket(self):
model = ReducerModule()
parameters = list(model.parameters())
buckets = [list(range(len(parameters)))]
dist.Reducer([parameters], buckets, self.process_group)
def _create_mixed_precision_model(self):
model = ReducerModule()
model.float()
model.fc1.double()
return model
def test_multi_dtype_single_bucket(self):
model = self._create_mixed_precision_model()
# Raise if there are multiple types per bucket.
# In this case we create one bucket for all parameters.
with self.assertRaises(RuntimeError):
parameters = [list(model.parameters())]
buckets = [list(range(len(parameters[0])))]
dist.Reducer(parameters, buckets, self.process_group)
def test_multi_dtype_multi_bucket(self):
model = self._create_mixed_precision_model()
parameters = [list(model.parameters())]
group_by_type = groupby(
range(len(parameters[0])),
key=lambda i: parameters[0][i].type())
buckets = [list(indices) for _, indices in group_by_type]
dist.Reducer(parameters, buckets, self.process_group)
def _create_reducer_for_models(self, models):
parameters = [list(model.parameters()) for model in models]
group_by_type = groupby(
range(len(parameters[0])),
key=lambda i: parameters[0][i].type())
buckets = [list(indices) for _, indices in group_by_type]
return dist.Reducer(parameters, buckets, self.process_group)
def test_forward_backward_single_replica(self):
batch_size = 10
model = self._create_mixed_precision_model()
reducer = self._create_reducer_for_models([model])
loss = nn.CrossEntropyLoss()
input = torch.rand([batch_size, 2], dtype=torch.double)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)])
output = loss(model(input), target)
reducer.prepare_for_backward(output)
output.backward()
def test_forward_backward_multi_replica(self):
batch_size = 10
num_replicas = 2
models = [self._create_mixed_precision_model() for _ in range(num_replicas)]
reducer = self._create_reducer_for_models(models)
loss = nn.CrossEntropyLoss()
input = torch.rand([batch_size, 2], dtype=torch.double).chunk(num_replicas)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)])
outputs = [models[i](input[i]) for i in range(num_replicas)]
output = loss(torch.cat(outputs), target)
reducer.prepare_for_backward(output)
output.backward()
# The reducer will have reduced the gradients for all model replicas.
# Verify that they are equal across model replicas.
for parameters in zip(*[model.parameters() for model in models]):
for parameter in parameters:
self.assertEqual(parameters[0].grad, parameter.grad)
def test_forward_backward_unused_parameters(self):
batch_size = 10
model = self._create_mixed_precision_model()
reducer = self._create_reducer_for_models([model])
loss = nn.CrossEntropyLoss()
input = torch.rand([batch_size, 2], dtype=torch.double)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)])
output = loss(model(input, use_fc3=False), target)
# Check that the grad of fc3 is not set.
self.assertEqual(None, model.fc3.weight.grad)
# Compute and accumulate gradients.
reducer.prepare_for_backward(output)
output.backward()
# The reducer will have marked the grad of fc3 as ready, because
# it doesn't show up in the autograd graph of `output`. Since fc3.weight
# is considered being globally unused, it will be kept untouched as None.
self.assertEqual(None, model.fc3.weight.grad)
def test_forward_backward_optimizer(self):
batch_size = 10
model = self._create_mixed_precision_model()
reducer = self._create_reducer_for_models([model])
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
for i in range(3):
input = torch.rand([batch_size, 2], dtype=torch.double)
target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)])
# The `zero_grad` function calls `detach_` and `zero_` on the grad
# tensors of model parameters. If we tried to set the grad tensors
# to a view of the reducer's bucket tensors, this would blow up.
optimizer.zero_grad()
# Unused parameter only in the first iteration.
output = loss(model(input, use_fc3=(i > 0)), target)
reducer.prepare_for_backward(output)
output.backward()
optimizer.step()
class ComputeBucketAssignmentTest(TestCase):
def test_single_limit_single_dtype(self):
tensors = [
torch.empty([100], dtype=torch.float),
torch.empty([200], dtype=torch.float),
torch.empty([100], dtype=torch.float),
torch.empty([50], dtype=torch.float),
]
result = dist._compute_bucket_assignment_by_size(tensors, [400])
self.assertEqual([[0], [1], [2], [3]], result)
def test_single_limit_multi_dtype(self):
tensors = [
torch.empty([50], dtype=torch.float),
torch.empty([25], dtype=torch.double),
torch.empty([50], dtype=torch.float),
torch.empty([25], dtype=torch.double),
torch.empty([50], dtype=torch.float),
torch.empty([25], dtype=torch.double),
]
result = dist._compute_bucket_assignment_by_size(tensors, [400])
self.assertEqual([[0, 2], [1, 3], [4], [5]], result)
def test_multi_limit_single_dtype(self):
tensors = [
torch.empty([10], dtype=torch.float),
torch.empty([10], dtype=torch.float),
torch.empty([10], dtype=torch.float),
torch.empty([10], dtype=torch.float),
]
result = dist._compute_bucket_assignment_by_size(tensors, [40, 80])
self.assertEqual([[0], [1, 2], [3]], result)
def test_multi_limit_multi_dtype(self):
tensors = [
torch.empty([50], dtype=torch.float),
torch.empty([25], dtype=torch.double),
torch.empty([50], dtype=torch.float),
torch.empty([25], dtype=torch.double),
torch.empty([50], dtype=torch.float),
torch.empty([25], dtype=torch.double),
]
result = dist._compute_bucket_assignment_by_size(tensors, [200, 400])
self.assertEqual([[0], [1], [2, 4], [3, 5]], result)
@unittest.skipIf(TEST_WITH_TSAN, "TSAN is not fork-safe since we're forking in a multi-threaded environment")
class NcclErrorHandlingTest(MultiProcessTestCase):
def setUp(self):
super(NcclErrorHandlingTest, self).setUp()
# Need to skip return code checking for these tests since the child
# processes don't exit cleanly.
self.skip_return_code_checks = [
self._get_wrapped_func(self.test_nccl_errors_blocking_abort),
self._get_wrapped_func(self.test_nccl_errors_blocking_sigkill),
self._get_wrapped_func(self.test_nccl_errors_blocking_sigterm),
self._get_wrapped_func(self.test_nccl_errors_blocking_nonzero_exit),
]
self._fork_processes()
def tearDown(self):
super(NcclErrorHandlingTest, 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 _get_wrapped_func(self, func):
# Get the original function which was wrapped in the decorator.
if hasattr(func, '__wrapped__'):
# py3 way.
return func.__wrapped__
else:
# py2 way.
return func.func_closure[0].cell_contents
def _run_all_reduce(self, pg):
pg.allreduce(torch.rand(10).cuda(self.rank))
@requires_nccl()
@requires_nccl_version(2400, "Need NCCL 2.4+ for error checking")
@skip_if_not_multigpu
@skip_if_rocm
def test_nccl_errors_nonblocking(self):
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
process_group.allreduce(torch.rand(10).cuda(self.rank))
if self.rank == 0:
# This allreduce does not block Python thread as allreduce enqueues
# the cuda operation, and then wait only blocks the current cuda
# stream.
work = process_group.allreduce(torch.rand(10).cuda(self.rank))
work.wait()
# Now the work scheduled next should hang forever since the previous
# allreduce will never complete.
t = threading.Thread(target=self._run_all_reduce, args=(process_group,))
t.daemon = True
t.start()
t.join(int(get_timeout(self.id()) / 5))
self.assertTrue(t.is_alive())
def _test_nccl_errors_blocking(self, func):
os.environ["NCCL_BLOCKING_WAIT"] = "1"
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(
store,
self.rank,
self.world_size,
timeout=timedelta(seconds=self.op_timeout_sec))
process_group.allreduce(torch.rand(10).cuda(self.rank))
if self.rank == 0:
work = process_group.allreduce(torch.rand(10).cuda(self.rank))
with self.assertRaisesRegex(RuntimeError, "Operation timed out!"):
# Operation would time out in blocking mode.
work.wait()
# Run some GPU operations to make sure cuda does not stuck to
# run new events. It was observed cuda could stuck if not
# aborting nccl communicators before throwing Operation timed out
a = torch.rand(10).cuda(self.rank)
else:
func()
@requires_nccl()
@requires_nccl_version(2400, "Need NCCL 2.4+ for error checking")
@skip_if_not_multigpu
def test_nccl_errors_blocking_clean_exit(self):
self._test_nccl_errors_blocking(lambda : sys.exit(0))
@requires_nccl()
@requires_nccl_version(2400, "Need NCCL 2.4+ for error checking")
@skip_if_not_multigpu
def test_nccl_errors_blocking_nonzero_exit(self):
self._test_nccl_errors_blocking(lambda : sys.exit(1))
@requires_nccl()
@requires_nccl_version(2400, "Need NCCL 2.4+ for error checking")
@skip_if_not_multigpu
def test_nccl_errors_blocking_abort(self):
self._test_nccl_errors_blocking(lambda : os.abort())
@requires_nccl()
@requires_nccl_version(2400, "Need NCCL 2.4+ for error checking")
@skip_if_not_multigpu
def test_nccl_errors_blocking_sigkill(self):
self._test_nccl_errors_blocking(lambda : os.kill(os.getpid(), signal.SIGKILL))
@requires_nccl()
@requires_nccl_version(2400, "Need NCCL 2.4+ for error checking")
@skip_if_not_multigpu
def test_nccl_errors_blocking_sigterm(self):
self._test_nccl_errors_blocking(lambda : os.kill(os.getpid(), signal.SIGTERM))
def _run_invalid_nccl_blocking_wait_env(self, val):
os.environ["NCCL_BLOCKING_WAIT"] = val
store = c10d.FileStore(self.file_name, self.world_size)
with self.assertRaises(RuntimeError):
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
@requires_nccl()
@skip_if_not_multigpu
def test_invalid_nccl_blocking_wait_env(self):
self._run_invalid_nccl_blocking_wait_env('abc')
self._run_invalid_nccl_blocking_wait_env('-1')
self._run_invalid_nccl_blocking_wait_env('2147483647')
self._run_invalid_nccl_blocking_wait_env('4294967295')
@requires_nccl()
@skip_if_not_multigpu
@skip_if_rocm
def test_nccl_timeout(self):
store = c10d.FileStore(self.file_name, self.world_size)
os.environ["NCCL_BLOCKING_WAIT"] = "1"
# Initialize process_group.
timeout = 1
c10d.distributed_c10d.init_process_group(
backend=dist.Backend.NCCL, store=store, world_size=2, rank=self.rank,
timeout=timedelta(seconds=timeout))
c10d.distributed_c10d.all_reduce(torch.rand(10).cuda(self.rank))
if self.rank == 0:
# This should timeout in about 1 second.
start = time.time()
with self.assertRaisesRegex(RuntimeError, "Operation timed out!"):
c10d.distributed_c10d.all_reduce(torch.rand(10).cuda(self.rank))
else:
# Ensure the other rank sleeps to trigger timeout.
time.sleep(2 * timeout)
@unittest.skipIf(TEST_WITH_TSAN, "TSAN is not fork-safe since we're forking in a multi-threaded environment")
class CommTest(MultiProcessTestCase):
def setUp(self):
super(CommTest, self).setUp()
self._fork_processes()
def tearDown(self):
super(CommTest, 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_coalesced(self, process_group, device):
half = torch.float16
# No support for float16 for CPU tensors
if device == torch.device('cpu'):
half = torch.float32
target = torch.arange(60, dtype=half, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float32, device=device).chunk(5)
target += torch.arange(60, dtype=half, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float64, device=device).chunk(5)
target += torch.arange(60, dtype=half, device=device).chunk(5)
target += torch.arange(60, dtype=torch.float32, device=device).chunk(5)
# The tensors to pass to broadcast are idential to the target
# only on the process that is the root of the broadcast.
if self.rank == 0:
tensors = list(tensor.clone() for tensor in target)
else:
tensors = list(torch.empty_like(tensor) for tensor in target)
c10d._broadcast_coalesced(
process_group,
tensors,
buffer_size=256)
self.assertEqual(tensors, target)
@requires_nccl()
@skip_if_not_multigpu
def test_broadcast_coalesced_nccl(self):
store = c10d.FileStore(self.file_name, self.world_size)
process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
device = torch.device('cuda:%d' % self.rank)
self._test_broadcast_coalesced(process_group, device)
@requires_gloo()
@skip_if_not_multigpu
def test_broadcast_coalesced_gloo_cuda(self):
store = c10d.FileStore(self.file_name, self.world_size)
options = c10d.ProcessGroupGloo.Options()
options.devices = [c10d.ProcessGroupGloo.create_device(interface=LOOPBACK)]
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size, options)
device = torch.device('cuda:%d' % self.rank)
self._test_broadcast_coalesced(process_group, device)
@requires_gloo()
def test_broadcast_coalesced_gloo_cpu(self):
store = c10d.FileStore(self.file_name, self.world_size)
options = c10d.ProcessGroupGloo.Options()
options.devices = [c10d.ProcessGroupGloo.create_device(interface=LOOPBACK)]
process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size, options)
device = torch.device('cpu')
self._test_broadcast_coalesced(process_group, device)
if __name__ == '__main__':
assert not torch.cuda._initialized, "test_distributed must not have initialized CUDA context on main process"
run_tests()