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# Owner(s): ["oncall: distributed"]
import operator
import os
import sys
import threading
from functools import reduce
from unittest import skip, SkipTest
import torch
import torch.autograd
import torch.distributed as dist
from torch._C._distributed_c10d import ReduceOp
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
from torch.testing._internal.common_distributed import (
MultiThreadedTestCase,
skip_if_lt_x_gpu,
spawn_threads_and_init_comms,
)
from torch.testing._internal.common_utils import IS_SANDCASTLE, run_tests, TestCase
DEFAULT_WORLD_SIZE = 4
class TestCollectivesWithWrapper(TestCase):
@spawn_threads_and_init_comms(world_size=4)
def test_broadcast_object_list(self):
val = 99 if dist.get_rank() == 0 else None
object_list = [val] * dist.get_world_size()
dist.broadcast_object_list(object_list=object_list)
self.assertEqual(99, object_list[0])
def test_collective_error_on_rank_zero(self):
@spawn_threads_and_init_comms(world_size=4)
def _test_method(self):
input_tensor = torch.ones(3, 3) * dist.get_rank() # perform 1st all gather
output_tensors = [
torch.empty_like(input_tensor) for _ in range(dist.get_world_size())
]
dist.all_gather(output_tensors, input_tensor)
if dist.get_rank() == 0:
raise AssertionError("Mimic real test failure.") # fail on rank 0
dist.all_gather(output_tensors, input_tensor) # perform 2nd all gather
with self.assertRaises(RuntimeError):
_test_method(self)
def test_collective_error_on_rank_non_zero(self):
@spawn_threads_and_init_comms(world_size=4)
def _test_method(self):
input_tensor = torch.ones(3, 3) * dist.get_rank() # perform 1st all gather
output_tensors = [
torch.empty_like(input_tensor) for _ in range(dist.get_world_size())
]
dist.all_gather(output_tensors, input_tensor)
if dist.get_rank() == 1:
raise AssertionError("Mimic real test failure.") # fail on rank 1
dist.all_gather(output_tensors, input_tensor) # perform 2nd all gather
with self.assertRaises(RuntimeError):
_test_method(self)
def test_collective_error_on_rank_non_zero_all(self):
@spawn_threads_and_init_comms(world_size=4)
def _test_method(self):
input_tensor = torch.ones(3, 3) * dist.get_rank() # perform 1st all gather
output_tensors = [
torch.empty_like(input_tensor) for _ in range(dist.get_world_size())
]
dist.all_gather(output_tensors, input_tensor)
if dist.get_rank() > 0:
raise AssertionError(
"Mimic real test failure."
) # fail on all non-zero rank
dist.all_gather(output_tensors, input_tensor) # perform 2nd all gather
with self.assertRaises(RuntimeError):
_test_method(self)
def test_skip(self):
@spawn_threads_and_init_comms(world_size=4)
@skip("check if skip exception can be captured correctly.")
def _test_method(self):
pass
if not IS_SANDCASTLE:
with self.assertRaises(SkipTest):
_test_method(self)
@spawn_threads_and_init_comms(world_size=4)
def test_all_to_all_single_tensor(self):
rank = dist.get_rank()
world_size = dist.get_world_size()
send = torch.full((world_size, 2), rank)
sizes = torch.ones(world_size, dtype=torch.int64)
out = torch.zeros(world_size, 2, dtype=send.dtype)
dist.all_to_all_single(out, send, sizes, sizes)
self.assertEqual(out.tolist(), list(zip(range(world_size), range(world_size))))
@spawn_threads_and_init_comms(world_size=4)
def test_all_to_all_single_list(self):
rank = dist.get_rank()
world_size = dist.get_world_size()
send = torch.full((world_size, 2), rank)
sizes = [1] * world_size
out = torch.zeros(world_size, 2, dtype=send.dtype)
dist.all_to_all_single(out, send, sizes, sizes)
self.assertEqual(out.tolist(), list(zip(range(world_size), range(world_size))))
@spawn_threads_and_init_comms(world_size=4)
def test_all_to_all_single_none(self):
rank = dist.get_rank()
world_size = dist.get_world_size()
send = torch.full((world_size, 2), rank)
out = torch.zeros(world_size, 2, dtype=send.dtype)
dist.all_to_all_single(out, send)
self.assertEqual(out.tolist(), list(zip(range(world_size), range(world_size))))
class TestCollectivesWithBaseClass(MultiThreadedTestCase):
@property
def world_size(self):
return 4
def setUp(self):
os.environ["TORCH_DIST_INIT_BARRIER"] = "1"
super().setUp()
self._spawn_threads()
def tearDown(self):
super().tearDown()
os.environ["TORCH_DIST_INIT_BARRIER"] = "0"
def test_allgather(self):
input_tensor = torch.ones(3, 3) * dist.get_rank()
output_tensors = [
torch.empty_like(input_tensor) for _ in range(self.world_size)
]
dist.all_gather(output_tensors, input_tensor)
for rank, out_tensor in enumerate(output_tensors):
self.assertEqual(out_tensor, torch.ones(3, 3) * rank)
def test_broadcast(self):
input_tensor = torch.ones(3, 3) * dist.get_rank()
for rank in range(self.world_size):
cloned_input = input_tensor.clone()
dist.broadcast(cloned_input, src=rank)
self.assertEqual(cloned_input, torch.ones(3, 3) * rank)
def test_scatter(self):
if dist.get_rank() == 0:
scatter_list = [torch.ones(3, 3) * rank for rank in range(self.world_size)]
else:
scatter_list = None
output_tensor = torch.empty(3, 3)
dist.scatter(output_tensor, scatter_list)
self.assertEqual(output_tensor, torch.ones(3, 3) * dist.get_rank())
def test_reduce_scatter(self):
to_reduce_scatter = [torch.ones(3, 3) * rank for rank in range(self.world_size)]
output_tensor = torch.empty(3, 3)
dist.reduce_scatter(output_tensor, to_reduce_scatter)
expected_tensor = torch.ones(3, 3) * dist.get_rank() * self.world_size
self.assertEqual(output_tensor, expected_tensor)
output_tensor = torch.empty(3, 3)
dist.reduce_scatter(output_tensor, to_reduce_scatter, op=dist.ReduceOp.AVG)
expected_tensor = torch.ones(3, 3) * dist.get_rank()
self.assertEqual(output_tensor, expected_tensor)
def test_broadcast_object_list(self):
val = 99 if dist.get_rank() == 0 else None
object_list = [val] * dist.get_world_size()
print(f"{dist.get_rank()} -> {dist.get_world_size()}")
dist.broadcast_object_list(object_list=object_list)
self.assertEqual(99, object_list[0])
def test_all_reduce(self):
output = torch.ones(3, 3) * dist.get_rank()
dist.all_reduce(output)
res_num = ((0 + self.world_size - 1) * self.world_size) / 2
self.assertEqual(output, torch.ones(3, 3) * res_num)
def test_all_to_all(self):
rank = self.rank
world_size = self.world_size
input_tensor_list = [
torch.ones(3, 3) * x
for x in range(rank * world_size, (rank + 1) * world_size)
]
output_tensor_list = [torch.empty_like(tensor) for tensor in input_tensor_list]
dist.all_to_all(output_tensor_list, input_tensor_list)
expected_tensor_list = [
torch.ones(3, 3) * x
for x in range(rank, world_size * world_size, world_size)
]
self.assertEqual(expected_tensor_list, output_tensor_list)
def test_all_reduce_ops(self):
tensor = torch.tensor([dist.get_rank() + 1])
dist.all_reduce(tensor, op=ReduceOp.PRODUCT)
expected = reduce(operator.mul, range(1, self.world_size + 1))
self.assertEqual(expected, tensor.item())
tensor = torch.tensor([dist.get_rank() + 1])
dist.all_reduce(tensor, op=ReduceOp.MIN)
self.assertEqual(1, tensor.item())
tensor = torch.tensor([dist.get_rank() + 1])
dist.all_reduce(tensor, op=ReduceOp.MAX)
self.assertEqual(self.world_size, tensor.item())
tensor = torch.tensor([dist.get_rank() + 1])
dist.all_reduce(tensor, op=ReduceOp.BAND)
expected = reduce(operator.and_, range(1, self.world_size + 1))
self.assertEqual(expected, tensor.item())
tensor = torch.tensor([dist.get_rank() + 1])
dist.all_reduce(tensor, op=ReduceOp.BOR)
expected = reduce(operator.or_, range(1, self.world_size + 1))
self.assertEqual(expected, tensor.item())
tensor = torch.tensor([dist.get_rank() + 1])
dist.all_reduce(tensor, op=ReduceOp.BXOR)
expected = reduce(operator.xor, range(1, self.world_size + 1))
self.assertEqual(expected, tensor.item())
def test_assert_equal_on_rank(self):
# RNG is shared across threads. So instead of asserting on all threads
# we only assert on rank 0
self_tensor = torch.rand(3, 3)
rank_0_tensor = self_tensor.clone()
dist.broadcast(rank_0_tensor, src=0)
self.assertEqualOnRank(rank_0_tensor, self_tensor, rank=0)
self.assertNotEqualOnRank(rank_0_tensor, self_tensor, rank=1)
def test_subpg(self):
subpg0 = dist.new_group([0, 1])
subpg1 = dist.new_group([2, 3])
current_rank = dist.get_rank()
output = torch.ones(3, 3) * current_rank
# call all_reduce on subpg0 and subpg1 concurrently
if current_rank in [0, 1]:
dist.all_reduce(output, group=subpg0)
else:
dist.all_reduce(output, group=subpg1)
if current_rank in [0, 1]:
self.assertEqual(output, torch.ones(3, 3) * 1)
else:
self.assertEqual(output, torch.ones(3, 3) * 5)
def test_using_pg_from_another_thread(self):
def stuff_in_other_thread(pg):
x = torch.rand(4, requires_grad=True)
dist.all_reduce(x, group=pg)
t = threading.Thread(target=stuff_in_other_thread, args=(dist.group.WORLD,))
t.start()
t.join()
def test_gather(self):
if dist.get_rank() == 0:
gather_list = [torch.empty(3, 3) for _ in range(self.world_size)]
else:
gather_list = None
input_tensor = torch.ones(3, 3) * dist.get_rank()
dist.gather(input_tensor, gather_list)
if dist.get_rank() == 0:
for i in range(self.world_size):
self.assertEqual(gather_list[i], torch.ones(3, 3) * i)
def test_all_reduce_coalesced(self):
t0 = torch.ones(3, 3) * dist.get_rank()
t1 = torch.ones(3, 3) * dist.get_rank() * 2
dist.all_reduce_coalesced([t0, t1])
res_num = ((0 + self.world_size - 1) * self.world_size) / 2
self.assertEqual(t0, torch.ones(3, 3) * res_num)
self.assertEqual(t1, torch.ones(3, 3) * (res_num * 2))
@skip_if_lt_x_gpu(1)
def test_bwd_sees_fwd_pg(self):
fwd_tid = threading.current_thread().ident
class MyFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, rank):
result = rank * 2
ctx.save_for_backward(result, rank)
assert int(rank.item()) == dist.get_rank()
return result
@staticmethod
def backward(ctx, grad_output):
result, rank = ctx.saved_tensors
bwd_tid = threading.current_thread().ident
self.assertEqual(
fwd_tid,
bwd_tid,
f"bwd not running in the same thread a fwd for rank {rank.item()}",
)
self.assertTrue(dist.is_initialized())
self.assertEqual(int(rank.item()), dist.get_rank())
dist.all_reduce(result)
self.assertEqual(int(result.item()), 12) # (0 + 1 + 2 + 3) * 2
return grad_output * result
x = torch.tensor(
[dist.get_rank()], dtype=torch.float, device="cuda", requires_grad=True
)
x = MyFunc.apply(x)
x.sum().backward()
if __name__ == "__main__":
run_tests()
|
