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# Copyright (c) Meta Platforms, Inc. and affiliates
# Owner(s): ["oncall: distributed"]
import torch
from torch.distributed._tensor import DeviceMesh, DTensor, Replicate, Shard, zeros
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
with_comms,
)
class DTensorInitOpsTest(DTensorTestBase):
def _run_init_op(self, init_op, *args, **kwargs):
device_mesh = self.build_device_mesh()
shard_spec = [Shard(0)]
input_size = (8, 4)
input_tensor = torch.randn(*input_size, device=self.device_type)
dtensor = DTensor.from_local(input_tensor, device_mesh, shard_spec)
local_tensor_clone = torch.clone(input_tensor)
torch.manual_seed(self.rank)
local_tensor_clone = init_op(local_tensor_clone, *args, **kwargs)
torch.manual_seed(self.rank)
dtensor = init_op(dtensor, *args, **kwargs)
self.assertEqual(local_tensor_clone, dtensor.to_local())
@with_comms
def test_init_ops(self):
# NOTE: random init tests are moved to test_random_ops.py
self._run_init_op(torch.nn.init.constant_, 2.4)
class DTensorConstructorTest(DTensorTestBase):
@property
def world_size(self):
return 4
def _run_init_op(self, init_op, dist_init_op, eq_op, *args, **kwargs):
# 1d mesh test
device_mesh = DeviceMesh(self.device_type, list(range(self.world_size)))
placements_list = [[Shard(0)], [Shard(1)], [Shard(2)], [Replicate()]]
# even sharding
tensor_size = [4, 8, 12]
for placements in placements_list:
local_tensor_size = tensor_size.copy()
if isinstance(placements[0], Shard):
shard_dim = placements[0].dim
local_tensor_size[shard_dim] //= self.world_size
dist_tensor = dist_init_op(
tensor_size,
*args,
**kwargs,
device_mesh=device_mesh,
placements=placements,
)
ones_expected = init_op(local_tensor_size, *args, **kwargs)
eq_op(ones_expected, dist_tensor.to_local())
# uneven sharding
tensor_size = [5, 10, 15]
for placements in placements_list:
dist_tensor = dist_init_op(
tensor_size,
*args,
**kwargs,
device_mesh=device_mesh,
placements=placements,
)
if isinstance(placements[0], Shard):
shard_dim = placements[0].dim
exp_tensor_list = list(
torch.chunk(
init_op(tensor_size, *args, **kwargs),
self.world_size,
dim=shard_dim,
)
)
if self.rank < len(exp_tensor_list):
eq_op(exp_tensor_list[self.rank], dist_tensor.to_local())
else:
exp_tensor = init_op(tensor_size, *args, **kwargs)
eq_op(exp_tensor, dist_tensor.to_local())
# empty shape
local_tensor = dist_init_op(
[], *args, **kwargs, device_mesh=device_mesh, placements=[Replicate()]
).to_local()
expected_tensor = init_op([], *args, **kwargs)
eq_op(expected_tensor, local_tensor)
@with_comms
def test_ones(self):
self._run_init_op(
torch.ones,
torch.distributed._tensor.ones,
self.assertEqual,
requires_grad=True,
)
@with_comms
def test_empty(self):
self._run_init_op(
torch.empty,
torch.distributed._tensor.empty,
lambda x, y: (x.shape == y.shape)
and (x.dtype == y.dtype)
and (x.layout == y.layout),
requires_grad=True,
)
@with_comms
def test_full(self):
self._run_init_op(
torch.full,
torch.distributed._tensor.full,
self.assertEqual,
123.4,
requires_grad=True,
)
@with_comms
def test_zeros(self):
self._run_init_op(
torch.zeros,
torch.distributed._tensor.zeros,
self.assertEqual,
requires_grad=True,
)
@with_comms
def test_zeros_full_mesh(self):
# construct a cuda device 1d mesh
mesh = DeviceMesh(self.device_type, torch.arange(self.world_size))
placements = [Shard(0)]
size = [32, 3]
dist_tensor = zeros(size, device_mesh=mesh, placements=placements)
self.assertEqual(dist_tensor.size(), torch.Size(size))
local_tensor = dist_tensor.to_local()
self.assertEqual(local_tensor.size(), torch.Size([8, 3]))
local_tensor = torch.zeros(8, 3)
self.assertEqual(dist_tensor.to_local(), local_tensor)
self.assertEqual(dist_tensor.device.type, self.device_type)
# 1d sharded unevenly
size = [31, 3]
dist_tensor = zeros(size, device_mesh=mesh, placements=placements)
self.assertEqual(dist_tensor.size(), torch.Size(size))
local_tensor = dist_tensor.to_local()
if self.rank <= 2:
self.assertEqual(local_tensor.size(), torch.Size([8, 3]))
self.assertEqual(torch.zeros(8, 3), local_tensor)
else:
self.assertEqual(local_tensor.size(), torch.Size([7, 3]))
self.assertEqual(torch.zeros(7, 3), local_tensor)
# construct a cuda device mesh with 2d: shard, replicate
mesh = DeviceMesh(self.device_type, torch.arange(self.world_size).reshape(2, 2))
placements = [Shard(0), Replicate()]
size = [32, 4]
dist_tensor = zeros(size, device_mesh=mesh, placements=placements)
self.assertEqual(dist_tensor.size(), torch.Size(size))
local_tensor = dist_tensor.to_local()
self.assertEqual(local_tensor.size(), torch.Size([16, 4]))
self.assertEqual(local_tensor, torch.zeros([16, 4]))
# construct a cuda device mesh with 2d: shard, shard
placements = [Shard(0), Shard(1)]
size = [32, 4]
dist_tensor = zeros(size, device_mesh=mesh, placements=placements)
self.assertEqual(dist_tensor.size(), torch.Size(size))
local_tensor = dist_tensor.to_local()
self.assertEqual(local_tensor.size(), torch.Size([16, 2]))
self.assertEqual(local_tensor, torch.zeros([16, 2]))
# 2d sharded unevenly
placements = [Shard(0), Shard(1)]
size = [31, 3]
dist_tensor = zeros(size, device_mesh=mesh, placements=placements)
self.assertEqual(dist_tensor.size(), torch.Size(size))
local_tensor = dist_tensor.to_local()
if self.rank == 0:
self.assertEqual(local_tensor, torch.zeros([16, 2]))
elif self.rank == 1:
self.assertEqual(local_tensor, torch.zeros([16, 1]))
elif self.rank == 2:
self.assertEqual(local_tensor, torch.zeros([15, 2]))
elif self.rank == 3:
self.assertEqual(local_tensor, torch.zeros([15, 1]))
@with_comms
def test_zeros_submesh(self):
# default world_size is 4
# construct a cuda device 1d mesh, with no sub pg initialized
sub_mesh_list = [0, 3]
mesh = DeviceMesh(self.device_type, sub_mesh_list)
placements = [Shard(0)]
size = [32, 3]
dist_tensor = zeros(size, device_mesh=mesh, placements=placements)
self.assertEqual(dist_tensor.size(), torch.Size(size))
local_tensor = dist_tensor.to_local()
if self.rank in sub_mesh_list:
self.assertEqual(local_tensor.size(), torch.Size([16, 3]))
self.assertEqual(local_tensor, torch.zeros([16, 3]))
else:
self.assertEqual(local_tensor.size(), torch.Size([0]))
self.assertEqual(local_tensor, torch.zeros(0))
# construct a cuda device 1d mesh: unevenly, with subpg initialized
sub_mesh_list = [0, 1, 3]
mesh = DeviceMesh(self.device_type, sub_mesh_list)
placements = [Shard(0)]
size = [32, 3]
dist_tensor = zeros(size, device_mesh=mesh, placements=placements)
self.assertEqual(dist_tensor.size(), torch.Size(size))
local_tensor = dist_tensor.to_local()
if self.rank in sub_mesh_list:
if self.rank != 3:
self.assertEqual(local_tensor.size(), torch.Size([11, 3]))
self.assertEqual(local_tensor, torch.zeros([11, 3]))
else:
self.assertEqual(local_tensor.size(), torch.Size([10, 3]))
self.assertEqual(local_tensor, torch.zeros([10, 3]))
else:
self.assertEqual(local_tensor.size(), torch.Size([0]))
self.assertEqual(local_tensor, torch.tensor([]))
# construct a cuda device 2d mesh, with no subpg initialized
sub_mesh_list = [[0], [3]]
mesh = DeviceMesh(self.device_type, sub_mesh_list)
placements = [Shard(0), Shard(1)]
size = [32, 3]
dist_tensor = zeros(size, device_mesh=mesh, placements=placements)
self.assertEqual(dist_tensor.size(), torch.Size(size))
local_tensor = dist_tensor.to_local()
if self.rank in [0, 3]:
self.assertEqual(local_tensor.size(), torch.Size([16, 3]))
self.assertEqual(local_tensor, torch.zeros([16, 3]))
else:
self.assertEqual(local_tensor.size(), torch.Size([0]))
self.assertEqual(local_tensor, torch.tensor([]))
if __name__ == "__main__":
run_tests()
|
