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# Owner(s): ["oncall: distributed"]
import copy
import io
import torch
import torch.distributed as dist
import torch.distributed._functional_collectives as funcol
from torch.distributed._state_dict_utils import (
_check_state_dict_similarity,
_copy_state_dict,
_create_cpu_state_dict,
_distribute_tensors,
_gather_state_dict,
_offload_state_dict_to_cpu,
)
from torch.distributed._tensor import (
distribute_tensor,
DTensor,
init_device_mesh,
Shard,
)
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
skip_if_lt_x_gpu,
with_comms,
)
class TestStateDictUtils(DTensorTestBase):
@property
def world_size(self):
return min(4, torch.cuda.device_count())
@with_comms
@skip_if_lt_x_gpu(2)
def test_gather_state_dict_dtensor(self):
device_mesh = self.build_device_mesh()
shard_spec = [Shard(0)]
torch.random.manual_seed(dist.get_rank())
local_tensor = torch.randn(3, 3, 3)
dist_tensor = DTensor.from_local(local_tensor, device_mesh, shard_spec)
state_dict = {"dtensor": dist_tensor}
gathered_state_dict = _gather_state_dict(state_dict)
expected_gathered_dtensor = funcol.all_gather_tensor(
dist_tensor.to_local(), gather_dim=0, group=(device_mesh, 0)
)
self.assertEqual(expected_gathered_dtensor, gathered_state_dict["dtensor"])
self.assertTrue(gathered_state_dict["dtensor"].is_cuda)
@with_comms
@skip_if_lt_x_gpu(4)
def test_gather_with_cpu_and_ranks_only(self):
device_mesh = self.build_device_mesh()
shard_spec = [Shard(0)]
torch.random.manual_seed(dist.get_rank())
local_tensor = torch.randn(3, 3, 3)
dist_tensor = DTensor.from_local(local_tensor, device_mesh, shard_spec)
state_dict = {"dtensor": dist_tensor}
gathered_state_dict = _gather_state_dict(
state_dict, cpu_offload=True, ranks_only=(0, 2)
)
expected_gathered_dtensor = funcol.all_gather_tensor(
dist_tensor.to_local(), gather_dim=0, group=(device_mesh, 0)
)
if dist.get_rank() in (0, 2):
self.assertEqual(expected_gathered_dtensor, gathered_state_dict["dtensor"])
self.assertFalse(gathered_state_dict["dtensor"].is_cuda)
else:
self.assertEqual(gathered_state_dict, {})
@with_comms
@skip_if_lt_x_gpu(4)
def test_cpu_and_ranks_only(self):
device = torch.device("cuda")
state_dict = {
"tensor1": torch.arange(10, device=device),
"tensor2": torch.ones(10, device=device),
}
cpu_state_dict = _offload_state_dict_to_cpu(state_dict, ranks_only=(0, 2))
if dist.get_rank() in (0, 2):
for v in cpu_state_dict.values():
self.assertFalse(v.is_cuda)
self.assertEqual(cpu_state_dict["tensor1"], torch.arange(10))
self.assertEqual(cpu_state_dict["tensor2"], torch.ones(10))
else:
self.assertEqual(cpu_state_dict, {})
@with_comms
@skip_if_lt_x_gpu(4)
def test_complicated_dict(self):
def create_dtensor():
device_mesh = self.build_device_mesh()
shard_spec = [Shard(0)]
torch.random.manual_seed(dist.get_rank())
local_tensor = torch.randn(3, 3, 3)
dist_tensor = DTensor.from_local(local_tensor, device_mesh, shard_spec)
tensor = funcol.all_gather_tensor(
dist_tensor.to_local(), gather_dim=0, group=(device_mesh, 0)
)
return tensor, dist_tensor
ltensor, ldtensor = [], []
for i in range(10):
tensor, dtensor = create_dtensor()
ltensor.append(tensor)
ltensor.append(torch.ones(10, device=torch.device("cuda")))
ldtensor.append(dtensor)
ldtensor.append(torch.ones(10, device=torch.device("cuda")))
tensor, dtensor = create_dtensor()
dist_state_dict = {
"local": dtensor,
"list": ldtensor,
"arange": torch.arange(10, device=torch.device("cuda")),
}
state_dict = {
"local": tensor,
"list": ltensor,
"arange": torch.arange(10, device=torch.device("cuda")),
}
self.assertEqual(state_dict, _gather_state_dict(dist_state_dict))
@skip_if_lt_x_gpu(2)
def test_create_cpu_state_dict(self):
device = torch.device("cuda")
buffer = io.BytesIO()
torch.save(torch.ones(10), buffer)
buffer.seek(0)
state_dict = {
"tensor1": torch.arange(10, device=device),
"tensor2": torch.ones(10, device=device),
"non_tensor_bytes_io": copy.deepcopy(buffer),
"non_tensor_bytes": buffer.read(),
"step": torch.tensor(7, dtype=torch.float),
"lr": 1.5,
"nested": {"list": [1, 2, 3, 4]},
}
def _verify(cpu_state_dict):
# Verify the correctness of _check_state_dict_similarity()
self.assertTrue(_check_state_dict_similarity(state_dict, cpu_state_dict))
tensor1 = cpu_state_dict["tensor1"]
cpu_state_dict["tensor1"] = torch.arange(11)
self.assertFalse(_check_state_dict_similarity(state_dict, cpu_state_dict))
cpu_state_dict["tensor1"] = tensor1
_copy_state_dict(state_dict, cpu_state_dict)
# Verify if _copy_state_dict works
for v in cpu_state_dict.values():
if isinstance(v, torch.Tensor):
self.assertFalse(v.is_cuda)
self.assertEqual(cpu_state_dict["tensor1"], torch.arange(10))
self.assertEqual(cpu_state_dict["tensor2"], torch.ones(10))
buffer.seek(0)
cpu_state_dict["non_tensor_bytes_io"].seek(0)
self.assertEqual(
cpu_state_dict["non_tensor_bytes_io"].read(), buffer.read()
)
buffer.seek(0)
self.assertEqual(cpu_state_dict["non_tensor_bytes"], buffer.read())
self.assertEqual(cpu_state_dict["lr"], 1.5)
self.assertEqual(cpu_state_dict["step"], 7)
self.assertEqual(cpu_state_dict["nested"], {"list": [1, 2, 3, 4]})
cpu_state_dict = _create_cpu_state_dict(state_dict, pin_memory=True)
_verify(cpu_state_dict)
cpu_state_dict = _create_cpu_state_dict(state_dict, share_memory=True)
_verify(cpu_state_dict)
cpu_state_dict = _create_cpu_state_dict(
state_dict, share_memory=True, pin_memory=True
)
_verify(cpu_state_dict)
@with_comms
@skip_if_lt_x_gpu(2)
def test_state_dict_util_distribute_tensors(self):
even_tensor = torch.randn(self.world_size, 2)
uneven_tensor = torch.randn(1, 2)
mesh = init_device_mesh("cuda", mesh_shape=(self.world_size,))
even_dtensor = distribute_tensor(
torch.randn(self.world_size, 2), mesh, [Shard(0)]
)
uneven_dtensor = distribute_tensor(torch.randn(1, 2), mesh, [Shard(0)])
# the dtensor and tensor are different before _distribute_tensors is called.
local_state_dict = {
"even": [even_dtensor, even_tensor],
"uneven": [uneven_dtensor, uneven_tensor],
}
ref_local_state_dict = copy.deepcopy(local_state_dict)
keys = ["even", "uneven"]
_distribute_tensors(local_state_dict, keys, self.device_type)
for local_v, ref_v in zip(
local_state_dict.values(), ref_local_state_dict.values()
):
self.assertEqual(local_v.size(), ref_v[0].size())
self.assertEqual(local_v.stride(), ref_v[0].stride())
self.assertNotEqual(
local_v_full_tensor := local_v.full_tensor(), ref_v[0].full_tensor()
)
self.assertEqual(local_v_full_tensor, ref_v[1])
if __name__ == "__main__":
run_tests()
|
