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|
# Copyright (c) Meta Platforms, Inc. and affiliates
# Owner(s): ["oncall: distributed"]
import torch
import torch.nn as nn
from torch.distributed.tensor import (
DeviceMesh,
distribute_module,
distribute_tensor,
DTensor,
Replicate,
Shard,
)
from torch.distributed.tensor.debug import CommDebugMode
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
with_comms,
)
class MyModel(nn.Module):
def __init__(self, n_features, n_layers, device):
super().__init__()
self.seq = nn.Sequential(
*[nn.Linear(n_features, n_features, device=device) for _ in range(n_layers)]
)
def forward(self, x):
return self.seq(x)
def reset_parameters(self):
for m in self.seq:
m.reset_parameters()
c10d_ops = torch.ops.c10d
class DTensorAPITest(DTensorTestBase):
@property
def world_size(self) -> int:
# hard code world size to 4 as we need to test
# at least with 2d mesh
return 4
@with_comms
def test_distribute_tensor_rank(self):
comm_mode = CommDebugMode()
device_mesh = self.build_device_mesh()
shard_spec = [Shard(0)]
for requires_grad in [True, False]:
tensor_to_shard = torch.randn(
3 * self.world_size, 3, requires_grad=requires_grad
)
with comm_mode:
dist_tensor = distribute_tensor(
tensor_to_shard, device_mesh, shard_spec
)
self.assertEqual(comm_mode.get_comm_counts()[c10d_ops.scatter_], 1)
self.assertEqual(dist_tensor.size(), torch.Size([3 * self.world_size, 3]))
local_tensor = dist_tensor.to_local()
self.assertEqual(local_tensor.size(), torch.Size([3, 3]))
if requires_grad:
self.assertTrue(dist_tensor.requires_grad)
self.assertTrue(dist_tensor.is_leaf)
# test negative dim
shard_minus_spec = [Shard(-1)]
tensor_to_shard = torch.randn(3, 3 * self.world_size)
dist_tensor = distribute_tensor(tensor_to_shard, device_mesh, shard_minus_spec)
self.assertEqual(dist_tensor.placements[0].dim, 1)
placement_combs = [[Shard(0)], [Shard(1)], [Replicate()]]
# test src_data_rank == 1
# set seed differently for each rank
torch.manual_seed(self.rank)
for placement in placement_combs:
tensor_to_distribute = torch.randn(3 * self.world_size, 3 * self.world_size)
dtensor = distribute_tensor(
tensor_to_distribute, device_mesh, placement, src_data_rank=1
)
full_dtensor = dtensor.full_tensor()
if self.rank == 1:
self.assertEqual(full_dtensor, tensor_to_distribute)
# test src_data_rank = None, make sure it does not have communication
with comm_mode:
for placement in placement_combs:
if isinstance(placement[0], Shard):
shard_dim = placement[0].dim
shape = [3, 3]
shape[shard_dim] *= self.world_size
tensor_to_distribute = torch.randn(*shape)
else:
tensor_to_distribute = torch.randn(3, 3)
dtensor = distribute_tensor(
tensor_to_distribute, device_mesh, placement, src_data_rank=None
)
self.assertEqual(dtensor.to_local().shape, (3, 3))
self.assertEqual(comm_mode.get_total_counts(), 0)
@with_comms
def test_distribute_tensor_errors(self):
device_mesh = DeviceMesh(
self.device_type, torch.arange(self.world_size).reshape(2, 2)
)
tensor_shape = [3 * self.world_size, 3 * self.world_size]
tensor_to_distribute = torch.randn(*tensor_shape)
with self.assertRaisesRegex(ValueError, "must have the same length"):
shard_spec = [Shard(0)]
distribute_tensor(tensor_to_distribute, device_mesh, shard_spec)
with self.assertRaisesRegex(RuntimeError, "distribute leaf tensor"):
shard_spec = [Shard(0)]
global_tensor = torch.randn(*tensor_shape, requires_grad=True)
global_tensor_to_distribute = global_tensor + 2
distribute_tensor(global_tensor_to_distribute, device_mesh, shard_spec)
spec = [Shard(0), Shard(1)]
dtensor = distribute_tensor(tensor_to_distribute, device_mesh, spec)
with self.assertRaisesRegex(ValueError, "to a different device mesh"):
new_mesh = DeviceMesh(self.device_type, torch.arange(self.world_size))
distribute_tensor(dtensor, new_mesh, [Shard(0)])
with self.assertRaisesRegex(ValueError, "to a different placements"):
new_spec = [Shard(0), Replicate()]
distribute_tensor(dtensor, device_mesh, new_spec)
@with_comms
def test_distribute_tensor_uneven_sharding(self):
device_mesh = self.build_device_mesh()
input_sizes_and_shard_dims = [
((self.world_size * 3 + 1, 3, 3), 0),
((self.world_size * 3 + 2, 3, 3), 0),
((3, self.world_size * 3 + 1, 3), 1),
((3, self.world_size * 3 + 2, 3), 1),
((3, 3, self.world_size * 3 + 1), 2),
((3, 3, self.world_size * 3 + 2), 2),
]
for input_size, shard_dim in input_sizes_and_shard_dims:
shard_spec = [Shard(shard_dim)]
tensor_to_shard = torch.randn(input_size)
splitted_tensor_list = list(
torch.chunk(tensor_to_shard, self.world_size, dim=shard_dim)
)
dist_tensor = distribute_tensor(tensor_to_shard, device_mesh, shard_spec)
self.assertEqual(dist_tensor.size(), torch.Size(input_size))
local_tensor = dist_tensor.to_local()
self.assertEqual(local_tensor, splitted_tensor_list[self.rank])
@with_comms
def test_distribute_module(self):
device_mesh = self.build_device_mesh()
# fully shard all linear modules on dim 0
module_to_shard = MyModel(5 * self.world_size, 20, device=self.device_type)
shard_spec = [Shard(0)]
def shard_fn(name, module, device_mesh):
if isinstance(module, nn.Linear):
for name, param in module.named_parameters():
dist_param = torch.nn.Parameter(
distribute_tensor(param, device_mesh, shard_spec)
)
module.register_parameter(name, dist_param)
sharded_module = distribute_module(module_to_shard, device_mesh, shard_fn)
for param in sharded_module.parameters():
self.assertIsInstance(param, DTensor)
self.assertEqual(param.placements, shard_spec)
replica_spec = [Replicate()]
# fully replicate all modules without passing in partition_fn
module_to_replicate = MyModel(5, 20, device=self.device_type)
replica_module = distribute_module(module_to_replicate, device_mesh)
for param in replica_module.parameters():
self.assertIsInstance(param, DTensor)
self.assertEqual(param.placements, replica_spec)
# fully replicate all modules by passing in partition_fn
def replicate_fn(name, module, device_mesh):
if isinstance(module, nn.Linear):
for name, param in module.named_parameters():
dist_param = torch.nn.Parameter(
distribute_tensor(param, device_mesh, replica_spec)
)
module.register_parameter(name, dist_param)
module_to_replicate = MyModel(5, 20, device=self.device_type)
replica_module = distribute_module(
module_to_replicate, device_mesh, replicate_fn
)
for param in replica_module.parameters():
self.assertIsInstance(param, DTensor)
self.assertEqual(param.placements, replica_spec)
# only shard part of module, and rest of module should be replicate
def shard_fn(name, module, device_mesh):
if isinstance(module, nn.Linear) and (name == "seq.0" or name == "seq.8"):
for name, param in module.named_parameters():
dist_param = torch.nn.Parameter(
distribute_tensor(param, device_mesh, shard_spec)
)
module.register_parameter(name, dist_param)
module_to_distribute = MyModel(5 * self.world_size, 20, device=self.device_type)
dist_module = distribute_module(module_to_distribute, device_mesh, shard_fn)
for name, param in dist_module.named_parameters():
self.assertIsInstance(param, DTensor)
if name.startswith(("seq.0", "seq.8")):
self.assertEqual(param.placements, shard_spec)
else:
self.assertEqual(param.placements, replica_spec)
@with_comms
def test_distribute_module_input_fn_output_fn(self):
device_mesh = self.build_device_mesh()
# fully replicate all linear modules
module_to_replicate = MyModel(20, 1, device=self.device_type)
# mark input sharding on dim 0
def input_fn(mod, inputs, device_mesh):
return DTensor.from_local(inputs[0], device_mesh, [Shard(0)])
def output_fn(mod, outputs, device_mesh):
assert isinstance(outputs, DTensor)
return outputs.to_local()
replica_module = distribute_module(
module_to_replicate,
device_mesh,
input_fn=input_fn,
output_fn=output_fn,
)
input_tensor = torch.randn(5, 20, device=self.device_type)
local_out = replica_module(input_tensor)
self.assertIsInstance(local_out, torch.Tensor)
self.assertNotIsInstance(local_out, DTensor)
# full replicate (even on inputs)
model = MyModel(10, 10, device=self.device_type)
def replicate_input_fn(mod, inputs, device_mesh):
return DTensor.from_local(inputs[0], device_mesh, [Replicate()])
replica_model = distribute_module(
model,
device_mesh,
input_fn=replicate_input_fn,
)
input = torch.randn(10, 10, requires_grad=True)
output = replica_model(input)
output.sum().backward()
param_grad = next(iter(replica_model.parameters())).grad
self.assertTrue(isinstance(param_grad, DTensor))
self.assertTrue(isinstance(param_grad.placements[0], Replicate))
@with_comms
def test_distribute_module_input_fn_output_fn_warning(self):
device_mesh = self.build_device_mesh()
# fully replicate all linear modules
module_to_replicate = MyModel(20, 1, device=self.device_type)
# mark input sharding on dim 0
def input_fn(inputs, device_mesh):
return DTensor.from_local(inputs[0], device_mesh, [Shard(0)])
def output_fn(outputs, device_mesh):
assert isinstance(outputs, DTensor)
return outputs.to_local()
with self.assertWarnsRegex(FutureWarning, "Deprecating"):
replica_module = distribute_module(
module_to_replicate,
device_mesh,
input_fn=input_fn,
output_fn=output_fn,
)
input_tensor = torch.randn(5, 20, device=self.device_type)
local_out = replica_module(input_tensor)
self.assertIsInstance(local_out, torch.Tensor)
self.assertNotIsInstance(local_out, DTensor)
@with_comms
def test_distribute_module_casting(self):
device_mesh = self.build_device_mesh()
# check DTensor casting
dt = DTensor.from_local(torch.rand(10), device_mesh, [Replicate()])
dt = dt.to(torch.bfloat16)
self.assertEqual(dt.dtype, torch.bfloat16)
self.assertEqual(dt._local_tensor.dtype, torch.bfloat16)
# check distribute_tensor casting
dt = distribute_tensor(torch.rand(10), device_mesh, [Replicate()])
dt = dt.to(torch.bfloat16)
self.assertEqual(dt.dtype, torch.bfloat16)
self.assertEqual(dt._local_tensor.dtype, torch.bfloat16)
# check distribute_module casting
model = MyModel(10, 10, device=self.device_type)
replica_model = distribute_module(
model,
device_mesh,
)
replica_model = replica_model.to(torch.bfloat16)
self.assertEqual(replica_model.seq[0].weight.dtype, torch.bfloat16)
self.assertEqual(
replica_model.seq[0].weight._local_tensor.dtype, torch.bfloat16
)
# check autocast
# `distribute_module` is an in-place operation, so we need to create a
# new model
model = MyModel(10, 10, device=self.device_type)
dt = distribute_tensor(torch.rand(10), device_mesh, [Replicate()])
replica_model = distribute_module(
model,
device_mesh,
)
with torch.autocast(device_type=self.device_type, dtype=torch.bfloat16):
output = replica_model(dt)
self.assertEqual(output.dtype, torch.bfloat16)
@with_comms
def test_distribute_module_meta(self):
# If the model is too big, the user may first the create entire model on the meta device and then initialize
# it on the device in the partition function.
device_mesh = self.build_device_mesh()
# fully shard all parameters on dim 0
module_to_shard = MyModel(5 * self.world_size, 20, device="meta")
shard_spec = [Shard(0)]
def shard_fn(name, module, device_mesh):
for param_name, param in module._parameters.items():
dist_param = distribute_tensor(param, device_mesh, shard_spec)
dist_param = torch.empty_like(
dist_param, device=device_mesh.device_type
)
module.register_parameter(param_name, torch.nn.Parameter(dist_param))
sharded_module = distribute_module(module_to_shard, device_mesh, shard_fn)
for param in sharded_module.parameters():
self.assertIsInstance(param, DTensor)
self.assertFalse(param.is_meta)
self.assertTrue(param.device.type == device_mesh.device_type)
if __name__ == "__main__":
run_tests()
|
