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nn.Module: use swap_tensors for Tensor subclasses (#122755) #123106

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merged 1 commit into from Apr 2, 2024
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nn.Module: use swap_tensors for Tensor subclasses (#122755) #123106

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38 changes: 38 additions & 0 deletions test/distributed/_tensor/test_api.py
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Expand Up @@ -250,6 +250,44 @@ def output_fn(outputs, device_mesh):
self.assertIsInstance(local_out, torch.Tensor)
self.assertNotIsInstance(local_out, DTensor)

@with_comms
def test_distribute_module_casting(self):
device_mesh = DeviceMesh(self.device_type, list(range(self.world_size)))

# 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
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
Expand Down
9 changes: 7 additions & 2 deletions torch/nn/modules/module.py
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Expand Up @@ -13,6 +13,7 @@
from typing import Union, Tuple, Any, Callable, Iterator, Set, Optional, overload, TypeVar, Mapping, Dict, List
from typing_extensions import Self
from ...utils.hooks import RemovableHandle
from torch.utils._python_dispatch import is_traceable_wrapper_subclass

__all__ = ['register_module_forward_pre_hook', 'register_module_forward_hook',
'register_module_full_backward_pre_hook', 'register_module_backward_hook',
Expand Down Expand Up @@ -802,8 +803,12 @@ def compute_should_use_set_data(tensor, tensor_applied):
with torch.no_grad():
param_applied = fn(param)
p_should_use_set_data = compute_should_use_set_data(param, param_applied)

# subclasses may have multiple child tensors so we need to use swap_tensors
p_should_use_swap_tensors = should_use_swap_tensors or is_traceable_wrapper_subclass(param_applied)

param_grad = param.grad
if should_use_swap_tensors:
if p_should_use_swap_tensors:
try:
if param_grad is not None:
# Accessing param.grad makes its at::Tensor's use_count 2, which will prevent swapping.
Expand All @@ -829,7 +834,7 @@ def compute_should_use_set_data(tensor, tensor_applied):
with torch.no_grad():
grad_applied = fn(param_grad)
g_should_use_set_data = compute_should_use_set_data(param_grad, grad_applied)
if should_use_swap_tensors:
if p_should_use_swap_tensors:
grad_applied.requires_grad_(param_grad.requires_grad)
try:
torch.utils.swap_tensors(param_grad, grad_applied)
Expand Down