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# Owner(s): ["oncall: distributed"]
import contextlib
from copy import deepcopy
from functools import partial
import torch
import torch.nn as nn
from torch.distributed.fsdp.fully_sharded_data_parallel import (
FullyShardedDataParallel as FSDP,
CPUOffload,
)
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
checkpoint_wrapper,
)
from torch.testing._internal.common_distributed import (
skip_if_lt_x_gpu,
)
from torch.testing._internal.common_fsdp import (
FSDPTest,
_maybe_wrap_fsdp,
)
from torch.testing._internal.common_utils import (
run_tests,
parametrize,
instantiate_parametrized_tests,
)
from torch.utils.checkpoint import checkpoint
_save_on_cpu_called = False
def get_patched_save_on_cpu():
orig_save_on_cpu = torch.distributed.algorithms._checkpoint.checkpoint_wrapper.save_on_cpu
def patched_save_on_cpu(*args, **kwargs):
global _save_on_cpu_called
_save_on_cpu_called = True
return orig_save_on_cpu(*args, **kwargs)
return patched_save_on_cpu
@contextlib.contextmanager
def patch_save_on_cpu(new_save_on_cpu):
orig_save_on_cpu = torch.distributed.algorithms._checkpoint.checkpoint_wrapper.save_on_cpu
torch.distributed.algorithms._checkpoint.checkpoint_wrapper.save_on_cpu = new_save_on_cpu
try:
yield
finally:
torch.distributed.algorithms._checkpoint.checkpoint_wrapper.save_on_cpu = orig_save_on_cpu
class TestFSDPCheckpoint(FSDPTest):
class SequentialModule(nn.Module):
def __init__(
self,
checkpoint_layer=False,
offload_activations=False,
wrap_fsdp=False,
*fsdp_args,
**fsdp_kwargs,
):
torch.manual_seed(0)
torch.cuda.manual_seed(0)
super().__init__()
l1 = nn.Linear(3, 3).cuda()
l2 = nn.Linear(3, 3).cuda()
l3 = nn.Linear(3, 3).cuda()
if checkpoint_layer:
ckpt_wrapper = partial(
checkpoint_wrapper, offload_to_cpu=offload_activations
)
l1 = ckpt_wrapper(l1)
l2 = ckpt_wrapper(l2)
l3 = ckpt_wrapper(l3)
fsdp_wrapper = partial(
_maybe_wrap_fsdp, wrap_fsdp=wrap_fsdp, *fsdp_args, **fsdp_kwargs
)
self.ffn = nn.Sequential(
fsdp_wrapper(l1),
fsdp_wrapper(l2),
fsdp_wrapper(l3),
)
def forward(self, x):
return self.ffn(x)
def _verify_parity(self, losses, outputs, models):
assert losses
assert outputs
assert models
for (l, o) in zip(losses[1:], outputs[1:]):
self.assertEqual(losses[0], l)
self.assertEqual(outputs[0], o)
# Verify grads
ref_model = models[0]
ref_grads = [p.grad for p in ref_model.parameters()]
for m in models[1:]:
grads = [p.grad for p in m.parameters()]
for ref_g, g in zip(ref_grads, grads):
self.assertEqual(ref_g, g)
@skip_if_lt_x_gpu(2)
@parametrize(
"cpu_offload",
[CPUOffload(offload_params=True), CPUOffload(offload_params=False)],
)
@parametrize("offload_activations", [True, False])
def test_checkpoint_fsdp_wrapping(self, cpu_offload, offload_activations):
# Test checkpoint(FSDP(layer1), FSDP(layer2), ....)
ckpt_sequential_wrapped_fsdp = checkpoint_wrapper(
TestFSDPCheckpoint.SequentialModule(
wrap_fsdp=True, cpu_offload=cpu_offload
),
offload_to_cpu=offload_activations,
)
# Test FSDP(checkpoint(layer1)), FSDP(checkpoint(layer2)), ....
inner_ckpt = TestFSDPCheckpoint.SequentialModule(
checkpoint_layer=True,
offload_activations=offload_activations,
wrap_fsdp=True,
cpu_offload=cpu_offload,
)
baseline = TestFSDPCheckpoint.SequentialModule(
wrap_fsdp=True, cpu_offload=cpu_offload
)
# note that reentrant-based checkpointing requires inputs to have grad
# flag set.
inp = torch.randn(10, 3, device=torch.cuda.current_device(), requires_grad=True)
global _save_on_cpu_called
models = [ckpt_sequential_wrapped_fsdp, inner_ckpt, baseline]
with patch_save_on_cpu(get_patched_save_on_cpu()):
for i in range(2):
losses = []
outputs = []
for m in models:
check_offload = m != baseline and i == 0 and offload_activations
if check_offload:
self.assertFalse(_save_on_cpu_called)
out = m(inp)
if check_offload:
self.assertTrue(_save_on_cpu_called)
_save_on_cpu_called = False
loss = out.sum()
loss.backward()
losses.append(loss)
outputs.append(out)
self._verify_parity(losses, outputs, models)
@skip_if_lt_x_gpu(2)
@parametrize(
"cpu_offload",
[CPUOffload(offload_params=True), CPUOffload(offload_params=False)],
)
@parametrize("offload_activations", [True, False])
def test_basic_checkpoint_end_to_end(self, cpu_offload, offload_activations):
global _save_on_cpu_called
with patch_save_on_cpu(get_patched_save_on_cpu()):
seq = TestFSDPCheckpoint.SequentialModule().to(torch.cuda.current_device())
# Runs FSDP with no checkpointing
fsdp_only_seq = FSDP(deepcopy(seq), cpu_offload=cpu_offload)
# Runs checkpoint-wrapped FSDP
checkpointed_fsdp = checkpoint_wrapper(
FSDP(deepcopy(seq), cpu_offload=cpu_offload),
offload_to_cpu=offload_activations,
)
# Runs FSDP-wrapped checkpointed module
fsdp_wrapped_checkpoint = FSDP(
checkpoint_wrapper(deepcopy(seq), offload_to_cpu=offload_activations),
cpu_offload=cpu_offload,
)
# Runs FSDP with manual calls to checkpoint.
fsdp_call_checkpoint = FSDP(deepcopy(seq), cpu_offload=cpu_offload)
# note that reentrant-based checkpointing requires inputs to have grad
# flag set.
inp = torch.randn(10, 3, device=torch.cuda.current_device(), requires_grad=True)
models = [
fsdp_only_seq,
checkpointed_fsdp,
fsdp_wrapped_checkpoint,
fsdp_call_checkpoint,
]
# Ensure _save_on_cpu is not yet called
self.assertFalse(_save_on_cpu_called)
for i in range(6):
losses = []
outputs = []
for m in models:
check_offload = m != fsdp_only_seq and i == 0 and offload_activations
if m == fsdp_call_checkpoint:
# _save_on_cpu should not be called yet
self.assertFalse(_save_on_cpu_called)
offload_ctx = (
get_patched_save_on_cpu()(pin_memory=True)
if offload_activations
else contextlib.suppress()
)
with offload_ctx:
out = checkpoint(m, inp)
else:
# _save_on_cpu should not be called yet
self.assertFalse(_save_on_cpu_called)
out = m(inp)
if check_offload:
self.assertTrue(_save_on_cpu_called)
loss = out.sum()
loss.backward()
losses.append(loss)
outputs.append(out)
_save_on_cpu_called = False
self._verify_parity(losses, outputs, models)
instantiate_parametrized_tests(TestFSDPCheckpoint)
if __name__ == "__main__":
run_tests()
|
