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|
# Owner(s): ["oncall: distributed"]
import sys
import torch
import torch.distributed as dist
import torch.nn as nn
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.wrap import always_wrap_policy as always_wrap
from torch.distributed.fsdp.wrap import wrap, enable_wrap
from torch.testing._internal.common_fsdp import (
FSDPTest,
)
from torch.testing._internal.common_utils import (
TEST_WITH_DEV_DBG_ASAN,
run_tests,
parametrize,
instantiate_parametrized_tests,
sandcastle_skip_if,
)
from torch.testing._internal.common_distributed import (
skip_if_lt_x_gpu,
)
_TORCHDISTX_AVAIL = True
try:
from torchdistx import deferred_init
except ImportError:
_TORCHDISTX_AVAIL = False
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
def _reset_params_if_meta(is_meta, model):
# For torchdistX init, we don't need to call reset_params, as
# deferred_init(model).materialize() is equivalent to model().
if is_meta:
model.reset_parameters()
class MyLinear(nn.Linear):
"""
Linear layer with deterministic reset_parameters for testing.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def reset_parameters(self, *args, **kwargs):
with torch.no_grad():
self.weight.fill_(1)
class MyModel(nn.Module):
def __init__(self, device):
super().__init__()
self.lin1 = MyLinear(2, 2, bias=False, device=device)
self.lin2 = MyLinear(2, 2, bias=False, device=device)
def forward(self, x):
return self.lin2(self.lin1(x))
def reset_parameters(self, *args, **kwargs):
for m in [self.lin1, self.lin2]:
if not isinstance(m, FSDP):
m.reset_parameters()
class NestedModel(nn.Module):
def __init__(self, device):
super().__init__()
self.lin1 = MyLinear(2, 2, bias=False, device=device)
self.lin1 = wrap(self.lin1)
self.lin2 = MyLinear(2, 2, bias=False, device=device)
self.l3 = MyModel(device=device)
self.l3 = wrap(self.l3)
def forward(self, x):
return self.l3(self.lin2(self.lin1(x)))
def reset_parameters(self):
for m in [self.lin1, self.lin2, self.l3]:
if not isinstance(m, FSDP):
m.reset_parameters()
def _init_with_reset_params(module):
"""
to_empty + reset_parameters() init function example for modules
initailized with device="meta"
"""
is_meta = any(t.is_meta for t in module.parameters())
if is_meta:
module.to_empty(device=torch.cuda.current_device())
with torch.no_grad():
module.reset_parameters()
def _init_with_torchdistX(module):
"""
torchdistX-based deferred module initialization function example
using ``materialize_module``.
"""
assert _TORCHDISTX_AVAIL
def check_fn(k):
return not isinstance(k, FSDP)
deferred_init.materialize_module(module, check_fn=check_fn)
class TestFSDPWithMetaDevice(FSDPTest):
@property
def world_size(self):
return 2
@property
def process_group(self):
return dist.distributed_c10d._get_default_group()
def _compare_fsdp(self, fsdp1, fsdp2):
with FSDP.summon_full_params(fsdp1):
with FSDP.summon_full_params(fsdp2):
for p1, p2 in zip(fsdp1.parameters(), fsdp2.parameters()):
self.assertTrue(torch.allclose(p1, p2), f"{p1} vs {p2}")
def _test_simple_model_with_meta_device(self, meta_module_fn, init_fn=None):
# Create model on meta device and wrap with FSDP.
model = meta_module_fn()
is_meta = next(model.parameters()).is_meta
fsdp_meta = FSDP(
model,
auto_wrap_policy=always_wrap,
param_init_fn=init_fn,
)
meta_opt = torch.optim.SGD(fsdp_meta.parameters(), lr=1e-3)
# Test to make sure it is the same model parameters as regular FSDP
# approach.
regular = MyModel(device="cuda")
_reset_params_if_meta(is_meta, regular)
fsdp_regular = FSDP(regular, auto_wrap_policy=always_wrap)
regular_opt = torch.optim.SGD(fsdp_regular.parameters(), lr=1e-3)
self._compare_fsdp(fsdp_meta, fsdp_regular)
inp = torch.randn(10, 2, device='cuda')
fsdp_meta(inp).sum().backward()
fsdp_regular(inp).sum().backward()
meta_opt.step()
regular_opt.step()
self._compare_fsdp(fsdp_meta, fsdp_regular)
# Test that meta init works if all submodules are contained in only a
# single FSDP unit.
model = meta_module_fn()
fsdp_meta = FSDP(model, param_init_fn=init_fn)
meta_opt = torch.optim.SGD(fsdp_meta.parameters(), lr=1e-3)
regular = MyModel(device="cuda")
_reset_params_if_meta(is_meta, regular)
fsdp_regular = FSDP(regular, auto_wrap_policy=always_wrap)
regular_opt = torch.optim.SGD(fsdp_regular.parameters(), lr=1e-3)
# Run a forward + backward pass + optimizer step
fsdp_meta(inp).sum().backward()
fsdp_regular(inp).sum().backward()
meta_opt.step()
regular_opt.step()
self._compare_fsdp(fsdp_meta, fsdp_regular)
@skip_if_lt_x_gpu(2)
def test_simple_model_with_meta_device_reset_params(self):
def meta_module_fn():
return MyModel(device="meta")
self._test_simple_model_with_meta_device(
meta_module_fn, _init_with_reset_params
)
@skip_if_lt_x_gpu(2)
def test_simple_model_with_meta_device_default_init(self):
def meta_module_fn():
return MyModel(device="meta")
self._test_simple_model_with_meta_device(meta_module_fn)
@skip_if_lt_x_gpu(2)
@sandcastle_skip_if(
not _TORCHDISTX_AVAIL, "Test requires torchdistX: https://github.com/pytorch/torchdistX"
)
def test_simple_model_with_torchdistX_default_init(self):
def meta_module_fn():
return deferred_init.deferred_init(MyModel, device="cuda")
self._test_simple_model_with_meta_device(meta_module_fn)
@skip_if_lt_x_gpu(2)
@sandcastle_skip_if(
not _TORCHDISTX_AVAIL, "Test requires torchdistX: https://github.com/pytorch/torchdistX"
)
def test_simple_model_with_torchdistX_init_fn(self):
def meta_module_fn():
return deferred_init.deferred_init(MyModel, device="cuda")
self._test_simple_model_with_meta_device(meta_module_fn, init_fn=_init_with_torchdistX)
def _test_nested_model_with_meta_device(self, auto_wrap, meta_module_fn, init_fn=None):
if auto_wrap:
module = meta_module_fn()
is_meta = next(module.parameters()).is_meta
fsdp_meta = FSDP(
module,
auto_wrap_policy=always_wrap,
param_init_fn=init_fn,
)
meta_opt = torch.optim.SGD(fsdp_meta.parameters(), lr=1e-3)
module_regular = NestedModel(device="cuda")
_reset_params_if_meta(is_meta, module_regular)
fsdp_regular = FSDP(
module_regular,
auto_wrap_policy=always_wrap,
)
regular_opt = torch.optim.SGD(fsdp_regular.parameters(), lr=1e-3)
else:
with enable_wrap(
wrapper_cls=FSDP, param_init_fn=init_fn,
):
module = meta_module_fn()
is_meta = next(module.parameters()).is_meta
# Non FSDP modules will still be initialized because they bubble up
# to be part of a larger FSDP unit.
fsdp_meta = wrap(module)
meta_opt = torch.optim.SGD(fsdp_meta.parameters(), lr=1e-3)
# Init and reset parameters before wrapping so that reset_params
# matches up with meta device's initialization.
module_regular = NestedModel(device="cuda")
_reset_params_if_meta(is_meta, module_regular)
with enable_wrap(wrapper_cls=FSDP):
module_regular.lin1 = wrap(module_regular.lin1)
module_regular.l3 = wrap(module_regular.l3)
fsdp_regular = wrap(module_regular)
regular_opt = torch.optim.SGD(fsdp_regular.parameters(), lr=1e-3)
# Compare it before training
self._compare_fsdp(fsdp_meta, fsdp_regular)
inp = torch.randn(10, 2, device='cuda')
fsdp_meta(inp).sum().backward()
fsdp_regular(inp).sum().backward()
meta_opt.step()
regular_opt.step()
self._compare_fsdp(fsdp_meta, fsdp_regular)
@skip_if_lt_x_gpu(2)
@parametrize("auto_wrap", [True, False])
def test_nested_model_with_meta_device_reset_params(self, auto_wrap):
def meta_module_fn():
return NestedModel(device="meta")
self._test_nested_model_with_meta_device(
auto_wrap=auto_wrap, meta_module_fn=meta_module_fn, init_fn=_init_with_reset_params
)
@skip_if_lt_x_gpu(2)
@parametrize("auto_wrap", [True, False])
def test_nested_model_with_meta_device_default_init(self, auto_wrap):
def meta_module_fn():
return NestedModel(device="meta")
self._test_nested_model_with_meta_device(
auto_wrap=auto_wrap, meta_module_fn=meta_module_fn,
)
@skip_if_lt_x_gpu(2)
@sandcastle_skip_if(
not _TORCHDISTX_AVAIL, "Test requires torchdistX: https://github.com/pytorch/torchdistX"
)
@parametrize("auto_wrap", [True, False])
def test_nested_model_with_torchdistX_default_init(self, auto_wrap):
def meta_module_fn():
return deferred_init.deferred_init(NestedModel, device="cuda")
self._test_nested_model_with_meta_device(
auto_wrap=auto_wrap, meta_module_fn=meta_module_fn
)
@skip_if_lt_x_gpu(2)
@sandcastle_skip_if(
not _TORCHDISTX_AVAIL, "Test requires torchdistX: https://github.com/pytorch/torchdistX"
)
@parametrize("auto_wrap", [True, False])
def test_nested_model_with_torchdistX_init_fn(self, auto_wrap):
def meta_module_fn():
return deferred_init.deferred_init(NestedModel, device="cuda")
self._test_nested_model_with_meta_device(
auto_wrap=auto_wrap, meta_module_fn=meta_module_fn, init_fn=_init_with_torchdistX,
)
def _test_bad_arg(self, meta_module_fn):
mod = meta_module_fn()
with self.assertRaisesRegex(ValueError, "to be callable"):
FSDP(mod, param_init_fn=42)
@skip_if_lt_x_gpu(2)
@sandcastle_skip_if(
not _TORCHDISTX_AVAIL, "Test requires torchdistX: https://github.com/pytorch/torchdistX"
)
def test_bad_arg_torchdistx(self):
def meta_module_fn():
return deferred_init.deferred_init(NestedModel, "cuda")
self._test_bad_arg(meta_module_fn)
@skip_if_lt_x_gpu(2)
def test_bad_arg_meta(self):
def meta_module_fn():
return NestedModel(device="meta")
self._test_bad_arg(meta_module_fn)
instantiate_parametrized_tests(TestFSDPWithMetaDevice)
if __name__ == "__main__":
run_tests()
|
