def test_params_count_and_value(
self,
rank0_only: bool,
offload_to_cpu: bool,
mixed_precision: bool,
):
mixed_precision = MixedPrecision() if mixed_precision else None
model = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.NO_FSDP,
CUDAInitMode.CUDA_BEFORE,
deterministic=True,
)
fsdp_model = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_BEFORE,
deterministic=True,
)
dev = (torch.device("cpu") if offload_to_cpu else torch.device(
"cuda", torch.cuda.current_device()))
params_to_compare = ([p.to(dev) for p in model.module.parameters()]
if not rank0_only or self.rank == 0 else list(
p.clone() for p in fsdp_model.parameters()))
with FSDP.summon_full_params(fsdp_model,
rank0_only=rank0_only,
writeback=not rank0_only):
for p1, p2 in itertools.zip_longest(fsdp_model.parameters(),
params_to_compare):
self.assertEqual(p1, p2)
# CPU offload should restore the param device
param = next(fsdp_model.parameters())
self.assertTrue(
param.device == torch.device("cuda", torch.cuda.current_device()))
def test_named_parameters_buffers(self, prefix: str, recurse: bool):
"""Tests that ``named_parameters()`` and ``named_buffers()`` for a
top-level FSDP-wrapped model matches their behavior for the equivalent
non-wrapped model."""
model = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.NO_FSDP,
CUDAInitMode.CUDA_BEFORE,
deterministic=True,
)
model.register_buffer("buffer", torch.ones(1))
# `named_parameters()` and `named_buffers` will contain FSDP prefixes
# if called on a non-FSDP root module
fsdp_model = FSDP(
NestedWrappedModule.init(
self.process_group,
FSDPInitMode.NO_FSDP,
CUDAInitMode.CUDA_BEFORE,
deterministic=True,
),
self.process_group,
)
fsdp_model.register_buffer("buffer", torch.ones(1))
with FSDP.summon_full_params(fsdp_model):
for call in ["named_parameters", "named_buffers"]:
for (n1, p1), (n2, p2) in itertools.zip_longest(
getattr(fsdp_model, call)(prefix=prefix,
recurse=recurse),
getattr(model, call)(prefix=prefix, recurse=recurse),
):
self.assertEqual(n1, n2)
self.assertEqual(p1, p2)
def test_params_count_and_value(self, rank0_only, offload_to_cpu,
mixed_precision):
mixed_precision = MixedPrecision() if mixed_precision else None
fsdp_model = FSDP(
NestedWrappedModule(
group=dist.distributed_c10d._get_default_group(),
wrap_fsdp=True,
fsdp_init_mode=FSDPInitMode.CUDA_BEFORE,
mixed_precision=mixed_precision,
),
mixed_precision=mixed_precision,
)
model = NestedWrappedModule(
group=dist.distributed_c10d._get_default_group(),
wrap_fsdp=False,
fsdp_init_mode=FSDPInitMode.CUDA_BEFORE,
)
dev = (torch.device("cpu") if offload_to_cpu else torch.device(
"cuda", torch.cuda.current_device()))
params_to_compare = ([p.to(dev) for p in model.module.parameters()]
if not rank0_only or self.rank == 0 else list(
p.clone() for p in fsdp_model.parameters()))
with fsdp_model.summon_full_params(fsdp_model,
rank0_only=rank0_only,
writeback=not rank0_only):
for p1, p2 in itertools.zip_longest(fsdp_model.parameters(),
params_to_compare):
self.assertEqual(p1, p2)
# CPU offload should restore the param device
param = next(fsdp_model.parameters())
self.assertTrue(
param.device == torch.device("cuda", torch.cuda.current_device()))
def test_fsdp_cpu_init_stays_on_cpu(self):
"""
Ensure that CPU model input stays on CPU
after FSDP init and we log a warning.
"""
torch.cuda.set_device(self.rank)
regex = "Module is put on CPU"
context = self.assertWarnsRegex(expected_warning=UserWarning,
expected_regex=regex)
with context:
mod = NestedWrappedModule(
group=self.process_group,
wrap_fsdp=True,
wrap_everything=True,
fsdp_init_mode=FSDPInitMode.CUDA_NEVER,
)
fsdp = FSDP(mod)
devices = {p.device for p in fsdp.parameters()}
self.assertEqual(1, len(devices))
self.assertEqual(torch.device("cpu"), devices.pop())
fsdp = fsdp.cuda()
# Ensure fwd + backward can be performed after moving to CUDA.
# CPU input also tests that input is correctly moved to appropriate
# CUDA device.
inp = mod.get_input(device=torch.device("cpu"))
fsdp(inp[0]).sum().backward()
def test_fsdp_device_id(self, use_index):
"""
If CPU module is passed into FSDP with device_id
argument, it is moved to the GPU with that device_id.
"""
dev_id = (
torch.cuda.current_device() if use_index
else torch.device("cuda", torch.cuda.current_device())
)
def _check_device_matches(fsdp, dev_id):
devices = {p.device for p in fsdp.parameters()}
self.assertEqual(1, len(devices))
found_dev = devices.pop()
if use_index and not isinstance(dev_id, torch.device):
dev_id = torch.device("cuda", dev_id)
self.assertEqual(found_dev, dev_id)
mod = NestedWrappedModule(
group=self.process_group,
wrap_fsdp=True,
wrap_everything=True,
fsdp_init_mode=FSDPInitMode.CUDA_NEVER,
device_id=dev_id
)
fsdp = FSDP(mod, device_id=dev_id)
# Check FSDP parameters are moved.
_check_device_matches(fsdp, dev_id)
# device_id matching module device before FSDP construction
# should not throw errors.
mod = NestedWrappedModule(
group=self.process_group,
wrap_fsdp=True,
wrap_everything=True,
fsdp_init_mode=FSDPInitMode.CUDA_BEFORE,
device_id=dev_id
)
fsdp = FSDP(mod, device_id=dev_id)
_check_device_matches(fsdp, dev_id)
# Passing in torch.device("cuda") should work.
regex = "does not have explicit index"
context = self.assertWarnsRegex(
expected_warning=UserWarning, expected_regex=regex
)
with context:
mod = NestedWrappedModule(
group=self.process_group,
wrap_fsdp=True,
wrap_everything=True,
fsdp_init_mode=FSDPInitMode.CUDA_BEFORE,
device_id=torch.device("cuda")
)
fsdp = FSDP(mod, device_id=torch.device("cuda"))
_check_device_matches(fsdp, torch.device("cuda", torch.cuda.current_device()))
def _init_model(
self,
nested_model: bool,
sharding_strategy: ShardingStrategy,
device: torch.device,
):
fsdp_kwargs = {"sharding_strategy": sharding_strategy}
if nested_model:
model = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_AFTER,
fsdp_kwargs,
)
fsdp_model: FSDP = FSDP(
model,
self.process_group,
**fsdp_kwargs,
).to(device)
else:
fsdp_model: FSDP = TransformerWithSharedParams.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_BEFORE,
fsdp_kwargs,
)
return fsdp_model
def test_fsdp_cpu_init_stays_on_cpu(self):
"""Tests that passing a CPU module to FSDP preserves that the wrapped
module is on CPU after FSDP initialization, albeit after loging a
warning, and that FSDP moves CPU input to GPU before the forward."""
torch.cuda.set_device(self.rank)
regex = "Module is put on CPU"
context = self.assertWarnsRegex(
expected_warning=UserWarning, expected_regex=regex
)
with context:
nested_wrapped_module = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_NEVER,
)
fsdp_model = FSDP(nested_wrapped_module, self.process_group)
devices = {p.device for p in fsdp_model.parameters()}
self.assertEqual(1, len(devices))
self.assertEqual(torch.device("cpu"), devices.pop())
fsdp_model = fsdp_model.cuda()
# Ensure fwd + backward can be performed after moving to CUDA.
# CPU input also tests that input is correctly moved to appropriate
# CUDA device.
inp = fsdp_model.module.get_input(device=torch.device("cpu"))
fsdp_model(*inp).sum().backward()
def test_params_count_and_value(self):
fsdp_model = FSDP(
NestedWrappedModule(
group=dist.distributed_c10d._get_default_group(),
wrap_fsdp=True,
fsdp_init_mode=FSDPInitMode.CUDA_BEFORE,
))
model = NestedWrappedModule(
group=dist.distributed_c10d._get_default_group(),
wrap_fsdp=False,
fsdp_init_mode=FSDPInitMode.CUDA_BEFORE,
)
with fsdp_model.summon_full_params():
for p1, p2 in itertools.zip_longest(fsdp_model.parameters(),
model.module.parameters()):
self.assertEqual(p1, p2)
def _init_model(
self,
nested_model: bool,
sharding_strategy: ShardingStrategy,
device: torch.device,
):
group = dist.distributed_c10d._get_default_group()
if nested_model:
model = NestedWrappedModule(
group,
wrap_fsdp=True,
sharding_strategy=sharding_strategy,
)
fsdp_model: FSDP = FSDP(
model,
group,
sharding_strategy=sharding_strategy,
).to(device)
else:
fsdp_model: FSDP = self._get_wrapped_model(
group,
cuda_first=False,
config={"sharding_strategy": sharding_strategy},
)
return fsdp_model
def test_nested_module_apply(self):
"""Tests that ``apply()`` modifies parameter values in-place on a
non-FSDP-root nested FSDP-wrapped model."""
nested_wrapped_module = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_AFTER,
)
self._check_apply(nested_wrapped_module)
def test_nested_module_apply(self):
"""
Checks apply() modifies weights appropriately on a nested FSDP instance.
"""
nested_module = NestedWrappedModule(self.process_group,
wrap_fsdp=True,
wrap_everything=True)
fsdp_module = FSDP(nested_module, self.process_group).cuda(self.rank)
self._check_apply(fsdp_module)
def test_module_device_mismatches_device_id(self):
"""Tests that specifying a ``device_id`` argument to FSDP for a GPU
module that does not match the GPU device ID raises an error."""
context = (
self.assertRaisesRegex(
RuntimeError,
f"on rank {self.rank}.*cuda:0, but is on cuda:{self.rank}"
) if self.rank != 0 else suppress()
)
with context:
NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
# Move wrapped modules to CUDA before wrapping with FSDP
cuda_init_mode=CUDAInitMode.CUDA_BEFORE,
# Should raise error since rank 1 is given `device_id=0` when
# the model is on cuda:1
fsdp_kwargs={"device_id": 0},
)
def test_raises_rank0_with_writeback(self):
fsdp_model = FSDP(
NestedWrappedModule(
group=dist.distributed_c10d._get_default_group(),
wrap_fsdp=True,
fsdp_init_mode=FSDPInitMode.CUDA_BEFORE,
))
with self.assertRaisesRegex(ValueError, "is not supported"):
with fsdp_model.summon_full_params(rank0_only=True,
writeback=True):
pass
def test_raises_rank0_with_writeback(self):
"""Tests that ``summon_full_params()`` with both ``rank0_only=True``
and ``writeback=True`` raises an error."""
nested_wrapped_module = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_BEFORE,
)
with self.assertRaisesRegex(ValueError, "is not supported"):
with FSDP.summon_full_params(nested_wrapped_module,
rank0_only=True,
writeback=True):
pass
def test_named_parameters_buffers(self, prefix: str, recurse: bool):
fsdp_model = FSDP(
NestedWrappedModule(
group=dist.distributed_c10d._get_default_group(),
wrap_fsdp=True,
fsdp_init_mode=FSDPInitMode.CUDA_BEFORE,
)
)
fsdp_model.register_buffer("buffer", torch.ones(1))
model = NestedWrappedModule(
group=dist.distributed_c10d._get_default_group(),
wrap_fsdp=False,
fsdp_init_mode=FSDPInitMode.CUDA_BEFORE,
)
model.register_buffer("buffer", torch.ones(1))
with fsdp_model.summon_full_params(fsdp_model):
for call in ["named_parameters", "named_buffers"]:
for (n1, p1), (n2, p2) in itertools.zip_longest(
getattr(fsdp_model, call)(prefix=prefix, recurse=recurse),
getattr(model, call)(prefix=prefix, recurse=recurse),
):
self.assertEqual(n1, n2)
self.assertEqual(p1, p2)
def test_fsdp_modules(self):
group = dist.distributed_c10d._get_default_group()
model = NestedWrappedModule(group, wrap_fsdp=True)
modules = FSDP.fsdp_modules(model)
self.assertEquals(modules, [
model.module.get_submodule("1"),
model.module.get_submodule("1").get_submodule("0"),
model.module.get_submodule("2"),
])
modules = FSDP.fsdp_modules(model, root_only=True)
self.assertEqual(modules, [
model.module.get_submodule("1"),
model.module.get_submodule("2"),
])
def test_fsdp_modules(self):
nested_wrapped_module = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_BEFORE,
)
modules = FSDP.fsdp_modules(nested_wrapped_module)
self.assertEquals(modules, [
nested_wrapped_module.module.get_submodule("1"),
nested_wrapped_module.module.get_submodule("1").get_submodule("0"),
nested_wrapped_module.module.get_submodule("2"),
])
modules = FSDP.fsdp_modules(nested_wrapped_module, root_only=True)
self.assertEqual(modules, [
nested_wrapped_module.module.get_submodule("1"),
nested_wrapped_module.module.get_submodule("2"),
])
def test_module_device_mismatches_device_id(self):
"""
FSDP raises errors when module is on a GPU that does
not match device_id.
"""
context = (self.assertRaisesRegex(
RuntimeError,
f"on rank {self.rank}.*cuda:0, but is on cuda:{self.rank}")
if self.rank != 0 else suppress())
with context:
mod = NestedWrappedModule(
group=self.process_group,
wrap_fsdp=True,
wrap_everything=True,
# Would move module to current cuda device before
# wrapping with FSDP
fsdp_init_mode=FSDPInitMode.CUDA_BEFORE,
# Rank 1 is given device id 0, but model is on cuda:1,
# should throw errors.
device_id=0)
def test_cpu_init_with_sync_module_raises(self):
"""
CPU module with sync_module_states=True throws appropriate
error because it requires GPU comm.
"""
mod = NestedWrappedModule(
group=self.process_group,
wrap_fsdp=False,
wrap_everything=True,
fsdp_init_mode=FSDPInitMode.CUDA_NEVER,
)
with self.assertRaisesRegex(
ValueError,
"Module has CPU parameters, but sync_module_states=True is specified."
):
FSDP(mod, sync_module_states=True)
# Specifying device_id with sync_module_states=True works.
FSDP(mod,
device_id=torch.cuda.current_device(),
sync_module_states=True)
def test_cpu_init_with_sync_module_states(self):
"""Tests that passing ``sync_module_states=True`` raises an error for
a CPU module since the synchronization requires GPU communication,
while additionally passing ``device_id`` does not raise an error."""
nested_wrapped_module = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_NEVER,
)
with self.assertRaisesRegex(
ValueError,
"Module has CPU parameters, but sync_module_states=True is specified."
):
FSDP(nested_wrapped_module, self.process_group, sync_module_states=True)
# Specifying device_id with sync_module_states=True works.
FSDP(
nested_wrapped_module,
self.process_group,
device_id=torch.cuda.current_device(),
sync_module_states=True,
)
def test_fsdp_device_id(self, use_index):
"""
Tests the FSDP ``device_id`` argument:
- Wrapping a CPU module should move the module to the GPU matching
``device_id``
- Wrapping a GPU module already on the GPU matching ``device_id``
should not raise an error
- Wrapping a GPU module already on GPU and passing a GPU device
without specifying a device ID (i.e. ``torch.device("cuda")``) warns
"""
dev_id = (
torch.cuda.current_device() if use_index
else torch.device("cuda", torch.cuda.current_device())
)
def _check_device_matches(module, device_id):
"""Checks that the ``FlatParameter``s in ``module`` have device
matching ``device_id``."""
devices = {
p.device for p in module.parameters()
if isinstance(p, FlatParameter)
}
assert len(devices) > 0
self.assertEqual(1, len(devices))
found_device = devices.pop()
if use_index and not isinstance(device_id, torch.device):
device = torch.device("cuda", device_id)
else:
device = device_id
self.assertEqual(found_device, device)
# Check that FSDP parameters are moved to `device_id` for a CPU module
nested_wrapped_module = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_NEVER,
fsdp_kwargs={"device_id": dev_id},
)
_check_device_matches(nested_wrapped_module, dev_id)
# Check that specifying `device_id` for a GPU module already on that
# device does not raise an error
nested_wrapped_module = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_BEFORE,
fsdp_kwargs={"device_id": dev_id},
)
_check_device_matches(nested_wrapped_module, dev_id)
# Check that passing in `torch.device("cuda")` for a GPU module warns
regex = "does not have explicit index"
context = self.assertWarnsRegex(
expected_warning=UserWarning, expected_regex=regex
)
with context:
nested_wrapped_module = NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
CUDAInitMode.CUDA_BEFORE,
fsdp_kwargs={"device_id": torch.device("cuda")}
)
_check_device_matches(
nested_wrapped_module,
torch.device("cuda", torch.cuda.current_device())
)
def test_communication(
self,
nested_model: bool,
use_no_sync: bool,
sharding_strategy: ShardingStrategy,
):
"""
Tests FSDP's communication cost in terms of calls to collective
communication primitives (i.e. all-gather and reduce-scatter).
Arguments:
nested_model (bool): If ``True``, uses ``NestedWrappedModule``,
which has nested FSDP instances; if ``False``, uses the default
model, which does not have nested FSDP instances.
use_no_sync (bool): If ``True``, uses the ``no_sync()`` context
manager to accumulate gradients for one iteration before
synchronizing gradients in the second iteration; if ``False``,
only checks the communication cost of normal execution.
"""
# Initialize the model and inputs
group = dist.distributed_c10d._get_default_group()
device = torch.device("cuda")
if nested_model:
model = NestedWrappedModule(group,
wrap_fsdp=True,
sharding_strategy=sharding_strategy)
fsdp_model: FSDP = FSDP(
model, group, sharding_strategy=sharding_strategy).to(device)
else:
fsdp_model: FSDP = self._get_wrapped_model(
group,
cuda_first=False,
config={"sharding_strategy": sharding_strategy},
)
batch = fsdp_model.module.get_input(device)
# Count the number of FSDP instances
num_fsdp = 0
for m in fsdp_model.modules(): # includes `self`
if isinstance(m, FSDP) and len(m.params) > 0:
num_fsdp += 1
# Count the number of all-gathers and reduce-scatters by mocking
# `_all_gather_base()` and `_reducer_scatter_base()`
#
# with `no_sync()`:
# Forward: when no_sync mode, root will not free full parameters,
# thus there will be `num_fsdp-1` all-gathers.
# Backward: `num_fsdp` - 1 all-gathers (only excluding the root)
# without `no_sync()`:
# Forward: all instances free full parameters, thus there will be ``
# `num_fsdp` all-gathers.
# Backward: `num_fsdp` - 1 all-gathers (only excluding the root)
expected_num_all_gather_no_sync = (num_fsdp - 1) + (num_fsdp - 1)
expected_num_all_gather_sync = num_fsdp + (num_fsdp - 1)
expected_num_reduce_scatter_no_sync = 0
expected_num_reduce_scatter_sync = num_fsdp
num_no_sync_iters = 3
num_sync_iters = 3
with patch("torch.distributed._all_gather_base") as mock_all_gather, \
patch("torch.distributed._reduce_scatter_base") as mock_reduce_scatter:
def reset_mocks():
mock_all_gather.reset_mock()
mock_reduce_scatter.reset_mock()
if use_no_sync:
# Check the communication cost when using `no_sync()`
for i in range(num_no_sync_iters):
reset_mocks()
with fsdp_model.no_sync():
output = fsdp_model(*batch)
loss = fsdp_model.module.get_loss(batch, output)
loss.backward()
num_all_gather = mock_all_gather.call_count
num_reduce_scatter = mock_reduce_scatter.call_count
# in the first iteration, all fsdp instances including root
# need to all_gather shards in the forward pass.
if i == 0:
expected_num_all_gather_no_sync_updated = expected_num_all_gather_no_sync + 1
# in the first iteration, all fsdp instances need to all_gather shards
# in the forward pass
if sharding_strategy == ShardingStrategy.SHARD_GRAD_OP:
expected_num_all_gather_no_sync_updated = num_fsdp
else:
expected_num_all_gather_no_sync_updated = expected_num_all_gather_no_sync
# full parameters are not freed after first iteration in the no_sync mode
if sharding_strategy == ShardingStrategy.SHARD_GRAD_OP:
expected_num_all_gather_no_sync_updated = 0
self.assertEqual(
num_all_gather,
expected_num_all_gather_no_sync_updated,
f"Expected {expected_num_all_gather_no_sync_updated} "
f"all-gathers but saw {num_all_gather} all-gathers "
f"when using `no_sync()`")
self.assertEqual(
num_reduce_scatter,
expected_num_reduce_scatter_no_sync,
f"Expected {expected_num_reduce_scatter_no_sync} "
f"reduce-scatters but saw {num_reduce_scatter} "
"reduce-scatters when using `no_sync()`")
# Check the normal communication cost (when not using `no_sync()`)
for i in range(num_sync_iters):
reset_mocks()
output = fsdp_model(*batch)
loss = fsdp_model.module.get_loss(batch, output)
loss.backward()
num_all_gather = mock_all_gather.call_count
num_reduce_scatter = mock_reduce_scatter.call_count
# previous non-sync iteration does not free full parameters for
# the root instance.
if use_no_sync and i == 0:
expected_num_all_gather_sync_updated = expected_num_all_gather_sync - 1
# previous non-sync iteration does not free full parameters
if sharding_strategy == ShardingStrategy.SHARD_GRAD_OP:
expected_num_all_gather_sync_updated = 0
else:
expected_num_all_gather_sync_updated = expected_num_all_gather_sync
# no need to all_gather shards in the backward pass when in
# SHARD_GRAD_OP mode
if sharding_strategy == ShardingStrategy.SHARD_GRAD_OP:
expected_num_all_gather_sync_updated = num_fsdp
self.assertEqual(
num_all_gather, expected_num_all_gather_sync_updated,
f"Expected {expected_num_all_gather_sync_updated} all-gathers "
f"but saw {num_all_gather} all-gathers when not using "
"`no_sync()`")
self.assertEqual(
num_reduce_scatter, expected_num_reduce_scatter_sync,
f"Expected {expected_num_reduce_scatter_sync} reduce-"
f"scatters but saw {num_reduce_scatter} reduce-scatters "
"when not using `no_sync()`")
def test_communication(
self,
nested_model: bool,
use_no_sync: bool,
):
"""
Tests FSDP's communication cost in terms of calls to collective
communication primitives (i.e. all-gather and reduce-scatter).
Arguments:
nested_model (bool): If ``True``, uses ``NestedWrappedModule``,
which has nested FSDP instances; if ``False``, uses the default
model, which does not have nested FSDP instances.
use_no_sync (bool): If ``True``, uses the ``no_sync()`` context
manager to accumulate gradients for one iteration before
synchronizing gradients in the second iteration; if ``False``,
only checks the communication cost of normal execution.
"""
# Initialize the model and inputs
group = dist.distributed_c10d._get_default_group()
device = torch.device("cuda")
if nested_model:
model = NestedWrappedModule(group, wrap_fsdp=True)
fsdp_model: FSDP = FSDP(model, group).to(device)
else:
fsdp_model: FSDP = self._get_wrapped_model(group, cuda_first=False)
batch = fsdp_model.module.get_input(device)
# Count the number of FSDP instances
num_fsdp = 0
for m in fsdp_model.modules(): # includes `self`
if isinstance(m, FSDP) and len(m.params) > 0:
num_fsdp += 1
# Count the number of all-gathers and reduce-scatters by mocking
# `_all_gather_base()` and `_reducer_scatter_base()`
# Both with and without `no_sync()`:
# Forward: `num_fsdp` all-gathers
# Backward: `num_fsdp` - 1 all-gathers (only excluding the root)
expected_num_all_gather_no_sync = num_fsdp + (num_fsdp - 1)
expected_num_all_gather_sync = num_fsdp + (num_fsdp - 1)
expected_num_reduce_scatter_no_sync = 0
expected_num_reduce_scatter_sync = num_fsdp
num_no_sync_iters = 3
num_sync_iters = 3
with patch("torch.distributed._all_gather_base") as mock_all_gather, \
patch("torch.distributed._reduce_scatter_base") as mock_reduce_scatter:
def reset_mocks():
mock_all_gather.reset_mock()
mock_reduce_scatter.reset_mock()
if use_no_sync:
# Check the communication cost when using `no_sync()`
for _ in range(num_no_sync_iters):
reset_mocks()
with fsdp_model.no_sync():
output = fsdp_model(*batch)
loss = fsdp_model.module.get_loss(batch, output)
loss.backward()
num_all_gather = mock_all_gather.call_count
num_reduce_scatter = mock_reduce_scatter.call_count
assert num_all_gather == expected_num_all_gather_no_sync, \
f"Expected {expected_num_all_gather_no_sync} " \
f"all-gathers but saw {num_all_gather} all-gathers " \
f"when using `no_sync()`"
assert num_reduce_scatter == \
expected_num_reduce_scatter_no_sync, \
f"Expected {expected_num_reduce_scatter_no_sync} " \
f"reduce-scatters but saw {num_reduce_scatter} " \
"reduce-scatters when using `no_sync()`"
# Check the normal communication cost (when not using `no_sync()`)
for _ in range(num_sync_iters):
reset_mocks()
output = fsdp_model(*batch)
loss = fsdp_model.module.get_loss(batch, output)
loss.backward()
num_all_gather = mock_all_gather.call_count
num_reduce_scatter = mock_reduce_scatter.call_count
assert num_all_gather == expected_num_all_gather_sync, \
f"Expected {expected_num_all_gather_sync} all-gathers " \
f"but saw {num_all_gather} all-gathers when not using " \
"`no_sync()`"
assert num_reduce_scatter == \
expected_num_reduce_scatter_sync, \
f"Expected {expected_num_reduce_scatter_sync} reduce-" \
f"scatters but saw {num_reduce_scatter} reduce-scatters " \
"when not using `no_sync()`"
