def load_factors_from_dir(self, compute_inverses: bool = True) -> None:
"""Load factors from `factor_checkpoint_dir`."""
if self.factor_checkpoint_dir is None:
raise ValueError('factor_checkpoint_dir is None.')
if not os.path.isdir(self.factor_checkpoint_dir):
warnings.warn(
f'factor_checkpoint_dir={self.factor_checkpoint_dir} '
'is not a directory. Skipping KFAC checkpoint load.',
)
return
for name, layer in self._layers.values():
if (
cast(GPTNeoXAssignment, self._assignment).factor_worker(
name,
'A',
)
== get_rank()
):
filepath = os.path.join(self.factor_checkpoint_dir, name)
if os.path.exists(filepath):
logger.info(
f'loading KFAC factors for {name} on rank '
f'{get_rank()}',
)
state_dict = torch.load(filepath)
layer.load_state_dict(state_dict)
if compute_inverses:
layer.compute_a_inv(damping=self.damping)
layer.compute_g_inv(damping=self.damping)
def load_state_dict(
self,
state_dict: dict[str, Any],
compute_inverses: bool = True,
) -> None:
"""Load state dict."""
layers = state_dict.pop('layers', None)
super().load_state_dict(state_dict, compute_inverses=False)
if self.factor_checkpoint_dir is not None:
self.load_factors_from_dir(compute_inverses)
return
if layers is None:
return
for found_name, layer_state_dict in layers.items():
for name, layer in self._layers.values():
if (
found_name == name
and cast(
GPTNeoXAssignment,
self._assignment,
).factor_worker(name, 'A')
== get_rank()
):
assert isinstance(layer_state_dict['A'], torch.Tensor)
assert isinstance(layer_state_dict['G'], torch.Tensor)
layer.load_state_dict(layer_state_dict)
if compute_inverses:
layer.compute_a_inv(damping=self.damping)
layer.compute_g_inv(damping=self.damping)
torch.distributed.barrier()
def broadcast_g_inv(
self,
src: int,
group: dist.ProcessGroup | None = None,
) -> None:
"""Initiate G inv broadcast and store future to result.
Note:
all ranks must enter this function even if the rank is not
a part of the inverse broadcast group.
Args:
src (int): src rank that computed G inverse.
group (ProcessGroup): process group to which src should broadcast
G inv. All ranks in group should enter this function.
Defaults to None, the default process group.
"""
if self.g_inv is None:
if get_rank() == src:
raise RuntimeError(
f'Attempt to broadcast G inv from src={src} but this rank '
'has not computed G inv yet.', )
assert isinstance(self.g_factor, torch.Tensor)
self.g_inv = torch.empty(
self.g_factor.shape,
device=self.g_factor.device,
dtype=self.inv_dtype,
)
self.g_inv = self.tdc.broadcast( # type: ignore
self.g_inv,
src=src,
group=group,
symmetric=self.symmetric_factors and self.symmetry_aware,
)
def broadcast_grad(
self,
src: int,
group: dist.ProcessGroup | None = None,
) -> None:
"""Broadcast preconditioned gradient and store future to result.
Note:
all ranks must enter this function.
Args:
src (int): src rank that preconditioned the gradient.
group (ProcessGroup): process group to which src should broadcast
the gradient. All ranks in group should enter this function.
Defaults to None, the default process group.
"""
if self.grad is None:
if get_rank() == src:
raise RuntimeError(
f'Attempt to broadcast gradient from src={src} but this '
'rank has not computed the preconditioned gradient yet.', )
self.grad = torch.empty_like(self.module.get_grad())
self.grad = self.tdc.broadcast( # type: ignore
self.grad,
src=src,
group=group,
)
def reduce_g_factor(
self,
group: torch.distributed.ProcessGroup | None = None,
) -> None: # pragma: no cover
"""Initiate reduction of G and store future to result.
Note:
all ranks should enter this function.
Args:
group (ProcessGroup): ignored because the correct group depends
on if the parallelism is on the output or input of the layer.
"""
if self.primary_rank is None:
raise RuntimeError('primary rank has not been set yet.')
valid = (torch.distributed.ProcessGroup, type(None))
if not isinstance(self.data_parallel_group, valid) or not isinstance(
self.pipe_parallel_peer_group,
valid,
):
raise RuntimeError(
'data_parallel_group or pipe_parallel_peer_group has not '
'been set yet.', )
if self.parallelism == 'input':
super().reduce_g_factor(self.pipe_parallel_peer_group)
elif self.parallelism == 'output':
if get_rank() != self.primary_rank:
return
super().reduce_g_factor(self.data_parallel_group)
else:
raise AssertionError('Unreachable.')
def save_factors_to_dir(self) -> None:
"""Save factors to `factor_checkpoint_dir`.
Saves the state dict for each layer to a separate file in
`self.factor_checkpoint_dir` with the filename as the name of the
layer. Note: only the inverse worker for the layer will save the
layer.
"""
if self.factor_checkpoint_dir is None:
raise ValueError('factor_checkpoint_dir is None')
if get_rank() == 0:
os.makedirs(self.factor_checkpoint_dir, exist_ok=True)
torch.distributed.barrier()
for name, layer in self._layers.values():
if get_rank() == self._assignment.inv_worker(name, 'A'):
layer_state_dict = layer.state_dict()
filepath = os.path.join(self.factor_checkpoint_dir, name)
logger.info(f'saving KFAC factors for {name} to {filepath}')
torch.save(layer_state_dict, filepath)
def broadcast_a_inv(
self,
src: int,
group: dist.ProcessGroup | None = None,
) -> None:
"""Initiate A inv broadcast and store future to result.
Note:
all ranks must enter this function even if the rank is not
a part of the inverse broadcast group.
Args:
src (int): src rank that computed A inverse.
group (ProcessGroup): process group to which src should broadcast
A inv. All ranks in group should enter this function.
Defaults to None, the default process group.
"""
if self.qa is None or (not self.prediv_eigenvalues
and self.da is None):
if get_rank() == src:
raise RuntimeError(
f'Attempt to broadcast A inv from src={src} but this rank '
'has not computed A inv yet.', )
assert isinstance(self.a_factor, torch.Tensor)
self.qa = torch.empty(
self.a_factor.shape,
device=self.a_factor.device,
dtype=self.inv_dtype,
)
self.da = torch.empty(
self.a_factor.shape[0],
device=self.a_factor.device,
dtype=self.inv_dtype,
)
self.qa = self.tdc.broadcast( # type: ignore
self.qa,
src=src,
group=group,
)
if not self.prediv_eigenvalues:
assert self.da is not None
self.da = self.tdc.broadcast( # type: ignore
self.da,
src=src,
group=group,
)
def state_dict(self, include_factors: bool = True) -> dict[str, Any]:
"""Get state dict.
Note:
all ranks must enter this.
"""
state_dict = super().state_dict(include_factors=False)
if not include_factors:
return state_dict
if self.factor_checkpoint_dir is not None:
self.save_factors_to_dir()
return state_dict
partition: list[tuple[str, dict[str, Any]]] = []
for name, layer in self._layers.values():
# Inv worker for A and G is the same for GPT training
if get_rank() == self._assignment.inv_worker(name, 'A'):
layer_state_dict = layer.state_dict()
assert layer_state_dict['A'] is not None
assert layer_state_dict['G'] is not None
# Move to CPU where we have more RAM
layer_state_dict['A'] = layer_state_dict['A'].cpu()
layer_state_dict['G'] = layer_state_dict['G'].cpu()
partition.append((name, layer_state_dict))
partitions = [None for _ in range(get_world_size())]
# Use gloo group because we moved data to CPU for more RAM
group = torch.distributed.new_group(backend='gloo')
torch.distributed.all_gather_object(partitions, partition, group=group)
layers = {}
for partition in partitions: # type: ignore
for name, layer_state_dict in partition:
layers[name] = layer_state_dict
state_dict['layers'] = layers
torch.distributed.barrier(group)
return state_dict
def step(self) -> None:
"""Perform one K-FAC step.
Note:
This function should always be called before `optimizer.step()` as
it modifies the gradients and does not modify the weights.
Note:
Gradients must be averaged across ranks before calling `step()`.
This condition is guarenteed to be true if using the
`DistributedDataParallel` model wrapper as gradients are
communicated during `loss.backward()`.
"""
if (
not self._update_factors_in_hook
and self.steps % self.factor_update_steps == 0
):
for name, layer in reversed(list(self._layers.values())):
self._mini_steps[name] = 0
layer.update_a_factor(alpha=self.factor_decay)
layer.reduce_a_factor(self._assignment.factor_group(name, 'A'))
layer.update_g_factor(alpha=self.factor_decay)
layer.reduce_g_factor(self._assignment.factor_group(name, 'G'))
# Flush last allreduce bucket from forward/backward pass.
# Will be a no-op if bucketing was not used
self._tdc.flush_allreduce_buckets()
# Compute Inverses
if self.steps % self.inv_update_steps == 0:
for name, layer in reversed(list(self._layers.values())):
if get_rank() == self._assignment.inv_worker(name, 'A'):
layer.compute_a_inv(damping=self.damping)
if (
self._assignment.broadcast_inverses()
and self._assignment.is_grad_worker(name)
):
layer.broadcast_a_inv(
src=self._assignment.inv_worker(name, 'A'),
group=self._assignment.grad_worker_group(name),
)
if get_rank() == self._assignment.inv_worker(name, 'G'):
layer.compute_g_inv(damping=self.damping)
if (
self._assignment.broadcast_inverses()
and self._assignment.is_grad_worker(name)
):
layer.broadcast_g_inv(
src=self._assignment.inv_worker(name, 'G'),
group=self._assignment.grad_worker_group(name),
)
self._tdc.flush_allreduce_buckets()
# Compute Preconditioned Gradients
for name, layer in reversed(list(self._layers.values())):
if self._assignment.is_grad_worker(name):
layer.preconditioned_grad(damping=self.damping)
if self._assignment.broadcast_gradients():
layer.broadcast_grad(
src=self._assignment.src_grad_worker(name),
group=self._assignment.grad_receiver_group(name),
)
self._tdc.flush_allreduce_buckets()
scale = None if self.kl_clip is None else self._compute_grad_scale()
# Update gradients in-place
for _, layer in reversed(list(self._layers.values())):
layer.update_grad(scale=scale)
self._steps += 1
self._mini_steps = defaultdict(int)
def __init__(
self,
model: torch.nn.Module,
*,
factor_update_steps: Callable[[int], int] | int = 1,
inv_update_steps: Callable[[int], int] | int = 1,
# KFAC hyperparameters
damping: Callable[[int], float] | float = 0.001,
factor_decay: Callable[[int], float] | float = 0.95,
kl_clip: Callable[[int], float] | float = 0.001,
lr: Callable[[int], float] | float = 0.1,
# Distribution strategy
accumulation_steps: int = 1,
allreduce_bucket_cap_mb: float = 25.0,
assignment_strategy: (
AssignmentStrategy | str
) = AssignmentStrategy.COMPUTE,
compute_method: ComputeMethod | str = ComputeMethod.EIGEN,
compute_eigenvalue_outer_product: bool = False,
symmetry_aware: bool = False,
# DeepSpeed 3D parallelism
data_parallel_group: torch.distributed.ProcessGroup | None = None,
model_parallel_group: torch.distributed.ProcessGroup | None = None,
pipeline_parallel_group: torch.distributed.ProcessGroup | None = None,
# Optional other parameters
grad_scaler: (
torch.cuda.amp.GradScaler | Callable[[], float] | None
) = None,
factor_dtype: torch.dtype | None = None,
inv_dtype: torch.dtype = torch.float32,
factor_checkpoint_dir: str | None = None,
skip_layers: list[str] | None = None,
update_factors_in_hook: bool = True,
loglevel: int = logging.DEBUG,
) -> None:
"""Init KFACPreconditioner.
Args:
model (torch.nn.Module): model to precondition with KFAC.
factor_update_steps (Callable, int): steps between computing and
updating the running average of the Kronecker factors or
callable that takes the K-FAC step and returns the value.
inv_update_steps (Callble, int): steps between recomputing and
communicating the second-order information or callable that
takes the K-FAC step and returns the value.
damping (Callable, float): Tikhonov damping parameter or a callable
that takes the K-FAC step and returns the damping parameter
as a float (default: 0.001).
factor_decay (Callable, float): running average coefficient for
Kronecker factors or callable that takes the K-FAC step and
returns the factor_decay (default: 0.95).
kl_clip (Callable, float): clipping parameter for gradient scaling
or a callable that takes the K-FAC step and returns a float.
If None, no scaling/clipping will be applied (default: 0.001).
lr (Callable, float): learning rate or callable that takes the
K-FAC step and returns learning rate (default: 0.1).
accumulation_steps (int): number of forward/backward passes
between optimization steps (default: 1).
allreduce_bucket_cap_mb (float): maximum size in megabytes for
allreduce bucketing. If zero, bucketing is not used
(default: 25).
assignment_strategy (AssignmentStrategy, str): See
`AssignmentStrategy` for more details
(default: AssignmentStrategy.COMPUTE).
compute_method (ComputeMethod, str): See `ComputeMethod` for more
details (default: ComputeMethod.EIGEN).
compute_eigenvalue_outer_product (bool): when using the eigen
compute method, precompute the element-wise inverse of the
outer product of eigenvectors on the eigen decomposition worker
rather to reduce computation in the gradient preconditioning
stage. `colocate_factors` must be True (default: True).
symmetry_aware (bool): communicate only the upper triangle of
symmetric matrices. Can reduce communication time when factors
are large (default: False).
data_parallel_group (ProcessGroup): DeepSpeed data parallel group.
model_parallel_group (ProcessGroup): DeepSpeed model parallel
group.
pipeline_parallel_group (ProcessGroup): DeepSpeed pipeline parallel
group.
grad_scaler (torch.cuda.amp.GradScaler or callable): Gradient
scaler used for Torch AMP training. Used to unscale the G
factors as they are accumulated during the backward pass.
Alternatively can be a callable which will return the current
scale (default: None).
factor_dtype (torch.dtype): force data type for storing factors.
If None, defaults to data type of intermediate values in
forward/backward pass (default: None).
inv_dtype (torch.dtype): force data type for storing second-order
data (e.g., inverses or eigen decompositions)
(default: torch.float32).
factor_checkpoint_dir (str): directory to store factors
checkpoints in.
skip_layers (list): list of module names to ignore when registering
layers. Passing the name of parent modules will prevent
recursively registering child modules of the parent.
Case-insensitive (default: []).
update_factors_in_hook (bool): If True, running average of factors
is updated in the module hook and the async commmunication is
started. Otherwise, this will be performed at the start of
step() (default: True).
loglevel (int): logging level (default: logging.DEBUG).
"""
if deepspeed_import_error is not None: # pragma: no cover
raise deepspeed_import_error
warnings.warn(
'KFAC support for GPT-NeoX training is experimental.',
ExperimentalFeatureWarning,
)
if not isinstance(model, PipelineModule):
raise ValueError(
'model must be an instance of deepspeed.pipe.PipelineModule. '
f'Got an instance of {type(model)}.',
)
if allreduce_bucket_cap_mb < 0:
raise ValueError('allreduce_bucket_cap_mb must be >= 0')
if isinstance(assignment_strategy, str):
assignment_strategy = AssignmentStrategy[
assignment_strategy.upper()
]
if isinstance(compute_method, str):
compute_method = ComputeMethod[compute_method.upper()]
self.allreduce_bucket_cap_mb = allreduce_bucket_cap_mb
self.assignment_strategy = assignment_strategy
self.compute_eigenvalue_outer_product = (
compute_eigenvalue_outer_product
)
self.compute_method = compute_method
self.grad_scaler = grad_scaler
self.factor_dtype = factor_dtype
self.inv_dtype = inv_dtype
self.factor_checkpoint_dir = factor_checkpoint_dir
self.skip_layers = [] if skip_layers is None else skip_layers
self.symmetry_aware = symmetry_aware
self.data_parallel_group = data_parallel_group
self.model_parallel_group = model_parallel_group
self.pipeline_parallel_group = pipeline_parallel_group
if self.allreduce_bucket_cap_mb > 0:
self.allreduce_method = AllreduceMethod.ALLREDUCE_BUCKETED
else:
self.allreduce_method = AllreduceMethod.ALLREDUCE
self.tdc = TorchDistributedCommunicator(
bucket_cap_mb=self.allreduce_bucket_cap_mb,
)
layer_kwargs = dict(
allreduce_method=self.allreduce_method,
grad_scaler=self.grad_scaler,
factor_dtype=self.factor_dtype,
inv_dtype=self.inv_dtype,
symmetry_aware=self.symmetry_aware,
tdc=self.tdc,
)
if self.compute_method == ComputeMethod.EIGEN:
pass
elif self.compute_method == ComputeMethod.INVERSE:
raise ValueError('Inverse method not supported with GPT NeoX.')
else:
raise AssertionError(
f'Unknown compute_method={self.compute_method}',
)
kfac_layers = register_modules(
model,
model_parallel_group=self.model_parallel_group,
skip_layers=self.skip_layers,
**layer_kwargs,
)
data_parallel_ranks = [
-1 for _ in range(get_world_size(self.data_parallel_group))
]
torch.distributed.all_gather_object(
object_list=data_parallel_ranks,
obj=get_rank(),
group=self.data_parallel_group,
)
for name, kfac_layer in kfac_layers.values():
logger.log(
loglevel,
f'Registered name="{name}": {repr(kfac_layer)} on '
f'global-rank={get_rank()} and '
f'pipeline-rank={get_rank(self.pipeline_parallel_group)}',
)
if self.assignment_strategy == AssignmentStrategy.COMPUTE:
cost_func = lambda n: n**3 # noqa: E731
elif self.assignment_strategy == AssignmentStrategy.MEMORY:
cost_func = lambda n: n**2 # noqa: E731
else:
raise AssertionError(
f'Unknown assignment_strategy={self.assignment_strategy}',
)
work = {
name: {
'A': cost_func(kfac_layer.module.a_factor_shape[0]),
'G': cost_func(kfac_layer.module.g_factor_shape[0]),
}
for name, kfac_layer in kfac_layers.values()
}
assignment = GPTNeoXAssignment(
work,
local_rank=get_rank(),
topology=model.topology(),
data_parallel_group=self.data_parallel_group,
model_parallel_group=self.model_parallel_group,
)
logger.log(loglevel, f'KFAC layer assignments: {assignment}')
# Set primary rank for each layer
for name, kfac_layer in kfac_layers.values():
# Inv worker for A and G should be same because GPTNeoXAssignment
# uses gradient worker fraction 1/world_size (mem-opt)
if not isinstance(kfac_layer, GPTNeoXKFACEigenLayer):
raise AssertionError(
'GPTNeoXKFACPreconditioner only supports '
'GPTNeoXKFACEigenLayer.',
)
kfac_layer.primary_rank = assignment.factor_worker(name, 'A')
kfac_layer.data_parallel_group = assignment.data_parallel_group
kfac_layer.pipe_parallel_peer_group = (
assignment.pipe_parallel_peer_group
)
defaults = {
'allreduce_bucket_cap_mb': self.allreduce_bucket_cap_mb,
'allreduce_method': self.allreduce_method,
'assignment_strategy': self.assignment_strategy,
'compute_eigenvalue_outer_product': (
self.compute_eigenvalue_outer_product
),
'compute_method': self.compute_method,
'grad_scaler': self.grad_scaler is not None,
'factor_checkpoint_dir': self.factor_checkpoint_dir,
'factor_dtype': self.factor_dtype,
'inv_dtype': self.inv_dtype,
'skip_layers': self.skip_layers,
'symmetry_aware': self.symmetry_aware,
}
super().__init__(
kfac_layers,
factor_update_steps=factor_update_steps,
inv_update_steps=inv_update_steps,
factor_decay=factor_decay,
damping=damping,
kl_clip=kl_clip,
lr=lr,
accumulation_steps=accumulation_steps,
assignment=assignment,
update_factors_in_hook=update_factors_in_hook,
defaults=defaults,
tdc=self.tdc,
loglevel=loglevel,
)
def preconditioned_grad(
self,
damping: float = 0.001,
) -> None: # pragma: no cover
"""Compute precondition gradient of each weight in module.
Note:
Unlike KFACEigenLayer, every rank in the model parallel group
should enter this function.
Preconditioned gradients can be applied to the actual gradients with
`update_gradient()`. Note the steps are separate in the event that
intermediate steps will be applied to the preconditioned gradient.
Args:
damping (float, optional): damping to use if preconditioning using
the eigendecomposition method (default: 0.001).
"""
if self.primary_rank is None:
raise RuntimeError('primary rank has not been set yet.')
if get_rank() == self.primary_rank and (
self.qa is None or self.qg is None or
(not self.prediv_eigenvalues and self.da is None) or
(not self.prediv_eigenvalues and self.dg is None) or
(self.prediv_eigenvalues and self.dgda is None)):
raise RuntimeError(
'Eigendecompositions for both A and G have not been computed',
)
grad_partition = self.module.get_weight_grad()
grad = gather_from_model_parallel_region(
grad_partition,
dst=self.primary_rank,
model_parallel_group=self.model_parallel_group,
dim=-1 if self.parallelism == 'input' else 0,
)
if self.module.has_bias():
bias_grad_partition = self.module.get_bias_grad()
# Bias is only actually partitioned if parallelism is done on
# output
if self.parallelism == 'output':
bias_grad = gather_from_model_parallel_region(
bias_grad_partition,
dst=self.primary_rank,
model_parallel_group=self.model_parallel_group,
dim=0,
)
else:
bias_grad = bias_grad_partition
else:
bias_grad = None
if grad is not None:
# Only perform preconditioning on worker that got the full gradient
grad_shape = grad.size()
if self.module.has_bias():
assert bias_grad is not None
bias_grad_shape = bias_grad.size()
grad = torch.cat([grad, bias_grad.view(-1, 1)], 1)
# mypy won't know these are not none because they are properties
assert self.da is not None
assert self.dg is not None
assert self.qa is not None
assert self.qg is not None
grad_type = grad.dtype
grad = grad.to(self.qa.dtype)
v1 = self.qg.t() @ grad @ self.qa
if self.prediv_eigenvalues:
v2 = v1 * self.dgda
else:
v2 = v1 / (torch.outer(self.dg, self.da) + damping)
grad = (self.qg @ v2 @ self.qa.t()).to(grad_type)
if self.module.has_bias():
weight_grad = grad[:, :-1].view(grad_shape)
bias_grad = grad[:, -1:].view(bias_grad_shape).contiguous()
else:
weight_grad = grad.view(grad_shape)
weight_grads = list(
split_tensor_along_dim(
weight_grad,
get_world_size(self.model_parallel_group),
dim=-1 if self.parallelism == 'input' else 0,
contiguous_split_chunks=True,
), )
if self.module.has_bias() and self.parallelism == 'output':
assert bias_grad is not None
bias_grads = list(
split_tensor_along_dim(
bias_grad,
get_world_size(self.model_parallel_group),
dim=0,
contiguous_split_chunks=True,
), )
else:
weight_grads = [
torch.zeros_like(grad_partition)
for _ in range(get_world_size(self.model_parallel_group))
]
if self.parallelism == 'output':
bias_grads = [
torch.zeros_like(bias_grad_partition)
for _ in range(get_world_size(self.model_parallel_group))
]
# PyTorch NCCL does not support scatter but we can emulate it
# with reduce_scatter where the reduction operation is sum and the
# non_src ranks contribute zero filled tensors
if get_world_size(self.model_parallel_group) > 1:
torch.distributed.reduce_scatter(
grad_partition,
weight_grads,
group=self.model_parallel_group,
)
else:
grad_partition = weight_grads[0]
if self.module.has_bias():
if get_world_size(self.model_parallel_group) > 1:
if self.parallelism == 'output':
torch.distributed.reduce_scatter(
bias_grad_partition,
bias_grads,
group=self.model_parallel_group,
)
bias_grad = bias_grad_partition
else:
torch.distributed.broadcast(
bias_grad,
src=self.primary_rank,
group=self.model_parallel_group,
)
assert bias_grad is not None
self.grad = torch.cat([grad_partition, bias_grad.view(-1, 1)], 1)
else:
self.grad = grad_partition
def __init__(
self,
model: torch.nn.Module,
*,
factor_update_steps: Callable[[int], int] | int = 1,
inv_update_steps: Callable[[int], int] | int = 1,
# KFAC hyperparameters
damping: Callable[[int], float] | float = 0.001,
factor_decay: Callable[[int], float] | float = 0.95,
kl_clip: Callable[[int], float] | float = 0.001,
lr: Callable[[int], float] | float = 0.1,
# Distribution strategy
accumulation_steps: int = 1,
allreduce_bucket_cap_mb: float = 25.0,
assignment_strategy: (AssignmentStrategy
| str) = AssignmentStrategy.COMPUTE,
colocate_factors: bool = True,
compute_method: ComputeMethod | str = ComputeMethod.EIGEN,
compute_eigenvalue_outer_product: bool = True,
grad_worker_fraction: (DistributedStrategy
| float) = DistributedStrategy.COMM_OPT,
symmetry_aware: bool = False,
# Optional other parameters
grad_scaler: (torch.cuda.amp.GradScaler | Callable[[], float]
| None) = None,
factor_dtype: torch.dtype | None = None,
inv_dtype: torch.dtype = torch.float32,
skip_layers: list[str] | None = None,
update_factors_in_hook: bool = True,
loglevel: int = logging.DEBUG,
) -> None:
"""Init KFACPreconditioner.
Args:
model (torch.nn.Module): model to precondition with KFAC.
factor_update_steps (Callable, int): steps between computing and
updating the running average of the Kronecker factors or
callable that takes the K-FAC step and returns the value.
inv_update_steps (Callble, int): steps between recomputing and
communicating the second-order information or callable that
takes the K-FAC step and returns the value.
damping (Callable, float): Tikhonov damping parameter or a callable
that takes the K-FAC step and returns the damping parameter
as a float (default: 0.001).
factor_decay (Callable, float): running average coefficient for
Kronecker factors or callable that takes the K-FAC step and
returns the factor_decay (default: 0.95).
kl_clip (Callable, float): clipping parameter for gradient scaling
or a callable that takes the K-FAC step and returns a float.
If None, no scaling/clipping will be applied (default: 0.001).
lr (Callable, float): learning rate or callable that takes the
K-FAC step and returns learning rate (default: 0.1).
accumulation_steps (int): number of forward/backward passes
between optimization steps (default: 1).
allreduce_bucket_cap_mb (float): maximum size in megabytes for
allreduce bucketing. If zero, bucketing is not used
(default: 25).
assignment_strategy (AssignmentStrategy, str): See
`AssignmentStrategy` for more details
(default: AssignmentStrategy.COMPUTE).
colocate_factors (bool): assign both factors for a single layer to
the same worker. Reccomended when num_layers < world_size
(default: True).
compute_method (ComputeMethod, str): See `ComputeMethod` for more
details (default: ComputeMethod.EIGEN).
compute_eigenvalue_outer_product (bool): when using the eigen
compute method, precompute the element-wise inverse of the
outer product of eigenvectors on the eigen decomposition worker
rather to reduce computation in the gradient preconditioning
stage. `colocate_factors` must be True (default: True).
grad_worker_fraction (DistributedStrategy, float): controls the
fraction of workers assigned as gradient workers for each
layer. Optionally, predefined configurations can be passed
using the DistributedStrategy enum
(default: DistributedStrategy.COMM_OPT).
symmetry_aware (bool): communicate only the upper triangle of
symmetric matrices. Can reduce communication time when factors
are large (default: False).
grad_scaler (torch.cuda.amp.GradScaler or callable): Gradient
scaler used for Torch AMP training. Used to unscale the G
factors as they are accumulated during the backward pass.
Alternatively can be a callable which will return the current
scale (default: None).
factor_dtype (torch.dtype): force data type for storing factors.
If None, defaults to data type of intermediate values in
forward/backward pass (default: None).
inv_dtype (torch.dtype): force data type for storing second-order
data (e.g., inverses or eigen decompositions)
(default: torch.float32).
skip_layers (list[str]): regex patterns that if matched, will cause
the layer to not be registered. The patterns will be applied
against the layer's name and class name.
update_factors_in_hook (bool): If True, running average of factors
is updated in the module hook and the async commmunication is
started. Otherwise, this will be performed at the start of
step() (default: True).
loglevel (int): logging level (default: logging.DEBUG).
"""
if allreduce_bucket_cap_mb < 0:
raise ValueError('allreduce_bucket_cap_mb must be >= 0')
if (compute_method == ComputeMethod.EIGEN
and compute_eigenvalue_outer_product and not colocate_factors):
raise ValueError(
'colocate_factors must be True to use '
'compute_eigenvalue_outer_product', )
if isinstance(assignment_strategy, str):
assignment_strategy = AssignmentStrategy[
assignment_strategy.upper()]
if isinstance(compute_method, str):
compute_method = ComputeMethod[compute_method.upper()]
size = get_world_size()
if isinstance(grad_worker_fraction, DistributedStrategy):
distributed_strategy = grad_worker_fraction
if distributed_strategy == DistributedStrategy.COMM_OPT:
grad_worker_fraction = 1.0
elif distributed_strategy == DistributedStrategy.HYBRID_OPT:
grad_worker_fraction = 0.5
elif distributed_strategy == DistributedStrategy.MEM_OPT:
grad_worker_fraction = 1.0 / size
else:
raise AssertionError(f'Unknown enum {grad_worker_fraction}')
else:
if not 0 <= grad_worker_fraction or not 1 >= grad_worker_fraction:
raise ValueError('grad_worker_fraction must in [0, 1]')
if grad_worker_fraction == 0:
grad_worker_fraction = 1.0 / size
if size % max(1, round(size * grad_worker_fraction)) != 0:
raise ValueError(
'grad_worker_fraction must produce groups of '
'equal size', )
if grad_worker_fraction == 1:
grad_worker_fraction = 1.0 # ensure float
distributed_strategy = DistributedStrategy.COMM_OPT
elif grad_worker_fraction <= 1 / size:
distributed_strategy = DistributedStrategy.MEM_OPT
else:
distributed_strategy = DistributedStrategy.HYBRID_OPT
assert isinstance(grad_worker_fraction, float)
if (not colocate_factors
and distributed_strategy is DistributedStrategy.MEM_OPT):
warnings.warn(
'grad_worker_frac=1/world_size (MEM_OPT) requires '
'colocate_factors=True. Enabling colocate_factors.', )
colocate_factors = True
self.allreduce_bucket_cap_mb = allreduce_bucket_cap_mb
self.assignment_strategy = assignment_strategy
self.colocate_factors = colocate_factors
self.compute_eigenvalue_outer_product = (
compute_eigenvalue_outer_product)
self.compute_method = compute_method
self.distributed_strategy = distributed_strategy
self.grad_worker_fraction = grad_worker_fraction
self.grad_scaler = grad_scaler
self.factor_dtype = factor_dtype
self.inv_dtype = inv_dtype
self.skip_layers = [] if skip_layers is None else skip_layers
self.symmetry_aware = symmetry_aware
if self.allreduce_bucket_cap_mb > 0:
self.allreduce_method = AllreduceMethod.ALLREDUCE_BUCKETED
else:
self.allreduce_method = AllreduceMethod.ALLREDUCE
self.tdc = TorchDistributedCommunicator(
bucket_cap_mb=self.allreduce_bucket_cap_mb, )
layer_kwargs = dict(
allreduce_method=self.allreduce_method,
grad_scaler=self.grad_scaler,
factor_dtype=self.factor_dtype,
inv_dtype=self.inv_dtype,
symmetry_aware=self.symmetry_aware,
tdc=self.tdc,
)
layer_type: type[KFACBaseLayer]
if self.compute_method == ComputeMethod.EIGEN:
layer_type = KFACEigenLayer
layer_kwargs[
'prediv_eigenvalues'] = self.compute_eigenvalue_outer_product
elif self.compute_method == ComputeMethod.INVERSE:
layer_type = KFACInverseLayer
else:
raise AssertionError(
f'Unknown compute_method={self.compute_method}', )
kfac_layers = register_modules(
model,
kfac_layer_type=layer_type,
skip_layers=self.skip_layers,
**layer_kwargs,
)
for name, kfac_layer in kfac_layers.values():
logger.log(
loglevel,
f'Registered name="{name}": {repr(kfac_layer)}',
)
if self.assignment_strategy == AssignmentStrategy.COMPUTE:
cost_func = lambda n: n**3 # noqa: E731
elif self.assignment_strategy == AssignmentStrategy.MEMORY:
cost_func = lambda n: n**2 # noqa: E731
else:
raise AssertionError(
f'Unknown assignment_strategy={self.assignment_strategy}', )
work = {
name: {
'A': cost_func(kfac_layer.module.a_factor_shape[0]),
'G': cost_func(kfac_layer.module.g_factor_shape[0]),
}
for name, kfac_layer in kfac_layers.values()
}
new_group = cast(
Callable[[List[int]], dist.ProcessGroup],
dist.new_group,
)
mock_new_group: Callable[[list[int]], None] = lambda x: None
assignment = KAISAAssignment(
work,
local_rank=get_rank(),
world_size=get_world_size(),
grad_worker_fraction=self.grad_worker_fraction,
group_func=new_group if dist.is_initialized() else mock_new_group,
colocate_factors=self.colocate_factors,
)
logger.log(loglevel, f'KFAC layer assignments: {assignment}')
defaults = {
'allreduce_bucket_cap_mb':
self.allreduce_bucket_cap_mb,
'allreduce_method':
self.allreduce_method,
'assignment_strategy':
self.assignment_strategy,
'colocate_factors':
self.colocate_factors,
'compute_eigenvalue_outer_product':
(self.compute_eigenvalue_outer_product),
'compute_method':
self.compute_method,
'distributed_strategy':
self.distributed_strategy,
'grad_worker_fraction':
self.grad_worker_fraction,
'grad_scaler':
self.grad_scaler is not None,
'factor_dtype':
self.factor_dtype,
'inv_dtype':
self.inv_dtype,
'skip_layers':
self.skip_layers,
'symmetry_aware':
self.symmetry_aware,
}
super().__init__(
kfac_layers,
factor_update_steps=factor_update_steps,
inv_update_steps=inv_update_steps,
factor_decay=factor_decay,
damping=damping,
kl_clip=kl_clip,
lr=lr,
accumulation_steps=accumulation_steps,
assignment=assignment,
update_factors_in_hook=update_factors_in_hook,
defaults=defaults,
tdc=self.tdc,
loglevel=loglevel,
)
def test_distributed_not_initialized() -> None:
"""Test rank/world_size functions when not using distributed."""
assert get_rank() == 0
assert get_world_size() == 1
