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# mypy: allow-untyped-defs
import contextlib
import warnings
from typing import cast, Generator
import torch
import torch.distributed.fsdp._traversal_utils as traversal_utils
import torch.nn as nn
from torch.distributed.fsdp._common_utils import (
_FSDPState,
_get_module_fsdp_state,
_has_fsdp_params,
_module_handle,
HandleTrainingState,
TrainingState,
)
from torch.distributed.fsdp._runtime_utils import (
_lazy_init,
_reset_flat_param_grad_info_if_needed,
_reshard,
_reshard_grads,
_unshard,
_unshard_grads,
)
from torch.distributed.utils import _p_assert
from ._flat_param import FlatParamHandle
FLAT_PARAM = "_flat_param"
@torch.no_grad()
def _writeback_to_local_shard(
handle: FlatParamHandle,
writeback_grad: bool,
):
"""
For the handle, writes back the this rank's shard of the unsharded
flattened parameter to the sharded flattened parameter. If
``writeback_grad=True``, then writes back to the sharded gradient as
well.
Precondition: The handle's ``FlatParameter`` 's data points to the
padded unsharded flattened parameter.
"""
def _get_shard(flat_param_or_grad: torch.Tensor) -> torch.Tensor:
if handle.uses_sharded_strategy:
# For sharded strategies, get the *unpadded* shard instead of
# the *padded* shard to persist user changes to the padding
# (though FSDP does not explicitly support this)
shard, _ = FlatParamHandle._get_unpadded_shard(
flat_param_or_grad,
handle.rank,
handle.world_size,
)
return shard
# For `NO_SHARD`, the `flat_param` or its gradient may be modified,
# so we write it back directly
return flat_param_or_grad
param_shard = _get_shard(handle.flat_param)
handle.flat_param._local_shard[: param_shard.numel()].copy_(param_shard) # type: ignore[attr-defined]
if writeback_grad:
existing_grad = handle.sharded_grad
if existing_grad is not None:
assert handle.flat_param.grad is not None
grad_shard = _get_shard(handle.flat_param.grad)
existing_grad[: grad_shard.numel()].copy_(grad_shard)
def _deregister_flat_param(state: _FSDPState, module: nn.Module) -> None:
"""
De-registers the flattened parameter from the wrapped module, hiding it
from ``nn.Module`` methods.
We do not use ``del`` because we want ``FLAT_PARAM`` to always be an
attribute but dynamically change whether it is visible to ``nn.Module``
methods.
"""
if _has_fsdp_params(state, module):
# TODO: figure out the case for the composable APIs.
cast(nn.Module, module.module)._parameters.pop(FLAT_PARAM, None)
def _register_flat_param(state: _FSDPState, module: nn.Module) -> None:
"""
Registers the flattened parameter to the wrapped module, making it
visible to ``nn.Module`` methods.
We do not use :meth:`nn.Module.register_parameter` because we want
``FLAT_PARAM`` to always be an attribute but dynamically change whether
it is visible to ``nn.Module`` methods.
"""
handle = _module_handle(state, module)
if _has_fsdp_params(state, module):
# TODO: figure out the case for the composable APIs.
cast(nn.Module, module.module)._parameters[FLAT_PARAM] = handle.flat_param
@contextlib.contextmanager
def _unflatten_as_params(state: _FSDPState, module: nn.Module) -> Generator:
"""
Assumes that the flattened parameter is unsharded. When in the context,
de-registers the flattened parameter and unflattens the original
parameters as ``nn.Parameter`` views into the flattened parameter.
After the context, re-registers the flattened parameter and restores
the original parameters as ``Tensor`` views into the flattened
parameter.
"""
handle = _module_handle(state, module)
if not handle:
yield
else:
_deregister_flat_param(state, module)
try:
with handle.unflatten_as_params():
yield
finally:
if not handle._use_orig_params:
_register_flat_param(state, module)
def _validate_unshard_params_args(
state: _FSDPState,
writeback: bool,
rank0_only: bool,
offload_to_cpu: bool,
with_grads: bool,
) -> None:
if with_grads and (offload_to_cpu or not state._use_orig_params):
raise NotImplementedError(
f"with_grads={with_grads}, "
f"use_orig_params={state._use_orig_params}, "
f"offload_to_cpu={offload_to_cpu} "
f"is not supported yet"
)
if offload_to_cpu and state._handle and (not state._handle.uses_sharded_strategy):
raise NotImplementedError(
"offload_to_cpu=True and NO_SHARD is not supported yet"
)
if writeback and rank0_only:
# TODO: Rank 0 can broadcast the `FlatParameter` to allow all ranks to
# persist the changes.
raise NotImplementedError(
"writeback=True and rank0_only=True is not supported yet"
)
if offload_to_cpu and not rank0_only:
warnings.warn(
"offload_to_cpu=True and rank0_only=False may result in the"
"unsharded parameters being redundantly copied to CPU memory for "
"GPUs sharing the same CPU memory, which risks CPU OOM. We "
"recommend using offload_to_cpu=True with rank0_only=True."
)
@contextlib.contextmanager
def _unshard_fsdp_state_params(
module: nn.Module,
state: _FSDPState,
writeback: bool,
rank0_only: bool,
offload_to_cpu: bool,
with_grads: bool,
):
"""
This unshards the parameters for a single FSDP state ``state`` that
corresponds to ``module``.
"""
_validate_unshard_params_args(
state, writeback, rank0_only, offload_to_cpu, with_grads
)
state._device_handle.synchronize()
# If handles are shared by other module(s), the handle may be already unsharded.
maybe_handle = _module_handle(state, module)
handle = None
if (
maybe_handle
and maybe_handle._training_state != HandleTrainingState.SUMMON_FULL_PARAMS
):
handle = maybe_handle
if not handle:
yield
return
assert (
handle._training_state == HandleTrainingState.IDLE
), f"Expects the handle training to be IDLE but got {handle._training_state}"
handle._training_state = HandleTrainingState.SUMMON_FULL_PARAMS
_reset_flat_param_grad_info_if_needed(handle)
free_unsharded_flat_param = handle.needs_unshard()
# No need to call `wait_stream()` since we unshard in the computation
# stream directly
computation_stream = state._device_handle.current_stream()
_unshard(state, handle, computation_stream, computation_stream)
if with_grads:
_unshard_grads(handle)
if rank0_only and state.rank != 0:
# Free the unsharded flattened parameter early
_reshard(state, handle, free_unsharded_flat_param)
if with_grads:
_reshard_grads(handle)
try:
yield
finally:
handle._training_state = HandleTrainingState.IDLE
else:
# Unflatten the unsharded flattened parameters
with contextlib.ExitStack() as stack:
# Invariant: rank == 0 or !rank0_only
if offload_to_cpu and handle.uses_sharded_strategy:
stack.enter_context(handle.to_cpu())
# NOTE: Since PyTorch enforces that a parameter and its
# gradients need to match metadata (e.g. device), we must
# move gradients to CPU *after* we move parameters.
# NOTE: This assumes 1 `FlatParameter`
if not state._use_orig_params:
stack.enter_context(_unflatten_as_params(state, module))
try:
yield
finally:
stack.close()
if writeback:
_writeback_to_local_shard(handle, with_grads)
_reshard(state, handle, free_unsharded_flat_param)
if with_grads:
_reshard_grads(handle)
handle._training_state = HandleTrainingState.IDLE
@contextlib.contextmanager
def _unshard_params_for_summon(
module: nn.Module,
state: _FSDPState,
writeback: bool,
rank0_only: bool,
offload_to_cpu: bool,
with_grads: bool,
):
_validate_unshard_params_args(
state, writeback, rank0_only, offload_to_cpu, with_grads
)
_lazy_init(state, module)
if state.training_state == TrainingState.FORWARD_BACKWARD:
raise AssertionError(
"Cannot manually unshard parameters during forward/backward"
)
elif state.training_state == TrainingState.SUMMON_FULL_PARAMS:
raise AssertionError(
"Cannot manually unshard parameters when already unsharding parameters"
)
with _unshard_fsdp_state_params(
module=module,
state=state,
writeback=writeback,
rank0_only=rank0_only,
offload_to_cpu=offload_to_cpu,
with_grads=with_grads,
):
try:
state.training_state = TrainingState.SUMMON_FULL_PARAMS
yield
finally:
state.training_state = TrainingState.IDLE
@contextlib.contextmanager
def _unshard_params(
module: nn.Module,
recurse: bool,
writeback: bool,
rank0_only: bool,
offload_to_cpu: bool,
with_grads: bool,
):
"""
This unshards FSDP-managed parameters for all modules with FSDP applied in
the module tree rooted at ``module``.
"""
if not recurse:
optional_state = _get_module_fsdp_state(module)
if optional_state is None:
with contextlib.nullcontext():
yield
return
states_and_modules = ([optional_state], [module])
else:
states_and_modules = traversal_utils._get_fsdp_states_with_modules(module)
with contextlib.ExitStack() as stack:
for state, module in zip(*states_and_modules):
stack.enter_context(
_unshard_params_for_summon(
module=module,
state=state,
writeback=writeback,
rank0_only=rank0_only,
offload_to_cpu=offload_to_cpu,
with_grads=with_grads,
)
)
yield
def _deregister_orig_params(state: _FSDPState, module: nn.Module) -> None:
"""
Deregisters the original parameters; registers the ``FlatParameter``.
"""
handle = _module_handle(state, module)
if not handle:
return
_p_assert(
handle._use_orig_params,
f"Inconsistent `_use_orig_params` -- FSDP: {state._use_orig_params} "
f"handle: {handle._use_orig_params}",
)
handle._deregister_orig_params()
_register_flat_param(state, module)
def _register_orig_params(state: _FSDPState, module: nn.Module) -> None:
"""
Deregisters the ``FlatParameter``; registers the original parameters.
"""
handle = _module_handle(state, module)
if not handle:
return
_deregister_flat_param(state, module)
if handle.is_sharded(handle.flat_param):
handle._use_sharded_views()
handle._use_sharded_grad_views()
else:
handle._use_unsharded_views(as_params=True)
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