# mypy: allow-untyped-decorators # mypy: allow-untyped-defs import functools import logging from collections.abc import Callable, Sequence from typing import Any, Generic, TYPE_CHECKING, TypeVar import torch import torch.nn as nn from torch._logging import warning_once from torch.autograd import Variable from torch.autograd.graph import _MultiHandle from torch.distributed._composable_state import ( _get_module_state, _insert_module_state, _State, ) from torch.distributed.device_mesh import _get_device_handle from torch.distributed.fsdp._common_utils import collect_grad_tensors from torch.distributed.utils import _apply_to_tensors, _to_kwargs from ._fsdp_api import MixedPrecisionPolicy from ._fsdp_common import _cast_fp_tensor, _dynamo_disable, TrainingState from ._fsdp_param_group import FSDPCommContext, FSDPParamGroup if TYPE_CHECKING: from ._fsdp_param import FSDPParam logger = logging.getLogger("torch.distributed.fsdp.fully_shard") _StateType = TypeVar("_StateType", bound="FSDPState") class FSDPStateContext(Generic[_StateType]): """This has state shared across FSDP states.""" def __init__(self) -> None: # All FSDP states in the root state's module tree self.all_states: list[_StateType] = [] # Iteration's forward root runs the once-per-forward logic; this root # may not be the overall root set by lazy initialization in cases where # only a submodule runs forward (e.g. encoder-only for eval) self.iter_forward_root: _StateType | None = None # Final callback should only be queued once per backward self.post_backward_final_callback_queued: bool = False # Whether to finalize backward in this backward's final callback self.is_last_backward: bool = True # Optional user-provided event recorded after optimizer for the # all-gather streams to wait on in the root pre-forward self.post_optim_event: torch.Event | None = None class FSDPState(_State): # Name used in error messages; subclasses can override _state_name: str = "FSDP" def __init__(self) -> None: super().__init__() # Support multiple param groups for per-param mesh support. # Each group has params with the same mesh_info. self._fsdp_param_groups: list[FSDPParamGroup] = [] self._is_root: bool | None = None # root set during lazy init self._state_ctx = FSDPStateContext() self._comm_ctx = FSDPCommContext() self._training_state: TrainingState = TrainingState.IDLE self._states_to_forward_prefetch: list[FSDPState] = [] self._states_to_backward_prefetch: list[FSDPState] = [] self._modules_to_run_forward: set[nn.Module] = set() # ``False`` when user set reshard_after_forward # through ``fully_shard`` or ``set_reshard_after_forward`` self._auto_reshard_after_forward: bool | None = True def _get_state_for_module(self, module: nn.Module) -> "FSDPState | None": """Get the state for a module. Subclasses can override to use different state getters.""" return _get_module_fsdp_state(module) @property def _fsdp_param_group(self) -> FSDPParamGroup | None: """ Returns the param group for backward compatibility. This property is only valid when there is at most one param group. For per-param mesh support with multiple param groups, use ``_fsdp_param_groups`` instead. """ if len(self._fsdp_param_groups) > 1: group_fqns = [g._module_fqn for g in self._fsdp_param_groups] raise AssertionError( f"Expected at most 1 param group for backward compatibility, " f"but got {len(self._fsdp_param_groups)} (fqns: {group_fqns}). " f"Use `_fsdp_param_groups` (plural) to access all param groups " f"when using per-param mesh via shard_placement_fn returning " f"ShardPlacementResult." ) if self._fsdp_param_groups: return self._fsdp_param_groups[0] return None # Define a separate init since `__init__` is called in the contract def init( self, modules: tuple[nn.Module, ...], device: torch.device, mp_policy: MixedPrecisionPolicy, auto_reshard_after_forward: bool, ) -> None: for module in modules: _insert_module_state(module, self) self._modules = modules self._device = device self._device_handle = _get_device_handle(device.type) self._mp_policy = mp_policy self._auto_reshard_after_forward = auto_reshard_after_forward if len(modules) == 1: self._pre_forward_hook_handle = modules[0].register_forward_pre_hook( self._pre_forward, prepend=True, with_kwargs=True ) self._post_forward_hook_handle = modules[0].register_forward_hook( self._post_forward, prepend=False ) else: hook_handle = _register_group_forward_hooks( modules, self._pre_forward, self._post_forward, self._modules_to_run_forward, ) self._pre_forward_hook_handle = hook_handle self._post_forward_hook_handle = hook_handle def _root_pre_forward( self, module: nn.Module, args: tuple[Any, ...], kwargs: dict[str, Any] ) -> tuple[tuple[Any, ...], dict[str, Any]]: self._lazy_init() if self._state_ctx.iter_forward_root is not None: return args, kwargs logger.debug("FSDP::root_pre_forward") self._state_ctx.iter_forward_root = self with torch.profiler.record_function("FSDP::root_pre_forward"): # Wait for optimizer before implicitly prefetched all-gathers if (event := self._state_ctx.post_optim_event) is not None: self._comm_ctx.all_gather_copy_in_stream.wait_event(event) self._comm_ctx.all_gather_stream.wait_event(event) self._state_ctx.post_optim_event = None else: current_stream = self._device_handle.current_stream() self._comm_ctx.all_gather_copy_in_stream.wait_stream(current_stream) self._comm_ctx.all_gather_stream.wait_stream(current_stream) if self._device.type in [ "cuda", "hpu", "xpu", "mtia", torch._C._get_privateuse1_backend_name(), ]: with torch.profiler.record_function("FSDP::inputs_to_device"): args_tuple, kwargs_tuple = _to_kwargs( args, kwargs, self._device, False ) # same as DDP args, kwargs = args_tuple[0], kwargs_tuple[0] return args, kwargs def _lazy_init(self) -> None: """ Lazy initialization represents when all modules' parallelisms have finalized (e.g. FSDP has been applied to all desired modules). This means that we can determine which state is the root, and we do so by the 1st state to run forward. """ if self._is_root is not None: return # no-op: already initialized self._is_root = True if len(self._modules) > 1: raise RuntimeError( f"{self._state_name} requires a single root module but got {self._modules}" ) root_module = self._modules[0] visited_states: set[FSDPState] = set() for module_name, module in root_module.named_modules(): if (state := self._get_state_for_module(module)) is None: continue if module is not root_module: if state not in visited_states and state._is_root is not None: raise RuntimeError( f"{self._state_name} state has already been lazily initialized for " f"{module_name}\n{self._state_name} requires running forward through " "the root module first" ) state._is_root = False # A single state can map to multiple modules (e.g. # fully_shard([mod_a, mod_b, mod_c])), so dedup here. if state not in visited_states: self._state_ctx.all_states.append(state) visited_states.add(state) # For the root, do not reshard after forward since for training, # the parameters would be freed and all-gathered immediately if self._auto_reshard_after_forward: for fsdp_param_group in self._fsdp_param_groups: fsdp_param_group.post_forward_mesh_info = None self._init_fqns() self._init_shared_state() self._validate_no_duplicate_params() # Run parameter group lazy inits after initializing FQNs for improved # error messages for state in self._state_ctx.all_states: for fsdp_param_group in state._fsdp_param_groups: fsdp_param_group.lazy_init() def _validate_no_duplicate_params(self) -> None: seen: set[int] = set() for state in self._state_ctx.all_states: for fsdp_param_group in state._fsdp_param_groups: for fsdp_param in fsdp_param_group.fsdp_params: if fsdp_param._orig_param_uid in seen: raise ValueError( f"Parameter '{fsdp_param._param_fqn}' is shared with a " f"parameter already managed by another FSDP group. " f"For shared/tied parameters, use " f"fully_shard([module_a, module_b]) to place them in " f"the same FSDP group." ) seen.add(fsdp_param._orig_param_uid) def _init_shared_state(self) -> None: self._comm_ctx.lazy_init(self._device) for state in self._state_ctx.all_states: state._state_ctx = self._state_ctx state._comm_ctx = self._comm_ctx num_groups = len(state._fsdp_param_groups) for i, fsdp_param_group in enumerate(state._fsdp_param_groups): fsdp_param_group.comm_ctx = self._comm_ctx fsdp_param_group._param_group_index = i fsdp_param_group._num_param_groups = num_groups def _init_fqns(self) -> None: """Sets module and parameter FQN attributes for debugging.""" if not self._is_root: raise AssertionError("Expected _is_root to be True") root_module = self._modules[0] param_to_fsdp_param: dict[nn.Parameter, FSDPParam] = {} # Build a mapping from module to all its FSDPParamGroups (not just one) module_to_fsdp_param_groups: dict[nn.Module, list[FSDPParamGroup]] = {} for state in self._state_ctx.all_states: for fsdp_param_group in state._fsdp_param_groups: for fsdp_param in fsdp_param_group.fsdp_params: param_to_fsdp_param[fsdp_param.sharded_param] = fsdp_param for module in fsdp_param_group.modules: if module not in module_to_fsdp_param_groups: module_to_fsdp_param_groups[module] = [] module_to_fsdp_param_groups[module].append(fsdp_param_group) for param_name, param in root_module.named_parameters(): if param in param_to_fsdp_param: param_to_fsdp_param[param]._param_fqn = param_name for module_name, module in root_module.named_modules(): if module in module_to_fsdp_param_groups: # Set FQN for all param groups associated with this module for fsdp_param_group in module_to_fsdp_param_groups[module]: module_fqn = fsdp_param_group._module_fqn if module_fqn is None: fsdp_param_group._module_fqn = module_name else: if not isinstance(module_fqn, str): raise AssertionError( f"Expected module_fqn to be str, got {type(module_fqn)}: {module_fqn}" ) module_fqn += f", {module_name}" fsdp_param_group._module_fqn = module_fqn @_dynamo_disable def _pre_forward( self, module: nn.Module, args: tuple[Any, ...], kwargs: dict[str, Any] ) -> tuple[tuple[Any, ...], dict[str, Any]]: # When composing with module-hook-based activation checkpointing, the # pre-backward hook is responsible for the unshard if self._training_state == TrainingState.PRE_BACKWARD: # With nested FSDP and multiple forward passes before backward, # the params might have been resharded by a previous post_backward. # We need to ensure params are unsharded for AC recomputation. for fsdp_param_group in self._fsdp_param_groups: if not fsdp_param_group.is_unsharded: fsdp_param_group.unshard() fsdp_param_group.wait_for_unshard() return args, kwargs self._training_state = TrainingState.FORWARD args, kwargs = self._root_pre_forward(module, args, kwargs) if self._mp_policy.cast_forward_inputs and self._mp_policy.param_dtype: with torch.profiler.record_function("FSDP::cast_forward_inputs"): cast_fn = functools.partial( _cast_fp_tensor, self._mp_policy.param_dtype ) args, kwargs = ( _apply_to_tensors(cast_fn, args), _apply_to_tensors(cast_fn, kwargs), ) for fsdp_param_group in self._fsdp_param_groups: args, kwargs = fsdp_param_group.pre_forward(module, args, kwargs) for fsdp_state in self._states_to_forward_prefetch: # Forward order (not reversed) to match forward execution order; # contrast with reversed() in _pre_backward for backward order. for target_param_group in fsdp_state._fsdp_param_groups: FSDPParamGroup._prefetch_unshard(target_param_group, "forward") return args, kwargs @_dynamo_disable def _post_forward(self, module: nn.Module, input: Any, output: Any) -> Any: # When composing with module-hook-based activation checkpointing, the # post-backward hook is responsible for the reshard if self._training_state == TrainingState.PRE_BACKWARD: return output for fsdp_param_group in self._fsdp_param_groups: output = fsdp_param_group.post_forward(module, input, output) output = self._register_pre_backward_hook(output) self._training_state = TrainingState.IDLE if self._state_ctx.iter_forward_root is self: if all_gather_state := self._comm_ctx.all_gather_state: # Free the last all-gather result if needed; refer to # [Note: Overlapping all-gather copy-in and all-gather] self._comm_ctx.all_gather_copy_in_stream.wait_event( all_gather_state.event ) self._comm_ctx.all_gather_stream.wait_event(all_gather_state.event) self._comm_ctx.all_gather_state = None # free the all-gather result self._state_ctx.iter_forward_root = None if self._mp_policy.output_dtype is not None: with torch.profiler.record_function("FSDP::cast_forward_outputs"): output = _apply_to_tensors( functools.partial(_cast_fp_tensor, self._mp_policy.output_dtype), output, ) return output @_dynamo_disable def _pre_backward(self, grad: torch.Tensor) -> torch.Tensor: self._training_state = TrainingState.PRE_BACKWARD self._register_root_post_backward_final_callback() default_prefetch = len(self._states_to_backward_prefetch) == 0 for fsdp_param_group in self._fsdp_param_groups: fsdp_param_group.pre_backward(default_prefetch) for fsdp_state in self._states_to_backward_prefetch: # Reverse so higher-indexed groups are prefetched first, # matching backward execution order (reverse of forward). for target_param_group in reversed(fsdp_state._fsdp_param_groups): FSDPParamGroup._prefetch_unshard(target_param_group, "backward") return grad @_dynamo_disable def _root_post_backward_final_callback(self) -> None: logger.debug("FSDP::root_post_backward") with torch.profiler.record_function("FSDP::root_post_backward_callback"): for state in self._state_ctx.all_states: # Reverse so that the last param group (which gates the # reduce-scatter wait/clear) fires first, matching the # autograd backward order and preserving RS overlap for # per-param-mesh modules whose inputs lack gradients. for fsdp_param_group in reversed(state._fsdp_param_groups): if fsdp_param_group._training_state != TrainingState.POST_BACKWARD: # Run post-backward in case forward inputs did not require # gradient so the autograd backward did not run fsdp_param_group.post_backward() fsdp_param_group._training_state = TrainingState.IDLE state._training_state = TrainingState.IDLE if self._state_ctx.is_last_backward: state._finalize_backward() if self._state_ctx.is_last_backward: self._comm_ctx.post_forward_order.clear() # Catch the last module's RS states that no subsequent # module's group N-1 wait will clear. for rs_state in self._comm_ctx.reduce_scatter_states: if rs_state.event is not None: self._device_handle.current_stream().wait_event(rs_state.event) self._comm_ctx.reduce_scatter_states.clear() self._state_ctx.post_backward_final_callback_queued = False def _finalize_backward(self) -> None: if self._modules_to_run_forward: msg = ( f"{len(self._modules_to_run_forward)} of the {len(self._modules)} " f"modules passed to fully_shard did not run forward before backward, " "which is error-prone since FSDP post-forward/pre-backward logic " "will not run for these modules. We recommend passing only modules " "that run forward together. Modules that did not run forward: " f"{list(self._modules_to_run_forward)}" ) warning_once(logger, msg, stacklevel=2) # Clear since we want the next forward to run self._modules_to_run_forward.clear() for fsdp_param_group in self._fsdp_param_groups: fsdp_param_group.finalize_backward() def _register_pre_backward_hook(self, output: Any) -> Any: if not torch.is_grad_enabled(): return output # output is the forward return value — pass directly without wrapping # (unlike _register_post_backward_hook which wraps (args, kwargs)) tensors = collect_grad_tensors(output) for t in tensors: t.register_hook(self._pre_backward) return output def _register_root_post_backward_final_callback(self): if self._state_ctx.post_backward_final_callback_queued: return self._state_ctx.post_backward_final_callback_queued = True Variable._execution_engine.queue_callback( self._root_post_backward_final_callback ) def _get_module_fsdp_state(module: nn.Module) -> FSDPState | None: state = _get_module_state(module) if isinstance(state, FSDPState): return state return None def _register_group_forward_hooks( modules: Sequence[nn.Module], pre_hook: Callable, post_hook: Callable, modules_to_run: set[nn.Module], ): """ Registers group forward pre and post-hooks. The pre-hook runs upon the first module pre-forward, and the post-hook runs upon the last. If at least one module does not run forward, then the post-hook does not run. """ modules_set = set(modules) @_dynamo_disable @functools.wraps(pre_hook) def wrapped_pre_hook(*args: Any, **kwargs: Any): if len(modules_to_run) == 0: # first to run modules_to_run.update(modules_set) return pre_hook(*args, **kwargs) @_dynamo_disable def get_wrapped_post_hook(module: nn.Module): @functools.wraps(post_hook) def wrapped_post_hook(*args: Any, **kwargs: Any): modules_to_run.discard(module) if len(modules_to_run) == 0: return post_hook(*args, **kwargs) return wrapped_post_hook pre_handles = [ module.register_forward_pre_hook( wrapped_pre_hook, prepend=True, with_kwargs=True ) for module in modules ] post_handles = [ module.register_forward_hook( get_wrapped_post_hook(module), prepend=False, always_call=True ) for module in modules ] return _MultiHandle(tuple(pre_handles + post_handles))