Vehicle-cpp/hkpc/software/torch/distributed/fsdp/_common_utils.py

"""
This file includes private common utilities for FSDP.
"""

import traceback
import warnings
from enum import auto, Enum
from typing import (
    Callable,
    Dict,
    Generator,
    Iterable,
    List,
    no_type_check,
    Optional,
    Set,
)

import torch
import torch.distributed as dist
import torch.distributed.fsdp.flat_param as flat_param_file
import torch.nn as nn
from torch.distributed._composable_state import _get_module_state, _State
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
    _CHECKPOINT_PREFIX,
)

from .api import (
    FullOptimStateDictConfig,
    FullStateDictConfig,
    OptimStateDictConfig,
    ShardingStrategy,
    StateDictConfig,
    StateDictType,
)

FSDP_WRAPPED_MODULE = "_fsdp_wrapped_module"
FSDP_PREFIX = FSDP_WRAPPED_MODULE + "."
FSDP_FLATTENED = "_fsdp_flattened"


class _FSDPState(_State):
    def __init__(self) -> None:
        # TODO: Move all the attributes to this class to enable typing for
        # FSDP/fully_shard.
        self._ignored_modules: Set[nn.Module] = set()
        self._ignored_params: Set[nn.Parameter] = set()
        self.process_group: Optional[dist.ProcessGroup] = None
        self.rank: int = -1
        self.world_size: int = -1
        self.sharding_strategy = ShardingStrategy.FULL_SHARD
        self._use_orig_params: bool = False
        self.training_state = TrainingState.IDLE
        self._unshard_params_ctx: Dict[nn.Module, Generator] = {}
        self._state_dict_type: StateDictType = StateDictType.FULL_STATE_DICT
        self._state_dict_config: StateDictConfig = FullStateDictConfig()
        self._optim_state_dict_config: OptimStateDictConfig = FullOptimStateDictConfig()
        self._is_root: Optional[bool] = None
        self._handles: List[flat_param_file.FlatParamHandle] = []
        self._fully_sharded_module_to_handles: Dict[
            nn.Module, flat_param_file.FlatParamHandle
        ] = {}
        self.compute_device = torch.device("cuda", torch.cuda.current_device())


def _get_module_fsdp_state(module: nn.Module) -> Optional[_FSDPState]:
    state = _get_module_state(module)
    if state is None or not isinstance(state, _FSDPState):
        return None
    return state


def _get_module_fsdp_state_if_fully_sharded_module(
    module: nn.Module,
) -> Optional[_FSDPState]:
    state = _get_module_fsdp_state(module)
    if state is None:
        return None
    if state == module:  # FullyShardedDataParallel module case.
        return state
    if module in state._fully_sharded_module_to_handles:  # fully_shard case.
        return state
    return None


class TrainingState(Enum):
    """
    An enum that indicates the state of a ``FullyShardedDataParallel` instance.
    """

    IDLE = auto()
    FORWARD_BACKWARD = auto()
    SUMMON_FULL_PARAMS = auto()


class HandleTrainingState(Enum):
    """
    An enum that indicates the state of a ``FlatParamHandle`.
    """

    IDLE = auto()
    FORWARD = auto()
    BACKWARD_PRE = auto()
    BACKWARD_POST = auto()
    SUMMON_FULL_PARAMS = auto()


def _is_composable(state: _FSDPState):
    # TODO: This is a temporary hack for differentiate between code paths.
    return not isinstance(state, nn.Module)


@no_type_check
def _module_handles(state: _FSDPState, module: nn.Module) -> List:
    """
    Returns the ``FlatParamHandle`` s corresponding to ``module``. These are
    the handles that contain some parameter in ``module``.
    """
    if _is_composable(state):
        assert (
            module in state._fully_sharded_module_to_handles
        ), f"Expects a `comm_module` but got {module} on rank {state.rank}"
        return state._fully_sharded_module_to_handles[module][:]
    else:
        # NOTE: This assumes `module` is a `FullyShardedDataParallel` instance.
        return module._handles[:]


@no_type_check
def _has_fsdp_params(state: _FSDPState, module: nn.Module) -> bool:
    """Returns if ``module`` has parameters managed by FSDP."""
    return len(_module_handles(state, module)) > 0


def _get_sharding_strategy(handles: Iterable):
    """
    Returns the sharding strategy of the group of handles given by ``handles``
    or ``None`` if ``handles`` is empty. The input should be the handles
    corresponding to one module, so we enforce that they all share the same
    sharding strategy.
    """
    sharding_strategy = None
    for handle in handles:
        if sharding_strategy is None:
            sharding_strategy = handle._sharding_strategy
        elif (
            sharding_strategy is not None
            and sharding_strategy != handle._sharding_strategy
        ):
            raise AssertionError(
                "Expects each group of handles to have the same sharding "
                f"strategy but got {sharding_strategy} and {handle._sharding_strategy}"
            )
    return sharding_strategy


def clean_tensor_name(tensor_name: str) -> str:
    """
    Cleans the parameter or buffer name by removing any module wrapper
    prefixes.
    """
    tensor_name = tensor_name.replace(FSDP_PREFIX, "")
    # TODO: Explicitly replacing the checkpoint wrapper prefix is not ideal as
    # it couples `CheckpointWrapper` and FSDP and also does not scale for more
    # module wrappers.
    tensor_name = tensor_name.replace(_CHECKPOINT_PREFIX, "")
    return tensor_name


def _set_fsdp_flattened(tensor: torch.Tensor) -> None:
    """
    Sets an attribute on ``tensor`` to mark it as flattened by FSDP. This is to
    avoid re-flattening it during nested construction.
    """
    setattr(tensor, FSDP_FLATTENED, True)


def _is_fsdp_flattened(tensor: torch.Tensor) -> bool:
    """Returns if ``tensor`` has been marked as flattened by FSDP."""
    return getattr(tensor, FSDP_FLATTENED, False)


def _get_param_to_fqns(
    model: torch.nn.Module,
    dedup_shared_params: bool = True,
) -> Dict[nn.Parameter, List[str]]:
    """
    Constructs a mapping from parameter to a list of its FQNs. Each normal
    parameter maps to a singleton list containing its FQN, while each
    ``FlatParameter`` maps to a list of its original parameter FQNs, which may
    have length greater than one. All FQNs are prefixed starting from
    ``model``.

    Args:
        model (torch.nn.Module): Root module (which may or may not be a
            :class:`FullyShardedDataParallel` instance).
        dedup_shared_params (bool): For shared parameters, if ``True``, only
            includes the FQNs corresponding to the first encounter of the
            shared parameter in the module traversal; if ``False``, then
            includes the FQNs across all encounters. (Default: ``True``)
    """

    def module_fn(module, prefix, param_to_fqns):
        for param_name, param in module.named_parameters(recurse=False):
            local_fqns = (
                param._fqns
                if type(param) is flat_param_file.FlatParameter
                else [param_name]
            )  # prefixed from `module`
            global_fqns = [
                clean_tensor_name(prefix + name) for name in local_fqns
            ]  # prefixed from the top level `model` (i.e. including `prefix`)
            is_shared_param = param in param_to_fqns
            if not is_shared_param:
                param_to_fqns[param] = global_fqns
            else:
                if type(param) is flat_param_file.FlatParameter:
                    # DMP overwrites `named_parameters` and skip (advance to
                    # the next child module) the wrapped_module (e.g.,
                    # _dmp_wrapped_module and _fsdp_wrapped_module). When a user
                    # calls `named_child` to traverse the module recursively and
                    # calls `named_parameters` with `recurse=False`, parameters
                    # will be traversed more than once.
                    # This hack is specificed designed for DMP + FSDP. We
                    # overwite the flat_parameters traversal result to only obtain
                    # the last one, which happens to be the correct one.
                    #
                    # TODO: Remove this hack once DMP + FSDP is not supported.
                    warnings.warn(
                        "FlatParameter is being traversed more than once. "
                        "This case should only happen when using "
                        "DistributedModelParallel with FullyShardedDataParallel."
                    )
                    param_to_fqns[param] = global_fqns
                elif not dedup_shared_params:
                    param_to_fqns[param].extend(global_fqns)

    def return_fn(param_to_fqns):
        return param_to_fqns

    param_to_unflat_param_names: Dict[torch.nn.Parameter, List[str]] = {}
    return _apply_to_modules(
        model,
        module_fn,
        return_fn,
        [key for key, _ in model.named_parameters()],
        param_to_unflat_param_names,
    )


def _apply_to_modules(
    root_module: torch.nn.Module,
    module_fn: Callable,
    return_fn: Callable,
    filter_fqns: Optional[List[str]] = None,
    *args,
    **kwargs,
):
    """
    Performs a pre-order traversal of the modules in the hierarchy rooted at
    ``root_module``, applying ``module_fn`` at each module and finally
    returning a value using ``return_fn``. The traversal constructs the full
    module prefix name (e.g. "module.submodule." just like in model state dict)
    and makes that available to ``module_fn``.

    ``filter_fqns`` is used because some module may have its own prefix similar
    to ``FullyShardedDataParallel`` and the ``named_parameters()`` is overwritten
    to remove the prefix.
    """

    def f(module: torch.nn.Module, prefix: str, *args, **kwargs):
        # Call the module function before recursing over children (pre-order)
        module_fn(module, prefix, *args, **kwargs)
        for submodule_name, submodule in module.named_children():
            if submodule is None:
                continue
            new_prefix = prefix + submodule_name + "."
            if filter_fqns is not None:
                for fqn in filter_fqns:
                    if fqn.startswith(new_prefix):
                        break
                else:
                    # DMP's named_parameter() will mess up the traversal with
                    # ``named_children`` + `named_parameter(recurse=False)``.
                    # This hack is a must to make the travsersal work.
                    # TODO: Remove this hack once DMP + FSDP is not supported.
                    if (
                        submodule_name == "_fsdp_wrapped_module"
                        or submodule_name == "_dmp_wrapped_module"
                    ):
                        warnings.warn(
                            "An unexpected prefix is detected. This case "
                            " should only happen when using DMP with FSDP. "
                            f"prefix = {prefix}, "
                            f"submodule_name = {submodule_name}"
                        )
                        new_prefix = prefix
            f(submodule, new_prefix, *args, **kwargs)

    f(root_module, "", *args, **kwargs)
    return return_fn(*args, **kwargs)


@no_type_check
def _assert_in_training_states(
    state: _FSDPState,
    training_states: List[TrainingState],
) -> None:
    """Asserts that FSDP is in the states ``_training_states``."""
    # Raise a `ValueError` instead of using `assert` to ensure that these
    # logical assertions run even if `assert`s are disabled
    if state.training_state not in training_states:
        msg = (
            f"expected to be in states {training_states} but current state is "
            f"{state.training_state}"
        )
        # Print the error on rank 0 in case this is called in the backward pass
        if state.rank == 0:
            if isinstance(state, nn.Module):
                print(f"Asserting FSDP instance is: {state}")
            print(f"ERROR: {msg}")
            traceback.print_stack()
        raise ValueError(msg)


def _get_root_modules(modules: Set[nn.Module]) -> Set[nn.Module]:
    """
    Returns:
        Set[nn.Module]: The subset of ``modules`` that are root modules (i.e.
        parent-less) with respect to the modules in the set itself. In other
        words, these are the modules in ``modules`` that are not the child of
        any other module in ``modules``.
    """
    root_modules: Set[nn.Module] = set()
    module_to_submodules = {module: set(module.modules()) for module in modules}
    for candidate_module in modules:
        is_root_module = True
        for module, submodules in module_to_submodules.items():
            is_child_module = (
                candidate_module is not module and candidate_module in submodules
            )
            if is_child_module:
                is_root_module = False
                break
        if is_root_module:
            root_modules.add(candidate_module)
    return root_modules