# mypy: allow-untyped-defs
import inspect
import itertools
from collections.abc import Callable, Sequence
from dataclasses import dataclass, field, replace
from enum import auto, Enum
from typing import Any, cast, TYPE_CHECKING


if TYPE_CHECKING:
    from ._fsdp_api import DataParallelMeshDims

import torch
import torch.nn as nn
from torch._prims_common import make_contiguous_strides_for
from torch.distributed._functional_collectives import AsyncCollectiveTensor
from torch.distributed.device_mesh import DeviceMesh
from torch.distributed.fsdp._fully_shard._fsdp_common import DDPMeshInfo
from torch.distributed.tensor import DTensor, Replicate, Shard
from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
from torch.distributed.tensor.placement_types import _StridedShard, Placement

from ._fsdp_api import CPUOffloadPolicy, MixedPrecisionPolicy, OffloadPolicy
from ._fsdp_common import (
    _chunk_with_empty,
    _from_local_no_grad,
    _get_dim_chunked_size,
    _raise_assert_with_print,
    _to_dtype_if_needed,
    DataParallelMeshInfo,
    FSDPMeshInfo,
    HSDPMeshInfo,
    resolve_shard_placement,
    ShardPlacementFnResult,
)


_orig_param_uid_counter = itertools.count()


def _get_orig_param_uid(param: nn.Parameter) -> int:
    if not hasattr(param, "_fsdp_orig_uid"):
        uid = next(_orig_param_uid_counter)
        param._fsdp_orig_uid = uid  # pyrefly: ignore[missing-attribute]
    return param._fsdp_orig_uid  # pyrefly: ignore[missing-attribute]


"""
[Note: FSDP tensors]
FSDP considers the following tensors:
- Original parameter: parameter passed to :class:`FSDPParam`, i.e. the one
  on the module when applying FSDP
- Sharded parameter: sharding the original parameter on dim-0 (or a
  user-specified dim) as a DTensor over the main mesh
- All-gather inputs: the ``torch.Tensor`` or ``Tensor`` s passed to all-gather,
  derived from the sharded parameter
- All-gather output: the ``torch.Tensor`` or ``Tensor`` s resulting from
  all-gathering the all-gather inputs
- Unsharded parameter: parameter used for forward/backward computation, derived
  from the all-gather output; autograd leaf

We define these tensors to describe the general framework that can accommodate
extensions, where:
- all-gather-inputs = pre-all-gather-transform(sharded-parameter)
- unsharded-parameter = post-all-gather-transform(all-gather-outputs)

For the default ``torch.Tensor`` case, there is only one all-gather input, and
it shares the same underlying tensor data as the sharded parameter, meaning
that they can be thought of as the same tensors. The same applies for the
all-gather output and unsharded parameter. For non-``torch.Tensor`` extensions,
these equivalences may no longer hold due to the pre/post-all-gather
transforms, and some may have multiple all-gather inputs/outputs (e.g.
quantized data and scales).

[Note: FSDP and autograd]
FSDP dynamically frees and allocates the unsharded parameter. Since autograd
can pack a reference to it or a view to save for backward, we use storage
resizing to implement the freeing/allocation since that preserves the aliasing.
This implies that we construct the unsharded parameter object once and write to
it in-place thereafter. For the default ``torch.Tensor` original parameter
case, the all-gather output and unsharded parameter share the same
data, so we use storage resizing on the all-gather output.
"""

lib = torch.library.Library("fsdp", "FRAGMENT")

lib.define("copy_(Tensor(a!) tensor, Tensor data) -> ()")


@torch.library.impl(lib, "copy_", "Meta")
@torch.library.impl(lib, "copy_", "CUDA")
@torch.library.impl(lib, "copy_", "XPU")
@torch.library.impl(lib, "copy_", "HPU")
@torch.library.impl(lib, "copy_", "CPU")
@torch.library.impl(lib, "copy_", "MTIA")
def copy_(tensor, data):
    tensor.copy_(data)


@torch.library.impl(lib, "copy_", "Functionalize")
def copy__functionalize(tensor, data):
    torch._sync(tensor)
    torch._sync(data)
    tensor_inner = torch._from_functional_tensor(tensor)
    data_inner = torch._from_functional_tensor(data)
    with torch._C._ExcludeDispatchKeyGuard(
        torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize)
    ):
        torch.ops.fsdp.copy_.default(tensor_inner, data_inner)


torch.fx.node.has_side_effect(torch.ops.fsdp.copy_.default)


class ShardedState(Enum):
    """
    - ``SHARDED``: The sharded parameter is registered to the module. It is the
      only contributor to parameter memory.
    - ``SHARDED_POST_FORWARD``: The unsharded parameter is resharded to a
      smaller world size. Since this data should not be used for computation,
      we do not register it to the module. Users should reshard the module
      before any in-place modifications. Both it and the sharded parameter
      contribute to parameter memory.
    - ``UNSHARDED``: The unsharded parameter is registered to the module. Both
      it and the sharded parameter contribute to parameter memory.
    """

    SHARDED = auto()
    SHARDED_POST_FORWARD = auto()
    UNSHARDED = auto()


@dataclass
class ParamModuleInfo:
    """
    For a parameter, this stores the module and the parameter name to be able
    to do a parameter swap via ``setattr(module, param_name, ...)`` or to get
    the parameter via ``getattr(module, param_name)``. We additionally save
    shared modules and shared parameter names to update them accordingly.
    """

    # Parameter names are unprefixed, e.g. "weight", not "lin.weight"
    module: nn.Module
    param_name: str
    shared_modules: list[nn.Module] = field(default_factory=list)
    shared_param_names: list[str] = field(default_factory=list)


@dataclass
class ExtensionsData:
    # User-defined metadata passed from pre to post-all-gather
    all_gather_metadata: Any | None = None
    # Save the all-gather input sizes to unflatten the all-gather outputs to ND
    all_gather_input_sizes: Sequence[torch.Size] = ()  # ND

    def clear(self):
        self.all_gather_metadata = None
        self.all_gather_input_sizes = ()


class FSDPParam:
    """
    This class manages a parameter with FSDP or FSDP variants applied,
    implementing dim-0 per-parameter sharding.
    """

    orig_dtype: torch.dtype
    param_dtype: torch.dtype | None
    reduce_dtype: torch.dtype | None
    _orig_size: torch.Size  # ND
    sharded_size: torch.Size  # ND
    contiguous_sharded_stride: tuple[int, ...]
    padded_sharded_param_size: torch.Size  # ND
    sharded_post_forward_size: torch.Size  # ND
    contiguous_sharded_post_forward_stride: tuple[int, ...]
    _sharded_param_data: torch.Tensor  # 1D
    sharded_param: nn.Parameter  # ND
    _sharded_post_forward_param_data: torch.Tensor | None  # 1D
    _sharded_post_forward_param: nn.Parameter | None  # ND
    _unsharded_param: nn.Parameter  # ND
    unsharded_accumulated_grad: torch.Tensor | None  # ND
    _sharding_spec: DTensorSpec
    _unsharded_dtensor_spec: (
        DTensorSpec | None
    )  # set for DTensor params (SPMD or TP/EP)
    all_gather_outputs: list[torch.Tensor]  # 1D
    # All-gather extension attributes
    _extensions_data: ExtensionsData
    _unsharded_inner_tensors: list[torch.Tensor]
    _orig_param_uid: int

    def __init__(
        self,
        param: nn.Parameter,
        module_info: ParamModuleInfo,
        mesh_info: DataParallelMeshInfo,
        post_forward_mesh_info: FSDPMeshInfo | None,
        device: torch.device,
        shard_placement_fn: Callable[[nn.Parameter], ShardPlacementFnResult] | None,
        mp_policy: MixedPrecisionPolicy,
        offload_policy: OffloadPolicy,
    ):
        self._module_info: ParamModuleInfo = module_info
        self.post_forward_mesh_info = post_forward_mesh_info
        self.device = device
        self.mp_policy = mp_policy
        self.offload_to_cpu: bool = isinstance(offload_policy, CPUOffloadPolicy)
        self.pin_memory = (
            self.offload_to_cpu and cast(CPUOffloadPolicy, offload_policy).pin_memory
        )
        self.grad_offload_event: torch.Event | None = None
        self._init_sharded_param(param, device, shard_placement_fn, mesh_info)
        if self.post_forward_mesh_info:
            self._init_sharded_post_forward_param_metadata(param)
        self._init_extensions()
        self.all_gather_outputs: list[torch.Tensor] = []
        self.unsharded_accumulated_grad = None
        self._param_fqn: str | None = None  # prefixed from root module
        # TODO: Remove this padding logic once DTensor pads the local tensor:
        # https://github.com/pytorch/pytorch/issues/113045
        self._post_load_hook_handle = (
            module_info.module.register_load_state_dict_post_hook(
                lambda *args, **kwargs: self.reset_sharded_param()
            )
        )

    @torch.no_grad()
    def _init_sharded_param(
        self,
        param: nn.Parameter,
        device: torch.device,
        shard_placement_fn: Callable[[nn.Parameter], ShardPlacementFnResult] | None,
        mesh_info: DataParallelMeshInfo,
    ):
        if callable(shard_placement_fn):
            shard_result = resolve_shard_placement(
                shard_placement_fn(param),
                cast(FSDPMeshInfo, mesh_info),
            )
            self.mesh_info = shard_result.mesh_info
            fsdp_placement = shard_result.placement
        else:
            self.mesh_info = mesh_info  # pyrefly: ignore[bad-assignment]
            fsdp_placement = None
        self._shard_mesh = self._init_shard_mesh()
        if param.device != device and param.device.type != "meta":
            raise AssertionError(
                f"Expects the parameter to already be moved to device {device} but got {param.device}"
            )
        if not param.is_contiguous():
            raise NotImplementedError(
                f"FSDP does not support non-contiguous parameters yet: {param.shape=} {param.stride()=}"
            )
        if fsdp_placement is None:
            fsdp_placement = Shard(0)
        elif fsdp_placement.dim < 0:
            fsdp_placement = Shard(fsdp_placement.dim + param.ndim)
        if not isinstance(fsdp_placement, Shard):
            raise AssertionError(
                f"Expected Shard, got {type(fsdp_placement)}: {fsdp_placement}"
            )
        self.fsdp_placement = fsdp_placement
        shard_dim = fsdp_placement.dim
        # TODO: Replace the sharded DTensor parameter construction logic with
        # `distribute_tensor` after https://github.com/pytorch/pytorch/issues/116101
        # TODO: Simplify the following sharded parameter padding logic after
        # https://github.com/pytorch/pytorch/issues/113045
        self.is_dtensor = isinstance(param, DTensor)
        self._orig_param_uid = _get_orig_param_uid(param)
        param_data = self._init_sharding_spec(param, fsdp_placement, shard_dim)
        if not param_data.is_contiguous():
            raise AssertionError(
                f"Expected contiguous tensor, got {param_data.shape=} {param_data.stride()=}"
            )
        shard_dim = fsdp_placement.dim
        if shard_dim >= param_data.ndim:
            raise AssertionError(
                f"Shard dim {shard_dim} is invalid for {param_data.ndim}D tensor: {param.shape}"
            )
        self._orig_size = param_data.size()
        self._contiguous_orig_stride = make_contiguous_strides_for(self._orig_size)
        if isinstance(self.mesh_info, FSDPMeshInfo):  # FSDP or HSDP
            shard_rank = self.mesh_info.shard_mesh_rank
            shard_world_size = self.mesh_info.shard_mesh_size
        else:  # DDP
            shard_rank = 0
            shard_world_size = 1

        if shard_dim > 0 and param_data.size(shard_dim) % shard_world_size != 0:
            # If sharding on nonzero dim, require even sharding for now because
            # the uneven sharding (1) requires extra copies before/after FSDP
            # collectives and (2) introduces extra complexity to handle padding
            # and unpadding
            raise NotImplementedError(
                f"FSDP does not support uneven sharding on dim {shard_dim}: "
                f"{param_data.size()} (world size: {shard_world_size})"
            )
        chunks = _chunk_with_empty(param_data, shard_world_size, dim=shard_dim)
        sharded_param = chunks[shard_rank]
        self.sharded_size = _get_dim_chunked_size(
            sharded_param, param_data.size(), dim=shard_dim
        )
        self.contiguous_sharded_stride = make_contiguous_strides_for(self.sharded_size)
        padded_sharded_size = chunks[0].size()  # 0th always padded
        self.padded_sharded_param_size = padded_sharded_size
        # Pre-pad the sharded parameter to avoid padding before all-gather
        padded_sharded_param = param_data.new_zeros(padded_sharded_size)
        if sharded_param.numel() > 0:
            padded_sharded_param.narrow(
                dim=shard_dim, start=0, length=sharded_param.size(shard_dim)
            ).copy_(sharded_param)
        if self.offload_to_cpu and not padded_sharded_param.is_meta:
            padded_sharded_param = padded_sharded_param.cpu()
            if self.pin_memory:
                padded_sharded_param = padded_sharded_param.pin_memory()
        self._sharded_param_data = padded_sharded_param.view(-1)
        length = sharded_param.size(shard_dim) if sharded_param.numel() > 0 else 0
        sharded_param = padded_sharded_param.narrow(
            dim=shard_dim, start=0, length=length
        )
        if not sharded_param.is_contiguous():
            raise AssertionError(
                f"Expected contiguous tensor with {self.fsdp_placement=}"
            )
        self.sharded_param = nn.Parameter(
            self.to_sharded_dtensor(sharded_param),
            requires_grad=param.requires_grad,
        )
        # Let `param_data` be freed normally when its ref count reaches 0 when
        # the `fully_shard` call returns to allow provided parameters to alias
        self._setattr_on_modules(self.sharded_param)
        self.sharded_state = ShardedState.SHARDED

    def _init_sharding_spec(
        self,
        param: nn.Parameter,
        fsdp_placement: Shard,
        shard_dim: int,
    ) -> torch.Tensor:
        """
        Build ``_sharding_spec``, ``_spmd_mesh``, and ``_spmd_placements`` and
        return the local tensor data to be sharded.
        """
        self._unsharded_dtensor_spec = None
        if self.mesh_info.is_spmd_mesh and not self.is_dtensor:
            raise ValueError(
                "When dp_mesh_dims is provided, all parameters must be "
                "DTensors on the full SPMD mesh (e.g. via distribute_module). "
                f"Got plain tensor for parameter '{self._module_info.param_name}'."
            )
        if self.is_dtensor and self.mesh_info.is_spmd_mesh:
            return self._init_sharding_spec_spmd(param, fsdp_placement, shard_dim)
        if self.is_dtensor:
            return self._init_sharding_spec_tp(param, fsdp_placement, shard_dim)
        return self._init_sharding_spec_plain(param, fsdp_placement)

    def _init_sharding_spec_spmd(
        self,
        param: nn.Parameter,
        fsdp_placement: Shard,
        shard_dim: int,
    ) -> torch.Tensor:
        """SPMD path: param is a DTensor on the full SPMD mesh."""
        self._unsharded_dtensor_spec = cast(DTensor, param)._spec
        spmd_mesh = self._unsharded_dtensor_spec.mesh
        dp_dim_names = self.mesh_info.dp_mesh_dims
        if dp_dim_names is None:
            raise AssertionError("dp_dim_names must not be None for SPMD mesh")
        if spmd_mesh.mesh_dim_names is None:
            raise AssertionError("spmd_mesh.mesh_dim_names must not be None")
        if (
            self.mesh_info.spmd_mesh is not None
            and spmd_mesh is not self.mesh_info.spmd_mesh
        ):
            raise ValueError(
                "Expected param's DTensor mesh to be the same mesh passed "
                "to fully_shard, but got different mesh objects"
            )

        dp_shard_indices = [
            spmd_mesh.mesh_dim_names.index(n) for n in dp_dim_names.shard_names
        ]

        orig_placements = self._unsharded_dtensor_spec.placements
        for idx in dp_shard_indices:
            if not isinstance(orig_placements[idx], Replicate):
                raise ValueError(
                    f"Expected Replicate() on DP shard dim "
                    f"'{spmd_mesh.mesh_dim_names[idx]}' (index {idx}) "
                    f"but got {orig_placements[idx]}"
                )
        dp_replicate_indices = []
        for rep_name in dp_dim_names.replicate_names:
            rep_idx = spmd_mesh.mesh_dim_names.index(rep_name)
            dp_replicate_indices.append(rep_idx)
            if not isinstance(orig_placements[rep_idx], Replicate):
                raise ValueError(
                    f"Expected Replicate() on DP replicate dim "
                    f"'{spmd_mesh.mesh_dim_names[rep_idx]}' (index {rep_idx}) "
                    f"but got {orig_placements[rep_idx]}"
                )

        # Cache DP dim indices so _get_grad_inner_tensor can skip
        # redistribution on DP dims and let FSDP's reduce-scatter handle them.
        self._dp_dim_indices: frozenset[int] = frozenset(
            dp_shard_indices + dp_replicate_indices
        )

        new_placements = list(orig_placements)
        for dp_idx in dp_shard_indices:
            # split_factor = number of non-DP shards on shard_dim from
            # mesh dims with higher index (the "right-side" dims that
            # _StridedShard needs to interleave with)
            sf = 1
            for j in range(dp_idx + 1, spmd_mesh.ndim):
                p = orig_placements[j]
                if isinstance(p, (Shard, _StridedShard)) and p.dim == shard_dim:
                    sf *= spmd_mesh.size(j)
            new_placements[dp_idx] = (
                _StridedShard(shard_dim, split_factor=sf) if sf > 1 else fsdp_placement
            )

        self._spmd_mesh = spmd_mesh
        self._spmd_placements: tuple[Placement, ...] = tuple(new_placements)
        self._sharding_spec = self._build_spmd_sharding_spec(
            dp_dim_names,
            dp_shard_indices,
            fsdp_placement,
        )
        return cast(DTensor, param)._local_tensor

    def _build_spmd_sharding_spec(
        self,
        dp_dim_names: "DataParallelMeshDims",
        dp_shard_indices: list[int],
        fsdp_placement: Shard,
    ) -> DTensorSpec:
        """Build the DTensorSpec for the sharded parameter/gradient.

        When multiple DP shard dims exist (e.g. dp_shard + cp), flatten
        them into one axis so the parameter and gradient DTensor have one
        Shard axis for DP.
        """
        spmd_mesh = self._spmd_mesh
        spmd_placements = self._spmd_placements
        if self._unsharded_dtensor_spec is None:
            raise AssertionError("_unsharded_dtensor_spec cannot be None")
        tensor_meta = self._unsharded_dtensor_spec.tensor_meta

        if len(dp_shard_indices) <= 1:
            return DTensorSpec(spmd_mesh, spmd_placements, tensor_meta=tensor_meta)

        shard_names_set = set(dp_dim_names.shard_names)
        replicate_names_set = set(dp_dim_names.replicate_names)

        # Walk spmd_mesh.mesh_dim_names in order. Replace consecutive DP
        # shard dims with the flattened DP mesh; keep non-DP dims as-is.
        if spmd_mesh.mesh_dim_names is None:
            raise AssertionError("mesh_dim_names cannot be None")
        submeshes: list[DeviceMesh] = []
        spec_placements: list[Placement] = []
        skip = 0
        for i, name in enumerate(spmd_mesh.mesh_dim_names):
            if skip > 0:
                skip -= 1
                continue
            if name in shard_names_set:
                submeshes.append(self.mesh_info.mesh)
                if isinstance(self.mesh_info, HSDPMeshInfo):
                    spec_placements.append(Replicate())
                # Reuse the placement already computed for this DP shard dim
                # so that we don't loss _StridedShard.
                spec_placements.append(spmd_placements[i])
                skip = len(dp_dim_names.shard_names) - 1
            elif name in replicate_names_set and isinstance(
                self.mesh_info, HSDPMeshInfo
            ):
                # HSDP replicate is inserted by the shard branch above.
                continue
            else:
                submeshes.append(spmd_mesh[name])
                spec_placements.append(spmd_placements[i])

        spec_mesh = DeviceMesh._concatenate(submeshes)
        return DTensorSpec(spec_mesh, tuple(spec_placements), tensor_meta=tensor_meta)

    def _init_sharding_spec_tp(
        self,
        param: nn.Parameter,
        fsdp_placement: Shard,
        shard_dim: int,
    ) -> torch.Tensor:
        """TP/EP path: param is a DTensor, DP mesh is separate from TP mesh."""
        self._unsharded_dtensor_spec = cast(DTensor, param)._spec
        dp_mesh, tp_mesh = (self.mesh_info.mesh, self._unsharded_dtensor_spec.mesh)
        if dp_mesh is None or tp_mesh is None:
            raise AssertionError(
                "FSDP requires the DP and model parallel TP/EP mesh to be not None but got: \n"
                f"DP's mesh: {dp_mesh}\nTP/EP's mesh: {tp_mesh}"
            )
        self._spmd_mesh = DeviceMesh._concatenate([dp_mesh, tp_mesh])
        if len(self._unsharded_dtensor_spec.placements) > 2:
            raise NotImplementedError(
                f"FSDP only supports 1D TP/EP or 2D EP+TP, not {self._unsharded_dtensor_spec.placements}"
            )
        split_factor = self._unsharded_dtensor_spec.num_shards_map[shard_dim]
        if not (2 <= self._spmd_mesh.ndim <= 4):
            raise AssertionError(
                "_spmd_mesh.ndim can only be 2 (FSDP+TP/EP), 3 (FSDP+EP+TP, HSDP+TP/EP), "
                f"or 4 (HSDP+EP+TP) but got {self._spmd_mesh.ndim}."
            )
        if isinstance(self.mesh_info, FSDPMeshInfo):
            dp_shard_tp_placement = (
                (
                    _StridedShard(shard_dim, split_factor=split_factor)
                    if split_factor > 1
                    else fsdp_placement
                ),
                *self._unsharded_dtensor_spec.placements,
            )
        else:  # DDP
            dp_shard_tp_placement = (
                Replicate(),
                *self._unsharded_dtensor_spec.placements,
            )
        self._spmd_placements: tuple[Placement, ...]
        if isinstance(self.mesh_info, HSDPMeshInfo):
            if self.mesh_info.replicate_mesh_dim != 0:
                raise AssertionError(
                    f"Expected replicate_mesh_dim to be 0, got {self.mesh_info.replicate_mesh_dim}"
                )
            self._spmd_placements = (Replicate(),) + dp_shard_tp_placement
        else:
            self._spmd_placements = dp_shard_tp_placement

        self._sharding_spec = DTensorSpec(
            self._spmd_mesh,
            self._spmd_placements,
            tensor_meta=self._unsharded_dtensor_spec.tensor_meta,
        )
        return cast(DTensor, param)._local_tensor

    def _init_sharding_spec_plain(
        self,
        param: nn.Parameter,
        fsdp_placement: Shard,
    ) -> torch.Tensor:
        """Plain tensor path: param is not a DTensor."""
        self._spmd_mesh = self.mesh_info.mesh
        if isinstance(self.mesh_info, HSDPMeshInfo):
            self._spmd_placements = (Replicate(), fsdp_placement)
        elif isinstance(self.mesh_info, FSDPMeshInfo):
            self._spmd_placements = (fsdp_placement,)
        elif isinstance(self.mesh_info, DDPMeshInfo):
            self._spmd_placements = (Replicate(),)
        self._sharding_spec = DTensorSpec(
            self._spmd_mesh,
            self._spmd_placements,
            tensor_meta=TensorMeta(param.size(), param.stride(), param.dtype),
        )
        return param

    def _init_sharded_post_forward_param_metadata(self, param: torch.Tensor) -> None:
        mesh_info = self.post_forward_mesh_info
        if mesh_info is None:
            raise AssertionError("Expected post_forward_mesh_info to not be None")
        param_data = param._local_tensor if isinstance(param, DTensor) else param
        if isinstance(mesh_info, FSDPMeshInfo):
            chunks = _chunk_with_empty(param_data, mesh_info.shard_mesh_size, dim=0)
            self.sharded_post_forward_size = _get_dim_chunked_size(
                chunks[mesh_info.shard_mesh_rank],
                param_data.size(),
                dim=self.fsdp_placement.dim,
            )
        else:  # DDP
            chunks = _chunk_with_empty(param_data, 1, dim=0)
            self.sharded_post_forward_size = _get_dim_chunked_size(
                chunks[0],
                param_data.size(),
                dim=self.fsdp_placement.dim,
            )
        self.contiguous_sharded_post_forward_stride = make_contiguous_strides_for(
            self.sharded_post_forward_size
        )

    def init_dtype_attrs(self, mp_policy: MixedPrecisionPolicy):
        param_dtype, reduce_dtype = (mp_policy.param_dtype, mp_policy.reduce_dtype)
        self.orig_dtype = self.sharded_param.dtype
        # Clamp `reduce_dtype` to `None` if no casting is required: since
        # gradients are computed in `param_dtype`, if `reduce_dtype` matches,
        # then we do not need extra casting
        if reduce_dtype == param_dtype:
            reduce_dtype = None
        # Clamp `param_dtype` to `None` if no casting is required or if the
        # parameter is non-floating-point (mixed precision is only meaningful
        # for floating-point parameters)
        if param_dtype == self.orig_dtype or not self.orig_dtype.is_floating_point:
            param_dtype = None
        self.param_dtype = param_dtype
        self.reduce_dtype = reduce_dtype
        # None indicates that the mixed precision is not enabled

    def _init_extensions(self) -> None:
        inner_tensor = self._sharded_local_tensor
        has_fsdp_pre_all_gather = hasattr(inner_tensor, "fsdp_pre_all_gather")
        has_fsdp_post_all_gather = hasattr(inner_tensor, "fsdp_post_all_gather")
        if has_fsdp_pre_all_gather != has_fsdp_post_all_gather:
            raise AssertionError(
                "Both fsdp_pre_all_gather and fsdp_post_all_gather should be defined "
                f"if using all-gather extensions: {inner_tensor}"
            )
        if has_fsdp_pre_all_gather:
            self._extensions_data = ExtensionsData()
        self._unsharded_inner_tensors: list[torch.Tensor] = []

    def init_all_gather_outputs(
        self,
        all_gather_input_numels: list[int],
        all_gather_input_dtypes: list[torch.dtype],
        world_size: int,
        device: torch.device,
    ):
        if len(self.all_gather_outputs) > 0:
            return  # already initialized
        self.all_gather_outputs = [
            torch.empty(torch.Size([numel * world_size]), dtype=dtype, device=device)
            for numel, dtype in zip(all_gather_input_numels, all_gather_input_dtypes)
        ]

    def init_unsharded_param(self):
        if hasattr(self, "_unsharded_param"):  # after the 1st all-gather
            inner_tensor = self._sharded_local_tensor
            if not hasattr(inner_tensor, "fsdp_post_all_gather"):
                return  # already initialized
            for tensor in self._unsharded_inner_tensors:
                alloc_storage(tensor)
            all_gather_outputs = self._unflatten_all_gather_outputs()
            inner_tensor.fsdp_post_all_gather(
                all_gather_outputs,
                self._extensions_data.all_gather_metadata,
                self.param_dtype or self.orig_dtype,
                out=self._unsharded_param,
            )
            self._extensions_data.clear()
            return
        inner_tensor = self._sharded_local_tensor
        if hasattr(inner_tensor, "fsdp_post_all_gather"):
            all_gather_outputs = self._unflatten_all_gather_outputs()
            (
                unsharded_tensor,
                self._unsharded_inner_tensors,
            ) = inner_tensor.fsdp_post_all_gather(
                all_gather_outputs,
                self._extensions_data.all_gather_metadata,
                self.param_dtype or self.orig_dtype,
            )
            self._extensions_data.clear()
        else:
            # For the default path (no post-all-gather), the all-gather output
            # gives the unsharded parameter data directly
            if len(self.all_gather_outputs) != 1:
                raise AssertionError(
                    f"Expected 1 all_gather_output, got {len(self.all_gather_outputs)}"
                )
            unsharded_tensor = self.all_gather_outputs[0]
        unsharded_param = torch.as_strided(
            unsharded_tensor,
            self._orig_size,
            self._contiguous_orig_stride,
            storage_offset=0,
        )
        if self._unsharded_dtensor_spec is not None:
            unsharded_dtensor_spec = self._get_unsharded_dtensor_spec(unsharded_param)
            unsharded_param = _from_local_no_grad(
                unsharded_param, unsharded_dtensor_spec
            )
        self._unsharded_param = nn.Parameter(
            unsharded_param, requires_grad=self.sharded_param.requires_grad
        )

    def _get_unsharded_dtensor_spec(self, unsharded_param: torch.Tensor) -> DTensorSpec:
        if self._unsharded_dtensor_spec is None:
            raise AssertionError("Expected _unsharded_dtensor_spec for DTensor param")
        tensor_meta = self._unsharded_dtensor_spec.tensor_meta
        if tensor_meta is None or tensor_meta.dtype == unsharded_param.dtype:
            return self._unsharded_dtensor_spec
        return replace(
            self._unsharded_dtensor_spec,
            tensor_meta=TensorMeta(
                tensor_meta.shape,
                tensor_meta.stride,
                unsharded_param.dtype,
            ),
        )

    def _unflatten_all_gather_outputs(self) -> tuple[torch.Tensor, ...]:
        return tuple(
            t.view(-1, *s[1:])
            for t, s in zip(
                self.all_gather_outputs, self._extensions_data.all_gather_input_sizes
            )
        )

    def to_sharded(self) -> None:
        self._setattr_on_modules(self.sharded_param)
        self.free_unsharded_param()
        self.sharded_state = ShardedState.SHARDED

    def to_sharded_post_forward(self) -> None:
        if self.is_dtensor:
            raise NotImplementedError(
                "Resharding to smaller mesh is not supported for DTensor parameters yet"
            )
        self._assert_in_states(ShardedState.UNSHARDED)
        if self.post_forward_mesh_info is None:
            raise AssertionError("Expected post_forward_mesh_info to not be None")
        if len(self.all_gather_outputs) != 1:
            raise AssertionError(
                f"Expected 1 all_gather_output, got {len(self.all_gather_outputs)}"
            )
        shard_world_size = self.post_forward_mesh_info.shard_mesh_size
        if (numel := self.all_gather_outputs[0].numel()) % shard_world_size != 0:
            _raise_assert_with_print(
                f"All-gather output size ({numel}) must be divisible by the shard "
                f"world size ({shard_world_size})"
            )
        shard_rank = self.post_forward_mesh_info.shard_mesh_rank
        sharded_numel = numel // shard_world_size
        self._sharded_post_forward_param_data = (
            self.all_gather_outputs[0].narrow(
                0, sharded_numel * shard_rank, sharded_numel
            )
        ).clone()  # clone to be able to free all-gather output
        sharded_post_forward_tensor = torch.as_strided(
            self._sharded_post_forward_param_data,
            size=self.sharded_post_forward_size,
            stride=self.contiguous_sharded_post_forward_stride,
            storage_offset=0,
        )
        self._sharded_post_forward_param = nn.Parameter(
            self.to_sharded_post_forward_dtensor(sharded_post_forward_tensor),
            requires_grad=self.sharded_param.requires_grad,
        )
        self._setattr_on_modules(self._sharded_post_forward_param)
        self.free_unsharded_param()
        self.sharded_state = ShardedState.SHARDED_POST_FORWARD

    def to_unsharded(self) -> None:
        # Assume that the data has been allocated and all-gathered
        set_requires_grad_if_needed(self.sharded_param, self._unsharded_param)
        self._setattr_on_modules(self._unsharded_param)
        if self.sharded_state == ShardedState.SHARDED_POST_FORWARD:
            # The data is allocated in the default stream via the post-forward
            # reshard and must be kept alive for the next all-gather copy-in.
            # Since we call this method after the copy-out, the data's lifetime
            # is ensured without further synchronization.
            self._sharded_post_forward_param = None
            self._sharded_post_forward_param_data = None  # free
        self.sharded_state = ShardedState.UNSHARDED

    def _setattr_on_modules(self, param: nn.Parameter) -> None:
        unsafe_setattr_param(
            self._module_info.module, self._module_info.param_name, param
        )
        for shared_module, shared_param_name in zip(
            self._module_info.shared_modules, self._module_info.shared_param_names
        ):
            unsafe_setattr_param(shared_module, shared_param_name, param)

    def to_sharded_dtensor(self, tensor: torch.Tensor) -> DTensor:
        """
        Converts a local tensor representing either the sharded parameter or
        sharded gradient to DTensor.
        """
        if tensor.shape != self.sharded_size:
            _raise_assert_with_print(
                f"Expects size {self.sharded_size} but got {tensor.shape}"
            )
        return _from_local_no_grad(
            tensor,
            self._sharding_spec,
        )

    def to_sharded_post_forward_dtensor(self, tensor: torch.Tensor) -> DTensor:
        if tensor.shape != self.sharded_post_forward_size:
            _raise_assert_with_print(
                f"Expects size {self.sharded_post_forward_size} but got {tensor.shape}"
            )
        if not isinstance(self.post_forward_mesh_info, HSDPMeshInfo):
            raise AssertionError(
                f"Expected HSDPMeshInfo, got {type(self.post_forward_mesh_info)}"
            )
        # TODO: Prefer this DTensor to be read-only and generalize the
        # placement once we support TP.
        post_forward_sharding_spec = DTensorSpec(
            self.post_forward_mesh_info.mesh,
            (Replicate(), Shard(0)),
            tensor_meta=self._sharding_spec.tensor_meta,
        )
        return _from_local_no_grad(tensor, post_forward_sharding_spec)

    def to_accumulated_grad_if_needed(self) -> None:
        # Access `_unsharded_param` to bypass the sharded state check since we
        # prefer to reshard before upcasting the gradient to save memory
        if (
            self.reduce_dtype is None
            or self._unsharded_param.grad is None
            or self._unsharded_param.grad.dtype == self.reduce_dtype
        ):
            return
        unsharded_grad = self._unsharded_param.grad
        self._unsharded_param.grad = None
        self.unsharded_accumulated_grad = unsharded_grad.to(self.reduce_dtype)

    def accumulate_unsharded_grad_if_needed(self) -> None:
        if (
            self.unsharded_accumulated_grad is not None
            and self.unsharded_param.grad is not None
        ):
            self.unsharded_accumulated_grad += self.unsharded_param.grad
            self.unsharded_param.grad = None

    def alloc_all_gather_outputs(self) -> None:
        for tensor in self.all_gather_outputs:
            alloc_storage(tensor)

    def free_unsharded_param(self) -> None:
        for tensor in itertools.chain(
            self.all_gather_outputs, self._unsharded_inner_tensors
        ):
            free_storage(tensor)

    @property
    def all_gather_inputs(self) -> list[torch.Tensor]:  # 1D
        self._assert_in_states(ShardedState.SHARDED, ShardedState.SHARDED_POST_FORWARD)
        if self.sharded_state == ShardedState.SHARDED:
            if hasattr(self._sharded_local_tensor, "fsdp_pre_all_gather"):
                sharded_local_tensor = self._sharded_local_tensor
                if self.offload_to_cpu:
                    sharded_local_tensor = sharded_local_tensor.to(
                        self.device, non_blocking=True
                    )
                pre_all_gather_signature = inspect.signature(
                    # pyrefly: ignore [missing-attribute]
                    sharded_local_tensor.fsdp_pre_all_gather
                )
                num_fn_params = len(pre_all_gather_signature.parameters)
                # Old signature only passes mesh; keep for BC for now
                if num_fn_params not in (1, 5):
                    raise AssertionError(
                        f"Invalid fsdp_pre_all_gather: {pre_all_gather_signature}\n"
                        "Expects fsdp_pre_all_gather(self, mesh: DeviceMesh, "
                        "outer_size: torch.Size, outer_stride: tuple[int, ...], "
                        "module: nn.Module, mp_policy: MixedPrecisionPolicy)"
                    )
                if num_fn_params == 1:
                    (
                        all_gather_inputs,
                        self._extensions_data.all_gather_metadata,
                        # pyrefly: ignore [missing-attribute]
                    ) = sharded_local_tensor.fsdp_pre_all_gather(
                        self.shard_mesh_from_root
                    )
                else:
                    (
                        all_gather_inputs,
                        self._extensions_data.all_gather_metadata,
                        # pyrefly: ignore [missing-attribute]
                    ) = sharded_local_tensor.fsdp_pre_all_gather(
                        self.shard_mesh_from_root,
                        self._orig_size,
                        self._contiguous_orig_stride,
                        self._module_info.module,
                        self.mp_policy,
                    )
                    if (
                        sharded_local_tensor.size() != self.padded_sharded_param_size
                        and any(
                            all_gather_input.size() != self.padded_sharded_param_size
                            for all_gather_input in all_gather_inputs
                        )
                    ):
                        # NOTE: Since this error can only be raised on the
                        # ranks that have padding, this can manifest as a NCCL
                        # watchdog timeout, as the other ranks will not error.
                        raise AssertionError(
                            "When a parameter is unevenly sharded by FSDP "
                            f"(orig size={self._orig_size}, FSDP world size={self.mesh_info.mesh.size()}), "
                            "fsdp_pre_all_gather must return all-gather inputs with the padded sharded size "
                            f"{self.padded_sharded_param_size} but got {[t.size() for t in all_gather_inputs]}"
                        )
                self._extensions_data.all_gather_input_sizes = [
                    t.size() for t in all_gather_inputs
                ]
                return [t.view(-1) for t in all_gather_inputs]
            sharded_param_data = self._sharded_param_data
            if self.offload_to_cpu:
                sharded_param_data = sharded_param_data.to(
                    self.device, non_blocking=True
                )
            return [_to_dtype_if_needed(sharded_param_data, self.param_dtype)]
        elif self.sharded_state == ShardedState.SHARDED_POST_FORWARD:
            if hasattr(self._sharded_local_tensor, "fsdp_pre_all_gather"):
                raise NotImplementedError
            all_gather_input = _to_dtype_if_needed(
                cast(torch.Tensor, self._sharded_post_forward_param_data),
                self.param_dtype,
            )
            return [all_gather_input]
        return [torch.empty(0)]  # mypy

    @property
    def unsharded_param(self) -> nn.Parameter:  # ND
        return self._unsharded_param

    @property
    def unsharded_grad_data(self) -> torch.Tensor:
        grad = self.unsharded_param.grad
        if grad is None:
            raise AssertionError("Expects unsharded_param.grad to not be None")
        return self._get_grad_inner_tensor(grad)

    @property
    def unsharded_accumulated_grad_data(self) -> torch.Tensor:
        grad = self.unsharded_accumulated_grad
        if grad is None:
            raise AssertionError("Expects unsharded_accumulated_grad to not be None")
        return self._get_grad_inner_tensor(grad)

    @property
    def unsharded_zero_grad_data(self) -> torch.Tensor:
        return self._get_grad_inner_tensor(torch.zeros_like(self.unsharded_param))

    def _get_grad_inner_tensor(self, grad: torch.Tensor) -> torch.Tensor:
        if self.is_dtensor:
            if isinstance(grad, AsyncCollectiveTensor):
                grad = grad.wait()
            if not isinstance(grad, DTensor):
                raise AssertionError(f"Expected DTensor, got {type(grad)}")
            if self._unsharded_dtensor_spec is None:
                raise AssertionError(
                    "Expected _unsharded_dtensor_spec for DTensor param"
                )
            placements = self._unsharded_dtensor_spec.placements
            if self.mesh_info.is_spmd_mesh:
                # Only redistribute non-DP dims; keep Partial on DP dims
                # so FSDP's reduce-scatter handles them directly, avoiding
                # a redundant all-reduce on the DP dimensions.
                target_placements = tuple(
                    grad.placements[i] if i in self._dp_dim_indices else placements[i]
                    for i in range(len(placements))
                )
                if target_placements != grad.placements:
                    if len(placements) != len(grad.placements):
                        raise AssertionError(
                            f"Expected same placement length: {placements=} {grad.placements=}"
                        )
                    grad = grad.redistribute(placements=target_placements)
            else:
                if placements != grad.placements:
                    if len(placements) != len(grad.placements):
                        raise AssertionError(
                            f"Expected same placement length: {placements=} {grad.placements=}"
                        )
                    grad = grad.redistribute(placements=placements)
            grad = grad._local_tensor
        return grad

    @property
    def _sharded_local_tensor(self) -> torch.Tensor:
        return cast(DTensor, self.sharded_param)._local_tensor

    def _init_shard_mesh(self) -> DeviceMesh:
        mesh = self.mesh_info.mesh
        if mesh.ndim == 1:
            return mesh
        if mesh.mesh_dim_names is None:
            raise AssertionError("Expected mesh_dim_names to not be None")
        return mesh[mesh.mesh_dim_names[-1]]

    @property
    def shard_mesh(self):
        return self._shard_mesh

    @property
    def shard_mesh_from_root(self):
        return self.shard_mesh

    def _assert_in_states(self, *states: ShardedState) -> None:
        if self.sharded_state not in states:
            _raise_assert_with_print(
                f"Expects to be in one of {states}, not {self.sharded_state}"
            )

    def reset_sharded_param(self):
        # For ops like `nn.Module._apply` or `load_state_dict(assign=True)`
        # that change the sharded parameter tensor, we may need to re-pad the
        # sharded local tensor and re-save the reference.
        module_info = self._module_info
        new_param = getattr(module_info.module, module_info.param_name)
        if new_param is not self.sharded_param:
            if torch.__future__.get_swap_module_params_on_conversion():
                raise AssertionError(
                    f"Expects swap_tensors to preserve object but got {new_param} "
                    f"instead of {self.sharded_param}"
                )
            self.sharded_param = new_param

        local_tensor = new_param._local_tensor
        if local_tensor.is_meta:
            return
        updated_local_tensor = False
        # local_tensor can be padded twice
        # 1st time in fully_shard(model)
        # 2nd time in model(input) lazy_init
        # 2nd time should be no-op if parameters remain unchanged
        # 2nd time shouldn't be no-op if people call model.load_state_dict(...) before lazy_init
        # this makes it possible for trainer to call `sd = model.state_dict()` before the training loop
        # and use `sd` without calling .state_dict() per iteration
        same_local_tensor = False
        # TODO: need to support tensor subclass
        if type(self._sharded_param_data) is torch.Tensor:
            same_local_tensor = (
                # when sharding param with shape (1, ...) over 2 ranks
                # local_tensor on rank 1 can be size 0, data_ptr() can be 0
                self._sharded_param_data.untyped_storage().data_ptr() > 0
                and self._sharded_param_data.untyped_storage().data_ptr()
                == local_tensor.untyped_storage().data_ptr()
            )
        padded_sharded_size = self.padded_sharded_param_size
        shard_dim = self.fsdp_placement.dim
        length = local_tensor.size(shard_dim) if local_tensor.numel() > 0 else 0
        if local_tensor.size() != padded_sharded_size and not same_local_tensor:
            if shard_dim != 0:
                raise AssertionError(
                    f"Shard({shard_dim}) requires even sharding: {local_tensor.size()=}"
                )
            padded_local_tensor = local_tensor.new_zeros(padded_sharded_size)
            padded_local_tensor.narrow(dim=shard_dim, start=0, length=length).copy_(
                local_tensor
            )
            local_tensor = padded_local_tensor
            updated_local_tensor = True
        if self.pin_memory and not local_tensor.is_pinned():
            local_tensor = local_tensor.cpu().pin_memory()
            updated_local_tensor = True
        if not same_local_tensor:
            self._sharded_param_data = local_tensor.view(-1)
        if not isinstance(self.sharded_param, DTensor):
            raise AssertionError(f"Expected DTensor, got {type(self.sharded_param)}")
        if updated_local_tensor:
            # Only change the local tensor object if needed
            self.sharded_param._local_tensor = local_tensor.narrow(
                dim=shard_dim, start=0, length=length
            )
            if not self.sharded_param._local_tensor.is_contiguous():
                raise AssertionError(
                    "Expected sharded_param._local_tensor to be contiguous"
                )
        self._sharding_spec = self.sharded_param._spec

    def __repr__(self):
        return f"FSDPParam(fqn={self._param_fqn}, orig_size={self._orig_size})"


def alloc_storage(tensor: torch.Tensor) -> None:
    size = tensor.numel() * tensor.itemsize
    if (storage := tensor.untyped_storage()).size() != size:
        storage.resize_(size)


def free_storage(tensor: torch.Tensor) -> None:
    if (storage := tensor.untyped_storage()).size() != 0:
        storage.resize_(0)


# NOTE: These bypass `nn.Module.__setattr__` checks, which incur non-trivial
# CPU overhead, if the module did not override it. For FSDP, we know we do not
# need those checks when transitioning between sharded/unsharded parameters.
def unsafe_setattr_param(
    module: nn.Module, param_name: str, param: nn.Parameter
) -> None:
    if getattr(module.__setattr__, "__func__", None) is nn.Module.__setattr__:
        module._parameters[param_name] = param
    else:  # slow path
        setattr(module, param_name, param)


def set_requires_grad_if_needed(
    src_tensor: torch.Tensor, dst_tensor: torch.Tensor
) -> None:
    # Only call `requires_grad_` if needed to avoid the Python <> C++ context
    # switch overhead
    if src_tensor.requires_grad != dst_tensor.requires_grad:
        dst_tensor.requires_grad_(src_tensor.requires_grad)
