# Copyright (c) Meta Platforms, Inc. and affiliates

import dataclasses
from typing import Dict, List, Optional, Sequence, Tuple, Union, cast
from torch.distributed.checkpoint.planner import LoadPlan

import torch
import torch.distributed as dist
from torch.distributed._shard.sharded_tensor.api import ShardedTensor
from torch.distributed._shard.sharded_tensor.metadata import TensorProperties
from torch.distributed._shard.sharded_tensor.shard import Shard
from torch.distributed._shard.sharding_spec.chunk_sharding_spec import (
    ChunkShardingSpec,
)

import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.metadata import (
    BytesStorageMetadata,
    Metadata,
    MetadataIndex,
    STATE_DICT_TYPE,
    TensorStorageMetadata,
    ChunkStorageMetadata,
)
from torch.distributed.checkpoint.planner_helpers import (
    create_read_items_for_chunk_list,
    _create_read_items,
)
from torch.distributed.remote_device import _remote_device

from torch.distributed._tensor import DTensor
from torch.distributed.checkpoint.default_planner import (
    DefaultLoadPlanner,
)
from torch.distributed._shard.api import _shard_tensor

from torch.distributed.checkpoint._nested_dict import unflatten_state_dict
from torch.distributed.checkpoint.utils import (
    _element_wise_add,
    _element_wise_sub,
)

STATE_DICT_2D_LAYOUT = Dict[str, Tuple[Optional[Sequence[int]], Sequence[int]]]


# TODO: Update docstrings for optimizer.py
__all__ = [
    "load_sharded_optimizer_state_dict",
]


def _gen_rank_device(global_rank: int) -> str:
    if torch.cuda.is_available():
        return f"cuda:{global_rank % torch.cuda.device_count()}"
    return "cpu"


def _create_colwise_spec(
    pg: Optional[dist.ProcessGroup] = None,
) -> ChunkShardingSpec:
    if pg is None:
        placements = [
            f"rank:{idx}/{_gen_rank_device(idx)}"
            for idx in range(dist.get_world_size())
        ]
    else:
        placements = [
            f"rank:{idx}/{_gen_rank_device(dist.get_global_rank(pg, idx))}"
            for idx in range(pg.size())
        ]
    return ChunkShardingSpec(
        dim=0,
        placements=cast(List[Union[_remote_device, str]], placements),
    )


def _is_nested_tensor(val: torch.Tensor) -> bool:
    if type(val) is ShardedTensor:
        if len(val.local_shards()) == 0:
            return False
        if type(val.local_shards()[0].tensor) is ShardedTensor:
            return True
        if type(val.local_shards()[0].tensor) is DTensor:
            raise ValueError(
                "Cannot handle DTensor nested insided ShardedTensor"
            )
    elif type(val) is DTensor and (
        type(val._local_tensor) is DTensor
        or type(val._local_tensor) is ShardedTensor
    ):
        raise ValueError("Cannot handle nested DTensor")
    return False


def _alloc_tensor(props: TensorProperties, size: Sequence[int]) -> torch.Tensor:
    return torch.empty(
        size=size,
        dtype=props.dtype,
        layout=props.layout,
        requires_grad=props.requires_grad,
        pin_memory=props.pin_memory,
        device=cast(torch.device, torch.cuda.current_device()),
    )


def _get_state_dict_2d_layout(
    state_dict: STATE_DICT_TYPE,
) -> Tuple[STATE_DICT_2D_LAYOUT, Optional[dist.ProcessGroup]]:
    """
    We have to load the right TP slice of the optimizer state.
    This is not easy since the per-tensor slicing can't be inferred from checkpoint metadata.
    We take advantage of the model state_dict producing a sliced ST to figure out what we need to load.
    This is pretty fragile and it might be easier for FSDP to compute this info for us.
    Returns a dictionary where keys are the same of the state_dict and the value is a tuple of
    (offset, size) for the current rank TP slice.
    N.B. The state_dict *MUST* come from FSDP.sharded_state_dict.
    """
    specs: STATE_DICT_2D_LAYOUT = {}
    dp_pg: Optional[dist.ProcessGroup] = None
    for key, value in state_dict.items():
        specs[key] = (None, value.size())
        if _is_nested_tensor(value):
            assert (
                len(value.local_shards()) == 1
            ), "Cannot handle ST with multiple shards"
            assert isinstance(
                value, ShardedTensor
            ), "Can only handle nested ShardedTensor"
            shard = value.local_shards()[0]
            specs[key] = (
                shard.metadata.shard_offsets,
                shard.metadata.shard_sizes,
            )
            dp_pg = shard.tensor._process_group  # type: ignore[attr-defined]

    return (
        specs,
        dp_pg,
    )


class _ReaderWithOffset(DefaultLoadPlanner):
    translation: Dict[MetadataIndex, MetadataIndex]
    state_dict: STATE_DICT_TYPE
    metadata: Metadata

    def __init__(self, fqn_to_offset: Dict[str, Sequence[int]]) -> None:
        super().__init__()
        self.fqn_to_offset = fqn_to_offset
        self.metadata = Metadata({})
        self.state_dict = {}
        self.translation = {}

    def create_local_plan(self) -> LoadPlan:
        requests = []
        self.translation = {}
        for fqn, obj in self.state_dict.items():
            md = self.metadata.state_dict_metadata[fqn]
            if not isinstance(obj, ShardedTensor):
                requests += _create_read_items(fqn, md, obj)
                continue

            if fqn not in self.fqn_to_offset:
                requests += _create_read_items(fqn, md, obj)
                continue

            offset = self.fqn_to_offset[fqn]

            assert len(obj.local_shards()) == 1
            original_shard = obj.local_shards()[0]
            local_chunks = [
                ChunkStorageMetadata(
                    offsets=torch.Size(
                        _element_wise_add(
                            original_shard.metadata.shard_offsets, offset
                        )
                    ),
                    sizes=torch.Size(original_shard.metadata.shard_sizes),
                )
            ]

            reqs = create_read_items_for_chunk_list(
                fqn, cast(TensorStorageMetadata, md), local_chunks
            )
            # TODO: The ReadItems will have a displaced MetadataIndex, fix it.
            # TODO: we should change _create_sharded_read_items to have more ergonomic API
            for ri in reqs:
                assert ri.dest_index.offset is not None
                original_offset = _element_wise_sub(
                    ri.dest_index.offset, offset
                )
                original_index = dataclasses.replace(
                    ri.dest_index, offset=torch.Size(original_offset)
                )
                self.translation[ri.dest_index] = original_index

            requests += reqs
        return LoadPlan(requests)

    def lookup_tensor(self, index: MetadataIndex) -> torch.Tensor:
        return super().lookup_tensor(self.translation.get(index, index))


def load_sharded_optimizer_state_dict(
    model_state_dict: STATE_DICT_TYPE,
    optimizer_key: str,
    storage_reader: dist_cp.StorageReader,
) -> STATE_DICT_TYPE:
    """
    Loads a state_dict in conjunction with FSDP sharded optimizer state.
    This is the current recommended way to checkpoint FSDP.
    >>> # xdoctest: +SKIP
    >>> import torch.distributed.checkpoint as dist_cp
    >>> # Save
    >>> model: torch.nn.Model
    >>> optim_params = model.parameters()
    >>> optim = torch.optim.SGD(optim_params, lr=0.01)
    >>> # Save
    >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
    >>>     state_dict = {
    >>>         "optimizer": FSDP.optim_state_dict(model, optim),
    >>>         "model": model.state_dict()
    >>>     }
    >>>     dist_cp.save_state_dict(
    >>>         state_dict=optim_state,
    >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
    >>>         planner=dist_cp.DefaultSavePlanner(),
    >>>     )
    >>>
    >>> # Load
    >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
    >>>     model_state_dict = model_tp.state_dict()
    >>>     checkpoint = {
    >>>         "model": model_state_dict
    >>>     }
    >>>     dist_cp.load_state_dict(
    >>>         state_dict=checkpoint,
    >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
    >>>         planner=dist_cp.DefaultLoadPlanner(),
    >>>     )
    >>>     model.load_state_dict(checkpoint["model_state"])
    >>>
    >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
    >>>         model_state_dict,
    >>>         optimizer_key="optimizer",
    >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
    >>>     )
    >>>
    >>>     flattened_osd = FSDP.optim_state_dict_to_load(
    >>>        model, optim, optim_state["optimizer"]
    >>>     )
    >>>
    >>>     optim.load_state_dict(flattened_osd)
    """
    metadata = storage_reader.read_metadata()

    layout_specs, dp_pg = _get_state_dict_2d_layout(model_state_dict)

    if dp_pg is None:
        sharding_spec = ChunkShardingSpec(
            dim=0,
            placements=[
                f"rank:{i}/cuda:{i % torch.cuda.device_count()}"
                for i in range(dist.get_world_size())
            ],
        )
    else:
        sharding_spec = _create_colwise_spec(dp_pg)

    # Create a state_dict for optimizer state
    state_dict: STATE_DICT_TYPE = {}

    fqn_to_offset: Dict[str, Sequence[int]] = {}
    for key, value in metadata.state_dict_metadata.items():
        key_path = metadata.planner_data[key]
        if key_path[0] != optimizer_key:
            continue

        if isinstance(value, BytesStorageMetadata):
            state_dict[key] = "<bytes_io>"
            continue

        # value: TensorStorageMetadata
        if value.size.numel() == 1:
            state_dict[key] = _alloc_tensor(value.properties, value.size)
        elif dp_pg is None:
            state_dict[key] = _shard_tensor(
                _alloc_tensor(value.properties, value.size), sharding_spec
            )
        else:
            spec_key = key_path[2]
            alloc_size = layout_specs.get(spec_key, (None, value.size))[1]

            st_md = sharding_spec.build_metadata(
                torch.Size(alloc_size), value.properties
            )
            local_shards = []
            current_rank = dist.get_rank(dp_pg)
            for shard_md in st_md.shards_metadata:
                if (
                    cast(_remote_device, shard_md.placement).rank()
                    != current_rank
                ):
                    continue
                local_shards.append(
                    Shard(
                        tensor=_alloc_tensor(
                            value.properties, shard_md.shard_sizes
                        ),
                        metadata=shard_md,
                    )
                )

            st = ShardedTensor._init_from_local_shards_and_global_metadata(
                local_shards, st_md, process_group=dp_pg
            )

            if (
                spec_key in layout_specs
                and layout_specs[spec_key][0] is not None
            ):
                fqn_to_offset[key] = cast(
                    Sequence[int], layout_specs[spec_key][0]
                )

            state_dict[key] = st

    # Whether we unflatten before or after doesn't matter
    dist_cp.load_state_dict(
        state_dict=state_dict,
        storage_reader=storage_reader,
        # FIXME the type of planner is wrong in load_state_dict
        planner=_ReaderWithOffset(fqn_to_offset) if dp_pg is not None else None,
    )

    state_dict = unflatten_state_dict(state_dict, metadata.planner_data)

    return state_dict
