# mypy: allow-untyped-defs
import copy
import operator
from collections.abc import Sequence
from typing import Any, cast

import torch
from torch._subclasses.fake_tensor import FakeTensor
from torch.distributed.tensor import DeviceMesh, distribute_tensor, DTensor
from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
from torch.distributed.tensor._op_schema import (
    OpSchema,
    OpSpec,
    OutputSharding,
    OutputSpecType,
)
from torch.distributed.tensor._redistribute import redistribute_local_tensor
from torch.distributed.tensor.parallel.style import ColwiseParallel, ParallelStyle
from torch.distributed.tensor.placement_types import Placement, Replicate, Shard
from torch.export import ExportedProgram
from torch.export.exported_program import ExportGraphSignature
from torch.fx import GraphModule
from torch.fx.experimental.proxy_tensor import make_fx
from torch.fx.node import Node
from torch.fx.passes.infra.pass_base import PassBase, PassResult
from torch.fx.passes.shape_prop import _extract_tensor_metadata
from torch.utils import _pytree as pytree


__all__ = ["tensor_parallel_transformation"]

aten = torch.ops.aten


def tensor_parallel_transformation(
    exported_program: ExportedProgram,
    rank: int,
    world_size: int,
    device_type: str,
    parallel_strategies: dict[str, ParallelStyle],
) -> ExportedProgram:
    """
    The entry point function to perform graph transformations on an exported program
    to transform a single-device graph into a tensor parallel graph.

    .. warning::
        This API is experimental and subject to change.
    """

    gm = exported_program.graph_module
    sig = copy.deepcopy(exported_program.graph_signature)
    state_dict = copy.copy(exported_program.state_dict)

    with gm._set_replace_hook(sig.get_replace_hook()):
        res = _TensorParallelTransformPass(
            rank,
            world_size,
            device_type,
            state_dict,
            exported_program.graph_signature,
            parallel_strategies,
        )(gm)
        assert res is not None
        gm = res.graph_module

    return exported_program._update(gm, sig, state_dict=state_dict)


class _TensorParallelTransformPass(PassBase):
    """
    This pass is responsible for transforming a single-device graph into a tensor parallel
    graph. It will mark the OpSpec of each node in the graph, partition the graph into
    distributed graph, then shard the parameters/buffers accordingly.
    """

    def __init__(
        self,
        rank: int,
        world_size: int,
        device_type: str,
        state_dict: dict[str, torch.Tensor],
        graph_signature: ExportGraphSignature,
        parallel_strategies: dict[str, ParallelStyle],
    ) -> None:
        super().__init__()
        self.rank = rank
        self.mesh = DeviceMesh(device_type, torch.arange(world_size))
        self.state_dict: dict[str, torch.Tensor] = state_dict
        self.graph_signature = graph_signature
        self.parallel_strategies = parallel_strategies

    def call(self, graph_module) -> PassResult:
        gm = copy.deepcopy(graph_module)

        parameter_placements = _generate_parameter_and_buffer_placements(
            list(self.state_dict.keys()), self.parallel_strategies
        )
        placement_strategies = _mark_sharding(
            gm, self.graph_signature, self.mesh, parameter_placements
        )
        _partitioner(gm)
        _shard_state_dict(
            self.state_dict, placement_strategies, self.graph_signature, self.mesh
        )
        return PassResult(gm, True)


def _generate_parameter_and_buffer_placements(
    params_and_buffers: list[str],
    parallel_strategies: dict[str, ParallelStyle],
) -> dict[str, Placement]:
    """
    Build parameter placements based on the give parallel style of linear layers.
    """
    parameter_placements: dict[str, Placement] = {}
    for linear_fqn, parallel_style in parallel_strategies.items():
        weight_fqn = f"{linear_fqn}.weight"
        bias_fqn = f"{linear_fqn}.bias"
        assert weight_fqn in params_and_buffers
        parameter_placements[weight_fqn] = (
            Shard(0) if parallel_style == ColwiseParallel else Shard(1)
        )
        if bias_fqn in params_and_buffers:
            parameter_placements[bias_fqn] = (
                Shard(0) if parallel_style == ColwiseParallel else Replicate()
            )
    return parameter_placements


def _mark_tensor_parallel_shardings(
    gm: GraphModule,
    graph_signature: ExportGraphSignature,
    mesh: DeviceMesh,
    parameter_placements: dict[str, Placement],
) -> dict[Node, OpSpec]:
    """
    Mark the placement strategies of the parameter and buffer placeholder nodes.
    """
    placement_strategies: dict[Node, OpSpec] = {}
    num_params_and_buffers = len(graph_signature.inputs_to_parameters) + len(
        graph_signature.inputs_to_buffers
    )
    placeholder_idx: int = 0
    for node in gm.graph.nodes:
        if node.op == "placeholder":
            if placeholder_idx < num_params_and_buffers:
                fqn: str = _get_input_node_fqn(node.name, graph_signature)
                placement: Placement = (
                    parameter_placements[fqn]
                    if fqn in parameter_placements
                    else Replicate()
                )
                placement_strategies[node] = _create_placement_strategy(
                    node,
                    mesh,
                    placements=(placement,),
                )
                placeholder_idx += 1
            else:
                placement_strategies[node] = _create_placement_strategy(
                    node,
                    mesh,
                    placements=(Replicate(),),
                )
    return placement_strategies


def _get_input_node_fqn(input_name: str, graph_signature: ExportGraphSignature) -> str:
    """
    Return the FQN of an input node.
    """
    if input_name in graph_signature.inputs_to_parameters:
        return graph_signature.inputs_to_parameters[input_name]
    elif input_name in graph_signature.inputs_to_buffers:
        return graph_signature.inputs_to_buffers[input_name]
    else:
        raise ValueError(
            f"{input_name} not found in inputs_to_parameters or inputs_to_buffers"
        )


def _mark_sharding(
    gm: GraphModule,
    graph_signature: ExportGraphSignature,
    mesh: DeviceMesh,
    parameter_placements: dict[str, Placement],
) -> dict[Node, OpSpec]:
    """
    Mark the sharding strategy for each node in the graph module.
    """
    placement_strategies: dict[Node, OpSpec] = _mark_tensor_parallel_shardings(
        gm,
        graph_signature,
        mesh,
        parameter_placements,
    )

    for node in gm.graph.nodes:
        if node.op == "placeholder":
            if node not in placement_strategies:
                placement_strategies[node] = _create_placement_strategy(
                    node, mesh, placements=(Replicate(),)
                )
            node.meta["sharding"] = placement_strategies[node]
        elif node.op == "call_function":
            if node.target is operator.getitem:
                input_nodes = node.all_input_nodes
                assert len(input_nodes) == 1, (
                    f"non-compute op only support one input now, found node: {node} with length of inputs: {len(node.args)}"
                )
                arg_strategy = placement_strategies[input_nodes[0]]
                placement_strategies[node] = _create_placement_strategy(
                    node,
                    mesh,
                    placements=arg_strategy.output_spec.placements,
                    input_specs=_get_input_node_specs(node, placement_strategies),
                )
                node.meta["sharding"] = placement_strategies[node]
            else:
                op_schema = _get_op_schema(node, placement_strategies)

                # get DTensor specs for inputs and outputs
                if (
                    op_schema.op
                    not in DTensor._op_dispatcher.sharding_propagator.op_strategy_funcs
                    and op_schema.op
                    not in DTensor._op_dispatcher.sharding_propagator.op_to_rules
                ):
                    # Mark all as replicated
                    output_sharding = _generate_default_output_sharding(
                        node,
                        mesh,
                        op_schema,
                    )
                else:
                    output_sharding = DTensor._op_dispatcher.sharding_propagator.propagate_op_sharding(  # type: ignore[assignment]
                        op_schema,
                    )
                placement_strategies[node] = OpSpec(
                    # pyrefly: ignore [bad-argument-type]
                    output_specs=_get_output_spec_from_output_sharding(output_sharding),
                    # pyrefly: ignore [missing-attribute]
                    input_specs=output_sharding.redistribute_schema.args_spec
                    # pyrefly: ignore [missing-attribute]
                    if output_sharding.redistribute_schema is not None
                    else _get_input_node_specs(node, placement_strategies),
                )
                node.meta["sharding"] = placement_strategies[node]
        elif node.op == "output":
            node.meta["sharding"] = None
        else:
            raise RuntimeError(f"op code {node.op} not supported")
    return placement_strategies


def _get_output_spec_from_output_sharding(
    output_sharding: OutputSharding,
) -> DTensorSpec:
    """
    Util function to extract output spec from output sharding.
    """
    if isinstance(output_sharding.output_spec, DTensorSpec):
        return output_sharding.output_spec
    else:
        # For ops that return multiple outputs, the outputs should have the same output spec
        assert isinstance(output_sharding.output_spec, Sequence)
        assert output_sharding.output_spec[0] is not None
        output_sharding.output_spec[0].tensor_meta = None
        return output_sharding.output_spec[0]


def _create_placement_strategy(
    node: Node,
    mesh: DeviceMesh,
    placements: tuple[Placement, ...],
    input_specs: Sequence[DTensorSpec] | None = None,
) -> OpSpec:
    """
    Util function to construct an OpSpec for a given node.
    """
    placement = OpSpec(
        input_specs=input_specs,
        output_specs=DTensorSpec(
            mesh=mesh,
            placements=placements,
        ),
    )
    _populate_tensor_meta(node, placement.output_specs)
    return placement


def _populate_tensor_meta(node: Node, output_spec: OutputSpecType) -> None:
    """
    Util function to populate tensor meta of output_spec based on node metadata.
    """
    if isinstance(node.meta["val"], Sequence):
        assert isinstance(output_spec, Sequence)
        for spec, fake_tensor in zip(output_spec, node.meta["val"]):
            assert spec is not None
            spec.tensor_meta = TensorMeta(
                shape=fake_tensor.shape,
                stride=fake_tensor.stride(),
                dtype=fake_tensor.dtype,
            )
    else:
        assert isinstance(output_spec, DTensorSpec)
        output_spec.tensor_meta = TensorMeta(
            shape=node.meta["val"].shape,
            stride=node.meta["val"].stride(),
            dtype=node.meta["val"].dtype,
        )


def _generate_default_output_sharding(
    node: Node,
    mesh: DeviceMesh,
    op_schema: OpSchema,
) -> OutputSharding:
    """
    Util function to create a default output sharding that suggests Replicate placement for both args and outputs.
    """

    def update_arg_spec(arg_spec: DTensorSpec) -> DTensorSpec:
        return DTensorSpec(
            mesh=arg_spec.mesh,
            placements=(Replicate(),),
            tensor_meta=arg_spec.tensor_meta,
        )

    new_op_schema = OpSchema(
        op=op_schema.op,
        args_schema=pytree.tree_map_only(
            DTensorSpec, update_arg_spec, op_schema.args_schema
        ),
        kwargs_schema=op_schema.kwargs_schema,
    )

    def create_output_spec(tensor: FakeTensor) -> DTensorSpec:
        return DTensorSpec(
            mesh=mesh,
            placements=(Replicate(),),
            tensor_meta=TensorMeta(
                shape=tensor.shape,
                stride=tensor.stride(),
                dtype=tensor.dtype,
            ),
        )

    return OutputSharding(
        output_spec=pytree.tree_map_only(
            FakeTensor, create_output_spec, node.meta["val"]
        ),
        redistribute_schema=new_op_schema,
        needs_redistribute=True,
    )


def _partitioner(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
    """
    Graph partitioner that partitions the single device graph
    to distributed graph
    """
    for node in gm.graph.nodes:
        node_sharding = node.meta["sharding"]
        if node.op == "placeholder":
            out_spec = node_sharding.output_spec
            local_val = _partition_val(node.meta["val"], out_spec)
            # update node value
            node.meta["val"] = local_val
        elif node.op == "call_function":
            out_spec = node_sharding.output_spec
            # check if there's misaligned sharding, insert reshard if there is
            expected_input_specs = node_sharding.input_specs
            for idx, input_arg in enumerate(node.all_input_nodes):
                input_arg_sharding = input_arg.meta["sharding"]
                input_arg_spec = input_arg_sharding.output_spec
                desired_spec = (
                    out_spec
                    if expected_input_specs is None
                    else expected_input_specs[idx]
                )
                if input_arg_spec != desired_spec:
                    _insert_reshard_gm(
                        gm, node, input_arg, input_arg_spec, desired_spec
                    )
            # convert output val to its local component
            output_val = node.meta["val"]
            node.meta["val"] = _partition_val(output_val, out_spec)
        elif node.op == "output":
            for input_arg in node.all_input_nodes:
                # input args of output should be Replicate, otherwise redistribution is needed.
                input_args_to_check: Sequence[Node] = (
                    input_arg if isinstance(input_arg, Sequence) else [input_arg]
                )
                for arg in input_args_to_check:
                    arg_sharding = arg.meta["sharding"]
                    arg_spec = arg_sharding.output_spec
                    desired_spec = copy.copy(arg_spec)
                    desired_spec.placements = (Replicate(),)
                    if arg_spec != desired_spec:
                        _insert_reshard_gm(gm, node, arg, arg_spec, desired_spec)
        else:
            raise RuntimeError(f"op code {node} not supported")

    _clean_up_graph_metadata(gm)
    gm.graph.lint()
    gm.recompile()
    return gm


def _partition_val(val: Any, spec: DTensorSpec) -> Any:
    """
    util function to convert a full tensor val to its local component
    """
    if isinstance(val, torch.Tensor):
        local_shard = val
        if val.ndim == 0:
            # If it's already a scalar tensor, it is already local, we don't
            # need to do anything
            return local_shard

        for idx, placement in enumerate(spec.placements):
            if placement.is_shard():
                placement = cast(Shard, placement)
                num_chunks = spec.mesh.size(mesh_dim=idx)
                my_coord = spec.mesh.get_coordinate()
                assert my_coord is not None, "current rank not in mesh!"
                my_coord_on_mesh_dim = my_coord[idx]
                local_shard = placement._split_tensor(
                    local_shard, num_chunks, with_padding=False, contiguous=True
                )[0][my_coord_on_mesh_dim]
        return local_shard
    elif isinstance(val, (list, tuple)):
        return val.__class__(_partition_val(v, spec) for v in val)
    else:
        raise RuntimeError(f"val type {type(val)} not supported")


def _insert_reshard_gm(
    gm: torch.fx.GraphModule,
    node: Node,
    input_arg: Node,
    input_arg_spec: DTensorSpec,
    desired_spec: DTensorSpec,
) -> None:
    """
    Transform the graph for tensor redistribution.
    """
    input_arg_spec.tensor_meta = input_arg.meta["tensor_meta"]
    desired_spec.tensor_meta = input_arg.meta["tensor_meta"]
    input_arg_tensor = input_arg.meta["val"]

    # insert reshard operation
    def reshard_fn(local_tensor: torch.Tensor) -> torch.Tensor:
        return redistribute_local_tensor(
            local_tensor,
            input_arg_spec,
            desired_spec,
        )

    reshard_gm = make_fx(reshard_fn)(input_arg_tensor)
    reshard_gm_nodes = list(reshard_gm.graph.nodes)
    input_node = reshard_gm_nodes[0]
    with gm.graph.inserting_before(node):
        # copy nn_module_stack metadata for output, all-reduce nodes
        for reshard_node in reshard_gm.graph.nodes:
            if reshard_node.op not in ["placeholder", "output"]:
                reshard_node.meta["nn_module_stack"] = (
                    copy.copy(input_arg.meta["nn_module_stack"])
                    if input_arg.op != "placeholder"
                    else copy.copy(node.meta["nn_module_stack"])
                )
        output_node = gm.graph.graph_copy(
            reshard_gm.graph,
            val_map={
                input_node: input_arg,
            },
        )
    node.replace_input_with(input_arg, output_node)  # type: ignore[arg-type]


def _clean_up_graph_metadata(gm: torch.fx.GraphModule) -> None:
    """
    Clean up the graph by removing sharding and partitioning related metadata
    """
    for node in gm.graph.nodes:
        if "sharding" in node.meta:
            del node.meta["sharding"]
        if "val" in node.meta and isinstance(node.meta["val"], torch.Tensor):
            local_tensor_meta = _extract_tensor_metadata(node.meta["val"])
            node.meta["tensor_meta"] = local_tensor_meta


def _get_input_node_specs(
    node: Node, placement_strategies: dict[Node, OpSpec]
) -> tuple[DTensorSpec, ...]:
    """
    Get the input specs of a node.
    """
    input_specs_list: list[DTensorSpec] = []
    for input_arg in node.all_input_nodes:
        if input_arg in placement_strategies:
            output_spec = placement_strategies[input_arg].output_specs
            assert isinstance(output_spec, DTensorSpec)
            input_specs_list.append(output_spec)
        else:
            raise ValueError(f"{input_arg} does not have output_spec populated.")
    return tuple(input_specs_list)


def _get_op_schema(node: Node, placement_strategies: dict[Node, OpSpec]) -> OpSchema:
    """
    Util function to construct the operator schema of a node.
    """
    args_schema_list = pytree.tree_map_only(
        Node, lambda arg: placement_strategies[arg].output_specs, node.args
    )
    op_schema = OpSchema(
        op=cast(torch._ops.OpOverload, node.target),
        args_schema=tuple(args_schema_list),
        kwargs_schema=cast(dict[str, object], node.kwargs),
    )
    return op_schema


def _shard_state_dict(
    state_dict: dict[str, torch.Tensor],
    placement_strategies: dict[Node, OpSpec],
    graph_signature: ExportGraphSignature,
    mesh: DeviceMesh,
) -> None:
    """
    Inplace partition the weights based on the OpSpec
    """
    for node, op_spec in placement_strategies.items():
        if node.op != "placeholder":
            continue
        if node.name in graph_signature.inputs_to_parameters:
            fqn = graph_signature.inputs_to_parameters[node.name]
        elif node.name in graph_signature.inputs_to_buffers:
            fqn = graph_signature.inputs_to_buffers[node.name]
        else:
            continue
        assert fqn in state_dict, f"{fqn} not found in state dict: {state_dict.keys()}"

        original_param = state_dict[fqn]
        dtensor_param = distribute_tensor(
            original_param,
            mesh,
            op_spec.output_spec.placements,
        )
        local_param = dtensor_param.to_local()
        state_dict[fqn] = (
            torch.nn.Parameter(local_param)
            if isinstance(original_param, torch.nn.Parameter)
            else local_param
        )
