# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from dataclasses import dataclass, field

import torch


@dataclass
class MoEPermuteScratch:
    # Reused metadata buffers for repeated grouped-MoE permutes.
    max_num_tokens: int
    topk: int
    num_experts: int
    num_local_experts: int
    device: torch.device
    hidden_size: int | None = None
    hidden_dtype: torch.dtype | None = None
    token_expert_indices: torch.Tensor = field(init=False)
    expert_first_token_offset: torch.Tensor = field(init=False)
    permuted_idx: torch.Tensor = field(init=False)
    inv_permuted_idx: torch.Tensor = field(init=False)
    permuted_hidden_states: torch.Tensor | None = field(init=False, default=None)
    sort_workspace: torch.Tensor = field(init=False)
    permuted_experts_id: torch.Tensor = field(init=False)
    sorted_row_idx: torch.Tensor = field(init=False)
    topk_ids_int32: torch.Tensor = field(init=False)
    topk_ids_for_sort: torch.Tensor = field(init=False)
    max_expanded_rows: int = field(init=False)

    def __post_init__(self) -> None:
        assert self.max_num_tokens > 0
        assert self.topk > 0
        assert self.num_experts > 0
        assert self.num_local_experts > 0
        if self.hidden_size is None:
            assert self.hidden_dtype is None
        else:
            assert self.hidden_dtype is not None

        self.max_expanded_rows = self.max_num_tokens * self.topk
        self.token_expert_indices = torch.arange(
            self.max_expanded_rows, dtype=torch.int32, device=self.device
        )
        self.expert_first_token_offset = torch.empty(
            self.num_local_experts + 1, dtype=torch.int64, device=self.device
        )
        self.permuted_idx = torch.empty(
            self.max_expanded_rows, dtype=torch.int32, device=self.device
        )
        self.inv_permuted_idx = torch.empty(
            self.max_expanded_rows, dtype=torch.int32, device=self.device
        )
        if self.hidden_size is not None:
            hidden_numel = self.max_expanded_rows * self.hidden_size
            self.permuted_hidden_states = torch.empty(
                hidden_numel, dtype=self.hidden_dtype, device=self.device
            )
        self.permuted_experts_id = torch.empty(
            self.max_expanded_rows, dtype=torch.int32, device=self.device
        )
        self.sorted_row_idx = torch.empty(
            self.max_expanded_rows, dtype=torch.int32, device=self.device
        )
        self.topk_ids_int32 = torch.empty(
            self.max_expanded_rows, dtype=torch.int32, device=self.device
        )
        self.topk_ids_for_sort = torch.empty(
            self.max_expanded_rows, dtype=torch.int32, device=self.device
        )
        sorter_size = torch.ops._moe_C.moe_permute_sort_workspace_size(
            self.max_expanded_rows, self.num_experts
        )
        self.sort_workspace = torch.empty(
            sorter_size, dtype=torch.int8, device=self.device
        )

    def validate(self, hidden_states: torch.Tensor, topk_ids: torch.Tensor) -> None:
        n_token, n_hidden = hidden_states.shape
        assert hidden_states.device == self.device
        assert topk_ids.device == self.device
        assert n_token <= self.max_num_tokens
        assert topk_ids.size(1) == self.topk
        assert topk_ids.size(0) == n_token
        if self.hidden_size is not None:
            assert n_hidden == self.hidden_size
            assert hidden_states.dtype == self.hidden_dtype
            assert self.permuted_hidden_states is not None

    def token_expert_indices_view(self, n_token: int) -> torch.Tensor:
        return self.token_expert_indices[: n_token * self.topk].view(n_token, self.topk)

    def prepare_topk_ids(self, topk_ids: torch.Tensor) -> torch.Tensor:
        if topk_ids.dtype == torch.int32:
            return topk_ids
        numel = topk_ids.numel()
        topk_ids_int32 = self.topk_ids_int32[:numel].view_as(topk_ids)
        topk_ids_int32.copy_(topk_ids)
        return topk_ids_int32


def moe_permute(
    hidden_states: torch.Tensor,
    a1q_scale: torch.Tensor | None,
    topk_ids: torch.Tensor,
    n_expert: int,
    n_local_expert: int = -1,
    expert_map: torch.Tensor | None = None,
    permuted_hidden_states: torch.Tensor | None = None,
    scratch: MoEPermuteScratch | None = None,
) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    This function expands and permutes activation to gather uncontinuous tokens
      for each expert.
    Parameters:
    - hidden_states (torch.Tensor): The input tensor to the MoE layer.
    - a1q_scale (Optional[torch.Tensor]): quant scale for hidden_states
    - topk_ids (torch.Tensor): topk expert route id for each token.
    - n_expert (int): The number of expert.
    - n_local_expert (int): The number of expert in current EP rank.
    - expert_map (Optional[torch.Tensor]):  A tensor mapping expert indices
        from the global expert space to the local expert space of the expert
        parallel shard.
    - permuted_hidden_states (Optional[torch.Tensor]): Optional output tensor.
        If None, the output tensor will be created in this function.
    Returns:
    - permuted_hidden_states (torch.Tensor): permuted activation.
    - a1q_scale (Optional[torch.Tensor]): permuted quant scale for hidden_states
        if original scale not per-tensor scaling
    - expert_first_token_offset (torch.Tensor): offset of the first token
       of each expert for standard grouped gemm.
    - inv_permuted_idx (torch.Tensor): idx map for moe_unpermute.
    - permuted_idx (torch.Tensor): idx map from hidden to permuted_hidden.
    """
    n_token, n_hidden = hidden_states.size()
    topk = topk_ids.size(1)
    assert (n_hidden * hidden_states.element_size()) % 16 == 0, (
        "permue kernel need hidden dim align to 16B"
    )
    permuted_row_size = n_token * topk
    if n_local_expert == -1:
        n_local_expert = n_expert
    if permuted_hidden_states is None:
        if scratch is None:
            permuted_hidden_states = torch.empty(
                (permuted_row_size, n_hidden),
                dtype=hidden_states.dtype,
                device=hidden_states.device,
            )
        else:
            scratch.validate(hidden_states, topk_ids)
            hidden_numel = permuted_row_size * n_hidden
            scratch_hidden_states = scratch.permuted_hidden_states
            assert scratch_hidden_states is not None
            permuted_hidden_states = scratch_hidden_states[:hidden_numel].view(
                permuted_row_size, n_hidden
            )
    assert permuted_hidden_states.size() == (permuted_row_size, n_hidden), (
        f"Expected permuted hidden states to be {(permuted_row_size, n_hidden)}"
        f" but got {permuted_hidden_states.size()}"
    )

    if scratch is None:
        token_expert_indices = torch.arange(
            0, n_token * topk, dtype=torch.int32, device=hidden_states.device
        ).reshape((n_token, topk))

        expert_first_token_offset = torch.empty(
            n_local_expert + 1, dtype=torch.int64, device=hidden_states.device
        )
        permuted_idx = torch.full(
            (permuted_row_size,),
            n_token * topk,
            dtype=torch.int32,
            device=hidden_states.device,
        )
        inv_permuted_idx = torch.empty(
            (n_token, topk), dtype=torch.int32, device=hidden_states.device
        )
        topk_ids_int32 = topk_ids.to(torch.int32)
        torch.ops._moe_C.moe_permute(
            hidden_states,
            topk_ids_int32,
            token_expert_indices,
            expert_map,
            n_expert,
            n_local_expert,
            topk,
            permuted_hidden_states,
            expert_first_token_offset,
            inv_permuted_idx,
            permuted_idx,
        )
    else:
        scratch.validate(hidden_states, topk_ids)
        assert n_expert == scratch.num_experts
        assert n_local_expert == scratch.num_local_experts
        token_expert_indices = scratch.token_expert_indices_view(n_token)
        expert_first_token_offset = scratch.expert_first_token_offset
        permuted_idx = scratch.permuted_idx[:permuted_row_size]
        permuted_idx.fill_(permuted_row_size)
        inv_permuted_idx = scratch.inv_permuted_idx[:permuted_row_size].view(
            n_token, topk
        )
        permuted_experts_id = scratch.permuted_experts_id[:permuted_row_size].view(
            n_token, topk
        )
        sorted_row_idx = scratch.sorted_row_idx[:permuted_row_size].view(n_token, topk)
        topk_ids_for_sort = scratch.topk_ids_for_sort[:permuted_row_size].view(
            n_token, topk
        )
        topk_ids_int32 = scratch.prepare_topk_ids(topk_ids)
        torch.ops._moe_C.moe_permute_with_scratch(
            hidden_states,
            topk_ids_int32,
            token_expert_indices,
            expert_map,
            n_expert,
            n_local_expert,
            topk,
            permuted_hidden_states,
            expert_first_token_offset,
            inv_permuted_idx,
            permuted_idx,
            scratch.sort_workspace,
            permuted_experts_id,
            sorted_row_idx,
            topk_ids_for_sort,
        )

    if a1q_scale is not None and a1q_scale.dim() > 1:
        a1q_scale = a1q_scale[permuted_idx.clamp(max=n_token * topk - 1) // topk]
    return (
        permuted_hidden_states,
        a1q_scale,
        expert_first_token_offset,
        inv_permuted_idx.flatten(),
        permuted_idx,
    )


def moe_unpermute(
    out: torch.Tensor,
    permuted_hidden_states: torch.Tensor,
    topk_weights: torch.Tensor,
    inv_permuted_idx: torch.Tensor,
    expert_first_token_offset: torch.Tensor | None = None,
) -> None:
    """
    This function expands and permutes activation to gathering uncontinuous
      tokens for each expert.
    Parameters:
    - out (torch.Tensor): output tensor
    - permuted_hidden_states (torch.Tensor): permuted activation.
    - topk_weights (torch.Tensor): topk expert route weight for each token.
    - inv_permuted_idx (torch.Tensor): row idx map for moe_unpermute.
    - expert_first_token_offset (Optional[torch.Tensor]): offset of the first
      token of each expert for grouped gemm.
    Returns:
    - hidden_states (torch.Tensor): The reduced and unpermuted activation
      tensor.
    """
    topk = topk_weights.size(1)
    n_hidden = permuted_hidden_states.size(-1)
    assert (n_hidden * permuted_hidden_states.element_size()) % 16 == 0, (
        "unpermue kernel need hidden dim align to 16B"
    )

    torch.ops._moe_C.moe_unpermute(
        permuted_hidden_states,
        topk_weights,
        inv_permuted_idx,
        expert_first_token_offset,
        topk,
        out,
    )


def moe_permute_unpermute_supported():
    return torch.ops._moe_C.moe_permute_unpermute_supported()
