[Refactor] Remove unused _moe_permute function (#33108)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/layers/fused_moe/moe_permute_unpermute.py

@@ -3,70 +3,6 @@
 
 import torch
 
-from vllm import _custom_ops as ops
-from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
-    moe_align_block_size,
-)
-from vllm.model_executor.layers.fused_moe.utils import _fp8_perm
-
-
-def _moe_permute(
-    curr_hidden_states: torch.Tensor,
-    a1q_scale: torch.Tensor | None,
-    curr_topk_ids: torch.Tensor,
-    global_num_experts: int,
-    expert_map: torch.Tensor | None,
-    block_m: int,
-) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor, torch.Tensor, torch.Tensor]:
-    """
-    Determine the sorted_token_ids, expert_ids for the given problem size.
-    Permute the hidden states and scales according to `sorted_token_ids`.
-    """
-    top_k_num = curr_topk_ids.size(1)
-
-    tokens_in_chunk = curr_hidden_states.size(0)
-
-    sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
-        curr_topk_ids, block_m, global_num_experts, expert_map, pad_sorted_ids=True
-    )
-
-    inv_perm: torch.Tensor | None = None
-
-    num_tokens = top_k_num * tokens_in_chunk
-    expert_ids = torch.repeat_interleave(expert_ids, block_m, dim=0)
-    inv_perm = torch.argsort(sorted_token_ids)[:num_tokens]
-
-    # Permute according to sorted token ids.
-    sorted_token_ids = sorted_token_ids.clamp(max=num_tokens - 1)
-
-    curr_hidden_states = _fp8_perm(curr_hidden_states, sorted_token_ids // top_k_num)
-
-    if a1q_scale is not None:
-        a1q_scale = a1q_scale[sorted_token_ids // top_k_num]
-
-    return (curr_hidden_states, a1q_scale, sorted_token_ids, expert_ids, inv_perm)
-
-
-def _moe_unpermute_and_reduce(
-    out: torch.Tensor,
-    curr_hidden: torch.Tensor,
-    inv_perm: torch.Tensor | None,
-    topk_weight: torch.Tensor,
-    apply_router_weight_on_input: bool,
-) -> None:
-    """
-    Unpermute the final result and apply topk_weights, then perform the final
-    reduction on the hidden states.
-    """
-    M, topk = topk_weight.size()
-    K = curr_hidden.size(-1)
-    if inv_perm is not None:
-        curr_hidden = curr_hidden[inv_perm, ...]
-    curr_hidden = curr_hidden.view(-1, topk, K)
-    if not apply_router_weight_on_input:
-        curr_hidden.mul_(topk_weight.view(M, -1, 1))
-    ops.moe_sum(curr_hidden, out)
-
 
 def moe_permute(
     hidden_states: torch.Tensor,