reference/code/DMax/dInfer/tools/fuse_moe.py

# SPDX-License-Identifier: Apache-2.0
"""Fused MoE kernel."""
import functools
import json
import os
from typing import Any, Callable, Dict, List, Optional, Tuple

import torch
import triton
import triton.language as tl

import vllm.envs as envs
from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
    per_token_group_quant_fp8)
from vllm.platforms import current_platform
from vllm.utils import direct_register_custom_op

logger = init_logger(__name__)


@triton.jit
def fused_moe_kernel_gptq_awq(
        # Pointers to matrices
        a_ptr,
        b_ptr,
        c_ptr,
        b_scale_ptr,
        b_zp_ptr,
        topk_weights_ptr,
        sorted_token_ids_ptr,
        expert_ids_ptr,
        num_tokens_post_padded_ptr,
        # Matrix dimensions
        N: tl.constexpr,
        K: tl.constexpr,
        EM,
        num_valid_tokens,
        # The stride variables represent how much to increase the ptr by when
        # moving by 1 element in a particular dimension. E.g. `stride_am` is
        # how much to increase `a_ptr` by to get the element one row down
        # (A has M rows).
        stride_am,
        stride_ak,
        stride_be,
        stride_bk,
        stride_bn,
        stride_cm,
        stride_cn,
        stride_bse,
        stride_bsk,
        stride_bsn,
        stride_bze,
        stride_bzk,
        stride_bzn,
        block_k_diviable: tl.constexpr,
        group_size: tl.constexpr,
        # Meta-parameters
        BLOCK_SIZE_M: tl.constexpr,
        BLOCK_SIZE_N: tl.constexpr,
        BLOCK_SIZE_K: tl.constexpr,
        GROUP_SIZE_M: tl.constexpr,
        MUL_ROUTED_WEIGHT: tl.constexpr,
        top_k: tl.constexpr,
        compute_type: tl.constexpr,
        has_zp: tl.constexpr,
        use_int4_w4a16: tl.constexpr,
        use_int8_w8a16: tl.constexpr):
    """
    Implements the fused computation for a Mixture of Experts (MOE) using
    token and expert matrices.

    Key Parameters:
    - A: The input tensor representing tokens with shape (*, K), where '*' can
        be any shape representing batches and K is the feature dimension of
        each token.
    - B: The stacked MOE weight tensor with shape (E, N, K), where E is
        the number of experts, K is the input feature dimension, and N is
        the output feature dimension.
    - C: The output cache tensor with shape (M, topk, N), where M is the
        total number of tokens post padding, topk is the number of times
        each token is repeated, and N is the output feature dimension.
    - sorted_token_ids: A tensor containing the sorted indices of tokens,
        repeated topk times and arranged by the expert index they are
        assigned to.
    - expert_ids: A tensor containing the indices of the expert for each
        block. It determines which expert matrix from B should be used for
        each block in A.
    This kernel performs the multiplication of a token by its corresponding
    expert matrix as determined by `expert_ids`. The sorting of
    `sorted_token_ids` by expert index and padding ensures divisibility by
    BLOCK_SIZE_M, which is necessary to maintain consistency in block matrix
    multiplication across different blocks processed by the same expert.
    """
    # -----------------------------------------------------------
    # Map program ids `pid` to the block of C it should compute.
    # This is done in a grouped ordering to promote L2 data reuse.
    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(EM, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
    num_pid_in_group = GROUP_SIZE_M * num_pid_n
    group_id = pid // num_pid_in_group
    first_pid_m = group_id * GROUP_SIZE_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
    pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
    pid_n = (pid % num_pid_in_group) // group_size_m

    # ----------------------------------------------------------
    # Create pointers for the first blocks of A and B.
    # We will advance this pointer as we move in the K direction
    # and accumulate
    # `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
    # `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
    num_tokens_post_padded = tl.load(num_tokens_post_padded_ptr)
    if pid_m * BLOCK_SIZE_M >= num_tokens_post_padded:
        return
    offs_token_id = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M).to(
        tl.int64)
    offs_token = tl.load(sorted_token_ids_ptr + offs_token_id)
    token_mask = offs_token < num_valid_tokens

    offs_bn = (pid_n * BLOCK_SIZE_N +
               tl.arange(0, BLOCK_SIZE_N).to(tl.int64)) % N
    offs_k = tl.arange(0, BLOCK_SIZE_K)
    a_ptrs = a_ptr + (offs_token[:, None] // top_k * stride_am +
                      offs_k[None, :] * stride_ak)

    off_experts = tl.load(expert_ids_ptr + pid_m).to(tl.int64)

    if use_int4_w4a16:
        b_ptrs = b_ptr + off_experts * stride_be + \
            (offs_k[:, None] // 2) * stride_bk + offs_bn[None, :] * stride_bn
        b_shifter = (offs_k[:, None] % 2) * 4
    elif use_int8_w8a16:
        b_ptrs = b_ptr + off_experts * stride_be + \
            offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn

    if not has_zp and use_int4_w4a16:
        b_zp_num = 8
    if not has_zp and use_int8_w8a16:
        b_zp_num = 128
    elif has_zp and use_int4_w4a16:
        b_zp_shifter = (offs_bn[None, :] % 2) * 4

    # -----------------------------------------------------------
    # Iterate to compute a block of the C matrix.
    # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
    # of fp32 values for higher accuracy.
    # `accumulator` will be converted back to fp16 after the loop.
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
        # Load the next block of A and B, generate a mask by checking the
        # K dimension.

        if not block_k_diviable:
            k_mask = offs_k[:, None] < K - k * BLOCK_SIZE_K
            k_other = 0.0
        else:
            k_mask = None
            k_other = None

        a = tl.load(a_ptrs,
                    mask=token_mask[:, None] &
                    (offs_k[None, :] < K - k * BLOCK_SIZE_K),
                    other=0.0)
        b = tl.load(b_ptrs)
        if use_int4_w4a16:
            b = (b >> b_shifter) & 0xF

        b_scale_ptrs = b_scale_ptr + off_experts * stride_bse + \
            offs_bn[None, :] * stride_bsn + \
            ((offs_k[:, None] + BLOCK_SIZE_K * k) // group_size) * stride_bsk
        b_scale = tl.load(b_scale_ptrs, mask=k_mask, other=k_other)
        b_scale = b_scale.to(tl.float32)

        if has_zp and use_int4_w4a16:
            offs_k_true = (offs_k[:, None] + BLOCK_SIZE_K * k) // group_size
            b_zp_ptrs = b_zp_ptr + off_experts * stride_bze + \
                (offs_bn[None, :] // 2) * stride_bzn + \
                offs_k_true * stride_bzk
            b_zp = tl.load(b_zp_ptrs, mask=k_mask, other=k_other)
            b_zp = ((b_zp >> b_zp_shifter) & 0xF)
            b_zp = b_zp.to(tl.float32)
        elif has_zp and use_int8_w8a16:
            offs_k_true = (offs_k[:, None] + BLOCK_SIZE_K * k) // group_size
            b_zp_ptrs = b_zp_ptr + off_experts * stride_bze + \
                offs_bn[None, :] * stride_bzn + \
                offs_k_true * stride_bzk
            b_zp = tl.load(b_zp_ptrs, mask=k_mask, other=k_other)
            b_zp = b_zp.to(tl.float32)

        # We accumulate along the K dimension.
        if has_zp:
            b = ((b.to(tl.float32) - b_zp) * b_scale).to(compute_type)
        else:
            b = ((b.to(tl.float32) - b_zp_num) * b_scale).to(compute_type)
        accumulator = tl.dot(a, b, acc=accumulator)

        # Advance the ptrs to the next K block.
        a_ptrs += BLOCK_SIZE_K * stride_ak
        if use_int4_w4a16:
            b_ptrs += (BLOCK_SIZE_K // 2) * stride_bk
        else:
            b_ptrs += BLOCK_SIZE_K * stride_bk

    if MUL_ROUTED_WEIGHT:
        moe_weight = tl.load(topk_weights_ptr + offs_token,
                             mask=token_mask,
                             other=0)
        accumulator = accumulator * moe_weight[:, None]

    accumulator = accumulator.to(compute_type)
    # -----------------------------------------------------------
    # Write back the block of the output
    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
    c_ptrs = c_ptr + stride_cm * offs_token[:, None] + stride_cn * offs_cn[
        None, :]
    c_mask = token_mask[:, None] & (offs_cn[None, :] < N)
    tl.store(c_ptrs, accumulator, mask=c_mask)


@triton.jit
def fused_moe_kernel(
        # Pointers to matrices
        a_ptr,
        b_ptr,
        c_ptr,
        a_scale_ptr,
        b_scale_ptr,
        topk_weights_ptr,
        sorted_token_ids_ptr,
        expert_ids_ptr,
        num_tokens_post_padded_ptr,
        # Matrix dimensions
        N,
        K,
        EM,
        num_valid_tokens,
        # The stride variables represent how much to increase the ptr by when
        # moving by 1 element in a particular dimension. E.g. `stride_am` is
        # how much to increase `a_ptr` by to get the element one row down
        # (A has M rows).
        stride_am,
        stride_ak,
        stride_be,
        stride_bk,
        stride_bn,
        stride_cm,
        stride_cn,
        stride_asm,
        stride_ask,
        stride_bse,
        stride_bsk,
        stride_bsn,
        # Block size for block-wise quantization
        group_n: tl.constexpr,
        group_k: tl.constexpr,
        # Meta-parameters
        BLOCK_SIZE_M: tl.constexpr,
        BLOCK_SIZE_N: tl.constexpr,
        BLOCK_SIZE_K: tl.constexpr,
        GROUP_SIZE_M: tl.constexpr,
        MUL_ROUTED_WEIGHT: tl.constexpr,
        top_k: tl.constexpr,
        compute_type: tl.constexpr,
        use_fp8_w8a8: tl.constexpr,
        use_int8_w8a16: tl.constexpr):
    """
    Implements the fused computation for a Mixture of Experts (MOE) using
    token and expert matrices.

    Key Parameters:
    - A: The input tensor representing tokens with shape (*, K), where '*' can
        be any shape representing batches and K is the feature dimension of
        each token.
    - B: The stacked MOE weight tensor with shape (E, N, K), where E is
        the number of experts, K is the input feature dimension, and N is
        the output feature dimension.
    - C: The output cache tensor with shape (M, topk, N), where M is the
        total number of tokens post padding, topk is the number of times
        each token is repeated, and N is the output feature dimension.
    - sorted_token_ids: A tensor containing the sorted indices of tokens,
        repeated topk times and arranged by the expert index they are
        assigned to.
    - expert_ids: A tensor containing the indices of the expert for each
        block. It determines which expert matrix from B should be used for
        each block in A.
    This kernel performs the multiplication of a token by its corresponding
    expert matrix as determined by `expert_ids`. The sorting of
    `sorted_token_ids` by expert index and padding ensures divisibility by
    BLOCK_SIZE_M, which is necessary to maintain consistency in block matrix
    multiplication across different blocks processed by the same expert.
    """
    # -----------------------------------------------------------
    # Map program ids `pid` to the block of C it should compute.
    # This is done in a grouped ordering to promote L2 data reuse.
    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(EM, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
    num_pid_in_group = GROUP_SIZE_M * num_pid_n
    group_id = pid // num_pid_in_group
    first_pid_m = group_id * GROUP_SIZE_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
    pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
    pid_n = (pid % num_pid_in_group) // group_size_m

    # ----------------------------------------------------------
    # Create pointers for the first blocks of A and B.
    # We will advance this pointer as we move in the K direction
    # and accumulate
    # `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
    # `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
    num_tokens_post_padded = tl.load(num_tokens_post_padded_ptr)
    if pid_m * BLOCK_SIZE_M >= num_tokens_post_padded:
        return
    offs_token_id = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M).to(
        tl.int64)
    offs_token = tl.load(sorted_token_ids_ptr + offs_token_id)
    token_mask = offs_token < num_valid_tokens

    offs_bn = (pid_n * BLOCK_SIZE_N +
               tl.arange(0, BLOCK_SIZE_N).to(tl.int64)) % N
    offs_k = tl.arange(0, BLOCK_SIZE_K)
    a_ptrs = a_ptr + (offs_token[:, None] // top_k * stride_am +
                      offs_k[None, :] * stride_ak)

    off_experts = tl.load(expert_ids_ptr + pid_m).to(tl.int64)
    b_ptrs = b_ptr + off_experts * stride_be + (offs_k[:, None] * stride_bk +
                                                offs_bn[None, :] * stride_bn)
    if use_int8_w8a16:
        b_scale_ptrs = b_scale_ptr + off_experts * stride_bse + offs_bn[
            None, :] * stride_bsn
        b_scale = tl.load(b_scale_ptrs)

    if use_fp8_w8a8:
        if group_k > 0 and group_n > 0:
            a_scale_ptrs = a_scale_ptr + (offs_token // top_k) * stride_asm
            offs_bsn = offs_bn // group_n
            b_scale_ptrs = (b_scale_ptr + off_experts * stride_bse +
                            offs_bsn * stride_bsn)
        else:
            a_scale = tl.load(a_scale_ptr)
            b_scale = tl.load(b_scale_ptr + off_experts)

    # -----------------------------------------------------------
    # Iterate to compute a block of the C matrix.
    # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
    # of fp32 values for higher accuracy.
    # `accumulator` will be converted back to fp16 after the loop.
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)

    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
        # Load the next block of A and B, generate a mask by checking the
        # K dimension.
        a = tl.load(a_ptrs,
                    mask=token_mask[:, None] &
                    (offs_k[None, :] < K - k * BLOCK_SIZE_K),
                    other=0.0)
        b = tl.load(b_ptrs,
                    mask=offs_k[:, None] < K - k * BLOCK_SIZE_K,
                    other=0.0)
        # We accumulate along the K dimension.
        if use_int8_w8a16:
            accumulator = tl.dot(a, b.to(compute_type), acc=accumulator)
        elif use_fp8_w8a8:
            if group_k > 0 and group_n > 0:
                k_start = k * BLOCK_SIZE_K
                offs_ks = k_start // group_k
                a_scale = tl.load(a_scale_ptrs + offs_ks * stride_ask,
                                  mask=token_mask,
                                  other=0.0)
                b_scale = tl.load(b_scale_ptrs + offs_ks * stride_bsk)

                accumulator += tl.dot(a, b) * a_scale[:,
                                                      None] * b_scale[None, :]
            else:
                accumulator = tl.dot(a, b, acc=accumulator)
        else:
            accumulator += tl.dot(a, b)
        # Advance the ptrs to the next K block.
        a_ptrs += BLOCK_SIZE_K * stride_ak
        b_ptrs += BLOCK_SIZE_K * stride_bk

    if MUL_ROUTED_WEIGHT:
        moe_weight = tl.load(topk_weights_ptr + offs_token,
                             mask=token_mask,
                             other=0)
        accumulator = accumulator * moe_weight[:, None]
    if use_int8_w8a16:
        accumulator = (accumulator * b_scale).to(compute_type)
    elif use_fp8_w8a8:
        if group_k > 0 and group_n > 0:
            accumulator = accumulator.to(compute_type)
        else:
            accumulator = (accumulator * a_scale * b_scale).to(compute_type)
    else:
        accumulator = accumulator.to(compute_type)
    # -----------------------------------------------------------
    # Write back the block of the output
    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
    c_ptrs = c_ptr + stride_cm * offs_token[:, None] + stride_cn * offs_cn[
        None, :]
    c_mask = token_mask[:, None] & (offs_cn[None, :] < N)
    tl.store(c_ptrs, accumulator, mask=c_mask)


def ceil_div(a, b):
    return (a + b - 1) // b


@triton.jit
def moe_align_block_size_stage1(
    topk_ids_ptr,
    tokens_cnts_ptr,
    num_experts: tl.constexpr,
    numel: tl.constexpr,
    tokens_per_thread: tl.constexpr,
):
    pid = tl.program_id(0)

    start_idx = pid * tokens_per_thread

    off_c = (pid + 1) * num_experts

    for i in range(tokens_per_thread):
        if start_idx + i < numel:
            idx = tl.load(topk_ids_ptr + start_idx + i)
            token_cnt = tl.load(tokens_cnts_ptr + off_c + idx)
            tl.store(tokens_cnts_ptr + off_c + idx, token_cnt + 1)


@triton.jit
def moe_align_block_size_stage2(
    tokens_cnts_ptr,
    num_experts: tl.constexpr,
):
    pid = tl.program_id(0)

    last_cnt = 0
    for i in range(1, num_experts + 1):
        token_cnt = tl.load(tokens_cnts_ptr + i * num_experts + pid)
        last_cnt = last_cnt + token_cnt
        tl.store(tokens_cnts_ptr + i * num_experts + pid, last_cnt)


@triton.jit
def moe_align_block_size_stage3(
    total_tokens_post_pad_ptr,
    tokens_cnts_ptr,
    cumsum_ptr,
    num_experts: tl.constexpr,
    block_size: tl.constexpr,
):
    last_cumsum = 0
    off_cnt = num_experts * num_experts
    for i in range(1, num_experts + 1):
        token_cnt = tl.load(tokens_cnts_ptr + off_cnt + i - 1)
        last_cumsum = last_cumsum + tl.cdiv(token_cnt, block_size) * block_size
        tl.store(cumsum_ptr + i, last_cumsum)
    tl.store(total_tokens_post_pad_ptr, last_cumsum)


@triton.jit
def moe_align_block_size_stage4(
    topk_ids_ptr,
    sorted_token_ids_ptr,
    expert_ids_ptr,
    tokens_cnts_ptr,
    cumsum_ptr,
    num_experts: tl.constexpr,
    block_size: tl.constexpr,
    numel: tl.constexpr,
    tokens_per_thread: tl.constexpr,
):
    pid = tl.program_id(0)
    start_idx = tl.load(cumsum_ptr + pid)
    end_idx = tl.load(cumsum_ptr + pid + 1)

    for i in range(start_idx, end_idx, block_size):
        tl.store(expert_ids_ptr + i // block_size, pid)

    start_idx = pid * tokens_per_thread
    off_t = pid * num_experts

    for i in range(start_idx, tl.minimum(start_idx + tokens_per_thread,
                                         numel)):
        expert_id = tl.load(topk_ids_ptr + i)
        token_cnt = tl.load(tokens_cnts_ptr + off_t + expert_id)
        rank_post_pad = token_cnt + tl.load(cumsum_ptr + expert_id)
        tl.store(sorted_token_ids_ptr + rank_post_pad, i)
        tl.store(tokens_cnts_ptr + off_t + expert_id, token_cnt + 1)


# Triton implementation based on:
# https://github.com/sgl-project/sglang/commit/ba5112ff691d791a9e38c6c71f59324a5fcb49d0
def moe_align_block_size_triton(
    topk_ids: torch.Tensor,
    num_experts: int,
    block_size: int,
    sorted_token_ids: torch.Tensor,
    expert_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
) -> None:
    numel = topk_ids.numel()
    grid = (num_experts, )
    tokens_cnts = torch.zeros((num_experts + 1, num_experts),
                              dtype=torch.int32,
                              device=topk_ids.device)
    cumsum = torch.zeros((num_experts + 1, ),
                         dtype=torch.int32,
                         device=topk_ids.device)
    tokens_per_thread = ceil_div(numel, num_experts)

    moe_align_block_size_stage1[grid](
        topk_ids,
        tokens_cnts,
        num_experts,
        numel,
        tokens_per_thread,
    )
    moe_align_block_size_stage2[grid](
        tokens_cnts,
        num_experts,
    )
    moe_align_block_size_stage3[(1, )](
        num_tokens_post_pad,
        tokens_cnts,
        cumsum,
        num_experts,
        block_size,
    )
    moe_align_block_size_stage4[grid](
        topk_ids,
        sorted_token_ids,
        expert_ids,
        tokens_cnts,
        cumsum,
        num_experts,
        block_size,
        numel,
        tokens_per_thread,
    )


def moe_align_block_size(
        topk_ids: torch.Tensor, block_size: int,
        num_experts: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    Aligns the token distribution across experts to be compatible with block
    size for matrix multiplication.

    Parameters:
    - topk_ids: A tensor of shape [total_tokens, top_k] representing the
        top-k expert indices for each token.
    - block_size: The block size used in block matrix multiplication.
    - num_experts: The total number of experts.

    Returns:
    - sorted_token_ids: A tensor containing the sorted token indices according
        to their allocated expert.
    - expert_ids: A tensor indicating the assigned expert index for each block.
    - num_tokens_post_padded: The total number of tokens after padding,
        ensuring divisibility by block_size.

    This function pads the number of tokens that each expert needs to process
    so that it is divisible by block_size.
    Padding ensures that during block matrix multiplication, the dimensions
    align correctly.

    Example:
    Given topk_ids = [[2, 3, 4], [1, 2, 4], [1, 3, 4], [1, 2, 3]],
    block_size = 4, and num_experts = 4:
    - We initially have 12 tokens (after repeating 'top_k' times) and 4 experts,
        with each expert needing to process 3 tokens.
    - As block_size is 4, we pad 1 token for each expert.
    - First, flatten topk_ids to [2, 3, 4, 1, 2, 4, 1, 3, 4, 1, 2, 3].
    - Then append padding tokens [12, 12, 12, 12] for each block.
    - After sorting by expert index, we obtain token_ids
        [3, 6, 9, 12, 0, 4, 10, 12, 1, 7, 11, 12, 2, 5, 8, 12].
        Tokens 12 are non-existent (padding) and are ignored in
        the subsequent matrix multiplication.
    - The padding ensures that the total number of tokens is now divisible
        by block_size for proper block matrix operations.
    """
    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
    sorted_ids = torch.empty((max_num_tokens_padded, ),
                             dtype=torch.int32,
                             device=topk_ids.device)
    sorted_ids.fill_(topk_ids.numel())
    max_num_m_blocks = triton.cdiv(max_num_tokens_padded, block_size)
    expert_ids = torch.empty((max_num_m_blocks, ),
                             dtype=torch.int32,
                             device=topk_ids.device)
    num_tokens_post_pad = torch.empty((1),
                                      dtype=torch.int32,
                                      device=topk_ids.device)
    if num_experts >= 224:
        if envs.VLLM_ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON:
            moe_align_block_size_triton(
                topk_ids,
                num_experts,
                block_size,
                sorted_ids,
                expert_ids,
                num_tokens_post_pad,
            )
        else:
            ops.sgl_moe_align_block_size(
                topk_ids,
                num_experts,
                block_size,
                sorted_ids,
                expert_ids,
                num_tokens_post_pad,
            )
    else:
        ops.moe_align_block_size(topk_ids, num_experts, block_size, sorted_ids,
                                 expert_ids, num_tokens_post_pad)
    return sorted_ids, expert_ids, num_tokens_post_pad


def invoke_fused_moe_kernel(A: torch.Tensor,
                            B: torch.Tensor,
                            C: torch.Tensor,
                            A_scale: Optional[torch.Tensor],
                            B_scale: Optional[torch.Tensor],
                            B_zp: Optional[torch.Tensor],
                            topk_weights: torch.Tensor,
                            topk_ids: torch.Tensor,
                            sorted_token_ids: torch.Tensor,
                            expert_ids: torch.Tensor,
                            num_tokens_post_padded: torch.Tensor,
                            mul_routed_weight: bool,
                            top_k: int,
                            config: Dict[str, Any],
                            compute_type: tl.dtype,
                            use_fp8_w8a8: bool,
                            use_int8_w8a16: bool,
                            use_int4_w4a16: bool,
                            block_shape: Optional[List[int]] = None) -> None:
    assert topk_weights.stride(1) == 1
    assert sorted_token_ids.stride(0) == 1

    if use_fp8_w8a8:
        assert B_scale is not None
        if block_shape is None:
            A, A_scale = ops.scaled_fp8_quant(A, A_scale)
        else:
            assert len(block_shape) == 2
            block_n, block_k = block_shape[0], block_shape[1]
            A, A_scale = per_token_group_quant_fp8(A, block_k)
            assert triton.cdiv(A.shape[-1], block_k) == A_scale.shape[-1]
            assert triton.cdiv(B.shape[-2], block_n) == B_scale.shape[-2]
            assert triton.cdiv(B.shape[-1], block_k) == B_scale.shape[-1]
    elif use_int8_w8a16 or use_int4_w4a16:
        assert B_scale is not None
        assert block_shape is None or block_shape[0] == 0
    else:
        assert A_scale is None
        assert B_scale is None

    EM = sorted_token_ids.shape[0]
    if A.shape[0] < config["BLOCK_SIZE_M"]:
        # optimize for small batch_size.
        # We assume that top_ids of each token is unique, so
        # so num_valid_experts <= batch_size <= BLOCK_SIZE_M,
        # and we can skip some invalid blocks.
        EM = min(sorted_token_ids.shape[0],
                 A.shape[0] * top_k * config['BLOCK_SIZE_M'])
    grid = lambda META: (triton.cdiv(EM, META['BLOCK_SIZE_M']) * triton.cdiv(
        B.shape[1], META['BLOCK_SIZE_N']), )

    if (use_int8_w8a16 or use_int4_w4a16) and \
            block_shape is not None and block_shape[1] > 0:
        assert B_scale is not None and B_scale.ndim == 3
        assert B_zp is None or B_zp.ndim == 3

        fused_moe_kernel_gptq_awq[grid](
            A,
            B,
            C,
            B_scale,
            B_zp,
            topk_weights,
            sorted_token_ids,
            expert_ids,
            num_tokens_post_padded,
            B.shape[1],
            A.shape[1],
            EM,
            topk_ids.numel(),
            A.stride(0),
            A.stride(1),
            B.stride(0),
            B.stride(2),
            B.stride(1),
            C.stride(1),
            C.stride(2),
            B_scale.stride(0),
            B_scale.stride(2),
            B_scale.stride(1),
            B_zp.stride(0) if B_zp is not None else 0,
            B_zp.stride(2) if B_zp is not None else 0,
            B_zp.stride(1) if B_zp is not None else 0,
            block_k_diviable=A.shape[1] % config["BLOCK_SIZE_K"] == 0,
            group_size=block_shape[1],
            MUL_ROUTED_WEIGHT=mul_routed_weight,
            top_k=top_k,
            compute_type=compute_type,
            has_zp=B_zp is not None,
            use_int4_w4a16=use_int4_w4a16,
            use_int8_w8a16=use_int8_w8a16,
            **config,
        )

    else:
        fused_moe_kernel[grid](
            A,
            B,
            C,
            A_scale,
            B_scale,
            topk_weights,
            sorted_token_ids,
            expert_ids,
            num_tokens_post_padded,
            B.shape[1],
            A.shape[1],
            EM,
            topk_ids.numel(),
            A.stride(0),
            A.stride(1),
            B.stride(0),
            B.stride(2),
            B.stride(1),
            C.stride(1),
            C.stride(2),
            A_scale.stride(0)
            if A_scale is not None and A_scale.ndim == 2 else 0,
            A_scale.stride(1)
            if A_scale is not None and A_scale.ndim == 2 else 0,
            B_scale.stride(0)
            if B_scale is not None and B_scale.ndim >= 2 else 0,
            B_scale.stride(2)
            if B_scale is not None and B_scale.ndim == 3 else 0,
            B_scale.stride(1)
            if B_scale is not None and B_scale.ndim >= 2 else 0,
            0 if block_shape is None else block_shape[0],
            0 if block_shape is None else block_shape[1],
            MUL_ROUTED_WEIGHT=mul_routed_weight,
            top_k=top_k,
            compute_type=compute_type,
            use_fp8_w8a8=use_fp8_w8a8,
            use_int8_w8a16=use_int8_w8a16,
            **config,
        )


# Adapted from: https://github.com/sgl-project/sglang/pull/2628
def get_config_file_name(E: int,
                         N: int,
                         dtype: Optional[str],
                         block_shape: Optional[List[int]] = None) -> str:
    device_name = current_platform.get_device_name().replace(" ", "_")
    dtype_selector = "" if not dtype else f",dtype={dtype}"
    block_shape_selector = ("" if not block_shape or not all(block_shape) else
                            f",block_shape={block_shape}").replace(" ", "")
    return f"E={E},N={N},device_name={device_name}{dtype_selector}{block_shape_selector}.json"  # noqa: E501


# Adapted from: https://github.com/sgl-project/sglang/pull/2628
@functools.lru_cache
def get_moe_configs(
    E: int,
    N: int,
    dtype: Optional[str],
    block_n: Optional[int] = None,
    block_k: Optional[int] = None,
) -> Optional[Dict[int, Any]]:
    """
    Return optimized configurations for the fused MoE kernel.

    The return value will be a dictionary that maps an irregular grid of
    batch sizes to configurations of the fused_moe kernel. To evaluate the
    kernel on a given batch size bs, the closest batch size in the grid should
    be picked and the associated configuration chosen to invoke the kernel.
    """

    # First look up if an optimized configuration is available in the configs
    # directory
    block_shape = [block_n, block_k] if block_n and block_k else None
    json_file_name = get_config_file_name(E, N, dtype, block_shape)

    config_file_path = os.path.join(
        os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name)
    if os.path.exists(config_file_path):
        with open(config_file_path) as f:
            logger.info("Using configuration from %s for MoE layer.",
                        config_file_path)
            # If a configuration has been found, return it
            return {int(key): val for key, val in json.load(f).items()}

    # If no optimized configuration is available, we will use the default
    # configuration
    logger.warning(
        ("Using default MoE config. Performance might be sub-optimal! "
         "Config file not found at %s"), config_file_path)
    return None


def get_default_config(
    M: int,
    E: int,
    N: int,
    K: int,
    topk: int,
    dtype: Optional[str],
    is_marlin: bool,
    block_shape: Optional[List[int]] = None,
) -> Dict[str, int]:
    if dtype == "fp8_w8a8" and block_shape is not None:
        # Block-wise quant: BLOCK_SIZE_N must be divisible by block_shape[0]
        # BLOCK_SIZE_K must be divisible by block_shape[1]
        config = {
            "BLOCK_SIZE_M": 64,
            "BLOCK_SIZE_N": block_shape[0],
            "BLOCK_SIZE_K": block_shape[1],
            "GROUP_SIZE_M": 32,
            "num_warps": 4,
            "num_stages": 3,
        }
    else:
        config = {
            "BLOCK_SIZE_M": 64,
            "BLOCK_SIZE_N": 64,
            "BLOCK_SIZE_K": 32,
            "GROUP_SIZE_M": 8,
        }
        # A heuristic: fused marlin works faster with this config for small M
        if M <= E or (is_marlin and M <= 32):
            config = {
                "BLOCK_SIZE_M": 16,
                "BLOCK_SIZE_N": 32,
                "BLOCK_SIZE_K": 64,
                "GROUP_SIZE_M": 1,
            }
    return config


def try_get_optimal_moe_config(
    w1_shape: Tuple[int, ...],
    w2_shape: Tuple[int, ...],
    top_k: int,
    dtype: Optional[str],
    M: int,
    is_marlin: bool = False,
    block_shape: Optional[List[int]] = None,
):
    # from vllm.model_executor.layers.fused_moe import get_config
    # override_config = get_config()
    if False:
        config = override_config
    else:
        # First try to load optimal config from the file
        E, _, N = w2_shape
        block_n = block_shape[0] if block_shape else 0
        block_k = block_shape[1] if block_shape else 0
        configs = get_moe_configs(E, N, dtype, block_n, block_k)

        if configs:
            # If an optimal configuration map has been found, look up the
            # optimal config
            config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
        else:
            # Else use the default config
            config = get_default_config(M, E, N, w1_shape[2], top_k, dtype,
                                        is_marlin, block_shape)
    return config


def fused_topk(
    hidden_states: torch.Tensor,
    gating_output: torch.Tensor,
    topk: int,
    renormalize: bool,
):
    assert hidden_states.shape[0] == gating_output.shape[0], (
        "Number of tokens mismatch")

    M, _ = hidden_states.shape

    topk_weights = torch.empty(M,
                               topk,
                               dtype=torch.float32,
                               device=hidden_states.device)
    topk_ids = torch.empty(M,
                           topk,
                           dtype=torch.int32,
                           device=hidden_states.device)
    token_expert_indicies = torch.empty(M,
                                        topk,
                                        dtype=torch.int32,
                                        device=hidden_states.device)

    ops.topk_softmax(
        topk_weights,
        topk_ids,
        token_expert_indicies,
        gating_output.float(),  # TODO(woosuk): Optimize this.
    )
    del token_expert_indicies  # Not used. Will be used in the future.

    if renormalize:
        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)

    return topk_weights, topk_ids


# This is used by the Deepseek-V2 and Deepseek-V3 model
@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
def grouped_topk(hidden_states: torch.Tensor,
                 gating_output: torch.Tensor,
                 topk: int,
                 renormalize: bool,
                 num_expert_group: int = 0,
                 topk_group: int = 0,
                 scoring_func: str = "softmax",
                 e_score_correction_bias: Optional[torch.Tensor] = None):

    assert hidden_states.shape[0] == gating_output.shape[0], (
        "Number of tokens mismatch")

    if scoring_func == "softmax":
        scores = torch.softmax(gating_output, dim=-1)
    elif scoring_func == "sigmoid":
        scores = gating_output.sigmoid()
    else:
        raise ValueError(f"Unsupported scoring function: {scoring_func}")

    if e_score_correction_bias is not None:
        # Store original scores before applying correction bias. We use biased
        # scores for expert selection but original scores for routing weights
        original_scores = scores
        scores = scores + e_score_correction_bias.unsqueeze(0)

    num_token = scores.shape[0]
    group_scores = scores.view(num_token, num_expert_group,
                               -1).max(dim=-1).values  # [n, n_group]
    group_idx = torch.topk(group_scores, k=topk_group, dim=-1,
                           sorted=False)[1]  # [n, top_k_group]
    group_mask = torch.zeros_like(group_scores)  # [n, n_group]
    group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
    score_mask = group_mask.unsqueeze(-1).expand(
        num_token, num_expert_group,
        scores.shape[-1] // num_expert_group).reshape(num_token, -1)  # [n, e]
    tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]

    if e_score_correction_bias is not None:
        topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)[1]
        # Use original unbiased scores for the routing weights
        topk_weights = original_scores.gather(1, topk_ids)
    else:
        topk_weights, topk_ids = torch.topk(tmp_scores,
                                            k=topk,
                                            dim=-1,
                                            sorted=False)

    if renormalize:
        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)

    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)


def get_config_dtype_str(dtype: torch.dtype,
                         use_int4_w4a16: Optional[bool] = False,
                         use_int8_w8a16: Optional[bool] = False,
                         use_fp8_w8a8: Optional[bool] = False):
    if use_fp8_w8a8:
        return "fp8_w8a8"
    elif use_int8_w8a16:
        return "int8_w8a16"
    elif use_int4_w4a16:
        return "int4_w8a16"
    elif dtype == torch.float:
        # avoiding cases where kernel fails when float32 MoE
        # use fp16/bfloat16 configs
        return "float32"
    return None


def inplace_fused_experts(hidden_states: torch.Tensor,
                          w1: torch.Tensor,
                          w2: torch.Tensor,
                          topk_weights: torch.Tensor,
                          topk_ids: torch.Tensor,
                          use_fp8_w8a8: bool = False,
                          use_int8_w8a16: bool = False,
                          use_int4_w4a16: bool = False,
                          w1_scale: Optional[torch.Tensor] = None,
                          w2_scale: Optional[torch.Tensor] = None,
                          w1_zp: Optional[torch.Tensor] = None,
                          w2_zp: Optional[torch.Tensor] = None,
                          a1_scale: Optional[torch.Tensor] = None,
                          a2_scale: Optional[torch.Tensor] = None,
                          block_shape: Optional[List[int]] = None) -> None:
    fused_experts_impl(hidden_states, w1, w2, topk_weights, topk_ids, True,
                       use_fp8_w8a8, use_int8_w8a16, use_int4_w4a16, w1_scale,
                       w2_scale, w1_zp, w2_zp, a1_scale, a2_scale, block_shape)


def inplace_fused_experts_fake(
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        use_fp8_w8a8: bool = False,
        use_int8_w8a16: bool = False,
        use_int4_w4a16: bool = False,
        w1_scale: Optional[torch.Tensor] = None,
        w2_scale: Optional[torch.Tensor] = None,
        w1_zp: Optional[torch.Tensor] = None,
        w2_zp: Optional[torch.Tensor] = None,
        a1_scale: Optional[torch.Tensor] = None,
        a2_scale: Optional[torch.Tensor] = None,
        block_shape: Optional[List[int]] = None) -> None:
    pass


direct_register_custom_op(
    op_name="inplace_fused_experts",
    op_func=inplace_fused_experts,
    mutates_args=["hidden_states"],
    fake_impl=inplace_fused_experts_fake,
)


def outplace_fused_experts(
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        use_fp8_w8a8: bool = False,
        use_int8_w8a16: bool = False,
        use_int4_w4a16: bool = False,
        w1_scale: Optional[torch.Tensor] = None,
        w2_scale: Optional[torch.Tensor] = None,
        w1_zp: Optional[torch.Tensor] = None,
        w2_zp: Optional[torch.Tensor] = None,
        a1_scale: Optional[torch.Tensor] = None,
        a2_scale: Optional[torch.Tensor] = None,
        block_shape: Optional[List[int]] = None) -> torch.Tensor:
    return fused_experts_impl(hidden_states, w1, w2, topk_weights, topk_ids,
                              False, use_fp8_w8a8, use_int8_w8a16,
                              use_int4_w4a16, w1_scale, w2_scale, w1_zp, w2_zp,
                              a1_scale, a2_scale, block_shape)


def outplace_fused_experts_fake(
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        use_fp8_w8a8: bool = False,
        use_int8_w8a16: bool = False,
        use_int4_w4a16: bool = False,
        w1_scale: Optional[torch.Tensor] = None,
        w2_scale: Optional[torch.Tensor] = None,
        w1_zp: Optional[torch.Tensor] = None,
        w2_zp: Optional[torch.Tensor] = None,
        a1_scale: Optional[torch.Tensor] = None,
        a2_scale: Optional[torch.Tensor] = None,
        block_shape: Optional[List[int]] = None) -> torch.Tensor:
    return torch.empty_like(hidden_states)


direct_register_custom_op(
    op_name="outplace_fused_experts",
    op_func=outplace_fused_experts,
    mutates_args=[],
    fake_impl=outplace_fused_experts_fake,
)


def fused_experts(hidden_states: torch.Tensor,
                  w1: torch.Tensor,
                  w2: torch.Tensor,
                  topk_weights: torch.Tensor,
                  topk_ids: torch.Tensor,
                  inplace: bool = False,
                  use_fp8_w8a8: bool = False,
                  use_int8_w8a16: bool = False,
                  use_int4_w4a16: bool = False,
                  w1_scale: Optional[torch.Tensor] = None,
                  w2_scale: Optional[torch.Tensor] = None,
                  w1_zp: Optional[torch.Tensor] = None,
                  w2_zp: Optional[torch.Tensor] = None,
                  a1_scale: Optional[torch.Tensor] = None,
                  a2_scale: Optional[torch.Tensor] = None,
                  block_shape: Optional[List[int]] = None):
    if inplace:
        torch.ops.vllm.inplace_fused_experts(hidden_states, w1, w2,
                                             topk_weights, topk_ids,
                                             use_fp8_w8a8, use_int8_w8a16,
                                             use_int4_w4a16, w1_scale,
                                             w2_scale, w1_zp, w2_zp, a1_scale,
                                             a2_scale, block_shape)
        return hidden_states
    else:
        return torch.ops.vllm.outplace_fused_experts(
            hidden_states, w1, w2, topk_weights, topk_ids, use_fp8_w8a8,
            use_int8_w8a16, use_int4_w4a16, w1_scale, w2_scale, w1_zp, w2_zp,
            a1_scale, a2_scale, block_shape)


def fused_experts_impl(hidden_states: torch.Tensor,
                       w1: torch.Tensor,
                       w2: torch.Tensor,
                       topk_weights: torch.Tensor,
                       topk_ids: torch.Tensor,
                       inplace: bool = False,
                       use_fp8_w8a8: bool = False,
                       use_int8_w8a16: bool = False,
                       use_int4_w4a16: bool = False,
                       w1_scale: Optional[torch.Tensor] = None,
                       w2_scale: Optional[torch.Tensor] = None,
                       w1_zp: Optional[torch.Tensor] = None,
                       w2_zp: Optional[torch.Tensor] = None,
                       a1_scale: Optional[torch.Tensor] = None,
                       a2_scale: Optional[torch.Tensor] = None,
                       block_shape: Optional[List[int]] = None):
    # Check constraints.
    if use_int4_w4a16:
        assert hidden_states.shape[1] // 2 == w1.shape[
            2], "Hidden size mismatch"
    else:
        assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"

    assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
    assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
    assert w1.is_contiguous(), "Expert weights1 must be contiguous"
    assert w2.is_contiguous(), "Expert weights2 must be contiguous"
    assert hidden_states.dtype in [
        torch.float32, torch.float16, torch.bfloat16
    ]

    num_tokens, _ = hidden_states.shape
    E, N, _ = w1.shape
    # We execute the fused_moe kernel in chunks to circumvent this issue:
    # https://github.com/vllm-project/vllm/issues/5938
    CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
    M = min(num_tokens, CHUNK_SIZE)
    config_dtype = get_config_dtype_str(use_fp8_w8a8=use_fp8_w8a8,
                                        use_int8_w8a16=use_int8_w8a16,
                                        use_int4_w4a16=use_int4_w4a16,
                                        dtype=hidden_states.dtype)

    get_config_func = functools.partial(
        try_get_optimal_moe_config,
        w1.shape,
        w2.shape,
        topk_ids.shape[1],
        config_dtype,
        block_shape=block_shape,
    )

    config = get_config_func(M)

    intermediate_cache1 = torch.empty((M, topk_ids.shape[1], N),
                                      device=hidden_states.device,
                                      dtype=hidden_states.dtype)
    intermediate_cache2 = torch.empty((M * topk_ids.shape[1], N // 2),
                                      device=hidden_states.device,
                                      dtype=hidden_states.dtype)
    intermediate_cache3 = torch.empty((M, topk_ids.shape[1], w2.shape[1]),
                                      device=hidden_states.device,
                                      dtype=hidden_states.dtype)

    if hidden_states.dtype == torch.bfloat16:
        compute_type = tl.bfloat16
    elif hidden_states.dtype == torch.float16:
        compute_type = tl.float16
    elif hidden_states.dtype == torch.float32:
        compute_type = tl.float32
    else:
        raise ValueError(f"Unsupported compute_type: {hidden_states.dtype}")

    if inplace:
        out_hidden_states = hidden_states
    else:
        out_hidden_states = torch.empty_like(hidden_states)

    for chunk in range((num_tokens // CHUNK_SIZE) + 1):
        begin_chunk_idx, end_chunk_idx = (chunk * CHUNK_SIZE,
                                          min((chunk + 1) * CHUNK_SIZE,
                                              num_tokens))
        curr_hidden_states = hidden_states[begin_chunk_idx:end_chunk_idx]
        tokens_in_chunk, _ = curr_hidden_states.shape

        if tokens_in_chunk == 0:
            break

        if tokens_in_chunk < CHUNK_SIZE and chunk > 0:
            # Adjust the intermediate cache size and config for the last
            # chunk. Note that in most cases we only have one chunk
            # so the cache size and config are already set correctly and
            # do not need to be adjusted.
            intermediate_cache1 = intermediate_cache1[:tokens_in_chunk]
            intermediate_cache2 = intermediate_cache2[:tokens_in_chunk]
            intermediate_cache3 = intermediate_cache3[:tokens_in_chunk]
            config = get_config_func(tokens_in_chunk)

        curr_topk_ids = topk_ids[begin_chunk_idx:end_chunk_idx]
        curr_topk_weights = topk_weights[begin_chunk_idx:end_chunk_idx]

        sorted_token_ids, expert_ids, num_tokens_post_padded = (
            moe_align_block_size(curr_topk_ids, config['BLOCK_SIZE_M'], E))

        invoke_fused_moe_kernel(curr_hidden_states,
                                w1,
                                intermediate_cache1,
                                a1_scale,
                                w1_scale,
                                w1_zp,
                                curr_topk_weights,
                                curr_topk_ids,
                                sorted_token_ids,
                                expert_ids,
                                num_tokens_post_padded,
                                False,
                                topk_ids.shape[1],
                                config,
                                compute_type=compute_type,
                                use_fp8_w8a8=use_fp8_w8a8,
                                use_int8_w8a16=use_int8_w8a16,
                                use_int4_w4a16=use_int4_w4a16,
                                block_shape=block_shape)

        torch.ops._C.silu_and_mul(intermediate_cache2,
                                  intermediate_cache1.view(-1, N))

        invoke_fused_moe_kernel(intermediate_cache2,
                                w2,
                                intermediate_cache3,
                                a2_scale,
                                w2_scale,
                                w2_zp,
                                curr_topk_weights,
                                curr_topk_ids,
                                sorted_token_ids,
                                expert_ids,
                                num_tokens_post_padded,
                                True,
                                1,
                                config,
                                compute_type=compute_type,
                                use_fp8_w8a8=use_fp8_w8a8,
                                use_int8_w8a16=use_int8_w8a16,
                                use_int4_w4a16=use_int4_w4a16,
                                block_shape=block_shape)

        ops.moe_sum(intermediate_cache3.view(*intermediate_cache3.shape),
                    out_hidden_states[begin_chunk_idx:end_chunk_idx])
    return out_hidden_states


def fused_moe(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    renormalize: bool = False,
    inplace: bool = False,
    use_grouped_topk: bool = False,
    num_expert_group: Optional[int] = None,
    topk_group: Optional[int] = None,
    custom_routing_function: Optional[Callable] = None,
    use_fp8_w8a8: bool = False,
    use_int8_w8a16: bool = False,
    use_int4_w4a16: bool = False,
    w1_scale: Optional[torch.Tensor] = None,
    w2_scale: Optional[torch.Tensor] = None,
    w1_zp: Optional[torch.Tensor] = None,
    w2_zp: Optional[torch.Tensor] = None,
    a1_scale: Optional[torch.Tensor] = None,
    a2_scale: Optional[torch.Tensor] = None,
    block_shape: Optional[List[int]] = None,
) -> torch.Tensor:
    """
    This function computes a Mixture of Experts (MoE) layer using two sets of
    weights, w1 and w2, and top-k gating mechanism.

    Parameters:
    - hidden_states (torch.Tensor): The input tensor to the MoE layer.
    - w1 (torch.Tensor): The first set of expert weights.
    - w2 (torch.Tensor): The second set of expert weights.
    - gating_output (torch.Tensor): The output of the gating operation
        (before softmax).
    - topk (int): The number of top-k experts to select.
    - renormalize (bool): If True, renormalize the top-k weights to sum to 1.
    - inplace (bool): If True, perform the operation in-place.
        Defaults to False.
    - num_expert_group: Optional[int]: additional parameter for grouped_topk
    - topk_group: Optional[int]: additional parameter for grouped_topk
    - use_grouped_topk: If True, use grouped_topk instead of fused_topk
        note: Deepseekv2 model uses grouped_topk
    - use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner
        products for w1 and w2. Defaults to False.
    - use_int8_w8a16 (bool): If True, use matmul of int8 weight and bf16/fp16
        activation to compute the inner products for w1 and w2.
        Defaults to False.
    - use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16
        activation to compute the inner products for w1 and w2.
        Defaults to False.
    - w1_scale (Optional[torch.Tensor]): Optional scale to be used for
        w1.
    - w2_scale (Optional[torch.Tensor]): Optional scale to be used for
        w2.
    - a1_scale (Optional[torch.Tensor]): Optional scale to be used for
        a1.
    - a2_scale (Optional[torch.Tensor]): Optional scale to be used for
        a2.
    - block_shape: (Optional[List[int]]): Optional block size for block-wise
        quantization.

    Returns:
    - torch.Tensor: The output tensor after applying the MoE layer.
    """

    return fused_experts(hidden_states,
                         w1,
                         w2,
                         topk_weights,
                         topk_ids,
                         inplace=inplace,
                         use_fp8_w8a8=use_fp8_w8a8,
                         use_int8_w8a16=use_int8_w8a16,
                         use_int4_w4a16=use_int4_w4a16,
                         w1_scale=w1_scale,
                         w2_scale=w2_scale,
                         w1_zp=w1_zp,
                         w2_zp=w2_zp,
                         a1_scale=a1_scale,
                         a2_scale=a2_scale,
                         block_shape=block_shape)