# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Fused MoE Triton kernels."""
import functools
import json
import os
# torch.compile needs typing.List. It will fail torch.library.infer_schema
# otherwise
from typing import List  # noqa: UP035
from typing import Any, Callable, Optional, Union

import torch
import torch.nn.functional as F

import vllm.envs as envs
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm import _custom_ops as ops
from vllm.logger import init_logger
# yapf: disable
from vllm.model_executor.layers.fused_moe.config import (
    FUSED_MOE_UNQUANTIZED_CONFIG, FusedMoEQuantConfig, _get_config_dtype_str)
from vllm.model_executor.layers.fused_moe.cutlass_moe import (
    _valid_cutlass_block_scaled_grouped_gemm,
    run_cutlass_block_scaled_fused_experts)
# yapf: enable
from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
    _valid_deep_gemm, deep_gemm_moe_fp8)
from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
    moe_align_block_size)
from vllm.model_executor.layers.fused_moe.prepare_finalize import (
    MoEPrepareAndFinalizeNoEP)
from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
    TopKWeightAndReduceNoOP)
from vllm.model_executor.layers.fused_moe.utils import (
    _resize_cache, activation_without_mul, moe_kernel_quantize_input)
from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
    dequant_mxfp4)
from vllm.platforms import current_platform
from vllm.triton_utils import tl, triton
from vllm.utils import direct_register_custom_op, is_torch_equal_or_newer
from vllm.utils.deep_gemm import is_deep_gemm_e8m0_used

from .rocm_aiter_fused_moe import is_rocm_aiter_moe_enabled

logger = init_logger(__name__)


@triton.jit
def write_zeros_to_output(c_ptr, stride_cm, stride_cn, pid_n, N, offs_token,
                          token_mask, BLOCK_SIZE_M, BLOCK_SIZE_N,
                          compute_type):
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=compute_type)
    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_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

    off_experts = tl.load(expert_ids_ptr + pid_m).to(tl.int64)
    if off_experts == -1:
        # -----------------------------------------------------------
        # Write back zeros to the output when the expert is not
        # in the current expert parallel rank.
        write_zeros_to_output(c_ptr, stride_cm, stride_cn, pid_n, N,
                              offs_token, token_mask, BLOCK_SIZE_M,
                              BLOCK_SIZE_N, compute_type)
        return

    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)

    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,
    b_bias_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,
    stride_bbe,  # bias expert stride
    stride_bbn,  # bias N stride
    # 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_w8a8: tl.constexpr,
    use_int8_w8a16: tl.constexpr,
    per_channel_quant: tl.constexpr,
    HAS_BIAS: 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

    off_experts = tl.load(expert_ids_ptr + pid_m).to(tl.int64)
    if off_experts == -1:
        # -----------------------------------------------------------
        # Write back zeros to the output when the expert is not
        # in the current expert parallel rank.
        write_zeros_to_output(c_ptr, stride_cm, stride_cn, pid_n, N,
                              offs_token, token_mask, BLOCK_SIZE_M,
                              BLOCK_SIZE_N, compute_type)
        return

    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)

    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 or use_int8_w8a8:
        # block-wise
        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)
        # channel-wise
        elif per_channel_quant:
            b_scale_ptrs = b_scale_ptr + off_experts * stride_bse + offs_bn[
                None, :] * stride_bsn
            b_scale = tl.load(b_scale_ptrs)
            # Load per-token scale for activations
            a_scale_ptrs = a_scale_ptr + (offs_token // top_k) * stride_asm
            a_scale = tl.load(a_scale_ptrs, mask=token_mask, other=0.0)[:,
                                                                        None]
        # tensor-wise
        else:
            a_scale = tl.load(a_scale_ptr)
            b_scale = tl.load(b_scale_ptr + off_experts)
    if HAS_BIAS:
        # bias shape: [num_experts, N]
        bias_ptrs = b_bias_ptr + off_experts * stride_bbe + offs_bn * stride_bbn
        bias = tl.load(bias_ptrs, mask=(offs_bn < N), other=0.0)
    # -----------------------------------------------------------
    # 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 or use_int8_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:
                if use_fp8_w8a8:
                    # acc used to enable fp8_fast_accum
                    accumulator = tl.dot(a, b, acc=accumulator)
                else:
                    accumulator += tl.dot(a, b)
        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 HAS_BIAS:
        accumulator = accumulator + bias[None, :]
    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 or use_int8_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 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: Optional[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_w8a8: bool,
                            use_int8_w8a16: bool,
                            use_int4_w4a16: bool,
                            per_channel_quant: bool,
                            block_shape: Optional[list[int]] = None,
                            B_bias: Optional[torch.Tensor] = None) -> None:
    assert topk_weights is not None or not mul_routed_weight
    assert topk_weights is None or topk_weights.stride(1) == 1
    assert sorted_token_ids.stride(0) == 1

    if use_fp8_w8a8 or use_int8_w8a8:
        assert B_scale is not None
        assert (block_shape is None
                or triton.cdiv(B.size(-2), block_shape[0]) == B_scale.size(-2))
        assert (block_shape is None
                or triton.cdiv(B.size(-1), block_shape[1]) == B_scale.size(-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

    M = A.size(0)
    num_tokens = M * top_k

    EM = sorted_token_ids.size(0)
    if A.size(0) < config["BLOCK_SIZE_M"]:
        # optimize for small batch_size.
        # We assume that top_ids of each token is unique,
        # so num_valid_experts <= batch_size <= BLOCK_SIZE_M,
        # and we can skip some invalid blocks.
        EM = min(sorted_token_ids.size(0),
                 A.size(0) * top_k * config['BLOCK_SIZE_M'])
    grid = lambda META: (triton.cdiv(EM, META['BLOCK_SIZE_M']) * triton.cdiv(
        B.size(1), META['BLOCK_SIZE_N']), )
    HAS_BIAS = B_bias is not None
    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

        use_moe_wna16_cuda = should_moe_wna16_use_cuda(
            num_valid_tokens=num_tokens,
            group_size=block_shape[1],
            num_experts=B.size(0),
            bit=4 if use_int4_w4a16 else 8)
        config = config.copy()
        config.update(
            get_moe_wna16_block_config(config=config,
                                       use_moe_wna16_cuda=use_moe_wna16_cuda,
                                       num_valid_tokens=num_tokens,
                                       size_k=A.size(1),
                                       size_n=B.size(1),
                                       num_experts=B.size(1),
                                       group_size=block_shape[1],
                                       real_top_k=top_k,
                                       block_size_m=config["BLOCK_SIZE_M"]))

        if use_moe_wna16_cuda:
            bit = 4 if use_int4_w4a16 else 8
            ops.moe_wna16_gemm(A, C, B, B_scale, B_zp,
                               topk_weights if mul_routed_weight else None,
                               sorted_token_ids, expert_ids,
                               num_tokens_post_padded, top_k,
                               config["BLOCK_SIZE_M"], config["BLOCK_SIZE_N"],
                               config["BLOCK_SIZE_K"], bit)
            return

        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.size(1),
            A.size(1),
            EM,
            num_tokens,
            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.size(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:
        config = config.copy()
        BLOCK_SIZE_K = config.pop("BLOCK_SIZE_K")
        if block_shape is not None:
            BLOCK_SIZE_K = min(BLOCK_SIZE_K, min(block_shape[0],
                                                 block_shape[1]))
        fused_moe_kernel[grid](
            A,
            B,
            C,
            B_bias,
            A_scale,
            B_scale,
            topk_weights,
            sorted_token_ids,
            expert_ids,
            num_tokens_post_padded,
            B.size(1),
            B.size(2),
            EM,
            num_tokens,
            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,
            B_bias.stride(0) if B_bias is not None else 0,
            B_bias.stride(1) if B_bias is not None 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_w8a8=use_int8_w8a8,
            use_int8_w8a16=use_int8_w8a16,
            per_channel_quant=per_channel_quant,
            HAS_BIAS=HAS_BIAS,
            BLOCK_SIZE_K=BLOCK_SIZE_K,
            **config,
        )


@triton.jit
def compute_identity_kernel(
    top_k: int,
    hidden_states_ptr: tl.tensor,
    expert_scales_ptr: tl.tensor,
    num_tokens: int,
    output_ptr: tl.tensor,
    hidden_dim: int,
    scales_stride: int,
    BLOCK_SIZE: tl.constexpr,
) -> None:
    pid = tl.program_id(0)

    batch_id = pid // (hidden_dim // BLOCK_SIZE)
    dim_offset = pid % (hidden_dim // BLOCK_SIZE) * BLOCK_SIZE

    if batch_id >= num_tokens or dim_offset >= hidden_dim:
        return

    h = tl.load(hidden_states_ptr + batch_id * hidden_dim + dim_offset +
                tl.arange(0, BLOCK_SIZE),
                mask=(dim_offset + tl.arange(0, BLOCK_SIZE)) < hidden_dim)

    result = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
    for i in range(top_k):
        scale = tl.load(expert_scales_ptr + batch_id * scales_stride + i)
        result += h * scale

    tl.store(output_ptr + batch_id * hidden_dim + dim_offset +
             tl.arange(0, BLOCK_SIZE),
             result,
             mask=(dim_offset + tl.arange(0, BLOCK_SIZE)) < hidden_dim)


def zero_experts_compute_triton(expert_indices: torch.Tensor,
                                expert_scales: torch.Tensor, num_experts: int,
                                zero_expert_type: str,
                                hidden_states: torch.Tensor) -> torch.Tensor:
    N = expert_indices.numel()
    top_k = expert_indices.size(-1)
    grid = lambda meta: (triton.cdiv(N, meta['BLOCK_SIZE']), )

    if zero_expert_type == "identity":
        zero_expert_mask = expert_indices < num_experts
        zero_expert_scales = expert_scales.clone()
        zero_expert_scales[zero_expert_mask] = 0.0

    normal_expert_mask = expert_indices >= num_experts
    expert_indices[normal_expert_mask] = 0
    expert_scales[normal_expert_mask] = 0.0

    output = torch.zeros_like(hidden_states).to(hidden_states.device)
    hidden_dim = hidden_states.size(-1)
    num_tokens = hidden_states.size(0)

    grid = lambda meta: (num_tokens * (hidden_dim // meta['BLOCK_SIZE']), )
    compute_identity_kernel[grid](
        top_k,
        hidden_states,
        zero_expert_scales,
        num_tokens,
        output,
        hidden_dim,
        zero_expert_scales.stride(0),
        BLOCK_SIZE=256,
    )

    return output


# 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_paths = []

    # note that we prioritize user defined config
    user_defined_config_folder = envs.VLLM_TUNED_CONFIG_FOLDER
    if user_defined_config_folder is not None:
        user_defined_config_file_path = os.path.join(
            user_defined_config_folder, json_file_name)
        config_file_paths.append(user_defined_config_file_path)

    default_config_file_path = os.path.join(
        os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name)
    config_file_paths.append(default_config_file_path)

    for config_file_path in config_file_paths:
        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
                tuned_config = json.load(f)
                # Delete triton_version from tuned_config
                tuned_config.pop("triton_version", None)
                return {int(key): val for key, val in tuned_config.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_paths)
    return None


def get_moe_wna16_block_config(config: dict[str,
                                            int], use_moe_wna16_cuda: bool,
                               num_valid_tokens: int, size_k: int, size_n: int,
                               num_experts: int, group_size: int,
                               real_top_k: int, block_size_m: int):
    if "BLOCK_SIZE_N" in config and "BLOCK_SIZE_K" in config:
        # optimal block config is set
        return {}
    if not use_moe_wna16_cuda:
        # triton moe wna16 kernel
        if num_valid_tokens // real_top_k == 1:
            # if bs=1, use a smaller BLOCK_SIZE_N
            return {"BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 64}
        else:
            return {"BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32}
    else:
        # cuda moe wna16 kernel
        # set default block_size 128, and increase them when num_blocks
        # is too large.
        block_size_n = 128
        block_size_k = 128
        if block_size_k <= group_size:
            block_size_k = group_size

        num_n_blocks = size_k // block_size_k
        num_k_blocks = size_n // block_size_k
        num_m_blocks = (num_valid_tokens + block_size_m - 1) / block_size_m + \
            num_experts
        if num_valid_tokens // real_top_k <= block_size_m:
            num_m_blocks = min(num_m_blocks, num_valid_tokens)
        num_blocks = num_m_blocks * num_n_blocks * num_k_blocks

        if size_k % 256 == 0 and num_blocks >= 256 and \
                block_size_k < 256:
            block_size_k = 256
            num_blocks = num_blocks // (256 // block_size_k)

        if num_m_blocks <= 16 and size_k % (block_size_k * 2) == 0 and \
                size_k % (block_size_k * 2) == 0 and block_size_k <= 512 and \
                num_blocks >= 512:
            block_size_k = block_size_k * 2
            num_blocks = num_blocks // 2

        if num_blocks > 1024:
            block_size_n = 256
            num_n_blocks = num_n_blocks // 2
            num_blocks = num_blocks // 2

        if size_n <= 1024 and num_blocks >= 1024:
            # The kernel performance got much better with BLOCK_SIZE_N=1024
            # when num_blocks is large, event when N is small.
            # Not sure why, maybe it force the CUDA SM process only one block
            # at the same time.
            block_size_n = 1024

        return {"BLOCK_SIZE_N": block_size_n, "BLOCK_SIZE_K": block_size_k}


def should_moe_wna16_use_cuda(num_valid_tokens: int, group_size: int,
                              num_experts: int, bit: int):
    return current_platform.is_cuda() and bit == 4 and \
        group_size in [32, 64, 128] and num_valid_tokens / num_experts <= 6


def get_default_config(
    M: int,
    E: int,
    N: int,
    K: int,
    topk: int,
    dtype: Optional[str],
    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]
        # num_stages=3 can cause triton.runtime.errors.OutOfResources
        # on ROCm, set it to 2 instead.
        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 if not current_platform.is_rocm() else 2,
        }
    elif dtype in ["int4_w4a16", "int8_w8a16"] and block_shape is not None:
        # moe wna16 kernels
        # only set BLOCK_SIZE_M
        # BLOCK_SIZE_N and BLOCK_SIZE_K would be set later
        bit = 4 if dtype == "int4_w4a16" else 8
        use_moe_wna16_cuda = should_moe_wna16_use_cuda(M * topk,
                                                       block_shape[1], E, bit)
        if use_moe_wna16_cuda:
            config = {"BLOCK_SIZE_M": min(16, M)}
        elif M <= 20:
            config = {"BLOCK_SIZE_M": 16, "GROUP_SIZE_M": 1}
        elif M <= 40:
            config = {"BLOCK_SIZE_M": 32, "GROUP_SIZE_M": 1}
        else:
            config = {"BLOCK_SIZE_M": 64, "GROUP_SIZE_M": 1}
    elif M <= E:
        config = {
            "BLOCK_SIZE_M": 16,
            "BLOCK_SIZE_N": 32,
            "BLOCK_SIZE_K": 64,
            "GROUP_SIZE_M": 1,
        }
    else:
        config = {
            "BLOCK_SIZE_M": 64,
            "BLOCK_SIZE_N": 64,
            "BLOCK_SIZE_K": 32,
            "GROUP_SIZE_M": 8,
        }
    return config


def try_get_optimal_moe_config(
    w1_shape: tuple[int, ...],
    w2_shape: tuple[int, ...],
    top_k: int,
    dtype: Optional[str],
    M: int,
    block_shape: Optional[list[int]] = None,
) -> dict[str, int]:
    from vllm.model_executor.layers.fused_moe import get_config
    override_config = get_config()
    if override_config:
        config = override_config
    else:
        # First try to load optimal config from the file
        E, _, N = w2_shape
        if dtype == "int4_w4a16":
            N = N * 2
        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,
                                        block_shape)
    return config


def vllm_topk_softmax(topk_weights: torch.Tensor, topk_indices: torch.Tensor,
                      token_expert_indices: torch.Tensor,
                      gating_output: torch.Tensor,
                      renormalize: bool) -> tuple[torch.Tensor, ...]:
    ops.topk_softmax(
        topk_weights,
        topk_indices,
        token_expert_indices,
        gating_output,
    )
    if renormalize:
        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)

    return topk_weights, topk_indices


def dispatch_topk_func() -> Callable[..., tuple[torch.Tensor, ...]]:
    if is_rocm_aiter_moe_enabled():
        from .rocm_aiter_fused_moe import rocm_aiter_topk_softmax
        return rocm_aiter_topk_softmax
    return vllm_topk_softmax


def fused_topk(
    hidden_states: torch.Tensor,
    gating_output: torch.Tensor,
    topk: int,
    renormalize: bool,
    indices_type: Optional[torch.dtype] = None,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    assert hidden_states.size(0) == gating_output.size(0), (
        "Number of tokens mismatch")

    M, _ = hidden_states.size()

    topk_weights = torch.empty(M,
                               topk,
                               dtype=torch.float32,
                               device=hidden_states.device)
    topk_ids = torch.empty(
        M,
        topk,
        dtype=torch.int32 if indices_type is None else indices_type,
        device=hidden_states.device)
    token_expert_indices = torch.empty(M,
                                       topk,
                                       dtype=torch.int32,
                                       device=hidden_states.device)

    gating_output_float = gating_output.float()  # TODO(woosuk): Optimize this.

    topk_func = dispatch_topk_func()
    topk_weights, topk_ids = topk_func(topk_weights, topk_ids,
                                       token_expert_indices,
                                       gating_output_float, renormalize)

    return topk_weights, topk_ids, token_expert_indices


def fused_topk_bias(
    hidden_states: torch.Tensor,
    gating_output: torch.Tensor,
    e_score_correction_bias: torch.Tensor,
    topk: int,
    renormalize: bool,
):
    n_routed_experts = gating_output.shape[-1]
    scores = gating_output.softmax(dim=-1)
    scores_for_choice = scores.view(
        -1, n_routed_experts) + e_score_correction_bias.unsqueeze(0)
    topk_indices = torch.topk(scores_for_choice, k=topk, dim=-1,
                              sorted=False)[1]
    topk_weights = scores.gather(1, topk_indices)
    if renormalize:
        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
    return topk_weights.to(torch.float32), topk_indices.to(torch.int32)


# 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",
    routed_scaling_factor: float = 1.0,
    e_score_correction_bias: Optional[torch.Tensor] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
    if envs.VLLM_USE_FUSED_MOE_GROUPED_TOPK and \
            current_platform.is_cuda() and \
            num_expert_group <= 32 and topk <= 32 and \
            e_score_correction_bias is not None:
        return fused_grouped_topk(
            hidden_states=hidden_states,
            gating_output=gating_output,
            topk=topk,
            renormalize=renormalize,
            e_score_correction_bias=e_score_correction_bias,
            num_expert_group=num_expert_group,
            topk_group=topk_group,
            scoring_func=scoring_func,
            routed_scaling_factor=routed_scaling_factor)

    assert hidden_states.size(0) == gating_output.size(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}")

    num_token = scores.size(0)
    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)
        group_scores = (scores.view(num_token, num_expert_group,
                                    -1).topk(2, dim=-1)[0].sum(dim=-1))
    else:
        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.size(-1) // num_expert_group).reshape(num_token, -1)  # [n, e]
    tmp_scores = scores.masked_fill(~score_mask.bool(),
                                    float("-inf"))  # [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)

    if routed_scaling_factor != 1.0:
        topk_weights = topk_weights * routed_scaling_factor
    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)


@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
def eplb_map_to_physical_and_record(
        topk_ids: torch.Tensor,
        expert_load_view: torch.Tensor,
        logical_to_physical_map: torch.Tensor,
        logical_replica_count: torch.Tensor,
        indices_type: Optional[torch.dtype] = None) -> torch.Tensor:
    '''
    Map the logical expert ids to physical expert ids
    and record the expert load metrics.

    This will select a pseudo-random replica for each logical expert.
    Only used for EPLB.

    Args:
        topk_ids: The logical expert ids.
        expert_load_view: The expert load view.
        logical_to_physical_map: The logical to physical map.
        logical_replica_count: The logical replica count.
        indices_type: The indices type.

    Returns:
        The physical expert ids.
    '''

    # 1. Convert the logical expert ids to physical expert ids
    # Directly select a random replica for each logical expert

    # In case `indices_type` is not `torch.long` or `torch.int`,
    # e.g. `torch.uint32` as required by dispatch/combine kernels
    topk_ids_long = topk_ids.long()
    # Use (token position) modulo (replica count)
    # to deterministically choose a replica
    replica_count = logical_replica_count[topk_ids_long]
    # Flatten-position based index, reshaped back to `topk_ids` shape
    pos_indices = torch.arange(topk_ids.numel(),
                               device=topk_ids.device,
                               dtype=torch.long).reshape_as(topk_ids)
    # Compute pseudo-random indices by modulo
    replica_indices = (pos_indices % replica_count).unsqueeze(-1)
    physical_ids = logical_to_physical_map[topk_ids_long].gather(
        -1, replica_indices).squeeze(-1)

    topk_ids = physical_ids

    # 2. Record expert load metrics.

    # TODO(bowen): When using `FusedMoEModularKernel`, this
    # can be done in a more unified way, since
    # `FusedMoEPrepareAndFinalize` will return the expert
    # token count, in some cases directly from the kernel.
    # However, now there are many code paths not using
    # the modular kernel, e.g. calling `fused_experts`,
    # so we decide to keep the logic here.
    #
    # If later refactor moved all the MoE kernel calls
    # to the modular kernel, we can move this logic there
    # to achieve better efficiency.

    # `expert_load_view`: (num_physical_experts,)

    # `torch.bincount` is not compilable, so use `scatter_add_` instead.
    topk_ids_flatten = topk_ids.flatten()
    expert_load_view.scatter_add_(
        dim=0,
        index=topk_ids_flatten.long(),
        src=torch.ones_like(topk_ids_flatten).to(expert_load_view))

    if indices_type is not None:
        topk_ids = topk_ids.to(dtype=indices_type)
    return topk_ids


def fused_grouped_topk(
    hidden_states: torch.Tensor,
    gating_output: torch.Tensor,
    topk: int,
    renormalize: bool,
    e_score_correction_bias: torch.Tensor,
    num_expert_group: int = 0,
    topk_group: int = 0,
    scoring_func: str = "softmax",
    routed_scaling_factor: float = 1.0,
) -> tuple[torch.Tensor, torch.Tensor]:
    assert hidden_states.size(0) == gating_output.size(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}")

    scores_with_bias = scores + e_score_correction_bias.unsqueeze(0)
    topk_values, topk_indices = ops.grouped_topk(
        scores, scores_with_bias.to(scores.dtype), num_expert_group,
        topk_group, topk, renormalize, routed_scaling_factor)
    return topk_values.to(torch.float32), topk_indices.to(torch.int32)


def inplace_fused_experts(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    activation: str = "silu",
    apply_router_weight_on_input: bool = False,
    use_fp8_w8a8: bool = False,
    use_int8_w8a8: bool = False,
    use_int8_w8a16: bool = False,
    use_int4_w4a16: bool = False,
    use_mxfp4_w4a4: bool = False,
    per_channel_quant: bool = False,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    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,  #noqa: UP006
    w1_bias: Optional[torch.Tensor] = None,
    w2_bias: Optional[torch.Tensor] = None,
) -> None:
    fused_experts_impl(hidden_states, w1, w2, topk_weights, topk_ids, True,
                       activation, apply_router_weight_on_input, use_fp8_w8a8,
                       use_int8_w8a8, use_int8_w8a16, use_int4_w4a16,
                       use_mxfp4_w4a4, per_channel_quant, global_num_experts,
                       expert_map, w1_scale, w2_scale, w1_zp, w2_zp, a1_scale,
                       a2_scale, block_shape, w1_bias, w2_bias)


def inplace_fused_experts_fake(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    activation: str = "silu",
    apply_router_weight_on_input: bool = False,
    use_fp8_w8a8: bool = False,
    use_int8_w8a8: bool = False,
    use_int8_w8a16: bool = False,
    use_int4_w4a16: bool = False,
    use_mxfp4_w4a4: bool = False,
    per_channel_quant: bool = False,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    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,  #noqa: UP006
    w1_bias: Optional[torch.Tensor] = None,
    w2_bias: Optional[torch.Tensor] = 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,
    tags=(() if is_torch_equal_or_newer("2.7.0") else
          (torch.Tag.needs_fixed_stride_order, )),
)


def outplace_fused_experts(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    activation: str = "silu",
    apply_router_weight_on_input: bool = False,
    use_fp8_w8a8: bool = False,
    use_int8_w8a8: bool = False,
    use_int8_w8a16: bool = False,
    use_int4_w4a16: bool = False,
    use_mxfp4_w4a4: bool = False,
    per_channel_quant: bool = False,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    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,  #noqa: UP006
    w1_bias: Optional[torch.Tensor] = None,
    w2_bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
    return fused_experts_impl(
        hidden_states, w1, w2, topk_weights, topk_ids, False, activation,
        apply_router_weight_on_input, use_fp8_w8a8, use_int8_w8a8,
        use_int8_w8a16, use_int4_w4a16, use_mxfp4_w4a4, per_channel_quant,
        global_num_experts, expert_map, w1_scale, w2_scale, w1_zp, w2_zp,
        a1_scale, a2_scale, block_shape, w1_bias, w2_bias)


def outplace_fused_experts_fake(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    activation: str = "silu",
    use_fp8_w8a8: bool = False,
    use_int8_w8a8: bool = False,
    use_int8_w8a16: bool = False,
    use_int4_w4a16: bool = False,
    use_mxfp4_w4a4: bool = False,
    per_channel_quant: bool = False,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    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,
    w1_bias: Optional[torch.Tensor] = None,
    w2_bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
    return torch.empty_like(hidden_states)


direct_register_custom_op(
    op_name="outplace_fused_experts",
    op_func=outplace_fused_experts,
    fake_impl=outplace_fused_experts_fake,
    tags=(() if is_torch_equal_or_newer("2.7.0") else
          (torch.Tag.needs_fixed_stride_order, )),
)


def torch_vllm_inplace_fused_experts(**kwargs) -> torch.Tensor:
    torch.ops.vllm.inplace_fused_experts(**kwargs)
    hidden_states = kwargs['hidden_states']
    return hidden_states


def torch_vllm_outplace_fused_experts(**kwargs) -> torch.Tensor:
    return torch.ops.vllm.outplace_fused_experts(**kwargs)


def dispatch_fused_experts_func(inplace: bool) -> Callable[..., torch.Tensor]:
    if inplace:
        return torch_vllm_inplace_fused_experts
    return torch_vllm_outplace_fused_experts


# TODO (bnell): replace this with modular op.  Can get rid of inplace/outplace
# torch ops.
def fused_experts(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    inplace: bool = False,
    activation: str = "silu",
    apply_router_weight_on_input: bool = False,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    quant_config: Optional[FusedMoEQuantConfig] = None,
    allow_deep_gemm: bool = False,
    allow_cutlass_block_scaled_grouped_gemm: bool = False,
) -> torch.Tensor:

    if quant_config is None:
        quant_config = FUSED_MOE_UNQUANTIZED_CONFIG
    use_fp8_w8a8 = quant_config.use_fp8_w8a8

    # For now, disable DeepGemm for small N (<= 512) until better
    # permute/unpermute ops are available.
    # However, on B200, we use DeepGemm for all cases because they only support
    # E8M0 scale, which means we requantize the weight and input to the specific
    # scale. Fallen back to cutlass or triton for some cases would cause
    # accuracy issue.
    if (allow_deep_gemm and quant_config.use_fp8_w8a8 and
        (is_deep_gemm_e8m0_used() or _valid_deep_gemm(hidden_states, w1, w2))):
        assert quant_config is not None
        assert apply_router_weight_on_input is False
        return deep_gemm_moe_fp8(
            hidden_states=hidden_states,
            w1=w1,
            w2=w2,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            inplace=inplace,
            activation=activation,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            w1_scale=quant_config.w1_scale,
            w2_scale=quant_config.w2_scale,
            a1_scale=quant_config.a1_scale,
            a2_scale=quant_config.a2_scale,
            apply_router_weight_on_input=apply_router_weight_on_input,
        )
    elif (allow_cutlass_block_scaled_grouped_gemm and use_fp8_w8a8
          and _valid_cutlass_block_scaled_grouped_gemm(
              w1, w2, inplace, activation, apply_router_weight_on_input,
              expert_map)):
        assert quant_config is not None
        return run_cutlass_block_scaled_fused_experts(
            a=hidden_states,
            w1=w1,
            w2=w2,
            w1_scale=quant_config.w1_scale,
            w2_scale=quant_config.w2_scale,
            topk_weights=topk_weights,
            topk_ids=topk_ids)
    else:
        return dispatch_fused_experts_func(inplace)(
            hidden_states=hidden_states,
            w1=w1,
            w2=w2,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            activation=activation,
            apply_router_weight_on_input=apply_router_weight_on_input,
            use_fp8_w8a8=quant_config.use_fp8_w8a8,
            use_int8_w8a8=quant_config.use_int8_w8a8,
            use_int8_w8a16=quant_config.use_int8_w8a16,
            use_int4_w4a16=quant_config.use_int4_w4a16,
            use_mxfp4_w4a4=quant_config.use_mxfp4_w4a4,
            per_channel_quant=quant_config.per_act_token_quant,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            w1_scale=quant_config.w1_scale,
            w2_scale=quant_config.w2_scale,
            w1_zp=quant_config.w1_zp,
            w2_zp=quant_config.w2_zp,
            a1_scale=quant_config.a1_scale,
            a2_scale=quant_config.a2_scale,
            block_shape=quant_config.block_shape,
            w1_bias=quant_config.w1_bias,
            w2_bias=quant_config.w2_bias)


SILU_NO_MUL: str = activation_without_mul("silu")
GELU_NO_MUL: str = activation_without_mul("gelu")


def _get_config_quant_dtype(
    use_fp8_w8a8: bool,
    use_int8_w8a8: bool,
    use_mxfp4_w4a4: bool,
) -> Union[None, torch.dtype, str]:
    """
    Get the quantization type based on the quantization strategy flags.
    We don't have a quant_config at this point so we need to work backwards.
    A return type of None means no quantization is required because the
    input is unquantized or has been quantized prior to calling
    fused_experts_impl.
    """
    if use_fp8_w8a8:
        return torch.float8_e4m3fn
    elif use_int8_w8a8:
        return torch.int8
    elif use_mxfp4_w4a4:
        return "mxfp4"
    return None


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,
    activation: str = "silu",
    apply_router_weight_on_input: bool = False,
    use_fp8_w8a8: bool = False,
    use_int8_w8a8: bool = False,
    use_int8_w8a16: bool = False,
    use_int4_w4a16: bool = False,
    use_mxfp4_w4a4: bool = False,
    per_channel_quant: bool = False,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    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,
    w1_bias: Optional[torch.Tensor] = None,
    w2_bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
    # Check constraints.
    if use_int4_w4a16:
        assert hidden_states.size(1) // 2 == w1.size(2), (
            "Hidden size mismatch")
    elif use_mxfp4_w4a4:
        # 16bit activation and fp4x2 packed weight
        assert hidden_states.size(1) // 2 == w1.size(2), "hidden size mismatch"
    else:
        assert hidden_states.size(1) == w1.size(2), (
            f"Hidden size mismatch {hidden_states.size(1)} != {w1.size(2)}")

    assert topk_weights.size() == topk_ids.size(), "topk shape mismatch"
    assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
    assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
    assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
    assert hidden_states.dtype in [
        torch.float32, torch.float16, torch.bfloat16
    ]

    num_tokens = hidden_states.size(0)
    E, N, _ = w1.size()
    K = w2.size(1)
    if global_num_experts == -1:
        global_num_experts = E
    top_k_num = topk_ids.size(1)
    # 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,
                                         use_mxfp4_w4a4=use_mxfp4_w4a4,
                                         dtype=hidden_states.dtype)

    # Note: for use_int8_w8a16 or use_int4_w4a16, the activations are
    # quantized prior to calling fused_experts.
    quant_dtype = _get_config_quant_dtype(use_fp8_w8a8=use_fp8_w8a8,
                                          use_int8_w8a8=use_int8_w8a8,
                                          use_mxfp4_w4a4=use_mxfp4_w4a4)

    get_config_func = functools.partial(
        try_get_optimal_moe_config,
        w1.size(),
        w2.size(),
        top_k_num,
        config_dtype,
        block_shape=block_shape,
    )

    config = get_config_func(M)

    # We can reuse the memory between these because by the time we need
    # cache3, we're done with cache1
    cache13 = torch.empty(M * top_k_num * max(N, K),
                          device=hidden_states.device,
                          dtype=hidden_states.dtype)
    intermediate_cache1 = cache13[:M * top_k_num * N].view(M, top_k_num, N)
    intermediate_cache3 = cache13[:M * top_k_num * K].view(M, top_k_num, K)

    # This needs separate memory since it's used concurrently with cache1
    intermediate_cache2 = torch.empty((M * top_k_num, N // 2),
                                      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)

    if use_mxfp4_w4a4:
        # Weight has to be dequantized for mxfp4 emulation.
        w1 = dequant_mxfp4(w1, w1_scale, hidden_states.dtype)
        w1_scale = None
        w2 = dequant_mxfp4(w2, w2_scale, hidden_states.dtype)
        w2_scale = None

    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.size()

        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 *
                                                      topk_ids.size(1)]
            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]
        qcurr_hidden_states, a1q_scale = moe_kernel_quantize_input(
            A=curr_hidden_states,
            A_scale=a1_scale,
            quant_dtype=quant_dtype,
            per_act_token_quant=per_channel_quant,
            block_shape=block_shape)

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

        invoke_fused_moe_kernel(qcurr_hidden_states,
                                w1,
                                intermediate_cache1,
                                a1q_scale,
                                w1_scale,
                                w1_zp,
                                curr_topk_weights,
                                sorted_token_ids,
                                expert_ids,
                                num_tokens_post_padded,
                                apply_router_weight_on_input,
                                top_k_num,
                                config,
                                compute_type=compute_type,
                                use_fp8_w8a8=use_fp8_w8a8,
                                use_int8_w8a8=use_int8_w8a8,
                                use_int8_w8a16=use_int8_w8a16,
                                use_int4_w4a16=use_int4_w4a16,
                                per_channel_quant=per_channel_quant,
                                block_shape=block_shape,
                                B_bias=w1_bias)

        # Activation function with multiplication
        if activation == "silu":
            torch.ops._C.silu_and_mul(intermediate_cache2,
                                      intermediate_cache1.view(-1, N))
        elif activation == "gelu":
            torch.ops._C.gelu_and_mul(intermediate_cache2,
                                      intermediate_cache1.view(-1, N))
        elif activation == "swigluoai":
            # alpha = 1.702, limit = 7.0
            torch.ops._C.swigluoai_and_mul(intermediate_cache2,
                                           intermediate_cache1.view(-1, N))
        # Activation function without multiplication
        elif activation == SILU_NO_MUL:
            intermediate_cache2 = F.silu(intermediate_cache1.view(-1, N))
        elif activation == GELU_NO_MUL:
            intermediate_cache2 = F.gelu(intermediate_cache1.view(-1, N))

        else:
            raise ValueError(f"Unsupported FusedMoe activation: {activation}.")

        qintermediate_cache2, a2q_scale = moe_kernel_quantize_input(
            A=intermediate_cache2,
            A_scale=a2_scale,
            quant_dtype=quant_dtype,
            per_act_token_quant=per_channel_quant,
            block_shape=block_shape)

        invoke_fused_moe_kernel(qintermediate_cache2,
                                w2,
                                intermediate_cache3,
                                a2q_scale,
                                w2_scale,
                                w2_zp,
                                curr_topk_weights,
                                sorted_token_ids,
                                expert_ids,
                                num_tokens_post_padded,
                                not apply_router_weight_on_input,
                                1,
                                config,
                                compute_type=compute_type,
                                use_fp8_w8a8=use_fp8_w8a8,
                                use_int8_w8a8=use_int8_w8a8,
                                use_int8_w8a16=use_int8_w8a16,
                                use_int4_w4a16=use_int4_w4a16,
                                per_channel_quant=per_channel_quant,
                                block_shape=block_shape,
                                B_bias=w2_bias)

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

    return out_hidden_states


class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):

    def __init__(
        self,
        quant_config: FusedMoEQuantConfig,
    ):
        super().__init__(quant_config)

    @property
    def activation_formats(
        self
    ) -> tuple[mk.FusedMoEActivationFormat, mk.FusedMoEActivationFormat]:
        return (mk.FusedMoEActivationFormat.Standard,
                mk.FusedMoEActivationFormat.Standard)

    def supports_chunking(self) -> bool:
        return True

    def supports_expert_map(self) -> bool:
        return True

    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
        return TopKWeightAndReduceNoOP()

    def workspace_shapes(
        self,
        a: torch.Tensor,
        aq: torch.Tensor,
        M: int,
        N: int,
        K: int,
        topk: int,
        global_num_experts: int,
        local_num_experts: int,
        expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], torch.dtype]:
        workspace1 = (M, topk, max(N // 2, K))
        workspace2 = (M, topk, max(N, K))
        output = (M, K)
        return (workspace1, workspace2, output, a.dtype)

    def apply(
        self,
        output: torch.Tensor,
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        activation: str,
        global_num_experts: int,
        expert_map: Optional[torch.Tensor],
        a1q_scale: Optional[torch.Tensor],
        a2_scale: Optional[torch.Tensor],
        workspace13: torch.Tensor,
        workspace2: torch.Tensor,
        expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
        apply_router_weight_on_input: bool,
    ):
        # Check constraints.
        if self.quant_config.use_int4_w4a16:
            assert hidden_states.size(-1) // 2 == w1.size(2), (
                "Hidden size mismatch")
        else:
            assert hidden_states.size(-1) == w1.size(2), \
                (f"Hidden size mismatch {hidden_states.size(-1)} "
                 f"!= {w1.size(2)}")

        assert hidden_states.is_contiguous(
        ), "Hidden_states must be contiguous"
        assert hidden_states.dim() == 2
        assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
        assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
        assert hidden_states.dtype in [
            torch.float32, torch.float16, torch.bfloat16, torch.float8_e4m3fn
        ]

        E, num_tokens, N, K, top_k_num = mk._moe_problem_size(
            hidden_states, w1, w2, topk_ids)

        if global_num_experts == -1:
            global_num_experts = E

        config = try_get_optimal_moe_config(
            w1.size(),
            w2.size(),
            top_k_num,
            self.quant_config.config_name(hidden_states.dtype),
            num_tokens,
            block_shape=self.block_shape,
        )

        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
        elif hidden_states.dtype == torch.float8_e4m3fn:
            compute_type = tl.bfloat16
        else:
            raise ValueError(
                f"Unsupported compute_type: {hidden_states.dtype}")

        # Note that the output tensor might be in workspace1
        intermediate_cache1 = _resize_cache(workspace2,
                                            (num_tokens, top_k_num, N))
        intermediate_cache2 = _resize_cache(workspace13,
                                            (num_tokens * top_k_num, N // 2))
        intermediate_cache3 = _resize_cache(workspace2,
                                            (num_tokens, top_k_num, K))

        sorted_token_ids, expert_ids, num_tokens_post_padded = (
            moe_align_block_size(topk_ids, config['BLOCK_SIZE_M'],
                                 global_num_experts, expert_map))

        invoke_fused_moe_kernel(
            hidden_states,
            w1,
            intermediate_cache1,
            a1q_scale,
            self.w1_scale,
            self.w1_zp,
            None,  # topk_weights
            sorted_token_ids,
            expert_ids,
            num_tokens_post_padded,
            False,  # mul_routed_weights
            top_k_num,
            config,
            compute_type=compute_type,
            use_fp8_w8a8=self.quant_config.use_fp8_w8a8,
            use_int8_w8a8=self.quant_config.use_int8_w8a8,
            use_int8_w8a16=self.quant_config.use_int8_w8a16,
            use_int4_w4a16=self.quant_config.use_int4_w4a16,
            per_channel_quant=self.per_act_token_quant,
            block_shape=self.block_shape,
            B_bias=self.w1_bias,
        )

        self.activation(activation, intermediate_cache2,
                        intermediate_cache1.view(-1, N))

        a2q_scale: Optional[torch.Tensor] = None

        qintermediate_cache2, a2q_scale = moe_kernel_quantize_input(
            intermediate_cache2, a2_scale, self.quant_dtype,
            self.per_act_token_quant, self.block_shape)

        invoke_fused_moe_kernel(
            qintermediate_cache2,
            w2,
            intermediate_cache3,
            a2q_scale,
            self.w2_scale,
            self.w2_zp,
            topk_weights,
            sorted_token_ids,
            expert_ids,
            num_tokens_post_padded,
            not apply_router_weight_on_input,
            1,
            config,
            compute_type=compute_type,
            use_fp8_w8a8=self.quant_config.use_fp8_w8a8,
            use_int8_w8a8=self.quant_config.use_int8_w8a8,
            use_int8_w8a16=self.quant_config.use_int8_w8a16,
            use_int4_w4a16=self.quant_config.use_int4_w4a16,
            per_channel_quant=self.per_act_token_quant,
            block_shape=self.block_shape,
            B_bias=self.w2_bias,
        )

        ops.moe_sum(intermediate_cache3, output)


def modular_triton_fused_moe(
        quant_config: FusedMoEQuantConfig) -> mk.FusedMoEModularKernel:
    return mk.FusedMoEModularKernel(
        MoEPrepareAndFinalizeNoEP(),
        TritonExperts(quant_config),
    )
