submission 531332

# from utils import make_match_reference
from task import input_t, output_t
# import torch
import torch.nn.functional as F
# from typing import Dict, Tuple, Optional
# import math

import aiter
from aiter import ActivationType, QuantType, dtypes
from aiter.fused_moe import fused_moe
# from aiter.utility import fp4_utils
# from aiter.ops.shuffle import shuffle_weight

# ──────────────────────────────────────────────────────────────────────
# ref_kernel: calls AITER fused_moe with MXFP4 quantized weights
# ──────────────────────────────────────────────────────────────────────
def custom_kernel(data: input_t) -> output_t:
    """
    Reference implementation using AITER's fused_moe kernel with MXFP4 quantized weights.

    Input data tuple (E = n_routed_experts + n_shared_experts, total_top_k = routed + shared):
        hidden_states:                [M, d_hidden]                           bf16
        gate_up_weight:               [E, 2*d_expert_pad, d_hidden_pad//2]    fp4x2  (raw, before shuffle)
        down_weight:                  [E, d_hidden_pad, d_expert_pad//2]      fp4x2  (raw, before shuffle)
        gate_up_weight_scale:         [E, 2*d_expert_pad, scale_K]            e8m0   (raw, before shuffle)
        down_weight_scale:            [E, d_hidden_pad, scale_K]              e8m0   (raw, before shuffle)
        gate_up_weight_shuffled:      [E, 2*d_expert_pad, d_hidden_pad//2]    fp4x2  (pre-shuffled)
        down_weight_shuffled:         [E, d_hidden_pad, d_expert_pad//2]      fp4x2  (pre-shuffled)
        gate_up_weight_scale_shuffled:[padded, flat]                          e8m0   (pre-shuffled)
        down_weight_scale_shuffled:   [padded, flat]                          e8m0   (pre-shuffled)
        topk_weights:                 [M, total_top_k]                        float32
        topk_ids:                     [M, total_top_k]                        int32
        config:                       dict

    Returns:
        output: [M, d_hidden] bf16
    """
    (
        hidden_states,
        gate_up_weight,
        down_weight,
        gate_up_weight_scale,
        down_weight_scale,
        gate_up_weight_shuffled,
        down_weight_shuffled,
        gate_up_weight_scale_shuffled,
        down_weight_scale_shuffled,
        topk_weights,
        topk_ids,
        config,
    ) = data

    hidden_pad = config["d_hidden_pad"] - config["d_hidden"]
    intermediate_pad = config["d_expert_pad"] - config["d_expert"]

    output = fused_moe(
        hidden_states,
        gate_up_weight_shuffled,
        down_weight_shuffled,
        topk_weights,
        topk_ids,
        expert_mask=None,
        activation=ActivationType.Silu,
        quant_type=QuantType.per_1x32,  # MXFP4 uses per_1x32 block scaling
        doweight_stage1=False,
        w1_scale=gate_up_weight_scale_shuffled,
        w2_scale=down_weight_scale_shuffled,
        a1_scale=None,
        a2_scale=None,
        hidden_pad=hidden_pad,
        intermediate_pad=intermediate_pad,
    )

    return output