submission 674660

#!POPCORN leaderboard amd-moe-mxfp4
#!POPCORN gpu MI355X

"""
AMD E2E SpeedRun - MXFP4 MoE Kernel (1500 pts)
================================================
DeepSeek-R1 style MXFP4 Mixture-of-Experts fused kernel for MI355X.

Flow:
  1. Quant activations to MXFP4 (per-1x32 dynamic)
  2. Stage 1: gate_up GEMM (a4w4) + SiLU activation
  3. Stage 2: down GEMM (a4w4)
  4. Weighted reduction across top-k experts

Input tuple:
  hidden_states:                [M, d_hidden]                           bf16
  gate_up_weight:               [E, 2*d_expert_pad, d_hidden_pad//2]    fp4x2
  down_weight:                  [E, d_hidden_pad, d_expert_pad//2]      fp4x2
  gate_up_weight_scale:         [E, 2*d_expert_pad, scale_K]            e8m0
  down_weight_scale:            [E, d_hidden_pad, scale_K]              e8m0
  gate_up_weight_shuffled:      [E, 2*d_expert_pad, d_hidden_pad//2]    fp4x2 (shuffled)
  down_weight_shuffled:         [E, d_hidden_pad, d_expert_pad//2]      fp4x2 (shuffled)
  gate_up_weight_scale_shuffled:[padded, flat]                          e8m0  (shuffled)
  down_weight_scale_shuffled:   [padded, flat]                          e8m0  (shuffled)
  topk_weights:                 [M, total_top_k]                        float32
  topk_ids:                     [M, total_top_k]                        int32
  config:                       dict

Output: [M, d_hidden] bf16

TODO: Optimize fused_moe kernel for MI355X
"""
import torch
from typing import Dict
from task import input_t, output_t

from aiter import ActivationType, QuantType
from aiter.fused_moe import fused_moe


def custom_kernel(data: input_t) -> output_t:
    """
    Baseline DeepSeek-R1 MXFP4 MoE using aiter's fused_moe.
    """
    (
        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,
        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