"""DSV4 Dense Router — fused BF16 GEMM + sqrt(softplus) + bias + top-k for decode.

Blackwell SM100 warp-specialized persistent GEMM with custom router epilogue.
See dense_router_decode_epilogue.py for the epilogue implementation.
"""

from __future__ import annotations
from typing import Tuple, Optional
import torch


def dense_router_dispatch(
    hidden_states: torch.Tensor,       # [N, hidden_size] BF16
    W_gate: torch.Tensor,              # [hidden_size, num_experts] BF16
    e_bias: torch.Tensor,              # [num_experts] FP32
    routed_scaling_factor: float,
    top_k: int,
    out_weights: torch.Tensor,         # [N, top_k] FP32, pre-allocated
    out_ids: torch.Tensor,             # [N, top_k] int32, pre-allocated
):
    """Dispatch the dense router kernel.

    For decode (N <= 64): uses the fused CuTeDSL kernel.
    For prefill (N > 64): uses torch.nn.functional.linear + activation_topk.
    """
    N = hidden_states.shape[0]

    if N <= 64:
        try:
            _run_fused_decode(
                hidden_states, W_gate, e_bias,
                routed_scaling_factor, top_k,
                out_weights, out_ids,
            )
            return
        except (ImportError, NotImplementedError):
            pass  # fall through to prefill path

    _run_prefill_path(
        hidden_states, W_gate, e_bias,
        routed_scaling_factor, top_k,
        out_weights, out_ids,
    )


def _run_prefill_path(
    hidden_states, W_gate, e_bias,
    routed_scaling_factor, top_k,
    out_weights, out_ids,
):
    """GEMM via torch.nn.functional.linear, then fused activation + top-k."""
    logits = torch.nn.functional.linear(hidden_states.float(), W_gate.float())
    from dsv4.kernels.router._activation_topk import run_fused_activation_topk
    run_fused_activation_topk(
        logits, e_bias, routed_scaling_factor, top_k,
        out_weights, out_ids,
    )


def _run_fused_decode(
    hidden_states, W_gate, e_bias,
    routed_scaling_factor, top_k,
    out_weights, out_ids,
):
    """Run the fused CuTeDSL decode kernel (BF16 GEMM + epilogue in one launch)."""
    from dsv4.kernels.router.dense_router_decode_kernel import DenseRouterDecodeKernel
    N = hidden_states.shape[0]
    E = W_gate.shape[1]
    K = W_gate.shape[0]

    kernel = DenseRouterDecodeKernel(
        mma_tiler_mn=(128, 128),
        cluster_shape_mn=(1, 1),
        top_k=top_k,
    )
    kernel.run(
        hidden_states, W_gate, e_bias,
        out_weights, out_ids,
        N, E, K,
        routed_scaling_factor, top_k,
    )