import math
from dataclasses import dataclass
import torch
from torch import nn
import torch.nn.functional as F
from task import input_t, output_t

# Global cache for RoPE matrices
_rope_cache = {}


def get_rope_cache(d_model: int, max_seq_len: int, device="cuda"):
    """Get or create cached RoPE rotation matrices"""
    cache_key = (d_model, max_seq_len, device)
    if cache_key not in _rope_cache:
        theta = 10000 ** (
            -torch.arange(0, d_model // 2, dtype=torch.bfloat16, device=device) / (d_model // 2)
        )
        positions = torch.arange(max_seq_len, dtype=torch.bfloat16, device=device)
        angles = torch.einsum("i,j->ij", positions, theta)
        angles = torch.cat([angles, angles], dim=-1)
        cos_cached = angles.cos()
        sin_cached = angles.sin()
        _rope_cache[cache_key] = (cos_cached, sin_cached)
    return _rope_cache[cache_key]


def apply_rope(x: torch.Tensor, cos_cached: torch.Tensor, sin_cached: torch.Tensor, start_pos: int):
    """Apply RoPE using precomputed cos/sin matrices"""
    seq_len = x.size(-2)
    cos = cos_cached[start_pos : start_pos + seq_len]
    sin = sin_cached[start_pos : start_pos + seq_len]

    # Rotate half efficiently
    x1, x2 = x.chunk(2, dim=-1)
    rotated = torch.cat((-x2, x1), dim=-1)

    return x * cos + rotated * sin


class KVCache(nn.Module):
    def __init__(self, kv_cache_shape: tuple) -> None:
        super().__init__()
        self.register_buffer(
            "data", torch.zeros(kv_cache_shape, dtype=torch.bfloat16, device="cuda")
        )
        self.seq_len = 0
        self.zero()

    def zero(self) -> None:
        self.data.zero_()

    def get_data(self) -> torch.Tensor:
        return self.data

    def forward(self, c_kv: torch.Tensor) -> torch.Tensor:
        assert self.seq_len + c_kv.size(1) <= self.data.size(1), "KV Cache Exceeded"
        self.data = self.data.to(c_kv.dtype)
        self.data[:, self.seq_len : self.seq_len + c_kv.size(1), :] = c_kv
        self.seq_len += c_kv.size(1)
        return self.data[:, : self.seq_len], self.seq_len


@dataclass
class Config:
    batch_size: int
    dim: int
    n_heads: int
    q_lora_rank: int
    kv_lora_rank: int
    qk_nope_head_dim: int
    qk_rope_head_dim: int
    v_head_dim: int
    seq_len: int
    max_seq_len: int
    kv_cache_shape: tuple
    Q_proj_down_weight: torch.Tensor
    Q_proj_up_weight: torch.Tensor
    KV_proj_down_weight: torch.Tensor
    KV_proj_up_weight: torch.Tensor
    wo_weight: torch.Tensor


def mla_forward(x: torch.Tensor, kv_cache: KVCache, config: Config) -> torch.Tensor:
    # seq_len = 1 always here
    batch_size, seq_len, model_dim = x.size()

    # Get cached RoPE matrices
    cos_cached, sin_cached = get_rope_cache(config.qk_rope_head_dim, config.max_seq_len)

    ################################################################################
    #                 Step 1: Handle down-projection + KV cache                    #
    ################################################################################
    q_lora = F.linear(x, config.Q_proj_down_weight)
    kv_lora = F.linear(x, config.KV_proj_down_weight)
    kv_lora, kv_len = kv_cache(kv_lora)
    query_pos = kv_len - 1

    ################################################################################
    #                  Step 2: Up-project and prepare NoPE + RoPE                  #
    ################################################################################
    # Handle queries Q first
    q_nope_and_rope = F.linear(q_lora, config.Q_proj_up_weight).view(
        batch_size, seq_len, config.n_heads, config.qk_nope_head_dim + config.qk_rope_head_dim
    )
    q_nope, q_rope_tensor = q_nope_and_rope.split(
        [config.qk_nope_head_dim, config.qk_rope_head_dim], dim=-1
    )

    # Handle keys and values K/V. V does not need RoPE
    kv_nope, k_rope_tensor = kv_lora.split([config.kv_lora_rank, config.qk_rope_head_dim], dim=-1)
    kv_nope = F.linear(kv_nope, config.KV_proj_up_weight).view(
        batch_size, kv_len, config.n_heads, config.qk_nope_head_dim + config.v_head_dim
    )
    k_nope, v = kv_nope.split([config.qk_nope_head_dim, config.v_head_dim], dim=-1)

    ################################################################################
    #                    Step 3: Handle RoPE Stream (OPTIMIZED)                    #
    ################################################################################
    # Compute RoPE for queries - use cached matrices
    q_rope_tensor = q_rope_tensor.permute(0, 2, 1, 3)  # bs x n_heads x seq_len x rope_head_dim
    q_rope_tensor = apply_rope(q_rope_tensor, cos_cached, sin_cached, query_pos)
    q_nope = q_nope.permute(0, 2, 1, 3)  # bs x n_heads x seq_len x nope_head_dim
    q = torch.cat([q_nope, q_rope_tensor], dim=-1)

    # Compute RoPE for keys - use cached matrices and optimize expansion
    k_rope_tensor = k_rope_tensor.unsqueeze(1)  # bs x 1 x kv_len x rope_head_dim
    k_rope_tensor = apply_rope(k_rope_tensor, cos_cached, sin_cached, 0)
    k_rope_tensor = k_rope_tensor.expand(
        -1, config.n_heads, -1, -1
    )  # bs x n_heads x kv_len x rope_head_dim
    k_nope = k_nope.permute(0, 2, 1, 3)  # bs x n_heads x kv_len x nope_head_dim
    k = torch.cat([k_nope, k_rope_tensor], dim=-1)

    ################################################################################
    #                        Compute Multi-head Attention                          #
    ################################################################################
    v = v.permute(0, 2, 1, 3)  # bs x n_heads x kv_len x v_head_dim

    # Fused attention computation
    scale = math.sqrt(config.qk_rope_head_dim + config.qk_nope_head_dim)
    scores = torch.matmul(q, k.transpose(-1, -2)) / scale
    attn = F.softmax(scores, dim=-1).to(torch.bfloat16)
    y = torch.matmul(attn, v).view(batch_size, 1, -1)
    y = F.linear(y, config.wo_weight)
    return y, kv_cache.get_data()


# entry point for evaluation
def custom_kernel(data: input_t) -> output_t:
    # via generate_input
    config, x, kv_cache = data
    output, kv_cache = mla_forward(x, kv_cache, config)
    return output, kv_cache

python eval.py leaderboard /tmp/tmpu42g1hjr
Buff name: data, shape: torch.Size([128, 8192, 576])
Buff name: data, shape: torch.Size([128, 8192, 576])
Buff name: data, shape: torch.Size([128, 8192, 576])
Buff name: data, shape: torch.Size([128, 8192, 576])
