model.py

"""
SID-GPT v2 - LLaMA-style transformer for SID register prediction.

258-token vocabulary (0-255 byte values, 256=SEP, 257=FRAME).
Predicts next SID register token given sequence of previous tokens.
"""

import math
from dataclasses import dataclass
from typing import Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F


@dataclass
class ModelConfig:
    n_embd: int = 512
    n_layer: int = 8
    n_head: int = 8
    n_kv_head: int = 2
    intermediate_size: int = 1408
    block_size: int = 4096
    vocab_size: int = 258
    rope_theta: float = 10000.0
    bias: bool = False
    dropout: float = 0.0

    @staticmethod
    def small() -> "ModelConfig":
        return ModelConfig(
            n_embd=512,
            n_layer=8,
            n_head=8,
            n_kv_head=2,
            intermediate_size=1408,
        )

    @staticmethod
    def large() -> "ModelConfig":
        return ModelConfig(
            n_embd=1024,
            n_layer=24,
            n_head=16,
            n_kv_head=4,
            intermediate_size=2816,
        )


class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Norm in float32 for stability
        norm_x = x.float()
        norm_x = norm_x * torch.rsqrt(
            norm_x.pow(2).mean(-1, keepdim=True) + self.eps
        )
        return (norm_x * self.weight).to(x.dtype)


def precompute_rope_freqs(
    dim: int,
    max_seq_len: int,
    theta: float = 10000.0,
    device: Optional[torch.device] = None,
) -> torch.Tensor:
    """
    Precompute complex-valued RoPE frequency table.

    For each position p and frequency index i:
      freq_i = 1 / (theta^(2i/dim))
      rope[p, i] = exp(j * p * freq_i)

    Returns complex tensor of shape (max_seq_len, dim//2).
    """
    freqs = 1.0 / (
        theta
        ** (torch.arange(0, dim, 2, device=device).float() / dim)
    )
    t = torch.arange(max_seq_len, device=device).float()
    freqs = torch.outer(t, freqs)
    return torch.polar(torch.ones_like(freqs), freqs)


def apply_rope(
    xq: torch.Tensor,
    xk: torch.Tensor,
    freqs: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Apply rotary embeddings to Q and K via complex multiplication.

    Reshapes last dim of Q/K into pairs -> complex,
    multiplies by precomputed freqs, converts back to real.
    """
    xq_c = torch.view_as_complex(
        xq.float().reshape(*xq.shape[:-1], -1, 2)
    )
    xk_c = torch.view_as_complex(
        xk.float().reshape(*xk.shape[:-1], -1, 2)
    )
    # xq_c/xk_c: (B, heads, T, head_dim//2)
    # freqs: (T, head_dim//2) -> (1, 1, T, head_dim//2)
    freqs = freqs.unsqueeze(0).unsqueeze(1)
    xq_out = torch.view_as_real(xq_c * freqs).flatten(-2)
    xk_out = torch.view_as_real(xk_c * freqs).flatten(-2)
    return xq_out.to(xq.dtype), xk_out.to(xk.dtype)


class GQAAttention(nn.Module):
    """
    Grouped-Query Attention: fewer KV heads than Q heads.
    Group size = n_head / n_kv_head. KV heads are expanded
    via repeat_interleave to match Q head count.
    """

    def __init__(self, config: ModelConfig):
        super().__init__()
        self.n_head = config.n_head
        self.n_kv_head = config.n_kv_head
        self.head_dim = config.n_embd // config.n_head
        self.n_rep = config.n_head // config.n_kv_head
        self.block_size = config.block_size

        self.q_proj = nn.Linear(
            config.n_embd,
            config.n_head * self.head_dim,
            bias=config.bias,
        )
        self.k_proj = nn.Linear(
            config.n_embd,
            config.n_kv_head * self.head_dim,
            bias=config.bias,
        )
        self.v_proj = nn.Linear(
            config.n_embd,
            config.n_kv_head * self.head_dim,
            bias=config.bias,
        )
        self.o_proj = nn.Linear(
            config.n_embd,
            config.n_embd,
            bias=config.bias,
        )
        self.attn_dropout = nn.Dropout(config.dropout)
        self.resid_dropout = nn.Dropout(config.dropout)

        # KV cache buffers (populated during inference)
        self.cache_k: Optional[torch.Tensor] = None
        self.cache_v: Optional[torch.Tensor] = None

    def forward(
        self,
        x: torch.Tensor,
        freqs: torch.Tensor,
        start_pos: Optional[int] = None,
    ) -> torch.Tensor:
        B, T, _ = x.shape

        q = self.q_proj(x)
        k = self.k_proj(x)
        v = self.v_proj(x)

        q = q.view(B, T, self.n_head, self.head_dim)
        k = k.view(B, T, self.n_kv_head, self.head_dim)
        v = v.view(B, T, self.n_kv_head, self.head_dim)

        # Transpose to (B, heads, T, head_dim)
        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)

        # Apply RoPE to Q and K
        q, k = apply_rope(q, k, freqs)

        if start_pos is not None:
            # Inference with KV-cache
            if self.cache_k is None or start_pos == 0:
                self.cache_k = torch.zeros(
                    B, self.n_kv_head, self.block_size,
                    self.head_dim,
                    device=x.device, dtype=x.dtype,
                )
                self.cache_v = torch.zeros_like(self.cache_k)

            end_pos = start_pos + T
            self.cache_k[:, :, start_pos:end_pos, :] = k
            self.cache_v[:, :, start_pos:end_pos, :] = v

            k = self.cache_k[:, :, :end_pos, :]
            v = self.cache_v[:, :, :end_pos, :]

        # Expand KV heads to match Q heads
        if self.n_rep > 1:
            k = k.repeat_interleave(self.n_rep, dim=1)
            v = v.repeat_interleave(self.n_rep, dim=1)

        is_causal = start_pos is None or start_pos == 0
        if start_pos is not None and start_pos > 0:
            # Single-token decode: no causal mask needed
            # (attending to all cached positions)
            is_causal = False

        y = F.scaled_dot_product_attention(
            q, k, v,
            dropout_p=(
                self.attn_dropout.p if self.training else 0.0
            ),
            is_causal=is_causal,
        )

        y = y.transpose(1, 2).contiguous().view(B, T, -1)
        return self.resid_dropout(self.o_proj(y))


class SwiGLUFFN(nn.Module):
    """
    SwiGLU feed-forward: down(silu(gate(x)) * up(x))
    Three projections, no bias.
    """

    def __init__(self, config: ModelConfig):
        super().__init__()
        self.gate_proj = nn.Linear(
            config.n_embd,
            config.intermediate_size,
            bias=config.bias,
        )
        self.up_proj = nn.Linear(
            config.n_embd,
            config.intermediate_size,
            bias=config.bias,
        )
        self.down_proj = nn.Linear(
            config.intermediate_size,
            config.n_embd,
            bias=config.bias,
        )
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.dropout(
            self.down_proj(
                F.silu(self.gate_proj(x)) * self.up_proj(x)
            )
        )


class TransformerBlock(nn.Module):
    """Pre-norm residual: x + attn(norm(x)), h + ffn(norm(h))"""

    def __init__(self, config: ModelConfig):
        super().__init__()
        self.attn_norm = RMSNorm(config.n_embd)
        self.attn = GQAAttention(config)
        self.ffn_norm = RMSNorm(config.n_embd)
        self.ffn = SwiGLUFFN(config)

    def forward(
        self,
        x: torch.Tensor,
        freqs: torch.Tensor,
        start_pos: Optional[int] = None,
    ) -> torch.Tensor:
        h = x + self.attn(self.attn_norm(x), freqs, start_pos)
        return h + self.ffn(self.ffn_norm(h))


class Transformer(nn.Module):
    def __init__(self, config: ModelConfig):
        super().__init__()
        self.config = config

        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
        self.drop = nn.Dropout(config.dropout)
        self.blocks = nn.ModuleList(
            [TransformerBlock(config) for _ in range(config.n_layer)]
        )
        self.norm = RMSNorm(config.n_embd)
        self.lm_head = nn.Linear(
            config.n_embd, config.vocab_size, bias=False
        )

        # Weight tying
        self.lm_head.weight = self.tok_emb.weight

        # Precompute RoPE frequencies
        head_dim = config.n_embd // config.n_head
        self.register_buffer(
            "rope_freqs",
            precompute_rope_freqs(
                head_dim, config.block_size, config.rope_theta
            ),
            persistent=False,
        )

        self.apply(self._init_weights)
        # Scale residual projections
        res_scale = 1.0 / math.sqrt(2 * config.n_layer)
        for block in self.blocks:
            block.attn.o_proj.weight.data *= res_scale
            block.ffn.down_proj.weight.data *= res_scale

    def _init_weights(self, module: nn.Module):
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(
        self,
        idx: torch.Tensor,
        targets: Optional[torch.Tensor] = None,
        start_pos: Optional[int] = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        B, T = idx.shape
        assert T <= self.config.block_size, (
            f"Sequence length {T} exceeds block_size "
            f"{self.config.block_size}"
        )

        x = self.drop(self.tok_emb(idx))

        if start_pos is not None:
            freqs = self.rope_freqs[start_pos : start_pos + T]
        else:
            freqs = self.rope_freqs[:T]

        for block in self.blocks:
            x = block(x, freqs, start_pos)

        x = self.norm(x)
        logits = self.lm_head(x)

        loss = None
        if targets is not None:
            loss = F.cross_entropy(
                logits.view(-1, logits.size(-1)),
                targets.view(-1),
            )

        return logits, loss

    def count_params(self) -> int:
        # Subtract lm_head since it's tied
        n = sum(p.numel() for p in self.parameters())
        n -= self.lm_head.weight.numel()
        return n