model.py

"""
Tiny Transformer with modern components:
- RoPE (Rotary Position Embeddings)
- RMSNorm
- SwiGLU activation
- Weight tying
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math

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):
        norm = torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
        return x * norm * self.weight


class RotaryEmbedding(nn.Module):
    def __init__(self, dim: int, max_seq_len: int = 512, base: int = 10000):
        super().__init__()
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq)
        self.max_seq_len = max_seq_len
        
    def forward(self, x, seq_len: int):
        t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        return emb.cos(), emb.sin()


def rotate_half(x):
    x1, x2 = x.chunk(2, dim=-1)
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin):
    cos = cos.unsqueeze(0).unsqueeze(0)  # [1, 1, seq_len, dim]
    sin = sin.unsqueeze(0).unsqueeze(0)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


class SwiGLU(nn.Module):
    def __init__(self, hidden_size: int, intermediate_size: int):
        super().__init__()
        self.w1 = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.w2 = nn.Linear(intermediate_size, hidden_size, bias=False)
        self.w3 = nn.Linear(hidden_size, intermediate_size, bias=False)

    def forward(self, x):
        return self.w2(F.silu(self.w1(x)) * self.w3(x))


class Attention(nn.Module):
    def __init__(self, hidden_size: int, num_heads: int, dropout: float = 0.0):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads
        
        self.q_proj = nn.Linear(hidden_size, hidden_size, bias=False)
        self.k_proj = nn.Linear(hidden_size, hidden_size, bias=False)
        self.v_proj = nn.Linear(hidden_size, hidden_size, bias=False)
        self.o_proj = nn.Linear(hidden_size, hidden_size, bias=False)
        
        self.rotary = RotaryEmbedding(self.head_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, mask=None):
        B, T, C = x.shape
        
        q = self.q_proj(x).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
        k = self.k_proj(x).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
        v = self.v_proj(x).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
        
        cos, sin = self.rotary(x, T)
        q, k = apply_rotary_pos_emb(q, k, cos, sin)
        
        # Scaled dot-product attention
        scale = 1.0 / math.sqrt(self.head_dim)
        attn = torch.matmul(q, k.transpose(-2, -1)) * scale
        
        if mask is not None:
            attn = attn.masked_fill(mask == 0, float('-inf'))
        
        attn = F.softmax(attn, dim=-1)
        attn = self.dropout(attn)
        
        out = torch.matmul(attn, v)
        out = out.transpose(1, 2).contiguous().view(B, T, C)
        return self.o_proj(out)


class TransformerBlock(nn.Module):
    def __init__(self, hidden_size: int, num_heads: int, intermediate_size: int, dropout: float = 0.0):
        super().__init__()
        self.norm1 = RMSNorm(hidden_size)
        self.attn = Attention(hidden_size, num_heads, dropout)
        self.norm2 = RMSNorm(hidden_size)
        self.ffn = SwiGLU(hidden_size, intermediate_size)

    def forward(self, x, mask=None):
        x = x + self.attn(self.norm1(x), mask)
        x = x + self.ffn(self.norm2(x))
        return x


class TinyLLM(nn.Module):
    def __init__(
        self,
        vocab_size: int = 32000,
        hidden_size: int = 512,
        num_layers: int = 12,
        num_heads: int = 8,
        intermediate_size: int = 1408,
        max_position_embeddings: int = 512,
        dropout: float = 0.0,
        tie_weights: bool = True,
    ):
        super().__init__()
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        
        self.embed_tokens = nn.Embedding(vocab_size, hidden_size)
        self.layers = nn.ModuleList([
            TransformerBlock(hidden_size, num_heads, intermediate_size, dropout)
            for _ in range(num_layers)
        ])
        self.norm = RMSNorm(hidden_size)
        self.lm_head = nn.Linear(hidden_size, vocab_size, bias=False)
        
        if tie_weights:
            self.lm_head.weight = self.embed_tokens.weight
        
        # Causal mask
        self.register_buffer(
            "causal_mask",
            torch.tril(torch.ones(max_position_embeddings, max_position_embeddings))
        )
        
        self._init_weights()
    
    def _init_weights(self):
        for module in self.modules():
            if isinstance(module, nn.Linear):
                torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            elif isinstance(module, nn.Embedding):
                torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
    
    def forward(self, input_ids, labels=None):
        B, T = input_ids.shape
        
        x = self.embed_tokens(input_ids)
        mask = self.causal_mask[:T, :T]
        
        for layer in self.layers:
            x = layer(x, mask)
        
        x = self.norm(x)
        logits = self.lm_head(x)
        
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss = F.cross_entropy(
                shift_logits.view(-1, self.vocab_size),
                shift_labels.view(-1),
                ignore_index=-100
            )
        
        return {"loss": loss, "logits": logits}
    
    def count_parameters(self):
        return sum(p.numel() for p in self.parameters())


if __name__ == "__main__":
    # Test model
    model = TinyLLM()
    print(f"Parameters: {model.count_parameters() / 1e6:.2f}M")
    
    x = torch.randint(0, 32000, (2, 128))
    out = model(x, labels=x)
    print(f"Loss: {out['loss'].item():.4f}")
    print(f"Logits shape: {out['logits'].shape}")