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model.py

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+"""
+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}")