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model.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from dataclasses import dataclass
+
+@dataclass
+class ModelConfig:
+    """Configuration matching SmolLM2-135M"""
+    vocab_size: int = 49152
+    hidden_size: int = 576
+    num_hidden_layers: int = 30
+    num_attention_heads: int = 9
+    intermediate_size: int = 1536
+    max_position_embeddings: int = 2048
+    layer_norm_eps: float = 1e-5
+    hidden_dropout_prob: float = 0.1
+    attention_dropout_prob: float = 0.1
+    
+    @property
+    def head_dim(self):
+        return self.hidden_size // self.num_attention_heads
+
+
+class RotaryEmbedding(nn.Module):
+    """Rotary Position Embedding (RoPE)"""
+    def __init__(self, dim, max_position_embeddings=2048, base=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_cached = max_position_embeddings
+        
+        t = torch.arange(self.max_seq_len_cached, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos(), persistent=False)
+        self.register_buffer("sin_cached", emb.sin(), persistent=False)
+    
+    def forward(self, x, seq_len):
+        return (
+            self.cos_cached[:seq_len, ...],
+            self.sin_cached[:seq_len, ...],
+        )
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin):
+    """Apply rotary position embedding to query and key tensors."""
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class MultiHeadAttention(nn.Module):
+    """Multi-head attention with RoPE"""
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.head_dim
+        self.hidden_size = config.hidden_size
+        
+        self.q_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.k_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.v_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        
+        self.rotary_emb = RotaryEmbedding(self.head_dim, config.max_position_embeddings)
+        self.dropout = nn.Dropout(config.attention_dropout_prob)
+    
+    def forward(self, hidden_states, attention_mask=None):
+        batch_size, seq_len, _ = hidden_states.shape
+        
+        # Project to Q, K, V
+        q = self.q_proj(hidden_states)
+        k = self.k_proj(hidden_states)
+        v = self.v_proj(hidden_states)
+        
+        # Reshape for multi-head attention
+        q = q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        
+        # Apply rotary embeddings
+        cos, sin = self.rotary_emb(v, seq_len)
+        q, k = apply_rotary_pos_emb(q, k, cos, sin)
+        
+        # Attention scores
+        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+        
+        attn_weights = F.softmax(attn_weights, dim=-1)
+        attn_weights = self.dropout(attn_weights)
+        
+        # Apply attention to values
+        attn_output = torch.matmul(attn_weights, v)
+        
+        # Reshape and project
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(batch_size, seq_len, self.hidden_size)
+        attn_output = self.o_proj(attn_output)
+        
+        return attn_output
+
+
+class MLP(nn.Module):
+    """Feed-forward network"""
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+    
+    def forward(self, x):
+        # SwiGLU activation
+        gate = F.silu(self.gate_proj(x))
+        up = self.up_proj(x)
+        return self.dropout(self.down_proj(gate * up))
+
+
+class TransformerBlock(nn.Module):
+    """Single transformer block"""
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.attention = MultiHeadAttention(config)
+        self.mlp = MLP(config)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+    
+    def forward(self, hidden_states, attention_mask=None):
+        # Pre-norm architecture
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.attention(hidden_states, attention_mask)
+        hidden_states = residual + hidden_states
+        
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        
+        return hidden_states
+
+
+class CustomSmolLM(nn.Module):
+    """Custom implementation mimicking SmolLM2-135M"""
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.config = config
+        
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([
+            TransformerBlock(config) for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        
+        # Tie weights
+        self.lm_head.weight = self.embed_tokens.weight
+        
+        self.apply(self._init_weights)
+    
+    def _init_weights(self, module):
+        std = 0.02
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+    
+    def forward(self, input_ids, attention_mask=None, labels=None):
+        batch_size, seq_len = input_ids.shape
+        
+        # Create causal mask
+        if attention_mask is None:
+            causal_mask = torch.triu(
+                torch.full((seq_len, seq_len), float('-inf'), device=input_ids.device),
+                diagonal=1
+            )
+            causal_mask = causal_mask.unsqueeze(0).unsqueeze(0)
+        else:
+            causal_mask = None  # Simplified for this example
+        
+        # Embed tokens
+        hidden_states = self.embed_tokens(input_ids)
+        
+        # Pass through transformer blocks
+        for layer in self.layers:
+            hidden_states = layer(hidden_states, causal_mask)
+        
+        hidden_states = self.norm(hidden_states)
+        logits = self.lm_head(hidden_states)
+        
+        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.config.vocab_size),
+                shift_labels.view(-1)
+            )
+        
+        return {'loss': loss, 'logits': logits}