Add model.py

model.py

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+"""
+Sheikh-2.5-Coder Model Implementation
+====================================
+
+This module implements the Sheikh-2.5-Coder model architecture, a 3B parameter
+transformer model optimized for code generation and on-device deployment.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+from dataclasses import dataclass
+from transformers import (
+    PreTrainedModel,
+    PreTrainedTokenizer,
+    AutoConfig,
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    BitsAndBytesConfig,
+    TrainingArguments
+)
+import json
+
+@dataclass
+class SheikhConfig:
+    """Configuration class for Sheikh-2.5-Coder model."""
+    
+    # Model architecture
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 2
+    hidden_size: int = 3072
+    intermediate_size: int = 8192
+    num_hidden_layers: int = 36
+    vocab_size: int = 50257
+    
+    # Position embeddings
+    max_position_embeddings: int = 32768
+    rope_theta: float = 10000.0
+    
+    # Attention
+    attention_dropout: float = 0.1
+    hidden_dropout: float = 0.1
+    
+    # Normalization
+    layer_norm_epsilon: float = 1e-6
+    rms_norm_eps: float = 1e-6
+    
+    # Activation
+    activation_function: str = "swiglu"
+    
+    # Precision
+    torch_dtype: str = "bfloat16"
+    
+    # Cache
+    use_cache: bool = True
+    
+    # Tie word embeddings
+    tie_word_embeddings: bool = True
+
+class SheikhRMSNorm(nn.Module):
+    """Root Mean Square Layer Normalization."""
+    
+    def __init__(self, hidden_size: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        input_dtype = x.dtype
+        x = x.float()
+        variance = x.pow(2).mean(-1, keepdim=True)
+        x = x * torch.rsqrt(variance + self.eps)
+        return (self.weight * x).to(input_dtype)
+
+class SheikhRotaryEmbedding(nn.Module):
+    """Rotary Positional Embedding."""
+    
+    def __init__(self, dim: int, max_position_embeddings: int = 32768, base: int = 10000):
+        super().__init__()
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
+        )
+    
+    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(seq_len, device=device, 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()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+    
+    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+class SheikhAttention(nn.Module):
+    """Multi-head attention with Grouped Query Attention."""
+    
+    def __init__(self, config: SheikhConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        
+        self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+        
+        self.rotary_emb = SheikhRotaryEmbedding(
+            self.head_dim, max_position_embeddings=config.max_position_embeddings
+        )
+    
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ):
+        bsz, q_len, _ = hidden_states.size()
+        
+        # Query, Key, Value projections
+        q = self.q_proj(hidden_states)
+        k = self.k_proj(hidden_states)
+        v = self.v_proj(hidden_states)
+        
+        # Reshape for grouped query attention
+        q = q.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        k = k.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        v = v.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        
+        # Apply rotary embeddings
+        cos, sin = self.rotary_emb(v, seq_len=q_len)
+        q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
+        
+        # Group key and value for grouped query attention
+        k = repeat_kv(k, self.num_key_value_groups)
+        v = repeat_kv(v, self.num_key_value_groups)
+        
+        # Scaled dot-product attention
+        attn_output = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=attention_mask, dropout_p=0.0, is_causal=True
+        )
+        
+        # Reshape and project output
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+        attn_output = self.o_proj(attn_output)
+        
+        if not output_attentions:
+            attn_weights = None
+        
+        outputs = (attn_output,)
+        if output_attentions:
+            outputs += (attn_weights,)
+        
+        return outputs
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """Repeat key/value states for grouped query attention."""
+    batch, slen, num_key_value_heads, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, :, None, :].repeat(1, 1, 1, n_rep, 1)
+    return hidden_states.reshape(batch, slen, num_key_value_heads * n_rep, head_dim)
+
+def apply_rotary_pos_emb(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: torch.Tensor):
+    """Apply rotary positional embeddings."""
+    def rotate_half(x):
+        x1 = x[..., : x.shape[-1] // 2]
+        x2 = x[..., x.shape[-1] // 2 :]
+        return torch.cat((-x2, x1), dim=-1)
+    
+    cos = cos.squeeze(1).squeeze(0)
+    sin = sin.squeeze(1).squeeze(0)
+    
+    cos = cos[position_ids].unsqueeze(1)
+    sin = sin[position_ids].unsqueeze(1)
+    
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+class SheikhMLP(nn.Module):
+    """SwiGLU MLP."""
+    
+    def __init__(self, config: SheikhConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+
+class SheikhTransformerBlock(nn.Module):
+    """Transformer block for Sheikh-2.5-Coder."""
+    
+    def __init__(self, config: SheikhConfig):
+        super().__init__()
+        self.self_attn = SheikhAttention(config)
+        self.mlp = SheikhMLP(config)
+        self.input_layernorm = SheikhRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = SheikhRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+    
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ):
+        # Self-attention
+        attn_output, _ = self.self_attn(
+            self.input_layernorm(hidden_states),
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = hidden_states + attn_output
+        
+        # MLP
+        mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
+        hidden_states = hidden_states + mlp_output
+        
+        return hidden_states
+
+class SheikhModel(PreTrainedModel):
+    """Sheikh-2.5-Coder base model."""
+    
+    def __init__(self, config: SheikhConfig):
+        super().__init__(config)
+        self.config = config
+        
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([SheikhTransformerBlock(config) for _ in range(config.num_hidden_layers)])
+        self.norm = SheikhRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        
+        # Initialize weights
+        self.apply(self._init_weights)
+    
+    def _init_weights(self, module):
+        """Initialize model weights."""
+        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 get_input_embeddings(self):
+        return self.embed_tokens
+    
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+    
+    def forward(
+        self,
+        input_ids: torch.Tensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.Tensor]] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        # Implementation continues...
+        pass
+
+# Model loading utilities
+def load_sheikh_model(
+    model_name_or_path: str,
+    device_map: Optional[str] = "auto",
+    torch_dtype: torch.dtype = torch.bfloat16,
+    load_in_8bit: bool = False,
+    load_in_4bit: bool = False,
+) -> AutoModelForCausalLM:
+    """Load Sheikh-2.5-Coder model with optional quantization."""
+    
+    # Setup quantization config
+    quantization_config = None
+    if load_in_8bit:
+        quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+    elif load_in_4bit:
+        quantization_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=torch.bfloat16,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type="nf4",
+        )
+    
+    # Load tokenizer and model
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name_or_path,
+        device_map=device_map,
+        torch_dtype=torch_dtype,
+        quantization_config=quantization_config,
+    )
+    
+    return model, tokenizer
+
+# Model training utilities
+def setup_training_args(output_dir: str, learning_rate: float = 1e-4) -> TrainingArguments:
+    """Setup training arguments for Sheikh-2.5-Coder."""
+    
+    return TrainingArguments(
+        output_dir=output_dir,
+        learning_rate=learning_rate,
+        per_device_train_batch_size=8,
+        per_device_eval_batch_size=8,
+        num_train_epochs=3,
+        max_steps=100000,
+        logging_steps=100,
+        save_steps=2000,
+        eval_steps=1000,
+        warmup_steps=2000,
+        fp16=True,
+        bf16=True,
+        gradient_accumulation_steps=4,
+        gradient_checkpointing=True,
+        remove_unused_columns=False,
+        dataloader_pin_memory=True,
+        report_to="wandb",
+        run_name="sheikh-2.5-coder",
+    )
+
+if __name__ == "__main__":
+    # Example usage
+    config = SheikhConfig()
+    model = SheikhModel(config)
+    
+    # Save configuration
+    with open("config.json", "w") as f:
+        json.dump(config.__dict__, f, indent=2)
+    
+    print("Sheikh-2.5-Coder model configuration created successfully!")
+    print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")