Update app.py

app.py

@@ -0,0 +1,466 @@
+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel, PretrainedConfig
+from huggingface_hub import hf_hub_download
+import json
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+# Define the model architecture
+class SmolLM2Config(PretrainedConfig):
+    model_type = "smollm2"
+    
+    def __init__(
+        self,
+        vocab_size=49152,
+        hidden_size=576,
+        intermediate_size=1536,
+        num_hidden_layers=30,
+        num_attention_heads=9,
+        num_key_value_heads=3,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.041666666666666664,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=0,
+        eos_token_id=0,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        **kwargs
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs
+        )
+
+# Register the model architecture
+from transformers import AutoConfig
+AutoConfig.register("smollm2", SmolLM2Config)
+
+class RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-5):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.eps = eps
+        
+    def forward(self, x):
+        variance = x.pow(2).mean(-1, keepdim=True)
+        x = x * torch.rsqrt(variance + self.eps)
+        return self.weight * x
+
+def precompute_rope_frequencies(dim: int, max_position_embeddings: int, theta: float = 10000.0):
+    position = torch.arange(max_position_embeddings).unsqueeze(1)  # [seq_len, 1]
+    div_term = theta ** (torch.arange(0, dim, 2).float() / dim)   # [dim/2]
+    freqs = position / div_term  # [seq_len, dim/2]
+    return freqs
+
+def apply_rotary_embeddings(x: torch.Tensor, freqs: torch.Tensor):
+    # x shape: [batch, seq_len, heads, head_dim]
+    # freqs shape: [seq_len, head_dim/2]
+    x_rot = x.float()
+    
+    # Reshape freqs to match x's dimensions
+    freqs = freqs.unsqueeze(0).unsqueeze(2)  # [1, seq_len, 1, dim/2]
+    
+    # Split channels for rotation
+    x1, x2 = x_rot[..., :x_rot.shape[-1]//2], x_rot[..., x_rot.shape[-1]//2:]
+    
+    # Apply rotary embeddings
+    cos = torch.cos(freqs).to(x.device)
+    sin = torch.sin(freqs).to(x.device)
+    
+    # Ensure broadcasting dimensions match
+    cos = cos.expand_as(x1)
+    sin = sin.expand_as(x1)
+    
+    # Rotate x1 and x2
+    x1_rot = x1 * cos - x2 * sin
+    x2_rot = x2 * cos + x1 * sin
+    
+    # Concatenate back
+    return torch.cat([x1_rot, x2_rot], dim=-1).to(x.dtype)
+
+class LlamaAttention(nn.Module):
+    def __init__(self, config: SmolLM2Config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_key_value_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        
+        # Adjust projections to match head dimensions
+        self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, config.hidden_size, bias=False)
+        
+        # Initialize rotary embeddings
+        self.register_buffer(
+            "rope_freqs",
+            precompute_rope_frequencies(
+                self.head_dim,  # Use full head_dim for frequencies
+                config.max_position_embeddings,
+                config.rope_theta
+            ),
+            persistent=False
+        )
+
+    def forward(self, hidden_states, attention_mask=None):
+        batch_size, seq_length, _ = hidden_states.size()
+        
+        # Project and reshape
+        q = self.q_proj(hidden_states).view(batch_size, seq_length, self.num_heads, self.head_dim)
+        k = self.k_proj(hidden_states).view(batch_size, seq_length, self.num_kv_heads, self.head_dim)
+        v = self.v_proj(hidden_states).view(batch_size, seq_length, self.num_kv_heads, self.head_dim)
+        
+        # Apply rotary embeddings
+        q = apply_rotary_embeddings(q, self.rope_freqs[:seq_length])
+        k = apply_rotary_embeddings(k, self.rope_freqs[:seq_length])
+        
+        # Repeat k/v heads if num_kv_heads < num_heads
+        if self.num_kv_heads < self.num_heads:
+            k = k.repeat_interleave(self.num_heads // self.num_kv_heads, dim=2)
+            v = v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=2)
+        
+        # Scaled dot-product attention
+        q = q.transpose(1, 2)  # (batch, num_heads, seq_len, head_dim)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+        
+        attention_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        
+        if attention_mask is not None:
+            attention_scores = attention_scores + attention_mask
+            
+        attention_probs = F.softmax(attention_scores, dim=-1)
+        context = torch.matmul(attention_probs, v)
+        
+        context = context.transpose(1, 2).contiguous()
+        context = context.view(batch_size, seq_length, -1)
+        
+        return self.o_proj(context)
+
+class LlamaMLP(nn.Module):
+    def __init__(self, config: SmolLM2Config):
+        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.act_fn = nn.SiLU()
+
+    def forward(self, x):
+        gate = self.act_fn(self.gate_proj(x))
+        up = self.up_proj(x)
+        return self.down_proj(gate * up)
+
+class LlamaDecoderLayer(nn.Module):
+    def __init__(self, config: SmolLM2Config):
+        super().__init__()
+        self.self_attn = LlamaAttention(config)
+        self.mlp = LlamaMLP(config)
+        self.input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+
+    def forward(self, hidden_states, attention_mask=None):
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(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 SmolLM2ForCausalLM(PreTrainedModel):
+    config_class = SmolLM2Config
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        
+        # Add lm_head before weight tying
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        
+        # Initialize weights
+        self.apply(self._init_weights)
+        
+        # Tie weights if configured
+        if config.tie_word_embeddings:
+            self.lm_head.weight = self.embed_tokens.weight
+            
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            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=self.config.initializer_range)
+
+    def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
+        hidden_states = self.embed_tokens(input_ids)
+        
+        # Create causal attention mask if none provided
+        if attention_mask is None:
+            # Create causal mask
+            seq_length = input_ids.size(1)
+            # [batch_size, 1, seq_length, seq_length]
+            causal_mask = torch.triu(
+                torch.ones((seq_length, seq_length), dtype=torch.bool, device=input_ids.device),
+                diagonal=1
+            ).unsqueeze(0).unsqueeze(0)
+            attention_mask = torch.zeros(
+                (1, 1, seq_length, seq_length),
+                dtype=hidden_states.dtype,
+                device=hidden_states.device
+            )
+            attention_mask.masked_fill_(causal_mask, float("-inf"))
+        
+        for layer in self.layers:
+            hidden_states = layer(hidden_states, attention_mask)
+            
+        hidden_states = self.norm(hidden_states)
+        logits = self.lm_head(hidden_states)
+        
+        loss = None
+        if labels is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1))
+            
+        return logits if loss is None else (loss, logits)
+    
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        return {
+            "input_ids": input_ids,
+            "attention_mask": kwargs.get("attention_mask", None)
+        }
+
+    def generate(
+        self,
+        input_ids,
+        max_length=100,
+        temperature=0.7,
+        top_k=50,
+        do_sample=True,
+        num_return_sequences=1,
+        pad_token_id=None,
+        eos_token_id=None,
+        **kwargs
+    ):
+        cur_len = input_ids.shape[1]
+        batch_size = input_ids.shape[0]
+        
+        if max_length < cur_len:
+            max_length = cur_len
+            
+        unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
+        
+        while cur_len < max_length:
+            # Prepare model inputs
+            model_inputs = self.prepare_inputs_for_generation(input_ids)
+            
+            # Forward pass
+            with torch.no_grad():
+                outputs = self(**model_inputs)
+                next_token_logits = outputs[:, -1, :]
+            
+            # Temperature scaling
+            if temperature != 1.0 and temperature > 0:
+                next_token_logits = next_token_logits / temperature
+                
+            # Top-k filtering
+            if top_k > 0:
+                indices_to_remove = next_token_logits < torch.topk(next_token_logits, top_k)[0][..., -1, None]
+                next_token_logits[indices_to_remove] = float('-inf')
+            
+            # Sample or greedy
+            if do_sample:
+                probs = F.softmax(next_token_logits, dim=-1)
+                next_tokens = torch.multinomial(probs, num_samples=1)
+            else:
+                next_tokens = torch.argmax(next_token_logits, dim=-1)
+                next_tokens = next_tokens.unsqueeze(-1)
+            
+            # Append next tokens
+            input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+            cur_len = input_ids.shape[1]
+            
+            # Early stopping if all sequences have reached the EOS token
+            if eos_token_id is not None:
+                unfinished_sequences = unfinished_sequences.mul(
+                    next_tokens.squeeze(-1).ne(eos_token_id).long()
+                )
+                if unfinished_sequences.max() == 0:
+                    break
+        
+        return input_ids
+
+# Register the model
+AutoModelForCausalLM.register(SmolLM2Config, SmolLM2ForCausalLM)
+
+# Cache for model and tokenizer
+MODEL = None
+TOKENIZER = None
+CONFIG = None
+
+def initialize():
+    global MODEL, TOKENIZER, CONFIG
+    
+    if MODEL is None:
+        print("Loading model and tokenizer...")
+        model_id = "jatingocodeo/SmolLM2"
+        
+        try:
+            # Download and load config
+            print("Loading config...")
+            config_path = hf_hub_download(repo_id=model_id, filename="config.json")
+            with open(config_path, 'r') as f:
+                config_dict = json.load(f)
+            CONFIG = SmolLM2Config(**config_dict)
+            
+            # Load tokenizer
+            print("Loading tokenizer...")
+            TOKENIZER = AutoTokenizer.from_pretrained(
+                model_id,
+                model_max_length=CONFIG.max_position_embeddings,
+                padding_side="left",
+                truncation_side="left",
+                trust_remote_code=True
+            )
+            
+            # Make sure we're using the correct special tokens
+            special_tokens = {
+                'bos_token': '<|endoftext|>',
+                'eos_token': '<|endoftext|>',
+                'unk_token': '<|endoftext|>',
+                'pad_token': '<|endoftext|>'  # Using endoftext as pad token since it's not specified
+            }
+            TOKENIZER.add_special_tokens(special_tokens)
+            
+            # Load model weights
+            print("Loading model...")
+            weights_path = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin")
+            
+            # Initialize model
+            MODEL = SmolLM2ForCausalLM(CONFIG)
+            
+            # Resize token embeddings to match tokenizer
+            MODEL.resize_token_embeddings(len(TOKENIZER))
+            
+            # Load state dict
+            state_dict = torch.load(weights_path, map_location="cpu")
+            MODEL.load_state_dict(state_dict)
+            
+            # Move model to device
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            MODEL = MODEL.to(device)
+            
+            print(f"Model loaded successfully on {device}")
+            
+        except Exception as e:
+            print(f"Error initializing: {str(e)}")
+            raise
+
+def generate_text(prompt, max_length=100, temperature=0.7, top_k=50):
+    # Initialize if not already done
+    if MODEL is None:
+        try:
+            initialize()
+        except Exception as e:
+            return f"Failed to initialize model: {str(e)}"
+    
+    try:
+        # Process prompt
+        if not prompt.strip():
+            return "Please enter a prompt."
+        
+        # Add BOS token if needed
+        if not prompt.startswith(TOKENIZER.bos_token):
+            prompt = TOKENIZER.bos_token + prompt
+        
+        # Encode prompt
+        encoded = TOKENIZER.encode_plus(
+            prompt,
+            add_special_tokens=True,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=CONFIG.max_position_embeddings
+        )
+        input_ids = encoded["input_ids"].to(MODEL.device)
+        attention_mask = encoded["attention_mask"].to(MODEL.device)
+        
+        # Generate
+        with torch.no_grad():
+            outputs = MODEL.generate(
+                input_ids,
+                attention_mask=attention_mask,
+                max_length=min(max_length + len(input_ids[0]), CONFIG.max_position_embeddings),
+                temperature=max(0.1, min(temperature, 1.0)),  # Clamp temperature
+                top_k=max(1, min(top_k, 100)),  # Clamp top_k
+                do_sample=True if temperature > 0 else False,
+                num_return_sequences=1,
+                pad_token_id=TOKENIZER.pad_token_id,
+                eos_token_id=TOKENIZER.eos_token_id,
+            )
+        
+        # Decode and return
+        generated_text = TOKENIZER.decode(outputs[0], skip_special_tokens=True)
+        return generated_text.strip()
+        
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return f"Error during text generation: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Enter your prompt here...", lines=2),
+        gr.Slider(minimum=10, maximum=200, value=100, step=1, label="Max Length"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=1, maximum=100, value=50, step=1, label="Top K"),
+    ],
+    outputs=gr.Textbox(label="Generated Text", lines=5),
+    title="SmolLM2 Text Generator",
+    description="Generate text using the fine-tuned SmolLM2 model. Adjust parameters to control the generation.",
+    examples=[
+        ["Once upon a time", 100, 0.7, 50],
+        ["The quick brown fox", 150, 0.8, 40],
+    ],
+    allow_flagging="never"
+)
+
+# Initialize on startup
+try:
+    initialize()
+except Exception as e:
+    print(f"Warning: Model initialization failed: {str(e)}")
+    print("Model will be initialized on first request")
+
+if __name__ == "__main__":
+    iface.launch()