Add modeling_nqlm.py

modeling_nqlm.py

@@ -0,0 +1,228 @@
+"""
+NeuralQuantum NQLM Model Implementation for Hugging Face Transformers
+"""
+
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from configuration_nqlm import NeuralQuantumNQLMConfig
+
+
+class QuantumLayer(nn.Module):
+    """Quantum-inspired layer for enhanced processing"""
+    
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.quantum_circuit_depth = config.quantum_circuit_depth
+        self.hidden_size = config.hidden_size
+        
+        # Quantum-inspired parameters
+        self.quantum_weights = nn.Parameter(torch.randn(self.quantum_circuit_depth, self.hidden_size, self.hidden_size))
+        self.quantum_bias = nn.Parameter(torch.randn(self.hidden_size))
+        
+    def forward(self, hidden_states):
+        # Simulate quantum circuit operations
+        for i in range(self.quantum_circuit_depth):
+            # Apply quantum-inspired transformation
+            hidden_states = torch.matmul(hidden_states, self.quantum_weights[i])
+            hidden_states = torch.tanh(hidden_states)  # Non-linear activation
+        
+        return hidden_states + self.quantum_bias
+
+
+class NeuralQuantumAttention(nn.Module):
+    """Quantum-enhanced attention mechanism"""
+    
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.num_attention_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_dim = self.hidden_size // self.num_attention_heads
+        
+        self.query = nn.Linear(self.hidden_size, self.hidden_size)
+        self.key = nn.Linear(self.hidden_size, self.hidden_size)
+        self.value = nn.Linear(self.hidden_size, self.hidden_size)
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        
+        # Quantum enhancement layer
+        self.quantum_layer = QuantumLayer(config)
+        
+    def forward(self, hidden_states, attention_mask=None):
+        batch_size, seq_len, hidden_size = hidden_states.size()
+        
+        # Apply quantum enhancement
+        quantum_enhanced = self.quantum_layer(hidden_states)
+        
+        # Standard attention computation
+        query = self.query(quantum_enhanced)
+        key = self.key(quantum_enhanced)
+        value = self.value(quantum_enhanced)
+        
+        # Reshape for multi-head attention
+        query = query.view(batch_size, seq_len, self.num_attention_heads, self.head_dim).transpose(1, 2)
+        key = key.view(batch_size, seq_len, self.num_attention_heads, self.head_dim).transpose(1, 2)
+        value = value.view(batch_size, seq_len, self.num_attention_heads, self.head_dim).transpose(1, 2)
+        
+        # Compute attention scores
+        attention_scores = torch.matmul(query, key.transpose(-2, -1)) / (self.head_dim ** 0.5)
+        
+        if attention_mask is not None:
+            attention_scores = attention_scores.masked_fill(attention_mask == 0, -1e9)
+        
+        attention_probs = torch.softmax(attention_scores, dim=-1)
+        attention_probs = self.dropout(attention_probs)
+        
+        # Apply attention to values
+        context = torch.matmul(attention_probs, value)
+        context = context.transpose(1, 2).contiguous().view(batch_size, seq_len, hidden_size)
+        
+        return context
+
+
+class NeuralQuantumBlock(nn.Module):
+    """NeuralQuantum transformer block"""
+    
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.attention = NeuralQuantumAttention(config)
+        self.ln_1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp = nn.Sequential(
+            nn.Linear(config.hidden_size, config.intermediate_size),
+            nn.GELU(),
+            nn.Linear(config.intermediate_size, config.hidden_size),
+            nn.Dropout(config.hidden_dropout_prob)
+        )
+        self.ln_2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        
+    def forward(self, hidden_states, attention_mask=None):
+        # Self-attention with residual connection
+        attn_output = self.attention(hidden_states, attention_mask)
+        hidden_states = self.ln_1(hidden_states + attn_output)
+        
+        # MLP with residual connection
+        mlp_output = self.mlp(hidden_states)
+        hidden_states = self.ln_2(hidden_states + mlp_output)
+        
+        return hidden_states
+
+
+class NeuralQuantumNQLMForCausalLM(PreTrainedModel):
+    """NeuralQuantum NQLM model for causal language modeling"""
+    
+    config_class = NeuralQuantumNQLMConfig
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        
+        # Embeddings
+        self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.wpe = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+        self.drop = nn.Dropout(config.hidden_dropout_prob)
+        
+        # Transformer blocks
+        self.h = nn.ModuleList([
+            NeuralQuantumBlock(config) for _ in range(config.num_hidden_layers)
+        ])
+        
+        # Output layer
+        self.ln_f = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        
+        # Initialize weights
+        self.init_weights()
+        
+    def get_input_embeddings(self):
+        return self.wte
+        
+    def set_input_embeddings(self, new_embeddings):
+        self.wte = new_embeddings
+        
+    def get_output_embeddings(self):
+        return self.lm_head
+        
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+        
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        batch_size, seq_len = input_ids.size()
+        
+        # Position embeddings
+        if position_ids is None:
+            position_ids = torch.arange(0, seq_len, dtype=torch.long, device=input_ids.device)
+            position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
+            
+        # Input embeddings
+        inputs_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = inputs_embeds + position_embeds
+        hidden_states = self.drop(hidden_states)
+        
+        # Transformer blocks
+        for i, block in enumerate(self.h):
+            hidden_states = block(hidden_states, attention_mask)
+            
+        # Final layer norm
+        hidden_states = self.ln_f(hidden_states)
+        
+        # Language modeling head
+        logits = self.lm_head(hidden_states)
+        
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+            
+        if not return_dict:
+            output = (logits,) + (None,) * 6
+            return ((loss,) + output) if loss is not None else output
+            
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=None,
+            hidden_states=None,
+            attentions=None,
+        )
+        
+    def generate(self, input_ids, max_length=50, temperature=1.0, do_sample=True, **kwargs):
+        """Generate text using the model"""
+        self.eval()
+        
+        with torch.no_grad():
+            for _ in range(max_length - input_ids.size(1)):
+                # Get logits for the last token
+                outputs = self.forward(input_ids)
+                logits = outputs.logits[:, -1, :] / temperature
+                
+                if do_sample:
+                    probs = torch.softmax(logits, dim=-1)
+                    next_token = torch.multinomial(probs, 1)
+                else:
+                    next_token = torch.argmax(logits, dim=-1, keepdim=True)
+                    
+                input_ids = torch.cat([input_ids, next_token], dim=1)
+                
+        return input_ids