""" 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