modeling_ollama.py

"""
NeuralQuantum Ollama 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_ollama import NeuralQuantumOllamaConfig


class QuantumOllamaLayer(nn.Module):
    """Quantum-inspired layer optimized for Ollama"""
    
    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 optimized for Ollama
        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))
        self.quantum_scale = nn.Parameter(torch.ones(self.hidden_size))
        
    def forward(self, hidden_states):
        # Simulate quantum circuit operations optimized for Ollama
        for i in range(self.quantum_circuit_depth):
            # Apply quantum-inspired transformation with scaling
            hidden_states = torch.matmul(hidden_states, self.quantum_weights[i])
            hidden_states = torch.tanh(hidden_states)  # Non-linear activation
            hidden_states = hidden_states * self.quantum_scale
        
        return hidden_states + self.quantum_bias


class NeuralQuantumOllamaAttention(nn.Module):
    """Quantum-enhanced attention mechanism optimized for Ollama"""
    
    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 optimized for Ollama
        self.quantum_layer = QuantumOllamaLayer(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 NeuralQuantumOllamaBlock(nn.Module):
    """NeuralQuantum Ollama transformer block"""
    
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.attention = NeuralQuantumOllamaAttention(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 NeuralQuantumOllamaForCausalLM(PreTrainedModel):
    """NeuralQuantum Ollama model for causal language modeling"""
    
    config_class = NeuralQuantumOllamaConfig
    
    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([
            NeuralQuantumOllamaBlock(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=0.7, top_p=0.9, top_k=40, do_sample=True, **kwargs):
        """Generate text using Ollama-optimized parameters"""
        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:
                    # Apply top-k filtering
                    if top_k > 0:
                        top_k_logits, top_k_indices = torch.topk(logits, top_k)
                        logits = torch.full_like(logits, -float('inf'))
                        logits.scatter_(1, top_k_indices, top_k_logits)
                    
                    # Apply top-p filtering
                    if top_p < 1.0:
                        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
                        cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
                        sorted_indices_to_remove = cumulative_probs > top_p
                        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
                        sorted_indices_to_remove[..., 0] = 0
                        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
                        logits[indices_to_remove] = -float('inf')
                    
                    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