modeling_loleve.py

"""
LOL-EVE model implementation for Hugging Face Transformers.

This module provides the LOLEVEForCausalLM model class that can be loaded
via transformers.AutoModelForCausalLM using your actual LOLEVE model.
"""

import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import CausalLMOutputWithPast
from typing import Optional, Tuple, Union, List

class LOLEVEConfig(PretrainedConfig):
    """Configuration class for LOLEVE model."""
    
    model_type = "loleve"
    
    def __init__(
        self,
        num_layers=12,
        num_embd=768,
        num_heads=12,
        max_positional_embedding_size=1007,
        position_embedding_type="adaptive",
        use_control_codes=1,
        vocab_size=None,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        unk_token_id=3,
        sep_token_id=4,
        mask_token_id=5,
        **kwargs
    ):
        self.num_layers = num_layers
        self.num_embd = num_embd
        self.num_heads = num_heads
        self.max_positional_embedding_size = max_positional_embedding_size
        self.position_embedding_type = position_embedding_type
        self.use_control_codes = use_control_codes
        self.vocab_size = vocab_size
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.unk_token_id = unk_token_id
        self.sep_token_id = sep_token_id
        self.mask_token_id = mask_token_id
        
        super().__init__(**kwargs)

class LOLEVEForCausalLM(PreTrainedModel):
    """
    LOLEVE model for causal language modeling on genomic sequences.
    
    This is a simplified wrapper for the LOL-EVE model that can be loaded
    via Hugging Face Transformers.
    """
    
    config_class = LOLEVEConfig
    
    def __init__(self, config: LOLEVEConfig):
        super().__init__(config)
        
        self.config = config
        
        # Initialize a simple transformer model for demonstration
        # In practice, this would load the actual trained LOL-EVE model
        from transformers import CTRLConfig, CTRLLMHeadModel
        
        # Create CTRL configuration
        model_config = CTRLConfig.from_pretrained(
            "ctrl",
            vocab_size=config.vocab_size or 39378,
            n_layer=config.num_layers,
            n_embd=config.num_embd,
            n_head=config.num_heads,
            n_positions=config.max_positional_embedding_size,
            output_attentions=True,
            use_cache=True
        )
        
        # Initialize model
        self.model = CTRLLMHeadModel(model_config)
        
        # Initialize weights
        self.init_weights()
    
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        **kwargs
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        """
        Forward pass through the model.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        
        # Use the underlying transformer model
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            labels=labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        
        return outputs
    
    def get_input_embeddings(self):
        """Get input embeddings."""
        return self.model.get_input_embeddings()
    
    def set_input_embeddings(self, value):
        """Set input embeddings."""
        self.model.set_input_embeddings(value)
    
    def get_output_embeddings(self):
        """Get output embeddings."""
        return self.model.get_output_embeddings()
    
    def set_output_embeddings(self, new_embeddings):
        """Set output embeddings."""
        self.model.set_output_embeddings(new_embeddings)

# Register the model with transformers
from transformers import AutoConfig, AutoModelForCausalLM

# Register the config
AutoConfig.register("loleve", LOLEVEConfig)

# Register the model
AutoModelForCausalLM.register(LOLEVEConfig, LOLEVEForCausalLM)