Update modeling_c_cubed.py

modeling_c_cubed.py

@@ -1,738 +1,710 @@
-# coding=utf-8
-"""PyTorch Ccubed model."""
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.utils.checkpoint
-from torch import nn
-
-from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
-from transformers.processing_utils import Unpack
-from transformers.image_processing_utils import select_best_resolution
-from transformers.modeling_outputs import ModelOutput
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs, _flash_attention_forward
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-    is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10
-)
-from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
-from .configuration_c_cubed import CcubedConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "CcubedConfig"
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-@dataclass
-class CcubedCausalLMOutputWithPast(ModelOutput):
-    """
-    Base class for Ccubed causal language model (or autoregressive) outputs.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Language modeling loss (for next-token prediction).
-        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            Tuple of `tuple(torch.FloatTensor)` of length `config.context_config.num_layers`, with each tuple having 2 tensors of shape
-            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
-
-            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
-            `past_key_values` input) to speed up sequential decoding.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-        context_hidden_states (`torch.FloatTensor`, *optional*):
-            A `torch.FloatTensor` of size (batch_size, sequence_length, hidden_size)`.
-            context_hidden_states of the model produced by the context encoder and after projecting the last hidden state.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    past_key_values: Optional[List[torch.FloatTensor]] = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-    context_hidden_states: Optional[torch.FloatTensor] = None
-
-
-class CcubedDynamicAttention(nn.Module):
-    """
-    Attention mechanism adapted for dynamic output size based on Mistral's architecture. This attention layer computes
-    the output attention scores which are used to determine the pooling size dynamically.
-    """
-
-    def __init__(self, config: CcubedConfig):
-        super().__init__()
-    
-        self.config = config
-        self.hidden_size = config.context_config.hidden_size
-        self.num_heads = config.context_config.num_attention_heads
-        self.head_dim = getattr(config.context_config, "head_dim", self.hidden_size // self.num_heads)
-        self.num_key_value_heads = config.context_config.num_key_value_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.scaling = self.head_dim ** -0.5
-        self.attention_dropout = getattr(self.config.context_config, "attention_dropout", 0.0)
-
-        # Query, Key, Value, and Output Projections
-        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, 1, bias=False)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ):
-        # Get input dimensions
-        bsz, seq_len, hidden_size = hidden_states.size()
-
-        # Query, Key, Value projections
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        # Reshape and transpose to [batch_size, num_heads, seq_len, head_dim]
-        query_states = query_states.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-
-        # Repeat key and value states for multi-head attention
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        # Compute attention scores
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        # Apply softmax to get attention probabilities
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        # Apply attention to values
-        attn_output = torch.matmul(attn_weights, value_states)
-
-        # Reshape attention output
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, seq_len, -1)
-
-        # Project to output dimension
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights
-
-
-class CcubedDynamicFlashAttention2(CcubedDynamicAttention):
-    def __init__(self, config: CcubedConfig):
-        super().__init__(config)
-        self.is_causal = False  # Assuming non-causal attention for this context
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ):
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-
-        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-
-        sliding_window = None
-        if getattr(self.config, "sliding_window", None) is not None:
-            sliding_window = self.config.sliding_window
-        
-        attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
-
-        attn_output, attn_weights = attention_interface(
-            self,
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            dropout=0.0 if not self.training else self.attention_dropout,
-            scaling=self.scaling,
-            sliding_window=sliding_window,  # main diff with Llama
-            **kwargs,
-        )
-
-        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-
-class CcubedDynamicWeightedAvgPool1d(nn.Module):
-    """
-    A module that dynamically determines the output size based on input
-    and performs weighted average pooling with separate attention mechanisms
-    for output size estimation and weighted pooling.
-    """
-    def __init__(self, config, output_size_min=32, output_size_max=131072):
-        super().__init__()
-        # Attention mechanism for estimating output size
-        self.size_estim_attn = CcubedDynamicFlashAttention2(config) # CcubedDynamicAttention(config)
-        # Attention mechanism for weighted pooling
-        self.imp_estim_attn = CcubedDynamicFlashAttention2(config) # CcubedDynamicAttention(config)
-        self.output_size_min = output_size_min
-        self.output_size_max = (
-            config.context_config.max_position_embeddings if config.context_config.max_position_embeddings is not None else output_size_max
-        )
-        self.scale_param = nn.Parameter(torch.tensor(0.01))
-
-    def forward(self, hidden_states, context_attention_mask=None):
-        """
-        Args:
-            x: Input tensor of shape (batch_size, seq_len, hidden_size)
-
-        Returns:
-            Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-                - pooled_output: Padded tensor of compressed sequences (batch_size, max_pooled_len, hidden_size)
-                - attention_mask: Binary mask indicating valid tokens (batch_size, max_pooled_len)
-                - dynamic_output_sizes: Dynamic output sizes for each batch (batch_size,)
-        """
-        batch_size, seq_len, hidden_size = hidden_states.size()
-        device = hidden_states.device
-
-        # Estimate output size using attention mechanism
-        # attn_output_size: (batch_size, seq_len, 1)
-        attn_output_size, _ = self.size_estim_attn(hidden_states)
-
-        # Calculate dynamic output sizes for each batch item
-        # (batch_size, seq_len, 1) -> (batch_size, 1)
-        batch_attn_means = torch.sigmoid(attn_output_size).mean(dim=1)
-        scaled_batch_means = batch_attn_means * self.scale_param.to(batch_attn_means.dtype)
-
-        # Calculate dynamic output sizes (batch_size,)
-        dynamic_output_sizes = (
-            (scaled_batch_means * (self.output_size_max - self.output_size_min)) + self.output_size_min
-        ).int().squeeze(-1)
-
-        max_pooled_len = dynamic_output_sizes.max().item()
-
-        # Compute attention weights for weighted pooling
-        # attn_output_weights: (batch_size, seq_len, 1)
-        attn_output_weights, _ = self.imp_estim_attn(hidden_states)
-        # Normalize with sigmoid function for use as weights
-        # attention_weights: (batch_size, seq_len)
-        attention_weights = torch.sigmoid(attn_output_weights).squeeze(-1)
-
-        # If context_attention_mask is provided, apply it to zero out weights for invalid tokens
-        if context_attention_mask is not None:
-            attention_weights = attention_weights * context_attention_mask
-
-        # Initialize output tensors
-        # pooled_output: (batch_size, max_pooled_len, hidden_size)
-        pooled_output = torch.zeros(
-            batch_size, max_pooled_len, hidden_size, 
-            device=device, dtype=hidden_states.dtype
-        )
-        # attention_mask: (batch_size, max_pooled_len)
-        attention_mask = torch.zeros(
-            batch_size, max_pooled_len, 
-            dtype=torch.bool, device=device
-        )
-
-        for batch_idx in range(batch_size):
-            output_size = dynamic_output_sizes[batch_idx].item()
-            item_input = hidden_states[batch_idx]  # Shape: (seq_len, hidden_size)
-            item_weights = attention_weights[batch_idx]  # Shape: (seq_len)
-   
-            # Perform weighted pooling
-            pooled_values = []
-            batch_attn_mask = torch.zeros(output_size, dtype=torch.bool, device=device)
-            # Split the sequence evenly
-            intervals = torch.linspace(0, seq_len, steps=output_size + 1).long()
-            for i in range(output_size):
-                start = intervals[i].item()
-                end = intervals[i + 1].item()
-                chunk_input = item_input[start:end]  # Shape: (chunk_size, hidden_size)
-                chunk_weights = item_weights[start:end]  # Shape: (chunk_size)
-                if chunk_weights.sum() == 0:
-                    # If the sum of weights is zero, add a zero vector
-                    pooled_value = torch.zeros(hidden_size, device=device, dtype=hidden_states.dtype)
-                else:
-                    # Calculate weighted average
-                    weighted_input = chunk_input * chunk_weights.unsqueeze(-1)  # Shape: (chunk_size, hidden_size)
-                    pooled_value = weighted_input.sum(dim=0) / (chunk_weights.sum() + 1e-8)  # Shape: (hidden_size)
-                    batch_attn_mask[i] = True
-                pooled_values.append(pooled_value)
-
-            if pooled_values:  # Only stack if there are values
-                # Convert the result to a tensor
-                pooled_values = torch.stack(pooled_values)  # Shape: (output_size, hidden_size)
-                # Store the result
-                pooled_output[batch_idx, -output_size:] = pooled_values
-                attention_mask[batch_idx, -output_size:] = batch_attn_mask
-
-        return pooled_output, attention_mask, dynamic_output_sizes
-
-
-class CcubedContextLanguageConnector(nn.Module):
-    def __init__(self, config: CcubedConfig):
-        super().__init__()
-
-        self.dynamic_pooling = CcubedDynamicWeightedAvgPool1d(config)
-
-        self.linear_1 = nn.Linear(
-            config.context_config.hidden_size,
-            config.text_config.hidden_size,
-            bias=True
-        )
-        self.act = ACT2FN[config.projector_hidden_act]
-        self.linear_2 = nn.Linear(
-            config.text_config.hidden_size,
-            config.text_config.hidden_size,
-            bias=True
-        )
-
-    def forward(self, context_features):
-        # context_features: [batch_size, seq_len, hidden_size]
-        # Apply dynamic adaptive average pooling with attention
-        pooled_output, attention_mask, dynamic_output_sizes = self.dynamic_pooling(
-            hidden_states=context_features
-        )
-
-        hidden_states = self.linear_1(pooled_output)
-        hidden_states = self.act(hidden_states)
-        hidden_states = self.linear_2(hidden_states)
-        
-        return hidden_states, attention_mask
-
-
-class CcubedContextTower(nn.Module):
-    def __init__(self, config: CcubedConfig):
-        super().__init__()
-
-        self.tower = AutoModelForCausalLM.from_config(
-            config.context_config,
-            attn_implementation="flash_attention_2" if is_flash_attn_2_available() else "eager"
-        )
-        self.select_layer = config.context_feature_layer
-    
-    def feature_select(self, llm_outputs):
-        hidden_states = llm_outputs.hidden_states
-        return hidden_states[self.select_layer]
-
-    def forward(
-        self,
-        input_ids,
-        inputs_embeds,
-        attention_mask
-    ):
-        outputs = self.tower(
-            input_ids=input_ids,
-            inputs_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            output_hidden_states=True
-        )
-        features = self.feature_select(outputs)
-        return features
-    
-
-class CcubedPreTrainedModel(PreTrainedModel):
-    config_class = CcubedConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = [] # ["CcubedContextLanguageConnector", "CcubedContextTower"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-    _supports_quantized_cache = True
-    _supports_static_cache = True
-
-    def _init_weights(self, module):
-        std = (
-            self.config.initializer_range
-            if hasattr(self.config, "initializer_range")
-            else self.config.text_config.initializer_range
-        )
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-
-class CcubedForConditionalGeneration(CcubedPreTrainedModel):
-    def __init__(self, config: CcubedConfig):
-        super().__init__(config)
-        self.context_tower = CcubedContextTower(config)
-        self.connector = CcubedContextLanguageConnector(config)
-
-        self.language_model = AutoModelForCausalLM.from_config(
-            config.text_config,
-            attn_implementation="flash_attention_2" if is_flash_attn_2_available() else "eager"
-        )
-
-        self.vocab_size = config.text_config.vocab_size
-        self.ignore_index = config.ignore_index if hasattr(config, 'ignore_index') else -100
-        self.start_of_context_token_id = config.start_of_context_token_id
-        self.end_of_context_token_id = config.end_of_context_token_id
-        
-        self.post_init()
-    
-    def get_input_embeddings(self):
-        return self.language_model.get_input_embeddings()
-
-    def get_context_input_embeddings(self):
-        return self.context_tower.tower.get_input_embeddings()
-    
-    def set_input_embeddings(self, value):
-        self.language_model.set_input_embeddings(value)
-
-    def set_context_input_embeddings(self, value):
-        self.context_tower.tower.set_input_embeddings(value)
-
-    def get_output_embeddings(self):
-        return self.language_model.get_output_embeddings()
-    
-    def get_context_output_embeddings(self):
-        return self.context_tower.tower.get_output_embeddings()
-    
-    def set_output_embeddings(self, new_embeddings):
-        self.language_model.set_output_embeddings(new_embeddings)
-
-    def set_context_output_embeddings(self, new_embeddings):
-        self.context_tower.tower.set_output_embeddings(new_embeddings)
-    
-    def set_decoder(self, decoder):
-        self.language_model.set_decoder(decoder)
-
-    def set_context_encoder(self, decoder):
-        self.context_tower.tower.set_decoder(decoder)
-    
-    def get_decoder(self):
-        return self.language_model.get_decoder()
-
-    def get_context_encoder(self):
-        return self.context_tower.tower.get_decoder()
-    
-    def tie_weights(self):
-        return self.language_model.tie_weights()
-    
-    def context_tie_weights(self):
-        return self.context_tower.tower.tie_weights()
-    
-    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
-        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
-        # update vocab size
-        self.config.text_config.vocab_size = model_embeds.num_embeddings
-        self.vocab_size = model_embeds.num_embeddings
-        return model_embeds
-    
-    def _merge_context_features(
-        self,
-        context_features = None,
-        inputs_embeds = None,
-        attention_mask = None,
-        context_attention_mask=None,
-        position_ids=None,
-        labels=None,
-    ):
-        if context_features is None:
-            return inputs_embeds, attention_mask, position_ids, labels
-
-        batch_size, seq_length, embed_dim = inputs_embeds.shape
-        context_seq_len = context_features.size(1)
-        
-        # Create embeddings for begin and end of context tokens
-        begin_context_embed = self.get_input_embeddings()(torch.tensor(self.start_of_context_token_id, device=context_features.device))
-        end_context_embed = self.get_input_embeddings()(torch.tensor(self.end_of_context_token_id, device=context_features.device))
-        
-        # Determine the actual lengths of context sequences (excluding padding)
-        if context_attention_mask is not None:
-            # context_attention_mask: [batch_size, context_seq_len, 1]
-            context_attention_mask = context_attention_mask.squeeze(-1)  # [batch_size, context_seq_len]
-            # Sum over sequence length to get actual lengths
-            context_lengths = context_attention_mask.sum(dim=1).long()  # [batch_size]
-        else:
-            # If no context_attention_mask is provided, assume full length
-            context_lengths = torch.full((batch_size,), context_seq_len, device=context_features.device, dtype=torch.long)
-            context_attention_mask = torch.ones(batch_size, context_seq_len, device=context_features.device, dtype=torch.long)
-        
-        # Rearrange context features to include padding at the beginning
-        # Identify the maximum context length (excluding padding)
-        max_context_length = context_lengths.max().item()
-        # Calculate the amount of padding needed for each sample
-        padding_lengths = context_seq_len - context_lengths  # [batch_size]
-        
-        # Create new context_features with padding at the beginning
-        new_context_features = []
-        for i in range(batch_size):
-            padding_len = padding_lengths[i].item()
-            # Create padding embeddings (zeros)
-            padding_embed = torch.zeros(padding_len, embed_dim, device=context_features.device, dtype=context_features.dtype)
-            # Get actual context features (excluding padding)
-            actual_context = context_features[i, padding_len:context_seq_len]
-            # Concatenate padding, begin token, actual context, end token
-            sample_context = torch.cat([
-                padding_embed,
-                begin_context_embed.unsqueeze(0),
-                actual_context,
-                end_context_embed.unsqueeze(0)
-            ], dim=0)  # [context_seq_len + 2, embed_dim]
-            new_context_features.append(sample_context)
-        # Stack to create [batch_size, new_context_seq_len, embed_dim]
-        context_features = torch.stack(new_context_features, dim=0)
-        new_context_seq_len = context_features.size(1)
-        
-        # Update context_attention_mask accordingly
-        new_context_attention_mask = []
-        for i in range(batch_size):
-            padding_len = padding_lengths[i].item()
-            # Create padding mask (zeros)
-            padding_mask = torch.zeros(padding_len, device=context_features.device, dtype=attention_mask.dtype)
-            # Begin and end token masks
-            begin_attention = torch.ones(1, device=context_features.device, dtype=attention_mask.dtype)
-            end_attention = torch.ones(1, device=context_features.device, dtype=attention_mask.dtype)
-            # Actual context attention mask (excluding padding)
-            actual_mask = context_attention_mask[i, padding_len:context_seq_len]
-            # Concatenate masks
-            sample_mask = torch.cat([
-                padding_mask,
-                begin_attention,
-                actual_mask,
-                end_attention
-            ], dim=0)  # [context_seq_len + 2]
-            new_context_attention_mask.append(sample_mask)
-        # Stack to create [batch_size, new_context_seq_len]
-        context_attention_mask = torch.stack(new_context_attention_mask, dim=0)
-        
-        # Concatenate context features with input embeddings
-        new_inputs_embeds = torch.cat([context_features, inputs_embeds], dim=1)  # [batch_size, total_seq_len, embed_dim]
-        
-        # Concatenate attention masks
-        new_attention_mask = torch.cat([context_attention_mask, attention_mask], dim=1)
-        
-        # Create new position_ids
-        total_seq_len = new_inputs_embeds.size(1)
-        new_position_ids = torch.arange(total_seq_len, device=inputs_embeds.device).unsqueeze(0).expand(batch_size, -1)
-        
-        # Update labels if provided
-        if labels is not None:
-            # Create ignore labels for context (including padding and special tokens)
-            context_labels = torch.full((batch_size, new_context_seq_len), self.ignore_index, device=labels.device, dtype=labels.dtype)
-            new_labels = torch.cat([context_labels, labels], dim=1)
-        else:
-            new_labels = None
-        
-        return new_inputs_embeds, new_attention_mask, new_position_ids, new_labels
-
-
-    @replace_return_docstrings(output_type=CcubedCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        context_input_ids: torch.LongTensor = None,
-        context_inputs_embeds: Optional[torch.FloatTensor] = None,
-        context_attention_mask: Optional[torch.Tensor] = None,
-        input_ids: torch.LongTensor = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[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,
-        cache_position: Optional[torch.LongTensor] = None,
-        logits_to_keep: int = 0,
-    ) -> Union[Tuple, CcubedCausalLMOutputWithPast]:
-        """
-        Perform a forward pass through the Ccubed model, optionally conditioning on context input.
-
-        Args:
-            context_input_ids (`torch.LongTensor` of shape `(batch_size, context_sequence_length)`, *optional*):
-                Token IDs of the context input sequence.
-            context_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, context_sequence_length, hidden_size)`, *optional*):
-                Pre-computed context embeddings. If provided, will not compute embeddings from context_input_ids.
-            context_attention_mask (`torch.Tensor` of shape `(batch_size, context_sequence_length)`, *optional*):
-                Attention mask for context input sequence.
-            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Token IDs of the input sequence.
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-                Optionally, instead of passing `input_ids`, you can pass an embedded representation directly.
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices.
-            position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Indices of positions of each input sequence token.
-            past_key_values (`List[torch.FloatTensor]`, *optional*):
-                Pre-computed hidden-states (key and value tensors) that can be used to speed up sequential decoding.
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-            use_cache (`bool`, *optional*):
-                If `True`, past key values will be used to speed up decoding.
-            output_attentions (`bool`, *optional*):
-                If `True`, return the attention tensors for each layer.
-            output_hidden_states (`bool`, *optional*):
-                If `True`, return the hidden states of all layers.
-            return_dict (`bool`, *optional*):
-                If `True`, return a `CcubedCausalLMOutputWithPast` instead of a plain tuple.
-            num_logits_to_keep (`int`, *optional*):
-                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
-                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-
-        Returns:
-            `Union[Tuple, CcubedCausalLMOutputWithPast]`: A tuple containing various model outputs or a `CcubedCausalLMOutputWithPast` instance.
-                The CcubedCausalLMOutputWithPast contains the following fields:
-                    - loss (`torch.FloatTensor`, *optional*): Language modeling loss if labels provided, None otherwise.
-                    - logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, vocab_size)`): Prediction scores.
-                    - past_key_values (`List[torch.FloatTensor]`, *optional*): Pre-computed hidden states for efficient decoding.
-                    - hidden_states (`Tuple[torch.FloatTensor]`, *optional*): Layer hidden states if output_hidden_states=True.
-                    - attentions (`Tuple[torch.FloatTensor]`, *optional*): Layer attention weights if output_attentions=True.
-                    - context_hidden_states (`torch.FloatTensor`, *optional*): Final hidden states from the context tower.
-        """
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-
-        all_inputs_none = (
-            input_ids is None and 
-            inputs_embeds is None and 
-            context_input_ids is None and 
-            context_inputs_embeds is None
-        )
-        
-        if all_inputs_none:
-            raise ValueError("You must provide either non-empty input_ids/inputs_embeds or context_input_ids/context_inputs_embeds.")
-
-
-        if context_input_ids is not None or context_inputs_embeds is not None:
-            context_features = self.context_tower(
-                input_ids=context_input_ids,
-                inputs_embeds=context_inputs_embeds,
-                attention_mask=context_attention_mask,
-            )
-            context_features, context_attention_mask = self.connector(
-                context_features=context_features
-            )
-        else:
-            context_features = None
-            context_attention_mask = None
-
-
-        if inputs_embeds is None and input_ids is not None:
-            inputs_embeds = self.get_input_embeddings()(input_ids)
-
-        if inputs_embeds is not None:
-            inputs_embeds, attention_mask, position_ids, labels = self._merge_context_features(
-                context_features=context_features,
-                inputs_embeds=inputs_embeds,
-                attention_mask=attention_mask,
-                context_attention_mask=context_attention_mask,
-                position_ids=position_ids,
-                labels=labels,
-            )
-        else:
-            inputs_embeds = context_features
-            attention_mask = context_attention_mask
-
-        outputs = self.language_model(
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=cache_position,
-            logits_to_keep=logits_to_keep,
-        )
-
-        logits = outputs[0]
-
-        loss = None
-        if labels is not None:
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            loss_fct = nn.CrossEntropyLoss(ignore_index=self.ignore_index)
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device))
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CcubedCausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            context_hidden_states=context_features,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        context_features=None,
-        **kwargs
-    ):
-        if past_key_values:
-            input_ids = input_ids[:, -1:]
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        model_inputs.update(
-            {
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "attention_mask": attention_mask,
-                "context_features": context_features,
-            }
-        )
-        return model_inputs
+# coding=utf-8
+"""PyTorch Ccubed model."""
+
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.processing_utils import Unpack
+from transformers.image_processing_utils import select_best_resolution
+from transformers.modeling_outputs import ModelOutput
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs, _flash_attention_forward
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10
+)
+from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
+from .configuration_c_cubed import CcubedConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "CcubedConfig"
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+@dataclass
+class CcubedCausalLMOutputWithPast(ModelOutput):
+    """
+    Base class for Ccubed causal language model (or autoregressive) outputs.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.context_config.num_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+            `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        context_hidden_states (`torch.FloatTensor`, *optional*):
+            A `torch.FloatTensor` of size (batch_size, sequence_length, hidden_size)`.
+            context_hidden_states of the model produced by the context encoder and after projecting the last hidden state.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    context_hidden_states: Optional[torch.FloatTensor] = None
+
+
+class CcubedDynamicAttention(nn.Module):
+    """
+    Attention mechanism adapted for dynamic output size based on Mistral's architecture. This attention layer computes
+    the output attention scores which are used to determine the pooling size dynamically.
+    """
+
+    def __init__(self, config: CcubedConfig):
+        super().__init__()
+    
+        self.config = config
+        self.hidden_size = config.context_config.hidden_size
+        self.num_heads = config.context_config.num_attention_heads
+        self.head_dim = getattr(config.context_config, "head_dim", self.hidden_size // self.num_heads)
+        self.num_key_value_heads = config.context_config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.scaling = self.head_dim ** -0.5
+        self.attention_dropout = getattr(self.config.context_config, "attention_dropout", 0.0)
+
+        # Query, Key, Value, and Output Projections
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, 1, bias=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ):
+        # Get input dimensions
+        bsz, seq_len, hidden_size = hidden_states.size()
+
+        # Query, Key, Value projections
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Reshape and transpose to [batch_size, num_heads, seq_len, head_dim]
+        query_states = query_states.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        # Repeat key and value states for multi-head attention
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # Compute attention scores
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        # Apply softmax to get attention probabilities
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+        # Apply attention to values
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        # Reshape attention output
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, seq_len, -1)
+
+        # Project to output dimension
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights
+
+
+class CcubedDynamicFlashAttention2(CcubedDynamicAttention):
+    def __init__(self, config: CcubedConfig):
+        super().__init__(config)
+        self.is_causal = False  # Assuming non-causal attention for this context
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ):
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        sliding_window = None
+        if getattr(self.config, "sliding_window", None) is not None:
+            sliding_window = self.config.sliding_window
+        
+        attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=sliding_window,  # main diff with Llama
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class CcubedDynamicWeightedAvgPool1d(nn.Module):
+    """
+    A module that dynamically determines the output size based on input
+    and performs weighted average pooling with separate attention mechanisms
+    for output size estimation and weighted pooling.
+    """
+    def __init__(self, config, output_size_min=32, output_size_max=131072):
+        super().__init__()
+        # Attention mechanism for estimating output size
+        self.size_estim_attn = CcubedDynamicFlashAttention2(config) # CcubedDynamicAttention(config)
+        # Attention mechanism for weighted pooling
+        self.imp_estim_attn = CcubedDynamicFlashAttention2(config) # CcubedDynamicAttention(config)
+        self.output_size_min = output_size_min
+        self.output_size_max = (
+            config.context_config.max_position_embeddings if config.context_config.max_position_embeddings is not None else output_size_max
+        )
+        self.scale_param = nn.Parameter(torch.tensor(0.01))
+
+    def forward(self, hidden_states, context_attention_mask=None):
+        """
+        Args:
+            x: Input tensor of shape (batch_size, seq_len, hidden_size)
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+                - pooled_output: Padded tensor of compressed sequences (batch_size, max_pooled_len, hidden_size)
+                - attention_mask: Binary mask indicating valid tokens (batch_size, max_pooled_len)
+                - dynamic_output_sizes: Dynamic output sizes for each batch (batch_size,)
+        """
+        batch_size, seq_len, hidden_size = hidden_states.size()
+        device = hidden_states.device
+
+        # Estimate output size using attention mechanism
+        # attn_output_size: (batch_size, seq_len, 1)
+        attn_output_size, _ = self.size_estim_attn(hidden_states)
+
+        # Calculate dynamic output sizes for each batch item
+        # (batch_size, seq_len, 1) -> (batch_size, 1)
+        batch_attn_means = torch.sigmoid(attn_output_size).mean(dim=1)
+        scaled_batch_means = batch_attn_means * self.scale_param.to(batch_attn_means.dtype)
+
+        # Calculate dynamic output sizes (batch_size,)
+        dynamic_output_sizes = (
+            (scaled_batch_means * (self.output_size_max - self.output_size_min)) + self.output_size_min
+        ).int().squeeze(-1)
+
+        max_pooled_len = dynamic_output_sizes.max().item()
+
+        # Compute attention weights for weighted pooling
+        # attn_output_weights: (batch_size, seq_len, 1)
+        attn_output_weights, _ = self.imp_estim_attn(hidden_states)
+        # Normalize with sigmoid function for use as weights
+        # attention_weights: (batch_size, seq_len)
+        attention_weights = torch.sigmoid(attn_output_weights).squeeze(-1)
+
+        # If context_attention_mask is provided, apply it to zero out weights for invalid tokens
+        if context_attention_mask is not None:
+            attention_weights = attention_weights * context_attention_mask
+
+        # Initialize output tensors
+        # pooled_output: (batch_size, max_pooled_len, hidden_size)
+        pooled_output = torch.zeros(
+            batch_size, max_pooled_len, hidden_size, 
+            device=device, dtype=hidden_states.dtype
+        )
+        # attention_mask: (batch_size, max_pooled_len)
+        attention_mask = torch.zeros(
+            batch_size, max_pooled_len, 
+            dtype=torch.bool, device=device
+        )
+
+        for batch_idx in range(batch_size):
+            output_size = dynamic_output_sizes[batch_idx].item()
+            item_input = hidden_states[batch_idx]  # Shape: (seq_len, hidden_size)
+            item_weights = attention_weights[batch_idx]  # Shape: (seq_len)
+   
+            # Perform weighted pooling
+            pooled_values = []
+            batch_attn_mask = torch.zeros(output_size, dtype=torch.bool, device=device)
+            # Split the sequence evenly
+            intervals = torch.linspace(0, seq_len, steps=output_size + 1).long()
+            for i in range(output_size):
+                start = intervals[i].item()
+                end = intervals[i + 1].item()
+                chunk_input = item_input[start:end]  # Shape: (chunk_size, hidden_size)
+                chunk_weights = item_weights[start:end]  # Shape: (chunk_size)
+                if chunk_weights.sum() == 0:
+                    # If the sum of weights is zero, add a zero vector
+                    pooled_value = torch.zeros(hidden_size, device=device, dtype=hidden_states.dtype)
+                else:
+                    # Calculate weighted average
+                    weighted_input = chunk_input * chunk_weights.unsqueeze(-1)  # Shape: (chunk_size, hidden_size)
+                    pooled_value = weighted_input.sum(dim=0) / (chunk_weights.sum() + 1e-8)  # Shape: (hidden_size)
+                    batch_attn_mask[i] = True
+                pooled_values.append(pooled_value)
+
+            if pooled_values:  # Only stack if there are values
+                # Convert the result to a tensor
+                pooled_values = torch.stack(pooled_values)  # Shape: (output_size, hidden_size)
+                # Store the result
+                pooled_output[batch_idx, -output_size:] = pooled_values
+                attention_mask[batch_idx, -output_size:] = batch_attn_mask
+
+        return pooled_output, attention_mask, dynamic_output_sizes
+
+
+class CcubedContextLanguageConnector(nn.Module):
+    def __init__(self, config: CcubedConfig):
+        super().__init__()
+
+        self.dynamic_pooling = CcubedDynamicWeightedAvgPool1d(config)
+
+        self.linear_1 = nn.Linear(
+            config.context_config.hidden_size,
+            config.text_config.hidden_size,
+            bias=True
+        )
+        self.act = ACT2FN[config.projector_hidden_act]
+        self.linear_2 = nn.Linear(
+            config.text_config.hidden_size,
+            config.text_config.hidden_size,
+            bias=True
+        )
+
+    def forward(self, context_features):
+        # context_features: [batch_size, seq_len, hidden_size]
+        # Apply dynamic adaptive average pooling with attention
+        pooled_output, attention_mask, dynamic_output_sizes = self.dynamic_pooling(
+            hidden_states=context_features
+        )
+
+        hidden_states = self.linear_1(pooled_output)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        
+        return hidden_states, attention_mask
+
+
+class CcubedContextTower(nn.Module):
+    def __init__(self, config: CcubedConfig):
+        super().__init__()
+
+        self.tower = AutoModelForCausalLM.from_config(
+            config.context_config,
+            attn_implementation="flash_attention_2" if is_flash_attn_2_available() else "eager"
+        )
+        self.select_layer = config.context_feature_layer
+    
+    def feature_select(self, llm_outputs):
+        hidden_states = llm_outputs.hidden_states
+        return hidden_states[self.select_layer]
+
+    def forward(
+        self,
+        input_ids,
+        inputs_embeds,
+        attention_mask
+    ):
+        outputs = self.tower(
+            input_ids=input_ids,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            output_hidden_states=True
+        )
+        features = self.feature_select(outputs)
+        return features
+    
+
+class CcubedPreTrainedModel(PreTrainedModel):
+    config_class = CcubedConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = [] # ["CcubedContextLanguageConnector", "CcubedContextTower"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = (
+            self.config.initializer_range
+            if hasattr(self.config, "initializer_range")
+            else self.config.text_config.initializer_range
+        )
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class CcubedForConditionalGeneration(CcubedPreTrainedModel):
+    def __init__(self, config: CcubedConfig):
+        super().__init__(config)
+        self.context_tower = CcubedContextTower(config)
+        self.connector = CcubedContextLanguageConnector(config)
+
+        self.language_model = AutoModelForCausalLM.from_config(
+            config.text_config,
+            attn_implementation="flash_attention_2" if is_flash_attn_2_available() else "eager"
+        )
+
+        self.vocab_size = config.text_config.vocab_size
+        self.ignore_index = config.ignore_index if hasattr(config, 'ignore_index') else -100
+        self.start_of_context_token_id = config.start_of_context_token_id
+        self.end_of_context_token_id = config.end_of_context_token_id
+        
+        self.post_init()
+    
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def get_context_input_embeddings(self):
+        return self.context_tower.tower.get_input_embeddings()
+    
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def set_context_input_embeddings(self, value):
+        self.context_tower.tower.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.language_model.get_output_embeddings()
+    
+    def get_context_output_embeddings(self):
+        return self.context_tower.tower.get_output_embeddings()
+    
+    def set_output_embeddings(self, new_embeddings):
+        self.language_model.set_output_embeddings(new_embeddings)
+
+    def set_context_output_embeddings(self, new_embeddings):
+        self.context_tower.tower.set_output_embeddings(new_embeddings)
+    
+    def set_decoder(self, decoder):
+        self.language_model.set_decoder(decoder)
+
+    def set_context_encoder(self, decoder):
+        self.context_tower.tower.set_decoder(decoder)
+    
+    def get_decoder(self):
+        return self.language_model.get_decoder()
+
+    def get_context_encoder(self):
+        return self.context_tower.tower.get_decoder()
+    
+    def tie_weights(self):
+        return self.language_model.tie_weights()
+    
+    def context_tie_weights(self):
+        return self.context_tower.tower.tie_weights()
+    
+    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
+        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+        # update vocab size
+        self.config.text_config.vocab_size = model_embeds.num_embeddings
+        self.vocab_size = model_embeds.num_embeddings
+        return model_embeds
+    
+    def _merge_context_features(
+        self,
+        context_features = None,
+        inputs_embeds = None,
+        attention_mask = None,
+        context_attention_mask=None,
+        position_ids=None,
+        labels=None,
+    ):
+        if context_features is None:
+            return inputs_embeds, attention_mask, position_ids, labels
+
+        batch_size, seq_length, embed_dim = inputs_embeds.shape
+        context_seq_len = context_features.size(1)
+        
+        # Create embeddings for begin and end of context tokens
+        begin_context_embed = self.get_input_embeddings()(torch.tensor(self.start_of_context_token_id, device=context_features.device))
+        end_context_embed = self.get_input_embeddings()(torch.tensor(self.end_of_context_token_id, device=context_features.device))
+        
+        # Determine the actual lengths of context sequences (excluding padding)
+        if context_attention_mask is not None:
+            # context_attention_mask: [batch_size, context_seq_len, 1]
+            context_attention_mask = context_attention_mask.squeeze(-1)  # [batch_size, context_seq_len]
+            # Sum over sequence length to get actual lengths
+            context_lengths = context_attention_mask.sum(dim=1).long()  # [batch_size]
+        else:
+            # If no context_attention_mask is provided, assume full length
+            context_lengths = torch.full((batch_size,), context_seq_len, device=context_features.device, dtype=torch.long)
+            context_attention_mask = torch.ones(batch_size, context_seq_len, device=context_features.device, dtype=torch.long)
+        
+        # Rearrange context features to include padding at the beginning
+        # Identify the maximum context length (excluding padding)
+        max_context_length = context_lengths.max().item()
+        # Calculate the amount of padding needed for each sample
+        padding_lengths = context_seq_len - context_lengths  # [batch_size]
+        
+        # Create new context_features with padding at the beginning
+        new_context_features = []
+        for i in range(batch_size):
+            padding_len = padding_lengths[i].item()
+            # Create padding embeddings (zeros)
+            padding_embed = torch.zeros(padding_len, embed_dim, device=context_features.device, dtype=context_features.dtype)
+            # Get actual context features (excluding padding)
+            actual_context = context_features[i, padding_len:context_seq_len]
+            # Concatenate padding, begin token, actual context, end token
+            sample_context = torch.cat([
+                padding_embed,
+                begin_context_embed.unsqueeze(0),
+                actual_context,
+                end_context_embed.unsqueeze(0)
+            ], dim=0)  # [context_seq_len + 2, embed_dim]
+            new_context_features.append(sample_context)
+        # Stack to create [batch_size, new_context_seq_len, embed_dim]
+        context_features = torch.stack(new_context_features, dim=0)
+        new_context_seq_len = context_features.size(1)
+        
+        # Update context_attention_mask accordingly
+        new_context_attention_mask = []
+        for i in range(batch_size):
+            padding_len = padding_lengths[i].item()
+            # Create padding mask (zeros)
+            padding_mask = torch.zeros(padding_len, device=context_features.device, dtype=attention_mask.dtype)
+            # Begin and end token masks
+            begin_attention = torch.ones(1, device=context_features.device, dtype=attention_mask.dtype)
+            end_attention = torch.ones(1, device=context_features.device, dtype=attention_mask.dtype)
+            # Actual context attention mask (excluding padding)
+            actual_mask = context_attention_mask[i, padding_len:context_seq_len]
+            # Concatenate masks
+            sample_mask = torch.cat([
+                padding_mask,
+                begin_attention,
+                actual_mask,
+                end_attention
+            ], dim=0)  # [context_seq_len + 2]
+            new_context_attention_mask.append(sample_mask)
+        # Stack to create [batch_size, new_context_seq_len]
+        context_attention_mask = torch.stack(new_context_attention_mask, dim=0)
+        
+        # Concatenate context features with input embeddings
+        new_inputs_embeds = torch.cat([context_features, inputs_embeds], dim=1)  # [batch_size, total_seq_len, embed_dim]
+        
+        # Concatenate attention masks
+        new_attention_mask = torch.cat([context_attention_mask, attention_mask], dim=1)
+        
+        # Create new position_ids
+        total_seq_len = new_inputs_embeds.size(1)
+        new_position_ids = torch.arange(total_seq_len, device=inputs_embeds.device).unsqueeze(0).expand(batch_size, -1)
+        
+        # Update labels if provided
+        if labels is not None:
+            # Create ignore labels for context (including padding and special tokens)
+            context_labels = torch.full((batch_size, new_context_seq_len), self.ignore_index, device=labels.device, dtype=labels.dtype)
+            new_labels = torch.cat([context_labels, labels], dim=1)
+        else:
+            new_labels = None
+        
+        return new_inputs_embeds, new_attention_mask, new_position_ids, new_labels
+
+
+    @replace_return_docstrings(output_type=CcubedCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        context_input_ids: torch.LongTensor = None,
+        context_inputs_embeds: Optional[torch.FloatTensor] = None,
+        context_attention_mask: Optional[torch.Tensor] = None,
+        input_ids: torch.LongTensor = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[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,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: int = 0,
+    ) -> Union[Tuple, CcubedCausalLMOutputWithPast]:
+        """
+        Perform a forward pass through the Ccubed model, optionally conditioning on context input.
+
+        Args:
+            context_input_ids (`torch.LongTensor` of shape `(batch_size, context_sequence_length)`, *optional*):
+                Token IDs of the context input sequence.
+            context_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, context_sequence_length, hidden_size)`, *optional*):
+                Pre-computed context embeddings. If provided, will not compute embeddings from context_input_ids.
+            context_attention_mask (`torch.Tensor` of shape `(batch_size, context_sequence_length)`, *optional*):
+                Attention mask for context input sequence.
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Token IDs of the input sequence.
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+                Optionally, instead of passing `input_ids`, you can pass an embedded representation directly.
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices.
+            position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Indices of positions of each input sequence token.
+            past_key_values (`List[torch.FloatTensor]`, *optional*):
+                Pre-computed hidden-states (key and value tensors) that can be used to speed up sequential decoding.
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+            use_cache (`bool`, *optional*):
+                If `True`, past key values will be used to speed up decoding.
+            output_attentions (`bool`, *optional*):
+                If `True`, return the attention tensors for each layer.
+            output_hidden_states (`bool`, *optional*):
+                If `True`, return the hidden states of all layers.
+            return_dict (`bool`, *optional*):
+                If `True`, return a `CcubedCausalLMOutputWithPast` instead of a plain tuple.
+            num_logits_to_keep (`int`, *optional*):
+                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
+                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+
+        Returns:
+            `Union[Tuple, CcubedCausalLMOutputWithPast]`: A tuple containing various model outputs or a `CcubedCausalLMOutputWithPast` instance.
+                The CcubedCausalLMOutputWithPast contains the following fields:
+                    - loss (`torch.FloatTensor`, *optional*): Language modeling loss if labels provided, None otherwise.
+                    - logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, vocab_size)`): Prediction scores.
+                    - past_key_values (`List[torch.FloatTensor]`, *optional*): Pre-computed hidden states for efficient decoding.
+                    - hidden_states (`Tuple[torch.FloatTensor]`, *optional*): Layer hidden states if output_hidden_states=True.
+                    - attentions (`Tuple[torch.FloatTensor]`, *optional*): Layer attention weights if output_attentions=True.
+                    - context_hidden_states (`torch.FloatTensor`, *optional*): Final hidden states from the context tower.
+        """
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        all_inputs_none = (
+            input_ids is None and 
+            inputs_embeds is None and 
+            context_input_ids is None and 
+            context_inputs_embeds is None
+        )
+        
+        if all_inputs_none:
+            raise ValueError("You must provide either non-empty input_ids/inputs_embeds or context_input_ids/context_inputs_embeds.")
+
+
+        if context_input_ids is not None or context_inputs_embeds is not None:
+            context_features = self.context_tower(
+                input_ids=context_input_ids,
+                inputs_embeds=context_inputs_embeds,
+                attention_mask=context_attention_mask,
+            )
+            context_features, context_attention_mask = self.connector(
+                context_features=context_features
+            )
+        else:
+            context_features = None
+            context_attention_mask = None
+
+
+        if inputs_embeds is None and input_ids is not None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if inputs_embeds is not None:
+            inputs_embeds, attention_mask, position_ids, labels = self._merge_context_features(
+                context_features=context_features,
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                context_attention_mask=context_attention_mask,
+                position_ids=position_ids,
+                labels=labels,
+            )
+        else:
+            inputs_embeds = context_features
+            attention_mask = context_attention_mask
+
+        outputs = self.language_model(
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            logits_to_keep=logits_to_keep,
+        )
+
+        logits = outputs[0]
+
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = nn.CrossEntropyLoss(ignore_index=self.ignore_index)
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CcubedCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            context_hidden_states=context_features,
+        )