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modeling_co_encoder.py

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+# coding=utf-8
+"""PyTorch CoEncoder 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 import PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.image_processing_utils import select_best_resolution
+from transformers.modeling_outputs import ModelOutput
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+    is_flash_attn_2_available,
+)
+from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
+from .configuration_co_encoder import CoEncoderConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "CoEncoderConfig"
+
+
+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 CoEncoderCausalLMOutputWithPast(ModelOutput):
+    """
+    Base class for CoEncoder 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 CoEncoderDynamicAttention(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: CoEncoderConfig):
+        super().__init__()
+
+        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
+
+        # 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,
+        output_attentions=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_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, seq_len, self.num_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 CoEncoderDynamicWeightedAvgPool1d(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_estimation_attention = CoEncoderDynamicAttention(config)
+        # Attention mechanism for weighted pooling
+        self.weighted_pooling_attention = CoEncoderDynamicAttention(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):
+        """
+        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_estimation_attention(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
+
+        # 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)
+
+        # Get the maximum output size across the batch
+        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.weighted_pooling_attention(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)
+
+        # 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)
+        # 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 = []
+            # 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)
+                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)
+                pooled_values.append(pooled_value)
+            # 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.squeeze(0)
+            attention_mask[batch_idx, -output_size:] = True
+
+        return pooled_output, attention_mask, dynamic_output_sizes
+
+
+class CoEncoderContextLanguageConnector(nn.Module):
+    def __init__(self, config: CoEncoderConfig):
+        super().__init__()
+
+        self.dynamic_pooling = CoEncoderDynamicWeightedAvgPool1d(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(context_features)
+        # pooled_output: [batch_size, max_pooled_len, hidden_size]
+
+        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 CoEncoderContextTower(nn.Module):
+    def __init__(self, config: CoEncoderConfig):
+        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, inputs):
+        outputs = self.tower(inputs, output_hidden_states=True)
+        features = self.feature_select(outputs)
+        return features
+    
+
+class CoEncoderPreTrainedModel(PreTrainedModel):
+    config_class = CoEncoderConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["CoEncoderContextLanguageConnector", "CoEncoderContextTower"]
+    _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 CoEncoderForConditionalGeneration(CoEncoderPreTrainedModel):
+    def __init__(self, config: CoEncoderConfig):
+        super().__init__(config)
+        self.context_tower = CoEncoderContextTower(config)
+        self.connector = CoEncoderContextLanguageConnector(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.begin_of_context_token_id = config.begin_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 set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.language_model.get_output_embeddings()
+    
+    def set_output_embeddings(self, new_embeddings):
+        self.language_model.set_output_embeddings(new_embeddings)
+    
+    def set_decoder(self, decoder):
+        self.language_model.set_decoder(decoder)
+    
+    def get_decoder(self):
+        return self.language_model.get_decoder()
+    
+    def tie_weights(self):
+        return self.language_model.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,
+        inputs_embeds,
+        input_ids,
+        attention_mask,
+        position_ids=None,
+        labels=None,
+        context_attention_mask=None,
+    ):
+        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.begin_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)
+            # 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=input_ids.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=CoEncoderCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        context_input_ids: torch.LongTensor = None,
+        context_attention_mask: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = 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,
+    ) -> Union[Tuple, CoEncoderCausalLMOutputWithPast]:
+        """
+        Perform a forward pass through the CoEncoder model, optionally conditioning on context input.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Token IDs of the input sequence.
+            context_input_ids (`torch.LongTensor` of shape `(batch_size, context_sequence_length)`, *optional*):
+                Token IDs of the context input sequence.
+            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.
+            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.
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the language modeling loss.
+            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 `CoEncoderCausalLMOutputWithPast` instead of a plain tuple.
+
+        Returns:
+            `Union[Tuple, CoEncoderCausalLMOutputWithPast]`: A tuple containing various model outputs or a `CoEncoderCausalLMOutputWithPast` instance.
+        """
+
+        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
+
+        # Process context input through ContextTower
+        if context_input_ids is not None:
+            context_features = self.context_tower(context_input_ids)
+            context_features, context_attention_mask = self.connector(context_features)
+        else:
+            context_features = None
+            context_attention_mask = None
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if context_features is not None:
+            inputs_embeds, attention_mask, position_ids, labels = self._merge_context_features(
+                context_features,
+                inputs_embeds,
+                input_ids,
+                attention_mask,
+                position_ids,
+                labels,
+                context_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,
+        )
+
+        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))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CoEncoderCausalLMOutputWithPast(
+            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

tokenization_co_encoder.py

@@ -0,0 +1,194 @@
+# coding=utf-8
+"""Tokenization classes for CoEncoder"""
+
+from typing import List, Union, Optional
+from transformers import AutoTokenizer
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+from transformers.utils import logging
+from transformers.feature_extraction_utils import BatchFeature
+
+logger = logging.get_logger(__name__)
+
+
+class CoEncoderDualTokenizerKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "context_kwargs": {
+            "padding": False,
+        },
+        "text_kwargs": {
+            "padding": False,
+        },
+    }
+
+
+class CoEncoderDualTokenizer(ProcessorMixin):
+    r"""
+    CoEncoderDualTokenizer is tokenizer for the CoEncoder model. It processes context and main text.
+
+    Args:
+        context_tokenizer ([`PreTrainedTokenizer`]):
+            The tokenizer for context.
+        text_tokenizer ([`PreTrainedTokenizer`]):
+            The tokenizer for main text.
+    """
+
+    attributes = ["context_tokenizer", "text_tokenizer"]
+    context_tokenizer_class = "AutoTokenizer"
+    text_tokenizer_class = "AutoTokenizer"
+
+    def __init__(self, context_tokenizer=None, text_tokenizer=None):
+        super().__init__(context_tokenizer, text_tokenizer)
+    
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
+        """
+        Load both context and text tokenizers from a given repository.
+
+        Args:
+            pretrained_model_name_or_path (str): The name or path of the Hugging Face repository.
+
+        Returns:
+            CoEncoderDualTokenizer: An instance of the tokenizer class.
+        """
+        # Load context_tokenizer from 'context_tokenizer' directory
+        context_tokenizer = AutoTokenizer.from_pretrained(f"{pretrained_model_name_or_path}/context_tokenizer", **kwargs)
+
+        # Load text_tokenizer from 'text_tokenizer' directory
+        text_tokenizer = AutoTokenizer.from_pretrained(f"{pretrained_model_name_or_path}/text_tokenizer", **kwargs)
+
+        # Return a new instance of CoEncoderDualTokenizer with both tokenizers loaded
+        return cls(context_tokenizer=context_tokenizer, text_tokenizer=text_tokenizer)
+
+    def __call__(
+        self,
+        context: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        return_tensors: Optional[str] = None,
+        **kwargs: Unpack[CoEncoderDualTokenizerKwargs]
+    ) -> BatchFeature:
+        """
+        Main method to prepare inputs for the CoEncoder model.
+
+        Args:
+            context: Context text input.
+            text: Main text input.
+            return_tensors: Type of tensors to return.
+
+        Returns:
+            BatchFeature: A BatchFeature object containing model inputs.
+        """
+        if context is None and text is None:
+            raise ValueError("You must provide either context or text.")
+
+        features = {}
+
+        if context is not None:
+            context_features = self.context_tokenizer(
+                context,
+                return_tensors=return_tensors,
+                **kwargs.get("context_kwargs", {})
+            )
+            features.update({f"context_{k}": v for k, v in context_features.items()})
+
+        if text is not None:
+            text_features = self.text_tokenizer(
+                text,
+                return_tensors=return_tensors,
+                **kwargs.get("text_kwargs", {})
+            )
+            features.update({k: v for k, v in text_features.items()})
+
+        return BatchFeature(data=features, tensor_type=return_tensors)
+
+    def pad(
+        self,
+        encoded_inputs,
+        padding=True,
+        max_length=None,
+        return_tensors=None,
+        **kwargs
+    ):
+        """
+        Pads the encoded inputs to the maximum length in the batch.
+
+        Args:
+            encoded_inputs: A list of dictionaries containing context and text features.
+            padding: Whether to pad sequences.
+            max_length: Maximum length for padding.
+            return_tensors: Type of tensors to return.
+
+        Returns:
+            A dictionary with padded sequences.
+        """
+        # Separate context and text features
+        context_features = []
+        text_features = []
+
+        for feature in encoded_inputs:
+            # Extract context features
+            context_feature = {
+                k[len("context_"):]: v
+                for k, v in feature.items()
+                if k.startswith("context_")
+            }
+            if context_feature:
+                context_features.append(context_feature)
+            # Extract text features
+            text_feature = {
+                k[len("input_"):]: v
+                for k, v in feature.items()
+                if k.startswith("input_")
+            }
+            if text_feature:
+                text_features.append(text_feature)
+
+        # Pad context features
+        if context_features:
+            context_padded = self.context_tokenizer.pad(
+                context_features,
+                padding=padding,
+                max_length=max_length,
+                return_tensors=return_tensors,
+                **kwargs.get("context_kwargs", {})
+            )
+            context_padded = {f"context_{k}": v for k, v in context_padded.items()}
+        else:
+            context_padded = {}
+
+        # Pad text features
+        if text_features:
+            text_padded = self.text_tokenizer.pad(
+                text_features,
+                padding=padding,
+                max_length=max_length,
+                return_tensors=return_tensors,
+                **kwargs.get("text_kwargs", {})
+            )
+            text_padded = {k: v for k, v in text_padded.items()}
+        else:
+            text_padded = {}
+
+        # Combine padded features
+        padded_features = {**context_padded, **text_padded}
+
+        return BatchFeature(data=padded_features, tensor_type=return_tensors)
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        Calls the batch_decode method of the text_tokenizer.
+        """
+        return self.text_tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        Calls the decode method of the text_tokenizer.
+        """
+        return self.text_tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        """
+        Returns the model input names.
+        """
+        return list(dict.fromkeys(self.context_tokenizer.model_input_names + self.text_tokenizer.model_input_names))