Upload modeling_backbone_components.py with huggingface_hub

modeling_backbone_components.py

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+"""Backbone components for Mimi models - shared attention transformers."""
+
+import math
+from typing import Optional, Union
+
+import torch
+from torch import nn
+
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.masking_utils import create_causal_mask
+from transformers.modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.utils import logging
+
+from configuration_mimi import MimiConfig
+from modeling_mimi_clean import (
+    MimiAttention, 
+    MimiMLP, 
+    MimiLayerScale,
+    MimiRotaryEmbedding,
+    apply_rotary_pos_emb,
+    MIMI_ATTENTION_CLASSES
+)
+
+logger = logging.get_logger(__name__)
+
+
+class CausalAttentionTransformer(nn.Module):
+    """
+    Standard causal attention transformer (decoder-only) consisting of *config.num_hidden_layers* layers.
+    Each layer is a [`MimiTransformerLayer`] with self-attention only.
+    
+    This is a standard decoder-only transformer architecture for causal language modeling.
+
+    Args:
+        config: MimiConfig
+    """
+
+    def __init__(self, config: MimiConfig):
+        super().__init__()
+        
+        self.layers = nn.ModuleList(
+            [MimiTransformerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.gradient_checkpointing = False
+        self.config = config
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = 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,
+    ) -> Union[tuple, BaseModelOutputWithPast]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Input embeddings or hidden states from previous layer
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+                config.max_position_embeddings - 1]`.
+
+                [What are position IDs?](../glossary#position-ids)
+            past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+                Pre-computed hidden-states (key and values in the self-attention blocks) that can be used to speed up 
+                sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous 
+                stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+                Two formats are allowed:
+                - a [`~cache_utils.Cache`] instance;
+                - Tuple of `tuple(torch.FloatTensor)` of length `config.num_hidden_layers`, with each tuple having 2 tensors of
+                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+                cache format.
+
+                The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+                legacy cache format will be returned.
+
+                If `past_key_values` are used, the user can optionally input only the last `hidden_states` of shape 
+                `(batch_size, 1, hidden_size)` instead of all `hidden_states` of shape `(batch_size, sequence_length, hidden_size)`.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+                `past_key_values`).
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+                tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+                more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence.
+        """
+        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
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if use_cache and not isinstance(past_key_values, Cache):
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            else:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                logger.warning_once(
+                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
+                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
+                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
+                )
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        # Create causal mask for self-attention
+        causal_mask = create_causal_mask(
+            config=self.config,
+            input_embeds=hidden_states,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        # Initialize output containers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        # Add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class MimiTransformerLayer(GradientCheckpointingLayer):
+    def __init__(self, config: MimiConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = MIMI_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+        self.mlp = MimiMLP(config)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+        self.self_attn_layer_scale = MimiLayerScale(config)
+        self.mlp_layer_scale = MimiLayerScale(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual + self.self_attn_layer_scale(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + self.mlp_layer_scale(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class CrossAttention(nn.Module):
+    """
+    Cross-attention layer with monotonic masking for decoder queries attending to encoder outputs.
+    Queries come from decoder, keys and values come from encoder.
+    Supports monotonic attention where each query can only attend to a progressive subset of keys.
+    """
+
+    def __init__(self, config: MimiConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.head_dim
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True  # Causal for queries, but can attend to all encoder positions
+        self.scaling = 1 / math.sqrt(config.head_dim)
+
+        if self.hidden_size % self.num_heads != 0:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        # Query projection for decoder hidden states
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        # Key and value projections for encoder hidden states
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+        
+        # Rotary embeddings only for queries (decoder positions)
+        self.rotary_emb = MimiRotaryEmbedding(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,  # Decoder hidden states (queries)
+        encoder_hidden_states: torch.Tensor,  # Encoder hidden states (keys, values)
+        attention_mask: Optional[torch.Tensor] = None,  # Mask for encoder positions
+        position_ids: Optional[torch.LongTensor] = None,  # Decoder position IDs
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        alignment_chunk_sizes: Optional[torch.Tensor] = None,  # Monotonic attention chunk sizes
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+        _, kv_len, _ = encoder_hidden_states.size()
+
+        # Queries from decoder
+        query_states = self.q_proj(hidden_states)
+        # Keys and values from encoder
+        key_states = self.k_proj(encoder_hidden_states)
+        value_states = self.v_proj(encoder_hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, kv_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, kv_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        # Apply rotary embeddings only to queries (decoder positions)
+        if position_ids is not None:
+            cos, sin = self.rotary_emb(value_states, position_ids)
+            query_states, _ = apply_rotary_pos_emb(query_states, query_states, cos, sin)
+
+        if past_key_value is not None:
+            # For cross attention, we typically cache encoder keys/values
+            cache_kwargs = {"sin": sin if position_ids is not None else None, 
+                          "cos": cos if position_ids is not None else None, 
+                          "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
+
+        # Apply monotonic attention mask if alignment_chunk_sizes is provided
+        if alignment_chunk_sizes is not None:
+            monotonic_mask = _create_monotonic_attention_mask(
+                alignment_chunk_sizes=alignment_chunk_sizes,
+                query_length=q_len,
+                key_length=kv_len,
+                device=attn_weights.device,
+                dtype=attn_weights.dtype,
+            )
+            attn_weights = attn_weights + monotonic_mask
+
+        # Apply additional attention mask for encoder positions (if provided)
+        if attention_mask is not None:
+            # attention_mask should mask invalid encoder positions
+            # Shape: [batch_size, 1, 1, encoder_seq_len] or [batch_size, 1, decoder_seq_len, encoder_seq_len]
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class CrossAttentionLayer(GradientCheckpointingLayer):
+    """
+    Cross-attention transformer layer with layer normalization and MLP.
+    Includes self-attention on decoder, cross-attention to encoder, and feed-forward.
+    """
+    
+    def __init__(self, config: MimiConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        # Self-attention for decoder
+        self.self_attn = MIMI_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+        
+        # Cross-attention to encoder
+        self.cross_attn = CrossAttention(config=config, layer_idx=layer_idx)
+
+        self.mlp = MimiMLP(config)
+        
+        # Layer norms
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+        self.post_cross_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+        
+        # Layer scales
+        self.self_attn_layer_scale = MimiLayerScale(config)
+        self.cross_attn_layer_scale = MimiLayerScale(config)
+        self.mlp_layer_scale = MimiLayerScale(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,  # Decoder hidden states
+        encoder_hidden_states: torch.Tensor,  # Encoder hidden states
+        attention_mask: Optional[torch.Tensor] = None,  # Causal mask for self-attention
+        encoder_attention_mask: Optional[torch.Tensor] = None,  # Mask for encoder positions
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        cross_past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        alignment_chunk_sizes: Optional[torch.Tensor] = None,  # Monotonic attention chunk sizes
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): decoder input of shape `(batch, seq_len, embed_dim)`
+            encoder_hidden_states (`torch.FloatTensor`): encoder output of shape `(batch, encoder_seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): causal attention mask for self-attention
+            encoder_attention_mask (`torch.FloatTensor`, *optional*): mask for encoder positions
+            position_ids (`torch.LongTensor`, *optional*): position IDs for decoder
+            past_key_value (`Cache`, *optional*): cached self-attention states
+            cross_past_key_value (`Cache`, *optional*): cached cross-attention states
+            output_attentions (`bool`, *optional*): whether to return attention weights
+            use_cache (`bool`, *optional*): whether to use caching
+            cache_position (`torch.LongTensor`, *optional*): cache positions
+        """
+        residual = hidden_states
+
+        # Pre-norm for self-attention
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self-attention on decoder
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual + self.self_attn_layer_scale(hidden_states)
+
+        # Cross-attention to encoder
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        
+        hidden_states, cross_attn_weights, cross_present_key_value = self.cross_attn(
+            hidden_states=hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=encoder_attention_mask,
+            position_ids=position_ids,
+            past_key_value=cross_past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            alignment_chunk_sizes=alignment_chunk_sizes,
+        )
+        hidden_states = residual + self.cross_attn_layer_scale(hidden_states)
+
+        # Feed Forward Network
+        residual = hidden_states
+        hidden_states = self.post_cross_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + self.mlp_layer_scale(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights, cross_attn_weights)
+
+        if use_cache:
+            outputs += (present_key_value, cross_present_key_value)
+
+        return outputs
+
+
+class CrossAttentionTransformer(nn.Module):
+    """
+    Cross-attention transformer consisting of N cross-attention layers.
+    Each layer performs self-attention on decoder and cross-attention to encoder.
+    
+    Args:
+        config: MimiConfig
+    """
+
+    def __init__(self, config: MimiConfig):
+        super().__init__()
+
+        self.layers = nn.ModuleList(
+            [CrossAttentionLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+
+        self.gradient_checkpointing = False
+        self.config = config
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,  # Decoder hidden states
+        encoder_hidden_states: torch.Tensor,  # Encoder hidden states
+        attention_mask: Optional[torch.Tensor] = None,  # Causal mask for decoder
+        encoder_attention_mask: Optional[torch.Tensor] = None,  # Mask for encoder
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+        cross_past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = 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,
+        alignment_chunk_sizes: Optional[torch.Tensor] = None,  # Monotonic attention chunk sizes
+    ) -> Union[tuple, BaseModelOutputWithPast]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): decoder input of shape `(batch_size, decoder_sequence_length, hidden_size)`
+            encoder_hidden_states (`torch.FloatTensor`): encoder output of shape `(batch_size, encoder_sequence_length, hidden_size)`
+            attention_mask (`torch.Tensor`, *optional*): causal attention mask for decoder self-attention
+            encoder_attention_mask (`torch.Tensor`, *optional*): attention mask for encoder positions
+            position_ids (`torch.LongTensor`, *optional*): position IDs for decoder
+            past_key_values (`Cache` or `list`, *optional*): cached self-attention states
+            cross_past_key_values (`Cache` or `list`, *optional*): cached cross-attention states
+            use_cache (`bool`, *optional*): whether to use caching
+            output_attentions (`bool`, *optional*): whether to return attention weights
+            output_hidden_states (`bool`, *optional*): whether to return hidden states
+            return_dict (`bool`, *optional*): whether to return ModelOutput
+            cache_position (`torch.LongTensor`, *optional*): cache positions
+            alignment_chunk_sizes (`torch.Tensor`, *optional*): tensor of shape `(decoder_sequence_length,)` specifying 
+                how many encoder positions each decoder position can attend to cumulatively. Enables monotonic attention 
+                where decoder position i can attend to encoder positions 0 through sum(alignment_chunk_sizes[:i+1])-1.
+        """
+        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
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if use_cache and past_key_values is None:
+            logger.warning_once("use_cache=True was passed, but no past_key_values were given. Creating new cache.")
+            past_key_values = DynamicCache()
+            
+        if use_cache and cross_past_key_values is None:
+            cross_past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        # Create causal mask for decoder self-attention
+        causal_mask = create_causal_mask(
+            config=self.config,
+            input_embeds=hidden_states,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        # Initialize output containers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_cross_attns = () if output_attentions else None
+        next_decoder_cache = None
+        next_cross_cache = None
+
+        for layer_idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            # Get past key values for this layer
+            layer_past_key_value = past_key_values[layer_idx] if past_key_values is not None else None
+            layer_cross_past_key_value = cross_past_key_values[layer_idx] if cross_past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    encoder_hidden_states,
+                    causal_mask,
+                    encoder_attention_mask,
+                    position_ids,
+                    layer_past_key_value,
+                    layer_cross_past_key_value,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    alignment_chunk_sizes,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=causal_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=layer_past_key_value,
+                    cross_past_key_value=layer_cross_past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    alignment_chunk_sizes=alignment_chunk_sizes,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                # Extract the cached states
+                if output_attentions:
+                    next_decoder_cache = layer_outputs[3]  # self attn cache
+                    next_cross_cache = layer_outputs[4]    # cross attn cache
+                else:
+                    next_decoder_cache = layer_outputs[1]  # self attn cache  
+                    next_cross_cache = layer_outputs[2]    # cross attn cache
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)   # self attention weights
+                all_cross_attns += (layer_outputs[2],)  # cross attention weights
+
+        # Add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        next_cross_cache = next_cross_cache if use_cache else None
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, next_cross_cache, all_hidden_states, all_self_attns, all_cross_attns] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+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)
+
+
+def _create_monotonic_attention_mask(
+    alignment_chunk_sizes: torch.Tensor,
+    query_length: int,
+    key_length: int,
+    device: torch.device,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """
+    Create a monotonic attention mask where each query can only attend to a progressive subset of keys.
+    
+    Args:
+        alignment_chunk_sizes: Tensor of shape (batch_size, query_length) where each element represents 
+                               how many keys the corresponding query can attend to cumulatively.
+        query_length: Number of queries (text tokens)
+        key_length: Number of keys (speech features)
+        device: Device to create the mask on
+        dtype: Data type for the mask
+        
+    Returns:
+        Attention mask of shape (batch_size, 1, query_length, key_length) where 
+        -inf masks out invalid positions, 0.0 allows attention.
+    """
+    batch_size = alignment_chunk_sizes.shape[0]
+    
+    # Create cumulative positions that each query can attend up to
+    cumulative_positions = torch.cumsum(alignment_chunk_sizes, dim=1)  # [batch_size, query_length]
+    
+    # Ensure we don't exceed the key length
+    cumulative_positions = torch.clamp(cumulative_positions, max=key_length)
+    
+    # Create position indices for keys
+    key_positions = torch.arange(key_length, device=device).unsqueeze(0).unsqueeze(0)  # [1, 1, key_length]
+    
+    # Expand cumulative positions for broadcasting
+    cumulative_positions = cumulative_positions.unsqueeze(2)  # [batch_size, query_length, 1]
+    
+    # Create mask: query i can attend to keys 0 to cumulative_positions[i]
+    mask = key_positions < cumulative_positions  # [batch_size, query_length, key_length]
+    
+    # Convert to attention mask format: True -> 0.0 (attend), False -> -inf (mask out)
+    attention_mask = torch.where(mask, 0.0, float('-inf'))
+    
+    # Add head dimension: [batch_size, 1, query_length, key_length]
+    attention_mask = attention_mask.unsqueeze(1)
+    
+    return attention_mask.to(dtype)
+
+
+
+__all__ = [
+    "CausalAttentionTransformer",
+    "MimiTransformerLayer", 
+    "CrossAttention",
+    "CrossAttentionLayer",
+    "CrossAttentionTransformer",
+    "_create_monotonic_attention_mask",
+]