import logging import re from typing import Any, Dict, Generator, Optional, Set, Tuple, TypeVar, Union import peft import torch import torch.nn as nn import torch.nn.functional as F import transformers import transformers.activations import transformers.modeling_outputs import transformers.models from transformers.generation.utils import GenerationMixin from transformers.models.whisper import modeling_whisper as whisper # We must use relative import in this directory to allow uploading to HF Hub # Even "from . import X" pattern doesn't work (undocumented and unclear why) from .ultravox_config import LossConfig from .ultravox_config import LossFunction from .ultravox_config import UltravoxConfig class UltravoxModel(transformers.LlamaPreTrainedModel, GenerationMixin): """ The Ultravox model which consists of an audio encoder and a language model. Audio input is processed by the audio encoder, then every `stack_factor` frames are stacked together and projected to the language model's embedding space using a few linear layers. The text is embedded by the language model as usual and then the audio and text embeddings are merged together. A special token `<|audio|>` is used to indicate the start of the audio embeddings in the merged embeddings. Parameters: config: Model configuration class with all the parameters of the model. """ config_class = UltravoxConfig config: UltravoxConfig # for type hinting # Usually we load encoder and LLM weights from a pretrained model separately, so they are allowed to be missing _keys_to_ignore_on_load_missing = ["audio_tower.*", "language_model.*"] # Since we have kwargs in forward, we need to set this to False, otherwise grad_accum_steps will cause incorrect train loss to be reported # see https://github.com/huggingface/transformers/issues/35856 and https://github.com/huggingface/trl/pull/2615/files accepts_loss_kwargs = False def __init__(self, config: UltravoxConfig): super().__init__(config) self._register_load_state_dict_pre_hook(self._pre_load_state_dict_hook) self.keep_params: Set[str] = set() self.vocab_size = config.vocab_size self.audio_tower = self._create_audio_tower(config) self.audio_tower_context_length: Optional[int] = None self.audio_tower_context_length = self.audio_tower.max_context_length self.multi_modal_projector = self._create_multi_modal_projector(config) self.language_model = self._create_language_model(config) if self.language_model._tied_weights_keys is not None: self._tied_weights_keys = [ f"language_model.{k}" for k in self.language_model._tied_weights_keys ] # Determine no_split_modules dynamically to use with FSDP auto_wrap policy. # FSDP throws an error if some of the layer types are not found in the model. # This would be something like ["LlamaDecoderLayer"] as we don't split audio encoder layers. self._no_split_modules = self.language_model._no_split_modules self.loss_config = LossConfig() self.post_init() @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs): model = super().from_pretrained(pretrained_model_name_or_path, *args, **kwargs) model._load_child_model_weights(*args, **kwargs) return model def _load_child_model_weights(self, *args, **kwargs) -> "UltravoxModel": if "torch_dtype" in kwargs: self.config.torch_dtype = kwargs.pop("torch_dtype") kwargs.pop("config", None) if ( self.config.text_model_id is not None and self.language_model.device.type == "meta" ): # Load the language model weights self.language_model = transformers.AutoModelForCausalLM.from_pretrained( self.config.text_model_id, torch_dtype=self.config.torch_dtype, *args, **kwargs, ) if ( self.config.audio_model_id is not None and self.audio_tower.device.type == "meta" ): # Load the audio tower weights self.audio_tower = transformers.AutoModel.from_pretrained( self.config.audio_model_id, torch_dtype=self.config.torch_dtype, *args, **kwargs, ) return self 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 set_loss_config(self, loss_config: LossConfig): self.loss_config = loss_config def _setup_cache( self, cache_cls, max_batch_size: int, max_cache_len: Optional[int] = None ): self.language_model._setup_cache(cache_cls, max_batch_size, max_cache_len) def _reorder_cache(self, past_key_values, beam_idx): return self.language_model._reorder_cache(past_key_values, beam_idx) def resize_token_embeddings( self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = 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.config.vocab_size = model_embeds.num_embeddings self.vocab_size = model_embeds.num_embeddings return model_embeds def _get_prediction_mask(self, labels: Optional[torch.Tensor]) -> torch.Tensor: """Get a boolean mask for positions where we want to compute KL divergence. For each label position, we want the position before it since that's where the model makes the prediction for that label. Args: labels: Tensor of shape (B, T) where B is batch size and T is sequence length, with -100 for masked positions and token ids for label positions Returns: Boolean tensor of shape (B, T) that's True for positions where we want to compute KL divergence """ if labels is None: raise ValueError("labels must be provided") # Shift the label mask right by 1 along the sequence dimension # This gives us positions where we make predictions for the next token label_mask = labels != -100 pred_mask = torch.zeros_like(label_mask) pred_mask[:, :-1] = label_mask[ :, 1: ] # shift right by 1 along sequence dimension return pred_mask def _compute_kl_loss( self, lm_output: transformers.modeling_outputs.CausalLMOutputWithPast, labels: Optional[torch.Tensor] = None, past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None, alt_input_ids: Optional[torch.Tensor] = None, alt_attention_mask: Optional[torch.Tensor] = None, alt_labels: Optional[torch.Tensor] = None, **kwargs, ): # disable gradient computation for the teacher model with torch.no_grad(): # compute the teacher (text-only) model's distribution alt_inputs_embeds = self.get_input_embeddings().forward(alt_input_ids) alt_lm_output = self.language_model.forward( inputs_embeds=alt_inputs_embeds, labels=alt_labels, attention_mask=alt_attention_mask, past_key_values=past_key_values, **kwargs, ) # compute the KL divergence loss between the two models kl_loss = F.kl_div( F.log_softmax( lm_output.logits[self._get_prediction_mask(labels)] / self.loss_config.kl_temperature, dim=-1, ), F.softmax( alt_lm_output.logits[self._get_prediction_mask(alt_labels)] / self.loss_config.kl_temperature, dim=-1, ), reduction="batchmean", ) return {"loss": kl_loss} def _audio_iter( self, audio_batch_size: torch.Tensor ) -> Generator[Tuple[int, int], None, None]: """ Iterate over the audio batch size and yield the batch index and audio index of each audio item. Args: audio_batch_size: A tensor of shape (B,) where B is the batch size. Returns: A generator that yields a tuple of (start index, length) for each audio item. """ audio_index = 0 for i_b, batch_count in enumerate(audio_batch_size): for _ in range(batch_count): yield i_b, audio_index audio_index += 1 def forward( self, input_ids: torch.Tensor, audio_values: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, audio_token_start_idx: Optional[torch.Tensor] = None, audio_lens: Optional[torch.Tensor] = None, audio_token_len: Optional[torch.Tensor] = None, audio_batch_size: Optional[torch.Tensor] = None, past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None, # the alt_* fields are needed for KL divergence loss alt_input_ids: Optional[torch.Tensor] = None, alt_attention_mask: Optional[torch.Tensor] = None, alt_labels: Optional[torch.Tensor] = None, **kwargs, ) -> Union[Tuple, transformers.modeling_outputs.CausalLMOutputWithPast]: """ Forward pass for the Ultravox model. `input_ids` are the tokenized text input. They are embedded by the language model as usual. `audio_values` are processed by the audio encoder and then every `stack_factor` frames are stacked together and projected to the language model's embedding space using a few linear layers. The audio and text embeddings are merged together. A special token `<|audio|>` is used to indicate the start of the audio embeddings in the merged embeddings. Args: input_ids: The tokenized text input. audio_values: The processed audio values. inputs_embeds: The embeddings for the input tokens. labels: The tokenized text labels. attention_mask: The attention mask for the input. position_ids: The position ids for the input. past_key_values: The past key value cache for the language model attention layers. **kwargs: Additional keyword arguments. Passed directly to the language model. """ if inputs_embeds is None: # B x T -> B x T x D inputs_embeds = self.get_input_embeddings().forward(input_ids) if audio_values is not None and len(audio_values) > 0: assert ( audio_token_start_idx is not None and audio_token_len is not None and audio_lens is not None and audio_batch_size is not None ), "audio_token_start_idx/audio_token_len/audio_lens must be provided if audio_values are provided." assert ( len(audio_token_start_idx) == len(audio_token_len) == len(audio_lens) == len(audio_values) ), "audio_token_start_idx/audio_token_len/audio_lens/audio_values must have the same batch size." assert len(audio_batch_size) == len( inputs_embeds ), "audio_batch_size and inputs_embeds must have the same batch size." # B x A/3200 x (D=max-audio-length-in-batch) audio_tower_output = self.audio_tower.forward( audio_values.to(self.audio_tower.dtype), audio_len=audio_lens, ).last_hidden_state audio_tower_output = audio_tower_output.to(inputs_embeds.dtype) audio_embeds = self.multi_modal_projector.forward(audio_tower_output) # combine audio and text embeddings for i_b, i_a in self._audio_iter(audio_batch_size): start_idx = audio_token_start_idx[i_a] token_len = audio_token_len[i_a] item_embedding = audio_embeds[i_a][:token_len] inputs_embeds[i_b][start_idx : start_idx + token_len] = item_embedding lm_output = self.language_model.forward( inputs_embeds=inputs_embeds, labels=labels, attention_mask=attention_mask, past_key_values=past_key_values, **kwargs, ) if self.training: if self.loss_config.loss_function == LossFunction.CrossEntropy: return lm_output elif self.loss_config.loss_function == LossFunction.KL_Divergence: return self._compute_kl_loss( lm_output=lm_output, labels=labels, past_key_values=past_key_values, alt_input_ids=alt_input_ids, alt_attention_mask=alt_attention_mask, alt_labels=alt_labels, **kwargs, ) else: raise ValueError( f"Unsupported loss function: {self.loss_config.loss_function}" ) else: return lm_output def prepare_inputs_for_generation( self, input_ids: torch.Tensor, audio_values: Optional[torch.FloatTensor] = None, audio_token_start_idx: Optional[torch.Tensor] = None, audio_token_len: Optional[torch.Tensor] = None, audio_lens: Optional[torch.Tensor] = None, audio_batch_size: Optional[torch.Tensor] = None, past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None, attention_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, cache_position: Optional[torch.Tensor] = None, **kwargs, ) -> Dict[str, Any]: model_input = self.language_model.prepare_inputs_for_generation( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, cache_position=cache_position, **kwargs, ) # include audio information in model_input only when it is needed during prefilling # audio_token_start_idx should always be relative to the current cache position prefill_start_idx: int | torch.Tensor = ( 0 if cache_position is None else cache_position[0] ) if ( audio_values is not None and audio_token_start_idx is not None and prefill_start_idx <= torch.max(audio_token_start_idx) ): model_input["audio_values"] = audio_values model_input["audio_token_start_idx"] = ( audio_token_start_idx - prefill_start_idx ) model_input["audio_token_len"] = audio_token_len model_input["audio_batch_size"] = audio_batch_size model_input["audio_lens"] = audio_lens return model_input @classmethod def _create_multi_modal_projector( cls, config: UltravoxConfig ) -> "UltravoxProjector": projector = UltravoxProjector(config) projector.to(config.torch_dtype) return projector @classmethod def _create_audio_tower( cls, config: UltravoxConfig ) -> Union[transformers.Wav2Vec2Model, "ModifiedWhisperEncoder"]: with transformers.modeling_utils.no_init_weights(): # we only ever use from_config if the weights are retrained, hence initializing is not # required. This makes the model quite creation faster since init on CPU is quite slow. if "whisper" in config.audio_config._name_or_path.lower(): audio_tower = ModifiedWhisperEncoder(config.audio_config) audio_tower.init_latency_mask( config.audio_latency_block_size, dtype=config.torch_dtype ) else: assert config.audio_latency_block_size in ( None, 0, ), "only whisper audio tower supports audio latency masking, got non-zero value for 'audio_latency_block_size'" audio_tower = transformers.AutoModel.from_config(config.audio_config) if isinstance( audio_tower, (transformers.Wav2Vec2BertModel, transformers.WhisperModel), ): # For these models we only need the encoder part # Wav2Vec2BertModel -> Wav2Vec2BertEncoder # WhisperModel -> WhisperEncoder audio_tower = audio_tower.encoder audio_tower = apply_lora(audio_tower, config.audio_model_lora_config) return audio_tower @classmethod def _create_language_model( cls, config: UltravoxConfig ) -> transformers.LlamaForCausalLM: with transformers.modeling_utils.no_init_weights(): # we only ever use from_config if the weights are retrained, hence initializing is not # required. This makes the model quite creation faster since init on CPU is quite slow. language_model = transformers.AutoModelForCausalLM.from_config( config.text_config, attn_implementation=config.text_config._attn_implementation, torch_dtype=config.torch_dtype, ) language_model = apply_lora(language_model, config.text_model_lora_config) return language_model def merge_and_unload(self): if isinstance(self.language_model, peft.PeftModel): self.language_model = self.language_model.merge_and_unload() # no need to download base language model weights anymore, so we can remove the id self.config.text_model_id = None self.keep_params.update( set( [ f"language_model.{name}" for name, _ in self.language_model.named_parameters() ] ) ) if isinstance(self.audio_tower, peft.PeftModel): self.audio_tower = self.audio_tower.merge_and_unload() # no need to download base audio model weights anymore, so we can remove the id self.config.audio_model_id = None self.keep_params.update( set( [ f"audio_tower.{name}" for name, _ in self.audio_tower.named_parameters() ] ) ) for param in ["text_model_lora_config", "audio_model_lora_config"]: if hasattr(self.config, param): delattr(self.config, param) def push_to_hub(self, *args, **kwargs): self.merge_and_unload() return super().push_to_hub(*args, **kwargs) def diff_state_dict( self, state_dict: Optional[Dict[str, Any]] = None ) -> Dict[str, Any]: if state_dict is None: state_dict = super().state_dict() trainable_params = {k for k, v in self.named_parameters() if v.requires_grad} # normalize the keys to match the original model # Example: audio_tower.base_model.model.layers.0._fsdp_wrapped_module.self_attn.k_proj.lora_B.default.weight trainable_params = { k.replace("_fsdp_wrapped_module.", "") for k in trainable_params } state_dict = { k: v for k, v in state_dict.items() if k in self.keep_params or k in trainable_params } return state_dict def save_pretrained( self, *args, state_dict: Optional[Dict[str, Any]] = None, **kwargs ): state_dict = self.diff_state_dict(state_dict) super().save_pretrained(*args, state_dict=state_dict, **kwargs) def _pre_load_state_dict_hook(self, state_dict: Dict[str, Any], *args, **kwargs): self.keep_params.update(set(state_dict.keys())) def print_trainable_parameters(self): """ Prints the number of trainable parameters in the model (reuses Peft model's method) """ count_params = peft.peft_model.PeftModel.get_nb_trainable_parameters trainable_params, all_param = count_params(self) logging.info( f"trainable params: {trainable_params:,d} || all params: {all_param:,d}" f" || trainable%: {100 * trainable_params / all_param:.1f}%" ) lm_trainable_params, lm_all_params = count_params(self.language_model) audio_trainable_params, audio_all_params = count_params(self.audio_tower) projector_trainable_params = ( trainable_params - lm_trainable_params - audio_trainable_params ) projector_all_params = all_param - lm_all_params - audio_all_params logging.info( f"Trainable%: " f" LLM: {100 * lm_trainable_params / lm_all_params:.1f}%" f" || Audio Encoder: {100 * audio_trainable_params / audio_all_params:.1f}%" f" || Projector: {100 * projector_trainable_params / projector_all_params:.1f}%" ) # TODO: refactor common parts to a shared module def is_cache_empty( past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]], ) -> bool: """ Check if the cache is empty. """ if past_key_values is None: return True if isinstance(past_key_values, tuple): return all(len(c) == 0 for c in past_key_values) return past_key_values.get_seq_length() == 0 T = TypeVar("T", bound=torch.nn.Module) def apply_lora(model: T, lora_config: dict) -> T: """ Applies LoRA finetuning to the model. If the `r` parameter is set to 0, the model is frozen instead. """ unfreeze_layers = lora_config.pop("unfreeze_layers", None) lora_config = peft.LoraConfig(**lora_config or {}) if lora_config.r == 0: # freeze the model entirely, except for the specified layers for name, param in model.named_parameters(): if not unfreeze_layers or not any( re.match(layer, name) for layer in unfreeze_layers ): param.requires_grad = False else: logging.info(f"Unfreezing layer: {name} with #{param.numel()} params") else: model = peft.get_peft_model(model, lora_config) return model class StackAudioFrames(nn.Module): """ Stack the audio embedding frames to reduce the sequence length by a factor of `stack_factor`. """ def __init__(self, stack_factor: int = 8): super().__init__() self.stack_factor = stack_factor def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor: B, T, C = audio_embeds.shape T_pad = (T + self.stack_factor - 1) // self.stack_factor * self.stack_factor audio_embeds = F.pad(audio_embeds, (0, 0, 0, T_pad - T)) B, T, C = audio_embeds.shape audio_embeds = audio_embeds.view( B, T // self.stack_factor, C * self.stack_factor ) return audio_embeds class RMSNorm(transformers.models.llama.modeling_llama.LlamaRMSNorm): def __init__(self, hidden_size: int, init: float = 1, eps: float = 1e-6): super().__init__(hidden_size=hidden_size, eps=eps) self.weight.data.fill_(init) class SwiGLU(nn.Module): def forward(self, x): x, gate = x.chunk(2, dim=-1) return F.silu(gate) * x class UltravoxProjector(nn.Module): def __init__(self, config: UltravoxConfig): super().__init__() self.hidden_dim = config.hidden_size self._pad_and_stack = StackAudioFrames(config.stack_factor) dim_in = config.audio_config.hidden_size * config.stack_factor self.ln_pre = RMSNorm(dim_in, init=config.norm_init) self.linear_1 = nn.Linear(dim_in, self.hidden_dim, bias=False) dim_mid = self.hidden_dim self.act = transformers.activations.get_activation(config.projector_act) dim_mid = dim_mid // 2 if config.projector_act == "swiglu" else dim_mid dim_out = config.text_config.hidden_size self.linear_2 = nn.Linear(dim_mid, dim_out, bias=False) # Ultravox v0.4.1 and below uses layer_norm after the second linear layer, # while v0.5.0 and above uses layer_norm after the first linear layer. if config.projector_ln_mid: self.ln_mid: nn.Module = RMSNorm(dim_mid, init=config.norm_init) self.ln_post: nn.Module = nn.Identity() else: self.ln_mid = nn.Identity() self.ln_post = RMSNorm(dim_out, init=config.norm_init) def forward(self, audio_features: torch.Tensor) -> torch.Tensor: """ Takes in audio features from the audio tower and projects them to the text model's embedding space. It reduces the number of frames by a factor of `stack_factor` and increases the number of channels by the same factor. If the number of audio frames are not a multiple of the stack factor, the last few frames will be padded with zeros. Input shape: audio_features: B, T*S, C Output shape: hidden_states: B, T, D Where: B: batch size F: number of frames in the audio tower T: number of output embeddings T = ceil(F / S) S: stack factor C: number of channels out of the encoder (aka audio tower) H: hidden size of the projector (config.hidden_size) D: dimension of the text model (config.text_config.hidden_size) """ # B, F, C -> B, T, C*S audio_features = self._pad_and_stack(audio_features) audio_features = self.ln_pre(audio_features) # B, T, C*S -> B, T, H hidden_states = self.linear_1(audio_features) # B, T, H -> B, T, H/2 (assuming swiglu) hidden_states = self.act(hidden_states) hidden_states = self.ln_mid(hidden_states) # B, T, H/2 -> B, T, D hidden_states = self.linear_2(hidden_states) hidden_states = self.ln_post(hidden_states) return hidden_states class ModifiedWhisperEncoder( whisper.WhisperEncoder, transformers.modeling_utils.ModuleUtilsMixin ): """ Encoder portion of OpenAI's Whisper model. This implementation is a slightly modified version of HF Transformers' Whisper Encoder, with only a few fixes: 1. base_model_prefix updated to allow for doing `.from_pretrained` directly on the encoder 2. allow less than 30 second of audio padding to be passed in: - relaxed ValueError check for `input_features` length to be less than or equal to `expected_seq_length` instead of strictly equal - embed_pos is now sliced to match the length of `inputs_embeds` Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py """ base_model_prefix = "model.encoder" _no_split_modules = ["WhisperEncoderLayer"] _keys_to_ignore_on_load_unexpected = ["model.decoder.*"] def __init__(self, config: transformers.WhisperConfig): super().__init__(config) self.config.is_decoder = False @property def max_context_length(self): return ( self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0] ) def init_latency_mask( self, audio_latency_block_size: int | None, dtype: torch.dtype ): if audio_latency_block_size is None: self.audio_streaming_mask = None return # Use max_context_length directly in the calculation max_seqlen = self.max_context_length assert ( max_seqlen > 0 ), f"maximum sequence length must be positive, got {max_seqlen}" assert ( max_seqlen % audio_latency_block_size == 0 ), f"audio_latency_block_size {audio_latency_block_size} must divide {max_seqlen} evenly." # Given the block size, we calculate number of blocks. audio_latency_nblocks = max_seqlen // audio_latency_block_size audio_streaming_mask = ( torch.tril( torch.ones(audio_latency_nblocks, audio_latency_nblocks), diagonal=0, ) .repeat_interleave(audio_latency_block_size, dim=0) .repeat_interleave(audio_latency_block_size, dim=1) ) audio_streaming_mask = (1.0 - audio_streaming_mask) * torch.finfo(dtype).min audio_streaming_mask = audio_streaming_mask[None, None, :, :] self.register_buffer( "audio_streaming_mask", audio_streaming_mask, persistent=False ) def forward( self, input_features, audio_len=None, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): expected_seq_length = self.max_context_length if input_features.shape[-1] > expected_seq_length: raise ValueError( f"Whisper expects the mel input features to be of length {expected_seq_length} or less, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}." ) 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 ) inputs_embeds = nn.functional.gelu(self.conv1(input_features)) inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds)) inputs_embeds = inputs_embeds.permute(0, 2, 1) embed_pos = self.embed_positions.weight[: inputs_embeds.size(-2)] hidden_states = inputs_embeds + embed_pos hidden_states = nn.functional.dropout( hidden_states, p=self.dropout, training=self.training ) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # Create attention mask based on audio lengths to mask out padding tokens # For each sample in batch: # - Convert raw audio length to feature length after convolutions # - Create boolean mask that is True for valid positions and False for padding # - Convert to extended attention mask format expected by transformer layers # (1.0 for positions to attend to, large negative for positions to ignore) # This masking ensures consistent behavior between training and inference # by preventing the model from attending to padding tokens in both cases attention_mask = None if audio_len != None: audio_feature_len = self._get_feat_extract_output_lengths(audio_len) max_seq_len = hidden_states.shape[1] attention_mask = torch.arange(max_seq_len, device=hidden_states.device)[ None, : ].lt(audio_feature_len.view(-1, 1)) attention_mask = self.get_extended_attention_mask( attention_mask, None, dtype=hidden_states.dtype, ) if self.audio_streaming_mask is not None: seqlen = hidden_states.size(-2) if attention_mask is not None: attention_mask = torch.minimum( self.audio_streaming_mask[:, :, :seqlen, :seqlen], attention_mask ) # merge else: attention_mask = self.audio_streaming_mask[:, :, :seqlen, :seqlen] attention_mask = attention_mask.to(hidden_states.dtype) # check if head_mask has a correct number of layers specified if desired if head_mask is not None: assert head_mask.size()[0] == ( len(self.layers) ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}." for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) to_drop = False if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: # skip the layer to_drop = True if to_drop: layer_outputs = (None, None) else: if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), output_attentions, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, layer_head_mask=( head_mask[idx] if head_mask is not None else None ), output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) hidden_states = self.layer_norm(hidden_states) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, encoder_states, all_attentions] if v is not None ) return transformers.modeling_outputs.BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions, ) UltravoxConfig.register_for_auto_class() UltravoxModel.register_for_auto_class() transformers.AutoConfig.register("ultravox", UltravoxConfig) transformers.AutoModel.register(UltravoxConfig, UltravoxModel) transformers.activations.ACT2FN["swiglu"] = SwiGLU