# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/higgs_audio_v2/modular_higgs_audio_v2.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_higgs_audio_v2.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections.abc import Callable from typing import Optional import torch import torch.nn as nn from ... import initialization as init from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func from ...masking_utils import create_causal_mask from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging from ...utils.generic import maybe_autocast from ...utils.output_capturing import capture_outputs from .configuration_higgs_audio_v2 import HiggsAudioV2Config from .generation_higgs_audio_v2 import HiggsAudioV2GenerationMixin logger = logging.get_logger(__name__) class HiggsAudioV2MLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj @use_kernel_forward_from_hub("RMSNorm") class HiggsAudioV2RMSNorm(nn.Module): def __init__(self, hidden_size, eps: float = 1e-6) -> None: """ HiggsAudioV2RMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) @use_kernel_func_from_hub("rotary_pos_emb") def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed 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 eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: torch.Tensor | None, scaling: float, dropout: float = 0.0, **kwargs: Unpack[TransformersKwargs], ): key_states = repeat_kv(key, module.num_key_value_groups) value_states = repeat_kv(value, module.num_key_value_groups) attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling if attention_mask is not None: attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights @use_kernelized_func(apply_rotary_pos_emb) class HiggsAudioV2Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: HiggsAudioV2Config, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout self.is_causal = True self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias ) self.k_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.v_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias ) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, attention_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor, torch.Tensor]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_values is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( self.config._attn_implementation, eager_attention_forward ) attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class HiggsAudioV2DecoderLayer(GradientCheckpointingLayer): def __init__(self, config: HiggsAudioV2Config, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = HiggsAudioV2Attention(config=config, layer_idx=layer_idx) self.mlp = HiggsAudioV2MLP(config) self.input_layernorm = HiggsAudioV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = HiggsAudioV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.audio_mlp = HiggsAudioV2MLP(config) self.audio_input_layernorm = HiggsAudioV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.audio_post_attention_layernorm = HiggsAudioV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None, attention_mask: torch.Tensor | None = None, audio_token_mask: torch.BoolTensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, use_cache: bool | None = False, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> torch.Tensor: residual = hidden_states if audio_token_mask is None: hidden_states = self.audio_input_layernorm(hidden_states) else: audio_token_mask = audio_token_mask.to(hidden_states.device) hidden_states = hidden_states.masked_scatter( audio_token_mask.unsqueeze(-1), self.audio_input_layernorm(hidden_states[audio_token_mask]).to(hidden_states.device), ) hidden_states = hidden_states.masked_scatter( ~audio_token_mask.unsqueeze(-1), self.input_layernorm(hidden_states[~audio_token_mask]).to(hidden_states.device), ) # Self Attention hidden_states, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = residual + hidden_states if audio_token_mask is None: audio_hidden_states = self.audio_post_attention_layernorm(hidden_states) audio_hidden_states = self.audio_mlp(audio_hidden_states) hidden_states = hidden_states + audio_hidden_states.to(hidden_states.device) else: text_hidden_states = self.post_attention_layernorm(hidden_states[~audio_token_mask]) audio_hidden_states = self.audio_post_attention_layernorm(hidden_states[audio_token_mask]) text_hidden_states = self.mlp(text_hidden_states) hidden_states[~audio_token_mask] += text_hidden_states.to(hidden_states.device) audio_hidden_states = self.audio_mlp(audio_hidden_states) hidden_states[audio_token_mask] += audio_hidden_states.to(hidden_states.device) return hidden_states class HiggsAudioV2Embeddings(nn.Module): def __init__(self, config): super().__init__() self.embed_audio_tokens = nn.Embedding((config.num_codebooks * config.codebook_size), config.hidden_size) self.register_buffer( "audio_tokens_offsets", torch.arange(config.num_codebooks) * config.codebook_size, persistent=False ) def forward(self, input_ids): inputs_embeds = self.embed_audio_tokens(input_ids + self.audio_tokens_offsets) inputs_embeds = inputs_embeds.sum(dim=-2) return inputs_embeds @auto_docstring class HiggsAudioV2PreTrainedModel(PreTrainedModel): config: HiggsAudioV2Config base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["HiggsAudioV2DecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn = True _supports_sdpa = True _supports_flex_attn = True _can_compile_fullgraph = True _supports_attention_backend = True _can_record_outputs = { "hidden_states": HiggsAudioV2DecoderLayer, "attentions": HiggsAudioV2Attention, } @torch.no_grad() def _init_weights(self, module): super()._init_weights(module) if isinstance(module, HiggsAudioV2Embeddings): init.copy_( module.audio_tokens_offsets, torch.arange(self.config.num_codebooks) * self.config.codebook_size ) class HiggsAudioV2RotaryEmbedding(nn.Module): inv_freq: torch.Tensor # fix linting for `register_buffer` def __init__(self, config: HiggsAudioV2Config, device=None): super().__init__() self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_type = self.config.rope_parameters["rope_type"] rope_init_fn: Callable = self.compute_default_rope_parameters if self.rope_type != "default": rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = rope_init_fn(self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) @staticmethod def compute_default_rope_parameters( config: HiggsAudioV2Config | None = None, device: Optional["torch.device"] = None, seq_len: int | None = None, ) -> tuple["torch.Tensor", float]: """ Computes the inverse frequencies according to the original RoPE implementation Args: config ([`~transformers.PreTrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. Unused for this type of RoPE. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). """ base = config.rope_parameters["rope_theta"] dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads attention_factor = 1.0 # Unused in this type of RoPE # Compute the inverse frequencies inv_freq = 1.0 / ( base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) ) return inv_freq, attention_factor @torch.no_grad() @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with maybe_autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * self.attention_scaling sin = emb.sin() * self.attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) @auto_docstring class HiggsAudioV2Model(HiggsAudioV2PreTrainedModel): def __init__(self, config: HiggsAudioV2Config): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList( [HiggsAudioV2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = HiggsAudioV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = HiggsAudioV2RotaryEmbedding(config=config) self.gradient_checkpointing = False self.embed_audio_tokens = HiggsAudioV2Embeddings(config) # Initialize weights and apply final processing self.post_init() @capture_outputs @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, audio_input_ids: torch.LongTensor | None = None, attention_mask: torch.LongTensor | None = None, audio_input_ids_mask: torch.BoolTensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, cache_position: torch.LongTensor | None = None, use_cache: bool | None = None, **kwargs: Unpack[TransformersKwargs], ) -> BaseModelOutputWithPast: r""" audio_input_ids (`torch.LongTensor` of shape `(batch_size, num_audio_frames, num_codebooks)`, *optional*): Indices of audio codebook tokens. Indices can be obtained using [`HiggsAudioV2TokenizerModel.encode`]. audio_input_ids_mask (`torch.BoolTensor` of shape `(batch_size, num_audio_frames)`, *optional*): Indicates which audio frames in `audio_input_ids` are valid. Returns: [`~models.modeling_outputs.BaseModelOutputWithPast`]: Usual decoder outputs with the placeholder positions already substituted by their corresponding audio embeddings. Example: ```python >>> from transformers import AutoProcessor, HiggsAudioV2Model >>> import torch >>> device = "cuda" if torch.cuda.is_available() else "cpu" >>> processor = AutoProcessor.from_pretrained("eustlb/higgs-audio-v2-generation-3B-base", device_map=device) >>> model = HiggsAudioV2Model.from_pretrained("eustlb/higgs-audio-v2-generation-3B-base", device_map=device) >>> conversation = [ ... { ... "role": "system", ... "content": [ ... { ... "type": "text", ... "text": "Generate audio following instruction." ... } ... ] ... }, ... { ... "role": "scene", ... "content": [ ... { ... "type": "text", ... "text": "Audio is recorded from a quiet room." ... } ... ] ... }, ... { ... "role": "user", ... "content": [ ... { ... "type": "text", ... "text": "It was the night before my birthday. Hooray! It's almost here! It may not be a holiday, but it's the best day of the year." ... } ... ] ... }, ... { ... "role": "assistant", ... "content": [ ... { ... "type": "audio", ... "url": "https://huggingface.co/datasets/eustlb/dummy-audio-samples-higgs/resolve/main/belinda.wav" ... } ... ] ... }, ... { ... "role": "user", ... "content": [ ... { ... "type": "text", ... "text": "The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years." ... } ... ] ... } ... ] >>> inputs = processor.apply_chat_template(conversation, return_dict=True, tokenize=True, sampling_rate=24000, return_tensors="pt") >>> inputs = inputs.to(model.device) >>> outputs = model(**inputs) ``` """ if (input_ids is None) and (inputs_embeds is None) and (audio_input_ids is None): raise ValueError("You must specify at least one of input_ids, inputs_embeds, or audio_input_ids") if (input_ids is not None) and (inputs_embeds is not None): raise ValueError("Only one of input_ids or inputs_embeds can be provided") audio_token_mask = self.get_placeholder_mask(input_ids, inputs_embeds, audio_input_ids_mask) if input_ids is not None: inputs_embeds = self.embed_tokens(input_ids) if audio_input_ids is not None: audio_embeds = self.embed_audio_tokens(audio_input_ids) if inputs_embeds is not None and audio_input_ids is not None: audio_embeds = ( audio_embeds[audio_input_ids_mask.to(audio_embeds.device)] if audio_input_ids_mask is not None else audio_embeds ) inputs_embeds = inputs_embeds.masked_scatter( audio_token_mask[..., None].expand_as(inputs_embeds), audio_embeds.to(inputs_embeds.device) ) elif audio_input_ids is not None: inputs_embeds = audio_embeds if use_cache and past_key_values is None: past_key_values = DynamicCache(config=self.config) 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.Tensor = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = create_causal_mask( config=self.config, inputs_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, past_key_values=past_key_values, position_ids=position_ids, ) hidden_states = inputs_embeds position_embeddings = self.rotary_emb(hidden_states, position_ids) for decoder_layer in self.layers[: self.config.num_hidden_layers]: hidden_states = decoder_layer( hidden_states, attention_mask=causal_mask, audio_token_mask=audio_token_mask, position_ids=position_ids, past_key_values=past_key_values, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = self.norm(hidden_states) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, ) def get_placeholder_mask( self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, audio_input_ids_mask: torch.LongTensor ): """ Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is equal to the length of audio_input_ids. If the lengths are different, an error is raised. If input_ids and inputs_embeds are None, we return None. Indeed this means we cannot determine the placeholder mask, the model is to be used in a audio-only mode, hence we return None. """ if input_ids is None and inputs_embeds is None: return None elif input_ids is None: special_audio_mask = inputs_embeds == self.embed_tokens( torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device) ) special_audio_mask = special_audio_mask.all(-1) else: special_audio_mask = (input_ids == self.config.audio_token_id) | ( input_ids == self.config.audio_delay_token_id ) return special_audio_mask @auto_docstring( custom_intro=""" The Higgs Audio model, a llama-like auto-regressive transformer model with dual-FFN. """ ) class HiggsAudioV2ForConditionalGeneration(HiggsAudioV2PreTrainedModel, HiggsAudioV2GenerationMixin): base_model_prefix = "model" _keys_to_ignore_on_load_unexpected = ["text_lm_head.weight"] def __init__(self, config: HiggsAudioV2Config, use_text_head: bool = False): r""" use_text_head (`bool`, *optional*, defaults to False): Whether to use a text language model head. Such head is not required for generation, but can be used to compute the text loss when training. """ super().__init__(config) self.model = HiggsAudioV2Model(config) self.audio_lm_head = nn.Linear(config.hidden_size, config.num_codebooks * config.codebook_size, bias=False) self.text_lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) if use_text_head else None self.post_init() def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, audio_input_ids: torch.LongTensor | None = None, audio_input_ids_mask: torch.LongTensor | None = None, **kwargs, ): model_inputs = super().prepare_inputs_for_generation(input_ids, **kwargs) if audio_input_ids is not None and model_inputs.get("past_key_values") is not None: current_cache_length = model_inputs["cache_position"][0] audio_token_mask = (input_ids == self.config.audio_token_id) | ( input_ids == self.config.audio_delay_token_id ) in_cache_num_audio_input_ids = audio_token_mask[:, :current_cache_length].sum(dim=-1) # already cached audio_input_ids should be masked # this surmise that audio_input_ids are right padded! valid_audio_input_ids = audio_input_ids_mask.cumsum(dim=-1) > in_cache_num_audio_input_ids[:, None] audio_input_ids_mask = audio_input_ids_mask & valid_audio_input_ids if audio_input_ids_mask is not None and (~audio_input_ids_mask[:, :-1]).all(): # in decoding mode, we only pass audio_input_ids audio_input_ids = audio_input_ids[:, -1:, :].clone(memory_format=torch.contiguous_format) model_inputs.pop("input_ids", None) audio_input_ids_mask = None model_inputs["audio_input_ids"] = audio_input_ids model_inputs["audio_input_ids_mask"] = audio_input_ids_mask return model_inputs @auto_docstring @can_return_tuple def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.BoolTensor | None = None, audio_input_ids: torch.LongTensor | None = None, audio_input_ids_mask: torch.LongTensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, labels: torch.LongTensor | None = None, audio_labels: torch.LongTensor | None = None, use_cache: bool | None = None, cache_position: torch.LongTensor | None = None, logits_to_keep: int | torch.Tensor = 0, **kwargs: Unpack[TransformersKwargs], ): r""" audio_input_ids (`torch.LongTensor` of shape `(batch_size, num_audio_frames, num_codebooks)`, *optional*): Indices of audio codebook tokens. Indices can be obtained using [`HiggsAudioV2TokenizerModel.encode`]. audio_input_ids_mask (`torch.BoolTensor` of shape `(batch_size, num_audio_frames)`, *optional*): Indicates which audio frames in `audio_input_ids` are valid. audio_labels (`torch.LongTensor` of shape `(batch_size, num_audio_frames, num_codebooks)`, *optional*): Labels for the audio codebook tokens for computing the masked language modeling loss. Indices should either be in `[0, ..., config.codebook_size]. Token with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.codebook_size]`. Can be obtained using `output_labels=True` when calling [`HiggsAudioV2Processor`]. Returns: [`~models.modeling_outputs.CausalLMOutputWithPast`]: A [`~models.modeling_outputs.CausalLMOutputWithPast`] containing the logits, loss (if labels are provided), and other outputs from the model. Example: ```python >>> from transformers import AutoProcessor, HiggsAudioV2ForConditionalGeneration >>> model_id = "eustlb/higgs-audio-v2-generation-3B-base" >>> processor = AutoProcessor.from_pretrained(model_id, device_map="auto") >>> model = HiggsAudioV2ForConditionalGeneration.from_pretrained(model_id, device_map="auto") >>> conversation = [ ... { ... "role": "system", ... "content": [ ... { ... "type": "text", ... "text": "Generate audio following instruction." ... } ... ] ... }, ... { ... "role": "scene", ... "content": [ ... { ... "type": "text", ... "text": "Audio is recorded from a quiet room." ... } ... ] ... }, ... { ... "role": "user", ... "content": [ ... { ... "type": "text", ... "text": "It was the night before my birthday. Hooray! It's almost here! It may not be a holiday, but it's the best day of the year." ... } ... ] ... }, ... { ... "role": "assistant", ... "content": [ ... { ... "type": "audio", ... "url": "https://huggingface.co/datasets/eustlb/dummy-audio-samples-higgs/resolve/main/belinda.wav" ... } ... ] ... }, ... { ... "role": "user", ... "content": [ ... { ... "type": "text", ... "text": "The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years." ... } ... ] ... } ... ] >>> inputs = processor.apply_chat_template(conversation, return_dict=True, tokenize=True, sampling_rate=24000, return_tensors="pt") >>> inputs = inputs.to(model.device) >>> outputs = model(**inputs) ``` """ outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, audio_input_ids=audio_input_ids, audio_input_ids_mask=audio_input_ids_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, cache_position=cache_position, **kwargs, ) hidden_states = outputs.last_hidden_state # Only compute necessary logits, and do not upcast them to float if we are not computing the loss slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.audio_lm_head(hidden_states[:, slice_indices, :]) loss = None if audio_labels is not None: audio_logits = logits.reshape(*logits.shape[:2], self.config.num_codebooks, self.config.codebook_size) audio_labels_expanded = input_ids.new_ones((*input_ids.shape[:2], 8)) * -100 audio_token_mask = self.model.get_placeholder_mask(input_ids, inputs_embeds, audio_input_ids_mask) audio_labels_expanded[audio_token_mask] = audio_labels[audio_input_ids_mask] codebook_losses = [] for codebook_idx in range(self.config.num_codebooks): codebook_logits = audio_logits[:, :, codebook_idx, :] codebook_labels = audio_labels_expanded[:, :, codebook_idx] codebook_losses.append( self.loss_function(codebook_logits, codebook_labels, self.config.codebook_size, **kwargs) ) loss = sum(codebook_losses) if labels is not None: if self.text_lm_head is not None: text_logits = self.text_lm_head(hidden_states[:, slice_indices, :]) text_loss = self.loss_function(text_logits, labels, self.config.vocab_size, **kwargs) loss = text_loss if loss is None else loss + text_loss else: logger.warning_once( f"`labels` provided to {self.__class__.__name__} but `text_lm_head` is disabled. " f"Text labels ignored. Set `use_text_head=True` in model init to enable text loss." ) return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) __all__ = ["HiggsAudioV2ForConditionalGeneration", "HiggsAudioV2PreTrainedModel", "HiggsAudioV2Model"]