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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/audioflamingo3/modular_audioflamingo3.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_audioflamingo3.py file directly. One of our CI enforces this.
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# Copyright 2025 NVIDIA CORPORATION and 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.
import math
from collections.abc import Callable
import torch
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache, EncoderDecoderCache
from ...generation import GenerationMixin
from ...masking_utils import create_bidirectional_mask
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPooling, CausalLMOutputWithPast
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 check_model_inputs
from ..auto import AutoModel, AutoModelForCausalLM
from .configuration_audioflamingo3 import AudioFlamingo3Config, AudioFlamingo3EncoderConfig
logger = logging.get_logger(__name__)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: torch.Tensor | None,
scaling: float | None = None,
dropout: float = 0.0,
**kwargs,
):
if scaling is None:
scaling = query.size(-1) ** -0.5
attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
if attention_mask is not None and attention_mask.ndim == 4:
attn_weights = attn_weights + attention_mask[:, :, :, : key.shape[-2]]
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
class AudioFlamingo3Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
layer_idx: int | None = None,
config: AudioFlamingo3Config | None = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.is_causal = is_causal
if layer_idx is None and is_decoder:
logger.warning_once(
f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
"will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.layer_idx = layer_idx
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: torch.Tensor | None = None,
past_key_values: Cache | None = None,
attention_mask: torch.Tensor | None = None,
output_attentions: bool = False,
cache_position: torch.Tensor | None = None,
# TODO: we need a refactor so that the different attention modules can get their specific kwargs
# ATM, we have mixed things encoder, decoder, and encoder-decoder attn
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
# determine input shapes
bsz, tgt_len = hidden_states.shape[:-1]
q_input_shape = (bsz, tgt_len, -1, self.head_dim)
# Scaling is susceptible to floating point arithmetics' inprecisions
# which can lead to different results (this is dependent from model
# to model, e.g. audioflamingo3 is one such case). We therefore keep the
# original order of scaling to follow the original implementation
# and enforce no scaling (1.0) in the attention call below.
query_states = self.q_proj(hidden_states) * self.scaling
query_states = query_states.view(*q_input_shape)
query_states = query_states.transpose(1, 2).contiguous()
# Check is encoder-decoder model is being used. Otherwise we'll get `DynamicCache`
if past_key_values is not None and isinstance(past_key_values, EncoderDecoderCache):
is_updated = past_key_values.is_updated.get(self.layer_idx)
if is_cross_attention:
# after the first generated id, we can subsequently re-use all key/value_states from cache
past_key_values.is_updated[self.layer_idx] = True
past_key_values = past_key_values.cross_attention_cache
else:
past_key_values = past_key_values.self_attention_cache
# use key_value_states if cross attention
current_states = key_value_states if key_value_states is not None else hidden_states
if is_cross_attention and past_key_values and is_updated:
# reuse k,v, cross_attentions
key_states = past_key_values.layers[self.layer_idx].keys
value_states = past_key_values.layers[self.layer_idx].values
else:
key_states = self.k_proj(current_states).view(bsz, -1, self.num_heads, self.head_dim)
value_states = self.v_proj(current_states).view(bsz, -1, self.num_heads, self.head_dim)
key_states = key_states.transpose(1, 2).contiguous()
value_states = value_states.transpose(1, 2).contiguous()
if past_key_values is not None:
# save all key/value_states to cache to be re-used for fast auto-regressive generation
cache_position = cache_position if not is_cross_attention else None
key_states, value_states = past_key_values.update(
key_states, value_states, self.layer_idx, {"cache_position": cache_position}
)
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.dropout,
scaling=1.0,
output_attentions=output_attentions,
**kwargs,
)
attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
class AudioFlamingo3EncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: AudioFlamingo3Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = AudioFlamingo3Attention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
config=config,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
output_attentions: bool = False,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative 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.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16:
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
return hidden_states, attn_weights
@auto_docstring
class AudioFlamingo3PreTrainedModel(PreTrainedModel):
config: AudioFlamingo3Config
base_model_prefix = "model"
input_modalities = ("audio", "text")
supports_gradient_checkpointing = True
_no_split_modules = ["AudioFlamingo3Attention"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn = True
_supports_sdpa = True
@auto_docstring(
custom_intro="""
The audio model from AudioFlamingo3 without any head or projection on top.
"""
)
class AudioFlamingo3Encoder(AudioFlamingo3PreTrainedModel):
"""
AudioFlamingo3 encoder: Whisper encoder, average pool (time/2), then LayerNorm.
"""
# Ignore copy
config: AudioFlamingo3EncoderConfig
main_input_name = "input_features"
input_modalities = "audio"
_no_split_modules = ["AudioFlamingo3EncoderLayer"]
_can_record_outputs = {
"hidden_states": AudioFlamingo3EncoderLayer,
"attentions": AudioFlamingo3Attention,
}
def __init__(self, config: AudioFlamingo3EncoderConfig):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.num_mel_bins = config.num_mel_bins
self.max_source_positions = config.max_source_positions
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1)
self.embed_positions = nn.Embedding(self.max_source_positions, embed_dim)
self.embed_positions.requires_grad_(False)
self.layers = nn.ModuleList([AudioFlamingo3EncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
# Ignore copy
self.avg_pooler = nn.AvgPool1d(2, stride=2)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def _freeze_parameters(self):
for param in self.parameters():
param.requires_grad = False
self._requires_grad = False
def get_input_embeddings(self) -> nn.Module:
return self.conv1
def set_input_embeddings(self, value: nn.Module):
self.conv1 = value
@check_model_inputs
def forward(
self,
input_features: torch.Tensor,
input_features_mask: torch.Tensor | None = None,
**kwargs,
) -> tuple | BaseModelOutputWithPooling:
r"""
Args:
input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`):
Log-Mel features extracted from raw audio. Use the processor/feature extractor to compute and pad
these features from waveform input.
input_features_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
"""
seq_len = (input_features.shape[-1] - 1) // 2 + 1 # After conv2 downsampling
input_features_lengths = input_features_mask.sum(-1)
input_features_lengths = (input_features_lengths - 1) // 2 + 1 # conv2 downsampling
input_features_mask = torch.arange(seq_len, device=input_features.device) < input_features_lengths[:, None]
# Conv front-end
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)
# Add positions, dropout
hidden_states = inputs_embeds + self.embed_positions.weight
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
attention_mask = create_bidirectional_mask(
config=self.config,
input_embeds=hidden_states,
attention_mask=input_features_mask,
)
# Transformer stack
for layer in self.layers:
drop = self.training and torch.rand([]) < self.layerdrop
if not drop:
hidden_states = layer(hidden_states, attention_mask)[0]
# AvgPool (time/2) + LayerNorm
hidden_states = hidden_states.permute(0, 2, 1)
hidden_states = self.avg_pooler(hidden_states).permute(0, 2, 1)
hidden_states = self.layer_norm(hidden_states)
return BaseModelOutputWithPooling(
last_hidden_state=hidden_states,
)
# Ignore copy
def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
"""
Computes the output length of the convolutional layers and the output length of the audio encoder
"""
input_lengths = (input_lengths - 1) // 2 + 1
output_lengths = (input_lengths - 2) // 2 + 1
return input_lengths, output_lengths
class AudioFlamingo3MultiModalProjector(nn.Module):
"""
Audio adaptor (small MLP) that projects AudioFlamingo3Encoder features
to the LLM embedding space so they can replace `<sound>` tokens.
"""
def __init__(self, config: AudioFlamingo3Config):
super().__init__()
self.linear_1 = nn.Linear(
config.audio_config.hidden_size, config.text_config.hidden_size, bias=config.projector_bias
)
self.act = ACT2FN[config.projector_hidden_act]
self.linear_2 = nn.Linear(
config.text_config.hidden_size, config.text_config.hidden_size, bias=config.projector_bias
)
def forward(self, audio_features):
hidden_states = self.linear_1(audio_features)
hidden_states = self.act(hidden_states)
hidden_states = self.linear_2(hidden_states)
return hidden_states
@auto_docstring(
custom_intro="""
The AudioFlamingo3 model which consists of a fine-tuned Whisper encoder, a multi-modal projector and a Qwen2 language model.
"""
)
class AudioFlamingo3ForConditionalGeneration(AudioFlamingo3PreTrainedModel, GenerationMixin):
_keep_in_fp32_modules_strict = None
_tp_plan = None
_pp_plan = None
def __init__(self, config):
super().__init__(config)
self.vocab_size = config.text_config.vocab_size
self.audio_tower = AutoModel.from_config(config.audio_config)
self.language_model = AutoModelForCausalLM.from_config(config.text_config)
self.multi_modal_projector = AudioFlamingo3MultiModalProjector(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.language_model.set_input_embeddings(value)
def get_output_embeddings(self):
return self.language_model.get_output_embeddings()
def set_output_embeddings(self, new_embeddings):
self.language_model.set_output_embeddings(new_embeddings)
def set_decoder(self, decoder):
self.language_model.set_decoder(decoder)
def get_decoder(self):
return self.language_model.get_decoder()
@can_return_tuple
@auto_docstring(
custom_intro="This method is used to get the audio embeddings from input features (a log mel spectrogram), meaning inferring the audio encoder and the multi-modal projector."
)
def get_audio_features(
self,
input_features: torch.FloatTensor,
input_features_mask: torch.Tensor,
**kwargs: Unpack[TransformersKwargs],
) -> tuple | BaseModelOutputWithPooling:
r"""
input_features (`torch.FloatTensor`):
Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
`numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
`input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
input_features_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`):
Mask to avoid performing attention on padded feature indices.
"""
# Encode audio
audio_output = self.audio_tower(
input_features, input_features_mask=input_features_mask, return_dict=True, **kwargs
)
audio_embeds = self.multi_modal_projector(audio_output.last_hidden_state)
# Mask according to avg pooling (which is after attention blocks)
post_lengths = (input_features_mask.sum(-1) - 2) // 2 + 1
valid_mask = torch.arange(audio_embeds.shape[1], device=post_lengths.device)[None, :] < post_lengths[:, None]
audio_embeds = audio_embeds[valid_mask.to(audio_embeds.device)]
audio_output.pooler_output = audio_embeds
return audio_output
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: torch.LongTensor | None = None,
input_features: torch.FloatTensor | None = None,
input_features_mask: torch.Tensor | None = None,
attention_mask: torch.Tensor | 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,
use_cache: bool | None = None,
cache_position: torch.LongTensor | None = None,
logits_to_keep: int | torch.Tensor = 0,
**kwargs: Unpack[TransformersKwargs],
) -> CausalLMOutputWithPast:
r"""
input_features_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`):
Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AudioFlamingo3ForConditionalGeneration, AutoProcessor
>>> model_id = "nvidia/audio-flamingo-3-hf"
>>> processor = AutoProcessor.from_pretrained(model_id)
>>> model = AudioFlamingo3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")
>>> conversations = [
>>> [
>>> {
>>> "role": "user",
>>> "content": [
>>> {"type": "text", "text": "Transcribe the input speech."},
>>> {
>>> "type": "audio",
>>> "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/t_837b89f2-26aa-4ee2-bdf6-f73f0dd59b26.wav",
>>> },
>>> ],
>>> }
>>> ],
>>> [
>>> {
>>> "role": "user",
>>> "content": [
>>> {
>>> "type": "text",
>>> "text": "This track feels really peaceful and introspective. What elements make it feel so calming and meditative?",
>>> },
>>> {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/FPSbCAANfbJLVSwD.mp3"},
>>> ],
>>> }
>>> ],
>>> ]
>>> inputs = processor.apply_chat_template(
>>> conversations,
>>> tokenize=True,
>>> add_generation_prompt=True,
>>> return_dict=True,
>>> ).to(model.device)
>>> outputs = model.generate(**inputs, max_new_tokens=500)
>>> decoded_outputs = processor.batch_decode(
>>> outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True
>>> )
>>> print(decoded_outputs)
["The spoken content of the audio is...", "The track's calming and meditative feel can be attributed to..."]
```"""
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings()(input_ids)
if input_features is not None and input_ids is not None:
audio_embeds = self.get_audio_features(input_features, input_features_mask, return_dict=True).pooler_output
# replace text-audio token placeholders with audio embeddings
audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
inputs_embeds = inputs_embeds.masked_scatter(
audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
)
outputs: CausalLMOutputWithPast = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
labels=labels,
use_cache=use_cache,
cache_position=cache_position,
logits_to_keep=logits_to_keep,
**kwargs,
)
return outputs
def prepare_inputs_for_generation(self, *args, **kwargs):
# Overwritten -- we should not pass input_features when we are in cached decoding stage
input_features = kwargs.pop("input_features", None)
input_features_mask = kwargs.pop("input_features_mask", None)
cache_position = kwargs.get("cache_position")
model_inputs = super().prepare_inputs_for_generation(*args, **kwargs)
if cache_position is not None and cache_position[0] == 0:
# input_features should only be passed when we are not in cached decoding stage
if input_features is not None:
model_inputs["input_features"] = input_features
if input_features_mask is not None:
model_inputs["input_features_mask"] = input_features_mask
return model_inputs
__all__ = ["AudioFlamingo3ForConditionalGeneration", "AudioFlamingo3PreTrainedModel", "AudioFlamingo3Encoder"]
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