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82e11f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | from typing import Any, Self
import torch
from torch import nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from src.modeling.adapter import AudioAdapter
from src.modeling.cross_attention import GatedCrossAttention
from src.modeling.lfm2_decoder import AudioLfm2ForCausalLM, add_cross_attention
class LambdaAudioModel(nn.Module):
def __init__(
self,
encoder: PreTrainedModel,
decoder: AudioLfm2ForCausalLM,
encoder_hidden_size: int = 768,
) -> None:
super().__init__()
self.backbones_trainable = True
self.encoder = encoder
self.decoder = decoder
self.adapter = AudioAdapter(encoder_hidden_size, decoder.config.hidden_size)
add_cross_attention(self.decoder)
def encode(
self,
input_values: torch.Tensor,
audio_padding_mask: torch.BoolTensor,
) -> tuple[torch.Tensor, torch.BoolTensor]:
# ---------------------------------------------------------
# Encode audio and create the mask for encoder output frames.
# ---------------------------------------------------------
encoder_output = self.encoder(
input_values=input_values,
padding_mask=audio_padding_mask,
)
encoder_padding_mask = self.encoder.forward_padding_mask(
encoder_output.last_hidden_state,
audio_padding_mask,
)
return self.adapter(
encoder_output.last_hidden_state,
encoder_padding_mask,
)
def forward(
self,
input_values: torch.Tensor,
audio_padding_mask: torch.BoolTensor,
input_ids: torch.LongTensor,
attention_mask: torch.Tensor,
labels: torch.LongTensor,
) -> CausalLMOutputWithPast:
audio_features, encoded_padding_mask = self.encode(
input_values,
audio_padding_mask,
)
audio_key_values = self.decoder.project_audio_key_values(audio_features)
return self.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
labels=labels,
audio_key_values=audio_key_values,
audio_padding_mask=encoded_padding_mask,
)
def generate(
self,
input_values: torch.Tensor,
audio_padding_mask: torch.BoolTensor,
input_ids: torch.LongTensor,
attention_mask: torch.Tensor,
**kwargs: Any,
) -> torch.LongTensor:
audio_features, encoded_padding_mask = self.encode(
input_values,
audio_padding_mask,
)
audio_key_values = self.decoder.project_audio_key_values(audio_features)
return self.decoder.generate(
input_ids=input_ids,
attention_mask=attention_mask,
audio_key_values=audio_key_values,
audio_padding_mask=encoded_padding_mask,
**kwargs,
)
def set_backbones_trainable(self, trainable: bool) -> None:
# ---------------------------------------------------------
# Keep audio conditioning trainable while freezing pretrained weights.
# ---------------------------------------------------------
self.backbones_trainable = trainable
self.requires_grad_(trainable)
self.adapter.requires_grad_(True)
for module in self.decoder.modules():
if isinstance(module, GatedCrossAttention):
module.requires_grad_(True)
self.train(self.training)
def train(self, mode: bool = True) -> Self:
super().train(mode)
if mode and not self.backbones_trainable:
self.encoder.eval()
self.decoder.eval()
for module in self.decoder.modules():
if isinstance(module, GatedCrossAttention):
module.train()
return self
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