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from dataclasses import dataclass
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutput
from transformers.utils import ModelOutput, logging
from .modeling_msp_audio import MSPAudioForCTC
from .modeling_msp_visual import MSPVisualForCTC
from .configuration_msp import MSPConfig
from .fusion import MSPFusionModel
logger = logging.get_logger(__name__)
@dataclass
class MSPModelOutput(ModelOutput):
"""Encoder and modality-aware fusion representations."""
last_hidden_state: torch.FloatTensor | None = None
padding_mask: torch.BoolTensor | None = None
audio_input_lengths: torch.LongTensor | None = None
visual_input_lengths: torch.LongTensor | None = None
audio_hidden_state: torch.FloatTensor | None = None
visual_hidden_state: torch.FloatTensor | None = None
cross_attentions: tuple | None = None
@dataclass
class MSPCTCOutput(CausalLMOutput):
"""MSP CTC output with optional training-only auxiliary losses."""
pass
def _compute_ctc_loss(
logits: torch.Tensor,
labels: torch.Tensor,
input_lengths: torch.Tensor,
blank: int,
reduction: str,
zero_infinity: bool,
) -> torch.Tensor:
labels_mask = labels >= 0
target_lengths = labels_mask.sum(dim=-1)
flattened_targets = labels.masked_select(labels_mask)
log_probs = F.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
with torch.backends.cudnn.flags(enabled=False):
return F.ctc_loss(
log_probs,
flattened_targets,
input_lengths,
target_lengths,
blank=blank,
reduction=reduction,
zero_infinity=zero_infinity,
)
class MSPPreTrainedModel(PreTrainedModel):
config_class = MSPConfig
base_model_prefix = "msp"
main_input_name = "input_values"
input_modalities = ["audio", "video"]
supports_gradient_checkpointing = False
all_tied_weights_keys = OrderedDict()
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
nn.init.zeros_(module.bias)
nn.init.ones_(module.weight)
@property
def dummy_inputs(self) -> dict:
return {
"input_values": torch.zeros(1, 16000, dtype=torch.float32),
"padding_mask": torch.ones(1, 16000, dtype=torch.long),
"pixel_values_videos": torch.zeros(1, 1, 25, 88, 88, dtype=torch.float32),
"padding_mask_videos": torch.ones(1, 25, dtype=torch.long),
"labels": torch.ones(1, 5, dtype=torch.long),
}
class MSPModel(MSPPreTrainedModel):
def __init__(self, config: MSPConfig):
super().__init__(config)
self.config = config
self.audio_model = MSPAudioForCTC(config=config.audio_config)
self.visual_model = MSPVisualForCTC(config=config.visual_config)
self.fusion_model = MSPFusionModel(config)
self.post_init()
def encode_audio(
self,
input_values: torch.Tensor,
padding_mask: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
if padding_mask is None:
padding_mask = torch.ones_like(input_values, dtype=torch.long)
if padding_mask.ndim != 2 or padding_mask.shape != input_values.shape[:2]:
raise ValueError(
"padding_mask must match input_values on batch and time axes."
)
outputs = self.audio_model.msp_audio.forward(
input_values=input_values,
padding_mask=padding_mask,
output_attentions=False,
output_hidden_states=False,
)
hidden_states = outputs.last_hidden_state
feature_mask = self.audio_model._get_feature_vector_attention_mask(
hidden_states.size(1), padding_mask
).to(device=hidden_states.device, dtype=torch.bool)
input_lengths = feature_mask.sum(dim=-1).to(torch.long)
return hidden_states, feature_mask, input_lengths
def encode_visual(
self,
pixel_values_videos: torch.Tensor,
padding_mask_videos: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
batch_size, _, num_frames = pixel_values_videos.shape[:3]
if padding_mask_videos is None:
padding_mask_videos = torch.ones(
batch_size,
num_frames,
dtype=torch.long,
device=pixel_values_videos.device,
)
if padding_mask_videos.ndim != 2 or tuple(padding_mask_videos.shape) != (
batch_size,
num_frames,
):
raise ValueError(
"padding_mask_videos must match pixel_values_videos on batch "
"and time axes."
)
outputs = self.visual_model.msp_visual.forward(
pixel_values_videos=pixel_values_videos,
padding_mask_videos=padding_mask_videos,
)
hidden_states = outputs.last_hidden_state
feature_mask = outputs.padding_mask_videos
if feature_mask is None:
feature_mask = torch.ones(
hidden_states.shape[:2],
dtype=torch.bool,
device=hidden_states.device,
)
feature_mask = feature_mask.to(device=hidden_states.device, dtype=torch.bool)
if tuple(feature_mask.shape) != tuple(hidden_states.shape[:2]):
raise ValueError(
"The visual encoder returned a padding mask that does not "
"match its hidden states."
)
input_lengths = feature_mask.sum(dim=-1).to(torch.long)
return hidden_states, feature_mask, input_lengths
def forward(
self,
input_values: torch.Tensor,
pixel_values_videos: torch.Tensor,
padding_mask: torch.Tensor | None = None,
padding_mask_videos: torch.Tensor | None = None,
**kwargs,
) -> MSPModelOutput:
if input_values is None or pixel_values_videos is None:
raise ValueError("MSP requires synchronized audio and video inputs.")
audio_states, audio_mask, audio_lengths = self.encode_audio(
input_values, padding_mask
)
visual_states, visual_mask, visual_lengths = self.encode_visual(
pixel_values_videos, padding_mask_videos
)
fusion_output = self.fusion_model.forward(
audio_features=audio_states,
video_features=visual_states,
audio_mask=audio_mask,
video_mask=visual_mask,
**kwargs,
)
return MSPModelOutput(
last_hidden_state=fusion_output.last_hidden_state,
padding_mask=fusion_output.padding_mask,
audio_input_lengths=audio_lengths,
visual_input_lengths=visual_lengths,
audio_hidden_state=audio_states,
visual_hidden_state=visual_states,
cross_attentions=fusion_output.cross_attentions,
)
class MSPForCTC(MSPPreTrainedModel):
def __init__(self, config: MSPConfig):
super().__init__(config)
if config.vocab_size is None:
raise ValueError(
"vocab_size must be set in MSPConfig to instantiate MSPForCTC."
)
self.msp = MSPModel(config=config)
# lm head
self.dropout = nn.Dropout(config.final_dropout)
self.lm_head = nn.Linear(config.fusion_hidden_size, config.vocab_size)
self.post_init()
def freeze_feature_encoder(self):
self.msp.audio_model.freeze_feature_encoder()
self.msp.visual_model.freeze_feature_encoder()
def freeze_feature_encoders(self):
self.freeze_feature_encoder()
def freeze_base_model(self):
self.msp.audio_model.freeze_base_model()
self.msp.visual_model.freeze_base_model()
def freeze_audio_branch(self):
for param in self.msp.audio_model.parameters():
param.requires_grad = False
def freeze_visual_branch(self):
for param in self.msp.visual_model.parameters():
param.requires_grad = False
def forward(
self,
input_values: torch.Tensor| None = None,
pixel_values_videos: torch.Tensor | None = None,
padding_mask: torch.Tensor | None = None,
padding_mask_videos: torch.Tensor | None = None,
labels: torch.Tensor | None = None,
**kwargs,
) -> MSPCTCOutput:
if not self.training:
if input_values is not None and pixel_values_videos is None:
outputs = self.msp.audio_model(
input_values = input_values,
padding_mask = padding_mask,
labels = labels
)
return MSPCTCOutput(
loss=outputs.loss,
logits=outputs.logits
)
if pixel_values_videos is not None and input_values is None:
outputs = self.msp.visual_model(
pixel_values_videos = pixel_values_videos,
padding_mask_videos = padding_mask_videos,
labels = labels
)
return MSPCTCOutput(
loss=outputs.loss,
logits=outputs.logits
)
if input_values is None or pixel_values_videos is None:
raise ValueError("MSPForCTC training requires both audio and video inputs.")
outputs = self.msp.forward(
input_values=input_values,
pixel_values_videos=pixel_values_videos,
padding_mask=padding_mask,
padding_mask_videos=padding_mask_videos,
)
hidden_states = self.dropout(outputs.last_hidden_state)
logits = self.lm_head(hidden_states)
input_lengths = outputs.padding_mask.sum(-1).long()
loss = msp_loss = audio_loss = visual_loss = None
audio_logits = visual_logits = None
if labels is not None and outputs.audio_hidden_state is not None:
audio_logits = self.msp.audio_model.lm_head(
self.msp.audio_model.dropout(outputs.audio_hidden_state)
)
if labels is not None and outputs.visual_hidden_state is not None:
visual_logits = self.msp.visual_model.lm_head(
self.msp.visual_model.dropout(outputs.visual_hidden_state)
)
if labels is not None:
valid_labels = labels[labels >= 0]
if valid_labels.numel() and valid_labels.max() >= self.config.vocab_size:
raise ValueError("A label id is outside the configured vocabulary.")
msp_loss = _compute_ctc_loss(
logits,
labels,
input_lengths,
self.config.pad_token_id,
self.config.ctc_loss_reduction,
self.config.ctc_zero_infinity,
)
loss = self.config.ctc_loss_msp_weight * msp_loss
# The pretrained branch heads are training-only deep supervision;
# audiovisual inference decodes the shared MSP head above.
if audio_logits is not None:
audio_loss = _compute_ctc_loss(
audio_logits,
labels,
outputs.audio_input_lengths,
self.config.pad_token_id,
self.config.ctc_loss_reduction,
self.config.ctc_zero_infinity,
)
loss = loss + self.config.ctc_loss_audio_weight * audio_loss
if visual_logits is not None:
visual_loss = _compute_ctc_loss(
visual_logits,
labels,
outputs.visual_input_lengths,
self.config.pad_token_id,
self.config.ctc_loss_reduction,
self.config.ctc_zero_infinity,
)
loss = loss + self.config.ctc_loss_visual_weight * visual_loss
return MSPCTCOutput(
loss=loss,
logits=logits,
)
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