# 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 dataclasses import dataclass from typing import Any import torch import torch.nn as nn from ... import initialization as init from ...masking_utils import create_bidirectional_mask from ...modeling_outputs import BaseModelOutputWithPooling, MaskedLMOutput from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel, eager_attention_forward from ...processing_utils import Unpack from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple from ...utils.generic import merge_with_config_defaults from ...utils.output_capturing import capture_outputs from ..auto import AutoModel from ..qwen3.modeling_qwen3 import Qwen3Attention, Qwen3DecoderLayer, Qwen3RMSNorm, Qwen3RotaryEmbedding from .configuration_pe_audio_video import PeAudioVideoConfig, PeAudioVideoEncoderConfig class PeAudioVideoMaskedGroupNorm(nn.GroupNorm): def forward(self, x, padding_mask=None): if padding_mask is None: return super().forward(x) batch_size, hidden_size, seq_len = x.shape group_size = hidden_size // self.num_groups grouped_shape = (batch_size, -1, group_size, seq_len) x_grouped = x.view(grouped_shape) padding_mask_grouped = padding_mask.reshape(grouped_shape).bool() mean = torch.masked.mean(x_grouped, mask=padding_mask_grouped, dim=(2, 3), keepdim=True) var = torch.masked.var(x_grouped, mask=padding_mask_grouped, dim=(2, 3), keepdim=True, unbiased=False) x_norm = (x_grouped - mean) / torch.sqrt(var + self.eps) x_norm = x_norm.view(x.shape) if self.affine: x_norm = x_norm * self.weight.view(1, -1, 1) + self.bias.view(1, -1, 1) return x_norm * padding_mask class PeAudioVideoConvBlock1d(nn.Module): def __init__(self, config): super().__init__() self.groupnorm = PeAudioVideoMaskedGroupNorm(num_groups=1, num_channels=config.hidden_size) self.activation = nn.SiLU() self.project = nn.Conv1d( in_channels=config.hidden_size, out_channels=config.hidden_size, kernel_size=3, padding="same", ) def forward(self, x, padding_mask=None): x = self.groupnorm(x, padding_mask=padding_mask) x = self.activation(x) return self.project(x) class PeAudioVideoResnetBlock1d(nn.Module): def __init__(self, config): super().__init__() self.block1 = PeAudioVideoConvBlock1d(config) self.block2 = PeAudioVideoConvBlock1d(config) def forward(self, hidden_states, padding_mask=None): """ Args: hidden_states: (batch_size, seq_len, hidden_size) padding_mask: (batch_size, seq_len) Returns: hidden_states: (batch_size, seq_len, hidden_size) """ # transpose for convolutions # (batch_size, seq_len, hidden_size) -> (batch_size, hidden_size, seq_len) hidden_states = hidden_states.transpose(1, 2) if padding_mask is not None: padding_mask = padding_mask.unsqueeze(1).expand_as(hidden_states) residual = hidden_states hidden_states = self.block1(hidden_states, padding_mask=padding_mask) hidden_states = self.block2(hidden_states, padding_mask=padding_mask) hidden_states = residual + hidden_states return hidden_states.transpose(1, 2) class PeAudioVideoEncoderPatchEmbedder(nn.Module): def __init__(self, config): super().__init__() self.resnet_block = PeAudioVideoResnetBlock1d(config) self.class_embedding = nn.Parameter(torch.randn(1, 1, config.hidden_size)) def forward(self, inputs_embeds, padding_mask=None): # Embedding step: prepend class token and run the ResNet block. hidden_states = torch.cat( [self.class_embedding.expand(inputs_embeds.size(0), -1, -1), inputs_embeds], dim=1, ) if padding_mask is not None: # TODO: any reason why we take padding_mask[0] and not just 1? padding_mask = torch.cat([padding_mask[:, [0]], padding_mask], dim=1) hidden_states = self.resnet_block(hidden_states, padding_mask=padding_mask) return hidden_states, padding_mask class PeAudioVideoContrastiveHead(nn.Module): def __init__( self, in_dim: int, out_dim: int, ) -> None: super().__init__() self.layer_norm = nn.LayerNorm(normalized_shape=in_dim, eps=1e-6) self.proj = nn.Linear(in_dim, out_dim, bias=False) def forward(self, x: torch.Tensor) -> torch.FloatTensor: return self.proj(self.layer_norm(x)) class PeAudioVideoEncoderEmbedder(nn.Module): def __init__(self, config: PeAudioVideoEncoderConfig): super().__init__() self.audio_encoder = AutoModel.from_config(config.audio_config) self.video_encoder = AutoModel.from_config(config.video_config) self.video_proj = nn.Conv1d(config.video_config.hidden_size, config.audio_config.hidden_size, 1) self.video_norm = nn.LayerNorm(config.audio_config.hidden_size) self.concat_modality_proj = nn.Linear( config.audio_config.hidden_size + config.video_config.hidden_size, config.hidden_size, ) self.data_proj = nn.Linear(config.hidden_size, config.hidden_size) def _align_video_hidden_state( self, video_hidden_state: torch.Tensor, audio_hidden_state: torch.Tensor, padding_mask_videos: torch.Tensor | None = None, padding_mask: torch.Tensor | None = None, ) -> torch.Tensor: """ Align video_hidden_state to audio_hidden_state by nearest neighbor interpolation. """ if video_hidden_state.shape[1] == audio_hidden_state.shape[1]: return video_hidden_state if padding_mask_videos is not None: video_lengths = padding_mask_videos.sum(dim=-1) else: video_lengths = video_hidden_state.shape[1] * video_hidden_state.new_ones( video_hidden_state.shape[0], dtype=torch.long ) if padding_mask is not None: audio_lengths = padding_mask.sum(dim=-1) else: audio_lengths = audio_hidden_state.shape[1] * audio_hidden_state.new_ones( audio_hidden_state.shape[0], dtype=torch.long ) if (audio_lengths == video_hidden_state.shape[1]).all() or ( video_lengths == audio_hidden_state.shape[1] ).all(): # no need to align taking into account the padding masks # note: when one of the above is true, we can expect the other to be true as there is no reason # to have masked audio without masked video and vice versa return nn.functional.interpolate( video_hidden_state.transpose(1, 2), size=audio_hidden_state.shape[1], mode="nearest" ).transpose(1, 2) aligned_shape = (*audio_hidden_state.shape[:2], video_hidden_state.shape[-1]) aligned_hidden_state = audio_hidden_state.new_zeros(aligned_shape) for i, (hidden_state, video_length, audio_length) in enumerate( zip(video_hidden_state, video_lengths, audio_lengths) ): hidden_state = hidden_state[:video_length] if hidden_state.numel() > 0 and audio_length > 0: interpolated_hidden_state = nn.functional.interpolate( hidden_state[None].transpose(1, 2), size=audio_length, mode="nearest" ).transpose(1, 2)[0] aligned_hidden_state[i, :audio_length, :] = interpolated_hidden_state return aligned_hidden_state 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, ): audio_output = self.audio_encoder(input_values, padding_mask=padding_mask) video_output = self.video_encoder(pixel_values_videos, padding_mask_videos=padding_mask_videos) audio_hidden_state = audio_output.last_hidden_state video_hidden_state = video_output.last_hidden_state padding_mask = audio_output.output_mask video_hidden_state = self.video_proj(video_hidden_state.transpose(1, 2)).transpose(1, 2) video_hidden_state = self._align_video_hidden_state( video_hidden_state=video_hidden_state, audio_hidden_state=audio_hidden_state, padding_mask_videos=padding_mask_videos, padding_mask=padding_mask, ) video_hidden_state = self.video_norm(video_hidden_state) inputs_embeds = torch.cat([audio_hidden_state, video_hidden_state], dim=-1) inputs_embeds = self.concat_modality_proj(inputs_embeds) inputs_embeds = self.data_proj(inputs_embeds) return inputs_embeds, padding_mask, audio_output, video_output class PeAudioVideoEncoderAttention(Qwen3Attention): def __init__(self, config, layer_idx): super().__init__(config, layer_idx) self.is_causal = False del self.sliding_window def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, attention_mask: torch.Tensor | 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_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2) key_states = self.k_norm(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) 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 PeAudioVideoEncoderLayer(Qwen3DecoderLayer): def __init__(self, config, layer_idx): super().__init__(config, layer_idx) del self.attention_type class PeAudioVideoEncoderRMSNorm(Qwen3RMSNorm): ... def stack_freqs(cos: torch.Tensor, sin: torch.Tensor): dim = cos.size(-1) cos = cos.narrow(-1, 0, dim // 2) sin = sin.narrow(-1, 0, dim // 2) freqs_cis = torch.stack((cos, -sin, sin, cos), dim=-1).view(*cos.size(), 2, 2) return freqs_cis def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): freqs_cis = stack_freqs(cos, sin) freqs_cis = freqs_cis.unsqueeze(unsqueeze_dim) q_ = q.reshape(*q.shape[:-1], -1, 1, 2) k_ = k.reshape(*k.shape[:-1], -1, 1, 2) return (q_ * freqs_cis).sum(5).flatten(3), (k_ * freqs_cis).sum(5).flatten(3) class PeAudioVideoEncoderRotaryEmbedding(Qwen3RotaryEmbedding): ... @auto_docstring class PeAudioVideoPreTrainedModel(PreTrainedModel): config: PeAudioVideoConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["PeAudioVideoEncoderLayer"] _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": PeAudioVideoEncoderLayer, "attentions": PeAudioVideoEncoderAttention, } def _init_weights(self, module): super()._init_weights(module) if hasattr(self.config, "initializer_range"): std = self.config.initializer_range else: # 0.02 is the standard default value across the library std = getattr(self.config.get_text_config(), "initializer_range", 0.02) if isinstance(module, PeAudioVideoEncoderPatchEmbedder): embed_dim = module.class_embedding.shape[-1] init.normal_(module.class_embedding, mean=0.0, std=embed_dim**-0.5 * std) @dataclass @auto_docstring( custom_intro=""" Class for outputs of [`PeAudioVideoEncoder`]. """ ) class PeAudioVideoEncoderOutput(BaseModelOutputWithPooling): audio_model_output: BaseModelOutputWithPooling | None = None video_model_output: BaseModelOutputWithPooling | None = None @auto_docstring( custom_intro=""" The PeAudioVideo Encoder model. """ ) class PeAudioVideoEncoder(PeAudioVideoPreTrainedModel): config: PeAudioVideoEncoderConfig main_input_name = "input_values" base_model_prefix = "audio_video_encoder" def __init__(self, config: PeAudioVideoEncoderConfig): super().__init__(config) self.embedder = PeAudioVideoEncoderEmbedder(config) self.patch_embedder = PeAudioVideoEncoderPatchEmbedder(config) self.layers = nn.ModuleList( [PeAudioVideoEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = PeAudioVideoEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = PeAudioVideoEncoderRotaryEmbedding(config=config) self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=False) self.gradient_checkpointing = False self.post_init() @can_return_tuple @merge_with_config_defaults @capture_outputs 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, **kwargs, ) -> tuple | PeAudioVideoEncoderOutput: inputs_embeds, padding_mask, audio_output, video_output = self.embedder( input_values, pixel_values_videos, padding_mask=padding_mask, padding_mask_videos=padding_mask_videos, ) inputs_embeds, attention_mask = self.patch_embedder(inputs_embeds, padding_mask=padding_mask) if attention_mask is not None: attention_mask = create_bidirectional_mask( config=self.config, inputs_embeds=inputs_embeds, attention_mask=attention_mask, ) position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0) position_embeddings = self.rotary_emb(inputs_embeds, position_ids) hidden_states = inputs_embeds for encoder_layer in self.layers[: self.config.num_hidden_layers]: hidden_states = encoder_layer( hidden_states, attention_mask=attention_mask, position_embeddings=position_embeddings, **kwargs, ) hidden_states = self.norm(hidden_states) hidden_states = self.output(hidden_states) return PeAudioVideoEncoderOutput( last_hidden_state=hidden_states[:, 1:], pooler_output=hidden_states[:, 0], audio_model_output=audio_output, video_model_output=video_output, ) @dataclass @auto_docstring( custom_intro=""" Class for outputs of [`PeAudioVideoModel`] when using text, audio, and/or video. """ ) class PeAudioVideoOutput(ModelOutput): # embeddings audio_embeds: torch.FloatTensor | None = None video_embeds: torch.FloatTensor | None = None audio_video_embeds: torch.FloatTensor | None = None text_audio_embeds: torch.FloatTensor | None = None text_video_embeds: torch.FloatTensor | None = None text_audio_video_embeds: torch.FloatTensor | None = None audio_plus_text_embeds: torch.FloatTensor | None = None video_plus_text_embeds: torch.FloatTensor | None = None # model outputs # TODO: update types to the correct ones text_outputs: MaskedLMOutput | None = None audio_outputs: BaseModelOutputWithPooling | None = None video_outputs: BaseModelOutputWithPooling | None = None audio_video_outputs: BaseModelOutputWithPooling | None = None # logits logits_audio_text: torch.FloatTensor | None = None logits_video_text: torch.FloatTensor | None = None logits_audio_video: torch.FloatTensor | None = None logits_audio_video_text: torch.FloatTensor | None = None logits_audio_plus_text_video: torch.FloatTensor | None = None logits_video_plus_text_audio: torch.FloatTensor | None = None audio_text_loss: torch.FloatTensor | None = None video_text_loss: torch.FloatTensor | None = None audio_video_loss: torch.FloatTensor | None = None audio_video_text_loss: torch.FloatTensor | None = None audio_plus_text_video_loss: torch.FloatTensor | None = None video_plus_text_audio_loss: torch.FloatTensor | None = None loss: torch.FloatTensor | None = None def to_tuple(self) -> tuple[Any]: return tuple(self[k] if not k.endswith("model_output") else getattr(self, k).to_tuple() for k in self.keys()) @dataclass class AudioVideoEmbeddings(ModelOutput): audio_embeds: torch.FloatTensor | None = None video_embeds: torch.FloatTensor | None = None audio_video_embeds: torch.FloatTensor | None = None class PeAudioVideoModel(PeAudioVideoPreTrainedModel): _tied_weights_keys = { r"audio_model\.text_model\.(?!rotary_emb)": r"^text_model\.(?!rotary_emb)", r"video_model\.text_model\.(?!rotary_emb)": r"^text_model\.(?!rotary_emb)", r"audio_video_encoder\.embedder\.audio_encoder\.(?!rotary_emb)": r"audio_model\.audio_encoder\.(?!rotary_emb)", r"audio_video_encoder\.embedder\.video_encoder\.(?!rotary_emb|.*\.rope\.pos_embed)": r"video_model\.video_encoder\.(?!rotary_emb|.*\.rope\.pos_embed)", } def __init__(self, config: PeAudioVideoConfig): super().__init__(config) self.text_model = AutoModel.from_config(config.text_config) self.audio_model = AutoModel.from_config(config.audio_config) self.video_model = AutoModel.from_config(config.video_config) self.audio_video_encoder = PeAudioVideoEncoder(config.audio_video_config) text_hidden_size = config.text_config.hidden_size audio_hidden_size = config.audio_video_config.audio_config.hidden_size video_hidden_size = config.audio_video_config.video_config.hidden_size # audio-video self.audio_video_head = PeAudioVideoContrastiveHead(config.audio_video_config.hidden_size, text_hidden_size) self.text_audio_video_head = PeAudioVideoContrastiveHead(text_hidden_size, text_hidden_size) self.audio_video_logit_scale = nn.Parameter(torch.zeros(1)) self.audio_video_logit_bias = nn.Parameter(torch.zeros(1)) self.text_audio_video_logit_scale = nn.Parameter(torch.zeros(1)) self.text_audio_video_logit_bias = nn.Parameter(torch.zeros(1)) # text-audio self.audio_plus_text_head = PeAudioVideoContrastiveHead(text_hidden_size + audio_hidden_size, text_hidden_size) self.audio_plus_text_logit_scale = nn.Parameter(torch.zeros(1)) self.audio_plus_text_logit_bias = nn.Parameter(torch.zeros(1)) # text-video self.video_plus_text_head = PeAudioVideoContrastiveHead(text_hidden_size + video_hidden_size, text_hidden_size) self.video_plus_text_logit_scale = nn.Parameter(torch.zeros(1)) self.video_plus_text_logit_bias = nn.Parameter(torch.zeros(1)) self.post_init() def _contrastive_loss(self, logits: torch.Tensor) -> torch.Tensor: labels = torch.eye(logits.shape[0], device=logits.device) loss = -nn.functional.logsigmoid(labels * logits).sum() / logits.shape[0] return loss def get_text_audio_embeds(self, input_ids, attention_mask=None): return self.audio_model.get_text_embeds(input_ids, attention_mask) def get_text_video_embeds(self, input_ids, attention_mask=None): return self.video_model.get_text_embeds(input_ids, attention_mask) def get_text_audio_video_embeds(self, input_ids, attention_mask=None): text_outputs: MaskedLMOutput = self.text_model( input_ids=input_ids, attention_mask=attention_mask, return_dict=True, ) text_embeds = text_outputs.hidden_states[-1][:, 0] return self.text_audio_video_head(text_embeds) def get_audio_embeds(self, input_values, padding_mask=None): return self.audio_model.get_audio_embeds(input_values, padding_mask) def get_video_embeds(self, pixel_values_videos, padding_mask_videos=None): return self.video_model.get_video_embeds(pixel_values_videos, padding_mask_videos) def get_audio_video_embeds( self, input_values: torch.Tensor, pixel_values_videos: torch.Tensor, padding_mask: torch.Tensor | None = None, padding_mask_videos: torch.Tensor | None = None, return_audio_embeds: bool = False, return_video_embeds: bool = False, **kwargs, ) -> AudioVideoEmbeddings: audio_video_outputs = self.audio_video_encoder( input_values=input_values, pixel_values_videos=pixel_values_videos, padding_mask=padding_mask, padding_mask_videos=padding_mask_videos, **kwargs, ) if return_audio_embeds: audio_embeds = self.audio_model.audio_head(audio_video_outputs.audio_model_output.pooler_output) if return_video_embeds: video_embeds = self.video_model.video_head(audio_video_outputs.video_model_output.pooler_output) audio_video_embeds = self.audio_video_head(audio_video_outputs.pooler_output) return AudioVideoEmbeddings( audio_embeds=audio_embeds if return_audio_embeds else None, video_embeds=video_embeds if return_video_embeds else None, audio_video_embeds=audio_video_embeds, ) def get_audio_plus_text_embeds( self, input_ids: torch.Tensor, input_values: torch.Tensor, attention_mask: torch.Tensor | None = None, padding_mask: torch.Tensor | None = None, ) -> torch.Tensor: audio_embeds = self.audio_model.audio_encoder( input_values=input_values, padding_mask=padding_mask, return_dict=True, ) text_outputs: MaskedLMOutput = self.text_model( input_ids=input_ids, attention_mask=attention_mask, return_dict=True, ) text_embeds = text_outputs.hidden_states[-1][:, 0] audio_plus_text_embeds = torch.cat([text_embeds, audio_embeds], dim=-1) return self.audio_plus_text_head(audio_plus_text_embeds) def get_video_plus_text_embeds( self, input_ids: torch.Tensor, pixel_values_videos: torch.Tensor, attention_mask: torch.Tensor | None = None, padding_mask_videos: torch.Tensor | None = None, ) -> torch.Tensor: video_embeds = self.video_model.video_encoder( pixel_values_videos=pixel_values_videos, padding_mask_videos=padding_mask_videos, return_dict=True, ) text_outputs: MaskedLMOutput = self.text_model( input_ids=input_ids, attention_mask=attention_mask, return_dict=True, ) text_embeds = text_outputs.hidden_states[-1][:, 0] video_plus_text_embeds = torch.cat([text_embeds, video_embeds], dim=-1) return self.video_plus_text_head(video_plus_text_embeds) @can_return_tuple def forward( self, input_ids: torch.Tensor | None = None, pixel_values_videos: torch.Tensor | None = None, input_values: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, padding_mask_videos: torch.Tensor | None = None, padding_mask: torch.Tensor | None = None, return_loss=False, **kwargs, ) -> PeAudioVideoOutput: if sum([input_ids is not None, pixel_values_videos is not None, input_values is not None]) < 2: raise ValueError("At least two of input_ids, pixel_values_videos, or input_values must be provided") if pixel_values_videos is None: outputs = self.audio_model( input_ids=input_ids, input_values=input_values, attention_mask=attention_mask, padding_mask=padding_mask, return_dict=True, ) audio_plus_text_embeds = torch.cat( [outputs.audio_outputs.pooler_output, outputs.text_outputs.hidden_states[-1][:, 0]], dim=-1 ) audio_plus_text_embeds = self.audio_plus_text_head(audio_plus_text_embeds) return PeAudioVideoOutput(audio_plus_text_embeds=audio_plus_text_embeds, **outputs) if input_values is None: outputs = self.video_model( input_ids=input_ids, pixel_values_videos=pixel_values_videos, attention_mask=attention_mask, padding_mask_videos=padding_mask_videos, return_dict=True, ) video_plus_text_embeds = torch.cat( [outputs.video_outputs.pooler_output, outputs.text_outputs.hidden_states[-1][:, 0]], dim=-1 ) video_plus_text_embeds = self.video_plus_text_head(video_plus_text_embeds) return PeAudioVideoOutput(video_plus_text_embeds=video_plus_text_embeds, **outputs) audio_video_outputs = self.audio_video_encoder( input_values=input_values, pixel_values_videos=pixel_values_videos, padding_mask=padding_mask, padding_mask_videos=padding_mask_videos, **kwargs, ) audio_embeds = audio_video_outputs.audio_model_output.pooler_output video_embeds = audio_video_outputs.video_model_output.pooler_output audio_video_embeds = audio_video_outputs.pooler_output audio_embeds = self.audio_model.audio_head(audio_embeds) video_embeds = self.video_model.video_head(video_embeds) audio_video_embeds = self.audio_video_head(audio_video_embeds) logits_audio_video = audio_embeds @ video_embeds.T logits_audio_video = logits_audio_video * self.audio_video_logit_scale + self.audio_video_logit_bias audio_video_loss = self._contrastive_loss(logits_audio_video) if return_loss else None if input_ids is None: return PeAudioVideoOutput( logits_audio_video=logits_audio_video, audio_embeds=audio_embeds, video_embeds=video_embeds, audio_video_embeds=audio_video_embeds, loss=audio_video_loss, audio_video_loss=audio_video_loss, ) kwargs["output_hidden_states"] = True text_outputs = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) text_embeds = text_outputs.hidden_states[-1][:, 0] audio_plus_text_embeds = torch.cat([audio_video_outputs.audio_model_output.pooler_output, text_embeds], dim=-1) video_plus_text_embeds = torch.cat([audio_video_outputs.video_model_output.pooler_output, text_embeds], dim=-1) text_audio_embeds = self.audio_model.text_audio_head(text_embeds) text_video_embeds = self.video_model.text_video_head(text_embeds) text_audio_video_embeds = self.text_audio_video_head(text_embeds) audio_plus_text_embeds = self.audio_plus_text_head(audio_plus_text_embeds) video_plus_text_embeds = self.video_plus_text_head(video_plus_text_embeds) logits_audio_text = audio_embeds @ text_audio_embeds.T logits_video_text = video_embeds @ text_video_embeds.T logits_audio_video_text = audio_video_embeds @ text_audio_video_embeds.T logits_audio_plus_text_video = audio_plus_text_embeds @ video_embeds.T logits_video_plus_text_audio = video_plus_text_embeds @ audio_embeds.T logits_audio_text = ( logits_audio_text * self.audio_model.text_audio_logit_scale + self.audio_model.text_audio_logit_bias ) logits_video_text = ( logits_video_text * self.video_model.text_video_logit_scale + self.video_model.text_video_logit_bias ) logits_audio_video_text = ( logits_audio_video_text * self.text_audio_video_logit_scale + self.text_audio_video_logit_bias ) logits_audio_plus_text_video = ( logits_audio_plus_text_video * self.audio_plus_text_logit_scale + self.audio_plus_text_logit_bias ) logits_video_plus_text_audio = ( logits_video_plus_text_audio * self.video_plus_text_logit_scale + self.video_plus_text_logit_bias ) if return_loss: audio_text_loss = self._contrastive_loss(logits_audio_text) video_text_loss = self._contrastive_loss(logits_video_text) audio_video_text_loss = self._contrastive_loss(logits_audio_video_text) audio_plus_text_video_loss = self._contrastive_loss(logits_audio_plus_text_video) video_plus_text_audio_loss = self._contrastive_loss(logits_video_plus_text_audio) loss = ( audio_video_text_loss + audio_text_loss + video_text_loss + audio_video_loss + audio_plus_text_video_loss + video_plus_text_audio_loss ) return PeAudioVideoOutput( # embeddings audio_embeds=audio_embeds, video_embeds=video_embeds, audio_video_embeds=audio_video_embeds, text_audio_embeds=text_audio_embeds, text_video_embeds=text_video_embeds, text_audio_video_embeds=text_audio_video_embeds, audio_plus_text_embeds=audio_plus_text_embeds, video_plus_text_embeds=video_plus_text_embeds, # model outputs text_outputs=text_outputs, audio_outputs=audio_video_outputs.audio_model_output, video_outputs=audio_video_outputs.video_model_output, audio_video_outputs=audio_video_outputs, # logits logits_audio_text=logits_audio_text, logits_video_text=logits_video_text, logits_audio_video=logits_audio_video, logits_audio_video_text=logits_audio_video_text, logits_audio_plus_text_video=logits_audio_plus_text_video, logits_video_plus_text_audio=logits_video_plus_text_audio, # losses audio_text_loss=audio_text_loss if return_loss else None, video_text_loss=video_text_loss if return_loss else None, audio_video_loss=audio_video_loss if return_loss else None, audio_video_text_loss=audio_video_text_loss if return_loss else None, audio_plus_text_video_loss=audio_plus_text_video_loss if return_loss else None, video_plus_text_audio_loss=video_plus_text_audio_loss if return_loss else None, loss=loss if return_loss else None, ) __all__ = [ "PeAudioVideoModel", "PeAudioVideoEncoder", ]