# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.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_qwen2_5_omni.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 The Qwen team, Alibaba Group 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 dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import torch import torch.nn.functional as F from torch import nn from torch.nn import Parameter from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, ModelOutput from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...utils import ( auto_docstring, check_torch_load_is_safe, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, is_torch_flex_attn_available, logging, ) from ...utils.hub import cached_file from .configuration_qwen2_5_omni import ( Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniBigVGANConfig, Qwen2_5OmniConfig, Qwen2_5OmniDiTConfig, Qwen2_5OmniTalkerConfig, Qwen2_5OmniTextConfig, Qwen2_5OmniThinkerConfig, Qwen2_5OmniToken2WavConfig, Qwen2_5OmniVisionEncoderConfig, ) if is_flash_attn_2_available(): from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_varlen_func from flash_attn.layers.rotary import apply_rotary_emb else: flash_attn_varlen_func = None apply_rotary_emb = None if is_torch_flex_attn_available(): from torch.nn.attention.flex_attention import BlockMask from ...integrations.flex_attention import make_flex_block_causal_mask if is_flash_attn_available(): from ...modeling_flash_attention_utils import _flash_attention_forward logger = logging.get_logger(__name__) class Qwen2RMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Qwen2RMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): 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}" @auto_docstring class Qwen2_5OmniPreTrainedModel(PreTrainedModel): config_class = Qwen2_5OmniConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["Qwen2_5OmniDecoderLayer", "Qwen2_5OmniVisionBlock"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_sdpa = True _supports_cache_class = True _supports_static_cache = True def _init_weights(self, module): # important: this ported version of Qwen2.5OmniThinker isn't meant for training from scratch - only # inference and fine-tuning - so the proper init weights code has been removed std = self.config.initializer_range if hasattr(self.config, "initializer_range") else 0.02 if isinstance(module, (nn.Linear, nn.Conv1d, nn.Conv3d, nn.ConvTranspose1d)): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): if module.weight is not None: module.weight.data.fill_(1.0) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, Qwen2RMSNorm): module.weight.data.fill_(1.0) class Qwen2_5OmniPreTrainedModelForConditionalGeneration(Qwen2_5OmniPreTrainedModel): def _prepare_4d_causal_attention_mask_with_cache_position( self, attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, min_dtype: float, cache_position: torch.Tensor, batch_size: int, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to place the 4D attention mask on. min_dtype (`float`): The minimum value representable with the dtype `dtype`. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask def get_llm_pos_ids_for_vision( self, start_idx: int, vision_idx: int, spatial_merge_size: int, t_index: List[int], grid_hs: List[int], grid_ws: List[int], ): llm_pos_ids_list = [] llm_grid_h = grid_hs[vision_idx] // spatial_merge_size llm_grid_w = grid_ws[vision_idx] // spatial_merge_size h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(len(t_index), -1, llm_grid_w).flatten() w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(len(t_index), llm_grid_h, -1).flatten() t_index = torch.Tensor(t_index).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten().long() _llm_pos_ids = torch.stack([t_index, h_index, w_index]) llm_pos_ids_list.append(_llm_pos_ids + start_idx) # + 1 ) # 12.09 by malinhan llm_pos_ids = torch.cat(llm_pos_ids_list, dim=1) return llm_pos_ids def get_chunked_index( self, token_indices: torch.Tensor, tokens_per_chunk: int, remove_index: int ) -> list[tuple[int, int]]: """ Splits token index list into chunks based on token value ranges. Given a list of token indices, returns a list of (start, end) index tuples representing slices of the list where the token values fall within successive ranges of `t_ntoken_per_chunk`. For example, if `t_ntoken_per_chunk` is 1000, the function will create chunks such that: - the first chunk contains token values < 1000, - the second chunk contains values >= 1000 and < 2000, and so on. Parameters: token_indices (`torch.Tensor` of shape `(seq_len, )`): A monotonically increasing list of token index values. t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold). remove_index (`int`) An index id to subtract from `token_indices` before chunking Returns: `List[Tuple[int, int]]`: A list of tuples, each representing the start (inclusive) and end (exclusive) indices of a chunk in `token_indices`. """ def _iter(): i, start_idx = 0, 0 # skip bos token current_chunk = 1 while i < len(token_indices): # skip eos token if token_indices[i] - remove_index >= current_chunk * tokens_per_chunk: yield (start_idx, i) start_idx = i current_chunk += 1 i += 1 yield (start_idx, len(token_indices)) return list(_iter()) def get_rope_index( self, input_ids: Optional[torch.LongTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, use_audio_in_video: bool = False, audio_seqlens: Optional[torch.LongTensor] = None, second_per_grids: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Calculate the 3D rope index based on image and video's temporal, height and width in LLM. Explanation: Each embedding sequence contains vision embedding and text embedding or just contains text embedding. For pure text embedding sequence, the rotary position embedding has no difference with modern LLMs. Examples: input_ids: [T T T T T], here T is for text. temporal position_ids: [0, 1, 2, 3, 4] height position_ids: [0, 1, 2, 3, 4] width position_ids: [0, 1, 2, 3, 4] For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part and 1D rotary position embedding for text part. Examples: Temporal (Time): 3 patches, representing different segments of the video in time. Height: 2 patches, dividing each frame vertically. Width: 2 patches, dividing each frame horizontally. We also have some important parameters: fps (Frames Per Second): The video's frame rate, set to 1. This means one frame is processed each second. tokens_per_second: This is a crucial parameter. It dictates how many "time-steps" or "temporal tokens" are conceptually packed into a one-second interval of the video. In this case, we have 25 tokens per second. So each second of the video will be represented with 25 separate time points. It essentially defines the temporal granularity. temporal_patch_size: The number of frames that compose one temporal patch. Here, it's 2 frames. interval: The step size for the temporal position IDs, calculated as tokens_per_second * temporal_patch_size / fps. In this case, 25 * 2 / 1 = 50. This means that each temporal patch will be have a difference of 50 in the temporal position IDs. input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision. vision temporal position_ids: [0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100] vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] text temporal position_ids: [101, 102, 103, 104, 105] text height position_ids: [101, 102, 103, 104, 105] text width position_ids: [101, 102, 103, 104, 105] Here we calculate the text start position_ids as the max vision position_ids plus 1. Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. use_audio_in_video (`bool`, *optional*): If set to `True`, use the audio in video. audio_seqlens (`torch.LongTensor` of shape `(num_audios)`, *optional*): The length of feature shape of each audio in LLM. second_per_grids (`torch.LongTensor` of shape `(num_videos)`, *optional*): The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. Returns: position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`) mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`) """ spatial_merge_size = self.spatial_merge_size image_token_id = self.config.image_token_id video_token_id = self.config.video_token_id audio_token_id = self.config.audio_token_id vision_start_token_id = self.config.vision_start_token_id audio_start_token_id = self.config.audio_start_token_id position_id_per_seconds = self.config.position_id_per_seconds seconds_per_chunk = self.config.seconds_per_chunk mrope_position_deltas = [] if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None): total_input_ids = input_ids if attention_mask is None: attention_mask = torch.ones_like(total_input_ids) position_ids = torch.ones( 3, input_ids.shape[0], input_ids.shape[1], dtype=input_ids.dtype, device=input_ids.device, ) image_idx, video_idx, audio_idx = 0, 0, 0 attention_mask = attention_mask.to(total_input_ids.device) for i, input_ids in enumerate(total_input_ids): input_ids = input_ids[attention_mask[i] == 1] image_nums, video_nums, audio_nums = 0, 0, 0 vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1) vision_tokens = input_ids[vision_start_indices + 1] audio_nums = torch.sum(input_ids == audio_start_token_id) image_nums = (vision_tokens == image_token_id).sum() video_nums = ( (vision_tokens == audio_start_token_id).sum() if use_audio_in_video else (vision_tokens == video_token_id).sum() ) input_tokens = input_ids.tolist() llm_pos_ids_list: list = [] st = 0 remain_images, remain_videos, remain_audios = image_nums, video_nums, audio_nums multimodal_nums = ( image_nums + audio_nums if use_audio_in_video else image_nums + video_nums + audio_nums ) for _ in range(multimodal_nums): st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 if image_token_id in input_tokens and remain_images > 0: ed_image = input_tokens.index(image_token_id, st) else: ed_image = len(input_tokens) + 1 if video_token_id in input_tokens and remain_videos > 0: ed_video = input_tokens.index(video_token_id, st) else: ed_video = len(input_tokens) + 1 if audio_token_id in input_tokens and remain_audios > 0: ed_audio = input_tokens.index(audio_token_id, st) else: ed_audio = len(input_tokens) + 1 min_ed = min(ed_image, ed_video, ed_audio) if min_ed == ed_audio: text_len = min_ed - st - 1 if text_len != 0: st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 bos_len = 1 llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 audio_len = ((audio_seqlens[audio_idx] - 1) // 2 + 1 - 2) // 2 + 1 llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx llm_pos_ids_list.append(llm_pos_ids) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 eos_len = 1 llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) st += text_len + bos_len + audio_len + eos_len audio_idx += 1 remain_audios -= 1 elif min_ed == ed_image: text_len = min_ed - st - 1 if text_len != 0: st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 bos_len = 1 llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 grid_t = image_grid_thw[image_idx][0] grid_hs = image_grid_thw[:, 1] grid_ws = image_grid_thw[:, 2] t_index = (torch.arange(grid_t) * 1 * position_id_per_seconds).long() llm_pos_ids = self.get_llm_pos_ids_for_vision( st_idx, image_idx, spatial_merge_size, t_index, grid_hs, grid_ws ) image_len = image_grid_thw[image_idx].prod() // (spatial_merge_size**2) llm_pos_ids_list.append(llm_pos_ids) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 eos_len = 1 llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) st += text_len + bos_len + image_len + eos_len image_idx += 1 remain_images -= 1 elif min_ed == ed_video and not use_audio_in_video: text_len = min_ed - st - 1 if text_len != 0: st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 bos_len = 1 llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 grid_t = video_grid_thw[video_idx][0] grid_hs = video_grid_thw[:, 1] grid_ws = video_grid_thw[:, 2] t_index = ( torch.arange(grid_t) * second_per_grids[video_idx].cpu().float() * position_id_per_seconds ).long() llm_pos_ids = self.get_llm_pos_ids_for_vision( st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws ) video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2) llm_pos_ids_list.append(llm_pos_ids) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 eos_len = 1 llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) st += text_len + bos_len + video_len + eos_len video_idx += 1 remain_videos -= 1 elif min_ed == ed_video and use_audio_in_video: text_len = min_ed - st - 2 if text_len != 0: st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 bos_len = 1 llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 audio_len = ((audio_seqlens[audio_idx] - 1) // 2 + 1 - 2) // 2 + 1 audio_llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx grid_t = video_grid_thw[video_idx][0] grid_hs = video_grid_thw[:, 1] grid_ws = video_grid_thw[:, 2] t_index = ( torch.arange(grid_t) * second_per_grids[video_idx].cpu().float() * position_id_per_seconds ).long() video_llm_pos_ids = self.get_llm_pos_ids_for_vision( st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws ) t_ntoken_per_chunk = int(position_id_per_seconds * seconds_per_chunk) video_chunk_indexes = self.get_chunked_index(video_llm_pos_ids[0], t_ntoken_per_chunk, st_idx) audio_chunk_indexes = self.get_chunked_index(audio_llm_pos_ids[0], t_ntoken_per_chunk, st_idx) sub_len = 0 for j in range(max(len(video_chunk_indexes), len(audio_chunk_indexes))): video_chunk_index = video_chunk_indexes[j] if j < len(video_chunk_indexes) else None audio_chunk_index = audio_chunk_indexes[j] if j < len(audio_chunk_indexes) else None if video_chunk_index is not None: sub_len += video_chunk_index[1] - video_chunk_index[0] llm_pos_ids_list.append( video_llm_pos_ids[:, video_chunk_index[0] : video_chunk_index[1]] ) if audio_chunk_index is not None: sub_len += audio_chunk_index[1] - audio_chunk_index[0] llm_pos_ids_list.append( audio_llm_pos_ids[:, audio_chunk_index[0] : audio_chunk_index[1]] ) video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2) st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 eos_len = 1 llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) st += text_len + bos_len * 2 + audio_len + video_len + eos_len * 2 audio_idx += 1 video_idx += 1 remain_videos -= 1 remain_audios -= 1 if st < len(input_tokens): st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 text_len = len(input_tokens) - st llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device) mrope_position_deltas.append(llm_positions.max() + 1 - len(input_ids)) mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1) return position_ids, mrope_position_deltas else: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device) max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0] mrope_position_deltas = max_position_ids + 1 - torch.sum(attention_mask, dim=-1, keepdim=True) return position_ids, mrope_position_deltas ############################ # Start Thinker # ############################ @dataclass class Qwen2_5OmniThinkerCausalLMOutputWithPast(ModelOutput): """ Base class for Qwen2.5OmniThinker causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`, *optional*): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): The rope index difference between sequence length and multimodal rope. """ loss: Optional[torch.FloatTensor] = None logits: Optional[torch.FloatTensor] = None past_key_values: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None rope_deltas: Optional[torch.LongTensor] = None class Qwen2_5OmniAudioAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, config: Qwen2_5OmniAudioEncoderConfig, ): super().__init__() self.embed_dim = config.d_model self.num_heads = config.encoder_attention_heads self.dropout = config.attention_dropout self.head_dim = self.embed_dim // self.num_heads self.config = config if (self.head_dim * self.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`: {self.num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = False self.is_causal = False self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) def forward( self, hidden_states: torch.Tensor, cu_seqlens: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" seq_length, _ = hidden_states.size() query_states = self.q_proj(hidden_states).reshape(seq_length, self.num_heads, -1) key_states = self.k_proj(hidden_states).reshape(seq_length, self.num_heads, -1) value_states = self.v_proj(hidden_states).reshape(seq_length, self.num_heads, -1) query_states = query_states.transpose(0, 1) key_states = key_states.transpose(0, 1) value_states = value_states.transpose(0, 1) attn_weights = torch.matmul(query_states, key_states.transpose(1, 2)) / math.sqrt(self.head_dim) attention_mask = torch.full( [1, seq_length, key_states.shape[1]], torch.finfo(query_states.dtype).min, device=query_states.device, dtype=query_states.dtype, ) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1).to(query_states.dtype) attn_output = torch.matmul(attn_weights, value_states).transpose(0, 1).reshape(seq_length, self.embed_dim) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = self.out_proj(attn_output) return attn_output class Qwen2_5OmniAudioFlashAttention2(Qwen2_5OmniAudioAttention): """ Qwen2.5OmniThinker flash attention module. This module inherits from `Qwen2_5OmniAudioAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def forward( self, hidden_states: torch.Tensor, cu_seqlens: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: seq_length, all_dim = hidden_states.size() query_states = self.q_proj(hidden_states) query_states = query_states.reshape(seq_length, self.num_heads, -1) key_states = self.k_proj(hidden_states) key_states = key_states.reshape(seq_length, self.num_heads, -1) value_states = self.v_proj(hidden_states) value_states = value_states.reshape(seq_length, self.num_heads, -1) max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item() attn_output = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen, dropout_p=0.0 ) attn_output = attn_output.reshape(seq_length, all_dim) attn_output = self.out_proj(attn_output) return attn_output class Qwen2_5OmniAudioSdpaAttention(Qwen2_5OmniAudioAttention): def forward( self, hidden_states: torch.Tensor, cu_seqlens: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" seq_length, _ = hidden_states.size() query_states = self.q_proj(hidden_states).reshape(seq_length, self.num_heads, -1) key_states = self.k_proj(hidden_states).reshape(seq_length, self.num_heads, -1) value_states = self.v_proj(hidden_states).reshape(seq_length, self.num_heads, -1) attention_mask = torch.zeros( [1, seq_length, key_states.shape[0]], device=query_states.device, dtype=torch.bool ) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1. query_states = query_states.transpose(0, 1) key_states = key_states.transpose(0, 1) value_states = value_states.transpose(0, 1) # NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask, # but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577 attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=attention_mask, dropout_p=self.dropout if self.training else 0.0, ) attn_output = attn_output.transpose(0, 1) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(seq_length, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output QWEN2_5_OMNI_AUDIO_ATTENTION_CLASSES = { "eager": Qwen2_5OmniAudioAttention, "flash_attention_2": Qwen2_5OmniAudioFlashAttention2, "sdpa": Qwen2_5OmniAudioSdpaAttention, } class Qwen2_5OmniAudioEncoderLayer(nn.Module): def __init__(self, config: Qwen2_5OmniAudioEncoderConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = QWEN2_5_OMNI_AUDIO_ATTENTION_CLASSES[config._attn_implementation](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, cu_seqlens: torch.Tensor, ) -> 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. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. 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 = self.self_attn( hidden_states=hidden_states, cu_seqlens=cu_seqlens, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) 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) outputs = (hidden_states,) return outputs class SinusoidsPositionEmbedding(nn.Module): def __init__(self, length, channels, max_timescale=10000): super().__init__() if channels % 2 != 0: raise ValueError("SinusoidsPositionEmbedding needs even channels input") log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float()) scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] self.register_buffer( "positional_embedding", torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1), persistent=False, ) def forward(self, seqlen: int): return self.positional_embedding[:seqlen, :] @auto_docstring( custom_intro=""" Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`Qwen2_5OmniAudioEncoderLayer`]. """ ) class Qwen2_5OmniAudioEncoder(Qwen2_5OmniPreTrainedModel): config_class = Qwen2_5OmniAudioEncoderConfig main_input_name = "input_features" _no_split_modules = ["Qwen2_5OmniAudioEncoderLayer"] _supports_sdpa = True def __init__(self, config: Qwen2_5OmniAudioEncoderConfig): super().__init__(config) self.dropout = config.dropout 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.n_window = config.n_window 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.positional_embedding = SinusoidsPositionEmbedding(self.max_source_positions, embed_dim) self.audio_bos_eos_token = nn.Embedding(2, config.output_dim) self.layers = nn.ModuleList([Qwen2_5OmniAudioEncoderLayer(config) for _ in range(config.encoder_layers)]) self.ln_post = nn.LayerNorm(config.d_model) self.avg_pooler = nn.AvgPool1d(2, stride=2) self.proj = nn.Linear(config.d_model, config.output_dim) 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 @auto_docstring def forward( self, input_features, feature_lens=None, aftercnn_lens=None, ): r""" input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`): 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__`] feature_lens (`torch.LongTensor` of shape `(batch_size,)`): mel length aftercnn_lens (`torch.LongTensor` of shape `(batch_size,)`): mel length after cnn """ chunk_num = torch.ceil(feature_lens / (self.n_window * 2)).long() chunk_lengths = torch.tensor( [self.n_window * 2] * chunk_num.sum(), dtype=torch.long, device=feature_lens.device, ) tail_chunk_index = F.pad(chunk_num, (1, 0), value=-1).cumsum(0)[1:] chunk_lengths[tail_chunk_index] = feature_lens % (self.n_window * 2) chunk_lengths = torch.where(chunk_lengths == 0, self.n_window * 2, chunk_lengths) chunk_list = input_features.split(chunk_lengths.tolist(), dim=1) padded_feature, padded_mask, padded_mask_after_cnn = self.padded_and_mask_function( chunk_list, chunk_lengths, padding_value=0, padding_side="right" ) padded_embed = nn.functional.gelu(self.conv1(padded_feature)) * padded_mask padded_embed = nn.functional.gelu(self.conv2(padded_embed)).transpose(1, 2) padded_embed = padded_embed + self.positional_embedding.positional_embedding[ : padded_embed.shape[1], : ].unsqueeze(0).to(padded_embed.dtype) hidden_states = padded_embed[padded_mask_after_cnn] cu_seqlens = torch.cat( ( torch.zeros(1, device=padded_mask_after_cnn.device, dtype=torch.int32), padded_mask_after_cnn.sum(1).cumsum(0), ) ).to(torch.int32) for idx, encoder_layer in enumerate(self.layers): if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, cu_seqlens, ) else: layer_outputs = encoder_layer( hidden_states, cu_seqlens, ) hidden_states = layer_outputs[0] hidden_states_list = hidden_states.split(aftercnn_lens.tolist(), dim=0) token_audio_list = [] for each_audio_states in hidden_states_list: each_audio_states = self.avg_pooler(each_audio_states.transpose(0, 1)).transpose_(0, 1) each_audio_states = self.ln_post(each_audio_states) each_audio_states = self.proj(each_audio_states) token_audio_list.append(each_audio_states) token_audio = torch.cat(token_audio_list, dim=0) return BaseModelOutput(last_hidden_state=token_audio) def padded_and_mask_function(self, tensor_list, tensor_len, padding_value=0, padding_side="right"): """ Pads a sequence of tensors to their maximum length on indicated `padding_side`. Then prepares a mask so that pad tokens are not attended to. """ max_len = tensor_len.max() dim = tensor_list[0].shape[0] padded_tensor = torch.full( size=(len(tensor_list), dim, max_len), fill_value=padding_value, dtype=self.dtype, device=tensor_list[0].device, ) batch_mask = torch.zeros( (len(tensor_len), max_len), dtype=torch.long, device=padded_tensor.device, ) for i, length in enumerate(tensor_len): batch_mask[i, :length] = 1 padded_tensor[i, :, :length] = tensor_list[i] feature_lens_after_cnn = (tensor_len - 1) // 2 + 1 max_len_after_cnn = feature_lens_after_cnn.max() batch_mask_after_cnn = torch.zeros( (len(tensor_len), max_len_after_cnn), dtype=torch.long, device=padded_tensor.device, ) for i, length in enumerate(feature_lens_after_cnn): batch_mask_after_cnn[i, :length] = 1 return ( padded_tensor, batch_mask.unsqueeze(1), batch_mask_after_cnn.bool(), ) # 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 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) def apply_rotary_pos_emb_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor: orig_dtype = tensor.dtype tensor = tensor.float() cos = freqs.cos() sin = freqs.sin() cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float() sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float() output = (tensor * cos) + (rotate_half(tensor) * sin) output = output.to(orig_dtype) return output class Qwen2_5OmniVisionAttention(nn.Module): def __init__(self, dim: int, num_heads: int = 16) -> None: super().__init__() self.num_heads = num_heads self.head_dim = dim // num_heads self.q = nn.Linear(dim, dim, bias=True) self.k = nn.Linear(dim, dim, bias=True) self.v = nn.Linear(dim, dim, bias=True) self.proj = nn.Linear(dim, dim) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor = None ) -> torch.Tensor: seq_length = hidden_states.shape[0] q = self.q(hidden_states).reshape(seq_length, self.num_heads, -1) k = self.k(hidden_states).reshape(seq_length, self.num_heads, -1) v = self.v(hidden_states).reshape(seq_length, self.num_heads, -1) q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0) k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0) attention_mask = torch.full( [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype ) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 q = q.transpose(0, 1) k = k.transpose(0, 1) v = v.transpose(0, 1) attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim) attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) attn_output = torch.matmul(attn_weights, v) attn_output = attn_output.transpose(0, 1) attn_output = attn_output.reshape(seq_length, -1) attn_output = self.proj(attn_output) return attn_output class Qwen2_5OmniVisionFlashAttention2(nn.Module): def __init__(self, dim: int, num_heads: int = 16) -> None: super().__init__() self.num_heads = num_heads self.q = nn.Linear(dim, dim, bias=True) self.k = nn.Linear(dim, dim, bias=True) self.v = nn.Linear(dim, dim, bias=True) self.proj = nn.Linear(dim, dim) def _apply_rotary_pos_emb_flashatt(self, tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor: tensor_ = tensor.float() cos = freqs.cos() # .type_as(tensor_) sin = freqs.sin() # .type_as(tensor_) output = apply_rotary_emb(tensor_, cos, sin).type_as(tensor) return output def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor = None ) -> torch.Tensor: seq_length = hidden_states.shape[0] q = self.q(hidden_states).reshape(seq_length, self.num_heads, -1) k = self.k(hidden_states).reshape(seq_length, self.num_heads, -1) v = self.v(hidden_states).reshape(seq_length, self.num_heads, -1) q = self._apply_rotary_pos_emb_flashatt(q.unsqueeze(0), rotary_pos_emb).squeeze(0) k = self._apply_rotary_pos_emb_flashatt(k.unsqueeze(0), rotary_pos_emb).squeeze(0) max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item() attn_output = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape( seq_length, -1 ) attn_output = self.proj(attn_output) return attn_output class Qwen2_5OmniVisionSdpaAttention(nn.Module): def __init__(self, dim: int, num_heads: int = 16) -> None: super().__init__() self.num_heads = num_heads self.q = nn.Linear(dim, dim, bias=True) self.k = nn.Linear(dim, dim, bias=True) self.v = nn.Linear(dim, dim, bias=True) self.proj = nn.Linear(dim, dim) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor = None ) -> torch.Tensor: seq_length = hidden_states.shape[0] q = self.q(hidden_states).reshape(seq_length, self.num_heads, -1) k = self.k(hidden_states).reshape(seq_length, self.num_heads, -1) v = self.v(hidden_states).reshape(seq_length, self.num_heads, -1) q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0) k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0) attention_mask = torch.zeros([1, seq_length, seq_length], device=q.device, dtype=torch.bool) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True q = q.transpose(0, 1) k = k.transpose(0, 1) v = v.transpose(0, 1) attn_output = F.scaled_dot_product_attention(q, k, v, attention_mask, dropout_p=0.0) attn_output = attn_output.transpose(0, 1) attn_output = attn_output.reshape(seq_length, -1) attn_output = self.proj(attn_output) return attn_output class Qwen2_5OmniMLP(nn.Module): def __init__(self, config, bias: bool = False): super().__init__() self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias) self.act_fn = ACT2FN[config.hidden_act] def forward(self, hidden_state): return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)) QWEN2_5_OMNI_VISION_ATTENTION_CLASSES = { "eager": Qwen2_5OmniVisionAttention, "flash_attention_2": Qwen2_5OmniVisionFlashAttention2, "sdpa": Qwen2_5OmniVisionSdpaAttention, } class Qwen2_5OmniVisionBlock(nn.Module): def __init__(self, config: Qwen2_5OmniVisionEncoderConfig) -> None: super().__init__() self.norm1 = Qwen2RMSNorm(config.hidden_size, eps=1e-6) self.norm2 = Qwen2RMSNorm(config.hidden_size, eps=1e-6) self.attn = QWEN2_5_OMNI_VISION_ATTENTION_CLASSES[config._attn_implementation]( config.hidden_size, num_heads=config.num_heads ) self.mlp = Qwen2_5OmniMLP(config, bias=True) def forward(self, hidden_states, cu_seqlens, rotary_pos_emb) -> torch.Tensor: hidden_states = hidden_states + self.attn( self.norm1(hidden_states), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb ) hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) return hidden_states class Qwen2_5_VisionPatchEmbed(nn.Module): def __init__( self, patch_size: int = 14, temporal_patch_size: int = 2, in_channels: int = 3, embed_dim: int = 1152, ) -> None: super().__init__() self.patch_size = patch_size self.temporal_patch_size = temporal_patch_size self.in_channels = in_channels self.embed_dim = embed_dim kernel_size = [temporal_patch_size, patch_size, patch_size] self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: target_dtype = self.proj.weight.dtype hidden_states = hidden_states.view( -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size ) hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim) return hidden_states class Qwen2_5_VisionRotaryEmbedding(nn.Module): def __init__(self, dim: int, theta: float = 10000.0) -> None: super().__init__() inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) def forward(self, seqlen: int) -> torch.Tensor: seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype) freqs = torch.outer(seq, self.inv_freq) return freqs class Qwen2_5OmniPatchMerger(nn.Module): def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None: super().__init__() self.hidden_size = context_dim * (spatial_merge_size**2) self.ln_q = Qwen2RMSNorm(context_dim, eps=1e-6) self.mlp = nn.Sequential( nn.Linear(self.hidden_size, self.hidden_size), nn.GELU(), nn.Linear(self.hidden_size, dim), ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.mlp(self.ln_q(x).view(-1, self.hidden_size)) return x class Qwen2_5OmniVisionEncoder(Qwen2_5OmniPreTrainedModel): config_class = Qwen2_5OmniVisionEncoderConfig _no_split_modules = ["Qwen2_5OmniVisionBlock"] def __init__(self, config: Qwen2_5OmniVisionEncoderConfig, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.spatial_merge_size = config.spatial_merge_size self.patch_size = config.patch_size self.fullatt_block_indexes = config.fullatt_block_indexes self.window_size = config.window_size self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size self.patch_embed = Qwen2_5_VisionPatchEmbed( patch_size=config.patch_size, temporal_patch_size=config.temporal_patch_size, in_channels=config.in_channels, embed_dim=config.hidden_size, ) head_dim = config.hidden_size // config.num_heads self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2) self.blocks = nn.ModuleList([Qwen2_5OmniVisionBlock(config) for _ in range(config.depth)]) self.merger = Qwen2_5OmniPatchMerger( dim=config.out_hidden_size, context_dim=config.hidden_size, spatial_merge_size=config.spatial_merge_size, ) self.gradient_checkpointing = False def rot_pos_emb(self, grid_thw): pos_ids = [] for t, h, w in grid_thw: hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) hpos_ids = hpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ) hpos_ids = hpos_ids.permute(0, 2, 1, 3) hpos_ids = hpos_ids.flatten() wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) wpos_ids = wpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ) wpos_ids = wpos_ids.permute(0, 2, 1, 3) wpos_ids = wpos_ids.flatten() pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) pos_ids = torch.cat(pos_ids, dim=0) max_grid_size = grid_thw[:, 1:].max() rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size) rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1) return rotary_pos_emb def get_window_index(self, grid_thw): window_index: list = [] cu_window_seqlens: list = [0] window_index_id = 0 vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size for grid_t, grid_h, grid_w in grid_thw: llm_grid_h, llm_grid_w = ( grid_h // self.spatial_merge_size, grid_w // self.spatial_merge_size, ) index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) index_padded = index_padded.reshape( grid_t, num_windows_h, vit_merger_window_size, num_windows_w, vit_merger_window_size, ) index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( grid_t, num_windows_h * num_windows_w, vit_merger_window_size, vit_merger_window_size, ) seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) index_padded = index_padded.reshape(-1) index_new = index_padded[index_padded != -100] window_index.append(index_new + window_index_id) cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1] cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) window_index_id += (grid_t * llm_grid_h * llm_grid_w).item() window_index = torch.cat(window_index, dim=0) return window_index, cu_window_seqlens def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor: """ Args: hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`): The final hidden states of the model. grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`): The temporal, height and width of feature shape of each image in LLM. Returns: `torch.Tensor`: hidden_states. """ hidden_states = self.patch_embed(hidden_states) rotary_pos_emb = self.rot_pos_emb(grid_thw) window_index, cu_window_seqlens = self.get_window_index(grid_thw) cu_window_seqlens = torch.tensor( cu_window_seqlens, device=hidden_states.device, dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, ) cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) seq_len, _ = hidden_states.size() hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) hidden_states = hidden_states[window_index, :, :] hidden_states = hidden_states.reshape(seq_len, -1) rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) rotary_pos_emb = rotary_pos_emb[window_index, :, :] rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1) cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum( dim=0, # Select dtype based on the following factors: # - FA2 requires that cu_seqlens_q must have dtype int32 # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw # See https://github.com/huggingface/transformers/pull/34852 for more information dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, ) cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) # Modification here for layer_num, blk in enumerate(self.blocks): if layer_num in self.fullatt_block_indexes: cu_seqlens_now = cu_seqlens else: cu_seqlens_now = cu_window_seqlens if self.gradient_checkpointing and self.training: hidden_states = self._gradient_checkpointing_func( blk.__call__, hidden_states, cu_seqlens_now, rotary_pos_emb ) else: hidden_states = blk( hidden_states, cu_seqlens=cu_seqlens_now, rotary_pos_emb=rotary_pos_emb, ) hidden_states = self.merger(hidden_states) reverse_indices = torch.argsort(window_index) hidden_states = hidden_states[reverse_indices, :] return hidden_states class Qwen2_5OmniRotaryEmbedding(nn.Module): def __init__(self, config: Qwen2_5OmniThinkerConfig, device=None): super().__init__() # BC: "rope_type" was originally "type" if hasattr(config, "rope_scaling") and config.rope_scaling is not None: self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq @torch.no_grad() @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) def forward(self, x, position_ids): # In contrast to other models, Qwen2_5Omni has different position ids for the grids # So we expand the inv_freq to shape (3, ...) inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1) position_ids_expanded = position_ids[:, :, None, :].float() # shape (3, bs, 1, positions) device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3) 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) def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1): """Applies Rotary Position Embedding with Multimodal Sections to the query and key tensors (https://qwenlm.github.io/blog/qwen2-vl/). Explanation: Multimodal 3D rotary position embedding is an extension to 1D rotary position embedding. The input embedding sequence contains vision (images / videos) embedding and text embedding or just contains text embedding. For vision embedding part, we apply rotary position embedding on temporal, height and width dimension separately. Here we split the channel dimension to 3 chunks for the temporal, height and width rotary position embedding. For text embedding part, we just apply 1D rotary position embedding. The three rotary position index (temporal, height and width) of text embedding is always the same, so the text embedding rotary position embedding has no difference with modern LLMs. 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. position_ids (`torch.Tensor`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. mrope_section(`List(int)`): Multimodal rope section is for channel dimension of temporal, height and width in rope calculation. 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. """ mrope_section = mrope_section * 2 cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze( unsqueeze_dim ) sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).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) class Qwen2_5OmniAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer and "Generating Long Sequences with Sparse Transformers". """ def __init__(self, config: Qwen2_5OmniConfig, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {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.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads) self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.is_causal = True self.attention_dropout = config.attention_dropout self.rope_scaling = config.rope_scaling self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) self.rotary_emb = Qwen2_5OmniRotaryEmbedding(config=config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_multimodal_rotary_pos_emb( query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] ) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask # Fix precision issues in Qwen2-VL float16 inference # Replace inf values with zeros in attention weights to prevent NaN propagation if query_states.dtype == torch.float16: attn_weights = torch.where(torch.isinf(attn_weights), torch.zeros_like(attn_weights), attn_weights) # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, -1) attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class Qwen2MLP(nn.Module): def __init__(self, config, bias: bool = False): super().__init__() self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias) self.act_fn = ACT2FN[config.hidden_act] def forward(self, hidden_state): return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)) class Qwen2_5OmniFlashAttention2(Qwen2_5OmniAttention): """ Qwen2_5Omni flash attention module, following Qwen2_5Omni attention module. This module inherits from `Qwen2_5OmniAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom config.max_window_layers layers. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC ): bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) # Because the input can be padded, the absolute sequence length depends on the max position id. cos, sin = position_embeddings query_states, key_states = apply_multimodal_rotary_pos_emb( query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] ) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) dropout_rate = 0.0 if not self.training else self.attention_dropout # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 just to be sure everything works as expected. input_dtype = query_states.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) # Reashape to the expected shape for Flash Attention query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) if ( self.config.use_sliding_window and getattr(self.config, "sliding_window", None) is not None and self.layer_idx >= self.config.max_window_layers ): sliding_window = self.config.sliding_window else: sliding_window = None attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, sliding_window=sliding_window, is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask, ) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class Qwen2_5OmniSdpaAttention(Qwen2_5OmniAttention): """ Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. """ # Adapted from Qwen2Attention.forward def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if output_attentions: # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. logger.warning_once( "Qwen2_5OmniModel is using Qwen2_5OmniSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) return super().forward( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_multimodal_rotary_pos_emb( query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] ) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) causal_mask = attention_mask if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, # Reference: https://github.com/pytorch/pytorch/issues/112577. if query_states.device.type == "cuda" and attention_mask is not None: query_states = query_states.contiguous() key_states = key_states.contiguous() value_states = value_states.contiguous() # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. is_causal = True if causal_mask is None and q_len > 1 else False attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=causal_mask, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=is_causal, ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.view(bsz, q_len, -1) attn_output = self.o_proj(attn_output) return attn_output, None, past_key_value QWEN2_5_OMNI_ATTENTION_CLASSES = { "eager": Qwen2_5OmniAttention, "flash_attention_2": Qwen2_5OmniFlashAttention2, "sdpa": Qwen2_5OmniSdpaAttention, } class Qwen2_5OmniDecoderLayer(nn.Module): def __init__(self, config: Qwen2_5OmniTextConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size if config.use_sliding_window and config._attn_implementation != "flash_attention_2": logger.warning_once( f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; " "unexpected results may be encountered." ) self.self_attn = QWEN2_5_OMNI_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) self.mlp = Qwen2MLP(config) self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch, sequence_length)` where padding elements are indicated by 0. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, with `head_dim` being the embedding dimension of each attention head. kwargs (`dict`, *optional*): Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code into the model """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs @auto_docstring class Qwen2_5OmniThinkerTextModel(Qwen2_5OmniPreTrainedModel): config_class = Qwen2_5OmniTextConfig _no_split_modules = ["Qwen2_5OmniDecoderLayer"] def __init__(self, config: Qwen2_5OmniTextConfig): 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( [Qwen2_5OmniDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self._attn_implementation = config._attn_implementation self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = Qwen2_5OmniRotaryEmbedding(config=config) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # torch.jit.trace() doesn't support cache objects in the output if use_cache and past_key_values is None and not torch.jit.is_tracing(): past_key_values = DynamicCache() if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) 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.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) # the hard coded `3` is for temporal, height and width. if position_ids is None: position_ids = cache_position.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1) elif position_ids.dim() == 2: position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, causal_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, position_embeddings, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) def _update_causal_mask( self, attention_mask: Union[torch.Tensor, "BlockMask"], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and past_key_values is not None: is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0] if is_padding_right: raise ValueError( "You are attempting to perform batched generation with padding_side='right'" " this may lead to unexpected behaviour for Flash Attention version of Qwen25OmniThinker. Make sure to " " call `tokenizer.padding_side = 'left'` before tokenizing the input. " ) if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None if self.config._attn_implementation == "flex_attention": if isinstance(attention_mask, torch.Tensor): attention_mask = make_flex_block_causal_mask(attention_mask) return attention_mask # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if ( self.config._attn_implementation == "sdpa" and not (using_static_cache or using_sliding_window_cache) and not output_attentions ): if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, sliding_window=self.config.sliding_window, is_training=self.training, ): return None dtype = input_tensor.dtype min_dtype = torch.finfo(dtype).min sequence_length = input_tensor.shape[1] # SlidingWindowCache or StaticCache if using_sliding_window_cache or using_static_cache: target_length = past_key_values.get_max_cache_shape() # DynamicCache or no cache else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, cache_position=cache_position, batch_size=input_tensor.shape[0], config=self.config, past_key_values=past_key_values, ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu", "npu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, config: Qwen2_5OmniConfig, past_key_values: Cache, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. config (`Qwen25OmniThinkerConfig`): The model's configuration class past_key_values (`Cache`): The cache class that is being used currently to generate """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device ) diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape( -1, 1 ) text_config = config.get_text_config() if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None: # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also # the check is needed to verify is current checkpoint was trained with sliding window or not if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length: sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= ( cache_position.reshape(-1, 1) - text_config.sliding_window ) diagonal_attend_mask.bitwise_or_(sliding_attend_mask) causal_mask *= diagonal_attend_mask causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit if attention_mask.shape[-1] > target_length: attention_mask = attention_mask[:, :target_length] mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask @auto_docstring( custom_intro=""" The Qwen2.5OmniThinker model which consists of a audio backbone and a language model. """ ) class Qwen2_5OmniThinkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForConditionalGeneration, GenerationMixin): config_class = Qwen2_5OmniThinkerConfig base_model_prefix = "thinker" _no_split_modules = ["Qwen2_5OmniAudioEncoder", "Qwen2_5OmniVisionEncoder"] def __init__(self, config: Qwen2_5OmniThinkerConfig): super().__init__(config) self.audio_tower = Qwen2_5OmniAudioEncoder._from_config( config.audio_config, attn_implementation=config._attn_implementation ) self.visual = Qwen2_5OmniVisionEncoder._from_config( config.vision_config, attn_implementation=config._attn_implementation ) self.vocab_size = config.text_config.vocab_size self.model = Qwen2_5OmniThinkerTextModel._from_config( config.text_config, attn_implementation=config._attn_implementation ) self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1 self.spatial_merge_size = config.vision_config.spatial_merge_size self.rope_deltas = None self.post_init() def get_input_embeddings(self): return self.model.get_input_embeddings() def set_input_embeddings(self, value): self.model.set_input_embeddings(value) def get_video_features( self, pixel_values_videos: torch.FloatTensor, video_grid_thw: Optional[torch.LongTensor] = None ): """ Encodes videos into continuous embeddings that can be forwarded to the language model. Args: pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): The tensors corresponding to the input videos. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. """ pixel_values_videos = pixel_values_videos.type(self.visual.dtype) video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw) return video_embeds def get_image_features(self, pixel_values: torch.FloatTensor, image_grid_thw: Optional[torch.LongTensor] = None): """ Encodes images into continuous embeddings that can be forwarded to the language model. Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): The tensors corresponding to the input images. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. """ pixel_values = pixel_values.type(self.visual.dtype) image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw) return image_embeds def get_audio_features( self, input_features: torch.FloatTensor, feature_attention_mask: Optional[torch.LongTensor] = None, audio_feature_lengths: Optional[torch.LongTensor] = None, ): """ Encodes audios into continuous embeddings that can be forwarded to the language model. Args: input_features (`torch.FloatTensor`): The tensors corresponding to the input audios. feature_attention_mask (`torch.LongTensor`, *optional*): Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`: audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*): The length of feature shape of each audio in LLM. """ if feature_attention_mask is not None: audio_feature_lengths = torch.sum(feature_attention_mask, dim=1) input_features = input_features.permute(0, 2, 1)[feature_attention_mask.bool()].permute(1, 0) else: audio_feature_lengths = None audio_feat_lengths, audio_output_lengths = self.audio_tower._get_feat_extract_output_lengths( audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1) ) feature_lens = audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1) audio_outputs = self.audio_tower( input_features, feature_lens=feature_lens, aftercnn_lens=audio_feat_lengths, ) audio_features = audio_outputs.last_hidden_state if audio_features.shape[0] != sum(audio_output_lengths.tolist()): raise ValueError("length of audio_features should match audio_output_lengths") return audio_features @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, input_features: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, feature_attention_mask: Optional[torch.Tensor] = None, audio_feature_lengths: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, rope_deltas: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, use_audio_in_video: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, video_second_per_grid: Optional[torch.LongTensor] = None, ) -> Union[Tuple, Qwen2_5OmniThinkerCausalLMOutputWithPast]: r""" input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, feature_sequence_length)`): Float values 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__`] pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size), *optional*): The tensors corresponding to the input videos. Pixel values can be obtained using [`AutoImageProcessor`]. See [`SiglipImageProcessor.__call__`] for details ([]`NewTaskModelProcessor`] uses [`SiglipImageProcessor`] for processing videos). image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. feature_attention_mask (`torch.Tensor` of shape `(batch_size, feature_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**. audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*): The length of feature shape of each audio in LLM. rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): The rope index difference between sequence length and multimodal rope. 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]`. use_audio_in_video (`bool`, *optional*): Whether or not use audio track in video, should same as the parameter in `process_audio_info`. video_second_per_grid (`torch.LongTensor` of shape `(num_videos)`, *optional*): Number of seconds per grid for each video, used for temporal feature mapping. Example: ```python >>> from io import BytesIO >>> from urllib.request import urlopen >>> import librosa >>> from qwen_vl_utils import process_vision_info >>> from transformers import Qwen2_5OmniProcessor, Qwen2_5OmniThinkerForConditionalGeneration >>> thinker = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-Omni-7B") >>> processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B") >>> conversations = [ >>> {'role': 'system', 'content': 'You are a helpful voice chat bot, and please respond to me in a casual conversation manner using random voice.'}, >>> {"role": "user", "content": [ >>> {"type": "image", "image_url": "https://www.ilankelman.org/stopsigns/australia.jpg"}, >>> {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3"}, >>> ]}, >>> ] >>> text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) >>> audios = [ librosa.load(BytesIO(urlopen( conversations[1]['content'][1]['audio_url'] ).read()), sr=self.processor.feature_extractor.sampling_rate) ] >>> images, videos = process_vision_info(conversations) >>> inputs = processor(text=text, audios=audios, images=images, videos=videos, return_tensors="pt", padding=True) >>> # Generate >>> inputs['use_audio_in_video'] = `True` or `False` >>> generation = thinker.generate(**inputs, max_new_tokens=2048) >>> generate_ids = generation[:, inputs.input_ids.size(1):] >>> response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if inputs_embeds is None: # 1. Extract the input embeddings inputs_embeds = self.get_input_embeddings()(input_ids) # 2. Merge text , audios , image and video if input_ids is not None and input_ids.shape[1] != 1: # Prefill stage if input_features is not None: audio_features = self.get_audio_features( input_features, feature_attention_mask=feature_attention_mask, audio_feature_lengths=audio_feature_lengths, ) audio_mask = ( (input_ids == self.config.audio_token_id) .unsqueeze(-1) .expand_as(inputs_embeds) .to(inputs_embeds.device) ) audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(audio_mask, audio_features) if pixel_values is not None: image_embeds = self.get_image_features(pixel_values, image_grid_thw) image_mask = ( (input_ids == self.config.image_token_id) .unsqueeze(-1) .expand_as(inputs_embeds) .to(inputs_embeds.device) ) image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) if pixel_values_videos is not None: video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw) video_mask = ( (input_ids == self.config.video_token_id) .unsqueeze(-1) .expand_as(inputs_embeds) .to(inputs_embeds.device) ) video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) if attention_mask is not None: attention_mask = attention_mask.to(inputs_embeds.device) if feature_attention_mask is not None: audio_feature_lengths = torch.sum(feature_attention_mask, dim=1) else: audio_feature_lengths = None if attention_mask is not None and position_ids is None: if ( cache_position is None or (cache_position is not None and cache_position[0] == 0) or self.rope_deltas is None ): delta0 = (1 - attention_mask).sum(dim=-1).unsqueeze(1) position_ids, rope_deltas = self.get_rope_index( input_ids, image_grid_thw, video_grid_thw, attention_mask, use_audio_in_video, audio_feature_lengths, video_second_per_grid, ) rope_deltas = rope_deltas - delta0 self.rope_deltas = rope_deltas else: batch_size, seq_length = input_ids.shape delta = cache_position[0] + self.rope_deltas if cache_position is not None else 0 position_ids = torch.arange(seq_length, device=input_ids.device) position_ids = position_ids.view(1, -1).expand(batch_size, -1) position_ids = position_ids.add(delta) position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) outputs = self.model( attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) loss = None if labels is not None: loss = self.loss_function( logits=logits, labels=labels, vocab_size=self.config.get_text_config().vocab_size ) if not return_dict: output = (logits,) + outputs return (loss,) + output if loss is not None else output return Qwen2_5OmniThinkerCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, rope_deltas=self.rope_deltas, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, position_ids=None, use_cache=True, pixel_values=None, pixel_values_videos=None, image_grid_thw=None, video_grid_thw=None, input_features=None, feature_attention_mask=None, use_audio_in_video=False, video_second_per_grid=None, **kwargs, ): model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, cache_position=cache_position, position_ids=position_ids, use_cache=use_cache, pixel_values=pixel_values, pixel_values_videos=pixel_values_videos, image_grid_thw=image_grid_thw, video_grid_thw=video_grid_thw, input_features=input_features, feature_attention_mask=feature_attention_mask, use_audio_in_video=use_audio_in_video, video_second_per_grid=video_second_per_grid, **kwargs, ) model_inputs["position_ids"] = None if cache_position[0] != 0: model_inputs["pixel_values"] = None model_inputs["pixel_values_videos"] = None return model_inputs ############################ # Start Talker # ############################ @dataclass class Qwen2_5OmniTalkerCausalLMOutputWithPast(ModelOutput): """ Base class for Qwen2.5OmniTalker causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): The rope index difference between sequence length and multimodal rope. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None rope_deltas: Optional[torch.LongTensor] = None thinker_reply_part: torch.FloatTensor = None @auto_docstring class Qwen2_5OmniTalkerModel(Qwen2_5OmniPreTrainedModel): config_class = Qwen2_5OmniTalkerConfig _no_split_modules = ["Qwen2_5OmniTalkerDecoderLayer"] def __init__(self, config: Qwen2_5OmniTalkerConfig): 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.embedding_size, self.padding_idx) self.layers = nn.ModuleList( [Qwen2_5OmniDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self._attn_implementation = config._attn_implementation self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = Qwen2_5OmniRotaryEmbedding(config=config) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # torch.jit.trace() doesn't support cache objects in the output if use_cache and past_key_values is None and not torch.jit.is_tracing(): past_key_values = DynamicCache() if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) 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.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) # the hard coded `3` is for temporal, height and width. if position_ids is None: position_ids = cache_position.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1) elif position_ids.dim() == 2: position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, causal_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, position_embeddings, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) def _update_causal_mask( self, attention_mask: Union[torch.Tensor, "BlockMask"], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and past_key_values is not None: is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0] if is_padding_right: raise ValueError( "You are attempting to perform batched generation with padding_side='right'" " this may lead to unexpected behaviour for Flash Attention version of Qwen2_5Omni. Make sure to " " call `tokenizer.padding_side = 'left'` before tokenizing the input. " ) if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None if self.config._attn_implementation == "flex_attention": if isinstance(attention_mask, torch.Tensor): attention_mask = make_flex_block_causal_mask(attention_mask) return attention_mask # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if ( self.config._attn_implementation == "sdpa" and not (using_static_cache or using_sliding_window_cache) and not output_attentions ): if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, sliding_window=self.config.sliding_window, is_training=self.training, ): return None dtype = input_tensor.dtype min_dtype = torch.finfo(dtype).min sequence_length = input_tensor.shape[1] # SlidingWindowCache or StaticCache if using_sliding_window_cache or using_static_cache: target_length = past_key_values.get_max_cache_shape() # DynamicCache or no cache else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, cache_position=cache_position, batch_size=input_tensor.shape[0], config=self.config, past_key_values=past_key_values, ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu", "npu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, config: Qwen2_5OmniConfig, past_key_values: Cache, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. config (`Qwen2_5OmniConfig`): The model's configuration class past_key_values (`Cache`): The cache class that is being used currently to generate """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device ) diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape( -1, 1 ) text_config = config.get_text_config() if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None: # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also # the check is needed to verify is current checkpoint was trained with sliding window or not if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length: sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= ( cache_position.reshape(-1, 1) - text_config.sliding_window ) diagonal_attend_mask.bitwise_or_(sliding_attend_mask) causal_mask *= diagonal_attend_mask causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit if attention_mask.shape[-1] > target_length: attention_mask = attention_mask[:, :target_length] mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask class Qwen2_5OmniTalkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForConditionalGeneration, GenerationMixin): config_class = Qwen2_5OmniTalkerConfig base_model_prefix = "talker" def __init__(self, config: Qwen2_5OmniTalkerConfig): super().__init__(config) self.thinker_to_talker_proj = nn.Linear(config.embedding_size, config.hidden_size) self.model = Qwen2_5OmniTalkerModel(config) self.codebook_size = config.vocab_size self.codec_head = nn.Linear(config.hidden_size, self.codebook_size, bias=False) self.codec_bos_token = config.tts_codec_start_token_id self.codec_eos_token = config.tts_codec_end_token_id self.codec_pad_token = config.tts_codec_pad_token_id self.codec_mask_token = config.tts_codec_mask_token_id self.text_bos_token = config.tts_text_start_token_id self.text_eos_token = config.tts_text_end_token_id self.text_pad_token = config.tts_text_pad_token_id self.spatial_merge_size = self.config.spatial_merge_size self.rope_deltas = None self.post_init() def get_input_embeddings(self): return self.model.get_input_embeddings() def set_input_embeddings(self, value): self.model.set_input_embeddings(value) @auto_docstring def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, thinker_reply_part: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, rope_deltas: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, input_text_ids: Optional[torch.LongTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, use_audio_in_video: Optional[bool] = None, audio_feature_lengths: Optional[torch.LongTensor] = None, video_second_per_grid: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Qwen2_5OmniTalkerCausalLMOutputWithPast]: r""" rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): The rope index difference between sequence length and multimodal rope. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*): The length of feature shape of each audio in LLM. thinker_reply_part (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Hidden states from the thinker model's output that represent the text reply part to be processed. input_text_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Input token IDs for text-only content, used for position calculation in multimodal contexts. use_audio_in_video (`bool`, *optional*): Whether or not use audio track in video, should same as the parameter in `process_audio_info`. video_second_per_grid (`torch.LongTensor` of shape `(num_videos)`, *optional*): Number of seconds per grid for each video, used for temporal feature mapping. Example: ```python >>> from io import BytesIO >>> from urllib.request import urlopen >>> import librosa >>> from transformers import AutoProcessor, Qwen2_5OmniTalkerForConditionalGeneration >>> model = Qwen2_5OmniTalkerForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B") >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B") >>> prompt = "<|audio_bos|><|AUDIO|><|audio_eos|>Generate the caption in English:" >>> url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3" >>> audio, _ = librosa.load(BytesIO(urlopen(url).read()), sr=self.processor.feature_extractor.sampling_rate) >>> inputs = processor(text=prompt, audios=audio, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(**inputs, max_length=30) >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Generate the caption in English: Glass is breaking." ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if attention_mask is not None and position_ids is None: if ( cache_position is None or (cache_position is not None and cache_position[0] == 0) or self.rope_deltas is None ): position_ids, rope_deltas = self.get_rope_index( input_text_ids, image_grid_thw, video_grid_thw, attention_mask, use_audio_in_video, audio_feature_lengths, video_second_per_grid, ) inputs_embeds[:, -1, :] += self.get_input_embeddings()( torch.tensor([self.codec_bos_token], dtype=torch.long, device=inputs_embeds.device) ) inputs_embeds[:, -2, :] += self.get_input_embeddings()( torch.tensor([self.codec_pad_token], dtype=torch.long, device=inputs_embeds.device) ) self.rope_deltas = rope_deltas else: batch_size, seq_length = input_ids.shape delta = cache_position[0] + self.rope_deltas if cache_position is not None else 0 position_ids = torch.arange(seq_length, device=input_ids.device) position_ids = position_ids.view(1, -1).expand(batch_size, -1) position_ids = position_ids.add(delta) position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) if inputs_embeds is None: # 1. Inference tokens after second token codec_embeds = self.get_input_embeddings()(input_ids) inputs_embeds = codec_embeds + thinker_reply_part[:, :1, :] if thinker_reply_part.shape[1] > 1: thinker_reply_part = thinker_reply_part[:, 1:, :] talker_lm_input = self.thinker_to_talker_proj(inputs_embeds) if attention_mask is not None: attention_mask = attention_mask.to(inputs_embeds.device) outputs = self.model( attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=talker_lm_input, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.codec_head(hidden_states) logits = logits.float() loss = None if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return Qwen2_5OmniTalkerCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=hidden_states, attentions=outputs.attentions, rope_deltas=self.rope_deltas, thinker_reply_part=thinker_reply_part, ) def _get_initial_cache_position(self, seq_length, device, model_kwargs): # Talker needs to calculate cache_position with input_ids, so pop inputs_embeds temporarily inputs_embeds = model_kwargs.pop("inputs_embeds") model_kwargs = super()._get_initial_cache_position(seq_length, device, model_kwargs) model_kwargs["inputs_embeds"] = inputs_embeds return model_kwargs # prepare inputs for talker lm generation def prepare_inputs_for_generation( self, input_ids, input_text_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, thinker_reply_part=None, cache_position=None, position_ids=None, use_cache=True, pixel_values=None, pixel_values_videos=None, image_grid_thw=None, video_grid_thw=None, input_audio_features=None, audio_feature_attention_mask=None, audio_feature_lengths=None, use_audio_in_video=False, video_second_per_grid=None, **kwargs, ): model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values, attention_mask, inputs_embeds, cache_position, use_cache=use_cache, thinker_reply_part=thinker_reply_part, input_text_ids=input_text_ids, image_grid_thw=image_grid_thw, video_grid_thw=video_grid_thw, use_audio_in_video=use_audio_in_video, audio_feature_lengths=audio_feature_lengths, video_second_per_grid=video_second_per_grid, **kwargs, ) model_inputs["position_ids"] = None return model_inputs def _update_model_kwargs_for_generation( self, outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False, num_new_tokens: int = 1, ) -> Dict[str, Any]: model_kwargs = super()._update_model_kwargs_for_generation( outputs, model_kwargs, is_encoder_decoder, num_new_tokens ) if getattr(outputs, "thinker_reply_part", None) is not None: model_kwargs["thinker_reply_part"] = outputs.thinker_reply_part return model_kwargs ############################ # Start Token2Wav # ############################ # Using custom RoPE, will use LlamaRotaryEmbedding next version class Qwen2_5OmniDiTRotaryEmbedding(nn.Module): def __init__(self, dim, base=10000): super().__init__() inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer("inv_freq", inv_freq) def forward(self, x): batch_size, seq_len = x.shape[0], x.shape[1] t = torch.arange(seq_len, device=x.device) device_type = x.device.type device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): freqs = t.unsqueeze(1).float() @ self.inv_freq.unsqueeze(0).float() freqs = torch.stack((freqs, freqs), dim=-1) freqs = freqs.reshape(*freqs.shape[:-2], -1) freqs = freqs.repeat(batch_size, *([1] * freqs.dim())) cos = freqs.cos() sin = freqs.sin() return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) class TimeDelayNetBlock(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, dilation, ): super().__init__() self.conv = nn.Conv1d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, dilation=dilation, padding="same", padding_mode="reflect", ) self.activation = nn.ReLU() def forward(self, hidden_states: torch.Tensor): return self.activation(self.conv(hidden_states)) class Res2NetBlock(torch.nn.Module): def __init__(self, in_channels, out_channels, scale=8, kernel_size=3, dilation=1): super().__init__() in_channel = in_channels // scale hidden_channel = out_channels // scale self.blocks = nn.ModuleList( [ TimeDelayNetBlock( in_channel, hidden_channel, kernel_size=kernel_size, dilation=dilation, ) for i in range(scale - 1) ] ) self.scale = scale def forward(self, hidden_states): outputs = [] for i, hidden_part in enumerate(torch.chunk(hidden_states, self.scale, dim=1)): if i == 0: output_part = hidden_part elif i == 1: output_part = self.blocks[i - 1](hidden_part) else: output_part = self.blocks[i - 1](hidden_part + output_part) outputs.append(output_part) output = torch.cat(outputs, dim=1) return output class SqueezeExcitationBlock(nn.Module): def __init__(self, in_channels, se_channels, out_channels): super().__init__() self.conv1 = nn.Conv1d( in_channels=in_channels, out_channels=se_channels, kernel_size=1, padding="same", padding_mode="reflect", ) self.relu = nn.ReLU(inplace=True) self.conv2 = nn.Conv1d( in_channels=se_channels, out_channels=out_channels, kernel_size=1, padding="same", padding_mode="reflect", ) self.sigmoid = nn.Sigmoid() def forward(self, hidden_states): hidden_states_mean = hidden_states.mean(dim=2, keepdim=True) hidden_states_mean = self.relu(self.conv1(hidden_states_mean)) hidden_states_mean = self.sigmoid(self.conv2(hidden_states_mean)) return hidden_states * hidden_states_mean class AttentiveStatisticsPooling(nn.Module): """This class implements an attentive statistic pooling layer for each channel. It returns the concatenated mean and std of the input tensor. """ def __init__(self, channels, attention_channels=128): super().__init__() self.eps = 1e-12 self.tdnn = TimeDelayNetBlock(channels * 3, attention_channels, 1, 1) self.tanh = nn.Tanh() self.conv = nn.Conv1d( in_channels=attention_channels, out_channels=channels, kernel_size=1, padding="same", padding_mode="reflect", ) def _length_to_mask(self, length, max_len=None, dtype=None, device=None): """Creates a binary mask for each sequence. Reference: https://discuss.pytorch.org/t/how-to-generate-variable-length-mask/23397/3 Arguments --------- length : torch.LongTensor Containing the length of each sequence in the batch. Must be 1D. max_len : int Max length for the mask, also the size of the second dimension. dtype : torch.dtype, default: None The dtype of the generated mask. device: torch.device, default: None The device to put the mask variable. Returns ------- mask : tensor The binary mask. """ if max_len is None: max_len = length.max().long().item() # using arange to generate mask mask = torch.arange(max_len, device=length.device, dtype=length.dtype).expand( len(length), max_len ) < length.unsqueeze(1) mask = torch.as_tensor(mask, dtype=dtype, device=device) return mask def _compute_statistics(self, x, m, dim=2): mean = (m * x).sum(dim) std = torch.sqrt((m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(self.eps)) return mean, std def forward(self, hidden_states): seq_length = hidden_states.shape[-1] lengths = torch.ones(hidden_states.shape[0], device=hidden_states.device) # Make binary mask of shape [N, 1, L] mask = self._length_to_mask( lengths * seq_length, max_len=seq_length, dtype=hidden_states.dtype, device=hidden_states.device ) mask = mask.unsqueeze(1) # Expand the temporal context of the pooling layer by allowing the # self-attention to look at global properties of the utterance. total = mask.sum(dim=2, keepdim=True) mean, std = self._compute_statistics(hidden_states, mask / total) mean = mean.unsqueeze(2).repeat(1, 1, seq_length) std = std.unsqueeze(2).repeat(1, 1, seq_length) attention = torch.cat([hidden_states, mean, std], dim=1) # Apply layers attention = self.conv(self.tanh(self.tdnn(attention))) # Filter out zero-paddings attention = attention.masked_fill(mask == 0, float("-inf")) attention = F.softmax(attention, dim=2) mean, std = self._compute_statistics(hidden_states, attention) # Append mean and std of the batch pooled_stats = torch.cat((mean, std), dim=1) pooled_stats = pooled_stats.unsqueeze(2) return pooled_stats class SqueezeExcitationRes2NetBlock(nn.Module): """An implementation of building block in ECAPA-TDNN, i.e., TDNN-Res2Net-TDNN-SqueezeExcitationBlock. """ def __init__( self, in_channels, out_channels, res2net_scale=8, se_channels=128, kernel_size=1, dilation=1, ): super().__init__() self.out_channels = out_channels self.tdnn1 = TimeDelayNetBlock( in_channels, out_channels, kernel_size=1, dilation=1, ) self.res2net_block = Res2NetBlock(out_channels, out_channels, res2net_scale, kernel_size, dilation) self.tdnn2 = TimeDelayNetBlock( out_channels, out_channels, kernel_size=1, dilation=1, ) self.se_block = SqueezeExcitationBlock(out_channels, se_channels, out_channels) def forward(self, hidden_state): residual = hidden_state hidden_state = self.tdnn1(hidden_state) hidden_state = self.res2net_block(hidden_state) hidden_state = self.tdnn2(hidden_state) hidden_state = self.se_block(hidden_state) return hidden_state + residual class ECAPA_TimeDelayNet(torch.nn.Module): """An implementation of the speaker embedding model in a paper. "ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification" (https://arxiv.org/abs/2005.07143). """ def __init__(self, config: Qwen2_5OmniDiTConfig): super().__init__() if len(config.enc_channels) != len(config.enc_kernel_sizes) or len(config.enc_channels) != len( config.enc_dilations ): raise ValueError("enc_channels, enc_kernel_sizes and enc_dilations should have same length") self.channels = config.enc_channels self.blocks = nn.ModuleList() # The initial TDNN layer self.blocks.append( TimeDelayNetBlock( config.mel_dim, config.enc_channels[0], config.enc_kernel_sizes[0], config.enc_dilations[0], ) ) # SE-Res2Net layers for i in range(1, len(config.enc_channels) - 1): self.blocks.append( SqueezeExcitationRes2NetBlock( config.enc_channels[i - 1], config.enc_channels[i], res2net_scale=config.enc_res2net_scale, se_channels=config.enc_se_channels, kernel_size=config.enc_kernel_sizes[i], dilation=config.enc_dilations[i], ) ) # Multi-layer feature aggregation self.mfa = TimeDelayNetBlock( config.enc_channels[-1], config.enc_channels[-1], config.enc_kernel_sizes[-1], config.enc_dilations[-1], ) # Attentive Statistical Pooling self.asp = AttentiveStatisticsPooling( config.enc_channels[-1], attention_channels=config.enc_attention_channels, ) # Final linear transformation self.fc = nn.Conv1d( in_channels=config.enc_channels[-1] * 2, out_channels=config.enc_dim, kernel_size=1, padding="same", padding_mode="reflect", ) def forward(self, hidden_states): # Minimize transpose for efficiency hidden_states = hidden_states.transpose(1, 2) hidden_states_list = [] for layer in self.blocks: hidden_states = layer(hidden_states) hidden_states_list.append(hidden_states) # Multi-layer feature aggregation hidden_states = torch.cat(hidden_states_list[1:], dim=1) hidden_states = self.mfa(hidden_states) # Attentive Statistical Pooling hidden_states = self.asp(hidden_states) # Final linear transformation hidden_states = self.fc(hidden_states) hidden_states = hidden_states.squeeze(-1) return hidden_states class DiTInputEmbedding(nn.Module): def __init__(self, config: Qwen2_5OmniDiTConfig): super().__init__() self.proj = nn.Linear( config.mel_dim + config.enc_dim + config.enc_emb_dim + config.emb_dim, config.hidden_size, ) self.spk_encoder = ECAPA_TimeDelayNet(config) def forward( self, hidden_states: torch.Tensor, speaker_embedding: torch.Tensor, condition_vector: torch.Tensor, code_embed: torch.Tensor, drop_audio_cond: Optional[bool] = False, code_embed_uncond: Optional[bool] = None, apply_cfg: Optional[bool] = True, ): if apply_cfg: hidden_states = torch.cat([hidden_states, hidden_states], dim=0) speaker_embedding = torch.cat([speaker_embedding, torch.zeros_like(speaker_embedding)], dim=0) condition_vector = torch.cat([condition_vector, torch.zeros_like(condition_vector)], dim=0) code_embed = torch.cat([code_embed, code_embed_uncond], dim=0) elif drop_audio_cond: # cfg for cond audio condition_vector = torch.zeros_like(condition_vector) speaker_embedding = torch.zeros_like(speaker_embedding) condition_vector = self.spk_encoder(condition_vector).unsqueeze(1).repeat(1, hidden_states.size(1), 1) hidden_states = self.proj(torch.cat((hidden_states, condition_vector, code_embed, speaker_embedding), dim=-1)) return hidden_states # Transformer backbone using DiT blocks class DiTCodecEmbedding(nn.Module): def __init__(self, codec_num_embeds, codec_dim, repeats): super().__init__() self.repeats = repeats self.codec_embed = nn.Embedding(codec_num_embeds + 1, codec_dim) def forward(self, code, drop_code=False): if drop_code: code = torch.zeros_like(code) code_embed = self.codec_embed(code) code_embed = torch.repeat_interleave(code_embed, repeats=self.repeats, dim=1) return code_embed # AdaLayerNormZero # return with modulated x for attn input, and params for later mlp modulation class Qwen2_5_OmniAdaLayerNormZero(nn.Module): def __init__(self, dim): super().__init__() self.silu = nn.SiLU() self.linear = nn.Linear(dim, dim * 6) self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) def forward(self, hidden_states, emb=None): emb = self.linear(self.silu(emb)) shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1) hidden_states = self.norm(hidden_states) * (1 + scale_msa[:, None]) + shift_msa[:, None] return hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp # AdaLayerNormZero for final layer # return only with modulated x for attn input, cuz no more mlp modulation class Qwen2_5_OmniAdaLayerNormZero_Final(nn.Module): def __init__(self, dim): super().__init__() self.silu = nn.SiLU() self.linear = nn.Linear(dim, dim * 2) self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) def forward(self, hidden_states, emb): emb = self.linear(self.silu(emb)) scale, shift = torch.chunk(emb, 2, dim=1) hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :] return hidden_states # FeedForward class DiTMLP(nn.Module): def __init__(self, dim, mult=4, dropout=0.0): super().__init__() inner_dim = int(dim * mult) self.ff = nn.ModuleList( [ nn.Linear(dim, inner_dim), nn.GELU(approximate="tanh"), nn.Dropout(dropout), nn.Linear(inner_dim, dim), ] ) def forward(self, hidden_states): for layer in self.ff: hidden_states = layer(hidden_states) return hidden_states # Modified from Llama with a different rotate function, will fixed in next release def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. 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. """ def rotate_half_codec(x): # x = rearrange(x, "... (d r) -> ... d r", r=2) x = x.reshape(*x.shape[:-1], -1, 2) x1, x2 = x.unbind(dim=-1) x = torch.stack((-x2, x1), dim=-1) return x.reshape(*x.shape[:-2], -1) cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half_codec(q) * sin) k_embed = (k * cos) + (rotate_half_codec(k) * sin) return q_embed, k_embed class DiTAttention(nn.Module): def __init__(self, config: Qwen2_5OmniDiTConfig): super().__init__() self.config = config self.dim = config.hidden_size self.heads = config.num_attention_heads self.inner_dim = config.head_dim * config.num_attention_heads self.dropout = config.dropout self._attn_implementation = config._attn_implementation self.is_causal = False self.to_q = nn.Linear(config.hidden_size, self.inner_dim) self.to_k = nn.Linear(config.hidden_size, self.inner_dim) self.to_v = nn.Linear(config.hidden_size, self.inner_dim) self.to_out = nn.ModuleList([nn.Linear(self.inner_dim, config.hidden_size), nn.Dropout(config.dropout)]) def forward( self, hidden_states, # noised input x position_embeddings=None, # rotary position embedding for x attention_mask=None, ) -> torch.Tensor: batch_size = hidden_states.shape[0] # `sample` projections. query = self.to_q(hidden_states) key = self.to_k(hidden_states) value = self.to_v(hidden_states) # attention inner_dim = key.shape[-1] head_dim = inner_dim // self.heads query = query.view(batch_size, -1, self.heads, head_dim).transpose(1, 2) key = key.view(batch_size, -1, self.heads, head_dim).transpose(1, 2) value = value.view(batch_size, -1, self.heads, head_dim).transpose(1, 2) # apply rotary position embedding # Due to training process, only first head is applied with RoPE, will be fixed at next release cos, sin = position_embeddings query[:, :1], key[:, :1] = apply_rotary_pos_emb(query[:, :1], key[:, :1], cos, sin) attention_interface = ALL_ATTENTION_FUNCTIONS[self._attn_implementation] attention_weights, _ = attention_interface( self, query, key, value, attention_mask=attention_mask, is_causal=False, ) # mask. e.g. inference got a batch with different target durations, mask out the padding attention_weights = attention_weights.reshape(batch_size, -1, self.heads * head_dim) attention_weights = attention_weights.to(query.dtype) # linear proj attention_output = self.to_out[0](attention_weights) attention_output = self.to_out[1](attention_output) return attention_output # time step conditioning embedding class SinusPositionEmbedding(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim def forward(self, hidden_states, scale=1000): device = hidden_states.device half_dim = self.dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) emb = scale * hidden_states.unsqueeze(1) * emb.unsqueeze(0) emb = torch.cat((emb.sin(), emb.cos()), dim=-1) return emb.type_as(hidden_states) class DiTTimestepEmbedding(nn.Module): def __init__(self, dim, freq_embed_dim=256): super().__init__() self.time_embed = SinusPositionEmbedding(freq_embed_dim) self.time_mlp = nn.ModuleList([nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)]) def forward(self, timestep): # noqa: F821 time_hidden = self.time_embed(timestep) time_hidden = time_hidden.to(timestep.dtype) for layer in self.time_mlp: time_hidden = layer(time_hidden) # b d return time_hidden class DiTDecoderLayer(nn.Module): def __init__(self, config: Qwen2_5OmniDiTConfig, look_ahead_block=0, look_backward_block=0): super().__init__() self.attn_norm = Qwen2_5_OmniAdaLayerNormZero(config.hidden_size) self.attn = DiTAttention(config) self.look_ahead_block = look_ahead_block self.look_backward_block = look_backward_block self.ff_norm = nn.LayerNorm(config.hidden_size, elementwise_affine=False, eps=1e-6) self.ff = DiTMLP(dim=config.hidden_size, mult=config.ff_mult, dropout=config.dropout) def forward( self, hidden_states, timestep, position_embeddings=None, block_diff=None ): # x: noised input, t: time embedding # pre-norm & modulation for attention input norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(hidden_states, emb=timestep) # attention attn_output = self.attn( hidden_states=norm, position_embeddings=position_embeddings, attention_mask=(block_diff >= -float(self.look_backward_block)) & (block_diff <= float(self.look_ahead_block)), ) # process attention output for input x hidden_states = hidden_states + gate_msa.unsqueeze(1) * attn_output norm = self.ff_norm(hidden_states) * (1 + scale_mlp[:, None]) + shift_mlp[:, None] ff_output = self.ff(norm) hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output return hidden_states class SnakeBeta(nn.Module): """ A modified Snake function which uses separate parameters for the magnitude of the periodic components Shape: - Input: (B, C, T) - Output: (B, C, T), same shape as the input Parameters: - alpha - trainable parameter that controls frequency - beta - trainable parameter that controls magnitude References: - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: https://arxiv.org/abs/2006.08195 """ def __init__(self, in_features, alpha=1.0): super().__init__() self.in_features = in_features # initialize alpha self.alpha = Parameter(torch.zeros(in_features) * alpha) self.beta = Parameter(torch.zeros(in_features) * alpha) self.no_div_by_zero = 0.000000001 def forward(self, hidden_states): """ Forward pass of the function. Applies the function to the input elementwise. SnakeBeta ∶= x + 1/b * sin^2 (xa) """ alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] beta = self.beta.unsqueeze(0).unsqueeze(-1) alpha = torch.exp(alpha) beta = torch.exp(beta) hidden_states = hidden_states + (1.0 / (beta + self.no_div_by_zero)) * torch.pow( torch.sin(hidden_states * alpha), 2 ) return hidden_states def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): """Generates a 1D Kaiser-windowed sinc filter. Args: cutoff (float): Normalized cutoff frequency (0 to 0.5). half_width (float): Transition bandwidth. kernel_size (int): Number of filter taps. Returns: torch.Tensor: A tensor of shape (1, 1, kernel_size) representing the filter. """ is_even = kernel_size % 2 == 0 half_size = kernel_size // 2 # Compute Kaiser window parameters delta_f = 4 * half_width attenuation = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 if attenuation > 50.0: beta = 0.1102 * (attenuation - 8.7) elif attenuation >= 21.0: beta = 0.5842 * (attenuation - 21) ** 0.4 + 0.07886 * (attenuation - 21.0) else: beta = 0.0 kaiser_window = torch.kaiser_window(kernel_size, beta=beta, periodic=False, dtype=torch.float32) # Compute time indices if is_even: time_indices = torch.arange(-half_size, half_size) + 0.5 else: time_indices = torch.arange(kernel_size) - half_size # Compute sinc filter if cutoff == 0: return torch.zeros((1, 1, kernel_size), dtype=torch.float32) # Ensures correct shape sinc_filter = torch.sinc(2 * cutoff * time_indices) normalized_filter = 2 * cutoff * kaiser_window * sinc_filter # Normalize to ensure sum = 1 (avoid leakage of constant component) normalized_filter /= normalized_filter.sum() return normalized_filter.view(1, 1, kernel_size) class UpSample1d(nn.Module): def __init__(self, ratio=2, kernel_size=None): super().__init__() self.ratio = ratio self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size self.stride = ratio self.pad = self.kernel_size // ratio - 1 self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size) self.register_buffer("filter", filter, persistent=False) def forward(self, hidden_states): channels = hidden_states.shape[1] hidden_states = F.pad(hidden_states, (self.pad, self.pad), mode="replicate") hidden_states = self.ratio * F.conv_transpose1d( hidden_states, self.filter.expand(channels, -1, -1), stride=self.stride, groups=channels ) hidden_states = hidden_states[..., self.pad_left : -self.pad_right] return hidden_states class DownSample1d(nn.Module): def __init__(self, ratio=2, kernel_size=None): super().__init__() cutoff = 0.5 / ratio half_width = 0.6 / ratio if cutoff < 0.0: raise ValueError("Minimum cutoff must be larger than zero.") if cutoff > 0.5: raise ValueError("A cutoff above 0.5 does not make sense.") self.even = kernel_size % 2 == 0 self.pad_left = kernel_size // 2 - int(self.even) self.pad_right = kernel_size // 2 self.stride = ratio filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) self.register_buffer("filter", filter, persistent=False) def forward(self, hidden_states): channels = hidden_states.shape[1] hidden_states = F.pad(hidden_states, (self.pad_left, self.pad_right), mode="replicate") out = F.conv1d(hidden_states, self.filter.expand(channels, -1, -1), stride=self.stride, groups=channels) return out class TorchActivation1d(nn.Module): def __init__( self, activation, up_ratio: int = 2, down_ratio: int = 2, up_kernel_size: int = 12, down_kernel_size: int = 12, ): super().__init__() if not callable(activation): raise ValueError("Activation function must be callable") self.act = activation self.upsample = UpSample1d(up_ratio, up_kernel_size) self.downsample = DownSample1d(down_ratio, down_kernel_size) def forward(self, hidden_states): hidden_states = self.upsample(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.downsample(hidden_states) return hidden_states class AMPBlock(torch.nn.Module): def __init__( self, channels, kernel_size=3, dilation=(1, 3, 5), ): super().__init__() self.convs1 = nn.ModuleList( [ nn.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[0], padding=self._get_padding(kernel_size, dilation[0]), ), nn.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[1], padding=self._get_padding(kernel_size, dilation[1]), ), nn.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[2], padding=self._get_padding(kernel_size, dilation[2]), ), ] ) self.convs2 = nn.ModuleList( [ nn.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=self._get_padding(kernel_size, 1), ), nn.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=self._get_padding(kernel_size, 1), ), nn.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=self._get_padding(kernel_size, 1), ), ] ) self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers self.activations = nn.ModuleList( [TorchActivation1d(activation=SnakeBeta(channels)) for _ in range(self.num_layers)] ) def _get_padding(self, kernel_size, dilation=1): return int((kernel_size * dilation - dilation) / 2) def forward(self, hidden_states): acts1, acts2 = self.activations[::2], self.activations[1::2] for conv1, conv2, act1, act2 in zip(self.convs1, self.convs2, acts1, acts2): residual = hidden_states hidden_states = act1(hidden_states) hidden_states = conv1(hidden_states) hidden_states = act2(hidden_states) hidden_states = conv2(hidden_states) hidden_states = residual + hidden_states return hidden_states @auto_docstring( custom_intro=""" The full Qwen2.5Omni Token2WavBigVGAN model. Which take mel spectrogram as input and predict waveform. """ ) class Qwen2_5OmniToken2WavBigVGANModel(Qwen2_5OmniPreTrainedModel): config_class = Qwen2_5OmniBigVGANConfig def __init__(self, config: Qwen2_5OmniBigVGANConfig): super().__init__(config) self.num_residual_blocks = len(config.resblock_kernel_sizes) self.num_upsample_layers = len(config.upsample_rates) self.conv_pre = nn.Conv1d(config.mel_dim, config.upsample_initial_channel, 7, 1, padding=3) # Removing extra ModuleList breaks official state dict ups = [ nn.ModuleList( [ nn.ConvTranspose1d( config.upsample_initial_channel // (2**layer_idx), config.upsample_initial_channel // (2 ** (layer_idx + 1)), kernel_size, stride, padding=(kernel_size - stride) // 2, ) ] ) for layer_idx, (stride, kernel_size) in enumerate(zip(config.upsample_rates, config.upsample_kernel_sizes)) ] self.ups = nn.ModuleList(ups) self.resblocks = nn.ModuleList( [ AMPBlock(config.upsample_initial_channel // (2 ** (layer_idx + 1)), kernel_size, dilation) for layer_idx in range(self.num_upsample_layers) for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes) ] ) self.activation_post = TorchActivation1d( activation=SnakeBeta(config.upsample_initial_channel // (2**self.num_upsample_layers)) ) self.conv_post = nn.Conv1d( config.upsample_initial_channel // (2**self.num_upsample_layers), 1, 7, 1, padding=3, bias=False ) def normalize_spectrogram(self, spectrogram, max_value, min_db): return torch.clamp((2 * max_value) * ((spectrogram - min_db) / (-min_db)) - max_value, -max_value, max_value) def amplitude_to_db(self, amplitude, min_db_level): min_level = torch.exp( torch.tensor(min_db_level / 20.0 * np.log(10), device=amplitude.device, dtype=amplitude.dtype) ) return 20 * torch.log10(torch.clamp(amplitude, min=min_level)) def process_mel_spectrogram(self, mel_spectrogram): amplitude_spectrum = torch.exp(mel_spectrogram) decibel_spectrum = self.amplitude_to_db(amplitude_spectrum, -115) - 20 return self.normalize_spectrogram(decibel_spectrum, 1, -115) def forward(self, mel_spectrogram): processed_spectrogram = self.process_mel_spectrogram(mel_spectrogram) hidden_representation = self.conv_pre(processed_spectrogram) for layer_index in range(self.num_upsample_layers): hidden_representation = self.ups[layer_index][0](hidden_representation) residual_output = sum( self.resblocks[layer_index * self.num_residual_blocks + block_index](hidden_representation) for block_index in range(self.num_residual_blocks) ) residual_output = residual_output / self.num_residual_blocks hidden_representation = residual_output hidden_representation = self.activation_post(hidden_representation) output_waveform = self.conv_post(hidden_representation) return torch.clamp(output_waveform, min=-1.0, max=1.0).squeeze().cpu() class RungeKutta4ODESolver: def __init__(self, function, initial_value): self.function = function self.initial_value = initial_value self._one_third = 1 / 3 self._two_thirds = 2 / 3 def _rk4_step(self, function, time_start, time_step, time_end, value_start, function_value_start=None): k1 = function_value_start if function_value_start is not None else function(time_start, value_start) k2 = function(time_start + time_step * self._one_third, value_start + time_step * k1 * self._one_third) k3 = function(time_start + time_step * self._two_thirds, value_start + time_step * (k2 - k1 * self._one_third)) k4 = function(time_end, value_start + time_step * (k1 - k2 + k3)) return (k1 + 3 * (k2 + k3) + k4) * time_step / 8 def _compute_step(self, function, time_start, time_step, time_end, value_start): function_value_start = function(time_start, value_start) return self._rk4_step( function, time_start, time_step, time_end, value_start, function_value_start=function_value_start ), function_value_start def _linear_interpolation(self, time_start, time_end, value_start, value_end, time_point): if time_point == time_start: return value_start if time_point == time_end: return value_end weight = (time_point - time_start) / (time_end - time_start) return value_start + weight * (value_end - value_start) def integrate(self, time_points): solution = torch.empty( len(time_points), *self.initial_value.shape, dtype=self.initial_value.dtype, device=self.initial_value.device, ) solution[0] = self.initial_value current_index = 1 current_value = self.initial_value for time_start, time_end in zip(time_points[:-1], time_points[1:]): time_step = time_end - time_start delta_value, _ = self._compute_step(self.function, time_start, time_step, time_end, current_value) next_value = current_value + delta_value while current_index < len(time_points) and time_end >= time_points[current_index]: solution[current_index] = self._linear_interpolation( time_start, time_end, current_value, next_value, time_points[current_index] ) current_index += 1 current_value = next_value return solution @auto_docstring( custom_intro=""" The full Qwen2.5Omni Token2WavDiT model. Which take speech tokens as input and predict mel spectrogram. """ ) class Qwen2_5OmniToken2WavDiTModel(Qwen2_5OmniPreTrainedModel): config_class = Qwen2_5OmniDiTConfig _no_split_modules = ["DiTDecoderLayer"] def __init__(self, config: Qwen2_5OmniDiTConfig): super().__init__(config) self.mel_dim = config.mel_dim self.repeats = config.repeats self.time_embed = DiTTimestepEmbedding(config.hidden_size) self.text_embed = DiTCodecEmbedding(config.num_embeds, config.emb_dim, config.repeats) self.input_embed = DiTInputEmbedding(config) self.rotary_embed = Qwen2_5OmniDiTRotaryEmbedding(config.head_dim) self.hidden_size = config.hidden_size self.layers = config.num_hidden_layers self.block_size = config.block_size self.num_attention_heads = config.num_attention_heads self.transformer_blocks = nn.ModuleList() for i in range(config.num_hidden_layers): self.transformer_blocks.append( DiTDecoderLayer( config, look_ahead_block=1 if i in config.look_ahead_layers else 0, look_backward_block=1 if i in config.look_backward_layers else 0, ) ) self.norm_out = Qwen2_5_OmniAdaLayerNormZero_Final(config.hidden_size) # final modulation self.proj_out = nn.Linear(config.hidden_size, config.mel_dim) def _create_block_diff(self, hidden_states): batch, seq_len = hidden_states.shape[0], hidden_states.shape[1] block_indices = torch.arange(seq_len, device=hidden_states.device) // self.block_size # [seq_length] block_i = block_indices.unsqueeze(1) # [seq_length, 1] block_j = block_indices.unsqueeze(0) # [1, seq_length] block_diff = block_j - block_i # (n, n) return block_diff.expand(batch, self.num_attention_heads, seq_len, seq_len) def forward( self, hidden_states, condition_vector, speaker_embedding, quantized_code, time_step, drop_audio_conditioning=False, drop_code=False, apply_cfg=True, ): batch_size = hidden_states.shape[0] if time_step.ndim == 0: time_step = time_step.repeat(batch_size) # Compute embeddings time_embedding = self.time_embed(time_step) text_embedding = self.text_embed(quantized_code, drop_code=False if apply_cfg else drop_code) text_embedding_unconditioned = self.text_embed(quantized_code, drop_code=True) if apply_cfg else None hidden_states = self.input_embed( hidden_states, speaker_embedding, condition_vector, text_embedding, drop_audio_cond=drop_audio_conditioning, code_embed_uncond=text_embedding_unconditioned, apply_cfg=apply_cfg, ) # Compute positional encodings position_embeddings = self.rotary_embed(hidden_states) blockwise_difference = self._create_block_diff(hidden_states) # Transformer blocks for transformer_block in self.transformer_blocks: hidden_states = transformer_block( hidden_states, time_embedding, position_embeddings=position_embeddings, block_diff=blockwise_difference, ) hidden_states = self.norm_out(hidden_states, time_embedding) output = self.proj_out(hidden_states) return output @torch.no_grad() def sample( self, conditioning_vector, reference_mel_spectrogram, quantized_code, num_steps=10, guidance_scale=0.5, sway_coefficient=-1.0, ): noise_initialization = torch.randn([1, 30000, self.mel_dim], dtype=reference_mel_spectrogram.dtype) maximum_duration = quantized_code.shape[1] * self.repeats initial_state = noise_initialization[:, :maximum_duration].to(quantized_code.device) batch_size = reference_mel_spectrogram.shape[0] conditioning_vector = conditioning_vector.unsqueeze(1).repeat(1, maximum_duration, 1) if batch_size != 1: raise ValueError("Only batch size = 1 is currently supported") def ode_function(time_step, hidden_states): if guidance_scale < 1e-5: prediction = self( hidden_states=hidden_states, speaker_embedding=conditioning_vector, condition_vector=reference_mel_spectrogram, quantized_code=quantized_code, time_step=time_step, drop_audio_conditioning=False, drop_code=False, ) return prediction model_output = self( hidden_states=hidden_states, quantized_code=quantized_code, speaker_embedding=conditioning_vector, condition_vector=reference_mel_spectrogram, time_step=time_step, apply_cfg=True, ) guided_prediction, null_prediction = torch.chunk(model_output, 2, dim=0) return guided_prediction + (guided_prediction - null_prediction) * guidance_scale initial_time = 0 time_embedding = torch.linspace( initial_time, 1, num_steps, device=quantized_code.device, dtype=conditioning_vector.dtype ) if sway_coefficient is not None: time_embedding += sway_coefficient * (torch.cos(torch.pi / 2 * time_embedding) - 1 + time_embedding) ode_solver = RungeKutta4ODESolver(function=ode_function, initial_value=initial_state) solution_trajectory = ode_solver.integrate(time_embedding) generated_waveform = solution_trajectory[-1] generated_mel_spectrogram = generated_waveform.permute(0, 2, 1) return generated_mel_spectrogram @auto_docstring( custom_intro=""" The full Qwen2.5Omni Token2Wav model. Consists a DiT model take speech tokens as input and predict mel spectrogram and a BigVGAN vocoder take mel spectrogram as input and predict waveform. """ ) class Qwen2_5OmniToken2WavModel(Qwen2_5OmniPreTrainedModel): config_class = Qwen2_5OmniToken2WavConfig base_model_prefix = "model" _no_split_modules = ["Qwen2_5OmniToken2WavDiTModel", "Qwen2_5OmniToken2WavBigVGANModel"] def __init__(self, config: Qwen2_5OmniToken2WavConfig): super().__init__(config) attn_impl = config._attn_implementation if config._attn_implementation == "flash_attention_2": logger.warning_once( "Qwen2_5OmniToken2WavModel must inference with fp32, but flash_attention_2 only supports fp16 and bf16, " "attention implementation of Qwen2_5OmniToken2WavModel will fallback to sdpa." ) attn_impl = "sdpa" elif config._attn_implementation == "eager": logger.warning_once( "Qwen2_5OmniToken2WavModel does not support eager attention implementation, fall back to sdpa" ) attn_impl = "sdpa" self.code2wav_dit_model = Qwen2_5OmniToken2WavDiTModel._from_config( config.dit_config, attn_implementation=attn_impl ) self.code2wav_bigvgan_model = Qwen2_5OmniToken2WavBigVGANModel._from_config( config.bigvgan_config, attn_implementation=attn_impl ) def forward( self, code, conditioning, reference_mel, num_steps=10, guidance_scale=0.5, sway_coefficient=-1.0, **kwargs, ): """Generates a waveform from input code and conditioning parameters.""" mel_spectrogram = self.code2wav_dit_model.sample( conditioning, reference_mel, code, num_steps=num_steps, guidance_scale=guidance_scale, sway_coefficient=sway_coefficient, ) waveform = self.code2wav_bigvgan_model(mel_spectrogram) return waveform ############################ # Start Qwen2.5Omni # ############################ @auto_docstring( custom_intro=""" The full Qwen2.5Omni model, a multimodal model composed of 3 sub-models: - [`Qwen2_5OmniThinkerForConditionalGeneration`]: a causal auto-regressive transformer takes text, audio, image, video as input and predict text tokens. - [`Qwen2_5OmniTalkerForConditionalGeneration`]: a causal auto-regressive transformer takes thinker hidden states and response as input and predict speech tokens. - [`Qwen2_5OmniToken2WavModel`]: a DiT model take speech tokens as input and predict mel spectrogram and a BigVGAN vocoder take mel spectrogram as input and predict waveform. """ ) class Qwen2_5OmniForConditionalGeneration(Qwen2_5OmniPreTrainedModel, GenerationMixin): config_class = Qwen2_5OmniConfig _no_split_modules = [ "Qwen2_5OmniTalkerForConditionalGeneration", "Qwen2_5OmniToken2WavModel", ] def __init__(self, config): super().__init__(config) self.thinker = Qwen2_5OmniThinkerForConditionalGeneration(config.thinker_config) self.has_talker = config.enable_audio_output self.speaker_map = {} if config.enable_audio_output: self.enable_talker() self.post_init() def enable_talker(self): self.talker = Qwen2_5OmniTalkerForConditionalGeneration(self.config.talker_config) self.token2wav = Qwen2_5OmniToken2WavModel(self.config.token2wav_config) self.token2wav.float() self.has_talker = True def load_speakers(self, path): check_torch_load_is_safe() for key, value in torch.load(path, weights_only=True).items(): self.speaker_map[key] = value logger.info("Speaker {} loaded".format(list(self.speaker_map.keys()))) def disable_talker(self): if hasattr(self, "talker"): del self.talker if hasattr(self, "token2wav"): del self.token2wav self.has_talker = False @classmethod def from_pretrained( cls, pretrained_model_name_or_path, *model_args, config=None, cache_dir=None, ignore_mismatched_sizes=False, force_download=False, local_files_only=False, token=None, revision="main", use_safetensors=None, weights_only=True, **kwargs, ): model = super().from_pretrained( pretrained_model_name_or_path, *model_args, config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes, force_download=force_download, local_files_only=local_files_only, token=token, revision=revision, use_safetensors=use_safetensors, weights_only=weights_only, **kwargs, ) spk_path = cached_file( pretrained_model_name_or_path, "spk_dict.pt", subfolder=kwargs.pop("subfolder", None), cache_dir=kwargs.pop("cache_dir", None), force_download=kwargs.pop("force_download", False), proxies=kwargs.pop("proxies", None), resume_download=kwargs.pop("resume_download", None), local_files_only=kwargs.pop("local_files_only", False), token=kwargs.pop("use_auth_token", None), revision=kwargs.pop("revision", None), ) if spk_path is None: raise ValueError(f"""{pretrained_model_name_or_path}/{spk_path} not exists""") model.load_speakers(spk_path) return model @torch.no_grad() # TODO: raushan, defaults should be saved in generation config def generate( self, input_ids: Optional[torch.Tensor] = None, speaker: str = "Chelsie", use_audio_in_video: bool = False, return_audio: Optional[bool] = None, thinker_max_new_tokens: int = 1024, talker_max_new_tokens: int = 4096, talker_do_sample: bool = True, talker_top_k: int = 40, talker_top_p: float = 0.8, talker_temperature: float = 0.9, talker_eos_token_id: list[int] = [8292, 8294], talker_repetition_penalty: float = 1.05, **kwargs, ): r""" Generate text response and audio from input. Args: input_ids (`Optional[torch.Tensor]`, *optional*): Input ids, should obtain from processor. speaker (`str` , defaults to "Chelsie"): Which speaker should be used in audio response. use_audio_in_video (`bool`, defaults to False): Whether or not use audio track in video, should same as the parameter in `process_audio_info`. return_audio (`Optional[bool]`, *optional*): Whether or not return response in audio format. When `return_audio=None`, this parameter is same as `config.enable_audio_output`. kwargs (*optional*): - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model. - With a *thinker_*, *talker_*, *token2wav_* prefix, they will be input for the `generate` method of the thinker, talker and token2wav respectively. It has the priority over the keywords without a prefix. Returns: When `return_audio=False`: - **Text** (`torch.Tensor`): Generated text token sequence. When `return_audio=True`: - **Text** (`torch.Tensor`): Generated text token sequence. - **Audio waveform** (`torch.Tensor`): Generated audio waveform. """ if speaker not in self.speaker_map: raise ValueError(f"{speaker} is not available, available speakers: {self.speaker_map.keys()}") if return_audio and not self.has_talker: raise ValueError( "Cannot use talker when talker module not initialized. Use `enable_talker` method or set enable_talker in config to enable talker." ) if return_audio is None: return_audio = self.has_talker if input_ids.shape[0] != 1 and return_audio: raise NotImplementedError("Qwen2.5-Omni currently does not support batched inference with audio output") shared_kwargs = {"use_audio_in_video": use_audio_in_video} thinker_kwargs = { "max_new_tokens": thinker_max_new_tokens, } talker_kwargs = { "max_new_tokens": talker_max_new_tokens, "do_sample": talker_do_sample, "top_k": talker_top_k, "top_p": talker_top_p, "temperature": talker_temperature, "eos_token_id": talker_eos_token_id, "repetition_penalty": talker_repetition_penalty, } token2wav_kwargs = {} for key, value in kwargs.items(): if key.startswith("thinker_"): thinker_kwargs[key[len("thinker_") :]] = value elif key.startswith("talker_"): talker_kwargs[key[len("talker_") :]] = value elif key.startswith("token2wav_"): token2wav_kwargs[key[len("token2wav_") :]] = value # Process special input values elif key == "feature_attention_mask": thinker_kwargs[key] = value talker_kwargs["audio_feature_lengths"] = torch.sum(value, dim=1) elif key == "input_features" or key == "attention_mask": thinker_kwargs[key] = value # Put other key to shared kwargs else: shared_kwargs[key] = value # Merge kwargs for key, value in shared_kwargs.items(): if key not in thinker_kwargs: thinker_kwargs[key] = value if key not in talker_kwargs: talker_kwargs[key] = value if key not in token2wav_kwargs: token2wav_kwargs[key] = value speaker_params = self.speaker_map[speaker] # 1. Generate from thinker module generate_audio = return_audio and self.has_talker if generate_audio: thinker_kwargs["output_hidden_states"] = True thinker_kwargs["return_dict_in_generate"] = True thinker_result = self.thinker.generate(input_ids=input_ids, **thinker_kwargs) if not generate_audio: return thinker_result # 2. Generate speech tokens from talker module embeds_to_talker = thinker_result.hidden_states[0][0].clone().to(self.talker.device) if thinker_kwargs.get("input_features", None) is not None: audio_ids_mask = input_ids == self.config.thinker_config.audio_token_index audio_mask = audio_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker).to(embeds_to_talker.device) audio_mask_tensor = torch.zeros( [audio_ids_mask.sum(), embeds_to_talker.shape[-1]], dtype=embeds_to_talker.dtype, device=self.talker.device, ) embeds_to_talker.masked_scatter_(audio_mask, audio_mask_tensor) if thinker_kwargs.get("pixel_values", None) is not None: image_ids_mask = input_ids == self.config.thinker_config.image_token_index image_mask = image_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker).to(embeds_to_talker.device) image_mask_tensor = torch.zeros( [image_ids_mask.sum(), embeds_to_talker.shape[-1]], dtype=embeds_to_talker.dtype, device=self.talker.device, ) embeds_to_talker.masked_scatter_(image_mask, image_mask_tensor) if thinker_kwargs.get("pixel_values_videos", None) is not None: video_ids_mask = input_ids == self.config.thinker_config.video_token_index video_mask = video_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker).to(embeds_to_talker.device) video_mask_tensor = torch.zeros( [video_ids_mask.sum(), embeds_to_talker.shape[-1]], dtype=embeds_to_talker.dtype, device=self.talker.device, ) embeds_to_talker.masked_scatter_(video_mask, video_mask_tensor) processed_thinker_hidden = ( (embeds_to_talker,) + thinker_result.hidden_states[0][1:], ) + thinker_result.hidden_states[1:] thinker_generate_ids = thinker_result.sequences[:, input_ids.size(1) :].to(self.talker.device) thinker_token_embeds = [ token_hidden_states[0].to(self.talker.device) for token_hidden_states in processed_thinker_hidden ] thinker_hidden_states = [ token_hidden_states[-1].to(self.talker.device) for token_hidden_states in processed_thinker_hidden ] talker_text_bos_token = speaker_params["bos_token"] talker_input_text_ids = torch.cat( [ input_ids.to(self.talker.device), torch.tensor([[talker_text_bos_token]], dtype=torch.long, device=self.talker.device), thinker_generate_ids[:, :1], ], dim=-1, ) talker_input_ids = torch.cat( [ torch.full_like(input_ids, fill_value=self.talker.codec_mask_token, device=self.talker.device), torch.tensor([[self.talker.codec_pad_token]], dtype=torch.long, device=self.talker.device), torch.tensor([[self.talker.codec_bos_token]], dtype=torch.long, device=self.talker.device), ], dim=1, ) thinker_embed_tokens = self.thinker.get_input_embeddings() thinker_reply_part = torch.cat(thinker_hidden_states[1:], dim=1) + torch.cat(thinker_token_embeds[1:], dim=1) talker_inputs_embeds = thinker_hidden_states[0] + thinker_token_embeds[0] talker_text_bos_token = torch.tensor([[talker_text_bos_token]], dtype=torch.long, device=self.thinker.device) talker_text_bos_embed = thinker_embed_tokens(talker_text_bos_token).to(self.talker.device) talker_inputs_embeds = torch.cat( [ talker_inputs_embeds, talker_text_bos_embed, thinker_reply_part[:, :1, :], ], dim=1, ) eos_embedding = thinker_embed_tokens( torch.tensor([[self.talker.text_eos_token]], dtype=torch.long, device=self.thinker.device) ).to(self.talker.device) pad_embedding = thinker_embed_tokens( torch.tensor([[self.talker.text_pad_token]], dtype=torch.long, device=self.thinker.device) ).to(self.talker.device) thinker_reply_part = torch.cat( [ thinker_reply_part[:, 1:, :], eos_embedding, pad_embedding, ], dim=1, ) talker_attention_mask = None if "attention_mask" in kwargs: talker_attention_mask = torch.cat( [kwargs["attention_mask"], kwargs["attention_mask"].new_ones((1, 2))], dim=1 ).to(self.talker.device) talker_result = self.talker.generate( input_ids=talker_input_ids, input_text_ids=talker_input_text_ids, thinker_reply_part=thinker_reply_part, inputs_embeds=talker_inputs_embeds, attention_mask=talker_attention_mask, suppress_tokens=[self.talker.codec_bos_token], **{k: (v.to(self.talker.device) if torch.is_tensor(v) else v) for k, v in talker_kwargs.items()}, ) talker_generate_codes = talker_result[:, talker_input_ids.shape[1] : -1] # 3. Generate wavs from code if self.token2wav.dtype != torch.float: self.token2wav.float() wav = self.token2wav( talker_generate_codes.to(self.token2wav.device), conditioning=speaker_params["cond"].to(self.token2wav.device).float(), reference_mel=speaker_params["ref_mel"].to(self.token2wav.device).float(), **token2wav_kwargs, ) return thinker_result.sequences, wav.float() __all__ = [ "Qwen2_5OmniForConditionalGeneration", "Qwen2_5OmniThinkerTextModel", "Qwen2_5OmniThinkerForConditionalGeneration", "Qwen2_5OmniTalkerModel", "Qwen2_5OmniTalkerForConditionalGeneration", "Qwen2_5OmniToken2WavDiTModel", "Qwen2_5OmniToken2WavBigVGANModel", "Qwen2_5OmniToken2WavModel", "Qwen2_5OmniPreTrainedModel", "Qwen2_5OmniPreTrainedModelForConditionalGeneration", ]