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397 lines
20 KiB
Python
397 lines
20 KiB
Python
from abc import ABC, abstractmethod
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import torch
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from .speech_encoder.builder import build_speech_encoder
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from .speech_projector.builder import build_speech_projector
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from opencompass.models.ola.constants import IGNORE_INDEX, SPEECH_TOKEN_INDEX
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from opencompass.models.ola.utils import lengths_to_padding_mask
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from .multimodal_encoder.builder import build_vision_tower
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from .multimodal_resampler.builder import build_vision_resampler
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from .multimodal_projector.builder import build_vision_projector
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from opencompass.models.ola.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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class OlaMetaModel:
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def __init__(self, config):
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super(OlaMetaModel, self).__init__(config)
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if hasattr(config, "speech_encoder"):
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self.speech_encoder = build_speech_encoder(config)
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self.speech_projector = build_speech_projector(config)
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if hasattr(config, "mm_vision_tower"):
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self.vision_tower = build_vision_tower(config, delay_load=True)
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self.vision_resampler = build_vision_resampler(config, vision_tower=self.vision_tower)
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self.mm_projector = build_vision_projector(config, vision_cfg=self.vision_tower.config)
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def get_speech_encoder(self):
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speech_encoder = getattr(self, 'speech_encoder', None)
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if type(speech_encoder) is list:
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speech_encoder = speech_encoder[0]
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return speech_encoder
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def get_vision_tower(self):
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vision_tower = getattr(self, 'vision_tower', None)
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if type(vision_tower) is list:
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vision_tower = vision_tower[0]
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return vision_tower
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def initialize_speech_modules(self, model_args, fsdp=None):
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self.config.speech_encoder = getattr(model_args, "speech_encoder", None)
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self.config.speech_encoder_type = getattr(model_args, "speech_encoder_type", None)
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self.config.speech_projector_type = getattr(model_args, 'speech_projector_type', 'linear')
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self.config.speech_encoder_ds_rate = getattr(model_args, 'speech_encoder_ds_rate', 5)
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self.config.speech_encoder_hidden_size = getattr(model_args, 'speech_encoder_hidden_size', 1280)
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self.config.music_encoder = getattr(model_args, 'music_encoder', None)
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if self.get_speech_encoder() is None:
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speech_encoder = build_speech_encoder(self.config)
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if fsdp is not None and len(fsdp) > 0:
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self.speech_encoder = [speech_encoder]
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else:
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self.speech_encoder = speech_encoder
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if getattr(self, 'speech_projector', None) is None:
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self.speech_projector = build_speech_projector(self.config)
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else:
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# In case it is frozen by LoRA
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for p in self.speech_projector.parameters():
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p.requires_grad = True
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if model_args.pretrain_speech_projector is not None:
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pretrain_speech_projector_weights = torch.load(model_args.pretrain_speech_projector, map_location='cpu')
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def get_w(weights, keyword):
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return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
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print('Loading pretrain speech projector weights')
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msg = self.speech_projector.load_state_dict(get_w(pretrain_speech_projector_weights, 'speech_projector'), strict=False)
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print(msg)
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def initialize_vision_modules(self, model_args, fsdp=None):
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vision_tower = model_args.vision_tower
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mm_vision_select_layer = model_args.mm_vision_select_layer
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mm_vision_select_feature = model_args.mm_vision_select_feature
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pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
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self.config.mm_vision_tower = vision_tower
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if self.get_vision_tower() is None:
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vision_tower = build_vision_tower(model_args)
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vision_resampler = build_vision_resampler(model_args, vision_tower=vision_tower)
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## Get the mm_spatial_pool_mode and mm_spatial_pool_stride
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for k, v in vision_resampler.config.items():
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setattr(self.config, k, v)
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if fsdp is not None and len(fsdp) > 0:
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self.vision_tower = [vision_tower]
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self.vision_resampler = [vision_resampler]
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else:
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self.vision_tower = vision_tower
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self.vision_resampler = vision_resampler
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else:
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if fsdp is not None and len(fsdp) > 0:
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vision_resampler = self.vision_resampler[0]
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vision_tower = self.vision_tower[0]
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else:
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vision_resampler = self.vision_resampler
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vision_tower = self.vision_tower
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vision_tower.load_model() # 已加载权重，故跳过
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# In case it is frozen by LoRA
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for p in self.vision_resampler.parameters():
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p.requires_grad = True
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self.config.use_mm_proj = True
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self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
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self.config.mm_hidden_size = getattr(vision_resampler, 'hidden_size', vision_tower.hidden_size)
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self.config.mm_vision_select_layer = mm_vision_select_layer
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self.config.mm_vision_select_feature = mm_vision_select_feature
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if getattr(self, 'mm_projector', None) is None:
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self.mm_projector = build_vision_projector(self.config, vision_cfg=vision_tower.config)
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else:
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for p in self.mm_projector.parameters():
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p.requires_grad = True
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if pretrain_mm_mlp_adapter is not None:
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mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
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def get_w(weights, keyword):
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return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
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self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
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print('Loading pretrain mm projector weights')
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incompatible_keys = self.vision_resampler.load_state_dict(get_w(mm_projector_weights, 'vision_resampler'), strict=False)
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print(incompatible_keys)
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class OlaMetaForCausalLM(ABC):
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@abstractmethod
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def get_model(self):
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pass
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def get_speech_encoder(self):
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return self.get_model().get_speech_encoder()
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def get_vision_tower(self):
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return self.get_model().get_vision_tower()
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def get_speech_projector(self):
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return self.get_model().speech_projector
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def encode_speech(self, speech, speech_lengths, speech_wav):
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# import pdb; pdb.set_trace()
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speech_encoder_type = self.config.speech_encoder_type
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speech_encoder = self.get_speech_encoder()
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if "whisper" in speech_encoder_type.lower():
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encoder_outs = speech_encoder(speech.permute(0, 2, 1))
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speech_lengths = (speech_lengths + 1) // 2
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else:
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encoder_outs = speech_encoder(speech.permute(0, 2, 1), raw_wav=speech_wav)
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speech_lengths = (speech_lengths + 1) // 2
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speech_projector_type = self.config.speech_projector_type
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speech_projector = self.get_speech_projector()
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if speech_projector_type == "linear":
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encoder_outs = speech_projector(encoder_outs)
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speech_lengths = speech_lengths // speech_projector.k
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else:
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raise ValueError(f'Unknown speech projector: {speech_projector_type}')
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# speech_features = [encoder_outs[i, :speech_lengths[i]] for i in range(len(encoder_outs))]
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return encoder_outs
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def prepare_inputs_labels_for_speech_vision_text(
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self, input_ids, position_ids, attention_mask, past_key_values, labels,
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speech, speech_lengths, speech_chunks, speech_wav, images, modalities, image_sizes=None, images_highres=None
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):
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speech_encoder = self.get_speech_encoder()
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vision_tower = self.get_vision_tower()
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if speech_encoder is None or input_ids.shape[1] == 1:
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return input_ids, position_ids, attention_mask, past_key_values, None, labels
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if vision_tower is None or input_ids.shape[1] == 1:
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return input_ids, position_ids, attention_mask, past_key_values, None, labels
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# encode speech
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if not isinstance(speech, list):
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speech = torch.split(speech, speech_chunks.tolist(), dim=0)
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speech_lengths = torch.split(speech_lengths, speech_chunks.tolist(), dim=0)
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speech_wav = torch.split(speech_wav, speech_chunks.tolist(), dim=0)
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speech_features = []
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for idx in range(len(speech)):
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speech_features.append(self.encode_speech(speech[idx], speech_lengths[idx], speech_wav[idx]))
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# encode vision
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if isinstance(modalities, str):
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modalities = [modalities]
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video_idx_in_batch = []
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for modal in range(len(modalities)):
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if 'video' in modalities[modal]:
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video_idx_in_batch.append(modal)
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aimg = images[-1]
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lowres_img = []
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for idx, img_feat in enumerate(images):
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if idx in video_idx_in_batch:
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img_feat = aimg.new(1, 3, 128, 128).fill_(0)
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lowres_img.append(img_feat)
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lowres_img_features, lowres_img_sizes = self.get_model().get_vision_tower()(lowres_img)
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highres_img_features = []
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highres_img_sizes = []
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for idx, img_feat in enumerate(images_highres):
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if img_feat.ndim == 5:
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img_feat = img_feat.squeeze(1)
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highres_img_feature, highres_img_size = self.get_model().get_vision_tower()(img_feat)
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highres_img_features.append(highres_img_feature)
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highres_img_sizes.append(highres_img_size)
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image_features = []
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for idx in range(len(modalities)):
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img_feat = self.get_model().mm_projector(lowres_img_features[idx],
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lowres_img_sizes[idx],
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highres_img_features[idx],
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highres_img_sizes[idx],
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modalities[idx])
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image_features.append(img_feat.flatten(0, 1))
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_labels = labels
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_position_ids = position_ids
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_attention_mask = attention_mask
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if attention_mask is None:
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attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
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else:
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attention_mask = attention_mask.bool()
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if position_ids is None:
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position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
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if labels is None:
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labels = torch.full_like(input_ids, IGNORE_INDEX)
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# remove the padding using attention_mask -- FIXME
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_input_ids = input_ids
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input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
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labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
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new_input_embeds = []
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new_labels = []
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cur_speech_idx = 0
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cur_image_idx = 0
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for batch_idx, cur_input_ids in enumerate(input_ids):
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num_speech = (cur_input_ids == SPEECH_TOKEN_INDEX).sum()
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num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
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num_speech_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum() + (cur_input_ids == SPEECH_TOKEN_INDEX).sum()
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if num_speech_images == 0:
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cur_speech_features = speech_features[cur_speech_idx]
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cur_images_features = image_features[cur_image_idx]
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cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
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cur_input_embeds = torch.cat([cur_input_embeds_1, cur_speech_features[0:0], cur_images_features[0:0]], dim=0)
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new_input_embeds.append(cur_input_embeds)
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new_labels.append(labels[batch_idx])
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cur_speech_idx += 1
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cur_image_idx += 1
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continue
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speech_image_token_indices = [-1] + torch.where((cur_input_ids == SPEECH_TOKEN_INDEX) | (cur_input_ids == IMAGE_TOKEN_INDEX))[0].tolist() + [cur_input_ids.shape[0]]
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cur_input_ids_nospeech_image = []
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cur_labels = labels[batch_idx]
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cur_labels_nospeech_image = []
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for i in range(len(speech_image_token_indices) - 1):
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cur_input_ids_nospeech_image.append(cur_input_ids[speech_image_token_indices[i]+1:speech_image_token_indices[i+1]])
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cur_labels_nospeech_image.append(cur_labels[speech_image_token_indices[i]+1:speech_image_token_indices[i+1]])
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split_sizes = [x.shape[0] for x in cur_labels_nospeech_image]
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cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_nospeech_image))
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cur_input_embeds_no_speech_image = torch.split(cur_input_embeds, split_sizes, dim=0)
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cur_new_input_embeds = []
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cur_new_labels = []
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for i in range(num_speech_images + 1):
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cur_new_input_embeds.append(cur_input_embeds_no_speech_image[i])
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cur_new_labels.append(cur_labels_nospeech_image[i])
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if i < num_speech_images:
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if i < num_images:
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cur_images_features = image_features[cur_image_idx]
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cur_image_idx += 1
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cur_new_input_embeds.append(cur_images_features)
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cur_new_labels.append(torch.full((cur_images_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
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else:
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cur_speech_features = speech_features[cur_speech_idx]
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cur_speech_idx += 1
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cur_new_input_embeds.append(cur_speech_features)
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cur_new_labels.append(torch.full((cur_speech_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
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cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]
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cur_new_input_embeds = torch.cat(cur_new_input_embeds)
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cur_new_labels = torch.cat(cur_new_labels)
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if num_images == 0:
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cur_new_input_embeds = torch.cat([cur_new_input_embeds, image_features[cur_image_idx][0:0]], dim=0)
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cur_image_idx += 1
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if num_speech == 0:
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cur_new_input_embeds = torch.cat([cur_new_input_embeds, speech_features[cur_speech_idx][0:0]], dim=0)
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cur_speech_idx += 1
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new_input_embeds.append(cur_new_input_embeds)
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new_labels.append(cur_new_labels)
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# Truncate sequences to max length as speech features can make the sequence longer
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tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
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if tokenizer_model_max_length is not None:
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new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
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new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
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# Combine them
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max_len = max(x.shape[0] for x in new_input_embeds)
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batch_size = len(new_input_embeds)
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new_input_embeds_padded = []
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new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
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attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
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position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
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for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
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cur_len = cur_new_embed.shape[0]
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if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
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new_input_embeds_padded.append(torch.cat((
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torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
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cur_new_embed
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), dim=0))
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if cur_len > 0:
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new_labels_padded[i, -cur_len:] = cur_new_labels
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attention_mask[i, -cur_len:] = True
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position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
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else:
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new_input_embeds_padded.append(torch.cat((
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cur_new_embed,
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torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
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), dim=0))
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if cur_len > 0:
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new_labels_padded[i, :cur_len] = cur_new_labels
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attention_mask[i, :cur_len] = True
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position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
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new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
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if _labels is None:
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new_labels = None
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else:
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new_labels = new_labels_padded
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if _attention_mask is None:
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attention_mask = None
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else:
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attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
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if _position_ids is None:
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position_ids = None
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return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
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def initialize_vision_tokenizer(self, model_args, tokenizer):
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if model_args.mm_use_im_patch_token:
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tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
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self.resize_token_embeddings(len(tokenizer))
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if model_args.mm_use_im_start_end:
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num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
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self.resize_token_embeddings(len(tokenizer))
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if num_new_tokens > 0:
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input_embeddings = self.get_input_embeddings().weight.data
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output_embeddings = self.get_output_embeddings().weight.data
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input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
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dim=0, keepdim=True)
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output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
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dim=0, keepdim=True)
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input_embeddings[-num_new_tokens:] = input_embeddings_avg
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output_embeddings[-num_new_tokens:] = output_embeddings_avg
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if model_args.tune_mm_mlp_adapter:
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for p in self.get_input_embeddings().parameters():
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p.requires_grad = True
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for p in self.get_output_embeddings().parameters():
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p.requires_grad = False
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if model_args.pretrain_mm_mlp_adapter:
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mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
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embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
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assert num_new_tokens == 2
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if input_embeddings.shape == embed_tokens_weight.shape:
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input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
|
|
elif embed_tokens_weight.shape[0] == num_new_tokens:
|
|
input_embeddings[-num_new_tokens:] = embed_tokens_weight
|
|
else:
|
|
raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
|
|
elif model_args.mm_use_im_patch_token:
|
|
if model_args.tune_mm_mlp_adapter:
|
|
for p in self.get_input_embeddings().parameters():
|
|
p.requires_grad = False
|
|
for p in self.get_output_embeddings().parameters():
|
|
p.requires_grad = False |