# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from .multimodal_encoder.builder import build_image_tower, build_video_tower
from .multimodal_projector.builder import build_vision_projector
from llava.constants import IGNORE_INDEX, X_TOKEN_INDEX, DEFAULT_X_PATCH_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
class LlavaMetaModel:
def __init__(self, config):
super(LlavaMetaModel, self).__init__(config)
if hasattr(config, "mm_image_tower"):
self.image_tower = build_image_tower(config, delay_load=True)
self.mm_projector = build_vision_projector(config)
if hasattr(config, "mm_video_tower"):
self.video_tower = build_video_tower(config, delay_load=True)
self.mm_projector = build_vision_projector(config)
def get_image_tower(self):
image_tower = getattr(self, 'image_tower', None)
if type(image_tower) is list:
image_tower = image_tower[0]
return image_tower
def get_video_tower(self):
video_tower = getattr(self, 'video_tower', None)
if type(video_tower) is list:
video_tower = video_tower[0]
return video_tower
def initialize_image_modules(self, model_args, fsdp=None):
image_tower = model_args.image_tower
mm_vision_select_layer = model_args.mm_vision_select_layer
mm_vision_select_feature = model_args.mm_vision_select_feature
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
self.config.mm_image_tower = image_tower
image_tower = build_image_tower(model_args)
if fsdp is not None and len(fsdp) > 0:
self.image_tower = [image_tower]
else:
self.image_tower = image_tower
self.config.use_mm_proj = True
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
self.config.mm_hidden_size = image_tower.hidden_size
self.config.mm_vision_select_layer = mm_vision_select_layer
self.config.mm_vision_select_feature = mm_vision_select_feature
self.mm_projector = build_vision_projector(self.config)
if pretrain_mm_mlp_adapter is not None:
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
def get_w(weights, keyword):
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
def initialize_video_modules(self, model_args, fsdp=None):
video_tower = model_args.video_tower
mm_vision_select_layer = model_args.mm_vision_select_layer
mm_vision_select_feature = model_args.mm_vision_select_feature
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
self.config.mm_video_tower = video_tower
video_tower = build_video_tower(model_args)
if fsdp is not None and len(fsdp) > 0:
self.video_tower = [video_tower]
else:
self.video_tower = video_tower
self.config.use_mm_proj = True
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
self.config.mm_hidden_size = video_tower.hidden_size
self.config.mm_vision_select_layer = mm_vision_select_layer
self.config.mm_vision_select_feature = mm_vision_select_feature
self.mm_projector = build_vision_projector(self.config)
if pretrain_mm_mlp_adapter is not None:
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
def get_w(weights, keyword):
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
class LlavaMetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def get_image_tower(self):
return self.get_model().get_image_tower()
def get_video_tower(self):
return self.get_model().get_video_tower()
def get_all_tower(self, keys):
tower = {key: getattr(self, f'get_{key}_tower') for key in keys}
return tower
def encode_images(self, images):
image_features = self.get_model().get_image_tower()(images)
image_features = self.get_model().mm_projector(image_features)
return image_features
def encode_videos(self, videos):
video_features = self.get_model().get_video_tower()(videos)
video_features = self.get_model().mm_projector(video_features)
return video_features
#
# def prepare_inputs_labels_for_multimodal(
# self, input_ids, attention_mask, past_key_values, labels, images
# ):
# vision_tower = self.get_vision_tower()
# if vision_tower is None or images is None or input_ids.shape[1] == 1:
# if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
# attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device)
# return input_ids, attention_mask, past_key_values, None, labels
#
# if type(images) is list or images.ndim == 5:
# concat_images = torch.cat([image for image in images], dim=0)
# image_features = self.encode_images(concat_images)
# split_sizes = [image.shape[0] for image in images]
# image_features = torch.split(image_features, split_sizes, dim=0)
# image_features = [x.flatten(0, 1) for x in image_features]
# else:
# image_features = self.encode_images(images)
#
# new_input_embeds = []
# new_labels = [] if labels is not None else None
# cur_image_idx = 0
# for batch_idx, cur_input_ids in enumerate(input_ids):
# if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:
# # multimodal LLM, but the current sample is not multimodal
# # FIXME: this is a hacky fix, for deepspeed zero3 to work
# half_len = cur_input_ids.shape[0] // 2
# cur_image_features = image_features[cur_image_idx]
# cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len])
# cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:])
# cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0)
# new_input_embeds.append(cur_input_embeds)
# if labels is not None:
# new_labels.append(labels[batch_idx])
# cur_image_idx += 1
# continue
# image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] # 把中间的imgtoken的位置找到
# cur_new_input_embeds = []
# if labels is not None:
# cur_labels = labels[batch_idx]
# cur_new_labels = []
# assert cur_labels.shape == cur_input_ids.shape
# while image_token_indices.numel() > 0:
# cur_image_features = image_features[cur_image_idx]
# image_token_start = image_token_indices[0]
# if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
# cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start-1]).detach())
# cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start-1:image_token_start]))
# cur_new_input_embeds.append(cur_image_features)
# cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start+1:image_token_start+2]))
# if labels is not None:
# cur_new_labels.append(cur_labels[:image_token_start])
# cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
# cur_new_labels.append(cur_labels[image_token_start:image_token_start+1])
# cur_labels = cur_labels[image_token_start+2:]
# else:
# cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start])) # imgtoken之前的text拿出来，好像都是模板套话
# cur_new_input_embeds.append(cur_image_features)
# if labels is not None:
# cur_new_labels.append(cur_labels[:image_token_start])
# cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
# cur_labels = cur_labels[image_token_start+1:]
# cur_image_idx += 1
# if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
# cur_input_ids = cur_input_ids[image_token_start+2:]
# else:
# cur_input_ids = cur_input_ids[image_token_start+1:] # imgtoken之后的text拿出来，是真的question
# image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
# if cur_input_ids.numel() > 0:
# if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
# cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids).detach())
# else:
# cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids))
# if labels is not None:
# cur_new_labels.append(cur_labels)
# cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] # 前面text+图片+后面question
# cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
# new_input_embeds.append(cur_new_input_embeds)
# if labels is not None:
# cur_new_labels = torch.cat(cur_new_labels, dim=0)
# new_labels.append(cur_new_labels)
#
# if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
# max_len = max(x.shape[0] for x in new_input_embeds)
#
# new_input_embeds_align = []
# for cur_new_embed in new_input_embeds:
# cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
# new_input_embeds_align.append(cur_new_embed)
# new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
#
# if labels is not None:
# new_labels_align = []
# _new_labels = new_labels
# for cur_new_label in new_labels:
# cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0)
# new_labels_align.append(cur_new_label)
# new_labels = torch.stack(new_labels_align, dim=0)
#
# if attention_mask is not None:
# new_attention_mask = []
# for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels):
# new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device)
# new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device)
# cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
# new_attention_mask.append(cur_new_attention_mask)
# attention_mask = torch.stack(new_attention_mask, dim=0)
# assert attention_mask.shape == new_labels.shape
# else:
# new_input_embeds = torch.stack(new_input_embeds, dim=0)
# if labels is not None:
# new_labels = torch.stack(new_labels, dim=0)
#
# if attention_mask is not None:
# new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device)
# attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
# assert attention_mask.shape == new_input_embeds.shape[:2]
#
# return None, attention_mask, past_key_values, new_input_embeds, new_labels
#
# def initialize_vision_tokenizer(self, model_args, tokenizer):
# if model_args.mm_use_im_patch_token:
# tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
# self.resize_token_embeddings(len(tokenizer))
#
# if model_args.mm_use_im_start_end:
# num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
# self.resize_token_embeddings(len(tokenizer))
#
# if num_new_tokens > 0:
# input_embeddings = self.get_input_embeddings().weight.data
# output_embeddings = self.get_output_embeddings().weight.data
#
# input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
# dim=0, keepdim=True)
# output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
# dim=0, keepdim=True)
#
# input_embeddings[-num_new_tokens:] = input_embeddings_avg
# output_embeddings[-num_new_tokens:] = output_embeddings_avg
#
# if model_args.tune_mm_mlp_adapter:
# for p in self.get_input_embeddings().parameters():
# p.requires_grad = True
# for p in self.get_output_embeddings().parameters():
# p.requires_grad = False
#
# if model_args.pretrain_mm_mlp_adapter:
# mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
# embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
# assert num_new_tokens == 2
# if input_embeddings.shape == embed_tokens_weight.shape:
# 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
def prepare_inputs_labels_for_multimodal(
self, input_ids, attention_mask, past_key_values, labels, X_modalities
):
'''
X_modalities [
[img_feature, img_feature, video_feature, audio_feature],
['image', 'image', 'video', 'audio']
]
'''
Xs, keys = X_modalities
all_tower = self.get_all_tower(set(keys)) if len(keys) > 0 else None
# print(2.5)
if all_tower is None or X_modalities[0][0] is None or input_ids.shape[1] == 1:
if past_key_values is not None and all_tower is not None and Xs is not None and input_ids.shape[1] == 1:
attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device)
return input_ids, attention_mask, past_key_values, None, labels
# if type(images) is list or images.ndim == 5:
# concat_images = torch.cat([image for image in images], dim=0)
# image_features = self.encode_images(concat_images)
# split_sizes = [image.shape[0] for image in images]
# image_features = torch.split(image_features, split_sizes, dim=0)
# image_features = [x.flatten(0, 1) for x in image_features]
# else:
print(keys)
X_features = [getattr(self, f'encode_{key}s')(X.unsqueeze(0)) for X, key in zip(Xs, keys)] # expand to get batchsize
# X_features = []
# # print(2.5, *[i.shape for i in Xs], keys)
# for X, key in zip(Xs, keys):
# temp_X = X.unsqueeze(0)
# # print(2.6)
# # fn = getattr(self, f'encode_{key}s')
# if key == 'image':
# out = self.encode_images(temp_X)
# # print(2.65, 'image', out.shape)
# elif key == 'video':
# out = self.encode_videos(temp_X)
# # print(2.65, 'video', out.shape)
# else:
# raise NameError(f'{key}')
# # print(2.8, out.shape)
# X_features.append(out)
X_features = [x.flatten(0, 1) for x in X_features]
# print([[j, i.shape] for i, j in zip(X_features, keys)])
new_input_embeds = []
new_labels = [] if labels is not None else None
cur_X_idx = 0
# print(2.9, input_ids.shape)
for batch_idx, cur_input_ids in enumerate(input_ids):
# print(333333)
if (torch.any(torch.stack([cur_input_ids == X_TOKEN_INDEX[key.upper()] for key in keys]), dim=0)).sum() == 0:
# multimodal LLM, but the current sample is not multimodal
# FIXME: this is a hacky fix, for deepspeed zero3 to work
half_len = cur_input_ids.shape[0] // 2
cur_X_features = X_features[cur_X_idx]
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len])
cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:])
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_X_features[0:0], cur_input_embeds_2], dim=0)
new_input_embeds.append(cur_input_embeds)
if labels is not None:
new_labels.append(labels[batch_idx])
cur_X_idx += 1 ############## 注意这里跳过了，如果一个sample是一个modal，那么就跳过1个全zero的modal，如果一个sample对应多个modal，这里的训练逻辑不对！！！
###### 但似乎不影响1个sample的inference
###### 一个text对应视频和图片，直接走下边了。只有1个text，传入none或者1个/2个全zero都无所谓，反正没有下一个数据了。
continue
X_token_indices = torch.where(torch.any(torch.stack([cur_input_ids == X_TOKEN_INDEX[key.upper()] for key in keys]), dim=0))[0] # 把中间的imgtoken的位置找到
cur_new_input_embeds = []
if labels is not None:
cur_labels = labels[batch_idx]
cur_new_labels = []
assert cur_labels.shape == cur_input_ids.shape
# print(4444444444)
while X_token_indices.numel() > 0:
cur_X_features = X_features[cur_X_idx]
X_token_start = X_token_indices[0]
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_x_start_end', False):
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:X_token_start-1]).detach())
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[X_token_start-1:X_token_start]))
cur_new_input_embeds.append(cur_X_features)
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[X_token_start+1:X_token_start+2]))
if labels is not None:
cur_new_labels.append(cur_labels[:X_token_start])
cur_new_labels.append(torch.full((cur_X_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
cur_new_labels.append(cur_labels[X_token_start:X_token_start+1])
cur_labels = cur_labels[X_token_start+2:]
else:
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:X_token_start])) # imgtoken之前的text拿出来，好像都是模板套话
cur_new_input_embeds.append(cur_X_features)
if labels is not None:
cur_new_labels.append(cur_labels[:X_token_start])
cur_new_labels.append(torch.full((cur_X_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
cur_labels = cur_labels[X_token_start+1:]
cur_X_idx += 1
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_x_start_end', False):
cur_input_ids = cur_input_ids[X_token_start+2:]
else:
cur_input_ids = cur_input_ids[X_token_start+1:] # imgtoken之后的text拿出来，是真的question
X_token_indices = torch.where(torch.any(torch.stack([cur_input_ids == X_TOKEN_INDEX[key.upper()] for key in keys]), dim=0))[0]
# print(55555555555555555)
if cur_input_ids.numel() > 0:
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_x_start_end', False):
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids).detach())
else:
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids))
if labels is not None:
cur_new_labels.append(cur_labels)
cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] # 前面text+图片+后面question
cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
new_input_embeds.append(cur_new_input_embeds)
if labels is not None:
cur_new_labels = torch.cat(cur_new_labels, dim=0)
new_labels.append(cur_new_labels)
if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
max_len = max(x.shape[0] for x in new_input_embeds)
new_input_embeds_align = []
for cur_new_embed in new_input_embeds:
cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
new_input_embeds_align.append(cur_new_embed)
new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
if labels is not None:
new_labels_align = []
_new_labels = new_labels
for cur_new_label in new_labels:
cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0)
new_labels_align.append(cur_new_label)
new_labels = torch.stack(new_labels_align, dim=0)
if attention_mask is not None:
new_attention_mask = []
for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels):
new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device)
new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device)
cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
new_attention_mask.append(cur_new_attention_mask)
attention_mask = torch.stack(new_attention_mask, dim=0)
assert attention_mask.shape == new_labels.shape
else:
new_input_embeds = torch.stack(new_input_embeds, dim=0)
if labels is not None:
new_labels = torch.stack(new_labels, dim=0)
if attention_mask is not None:
new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device)
attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
assert attention_mask.shape == new_input_embeds.shape[:2]
return None, attention_mask, past_key_values, new_input_embeds, new_labels
def initialize_X_tokenizer(self, model_args, tokenizer):
if model_args.mm_use_x_patch_token:
for x in model_args.X:
tokenizer.add_tokens([DEFAULT_X_PATCH_TOKEN[x.upper()]], special_tokens=True)
# tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if model_args.mm_use_x_start_end:
num_new_tokens = 0
for x in model_args.X:
num_new_tokens += tokenizer.add_tokens([DEFAULT_X_START_TOKEN[x.upper()], DEFAULT_X_END_TOKEN[x.upper()]], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = self.get_input_embeddings().weight.data
output_embeddings = self.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = True
for p in self.get_output_embeddings().parameters():
p.requires_grad = False
if model_args.pretrain_mm_mlp_adapter:
mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
assert num_new_tokens == 2
if input_embeddings.shape == embed_tokens_weight.shape:
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_x_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