VideoLLaMA2 / videollama2 /model /videollama2_arch.py
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# Adopted from https://github.com/haotian-liu/LLaVA. Below is the original copyright:
# 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.
import os
from abc import ABC, abstractmethod
import einops
import torch
import torch.nn as nn
from .multimodal_encoder.builder import build_vision_tower
from .multimodal_projector.builder import build_vision_projector
from ..mm_utils import get_anyres_image_grid_shape
from ..constants import NUM_FRAMES, IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN,DEFAULT_MMODAL_PATCH_TOKEN, DEFAULT_MMODAL_START_TOKEN, DEFAULT_MMODAL_END_TOKEN, MMODAL_TOKEN_INDEX
class Videollama2MetaModel:
def __init__(self, config):
super(Videollama2MetaModel, self).__init__(config)
if hasattr(config, "mm_vision_tower"):
self.vision_tower = build_vision_tower(config, delay_load=True)
self.mm_projector = build_vision_projector(config)
def get_vision_tower(self):
vision_tower = getattr(self, 'vision_tower', None)
if type(vision_tower) is list:
vision_tower = vision_tower[0]
return vision_tower
def initialize_vision_modules(self, model_args, fsdp=None):
vision_tower = model_args.vision_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_vision_tower = vision_tower
if self.get_vision_tower() is None:
vision_tower = build_vision_tower(model_args)
if fsdp is not None and len(fsdp) > 0:
self.vision_tower = [vision_tower]
else:
self.vision_tower = vision_tower
else:
if fsdp is not None and len(fsdp) > 0:
vision_tower = self.vision_tower[0]
else:
vision_tower = self.vision_tower
vision_tower.load_model()
self.config.use_mm_proj = True
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
self.config.mm_hidden_size = vision_tower.hidden_size
self.config.mm_vision_select_layer = mm_vision_select_layer
self.config.mm_vision_select_feature = mm_vision_select_feature
if getattr(self, 'mm_projector', None) is None:
self.mm_projector = build_vision_projector(self.config)
else:
# In case it is frozen by LoRA
for p in self.mm_projector.parameters():
p.requires_grad = True
if pretrain_mm_mlp_adapter is not None:
if os.path.exists(pretrain_mm_mlp_adapter):
is_local = True
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
else:
# Support loading projector weights from remote HuggingFace model hub
is_local = False
pretrain_mm_mlp_adapter = pretrain_mm_mlp_adapter.replace('mm_projector.bin', '')
pretrain_mm_mlp_adapter = pretrain_mm_mlp_adapter.strip('/').strip('\\').strip()
mm_projector_weights = load_mm_projector(pretrain_mm_mlp_adapter)
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'))
# set strict=False to avoid missing key error regarding bert.embeddings.position_ids
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'), strict=False)
class Videollama2MetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def num_frames(self):
if hasattr(self.config, 'num_frames'):
return self.config.num_frames
else:
return NUM_FRAMES
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def encode_images_or_videos(self, images_or_videos, modalities):
num_frames = self.config.num_frames if hasattr(self.config, 'num_frames') else NUM_FRAMES
videos = [x.unsqueeze(0).expand(num_frames, -1, -1, -1) if modal == 'image' else x for x, modal in zip(images_or_videos, modalities)]
videos = torch.stack(videos, dim=0)
assert len(videos.size()) == 5
batch_size = videos.size(0)
frames = einops.rearrange(videos, 'b t c h w -> (b t) c h w')
frames_features = self.get_model().get_vision_tower()(frames)
frames_features = einops.rearrange(frames_features, '(b t) n h -> b t n h', b = batch_size)
return self.temporal_aggregator(frames_features)
def temporal_aggregator(self, frames_features):
"""Temporal aggregation of frame features.
Args:
frames_features (torch.Tensor): Frame features with shape (b, t, n, h).
Returns:
torch.Tensor: Video features with shape (b, n, h).
"""
# TODO: improve the merging method.
# *********** mean pooling *************
if self.config.mm_projector_type == "mlp2x_gelu" or self.config.mm_projector_type == "linear":
video_features = self.get_model().mm_projector(frames_features.mean(1))
# *********** spatial convolution *************
elif self.config.mm_projector_type == "spatial_conv":
video_features = self.get_model().mm_projector(frames_features)
# *********** spatial pooling *************
elif self.config.mm_projector_type == "spatial_pool":
video_features = self.get_model().mm_projector(frames_features)
# *********** time ************
elif "tc_connector" in self.config.mm_projector_type or "tp_connector" in self.config.mm_projector_type:
video_features = self.get_model().mm_projector(frames_features)
else:
raise Exception(f"Unsupported projector type {self.config.mm_projector_type}!!!")
return video_features
def prepare_inputs_labels_for_multimodal(
self, input_ids, attention_mask, past_key_values, labels, X_modalities
):
vision_tower = self.get_vision_tower()
# NOTE: text-only situation
if vision_tower is None or X_modalities is None or input_ids.shape[1] == 1:
# if past_key_values is not None and vision_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
Xs, keys = X_modalities
X_features = self.encode_images_or_videos(Xs, keys)
new_input_embeds = []
new_labels = [] if labels is not None else None
cur_X_idx = 0
# replace image/video/audio tokens with pre-computed embeddings
for batch_idx, cur_input_ids in enumerate(input_ids):
# cur_X_features = X_features[batch_idx]
if (torch.any(torch.stack([cur_input_ids == MMODAL_TOKEN_INDEX[key.upper()] for key in keys]), dim=0)).sum() == 0:
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
continue
X_token_indices = torch.where(torch.any(torch.stack([cur_input_ids == MMODAL_TOKEN_INDEX[key.upper()] for key in keys]), dim=0))[0]
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
# X_index_inonesample = 0
while X_token_indices.numel() > 0:
cur_X_features = X_features[cur_X_idx]
X_token_start = X_token_indices[0]
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:X_token_start]))
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
cur_input_ids = cur_input_ids[X_token_start+1:]
X_token_indices = torch.where(torch.any(torch.stack([cur_input_ids == MMODAL_TOKEN_INDEX[key.upper()] for key in keys]), dim=0))[0]
if cur_input_ids.numel() > 0:
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]
# NOTE: one cur_new_input_embeds per each
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)
# padding
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 initialize_MM_tokenizer(self, model_args, tokenizer):
if model_args.mm_use_im_patch_token:
for modal in ['IMAGE', 'VIDEO', 'AUDIO']:
tokenizer.add_tokens([DEFAULT_MMODAL_PATCH_TOKEN[modal.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_im_start_end:
num_new_tokens = 0
for modal in ['IMAGE', 'VIDEO', 'AUDIO']:
num_new_tokens += tokenizer.add_tokens([DEFAULT_MMODAL_START_TOKEN[modal.upper()], DEFAULT_MMODAL_END_TOKEN[modal.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 == 6 # start/end tokens for image/video/audio
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