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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 .projector import load_mm_projector, build_vision_projector
from .encoder import build_vision_tower
from ..constants import IGNORE_INDEX, NUM_FRAMES, MODAL_INDEX_MAP
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
if os.path.isdir(pretrain_mm_mlp_adapter):
mm_projector_weights = load_mm_projector(pretrain_mm_mlp_adapter)
else:
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):
num_frames = self.config.num_frames if hasattr(self.config, 'num_frames') else NUM_FRAMES
data_batch = []
for i, (data, modal) in enumerate(images):
if modal == 'image':
data = data.expand(num_frames, -1, -1, -1)
else:
data = data
data_batch.append(data)
data_batch = torch.stack(data_batch, dim=0)
assert len(data_batch.size()) == 5
batch_size = data_batch.size(0)
frames = einops.rearrange(data_batch, '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, images
):
vision_tower = self.get_vision_tower()
# NOTE: text-only situation
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 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
mm_features = self.encode_images_or_videos(images)
new_input_embeds = []
new_labels = [] if labels is not None else None
cur_mm_idx = 0
# replace image/video/audio tokens with pre-computed embeddings
for batch_idx, cur_input_ids in enumerate(input_ids):
num_multimodals = sum((cur_input_ids == mm_token_idx).sum() for mm_token_idx in MODAL_INDEX_MAP.values())
# pure text input
if num_multimodals == 0:
half_len = cur_input_ids.shape[0] // 2
cur_mm_features = mm_features[cur_mm_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_mm_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_mm_idx += 1
continue
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
mm_token_indices = torch.where(sum([cur_input_ids == mm_token_idx for mm_token_idx in MODAL_INDEX_MAP.values()]))[0]
while mm_token_indices.numel() > 0:
cur_mm_features = mm_features[cur_mm_idx]
mm_token_start = mm_token_indices[0]
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:mm_token_start]))
cur_new_input_embeds.append(cur_mm_features)
if labels is not None:
cur_new_labels.append(cur_labels[:mm_token_start])
cur_new_labels.append(torch.full((cur_mm_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
cur_labels = cur_labels[mm_token_start+1:]
cur_mm_idx += 1
cur_input_ids = cur_input_ids[mm_token_start+1:]
mm_token_indices = torch.where(sum([cur_input_ids == mm_token_idx for mm_token_idx in MODAL_INDEX_MAP.values()]))[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
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