-
Notifications
You must be signed in to change notification settings - Fork 14
/
inference.py
190 lines (157 loc) · 7.45 KB
/
inference.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
import re
import os
import argparse
import torch
import math
import numpy as np
from typing import Dict, Optional, Sequence, List
import transformers
from transformers import AutoConfig
from PIL import Image
from oryx.conversation import conv_templates, SeparatorStyle
from oryx.model.builder import load_pretrained_model
from oryx.utils import disable_torch_init
from oryx.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria, process_anyres_video_genli
from oryx.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX
from decord import VideoReader, cpu
if 'EVAL_LARGE' in os.environ:
print("EVAL_LARGE is set")
EVAL_LARGE = True
else:
EVAL_LARGE = False
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
def preprocess_qwen(sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict:
roles = {"human": "<|im_start|>user", "gpt": "<|im_start|>assistant"}
im_start, im_end = tokenizer.additional_special_tokens_ids[:2]
nl_tokens = tokenizer("\n").input_ids
_system = tokenizer("system").input_ids + nl_tokens
_user = tokenizer("user").input_ids + nl_tokens
_assistant = tokenizer("assistant").input_ids + nl_tokens
# Apply prompt templates
input_ids, targets = [], []
source = sources
if roles[source[0]["from"]] != roles["human"]:
source = source[1:]
input_id, target = [], []
system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens
input_id += system
target += [im_start] + [IGNORE_INDEX] * (len(system) - 3) + [im_end] + nl_tokens
assert len(input_id) == len(target)
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
if has_image and sentence["value"] is not None and "<image>" in sentence["value"]:
num_image = len(re.findall(DEFAULT_IMAGE_TOKEN, sentence["value"]))
texts = sentence["value"].split('<image>')
_input_id = tokenizer(role).input_ids + nl_tokens
for i,text in enumerate(texts):
_input_id += tokenizer(text).input_ids
if i<len(texts)-1:
_input_id += [IMAGE_TOKEN_INDEX] + nl_tokens
_input_id += [im_end] + nl_tokens
assert sum([i==IMAGE_TOKEN_INDEX for i in _input_id])==num_image
else:
if sentence["value"] is None:
_input_id = tokenizer(role).input_ids + nl_tokens
else:
_input_id = tokenizer(role).input_ids + nl_tokens + tokenizer(sentence["value"]).input_ids + [im_end] + nl_tokens
input_id += _input_id
if role == "<|im_start|>user":
_target = [im_start] + [IGNORE_INDEX] * (len(_input_id) - 3) + [im_end] + nl_tokens
elif role == "<|im_start|>assistant":
_target = [im_start] + [IGNORE_INDEX] * len(tokenizer(role).input_ids) + _input_id[len(tokenizer(role).input_ids) + 1 : -2] + [im_end] + nl_tokens
else:
raise NotImplementedError
target += _target
input_ids.append(input_id)
targets.append(target)
input_ids = torch.tensor(input_ids, dtype=torch.long)
targets = torch.tensor(targets, dtype=torch.long)
return input_ids
def eval_model(args):
# Model
disable_torch_init()
model_path = args.model_path
model_name = get_model_name_from_path(model_path)
if args.overwrite == True:
overwrite_config = {}
overwrite_config["mm_resampler_type"] = "dynamic_compressor"
overwrite_config["patchify_video_feature"] = False
overwrite_config["attn_implementation"] = "sdpa" if torch.__version__ >= "2.1.2" else "eager"
cfg_pretrained = AutoConfig.from_pretrained(model_path)
if '7b' in model_path:
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name, device_map="cuda:0", overwrite_config=overwrite_config)
elif '34b' in model_path:
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name, device_map="auto", overwrite_config=overwrite_config)
if EVAL_LARGE:
model.eval()
else:
model.to('cuda').eval()
video_file = ""
vr = VideoReader(video_file, ctx=cpu(0))
total_frame_num = len(vr)
fps = round(vr.get_avg_fps())
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, args.frames_upbound, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
spare_frames = vr.get_batch(frame_idx).asnumpy()
video = [Image.fromarray(frame) for frame in spare_frames]
args.conv_mode = "qwen_1_5"
question = "Describe the video in detail."
question = "<image>\n" + question
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
if '7b' in model_path:
input_ids = preprocess_qwen([{'from': 'human','value': question},{'from': 'gpt','value': None}], tokenizer, has_image=True).cuda()
elif '34b' in model_path:
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to('cuda:0')
video_processed = []
for idx, frame in enumerate(video):
image_processor.do_resize = False
image_processor.do_center_crop = False
frame = process_anyres_video_genli(frame, image_processor)
video_processed.append(frame.unsqueeze(0))
if frame_idx is None:
frame_idx = np.arange(0, len(video_processed), dtype=int).tolist()
video_processed = torch.cat(video_processed, dim=0).bfloat16().cuda()
video_processed = (video_processed, video_processed)
video_data = (video_processed, (384, 384), "video")
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
inputs=input_ids,
images=video_data[0][0],
images_highres=video_data[0][1],
modalities=video_data[2],
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
max_new_tokens=128,
use_cache=True,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
outputs = outputs.strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
print(outputs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--frames-upbound", type=int, default=64)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--overwrite", type=bool, default=True)
parser.add_argument("--num_beams", type=int, default=1)
args = parser.parse_args()
eval_model(args)