File size: 15,410 Bytes
208b0eb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
# Modified from https://github.com/mit-han-lab/llm-awq/blob/main/tinychat/vlm_demo_new.py.
import argparse
import os
from pathlib import Path

import numpy as np
import pandas as pd
import torch
from accelerate import load_checkpoint_and_dispatch, PartialState
from accelerate.utils import gather_object
from decord import VideoReader
from PIL import Image
from natsort import natsorted
from tqdm import tqdm
from transformers import AutoConfig, AutoTokenizer

import tinychat.utils.constants
# from tinychat.models.llava_llama import LlavaLlamaForCausalLM
from tinychat.models.vila_llama import VilaLlamaForCausalLM
from tinychat.stream_generators.llava_stream_gen import LlavaStreamGenerator
from tinychat.utils.conversation_utils import gen_params
from tinychat.utils.llava_image_processing import process_images
from tinychat.utils.prompt_templates import (
    get_image_token,
    get_prompter,
    get_stop_token_ids,
)
from tinychat.utils.tune import (
    device_warmup,
    tune_llava_patch_embedding,
)

from utils.filter import filter
from utils.logger import logger

gen_params.seed = 1
gen_params.temp = 1.0
gen_params.top_p = 1.0


def extract_uniform_frames(video_path: str, num_sampled_frames: int = 8):
    vr = VideoReader(video_path)
    sampled_frame_idx_list = np.linspace(0, len(vr), num_sampled_frames, endpoint=False, dtype=int)
    sampled_frame_list = []
    for idx in sampled_frame_idx_list:
        sampled_frame = Image.fromarray(vr[idx].asnumpy())
        sampled_frame_list.append(sampled_frame)

    return sampled_frame_list


def stream_output(output_stream):
    for outputs in output_stream:
        output_text = outputs["text"]
        output_text = output_text.strip().split(" ")
        # print(f"output_text: {output_text}.")
    return " ".join(output_text)


def skip(*args, **kwargs):
    pass


def parse_args():
    parser = argparse.ArgumentParser(description="Recaption videos with VILA1.5.")
    parser.add_argument(
        "--video_metadata_path",
        type=str,
        default=None,
        help="The path to the video dataset metadata (csv/jsonl).",
    )
    parser.add_argument(
        "--video_path_column",
        type=str,
        default="video_path",
        help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
    )
    parser.add_argument(
        "--caption_column",
        type=str,
        default="caption",
        help="The column contains the caption.",
    )
    parser.add_argument(
        "--video_folder", type=str, default="", help="The video folder."
    )
    parser.add_argument("--input_prompt", type=str, default="<video>\\n Elaborate on the visual and narrative elements of the video in detail.")
    parser.add_argument(
        "--model_type", type=str, default="LLaMa", help="type of the model"
    )
    parser.add_argument(
        "--model_path", type=str, default="Efficient-Large-Model/Llama-3-VILA1.5-8b-AWQ"
    )
    parser.add_argument(
        "--quant_path",
        type=str,
        default=None,
    )
    parser.add_argument(
        "--precision", type=str, default="W4A16", help="compute precision"
    )
    parser.add_argument("--num_sampled_frames", type=int, default=8)
    parser.add_argument(
        "--saved_path",
        type=str,
        required=True,
        help="The save path to the output results (csv/jsonl).",
    )
    parser.add_argument(
        "--saved_freq",
        type=int,
        default=100,
        help="The frequency to save the output results.",
    )

    parser.add_argument(
        "--basic_metadata_path", type=str, default=None, help="The path to the basic metadata (csv/jsonl)."
    )
    parser.add_argument("--min_resolution", type=float, default=0, help="The resolution threshold.")
    parser.add_argument("--min_duration", type=float, default=-1, help="The minimum duration.")
    parser.add_argument("--max_duration", type=float, default=-1, help="The maximum duration.")
    parser.add_argument(
        "--asethetic_score_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
    )
    parser.add_argument("--min_asethetic_score", type=float, default=4.0, help="The asethetic score threshold.")
    parser.add_argument(
        "--asethetic_score_siglip_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
    )
    parser.add_argument("--min_asethetic_score_siglip", type=float, default=4.0, help="The asethetic score (SigLIP) threshold.")
    parser.add_argument(
        "--text_score_metadata_path", type=str, default=None, help="The path to the video text score metadata (csv/jsonl)."
    )
    parser.add_argument("--min_text_score", type=float, default=0.02, help="The text threshold.")
    parser.add_argument(
        "--motion_score_metadata_path", type=str, default=None, help="The path to the video motion score metadata (csv/jsonl)."
    )
    parser.add_argument("--min_motion_score", type=float, default=2, help="The motion threshold.")
    
    args = parser.parse_args()
    return args


def main(args):
    if args.video_metadata_path.endswith(".csv"):
        video_metadata_df = pd.read_csv(args.video_metadata_path)
    elif args.video_metadata_path.endswith(".jsonl"):
        video_metadata_df = pd.read_json(args.video_metadata_path, lines=True)
    else:
        raise ValueError("The video_metadata_path must end with .csv or .jsonl.")
    video_path_list = video_metadata_df[args.video_path_column].tolist()
    video_path_list = [os.path.basename(video_path) for video_path in video_path_list]

    if not (args.saved_path.endswith(".csv") or args.saved_path.endswith(".jsonl")):
        raise ValueError("The saved_path must end with .csv or .jsonl.")

    if os.path.exists(args.saved_path):
        if args.saved_path.endswith(".csv"):
            saved_metadata_df = pd.read_csv(args.saved_path)
        elif args.saved_path.endswith(".jsonl"):
            saved_metadata_df = pd.read_json(args.saved_path, lines=True)
        saved_video_path_list = saved_metadata_df[args.video_path_column].tolist()
        video_path_list = list(set(video_path_list).difference(set(saved_video_path_list)))
        logger.info(
            f"Resume from {args.saved_path}: {len(saved_video_path_list)} processed and {len(video_path_list)} to be processed."
        )
    
    video_path_list = filter(
        video_path_list,
        basic_metadata_path=args.basic_metadata_path,
        min_resolution=args.min_resolution,
        min_duration=args.min_duration,
        max_duration=args.max_duration,
        asethetic_score_metadata_path=args.asethetic_score_metadata_path,
        min_asethetic_score=args.min_asethetic_score,
        asethetic_score_siglip_metadata_path=args.asethetic_score_siglip_metadata_path,
        min_asethetic_score_siglip=args.min_asethetic_score_siglip,
        text_score_metadata_path=args.text_score_metadata_path,
        min_text_score=args.min_text_score,
        motion_score_metadata_path=args.motion_score_metadata_path,
        min_motion_score=args.min_motion_score,
    )
    video_path_list = [os.path.join(args.video_folder, video_path) for video_path in video_path_list]
    # Sorting to guarantee the same result for each process.
    video_path_list = natsorted(video_path_list)

    state = PartialState()

    # Accelerate model initialization
    setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
    setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
    torch.nn.init.kaiming_uniform_ = skip
    torch.nn.init.kaiming_normal_ = skip
    torch.nn.init.uniform_ = skip
    torch.nn.init.normal_ = skip

    tokenizer = AutoTokenizer.from_pretrained(os.path.join(args.model_path, "llm"), use_fast=False)
    tinychat.utils.constants.LLAVA_DEFAULT_IMAGE_PATCH_TOKEN_IDX = (
        tokenizer.convert_tokens_to_ids(
            [tinychat.utils.constants.LLAVA_DEFAULT_IMAGE_PATCH_TOKEN]
        )[0]
    )
    config = AutoConfig.from_pretrained(args.model_path, trust_remote_code=True)
    model = VilaLlamaForCausalLM(config).half()
    tinychat.utils.constants.LLAVA_DEFAULT_IMAGE_PATCH_TOKEN_IDX = (
        tokenizer.convert_tokens_to_ids(
            [tinychat.utils.constants.LLAVA_DEFAULT_IMAGE_PATCH_TOKEN]
        )[0]
    )
    vision_tower = model.get_vision_tower()
    # if not vision_tower.is_loaded:
    #     vision_tower.load_model()
    image_processor = vision_tower.image_processor
    # vision_tower = vision_tower.half()

    if args.precision == "W16A16":
        pbar = tqdm(range(1))
        pbar.set_description("Loading checkpoint shards")
        for i in pbar:
            model.llm = load_checkpoint_and_dispatch(
                model.llm,
                os.path.join(args.model_path, "llm"),
                no_split_module_classes=[
                    "OPTDecoderLayer",
                    "LlamaDecoderLayer",
                    "BloomBlock",
                    "MPTBlock",
                    "DecoderLayer",
                    "CLIPEncoderLayer",
                ],
            ).to(state.device)
        model = model.to(state.device)

    elif args.precision == "W4A16":
        from tinychat.utils.load_quant import load_awq_model
        # Auto load quant_path from the 3b/8b/13b/40b model.
        if args.quant_path is None:
            if "VILA1.5-3b-s2-AWQ" in args.model_path:
                args.quant_path = os.path.join(args.model_path, "llm/vila-1.5-3b-s2-w4-g128-awq-v2.pt")
            elif "VILA1.5-3b-AWQ" in args.model_path:
                args.quant_path = os.path.join(args.model_path, "llm/vila-1.5-3b-w4-g128-awq-v2.pt")
            elif "Llama-3-VILA1.5-8b-AWQ" in args.model_path:
                args.quant_path = os.path.join(args.model_path, "llm/llama-3-vila1.5-8b-w4-g128-awq-v2.pt")
            elif "VILA1.5-13b-AWQ" in args.model_path:
                args.quant_path = os.path.join(args.model_path, "llm/vila-1.5-13b-w4-g128-awq-v2.pt")
            elif "VILA1.5-40b-AWQ" in args.model_path:
                args.quant_path = os.path.join(args.model_path, "llm/vila-1.5-40b-w4-g128-awq-v2.pt")
        model.llm = load_awq_model(model.llm, args.quant_path, 4, 128, state.device)
        from tinychat.modules import (
            make_fused_mlp,
            make_fused_vision_attn,
            make_quant_attn,
            make_quant_norm,
        )

        make_quant_attn(model.llm, state.device)
        make_quant_norm(model.llm)
        # make_fused_mlp(model)
        # make_fused_vision_attn(model,state.device)
        model = model.to(state.device)

    else:
        raise NotImplementedError(f"Precision {args.precision} is not supported.")
    
    device_warmup(state.device)
    tune_llava_patch_embedding(vision_tower, device=state.device)

    stream_generator = LlavaStreamGenerator

    model_prompter = get_prompter(
        args.model_type, args.model_path, False, False
    )
    stop_token_ids = get_stop_token_ids(args.model_type, args.model_path)

    model.eval()

    index = len(video_path_list) - len(video_path_list) % state.num_processes
    # Avoid the NCCL timeout in the final gather operation.
    logger.info(f"Drop {len(video_path_list) % state.num_processes} videos to ensure each process handles the same number of videos.")
    video_path_list = video_path_list[:index]
    logger.info(f"{len(video_path_list)} videos are to be processed.")
    
    result_dict = {args.video_path_column: [], args.caption_column: []}
    with state.split_between_processes(video_path_list) as splitted_video_path_list:
        # TODO: Use VideoDataset.
        for i, video_path in enumerate(tqdm(splitted_video_path_list)):
            try:
                image_list = extract_uniform_frames(video_path, args.num_sampled_frames)
                image_num = len(image_list)
                # Similar operation in model_worker.py
                image_tensor = process_images(image_list, image_processor, model.config)
                if type(image_tensor) is list:
                    image_tensor = [
                        image.to(state.device, dtype=torch.float16) for image in image_tensor
                    ]
                else:
                    image_tensor = image_tensor.to(state.device, dtype=torch.float16)

                input_prompt = args.input_prompt
                # Insert image here
                image_token = get_image_token(model, args.model_path)
                image_token_holder = tinychat.utils.constants.LLAVA_DEFAULT_IM_TOKEN_PLACE_HOLDER
                im_token_count = input_prompt.count(image_token_holder)
                if im_token_count == 0:
                    model_prompter.insert_prompt(image_token * image_num + input_prompt)
                else:
                    assert im_token_count == image_num
                    input_prompt = input_prompt.replace(image_token_holder, image_token)
                    model_prompter.insert_prompt(input_prompt)
                output_stream = stream_generator(
                    model,
                    tokenizer,
                    model_prompter.model_input,
                    gen_params,
                    device=state.device,
                    stop_token_ids=stop_token_ids,
                    image_tensor=image_tensor,
                )
                outputs = stream_output(output_stream)
                if len(outputs) != 0:
                    result_dict[args.video_path_column].append(Path(video_path).name)
                    result_dict[args.caption_column].append(outputs)
            
            except Exception as e:
                logger.warning(f"VILA with {video_path} failed. Error is {e}.")

            if i != 0 and i % args.saved_freq == 0:
                state.wait_for_everyone()
                gathered_result_dict = {k: gather_object(v) for k, v in result_dict.items()}
                if state.is_main_process and len(gathered_result_dict[args.video_path_column]) != 0:
                    result_df = pd.DataFrame(gathered_result_dict)
                    if args.saved_path.endswith(".csv"):
                        header = False if os.path.exists(args.saved_path) else True
                        result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
                    elif args.saved_path.endswith(".jsonl"):
                        result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
                    logger.info(f"Save result to {args.saved_path}.")
                for k in result_dict.keys():
                    result_dict[k] = []
    
    state.wait_for_everyone()
    gathered_result_dict = {k: gather_object(v) for k, v in result_dict.items()}
    if state.is_main_process and len(gathered_result_dict[args.video_path_column]) != 0:
        result_df = pd.DataFrame(gathered_result_dict)
        if args.saved_path.endswith(".csv"):
            header = False if os.path.exists(args.saved_path) else True
            result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
        elif args.saved_path.endswith(".jsonl"):
            result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
        logger.info(f"Save result to {args.saved_path}.")


if __name__ == "__main__":
    args = parse_args()
    main(args)