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# 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)
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