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vlm_demo_new.py
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vlm_demo_new.py
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import argparse
import torch
from PIL import Image
from tqdm import tqdm
from transformers import AutoConfig, AutoTokenizer
from accelerate import load_checkpoint_and_dispatch
from tinychat.utils.tune import (
device_warmup,
tune_all_wqlinears,
tune_llava_patch_embedding,
)
from tinychat.utils.prompt_templates import (
get_prompter,
get_stop_token_ids,
get_image_token,
)
from tinychat.utils.llava_image_processing import (
process_images,
load_images,
vis_images,
)
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, stream_output, TimeStats
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
def image_parser(args):
out = args.image_file.split(args.im_sep)
return out
def skip(*args, **kwargs):
pass
def main(args):
# 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(args.device)
model = model.to(args.device)
elif args.precision == "W4A16":
from tinychat.utils.load_quant import load_awq_model
model.llm = load_awq_model(model.llm, args.quant_path, 4, 128, args.device)
from tinychat.modules import (
make_quant_norm,
make_quant_attn,
make_fused_mlp,
make_fused_vision_attn,
)
if args.flash_attn:
print("Enabling flash-attention!")
make_quant_attn(model.llm, args.device, 1)
else:
print("Disabling flash-attention!")
make_quant_attn(model.llm, args.device)
make_quant_norm(model.llm)
# make_fused_mlp(model)
# make_fused_vision_attn(model,args.device)
model = model.to(args.device)
else:
raise NotImplementedError(f"Precision {args.precision} is not supported.")
image_files = image_parser(args)
image_num = len(image_files)
images = load_images(image_files)
if args.vis_image:
print("=" * 50)
print("Input Image:")
vis_images(image_files)
# Similar operation in model_worker.py
image_tensor = process_images(images, image_processor, model.config)
if type(image_tensor) is list:
image_tensor = [
image.to(args.device, dtype=torch.float16) for image in image_tensor
]
else:
image_tensor = image_tensor.to(args.device, dtype=torch.float16)
device_warmup(args.device)
tune_llava_patch_embedding(vision_tower, device=args.device)
stream_generator = LlavaStreamGenerator
if args.max_seq_len <= 1024:
short_prompt = True
else:
short_prompt = False
model_prompter = get_prompter(
args.model_type, args.model_path, short_prompt, args.empty_prompt
)
stop_token_ids = get_stop_token_ids(args.model_type, args.model_path)
count = 0
if args.empty_prompt:
input_indicator = "Input: "
output_indicator = "Generated: "
else:
input_indicator = "USER: "
output_indicator = "ASSISTANT: "
model.eval()
time_stats = TimeStats()
start_pos = 0
while True:
# Get input from the user
print("=" * 50)
input_prompt = input(input_indicator)
print("-" * 50)
if input_prompt == "":
print("EXIT...")
time_stats.show()
break
if count == 0: # 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)
else:
model_prompter.insert_prompt(input_prompt)
if args.chunk_prefilling:
image_tensor = None # Can insert more images in future
output_stream = stream_generator(
model,
tokenizer,
model_prompter.model_input,
start_pos,
gen_params,
device=args.device,
stop_token_ids=stop_token_ids,
image_tensor=image_tensor,
chunk_prefilling=args.chunk_prefilling,
)
print(output_indicator, end="", flush=True)
if count == 0:
outputs, total_tokens = stream_output(output_stream, time_stats)
else:
outputs, total_tokens = stream_output(output_stream)
if args.chunk_prefilling:
start_pos += total_tokens
if (
args.single_round is not True and args.max_seq_len > 512
): # Only memorize previous conversations when kv_cache_size > 512
model_prompter.update_template(outputs, args.chunk_prefilling)
count += 1
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_type", type=str, default="LLaMa", help="type of the model"
)
parser.add_argument(
"--model-path", type=str, default="/data/llm/checkpoints/llava/llava-v1.5-7b"
)
parser.add_argument(
"--quant-path",
type=str,
default="/data/llm/checkpoints/llava/llava-v1.5-7b-w4-g128-awq.pt",
)
parser.add_argument(
"--precision", type=str, default="W4A16", help="compute precision"
)
parser.add_argument(
"--image-file",
type=str,
default="https://llava.hliu.cc/file=/nobackup/haotian/code/LLaVA/llava/serve/examples/extreme_ironing.jpg",
)
parser.add_argument(
"--im-sep",
type=str,
default=",",
)
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--max_seq_len", type=int, default=2048)
parser.add_argument(
"--single_round",
action="store_true",
help="whether to memorize previous conversations",
)
parser.add_argument(
"--vis-image",
action="store_true",
help="whether to visualize the image while chatting",
)
parser.add_argument(
"--empty-prompt",
action="store_true",
help="whether to use empty prompt template",
)
parser.add_argument(
"--flash_attn",
action="store_true",
help="whether to use flash attention",
)
parser.add_argument(
"--chunk_prefilling",
action="store_true",
help="If used, in context stage, the history tokens will not be recalculated, greatly speeding up the calculation",
)
args = parser.parse_args()
main(args)