[Question] Use api

[MartinGuo]

Question

How can I use API requests to call the model for the upper level application?

[Raidus]

The serve cli module might give you some hints.

        with torch.inference_mode():
            output_ids = model.generate(
                input_ids,
                images=image_tensor,
                do_sample=True,
                temperature=args.temperature,
                max_new_tokens=args.max_new_tokens,
                streamer=streamer,
                use_cache=True,
                stopping_criteria=[stopping_criteria])

        outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
        conv.messages[-1][-1] = outputs

Reference: https://github.com/haotian-liu/LLaVA/blob/main/llava/serve/cli.py

[userbox020]

im trying to modify the client to give the user the option to upload new images, but after upload the 3rd image i get oom error, im doing garbage colector and cleaning cache of vram but maybe im doing it wrong

import argparse
import torch

from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria

from PIL import Image

import requests
import traceback
from PIL import Image
from io import BytesIO
from transformers import TextStreamer

# Garbage Collector for PyTorch GPU Memory
import gc

def pytorch_gpu_garbage_collector():
    """Explicitly trigger Python's garbage collector and then clear PyTorch's GPU cache for all GPUs."""
    print("Triggering Python's garbage collector...")
    # Collect Python's garbage
    gc.collect()

    # Clear PyTorch's GPU cache for all GPUs
    if torch.cuda.is_available():
        for device_idx in range(torch.cuda.device_count()):
            torch.cuda.set_device(device_idx)
            print(f"Clearing PyTorch GPU cache for GPU {device_idx}...")
            torch.cuda.empty_cache()
    print("Garbage collection completed!")



def load_image(image_file):
    if image_file.startswith('http://') or image_file.startswith('https://'):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert('RGB')
    else:
        image = Image.open(image_file).convert('RGB')
    return image


def main(args):
    # Model
    disable_torch_init()

    model_name = get_model_name_from_path(args.model_path)
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device)
    
    if 'llama-2' in model_name.lower():
        conv_mode = "llava_llama_2"
    elif "v1" in model_name.lower():
        conv_mode = "llava_v1"
    elif "mpt" in model_name.lower():
        conv_mode = "mpt"
    else:
        conv_mode = "llava_v0"

    if args.conv_mode is not None and conv_mode != args.conv_mode:
        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
    else:
        args.conv_mode = conv_mode

    conv = conv_templates[args.conv_mode].copy()
    if "mpt" in model_name.lower():
        roles = ('user', 'assistant')
    else:
        roles = conv.roles



    while True:
        try:
            
            image_file = input('Image path:')
            #image = load_image(args.image_file)

            image = load_image(image_file)

            # Similar operation in model_worker.py
            image_tensor = process_images([image], image_processor, args)
            if type(image_tensor) is list:
                image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
            else:
                image_tensor = image_tensor.to(model.device, dtype=torch.float16)            
            
            inp = input(f"{roles[0]}: ")
        except EOFError:
            inp = ""
        if not inp:
            print("exit...")
            break

        try:
            print(f"{roles[1]}: ", end="")

            if image is not None:
                # first message
                if model.config.mm_use_im_start_end:
                    inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
                else:
                    inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
                conv.append_message(conv.roles[0], inp)
                image = None
            else:
                # later messages
                conv.append_message(conv.roles[0], inp)
            conv.append_message(conv.roles[1], None)
            prompt = conv.get_prompt()



            input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
            keywords = [stop_str]
            stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
            streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

            print('------------------------')
            print('input_ids:', input_ids)
            print('image_tensor:', image_tensor)
            print('args.temperature:', args.temperature)
            print('max_new_tokens:', args.max_new_tokens)
            print('streamer:', streamer)
            print('stopping_criteria:', [stopping_criteria])


            with torch.inference_mode():
                output_ids = model.generate(
                    input_ids,
                    images=image_tensor,
                    do_sample=True,
                    temperature=args.temperature,
                    max_new_tokens=args.max_new_tokens,
                    streamer=streamer,
                    use_cache=True,
                    stopping_criteria=[stopping_criteria])

            input('Debug...')

            outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
            conv.messages[-1][-1] = outputs
            print('------------------------')
            if args.debug:
                print("\n", {"prompt": prompt, "outputs": outputs}, "\n")


            pytorch_gpu_garbage_collector()
        
        
        except Exception as e:
            print('Error:',str(e))
            print('Traceback:', traceback.format_exc())


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    parser.add_argument("--model-base", type=str, default=None)
    #parser.add_argument("--image-file", type=str, required=True)
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--conv-mode", type=str, default=None)
    parser.add_argument("--temperature", type=float, default=0.2)
    parser.add_argument("--max-new-tokens", type=int, default=512)
    parser.add_argument("--load-8bit", action="store_true")
    parser.add_argument("--load-4bit", action="store_true")
    parser.add_argument("--debug", action="store_true")
    parser.add_argument("--image-aspect-ratio", type=str, default='pad')
    args = parser.parse_args()
    main(args)

[TonyUSTC]

请问解决了吗？遇到同样的困惑。

[vicgarfield]

The reason is that the request prompt will superimpose the previous image and text prompt token. Just re-initialize "conv" parameter every time you request it.

[userbox020]

@vicgarfield thanks bro, working beautiful everything its cleaning now memory like it should be. Im on loop 9 and not accumulating any extra vram. Im running it with only 12gb vram. here i left code

import argparse
import torch

from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria

from PIL import Image

import requests
import traceback
from PIL import Image
from io import BytesIO
from transformers import TextStreamer

# Garbage Collector for PyTorch GPU Memory
import gc

def pytorch_gpu_garbage_collector():
    """Explicitly trigger Python's garbage collector and then clear PyTorch's GPU cache for all GPUs."""
    print("Triggering Python's garbage collector...")
    # Collect Python's garbage
    gc.collect()

    # Clear PyTorch's GPU cache for all GPUs
    if torch.cuda.is_available():
        for device_idx in range(torch.cuda.device_count()):
            torch.cuda.set_device(device_idx)
            print(f"Clearing PyTorch GPU cache for GPU {device_idx}...")
            torch.cuda.empty_cache()
    print("Garbage collection completed!")



def load_image(image_file):
    if image_file.startswith('http://') or image_file.startswith('https://'):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert('RGB')
    else:
        image = Image.open(image_file).convert('RGB')
    return image


def main(args):
    # Model
    disable_torch_init()

    model_name = get_model_name_from_path(args.model_path)
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device)
    
    if 'llama-2' in model_name.lower():
        conv_mode = "llava_llama_2"
    elif "v1" in model_name.lower():
        conv_mode = "llava_v1"
    elif "mpt" in model_name.lower():
        conv_mode = "mpt"
    else:
        conv_mode = "llava_v0"

    if args.conv_mode is not None and conv_mode != args.conv_mode:
        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
    else:
        args.conv_mode = conv_mode




    while True:
        try:
            

            conv = conv_templates[args.conv_mode].copy()
            if "mpt" in model_name.lower():
                roles = ('user', 'assistant')
            else:
                roles = conv.roles


            image_file = input('Image path:')
            #image = load_image(args.image_file)

            image = load_image(image_file)

            # Similar operation in model_worker.py
            image_tensor = process_images([image], image_processor, args)
            if type(image_tensor) is list:
                image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
            else:
                image_tensor = image_tensor.to(model.device, dtype=torch.float16)            
            
            inp = input(f"{roles[0]}: ")
        except EOFError:
            inp = ""
        if not inp:
            print("exit...")
            break

        try:
            print(f"{roles[1]}: ", end="")

            if image is not None:
                # first message
                if model.config.mm_use_im_start_end:
                    inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
                else:
                    inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
                conv.append_message(conv.roles[0], inp)
                image = None
            else:
                # later messages
                conv.append_message(conv.roles[0], inp)
            conv.append_message(conv.roles[1], None)
            prompt = conv.get_prompt()



            input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
            keywords = [stop_str]
            stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
            streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

            print('------------------------')
            print('input_ids:', input_ids)
            print('image_tensor:', image_tensor)
            print('args.temperature:', args.temperature)
            print('max_new_tokens:', args.max_new_tokens)
            print('streamer:', streamer)
            print('stopping_criteria:', [stopping_criteria])


            with torch.inference_mode():
                output_ids = model.generate(
                    input_ids,
                    images=image_tensor,
                    do_sample=True,
                    temperature=args.temperature,
                    max_new_tokens=args.max_new_tokens,
                    streamer=streamer,
                    use_cache=True,
                    stopping_criteria=[stopping_criteria])


            input('Debug...')

            outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
            conv.messages[-1][-1] = outputs
            print('------------------------')
            if args.debug:
                print("\n", {"prompt": prompt, "outputs": outputs}, "\n")


            # Assuming you're done with output_ids and other tensors, start cleanup
            print("Deleting output_ids")
            del output_ids
            print("Deleting input_ids")
            del input_ids
            print("Deleting image_tensor")
            del image_tensor
            print("Deleting inp")
            del inp
            print("Deleting stop_str")
            del stop_str
            #print('Deleting conv')
            #del conv
            #print('Deleting model')
            #del model

            # --- MEMORY CLEANER            
            pytorch_gpu_garbage_collector()

        
        
        except Exception as e:
            print('Error:',str(e))
            print('Traceback:', traceback.format_exc())


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    parser.add_argument("--model-base", type=str, default=None)
    #parser.add_argument("--image-file", type=str, required=True)
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--conv-mode", type=str, default=None)
    parser.add_argument("--temperature", type=float, default=0.2)
    parser.add_argument("--max-new-tokens", type=int, default=512)
    parser.add_argument("--load-8bit", action="store_true")
    parser.add_argument("--load-4bit", action="store_true")
    parser.add_argument("--debug", action="store_true")
    parser.add_argument("--image-aspect-ratio", type=str, default='pad')
    args = parser.parse_args()
    main(args)

To run the model:

CUDA_VISIBLE_DEVICES=0,1 python -m llava.serve.tcli --model-path /media/10TB_HHD/_LLaVA/_models/liuhaotian_llava-v1.5-13b/ --load-4bit

[vicgarfield]

@userbox020 Perfect！

[pbenaim]

And tcli.py must be in ../LLaVA/llava/serve 👍

Thanks

[yinjiaoyuan]

VERY GOOD！！！

[hugsbrugs]

Hey,
I'm also trying to call to model through API but I can't figure out how you do your CURL (post ?) request once the model is loaded ?
You don't attach it to a controller or gradio web server ? so how to call it publicly ? I've tried to append --share but this option is not handled with tcli.py
Thanks for your help !

[Xingxiangrui]

Hey, I'm also trying to call to model through API but I can't figure out how you do your CURL (post ?) request once the model is loaded ? You don't attach it to a controller or gradio web server ? so how to call it publicly ? I've tried to append --share but this option is not handled with tcli.py Thanks for your help !

+1 for this

[userbox020]

Hey, I'm also trying to call to model through API but I can't figure out how you do your CURL (post ?) request once the model is loaded ? You don't attach it to a controller or gradio web server ? so how to call it publicly ? I've tried to append --share but this option is not handled with tcli.py Thanks for your help !

+1 for this

Sorry bro i confuse post, it wasnt an API it just some tricks to don't oom with 8 gb gpus.

But adding API will be easy enought, you can create a separate script and use the module subprocess and save the output or stream it. Then use any python http server to publish or stream the output.

Chatgpt must be able to do a good code template for that