THUDM

/

cogvlm-chat-hf

like 176

yes, try sth like this.

import torch
import requests
from PIL import Image
from transformers import AutoModelForCausalLM, LlamaTokenizer

tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5')
model = AutoModelForCausalLM.from_pretrained(
    'THUDM/cogvlm-chat-hf',
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True
).to('cuda').eval()

input_sample1 = model.build_conversation_input_ids(
    tokenizer,
    images=[Image.open(requests.get('https://github.com/THUDM/CogVLM/blob/main/openai_demo/demo.jpg?raw=true', stream=True).raw).convert('RGB'),],
    query='Do you think this is a spring or winter photo?',  # Q2
    history=[
        (
            "What's in this image?",   # Q1
            'The image displays a wooden boardwalk extending through a vibrant green grassy wetland.'  # A1
         )
        ], 
    )
input_sample2 = model.build_conversation_input_ids(
    tokenizer,
    images=[Image.open(requests.get('https://github.com/THUDM/CogVLM/blob/main/examples/1.png?raw=true', stream=True).raw).convert('RGB'),],
    query='Describe this image',  # Q1
    history=[], 
    )

def recur_move_to(item, tgt, criterion_func):
    if criterion_func(item):
        device_copy = item.to(tgt)
        return device_copy
    elif isinstance(item, list):
        return [recur_move_to(v, tgt, criterion_func) for v in item]
    elif isinstance(item, tuple):
        return tuple([recur_move_to(v, tgt, criterion_func) for v in item])
    elif isinstance(item, dict):
        return {k: recur_move_to(v, tgt, criterion_func) for k, v in item.items()}
    else:
        return item

def collate_fn(features, tokenizer) -> dict:
    images = [feature.pop('images') for feature in features]
    tokenizer.padding_side = 'left'
    padded_features = tokenizer.pad(features)
    inputs = {**padded_features, 'images': images}
    return inputs

input_batch = collate_fn([input_sample1, input_sample2], tokenizer)
input_batch = recur_move_to(input_batch, 'cuda', lambda x: isinstance(x, torch.Tensor))
input_batch = recur_move_to(input_batch, torch.bfloat16, lambda x: isinstance(x, torch.Tensor) and torch.is_floating_point(x))

gen_kwargs = {"max_length": 2048, "do_sample": False}

with torch.no_grad():
    outputs = model.generate(**input_batch, **gen_kwargs)
    outputs = outputs[:, input_batch['input_ids'].shape[1]:]
    print(tokenizer.batch_decode(outputs))