36fbe2144d
* add fuyu cpu examples * add gpu example * add comments * add license * remove gpu example * fix inference time
68 lines
3 KiB
Python
68 lines
3 KiB
Python
#
|
|
# Copyright 2016 The BigDL Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
from transformers import AutoModelForCausalLM, FuyuProcessor
|
|
import torch
|
|
import argparse
|
|
import time
|
|
from PIL import Image
|
|
from bigdl.llm import optimize_model
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Fuyu model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="adept/fuyu-8b",
|
|
help='The huggingface repo id for the Fuyu model to be downloaded'
|
|
', or the path to the huggingface checkpoint folder')
|
|
parser.add_argument('--prompt', type=str, default="Generate a coco-style caption.",
|
|
help='Prompt to infer')
|
|
parser.add_argument('--image-path', type=str, required=True,
|
|
help='Image path for the input image that the chat will focus on')
|
|
parser.add_argument('--n-predict', type=int, default=512, help='Max tokens to predict')
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
prompt = args.prompt
|
|
image = Image.open(args.image_path)
|
|
|
|
# Load model
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='cpu', trust_remote_code=True)
|
|
|
|
# With only one line to enable BigDL-LLM optimization on model
|
|
# For successful BigDL-LLM optimization on Fuyu, skip the 'vision_embed_tokens' module during optimization
|
|
model = optimize_model(model,
|
|
low_bit='sym_int4',
|
|
modules_to_not_convert=['vision_embed_tokens'])
|
|
|
|
# Load processor
|
|
processor = FuyuProcessor.from_pretrained(model_path)
|
|
|
|
# Generate predicted tokens
|
|
with torch.inference_mode():
|
|
inputs = processor(text=prompt, images=image, return_tensors="pt")
|
|
st = time.time()
|
|
generation_outputs = model.generate(**inputs,
|
|
max_new_tokens=args.n_predict)
|
|
end = time.time()
|
|
outputs = processor.batch_decode(generation_outputs[:, -args.n_predict:], skip_special_tokens=True)
|
|
print(f'Inference time: {end-st} s')
|
|
print('-'*20, 'Prompt', '-'*20)
|
|
print(prompt)
|
|
print('-'*20, 'Output', '-'*20)
|
|
for output in outputs:
|
|
# '\x04' is the "beginning of answer" token
|
|
# See https://huggingface.co/adept/fuyu-8b#how-to-use
|
|
answer = output.split('\x04 ', 1)[1] if '\x04' in output else ''
|
|
print(answer)
|