87 lines
3.9 KiB
Python
87 lines
3.9 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.
|
|
#
|
|
|
|
import os
|
|
import time
|
|
import torch
|
|
import argparse
|
|
import requests
|
|
|
|
from PIL import Image
|
|
from ipex_llm.transformers import AutoModelForCausalLM, init_pipeline_parallel
|
|
from transformers import AutoTokenizer
|
|
|
|
init_pipeline_parallel()
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for THUDM/glm-4v-9b model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/glm-4v-9b",
|
|
help='The huggingface repo id for the THUDM/glm-4v-9b model to be downloaded'
|
|
', or the path to the huggingface checkpoint folder')
|
|
parser.add_argument('--image-url-or-path', type=str,
|
|
default='http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg',
|
|
help='The URL or path to the image to infer')
|
|
parser.add_argument('--prompt', type=str, default="这是什么？",
|
|
help='Prompt to infer')
|
|
parser.add_argument('--n-predict', type=int, default=32,
|
|
help='Max tokens to predict')
|
|
parser.add_argument('--low-bit', type=str, default='sym_int4', help='The quantization type the model will convert to.')
|
|
parser.add_argument('--gpu-num', type=int, default=2, help='GPU number to use')
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
image_path = args.image_url_or_path
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_path,
|
|
load_in_low_bit=args.low_bit,
|
|
optimize_model=True,
|
|
trust_remote_code=True,
|
|
use_cache=True,
|
|
pipeline_parallel_stages=args.gpu_num)
|
|
model = model.half()
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
local_rank = torch.distributed.get_rank()
|
|
|
|
query = args.prompt
|
|
if os.path.exists(image_path):
|
|
image = Image.open(image_path)
|
|
else:
|
|
image = Image.open(requests.get(image_path, stream=True).raw)
|
|
|
|
# here the prompt tuning refers to https://huggingface.co/THUDM/glm-4v-9b/blob/main/README.md
|
|
inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}],
|
|
add_generation_prompt=True,
|
|
tokenize=True,
|
|
return_tensors="pt",
|
|
return_dict=True) # chat mode
|
|
inputs = inputs.to(f'xpu:{local_rank}')
|
|
all_input = [{'image': image_path}, {'text': query}]
|
|
|
|
# Generate predicted tokens
|
|
with torch.inference_mode():
|
|
gen_kwargs = {"max_new_tokens": args.n_predict, "do_sample": False,}
|
|
st = time.time()
|
|
outputs = model.generate(**inputs, **gen_kwargs)
|
|
outputs = outputs[:, inputs['input_ids'].shape[1]:]
|
|
end = time.time()
|
|
if local_rank == args.gpu_num - 1:
|
|
print(f'Inference time: {end-st} s')
|
|
output_str = tokenizer.decode(outputs[0])
|
|
print('-'*20, 'Input', '-'*20)
|
|
print(f'Message: {all_input}')
|
|
print('-'*20, 'Output', '-'*20)
|
|
print(output_str)
|