55ce091242
* Add GLM4-Edge-V examples * polish readme * revert wrong changes * polish readme * polish readme * little polish in reference info and indent * Small fix and sample output updates * Update main readme --------- Co-authored-by: Yuwen Hu <yuwen.hu@intel.com>
122 lines
4.8 KiB
Python
122 lines
4.8 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 torch
|
|
import time
|
|
import argparse
|
|
import requests
|
|
|
|
from ipex_llm.transformers import AutoModelForCausalLM
|
|
|
|
from PIL import Image
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for glm-edge-v model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str,
|
|
help='The Hugging Face or ModelScope repo id for the glm-edge-v model to be downloaded'
|
|
', or the path to the 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="What is in the image?",
|
|
help='Prompt to infer')
|
|
parser.add_argument('--n-predict', type=int, default=32,
|
|
help='Max tokens to predict')
|
|
parser.add_argument('--modelscope', action="store_true", default=False,
|
|
help="Use models from modelscope")
|
|
|
|
args = parser.parse_args()
|
|
|
|
if args.modelscope:
|
|
from modelscope import AutoTokenizer, AutoImageProcessor
|
|
model_hub = 'modelscope'
|
|
else:
|
|
from transformers import AutoTokenizer, AutoImageProcessor
|
|
model_hub = 'huggingface'
|
|
|
|
model_path = args.repo_id_or_model_path if args.repo_id_or_model_path else \
|
|
("ZhipuAI/glm-edge-v-5b" if args.modelscope else "THUDM/glm-edge-v-5b")
|
|
image_path = args.image_url_or_path
|
|
|
|
# Load model in 4 bit,
|
|
# which convert the relevant layers in the model into INT4 format
|
|
# When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function.
|
|
# This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
|
|
model = AutoModelForCausalLM.from_pretrained(model_path,
|
|
load_in_4bit=True,
|
|
optimize_model=True,
|
|
trust_remote_code=True,
|
|
modules_to_not_convert=["vision"],
|
|
use_cache=True,
|
|
model_hub=model_hub)
|
|
model = model.half().to('xpu')
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
|
|
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)
|
|
|
|
image_processor = AutoImageProcessor.from_pretrained(model_path, trust_remote_code=True)
|
|
|
|
with torch.inference_mode():
|
|
pixel_values = image_processor(images=[image], return_tensors='pt').pixel_values
|
|
pixel_values = pixel_values.to('xpu')
|
|
|
|
# The following code for generation is adapted from https://huggingface.co/THUDM/glm-edge-v-5b#inference
|
|
messages = [{
|
|
"role": "user",
|
|
"content": [{"type": "image"},
|
|
{"type": "text",
|
|
"text": args.prompt}]
|
|
}]
|
|
|
|
inputs = tokenizer.apply_chat_template(
|
|
messages,
|
|
add_generation_prompt=True,
|
|
return_dict=True,
|
|
tokenize=True,
|
|
return_tensors="pt"
|
|
)
|
|
inputs = inputs.to('xpu')
|
|
|
|
generate_kwargs = {
|
|
**inputs,
|
|
"pixel_values": pixel_values,
|
|
"max_new_tokens": args.n_predict,
|
|
}
|
|
|
|
# ipex_llm model needs a warmup, then inference time can be accurate
|
|
output = model.generate(**generate_kwargs)
|
|
|
|
st = time.time()
|
|
output = model.generate(**generate_kwargs)
|
|
torch.xpu.synchronize()
|
|
end = time.time()
|
|
|
|
output_str = tokenizer.decode(
|
|
output[0][len(inputs["input_ids"][0]):],
|
|
skip_special_tokens=True
|
|
)
|
|
|
|
print(f'Inference time: {end-st} s')
|
|
print('-'*20, 'Prompt', '-'*20)
|
|
print(args.prompt)
|
|
print('-'*20, 'Output', '-'*20)
|
|
print(output_str)
|