68a770745b
* Add moonlight GPU example and update table * Small fix * Fix based on comments * Small fix
77 lines
3.1 KiB
Python
77 lines
3.1 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 time
|
|
import argparse
|
|
import torch
|
|
from ipex_llm.transformers import AutoModelForCausalLM
|
|
from transformers import AutoTokenizer
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Moonlight model')
|
|
parser.add_argument('--converted-model-path', type=str, required=True,
|
|
help='Model path to the converted Moonlight model by convert.py')
|
|
parser.add_argument('--prompt', type=str, default="What is AI?",
|
|
help='Prompt to infer')
|
|
parser.add_argument('--n-predict', type=int, default=32,
|
|
help='Max tokens to predict')
|
|
|
|
args = parser.parse_args()
|
|
converted_model_path = args.converted_model_path
|
|
|
|
# Load model in 4 bit,
|
|
# which convert the relevant layers in the model into INT4 format
|
|
model = AutoModelForCausalLM.from_pretrained(converted_model_path,
|
|
load_in_4bit=True,
|
|
optimize_model=True,
|
|
trust_remote_code=True,
|
|
use_cache=True)
|
|
model = model.to('xpu')
|
|
|
|
# Load tokenizer
|
|
tokenizer = AutoTokenizer.from_pretrained(converted_model_path, trust_remote_code=True)
|
|
|
|
# Generate predicted tokens
|
|
with torch.inference_mode():
|
|
# here the prompt tuning refers to
|
|
# https://huggingface.co/moonshotai/Moonlight-16B-A3B-Instruct#inference-with-hugging-face-transformers
|
|
messages = [
|
|
{"role": "system", "content": "You are a helpful assistant provided by Moonshot-AI."},
|
|
{"role": "user", "content": args.prompt}
|
|
]
|
|
input_ids = tokenizer.apply_chat_template(
|
|
messages,
|
|
add_generation_prompt=True,
|
|
return_tensors="pt"
|
|
).to('xpu')
|
|
|
|
# ipex_llm model needs a warmup, then inference time can be accurate
|
|
output = model.generate(input_ids,
|
|
max_new_tokens=args.n_predict)
|
|
|
|
# start inference
|
|
st = time.time()
|
|
output = model.generate(input_ids,
|
|
max_new_tokens=args.n_predict)
|
|
torch.xpu.synchronize()
|
|
end = time.time()
|
|
|
|
output_str = tokenizer.decode(output[0], skip_special_tokens=False)
|
|
print(f'Inference time: {end-st} s')
|
|
print('-'*20, 'Prompt', '-'*20)
|
|
print(args.prompt)
|
|
print('-'*20, 'Output', '-'*20)
|
|
print(output_str)
|