65 lines
2.7 KiB
Python
65 lines
2.7 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 torch
|
|
import time
|
|
import argparse
|
|
|
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
|
|
from ipex_llm import optimize_model
|
|
|
|
# you could tune the prompt based on your own model,
|
|
FLAN_T5_PROMPT_FORMAT = "<|User|>:{prompt}"
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for flan-t5 model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="google/flan-t5-xxl",
|
|
help='The huggingface repo id for the flan-t5 model to be downloaded'
|
|
', or the path to the huggingface checkpoint folder')
|
|
parser.add_argument('--prompt', type=str, default="Translate to German: My name is Arthur",
|
|
help='Prompt to infer')
|
|
parser.add_argument('--n-predict', type=int, default=32,
|
|
help='Max tokens to predict')
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
|
|
# Load model
|
|
model = AutoModelForSeq2SeqLM.from_pretrained(model_path, trust_remote_code=True)
|
|
|
|
# With only one line to enable IPEX-LLM optimization on model
|
|
# "wo" module is not converted due to some issues of T5 model
|
|
# (https://github.com/huggingface/transformers/issues/20287),
|
|
# "lm_head" module is not converted to generate outputs with better quality
|
|
model = optimize_model(model, modules_to_not_convert=["wo", "lm_head"])
|
|
|
|
# Load tokenizer
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
|
|
# Generate predicted tokens
|
|
with torch.inference_mode():
|
|
prompt = FLAN_T5_PROMPT_FORMAT.format(prompt=args.prompt)
|
|
input_ids = tokenizer.encode(prompt, return_tensors="pt")
|
|
st = time.time()
|
|
output = model.generate(input_ids,
|
|
max_new_tokens=args.n_predict)
|
|
end = time.time()
|
|
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
|
print(f'Inference time: {end-st} s')
|
|
print('-'*20, 'Prompt', '-'*20)
|
|
print(prompt)
|
|
print('-'*20, 'Output', '-'*20)
|
|
print(output_str)
|