93 lines
4.1 KiB
Python
93 lines
4.1 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 torch
|
||
import time
|
||
import argparse
|
||
|
||
from ipex_llm.transformers import AutoModelForCausalLM
|
||
from transformers import LlamaTokenizer
|
||
|
||
# you could tune the prompt based on your own model,
|
||
# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style
|
||
DEFAULT_SYSTEM_PROMPT = """\
|
||
"""
|
||
|
||
def get_prompt(message: str, chat_history: list[tuple[str, str]],
|
||
system_prompt: str) -> str:
|
||
texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
|
||
# The first user input is _not_ stripped
|
||
do_strip = False
|
||
for user_input, response in chat_history:
|
||
user_input = user_input.strip() if do_strip else user_input
|
||
do_strip = True
|
||
texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
|
||
message = message.strip() if do_strip else message
|
||
texts.append(f'{message} [/INST]')
|
||
return ''.join(texts)
|
||
|
||
if __name__ == '__main__':
|
||
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
|
||
parser.add_argument('--repo-id-or-model-path', type=str, default="LinkSoul/Chinese-Llama-2-7b",
|
||
help='The huggingface repo id for the Chinese Llama2 (e.g. `LinkSoul/Chinese-Llama-2-7b`) to be downloaded'
|
||
', or the path to the huggingface checkpoint folder')
|
||
parser.add_argument('--prompt', type=str, default="AI是什么？",
|
||
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 in 4 bit,
|
||
# which convert the relevant layers in the model into INT4 format
|
||
# if your selected model is capable of utilizing previous key/value attentions
|
||
# to enhance decoding speed, but has `"use_cache": false` in its model config,
|
||
# it is important to set `use_cache=True` explicitly to obtain optimal
|
||
# performance with IPEX-LLM INT4 optimizations
|
||
# 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,
|
||
use_cache=True)
|
||
model = model.to('xpu')
|
||
|
||
# Load tokenizer
|
||
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||
|
||
# Generate predicted tokens
|
||
with torch.inference_mode():
|
||
prompt = get_prompt(args.prompt, [], system_prompt=DEFAULT_SYSTEM_PROMPT)
|
||
input_ids = tokenizer.encode(prompt, 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 = output.cpu()
|
||
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)
|