9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
89 lines
3.9 KiB
Python
89 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 torch
|
|
import time
|
|
import argparse
|
|
from ipex_llm import optimize_model
|
|
from ipex_llm.optimize import low_memory_init, load_low_bit
|
|
from transformers import AutoModelForCausalLM, 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
|
|
LLAMA2_PROMPT_FORMAT = """### HUMAN:
|
|
{prompt}
|
|
|
|
### RESPONSE:
|
|
"""
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Example of saving and loading the optimized model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-chat-hf",
|
|
help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded'
|
|
', or the path to the huggingface checkpoint folder')
|
|
parser.add_argument('--low-bit', type=str, default="sym_int4",
|
|
choices=['sym_int4', 'asym_int4', 'sym_int5', 'asym_int5', 'sym_int8'],
|
|
help='The quantization type the model will convert to.')
|
|
parser.add_argument('--save-path', type=str, default=None,
|
|
help='The path to save the low-bit model.')
|
|
parser.add_argument('--load-path', type=str, default=None,
|
|
help='The path to load the low-bit model.')
|
|
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()
|
|
model_path = args.repo_id_or_model_path
|
|
low_bit = args.low_bit
|
|
load_path = args.load_path
|
|
if load_path:
|
|
# Fast and low cost by loading model on meta device
|
|
with low_memory_init():
|
|
model = AutoModelForCausalLM.from_pretrained(load_path, torch_dtype="auto", trust_remote_code=True)
|
|
model = load_low_bit(model, load_path)
|
|
tokenizer = LlamaTokenizer.from_pretrained(load_path)
|
|
else:
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
|
|
model = optimize_model(model, low_bit=low_bit)
|
|
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
|
|
save_path = args.save_path
|
|
if save_path:
|
|
model.save_low_bit(save_path)
|
|
tokenizer.save_pretrained(save_path)
|
|
print(f"Model and tokenizer are saved to {save_path}")
|
|
|
|
# please save/load model before you run it on GPU
|
|
model = model.to('xpu')
|
|
|
|
# Generate predicted tokens
|
|
with torch.inference_mode():
|
|
prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
|
|
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
|
|
# ipex model needs a warmup, then inference time can be accurate
|
|
output = model.generate(input_ids,
|
|
max_new_tokens=args.n_predict)
|
|
|
|
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, 'Output', '-'*20)
|
|
print(output_str)
|