6930422b42
* codeshell fix wrong urls * restart runner * add RWKV CPU & GPU example (rwkv-4-world-7b) * restart runner * update submodule * fix runner * runner-test --------- Co-authored-by: Shengsheng Huang <shengsheng.huang@intel.com>
71 lines
3 KiB
Python
71 lines
3 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 ag8reed 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 AutoModel, AutoTokenizer, AutoModelForCausalLM
|
|
from bigdl.llm import optimize_model
|
|
|
|
# you could tune the prompt based on your own model,
|
|
# here the prompt tuning refers to https://huggingface.co/RWKV/rwkv-4-world-7b
|
|
RWKV_PROMPT_FORMAT = "Question: {prompt}\n\nAnswer:"
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for RWKV model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="RWKV/rwkv-4-world-7b",
|
|
help='The huggingface repo id for the RWKV model to be downloaded'
|
|
', or the path to the huggingface checkpoint folder')
|
|
parser.add_argument('--prompt', type=str, default="\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese.",
|
|
help='Prompt to infer')
|
|
parser.add_argument('--n-predict', type=int, default=40,
|
|
help='Max tokens to predict')
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
|
|
# First load the model in fp16 dtype
|
|
model = AutoModelForCausalLM.from_pretrained(model_path,
|
|
trust_remote_code=True,
|
|
low_cpu_mem_usage=True,
|
|
torch_dtype=torch.half)
|
|
|
|
# Call the `_rescale_layers` method, prepare to convert to int4
|
|
model.rwkv._rescale_layers()
|
|
|
|
# With only one line to enable BigDL-LLM optimization on model
|
|
model = optimize_model(model)
|
|
|
|
# Load tokenizer
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
|
|
# Generate predicted tokens
|
|
with torch.inference_mode():
|
|
prompt = RWKV_PROMPT_FORMAT.format(prompt = args.prompt)
|
|
|
|
inputs = tokenizer(prompt, return_tensors="pt")
|
|
st = time.time()
|
|
output = model.generate(inputs["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)
|