wehub-resource-sync
97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import json
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import os
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from functools import partial
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from types import SimpleNamespace
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import paddle
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from utils.data import convert_example_for_reft
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from paddlenlp.datasets import load_dataset
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from paddlenlp.peft.reft import ReFTModel, do_predict
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from paddlenlp.transformers import AutoModelForCausalLM, AutoTokenizer
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device = "gpu" if paddle.is_compiled_with_cuda() else "cpu"
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def get_intervention_info(reft_config_file):
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with open(os.path.join(reft_config_file, "config.json"), "r") as f:
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intervention_info = json.load(f)
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intervention_info["num_interventions"] = len(intervention_info["representations"])
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return intervention_info
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def reft_predict(predictor_args):
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intervention_info = get_intervention_info(predictor_args.reft_path)
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tokenizer = AutoTokenizer.from_pretrained(
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predictor_args.model_name_or_path,
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padding_side="right",
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)
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tokenizer.pad_token_id = tokenizer.eos_token_id
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dev_ds = load_dataset(
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"json",
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data_files=predictor_args.data_file,
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)[0]
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trans_func = partial(
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convert_example_for_reft,
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tokenizer=tokenizer,
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data_args=SimpleNamespace(
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**{
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"max_length": predictor_args.max_length,
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"src_length": predictor_args.src_length,
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"autoregressive": False,
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}
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),
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positions=intervention_info["position"],
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num_interventions=intervention_info["num_interventions"],
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)
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dev_ds = dev_ds.map(partial(trans_func, is_test=True, zero_padding=False, flash_mask=False))
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model = AutoModelForCausalLM.from_pretrained(predictor_args.model_name_or_path, dtype=paddle.bfloat16)
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reft_model = ReFTModel.from_pretrained(predictor_args.reft_path, model)
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do_predict(
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intervenable=reft_model,
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tokenizer=tokenizer,
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eval_dataset=dev_ds,
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batch_size=predictor_args.batch_size,
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predict_path=predictor_args.output_file,
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num_beams=predictor_args.num_beams,
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max_length=predictor_args.max_length,
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)
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def get_pred_parser():
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_name_or_path", type=str, help="The base model name or path")
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parser.add_argument("--reft_path", type=str, help="The reft model path")
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parser.add_argument("--output_file", type=str, help="The output file path")
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parser.add_argument("--batch_size", type=int, help="The batch size in prediction")
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parser.add_argument("--data_file", type=str, help="The dataset name or path")
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parser.add_argument("--max_length", type=int, default=1024, help="The maximum length of input sequences")
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parser.add_argument("--src_length", type=int, default=512, help="The source sequence length")
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parser.add_argument("--num_beams", type=int, default=4, help="The maximum length of input sequences")
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return parser.parse_args()
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def main():
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predictor_args = get_pred_parser()
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reft_predict(predictor_args)
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if __name__ == "__main__":
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main()
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