未验证 提交 34d44715 编写于 作者: C ceci3 提交者: GitHub

demo of ofa-bert link to demo in paddlenlp (#783)

* fix

* link demo to paddlenlp
上级 f43a57b2
[完整代码](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/model_compression/ofa)
# OFA压缩PaddleNLP-BERT模型
BERT-base模型是一个迁移能力很强的通用语义表示模型,但是模型中也有一些参数冗余。本教程将介绍如何使用PaddleSlim对[PaddleNLP](https://paddlenlp.readthedocs.io/zh/latest/)中BERT-base模型进行压缩。
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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 argparse
import logging
import os
import random
import time
import json
from functools import partial
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddlenlp.transformers import BertModel, BertForSequenceClassification, BertTokenizer
from paddlenlp.utils.log import logger
from paddleslim.nas.ofa import OFA, utils
from paddleslim.nas.ofa.convert_super import Convert, supernet
from paddleslim.nas.ofa.layers import BaseBlock
MODEL_CLASSES = {"bert": (BertForSequenceClassification, BertTokenizer), }
def parse_args():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()), )
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(
sum([
list(classes[-1].pretrained_init_configuration.keys())
for classes in MODEL_CLASSES.values()
], [])), )
parser.add_argument(
"--sub_model_output_dir",
default=None,
type=str,
required=True,
help="The output directory where the sub model predictions and checkpoints will be written.",
)
parser.add_argument(
"--static_sub_model",
default=None,
type=str,
help="The output directory where the sub static model will be written. If set to None, not export static model",
)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.", )
parser.add_argument(
"--n_gpu",
type=int,
default=1,
help="number of gpus to use, 0 for cpu.")
parser.add_argument(
'--width_mult',
type=float,
default=1.0,
help="width mult you want to export")
args = parser.parse_args()
return args
def export_static_model(model, model_path, max_seq_length):
input_shape = [
paddle.static.InputSpec(
shape=[None, max_seq_length], dtype='int64'),
paddle.static.InputSpec(
shape=[None, max_seq_length], dtype='int64')
]
net = paddle.jit.to_static(model, input_spec=input_shape)
paddle.jit.save(net, model_path)
def do_train(args):
paddle.set_device("gpu" if args.n_gpu else "cpu")
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config_path = os.path.join(args.model_name_or_path, 'model_config.json')
cfg_dict = dict(json.loads(open(config_path).read()))
num_labels = cfg_dict['num_classes']
model = model_class.from_pretrained(
args.model_name_or_path, num_classes=num_labels)
origin_model = model_class.from_pretrained(
args.model_name_or_path, num_classes=num_labels)
sp_config = supernet(expand_ratio=[1.0, args.width_mult])
model = Convert(sp_config).convert(model)
ofa_model = OFA(model)
sd = paddle.load(
os.path.join(args.model_name_or_path, 'model_state.pdparams'))
ofa_model.model.set_state_dict(sd)
best_config = utils.dynabert_config(ofa_model, args.width_mult)
ofa_model.export(
best_config,
input_shapes=[[1, args.max_seq_length], [1, args.max_seq_length]],
input_dtypes=['int64', 'int64'],
origin_model=origin_model)
for name, sublayer in origin_model.named_sublayers():
if isinstance(sublayer, paddle.nn.MultiHeadAttention):
sublayer.num_heads = int(args.width_mult * sublayer.num_heads)
output_dir = os.path.join(args.sub_model_output_dir,
"model_width_%.5f" % args.width_mult)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = origin_model
model_to_save.save_pretrained(output_dir)
if args.static_sub_model != None:
export_static_model(origin_model, args.static_sub_model,
args.max_seq_length)
def print_arguments(args):
"""print arguments"""
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).items()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
if __name__ == "__main__":
args = parse_args()
print_arguments(args)
do_train(args)
此差异已折叠。
此差异已折叠。
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