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

* fix

* link demo to paddlenlp

demo/ofa/bert/README.md

+[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。  

demo/ofa/bert/export_model.py

-# 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)