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 math
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 bert_forward(self,
                 input_ids,
                 token_type_ids=None,
                 position_ids=None,
                 attention_mask=None,
                 output_hidden_states=False):
    wtype = self.pooler.dense.fn.weight.dtype if hasattr(
        self.pooler.dense, 'fn') else self.pooler.dense.weight.dtype
    if attention_mask is None:
        attention_mask = paddle.unsqueeze(
            (input_ids == self.pad_token_id).astype(wtype) * -1e9, axis=[1, 2])
    else:
        if attention_mask.ndim == 2:
            # attention_mask [batch_size, sequence_length] -> [batch_size, 1, 1, sequence_length]
            attention_mask = attention_mask.unsqueeze(axis=[1, 2])

    embedding_output = self.embeddings(
        input_ids=input_ids,
        position_ids=position_ids,
        token_type_ids=token_type_ids)
    if output_hidden_states:
        output = embedding_output
        encoder_outputs = []
        for mod in self.encoder.layers:
            output = mod(output, src_mask=attention_mask)
            encoder_outputs.append(output)
        if self.encoder.norm is not None:
            encoder_outputs[-1] = self.encoder.norm(encoder_outputs[-1])
        pooled_output = self.pooler(encoder_outputs[-1])
    else:
        sequence_output = self.encoder(embedding_output, attention_mask)
        pooled_output = self.pooler(sequence_output)
    if output_hidden_states:
        return encoder_outputs, pooled_output
    else:
        return sequence_output, pooled_output


BertModel.forward = bert_forward


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")
    parser.add_argument(
        '--depth_mult',
        type=float,
        default=1.0,
        help="depth 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()))

    kept_layers_index = {}
    if args.depth_mult < 1.0:
        depth = round(cfg_dict["init_args"][0]['num_hidden_layers'] *
                      args.depth_mult)
        cfg_dict["init_args"][0]['num_hidden_layers'] = depth
        for idx, i in enumerate(range(1, depth + 1)):
            kept_layers_index[idx] = math.floor(i / args.depth_mult) - 1

    os.rename(config_path, config_path + '_bak')
    with open(config_path, "w", encoding="utf-8") as f:
        f.write(json.dumps(cfg_dict, ensure_ascii=False))

    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)

    os.rename(config_path + '_bak', config_path)

    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'))

    if len(kept_layers_index) == 0:
        ofa_model.model.set_state_dict(sd)
    else:
        for name, params in ofa_model.model.named_parameters():
            if 'encoder' not in name:
                params.set_value(sd[name])
            else:
                idx = int(name.strip().split('.')[3])
                mapping_name = name.replace(
                    '.' + str(idx) + '.',
                    '.' + str(kept_layers_index[idx]) + '.')
                params.set_value(sd[mapping_name])

    best_config = utils.dynabert_config(ofa_model, args.width_mult)
    for name, sublayer in ofa_model.model.named_sublayers():
        if isinstance(sublayer, paddle.nn.MultiHeadAttention):
            sublayer.num_heads = int(args.width_mult * sublayer.num_heads)

    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)