finetune_ner.py

#   Copyright (c) 2018 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 os
import re
import time
import logging
import six
import json
from random import random
from tqdm import tqdm
from collections import OrderedDict
from functools import reduce, partial
from pathlib import Path
from visualdl import LogWriter

import numpy as np
import multiprocessing
import pickle
import logging

from sklearn.metrics import f1_score
import paddle as P

from propeller import log
import propeller.paddle as propeller

log.setLevel(logging.DEBUG)
logging.getLogger().setLevel(logging.DEBUG)

from demo.utils import create_if_not_exists, get_warmup_and_linear_decay
from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification, ErnieModelForTokenClassification
from ernie.tokenizing_ernie import ErnieTokenizer
#from ernie.optimization import AdamW, LinearDecay

parser = propeller.ArgumentParser('NER model with ERNIE')
parser.add_argument('--max_seqlen', type=int, default=256)
parser.add_argument('--bsz', type=int, default=32)
parser.add_argument('--data_dir', type=str, required=True)
parser.add_argument('--epoch', type=int, default=6)
parser.add_argument(
    '--warmup_proportion',
    type=float,
    default=0.1,
    help='if use_lr_decay is set, '
    'learning rate will raise to `lr` at `warmup_proportion` * `max_steps` and decay to 0. at `max_steps`'
)
parser.add_argument(
    '--max_steps',
    type=int,
    required=True,
    help='max_train_steps, set this to EPOCH * NUM_SAMPLES / BATCH_SIZE, used in learning rate scheduler'
)
parser.add_argument(
    '--use_amp',
    action='store_true',
    help='only activate AMP(auto mixed precision accelatoin) on TensorCore compatible devices'
)

parser.add_argument('--from_pretrained', type=Path, required=True)
parser.add_argument('--lr', type=float, default=5e-5, help='learning rate')
parser.add_argument(
    '--save_dir', type=Path, required=True, help='model output directory')
parser.add_argument(
    '--wd', type=float, default=0.01, help='weight decay, aka L2 regularizer')
args = parser.parse_args()

tokenizer = ErnieTokenizer.from_pretrained(args.from_pretrained)


def tokenizer_func(inputs):
    ret = inputs.split(b'\2')
    tokens, orig_pos = [], []
    for i, r in enumerate(ret):
        t = tokenizer.tokenize(r)
        for tt in t:
            tokens.append(tt)
            orig_pos.append(i)
    assert len(tokens) == len(orig_pos)
    return tokens + orig_pos


def tokenizer_func_for_label(inputs):
    return inputs.split(b'\2')


feature_map = {
    b"B-PER": 0,
    b"I-PER": 1,
    b"B-ORG": 2,
    b"I-ORG": 3,
    b"B-LOC": 4,
    b"I-LOC": 5,
    b"O": 6,
}
other_tag_id = feature_map[b'O']

feature_column = propeller.data.FeatureColumns([
    propeller.data.TextColumn(
        'text_a',
        unk_id=tokenizer.unk_id,
        vocab_dict=tokenizer.vocab,
        tokenizer=tokenizer_func), propeller.data.TextColumn(
            'label',
            unk_id=other_tag_id,
            vocab_dict=feature_map,
            tokenizer=tokenizer_func_for_label, )
])


def before(seg, label):
    seg, orig_pos = np.split(seg, 2)
    aligned_label = label[orig_pos]
    seg, _ = tokenizer.truncate(seg, [], args.max_seqlen)
    aligned_label, _ = tokenizer.truncate(aligned_label, [], args.max_seqlen)
    orig_pos, _ = tokenizer.truncate(orig_pos, [], args.max_seqlen)

    sentence, segments = tokenizer.build_for_ernie(
        seg
    )  #utils.data.build_1_pair(seg, max_seqlen=args.max_seqlen, cls_id=cls_id, sep_id=sep_id)
    aligned_label = np.concatenate([[0], aligned_label, [0]], 0)
    orig_pos = np.concatenate([[0], orig_pos, [0]])

    assert len(aligned_label) == len(sentence) == len(orig_pos), (
        len(aligned_label), len(sentence), len(orig_pos))  # alinged
    return sentence, segments, aligned_label, label, orig_pos

train_ds = feature_column.build_dataset('train', data_dir=os.path.join(args.data_dir, 'train'), shuffle=True, repeat=False, use_gz=False) \
                               .map(before) \
                               .padded_batch(args.bsz, (0,0,-100, other_tag_id + 1, 0)) \

dev_ds = feature_column.build_dataset('dev', data_dir=os.path.join(args.data_dir, 'dev'), shuffle=False, repeat=False, use_gz=False) \
                               .map(before) \
                               .padded_batch(args.bsz, (0,0,-100, other_tag_id + 1,0)) \

test_ds = feature_column.build_dataset('test', data_dir=os.path.join(args.data_dir, 'test'), shuffle=False, repeat=False, use_gz=False) \
                               .map(before) \
                               .padded_batch(args.bsz, (0,0,-100, other_tag_id + 1,0)) \


def evaluate(model, dataset):
    model.eval()
    with P.no_grad():
        chunkf1 = propeller.metrics.ChunkF1(None, None, None, len(feature_map))
        for step, (ids, sids, aligned_label, label, orig_pos
                   ) in enumerate(P.io.DataLoader(
                       dataset, batch_size=None)):
            loss, logits = model(ids, sids)
            #print('\n'.join(map(str, logits.numpy().tolist())))

            assert orig_pos.shape[0] == logits.shape[0] == ids.shape[
                0] == label.shape[0]
            for pos, lo, la, id in zip(orig_pos.numpy(),
                                       logits.numpy(),
                                       label.numpy(), ids.numpy()):
                _dic = OrderedDict()
                assert len(pos) == len(lo) == len(id)
                for _pos, _lo, _id in zip(pos, lo, id):
                    if _id > tokenizer.mask_id:  # [MASK] is the largest special token
                        _dic.setdefault(_pos, []).append(_lo)
                merged_lo = np.array(
                    [np.array(l).mean(0) for _, l in six.iteritems(_dic)])
                merged_preds = np.argmax(merged_lo, -1)
                la = la[np.where(la != (other_tag_id + 1))]  #remove pad
                if len(la) > len(merged_preds):
                    log.warn(
                        'accuracy loss due to truncation: label len:%d, truncate to %d'
                        % (len(la), len(merged_preds)))
                    merged_preds = np.pad(merged_preds,
                                          [0, len(la) - len(merged_preds)],
                                          mode='constant',
                                          constant_values=7)
                else:
                    assert len(la) == len(
                        merged_preds
                    ), 'expect label == prediction, got %d vs %d' % (
                        la.shape, merged_preds.shape)
                chunkf1.update((merged_preds, la, np.array(len(la))))
        #f1 = f1_score(np.concatenate(all_label), np.concatenate(all_pred), average='macro')
        f1 = chunkf1.eval()
    model.train()
    return f1


model = ErnieModelForTokenClassification.from_pretrained(
    args.from_pretrained,
    num_labels=len(feature_map),
    name='',
    has_pooler=False)

g_clip = P.nn.ClipGradByGlobalNorm(1.0)  #experimental
param_name_to_exclue_from_weight_decay = re.compile(
    r'.*layer_norm_scale|.*layer_norm_bias|.*b_0')
lr_scheduler = P.optimizer.lr.LambdaDecay(
    args.lr,
    get_warmup_and_linear_decay(args.max_steps,
                                int(args.warmup_proportion * args.max_steps)))
opt = P.optimizer.AdamW(
    lr_scheduler,
    parameters=model.parameters(),
    weight_decay=args.wd,
    apply_decay_param_fun=lambda n: not param_name_to_exclue_from_weight_decay.match(n),
    grad_clip=g_clip)

scaler = P.amp.GradScaler(enable=args.use_amp)
with LogWriter(
        logdir=str(create_if_not_exists(args.save_dir / 'vdl'))) as log_writer:
    with P.amp.auto_cast(enable=args.use_amp):
        for epoch in range(args.epoch):
            for step, (
                    ids, sids, aligned_label, label, orig_pos
            ) in enumerate(P.io.DataLoader(
                    train_ds, batch_size=None)):
                loss, logits = model(ids, sids, labels=aligned_label)
                #loss, logits = model(ids, sids, labels=aligned_label, loss_weights=P.cast(ids != 0, 'float32'))
                loss = scaler.scale(loss)
                loss.backward()
                scaler.minimize(opt, loss)
                model.clear_gradients()
                lr_scheduler.step()

                if step % 10 == 0:
                    _lr = lr_scheduler.get_lr()
                    if args.use_amp:
                        _l = (loss / scaler._scale).numpy()
                        msg = '[step-%d] train loss %.5f lr %.3e scaling %.3e' % (
                            step, _l, _lr, scaler._scale.numpy())
                    else:
                        _l = loss.numpy()
                        msg = '[step-%d] train loss %.5f lr %.3e' % (step, _l,
                                                                     _lr)
                    log.debug(msg)
                    log_writer.add_scalar('loss', _l, step=step)
                    log_writer.add_scalar('lr', _lr, step=step)

                if step % 100 == 0:
                    f1 = evaluate(model, dev_ds)
                    log.debug('eval f1: %.5f' % f1)
                    log_writer.add_scalar('eval/f1', f1, step=step)
                    if args.save_dir is not None:
                        P.save(model.state_dict(), args.save_dir / 'ckpt.bin')

f1 = evaluate(model, dev_ds)
log.debug('final eval f1: %.5f' % f1)
log_writer.add_scalar('eval/f1', f1, step=step)
if args.save_dir is not None:
    P.save(model.state_dict(), args.save_dir / 'ckpt.bin')