run_squad.py

# Copyright (c) 2020 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 collections
import os
import random
import time

from functools import partial
import numpy as np
import paddle

from paddle.io import DataLoader
from args import parse_args

import paddlenlp as ppnlp

from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.transformers import BertForQuestionAnswering, BertTokenizer, ErnieForQuestionAnswering, ErnieTokenizer
from paddlenlp.metrics.squad import squad_evaluate, compute_predictions

MODEL_CLASSES = {
    "bert": (BertForQuestionAnswering, BertTokenizer),
    "ernie": (ErnieForQuestionAnswering, ErnieTokenizer)
}


def set_seed(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    paddle.seed(args.seed)


class CrossEntropyLossForSQuAD(paddle.nn.Layer):
    def __init__(self):
        super(CrossEntropyLossForSQuAD, self).__init__()

    def forward(self, y, label):
        start_logits, end_logits = y
        start_position, end_position = label
        start_position = paddle.unsqueeze(start_position, axis=-1)
        end_position = paddle.unsqueeze(end_position, axis=-1)
        start_loss = paddle.nn.functional.softmax_with_cross_entropy(
            logits=start_logits, label=start_position, soft_label=False)
        start_loss = paddle.mean(start_loss)
        end_loss = paddle.nn.functional.softmax_with_cross_entropy(
            logits=end_logits, label=end_position, soft_label=False)
        end_loss = paddle.mean(end_loss)

        loss = (start_loss + end_loss) / 2
        return loss


def evaluate(model, data_loader, args):
    model.eval()

    RawResult = collections.namedtuple(
        "RawResult", ["unique_id", "start_logits", "end_logits"])

    all_results = []
    tic_eval = time.time()

    for batch in data_loader:
        input_ids, segment_ids, unipue_ids = batch
        start_logits_tensor, end_logits_tensor = model(input_ids, segment_ids)

        for idx in range(unipue_ids.shape[0]):
            if len(all_results) % 1000 == 0 and len(all_results):
                print("Processing example: %d" % len(all_results))
                print('time per 1000:', time.time() - tic_eval)
                tic_eval = time.time()
            unique_id = int(unipue_ids[idx])
            start_logits = [float(x) for x in start_logits_tensor.numpy()[idx]]
            end_logits = [float(x) for x in end_logits_tensor.numpy()[idx]]
            all_results.append(
                RawResult(
                    unique_id=unique_id,
                    start_logits=start_logits,
                    end_logits=end_logits))

    all_predictions, all_nbest_json, scores_diff_json = compute_predictions(
        data_loader.dataset.examples, data_loader.dataset.data, all_results,
        args.n_best_size, args.max_answer_length, args.do_lower_case,
        args.version_2_with_negative, args.null_score_diff_threshold,
        args.verbose, data_loader.dataset.tokenizer)

    squad_evaluate(data_loader.dataset.examples, all_predictions,
                   scores_diff_json, 1.0)

    model.train()


def do_train(args):
    paddle.set_device("gpu" if args.n_gpu else "cpu")
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()

    args.model_type = args.model_type.lower()
    model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
    root = args.data_path

    set_seed(args)

    train_dataset = ppnlp.datasets.SQuAD(
        tokenizer=tokenizer,
        doc_stride=args.doc_stride,
        root=root,
        version_2_with_negative=args.version_2_with_negative,
        max_query_length=args.max_query_length,
        max_seq_length=args.max_seq_length,
        segment="train")

    train_batch_sampler = paddle.io.DistributedBatchSampler(
        train_dataset, batch_size=args.batch_size, shuffle=True)

    train_batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]),  # input
        Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]),  # segment
        Stack(),  # unipue_id
        Stack(dtype="int64"),  # start_pos
        Stack(dtype="int64")  # end_pos
    ): [data for i, data in enumerate(fn(samples)) if i != 2]

    train_data_loader = DataLoader(
        dataset=train_dataset,
        batch_sampler=train_batch_sampler,
        collate_fn=train_batchify_fn,
        return_list=True)

    dev_dataset = ppnlp.datasets.SQuAD(
        tokenizer=tokenizer,
        doc_stride=args.doc_stride,
        root=root,
        version_2_with_negative=args.version_2_with_negative,
        max_query_length=args.max_query_length,
        max_seq_length=args.max_seq_length,
        segment="dev")

    dev_batch_sampler = paddle.io.BatchSampler(
        dev_dataset, batch_size=args.batch_size, shuffle=False)

    dev_batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]),  # input
        Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]),  # segment
        Stack()  # unipue_id
    ): fn(samples)

    dev_data_loader = DataLoader(
        dataset=dev_dataset,
        batch_sampler=dev_batch_sampler,
        collate_fn=dev_batchify_fn,
        return_list=True)

    model = model_class.from_pretrained(args.model_name_or_path)

    if paddle.distributed.get_world_size() > 1:
        model = paddle.DataParallel(model)

    lr_scheduler = paddle.optimizer.lr.LambdaDecay(
        args.learning_rate,
        lambda current_step, warmup_proportion=args.warmup_proportion,
        num_training_steps=args.max_steps if args.max_steps > 0 else
        (len(train_dataset.examples)//args.batch_size*args.num_train_epochs): float(
            current_step) / float(max(1, warmup_proportion*num_training_steps))
        if current_step < warmup_proportion*num_training_steps else max(
            0.0,
            float(num_training_steps - current_step) / float(
                max(1, num_training_steps - warmup_proportion*num_training_steps))))

    optimizer = paddle.optimizer.AdamW(
        learning_rate=lr_scheduler,
        epsilon=args.adam_epsilon,
        parameters=model.parameters(),
        weight_decay=args.weight_decay,
        apply_decay_param_fun=lambda x: x in [
            p.name for n, p in model.named_parameters()
            if not any(nd in n for nd in ["bias", "norm"])
        ])
    criterion = CrossEntropyLossForSQuAD()

    global_step = 0
    tic_train = time.time()
    for epoch in range(args.num_train_epochs):
        for step, batch in enumerate(train_data_loader):
            global_step += 1
            input_ids, segment_ids, start_positions, end_positions = batch

            logits = model(input_ids=input_ids, token_type_ids=segment_ids)
            loss = criterion(logits, (start_positions, end_positions))

            if global_step % args.logging_steps == 0:
                print(
                    "global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
                    % (global_step, epoch, step, loss,
                       args.logging_steps / (time.time() - tic_train)))
                tic_train = time.time()
            loss.backward()
            optimizer.step()
            lr_scheduler.step()
            optimizer.clear_gradients()

            if global_step % args.save_steps == 0:
                if (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0:
                    output_dir = os.path.join(args.output_dir,
                                              "model_%d" % global_step)
                    if not os.path.exists(output_dir):
                        os.makedirs(output_dir)
                    # need better way to get inner model of DataParallel
                    model_to_save = model._layers if isinstance(
                        model, paddle.DataParallel) else model
                    model_to_save.save_pretrained(output_dir)
                    tokenizer.save_pretrained(output_dir)
                    print('Saving checkpoint to:', output_dir)

        if (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0:
            evaluate(model, dev_data_loader, args)


if __name__ == "__main__":
    args = parse_args()
    if args.n_gpu > 1:
        paddle.distributed.spawn(do_train, args=(args, ), nprocs=args.n_gpu)
    else:
        do_train(args)