run_squad.py

#   Copyright (c) 2019 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.
"""Finetuning on SQuAD."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import six
import sys
if six.PY2:
    reload(sys)
    sys.setdefaultencoding('utf8')

import argparse
import collections
import multiprocessing
import os
import time
import numpy as np
import paddle
import paddle.fluid as fluid

from reader.squad import DataProcessor, write_predictions
from model.bert import BertConfig, BertModel
from utils.args import ArgumentGroup, print_arguments, check_cuda
from optimization import optimization
from utils.init import init_pretraining_params, init_checkpoint

# yapf: disable
parser = argparse.ArgumentParser(__doc__)
model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
model_g.add_arg("bert_config_path",         str,  None,           "Path to the json file for bert model config.")
model_g.add_arg("init_checkpoint",          str,  None,           "Init checkpoint to resume training from.")
model_g.add_arg("init_pretraining_params",  str,  None,
                "Init pre-training params which preforms fine-tuning from. If the "
                 "arg 'init_checkpoint' has been set, this argument wouldn't be valid.")
model_g.add_arg("checkpoints",              str,  "checkpoints",  "Path to save checkpoints.")

train_g = ArgumentGroup(parser, "training", "training options.")
train_g.add_arg("epoch",             int,    3,      "Number of epoches for fine-tuning.")
train_g.add_arg("learning_rate",     float,  5e-5,   "Learning rate used to train with warmup.")
train_g.add_arg("lr_scheduler",      str,    "linear_warmup_decay",
                "scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay'])
train_g.add_arg("weight_decay",      float,  0.01,   "Weight decay rate for L2 regularizer.")
train_g.add_arg("warmup_proportion", float,  0.1,
                "Proportion of training steps to perform linear learning rate warmup for.")
train_g.add_arg("save_steps",        int,    1000,   "The steps interval to save checkpoints.")
train_g.add_arg("use_fp16",          bool,   False,  "Whether to use fp16 mixed precision training.")
train_g.add_arg("use_dynamic_loss_scaling",    bool,   True,   "Whether to use dynamic loss scaling in mixed precision training.")
train_g.add_arg("init_loss_scaling",           float,  2**32,
                "Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled.")
train_g.add_arg("incr_every_n_steps",          int,    1000,   "Increases loss scaling every n consecutive.")
train_g.add_arg("decr_every_n_nan_or_inf",     int,    2,
                "Decreases loss scaling every n accumulated steps with nan or inf gradients.")
train_g.add_arg("incr_ratio",                  float,  2.0,
                "The multiplier to use when increasing the loss scaling.")
train_g.add_arg("decr_ratio",                  float,  0.8,
                "The less-than-one-multiplier to use when decreasing.")

log_g = ArgumentGroup(parser, "logging", "logging related.")
log_g.add_arg("skip_steps",          int,    10,    "The steps interval to print loss.")
log_g.add_arg("verbose",             bool,   False, "Whether to output verbose log.")

data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
data_g.add_arg("train_file",                str,   None,  "SQuAD json for training. E.g., train-v1.1.json.")
data_g.add_arg("predict_file",              str,   None,  "SQuAD json for predictions. E.g. dev-v1.1.json or test-v1.1.json.")
data_g.add_arg("vocab_path",                str,   None,  "Vocabulary path.")
data_g.add_arg("version_2_with_negative",   bool,  False,
               "If true, the SQuAD examples contain some that do not have an answer. If using squad v2.0, it should be set true.")
data_g.add_arg("max_seq_len",               int,   512,   "Number of words of the longest seqence.")
data_g.add_arg("max_query_length",          int,   64,    "Max query length.")
data_g.add_arg("max_answer_length",         int,   30,    "Max answer length.")
data_g.add_arg("batch_size",                int,   12,    "Total examples' number in batch for training. see also --in_tokens.")
data_g.add_arg("in_tokens",                 bool,  False,
               "If set, the batch size will be the maximum number of tokens in one batch. "
               "Otherwise, it will be the maximum number of examples in one batch.")
data_g.add_arg("do_lower_case",             bool,  True,
               "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
data_g.add_arg("doc_stride",                int,   128,
               "When splitting up a long document into chunks, how much stride to take between chunks.")
data_g.add_arg("n_best_size",               int,   20,
               "The total number of n-best predictions to generate in the nbest_predictions.json output file.")
data_g.add_arg("null_score_diff_threshold", float, 0.0,
               "If null_score - best_non_null is greater than the threshold predict null.")
data_g.add_arg("random_seed",               int,   0,      "Random seed.")

run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
run_type_g.add_arg("use_cuda",                     bool,   True,  "If set, use GPU for training.")
run_type_g.add_arg("use_fast_executor",            bool,   False, "If set, use fast parallel executor (in experiment).")
run_type_g.add_arg("num_iteration_per_drop_scope", int,    1,     "Ihe iteration intervals to clean up temporary variables.")
run_type_g.add_arg("do_train",                     bool,   True,  "Whether to perform training.")
run_type_g.add_arg("do_predict",                   bool,   True,  "Whether to perform prediction.")

args = parser.parse_args()
# yapf: enable.

def create_model(bert_config, is_training=False):
    if is_training:
        input_fields = {
            'names': ['src_ids', 'pos_ids', 'sent_ids', 'input_mask', 'start_positions', 'end_positions'],
            'shapes': [[None, None], [None, None], [None, None],
                    [None, None, 1], [None, 1], [None, 1]],
            'dtypes': [
                'int64', 'int64', 'int64', 'float32', 'int64', 'int64'],
            'lod_levels': [0, 0, 0, 0, 0, 0],
        }
    else:
        input_fields = {
            'names': ['src_ids', 'pos_ids', 'sent_ids', 'input_mask', 'unique_id'],
            'shapes': [[None, None], [None, None], [None, None],
                    [None, None, 1], [None, 1]],
            'dtypes': [
                'int64', 'int64', 'int64', 'float32', 'int64'],
            'lod_levels': [0, 0, 0, 0, 0],
        }

    inputs = [fluid.data(name=input_fields['names'][i],
                      shape=input_fields['shapes'][i],
                      dtype=input_fields['dtypes'][i],
                      lod_level=input_fields['lod_levels'][i]) for i in range(len(input_fields['names']))]

    data_loader = fluid.io.DataLoader.from_generator(feed_list=inputs, capacity=50, iterable=False)

    if is_training:
        (src_ids, pos_ids, sent_ids, input_mask, start_positions, end_positions) = inputs
    else:
        (src_ids, pos_ids, sent_ids, input_mask, unique_id) = inputs

    bert = BertModel(
        src_ids=src_ids,
        position_ids=pos_ids,
        sentence_ids=sent_ids,
        input_mask=input_mask,
        config=bert_config,
        use_fp16=args.use_fp16)

    enc_out = bert.get_sequence_output()

    logits = fluid.layers.fc(
        input=enc_out,
        size=2,
        num_flatten_dims=2,
        param_attr=fluid.ParamAttr(
            name="cls_squad_out_w",
            initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
        bias_attr=fluid.ParamAttr(
            name="cls_squad_out_b", initializer=fluid.initializer.Constant(0.)))

    logits = fluid.layers.transpose(x=logits, perm=[2, 0, 1])
    start_logits, end_logits = fluid.layers.unstack(x=logits, axis=0)

    batch_ones = fluid.layers.fill_constant_batch_size_like(
        input=start_logits, dtype='int64', shape=[1], value=1)
    num_seqs = fluid.layers.reduce_sum(input=batch_ones)

    if is_training:

        def compute_loss(logits, positions):
            loss = fluid.layers.softmax_with_cross_entropy(
                logits=logits, label=positions)
            loss = fluid.layers.mean(x=loss)
            return loss

        start_loss = compute_loss(start_logits, start_positions)
        end_loss = compute_loss(end_logits, end_positions)
        total_loss = (start_loss + end_loss) / 2.0
        return data_loader, total_loss, num_seqs
    else:
        return data_loader, unique_id, start_logits, end_logits, num_seqs


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


def predict(test_exe, test_program, test_data_loader, fetch_list, processor):
    if not os.path.exists(args.checkpoints):
        os.makedirs(args.checkpoints)
    output_prediction_file = os.path.join(args.checkpoints, "predictions.json")
    output_nbest_file = os.path.join(args.checkpoints, "nbest_predictions.json")
    output_null_log_odds_file = os.path.join(args.checkpoints, "null_odds.json")

    test_data_loader.start()
    all_results = []
    time_begin = time.time()
    while True:
        try:
            np_unique_ids, np_start_logits, np_end_logits, np_num_seqs = test_exe.run(
                fetch_list=fetch_list, program=test_program)
            for idx in range(np_unique_ids.shape[0]):
                if len(all_results) % 1000 == 0:
                    print("Processing example: %d" % len(all_results))
                unique_id = int(np_unique_ids[idx])
                start_logits = [float(x) for x in np_start_logits[idx].flat]
                end_logits = [float(x) for x in np_end_logits[idx].flat]
                all_results.append(
                    RawResult(
                        unique_id=unique_id,
                        start_logits=start_logits,
                        end_logits=end_logits))
        except fluid.core.EOFException:
            test_data_loader.reset()
            break
    time_end = time.time()

    features = processor.get_features(
        processor.predict_examples, is_training=False)
    write_predictions(processor.predict_examples, features, all_results,
                      args.n_best_size, args.max_answer_length,
                      args.do_lower_case, output_prediction_file,
                      output_nbest_file, output_null_log_odds_file,
                      args.version_2_with_negative,
                      args.null_score_diff_threshold, args.verbose)


def train(args):
    bert_config = BertConfig(args.bert_config_path)
    bert_config.print_config()

    if not (args.do_train or args.do_predict):
        raise ValueError("For args `do_train` and `do_predict`, at "
                         "least one of them must be True.")

    if args.use_cuda:
        place = fluid.CUDAPlace(0)
        dev_count = fluid.core.get_cuda_device_count()
    else:
        place = fluid.CPUPlace()
        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
    exe = fluid.Executor(place)

    processor = DataProcessor(
        vocab_path=args.vocab_path,
        do_lower_case=args.do_lower_case,
        max_seq_length=args.max_seq_len,
        in_tokens=args.in_tokens,
        doc_stride=args.doc_stride,
        max_query_length=args.max_query_length)

    startup_prog = fluid.Program()
    if args.random_seed is not None:
        startup_prog.random_seed = args.random_seed

    if args.do_train:
        train_data_generator = processor.data_generator(
            data_path=args.train_file,
            batch_size=args.batch_size,
            phase='train',
            shuffle=True,
            dev_count=dev_count,
            version_2_with_negative=args.version_2_with_negative,
            epoch=args.epoch)

        num_train_examples = processor.get_num_examples(phase='train')
        if args.in_tokens:
            max_train_steps = args.epoch * num_train_examples // (
                args.batch_size // args.max_seq_len) // dev_count
        else:
            max_train_steps = args.epoch * num_train_examples // (
                args.batch_size) // dev_count
        warmup_steps = int(max_train_steps * args.warmup_proportion)
        print("Device count: %d" % dev_count)
        print("Num train examples: %d" % num_train_examples)
        print("Max train steps: %d" % max_train_steps)
        print("Num warmup steps: %d" % warmup_steps)

        train_program = fluid.Program()
        with fluid.program_guard(train_program, startup_prog):
            with fluid.unique_name.guard():
                train_data_loader, loss, num_seqs = create_model(
                    bert_config=bert_config,
                    is_training=True)

                scheduled_lr, loss_scaling = optimization(
                    loss=loss,
                    warmup_steps=warmup_steps,
                    num_train_steps=max_train_steps,
                    learning_rate=args.learning_rate,
                    train_program=train_program,
                    startup_prog=startup_prog,
                    weight_decay=args.weight_decay,
                    scheduler=args.lr_scheduler,
                    use_fp16=args.use_fp16,
                    use_dynamic_loss_scaling=args.use_dynamic_loss_scaling,
                    init_loss_scaling=args.init_loss_scaling,
                    incr_every_n_steps=args.incr_every_n_steps,
                    decr_every_n_nan_or_inf=args.decr_every_n_nan_or_inf,
                    incr_ratio=args.incr_ratio,
                    decr_ratio=args.decr_ratio)

    if args.do_predict:
        test_prog = fluid.Program()
        with fluid.program_guard(test_prog, startup_prog):
            with fluid.unique_name.guard():
                test_data_loader, unique_ids, start_logits, end_logits, num_seqs = create_model(
                    bert_config=bert_config,
                    is_training=False)

        test_prog = test_prog.clone(for_test=True)

    exe.run(startup_prog)

    if args.do_train:
        if args.init_checkpoint and args.init_pretraining_params:
            print(
                "WARNING: args 'init_checkpoint' and 'init_pretraining_params' "
                "both are set! Only arg 'init_checkpoint' is made valid.")
        if args.init_checkpoint:
            init_checkpoint(
                exe,
                args.init_checkpoint,
                main_program=startup_prog,
                use_fp16=args.use_fp16)
        elif args.init_pretraining_params:
            init_pretraining_params(
                exe,
                args.init_pretraining_params,
                main_program=startup_prog,
                use_fp16=args.use_fp16)
    elif args.do_predict:
        if not args.init_checkpoint:
            raise ValueError("args 'init_checkpoint' should be set if"
                             "only doing prediction!")
        init_checkpoint(
            exe,
            args.init_checkpoint,
            main_program=startup_prog,
            use_fp16=args.use_fp16)

    if args.do_train:
        exec_strategy = fluid.ExecutionStrategy()
        exec_strategy.use_experimental_executor = args.use_fast_executor
        exec_strategy.num_threads = dev_count
        exec_strategy.num_iteration_per_drop_scope = args.num_iteration_per_drop_scope

        train_compiled_program = fluid.CompiledProgram(train_program).with_data_parallel(
                 loss_name=loss.name, exec_strategy=exec_strategy)

        train_data_loader.set_batch_generator(train_data_generator, place)

        train_data_loader.start()
        steps = 0
        total_cost, total_num_seqs = [], []
        time_begin = time.time()
        while steps < max_train_steps:
            try:
                steps += 1
                if steps % args.skip_steps == 0:
                    if args.use_fp16:
                        fetch_list = [loss.name, scheduled_lr.name, num_seqs.name, loss_scaling.name]
                    else:
                        fetch_list = [loss.name, scheduled_lr.name, num_seqs.name]
                else:
                    fetch_list = []

                outputs = exe.run(train_compiled_program, fetch_list=fetch_list)

                if steps % args.skip_steps == 0:
                    if args.use_fp16:
                        np_loss, np_lr, np_num_seqs, np_scaling = outputs
                    else:
                        np_loss, np_lr, np_num_seqs = outputs
                    total_cost.extend(np_loss * np_num_seqs)
                    total_num_seqs.extend(np_num_seqs)

                    if args.verbose:
                        verbose = "train data_loader queue size: %d, " % train_data_loader.queue.size(
                        )
                        verbose += "learning rate: %f " % np_lr[0]
                        if args.use_fp16:
                            verbose += ", loss scaling: %f" % np_scaling[0]
                        print(verbose)

                    time_end = time.time()
                    used_time = time_end - time_begin
                    current_example, epoch = processor.get_train_progress()

                    print("epoch: %d, progress: %d/%d, step: %d, loss: %f, "
                          "speed: %f steps/s" %
                          (epoch, current_example, num_train_examples, steps,
                           np.sum(total_cost) / np.sum(total_num_seqs),
                           args.skip_steps / used_time))
                    total_cost, total_num_seqs = [], []
                    time_begin = time.time()

                if steps % args.save_steps == 0 or steps == max_train_steps:
                    save_path = os.path.join(args.checkpoints,
                                             "step_" + str(steps))
                    fluid.save( train_program, save_path )
            except fluid.core.EOFException:
                save_path = os.path.join(args.checkpoints,
                                         "step_" + str(steps) + "_final")
                fluid.save( train_program, save_path )
                train_data_loader.reset()
                break

    if args.do_predict:
        test_data_loader.set_batch_generator(
            processor.data_generator(
                data_path=args.predict_file,
                batch_size=args.batch_size,
                phase='predict',
                shuffle=False,
                dev_count=1,
                epoch=1), place)

        predict(exe, test_prog, test_data_loader, [
            unique_ids.name, start_logits.name, end_logits.name, num_seqs.name
        ], processor)


if __name__ == '__main__':
    print_arguments(args)
    check_cuda(args.use_cuda)
    train(args)