run_swag.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# 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.
"""BERT finetuning runner."""

import pandas as pd

import logging
import os
import argparse
import random
from tqdm import tqdm, trange

import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler

from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.modeling import BertForMultipleChoice
from pytorch_pretrained_bert.optimization import BertAdam
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE

logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                    datefmt = '%m/%d/%Y %H:%M:%S',
                    level = logging.INFO)
logger = logging.getLogger(__name__)


class SwagExample(object):
    """A single training/test example for the SWAG dataset."""
    def __init__(self,
                 swag_id,
                 context_sentence,
                 start_ending,
                 ending_0,
                 ending_1,
                 ending_2,
                 ending_3,
                 label = None):
        self.swag_id = swag_id
        self.context_sentence = context_sentence
        self.start_ending = start_ending
        self.endings = [
            ending_0,
            ending_1,
            ending_2,
            ending_3,
        ]
        self.label = label

    def __str__(self):
        return self.__repr__()

    def __repr__(self):
        l = [
            f"swag_id: {self.swag_id}",
            f"context_sentence: {self.context_sentence}",
            f"start_ending: {self.start_ending}",
            f"ending_0: {self.endings[0]}",
            f"ending_1: {self.endings[1]}",
            f"ending_2: {self.endings[2]}",
            f"ending_3: {self.endings[3]}",
        ]

        if self.label is not None:
            l.append(f"label: {self.label}")

        return ", ".join(l)


class InputFeatures(object):
    def __init__(self,
                 example_id,
                 choices_features,
                 label

    ):
        self.example_id = example_id
        self.choices_features = [
            {
                'input_ids': input_ids,
                'input_mask': input_mask,
                'segment_ids': segment_ids
            }
            for _, input_ids, input_mask, segment_ids in choices_features
        ]
        self.label = label


def read_swag_examples(input_file, is_training):
    input_df = pd.read_csv(input_file)

    if is_training and 'label' not in input_df.columns:
        raise ValueError(
            "For training, the input file must contain a label column.")

    examples = [
        SwagExample(
            swag_id = row['fold-ind'],
            context_sentence = row['sent1'],
            start_ending = row['sent2'], # in the swag dataset, the
                                         # common beginning of each
                                         # choice is stored in "sent2".
            ending_0 = row['ending0'],
            ending_1 = row['ending1'],
            ending_2 = row['ending2'],
            ending_3 = row['ending3'],
            label = row['label'] if is_training else None
        ) for _, row in input_df.iterrows()
    ]

    return examples

def convert_examples_to_features(examples, tokenizer, max_seq_length,
                                 is_training):
    """Loads a data file into a list of `InputBatch`s."""

    # Swag is a multiple choice task. To perform this task using Bert,
    # we will use the formatting proposed in "Improving Language
    # Understanding by Generative Pre-Training" and suggested by
    # @jacobdevlin-google in this issue
    # https://github.com/google-research/bert/issues/38.
    #
    # Each choice will correspond to a sample on which we run the
    # inference. For a given Swag example, we will create the 4
    # following inputs:
    # - [CLS] context [SEP] choice_1 [SEP]
    # - [CLS] context [SEP] choice_2 [SEP]
    # - [CLS] context [SEP] choice_3 [SEP]
    # - [CLS] context [SEP] choice_4 [SEP]
    # The model will output a single value for each input. To get the
    # final decision of the model, we will run a softmax over these 4
    # outputs.
    features = []
    for example_index, example in enumerate(examples):
        context_tokens = tokenizer.tokenize(example.context_sentence)
        start_ending_tokens = tokenizer.tokenize(example.start_ending)

        choices_features = []
        for ending_index, ending in enumerate(example.endings):
            # We create a copy of the context tokens in order to be
            # able to shrink it according to ending_tokens
            context_tokens_choice = context_tokens[:]
            ending_tokens = start_ending_tokens + tokenizer.tokenize(ending)
            # Modifies `context_tokens_choice` and `ending_tokens` in
            # place so that the total length is less than the
            # specified length.  Account for [CLS], [SEP], [SEP] with
            # "- 3"
            _truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)

            tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
            segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)

            input_ids = tokenizer.convert_tokens_to_ids(tokens)
            input_mask = [1] * len(input_ids)

            # Zero-pad up to the sequence length.
            padding = [0] * (max_seq_length - len(input_ids))
            input_ids += padding
            input_mask += padding
            segment_ids += padding

            assert len(input_ids) == max_seq_length
            assert len(input_mask) == max_seq_length
            assert len(segment_ids) == max_seq_length

            choices_features.append((tokens, input_ids, input_mask, segment_ids))

        label = example.label
        if example_index < 5:
            logger.info("*** Example ***")
            logger.info(f"swag_id: {example.swag_id}")
            for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
                logger.info(f"choice: {choice_idx}")
                logger.info(f"tokens: {' '.join(tokens)}")
                logger.info(f"input_ids: {' '.join(map(str, input_ids))}")
                logger.info(f"input_mask: {' '.join(map(str, input_mask))}")
                logger.info(f"segment_ids: {' '.join(map(str, segment_ids))}")
            if is_training:
                logger.info(f"label: {label}")

        features.append(
            InputFeatures(
                example_id = example.swag_id,
                choices_features = choices_features,
                label = label
            )
        )

    return features

def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()

def accuracy(out, labels):
    outputs = np.argmax(out, axis=1)
    return np.sum(outputs == labels)

def select_field(features, field):
    return [
        [
            choice[field]
            for choice in feature.choices_features
        ]
        for feature in features
    ]

def copy_optimizer_params_to_model(named_params_model, named_params_optimizer):
    """ Utility function for optimize_on_cpu and 16-bits training.
        Copy the parameters optimized on CPU/RAM back to the model on GPU
    """
    for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model):
        if name_opti != name_model:
            logger.error("name_opti != name_model: {} {}".format(name_opti, name_model))
            raise ValueError
        param_model.data.copy_(param_opti.data)

def set_optimizer_params_grad(named_params_optimizer, named_params_model, test_nan=False):
    """ Utility function for optimize_on_cpu and 16-bits training.
        Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model
    """
    is_nan = False
    for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model):
        if name_opti != name_model:
            logger.error("name_opti != name_model: {} {}".format(name_opti, name_model))
            raise ValueError
        if param_model.grad is not None:
            if test_nan and torch.isnan(param_model.grad).sum() > 0:
                is_nan = True
            if param_opti.grad is None:
                param_opti.grad = torch.nn.Parameter(param_opti.data.new().resize_(*param_opti.data.size()))
            param_opti.grad.data.copy_(param_model.grad.data)
        else:
            param_opti.grad = None
    return is_nan

def main():
    parser = argparse.ArgumentParser()

    ## Required parameters
    parser.add_argument("--data_dir",
                        default=None,
                        type=str,
                        required=True,
                        help="The input data dir. Should contain the .csv files (or other data files) for the task.")
    parser.add_argument("--bert_model", default=None, type=str, required=True,
                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
                             "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
    parser.add_argument("--output_dir",
                        default=None,
                        type=str,
                        required=True,
                        help="The output directory where the model checkpoints will be written.")

    ## Other parameters
    parser.add_argument("--max_seq_length",
                        default=128,
                        type=int,
                        help="The maximum total input sequence length after WordPiece tokenization. \n"
                             "Sequences longer than this will be truncated, and sequences shorter \n"
                             "than this will be padded.")
    parser.add_argument("--do_train",
                        default=False,
                        action='store_true',
                        help="Whether to run training.")
    parser.add_argument("--do_eval",
                        default=False,
                        action='store_true',
                        help="Whether to run eval on the dev set.")
    parser.add_argument("--do_lower_case",
                        default=False,
                        action='store_true',
                        help="Set this flag if you are using an uncased model.")
    parser.add_argument("--train_batch_size",
                        default=32,
                        type=int,
                        help="Total batch size for training.")
    parser.add_argument("--eval_batch_size",
                        default=8,
                        type=int,
                        help="Total batch size for eval.")
    parser.add_argument("--learning_rate",
                        default=5e-5,
                        type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument("--num_train_epochs",
                        default=3.0,
                        type=float,
                        help="Total number of training epochs to perform.")
    parser.add_argument("--warmup_proportion",
                        default=0.1,
                        type=float,
                        help="Proportion of training to perform linear learning rate warmup for. "
                             "E.g., 0.1 = 10%% of training.")
    parser.add_argument("--no_cuda",
                        default=False,
                        action='store_true',
                        help="Whether not to use CUDA when available")
    parser.add_argument("--local_rank",
                        type=int,
                        default=-1,
                        help="local_rank for distributed training on gpus")
    parser.add_argument('--seed',
                        type=int,
                        default=42,
                        help="random seed for initialization")
    parser.add_argument('--gradient_accumulation_steps',
                        type=int,
                        default=1,
                        help="Number of updates steps to accumulate before performing a backward/update pass.")
    parser.add_argument('--optimize_on_cpu',
                        default=False,
                        action='store_true',
                        help="Whether to perform optimization and keep the optimizer averages on CPU")
    parser.add_argument('--fp16',
                        default=False,
                        action='store_true',
                        help="Whether to use 16-bit float precision instead of 32-bit")
    parser.add_argument('--loss_scale',
                        type=float, default=128,
                        help='Loss scaling, positive power of 2 values can improve fp16 convergence.')

    args = parser.parse_args()

    if args.local_rank == -1 or args.no_cuda:
        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
        n_gpu = torch.cuda.device_count()
    else:
        device = torch.device("cuda", args.local_rank)
        n_gpu = 1
        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
        torch.distributed.init_process_group(backend='nccl')
        if args.fp16:
            logger.info("16-bits training currently not supported in distributed training")
            args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496)
    logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1))

    if args.gradient_accumulation_steps < 1:
        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
                            args.gradient_accumulation_steps))

    args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)

    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)

    if not args.do_train and not args.do_eval:
        raise ValueError("At least one of `do_train` or `do_eval` must be True.")

    if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
    os.makedirs(args.output_dir, exist_ok=True)

    tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)

    train_examples = None
    num_train_steps = None
    if args.do_train:
        train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True)
        num_train_steps = int(
            len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)

    # Prepare model
    model = BertForMultipleChoice.from_pretrained(args.bert_model,
        cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
        num_choices = 4
    )
    if args.fp16:
        model.half()
    model.to(device)
    if args.local_rank != -1:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
                                                          output_device=args.local_rank)
    elif n_gpu > 1:
        model = torch.nn.DataParallel(model)

    # Prepare optimizer
    if args.fp16:
        param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \
                            for n, param in model.named_parameters()]
    elif args.optimize_on_cpu:
        param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \
                            for n, param in model.named_parameters()]
    else:
        param_optimizer = list(model.named_parameters())
    no_decay = ['bias', 'gamma', 'beta']
    optimizer_grouped_parameters = [
        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01},
        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0}
        ]
    t_total = num_train_steps
    if args.local_rank != -1:
        t_total = t_total // torch.distributed.get_world_size()
    optimizer = BertAdam(optimizer_grouped_parameters,
                         lr=args.learning_rate,
                         warmup=args.warmup_proportion,
                         t_total=t_total)

    global_step = 0
    if args.do_train:
        train_features = convert_examples_to_features(
            train_examples, tokenizer, args.max_seq_length, True)
        logger.info("***** Running training *****")
        logger.info("  Num examples = %d", len(train_examples))
        logger.info("  Batch size = %d", args.train_batch_size)
        logger.info("  Num steps = %d", num_train_steps)
        all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long)
        all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
        all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
        all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
        train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
        if args.local_rank == -1:
            train_sampler = RandomSampler(train_data)
        else:
            train_sampler = DistributedSampler(train_data)
        train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)

        model.train()
        for _ in trange(int(args.num_train_epochs), desc="Epoch"):
            tr_loss = 0
            nb_tr_examples, nb_tr_steps = 0, 0
            for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
                batch = tuple(t.to(device) for t in batch)
                input_ids, input_mask, segment_ids, label_ids = batch
                loss = model(input_ids, segment_ids, input_mask, label_ids)
                if n_gpu > 1:
                    loss = loss.mean() # mean() to average on multi-gpu.
                if args.fp16 and args.loss_scale != 1.0:
                    # rescale loss for fp16 training
                    # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
                    loss = loss * args.loss_scale
                if args.gradient_accumulation_steps > 1:
                    loss = loss / args.gradient_accumulation_steps
                loss.backward()
                tr_loss += loss.item()
                nb_tr_examples += input_ids.size(0)
                nb_tr_steps += 1
                if (step + 1) % args.gradient_accumulation_steps == 0:
                    if args.fp16 or args.optimize_on_cpu:
                        if args.fp16 and args.loss_scale != 1.0:
                            # scale down gradients for fp16 training
                            for param in model.parameters():
                                if param.grad is not None:
                                    param.grad.data = param.grad.data / args.loss_scale
                        is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True)
                        if is_nan:
                            logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling")
                            args.loss_scale = args.loss_scale / 2
                            model.zero_grad()
                            continue
                        optimizer.step()
                        copy_optimizer_params_to_model(model.named_parameters(), param_optimizer)
                    else:
                        optimizer.step()
                    model.zero_grad()
                    global_step += 1

    if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True)
        eval_features = convert_examples_to_features(
            eval_examples, tokenizer, args.max_seq_length, True)
        logger.info("***** Running evaluation *****")
        logger.info("  Num examples = %d", len(eval_examples))
        logger.info("  Batch size = %d", args.eval_batch_size)
        all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long)
        all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
        all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
        all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
        eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
        # Run prediction for full data
        eval_sampler = SequentialSampler(eval_data)
        eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)

        model.eval()
        eval_loss, eval_accuracy = 0, 0
        nb_eval_steps, nb_eval_examples = 0, 0
        for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
            input_ids = input_ids.to(device)
            input_mask = input_mask.to(device)
            segment_ids = segment_ids.to(device)
            label_ids = label_ids.to(device)

            with torch.no_grad():
                tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids)
                logits = model(input_ids, segment_ids, input_mask)

            logits = logits.detach().cpu().numpy()
            label_ids = label_ids.to('cpu').numpy()
            tmp_eval_accuracy = accuracy(logits, label_ids)

            eval_loss += tmp_eval_loss.mean().item()
            eval_accuracy += tmp_eval_accuracy

            nb_eval_examples += input_ids.size(0)
            nb_eval_steps += 1

        eval_loss = eval_loss / nb_eval_steps
        eval_accuracy = eval_accuracy / nb_eval_examples

        result = {'eval_loss': eval_loss,
                  'eval_accuracy': eval_accuracy,
                  'global_step': global_step,
                  'loss': tr_loss/nb_tr_steps}

        output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
        with open(output_eval_file, "w") as writer:
            logger.info("***** Eval results *****")
            for key in sorted(result.keys()):
                logger.info("  %s = %s", key, str(result[key]))
                writer.write("%s = %s\n" % (key, str(result[key])))


if __name__ == "__main__":
    main()