train/finetune/run_classifier.py

"""
This script provides an example to wrap TencentPretrain for classification.
"""
import sys
import os
import random
import argparse
import torch
import torch.nn as nn

tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.append(tencentpretrain_dir)

from tencentpretrain.embeddings import *
from tencentpretrain.encoders import *
from tencentpretrain.utils.vocab import Vocab
from tencentpretrain.utils.constants import *
from tencentpretrain.utils import *
from tencentpretrain.utils.optimizers import *
from tencentpretrain.utils.config import load_hyperparam
from tencentpretrain.utils.seed import set_seed
from tencentpretrain.utils.logging import init_logger
from tencentpretrain.utils.misc import pooling
from tencentpretrain.model_saver import save_model
from tencentpretrain.opts import finetune_opts, tokenizer_opts, adv_opts


class Classifier(nn.Module):
    def __init__(self, args):
        super(Classifier, self).__init__()
        self.embedding = Embedding(args)
        for embedding_name in args.embedding:
            tmp_emb = str2embedding[embedding_name](args, len(args.tokenizer.vocab))
            self.embedding.update(tmp_emb, embedding_name)
        self.encoder = str2encoder[args.encoder](args)
        self.labels_num = args.labels_num
        self.pooling_type = args.pooling
        self.soft_targets = args.soft_targets
        self.soft_alpha = args.soft_alpha
        self.output_layer_1 = nn.Linear(args.hidden_size, args.hidden_size)
        self.output_layer_2 = nn.Linear(args.hidden_size, self.labels_num)

    def forward(self, src, tgt, seg, soft_tgt=None):
        """
        Args:
            src: [batch_size x seq_length]
            tgt: [batch_size]
            seg: [batch_size x seq_length]
        """
        # Embedding.
        emb = self.embedding(src, seg)
        # Encoder.
        output = self.encoder(emb, seg)
        # Target.
        output = pooling(output, seg, self.pooling_type)
        output = torch.tanh(self.output_layer_1(output))
        logits = self.output_layer_2(output)
        if tgt is not None:
            if self.soft_targets and soft_tgt is not None:
                loss = self.soft_alpha * nn.MSELoss()(logits, soft_tgt) + \
                       (1 - self.soft_alpha) * nn.NLLLoss()(nn.LogSoftmax(dim=-1)(logits), tgt.view(-1))
            else:
                loss = nn.NLLLoss()(nn.LogSoftmax(dim=-1)(logits), tgt.view(-1))
            return loss, logits
        else:
            return None, logits


def count_labels_num(path):
    labels_set, columns = set(), {}
    with open(path, mode="r", encoding="utf-8") as f:
        for line_id, line in enumerate(f):
            if line_id == 0:
                for i, column_name in enumerate(line.rstrip("\r\n").split("\t")):
                    columns[column_name] = i
                continue
            line = line.rstrip("\r\n").split("\t")
            label = int(line[columns["label"]])
            labels_set.add(label)
    return len(labels_set)


def load_or_initialize_parameters(args, model):
    if args.pretrained_model_path is not None:
        # Initialize with pretrained model.
        model.load_state_dict(torch.load(args.pretrained_model_path, map_location="cpu"), strict=False)
    else:
        # Initialize with normal distribution.
        for n, p in list(model.named_parameters()):
            if "gamma" not in n and "beta" not in n:
                p.data.normal_(0, 0.02)


def build_optimizer(args, model):
    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": 0.01},
        {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
    ]
    if args.optimizer in ["adamw"]:
        optimizer = str2optimizer[args.optimizer](optimizer_grouped_parameters, lr=args.learning_rate, correct_bias=False)
    else:
        optimizer = str2optimizer[args.optimizer](optimizer_grouped_parameters, lr=args.learning_rate,
                                                  scale_parameter=False, relative_step=False)
    if args.scheduler in ["constant"]:
        scheduler = str2scheduler[args.scheduler](optimizer)
    elif args.scheduler in ["constant_with_warmup"]:
        scheduler = str2scheduler[args.scheduler](optimizer, args.train_steps*args.warmup)
    else:
        scheduler = str2scheduler[args.scheduler](optimizer, args.train_steps*args.warmup, args.train_steps)
    return optimizer, scheduler


def batch_loader(batch_size, src, tgt, seg, soft_tgt=None):
    instances_num = src.size()[0]
    for i in range(instances_num // batch_size):
        src_batch = src[i * batch_size : (i + 1) * batch_size, :]
        tgt_batch = tgt[i * batch_size : (i + 1) * batch_size]
        seg_batch = seg[i * batch_size : (i + 1) * batch_size, :]
        if soft_tgt is not None:
            soft_tgt_batch = soft_tgt[i * batch_size : (i + 1) * batch_size, :]
            yield src_batch, tgt_batch, seg_batch, soft_tgt_batch
        else:
            yield src_batch, tgt_batch, seg_batch, None
    if instances_num > instances_num // batch_size * batch_size:
        src_batch = src[instances_num // batch_size * batch_size :, :]
        tgt_batch = tgt[instances_num // batch_size * batch_size :]
        seg_batch = seg[instances_num // batch_size * batch_size :, :]
        if soft_tgt is not None:
            soft_tgt_batch = soft_tgt[instances_num // batch_size * batch_size :, :]
            yield src_batch, tgt_batch, seg_batch, soft_tgt_batch
        else:
            yield src_batch, tgt_batch, seg_batch, None


def read_dataset(args, path):
    dataset, columns = [], {}
    with open(path, mode="r", encoding="utf-8") as f:
        for line_id, line in enumerate(f):
            if line_id == 0:
                for i, column_name in enumerate(line.rstrip("\r\n").split("\t")):
                    columns[column_name] = i
                continue
            line = line.rstrip("\r\n").split("\t")
            tgt = int(line[columns["label"]])
            if args.soft_targets and "logits" in columns.keys():
                soft_tgt = [float(value) for value in line[columns["logits"]].split(" ")]
            if "text_b" not in columns:  # Sentence classification.
                text_a = line[columns["text_a"]]
                src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN])
                seg = [1] * len(src)
            else:  # Sentence-pair classification.
                text_a, text_b = line[columns["text_a"]], line[columns["text_b"]]
                src_a = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN])
                src_b = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(text_b) + [SEP_TOKEN])
                src = src_a + src_b
                seg = [1] * len(src_a) + [2] * len(src_b)

            if len(src) > args.seq_length:
                src = src[: args.seq_length]
                seg = seg[: args.seq_length]
            PAD_ID = args.tokenizer.convert_tokens_to_ids([PAD_TOKEN])[0]
            while len(src) < args.seq_length:
                src.append(PAD_ID)
                seg.append(0)
            if args.soft_targets and "logits" in columns.keys():
                dataset.append((src, tgt, seg, soft_tgt))
            else:
                dataset.append((src, tgt, seg))

    return dataset


def train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch, soft_tgt_batch=None):
    model.zero_grad()

    src_batch = src_batch.to(args.device)
    tgt_batch = tgt_batch.to(args.device)
    seg_batch = seg_batch.to(args.device)
    if soft_tgt_batch is not None:
        soft_tgt_batch = soft_tgt_batch.to(args.device)

    loss, _ = model(src_batch, tgt_batch, seg_batch, soft_tgt_batch)
    if torch.cuda.device_count() > 1:
        loss = torch.mean(loss)

    if args.fp16:
        with args.amp.scale_loss(loss, optimizer) as scaled_loss:
            scaled_loss.backward()
    else:
        loss.backward()

    if args.use_adv and args.adv_type == "fgm":
        args.adv_method.attack(epsilon=args.fgm_epsilon)
        loss_adv, _ = model(src_batch, tgt_batch, seg_batch, soft_tgt_batch)
        if torch.cuda.device_count() > 1:
            loss_adv = torch.mean(loss_adv)
        loss_adv.backward()
        args.adv_method.restore()

    if args.use_adv and args.adv_type == "pgd":
        K = args.pgd_k
        args.adv_method.backup_grad()
        for t in range(K):
            # apply the perturbation to embedding
            args.adv_method.attack(epsilon=args.pgd_epsilon, alpha=args.pgd_alpha,
                                   is_first_attack=(t == 0))
            if t != K - 1:
                model.zero_grad()
            else:
                args.adv_method.restore_grad()
            loss_adv, _ = model(src_batch, tgt_batch, seg_batch, soft_tgt_batch)
            if torch.cuda.device_count() > 1:
                loss_adv = torch.mean(loss_adv)
            loss_adv.backward()
        args.adv_method.restore()

    optimizer.step()
    scheduler.step()

    return loss


def evaluate(args, dataset):
    src = torch.LongTensor([sample[0] for sample in dataset])
    tgt = torch.LongTensor([sample[1] for sample in dataset])
    seg = torch.LongTensor([sample[2] for sample in dataset])

    batch_size = args.batch_size

    correct = 0
    # Confusion matrix.
    confusion = torch.zeros(args.labels_num, args.labels_num, dtype=torch.long)

    args.model.eval()

    for i, (src_batch, tgt_batch, seg_batch, _) in enumerate(batch_loader(batch_size, src, tgt, seg)):
        src_batch = src_batch.to(args.device)
        tgt_batch = tgt_batch.to(args.device)
        seg_batch = seg_batch.to(args.device)
        with torch.no_grad():
            _, logits = args.model(src_batch, tgt_batch, seg_batch)
        pred = torch.argmax(nn.Softmax(dim=1)(logits), dim=1)
        gold = tgt_batch
        for j in range(pred.size()[0]):
            confusion[pred[j], gold[j]] += 1
        correct += torch.sum(pred == gold).item()

    args.logger.info("Confusion matrix:")
    args.logger.info(confusion)
    args.logger.info("Report precision, recall, and f1:")

    eps = 1e-9
    for i in range(confusion.size()[0]):
        p = confusion[i, i].item() / (confusion[i, :].sum().item() + eps)
        r = confusion[i, i].item() / (confusion[:, i].sum().item() + eps)
        f1 = 2 * p * r / (p + r + eps)
        args.logger.info("Label {}: {:.3f}, {:.3f}, {:.3f}".format(i, p, r, f1))

    args.logger.info("Acc. (Correct/Total): {:.4f} ({}/{}) ".format(correct / len(dataset), correct, len(dataset)))
    return correct / len(dataset), confusion


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

    finetune_opts(parser)

    tokenizer_opts(parser)

    parser.add_argument("--soft_targets", action='store_true',
                        help="Train model with logits.")
    parser.add_argument("--soft_alpha", type=float, default=0.5,
                        help="Weight of the soft targets loss.")

    adv_opts(parser)

    args = parser.parse_args()

    # Load the hyperparameters from the config file.
    args = load_hyperparam(args)
    # Count the number of labels.
    args.labels_num = count_labels_num(args.train_path)

    # Build tokenizer.
    args.tokenizer = str2tokenizer[args.tokenizer](args)
    set_seed(args.seed)

    # Build classification model.
    model = Classifier(args)

    # Load or initialize parameters.
    load_or_initialize_parameters(args, model)

    # Get logger.
    args.logger = init_logger(args)

    args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = model.to(args.device)

    # Training phase.
    trainset = read_dataset(args, args.train_path)
    instances_num = len(trainset)
    batch_size = args.batch_size

    args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1

    args.logger.info("Batch size: {}".format(batch_size))
    args.logger.info("The number of training instances: {}".format(instances_num))
    optimizer, scheduler = build_optimizer(args, model)

    if args.fp16:
        try:
            from apex import amp
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
        args.amp = amp

    if torch.cuda.device_count() > 1:
        args.logger.info("{} GPUs are available. Let's use them.".format(torch.cuda.device_count()))
        model = torch.nn.DataParallel(model)
    args.model = model

    if args.use_adv:
        args.adv_method = str2adv[args.adv_type](model)

    total_loss, result, best_result = 0.0, 0.0, 0.0

    args.logger.info("Start training.")
    for epoch in range(1, args.epochs_num + 1):
        random.shuffle(trainset)
        src = torch.LongTensor([example[0] for example in trainset])
        tgt = torch.LongTensor([example[1] for example in trainset])
        seg = torch.LongTensor([example[2] for example in trainset])
        if args.soft_targets:
            soft_tgt = torch.FloatTensor([example[3] for example in trainset])
        else:
            soft_tgt = None

        model.train()
        for i, (src_batch, tgt_batch, seg_batch, soft_tgt_batch) in enumerate(batch_loader(batch_size, src, tgt, seg, soft_tgt)):
            loss = train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch, soft_tgt_batch)
            total_loss += loss.item()
            if (i + 1) % args.report_steps == 0:
                args.logger.info("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".format(epoch, i + 1, total_loss / args.report_steps))
                total_loss = 0.0

        result = evaluate(args, read_dataset(args, args.dev_path))
        if result[0] > best_result:
            best_result = result[0]
            save_model(model, args.output_model_path)

    # Evaluation phase.
    if args.test_path is not None:
        args.logger.info("Test set evaluation.")
        if torch.cuda.device_count() > 1:
            args.model.module.load_state_dict(torch.load(args.output_model_path))
        else:
            args.model.load_state_dict(torch.load(args.output_model_path))
        evaluate(args, read_dataset(args, args.test_path))


if __name__ == "__main__":
    main()