train.py

import os
import ast
import time
import argparse
import logging

import paddle
from tqdm import tqdm
import paddle.nn as nn
from paddle.io import DataLoader
from paddlenlp.transformers import ErnieForGeneration
from paddlenlp.transformers import ErnieTokenizer, ErnieTinyTokenizer, BertTokenizer, ElectraTokenizer, RobertaTokenizer
from paddlenlp.datasets import Poetry
from paddlenlp.data import Stack, Tuple, Pad
from paddlenlp.metrics import Rouge1, Rouge2
from paddlenlp.utils.log import logger

from encode import convert_example, after_padding
from decode import beam_search_infilling, post_process, greedy_search_infilling

# yapf: disable
parser = argparse.ArgumentParser('seq2seq model with ERNIE-GEN')
parser.add_argument("--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: "+ ", ".join(list(ErnieTokenizer.pretrained_init_configuration.keys())))
parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.",)
parser.add_argument('--max_encode_len', type=int, default=5, help="The max encoding sentence length")
parser.add_argument('--max_decode_len', type=int, default=5, help="The max decoding sentence length")
parser.add_argument("--batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.", )
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.1, type=float, help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--num_epochs", default=3, type=int, help="Total number of training epochs to perform.", )
parser.add_argument("--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_epochs.",)
parser.add_argument("--warmup_proportion", default=0.1, type=float, help="Linear warmup proportion.")
parser.add_argument("--logging_steps", type=int, default=1, help="Log every X updates steps.")
parser.add_argument("--save_steps", type=int, default=100, help="Save checkpoint every X updates steps.")
parser.add_argument("--n_gpu", type=int, default=1, help="Number of gpus to use, 0 for cpu.")
parser.add_argument('--beam_width', type=int, default=1, help="Beam search width")
parser.add_argument('--noise_prob', type=float, default=0., help='Probability of token be repalced')
parser.add_argument('--use_random_noice', action='store_true', help='If set, replace target tokens with random token from vocabulary, else replace with `[NOISE]`')
parser.add_argument('--label_smooth', type=float, default=0., help="The soft label smooth rate")
parser.add_argument('--length_penalty', type=float, default=1.0, help="The length penalty during decoding")
parser.add_argument('--init_checkpoint', type=str, default=None, help='Checkpoint to warm start from')
parser.add_argument('--save_dir', type=str, default=None, help='Model output directory')
# yapf: enable

args = parser.parse_args()


def evaluate(model, data_loader, tokenizer, rouge1, rouge2, attn_id,
             tgt_type_id, args):
    model.eval()

    vocab = tokenizer.vocab
    eos_id = vocab[tokenizer.sep_token]
    sos_id = vocab[tokenizer.cls_token]
    pad_id = vocab[tokenizer.pad_token]
    unk_id = vocab[tokenizer.unk_token]
    vocab_size = len(vocab)
    evaluated_sentences_ids = []
    reference_sentences_ids = []
    logger.info("Evaluating...")
    for data in tqdm(data_loader):
        (src_ids, src_sids, src_pids, _, _, _, _, _, _, _, _,
         raw_tgt_labels) = data  # never use target when infer
        # Use greedy_search_infilling or beam_search_infilling to get predictions
        output_ids = beam_search_infilling(
            model,
            src_ids,
            src_sids,
            eos_id=eos_id,
            sos_id=sos_id,
            attn_id=attn_id,
            pad_id=pad_id,
            unk_id=unk_id,
            vocab_size=vocab_size,
            max_decode_len=args.max_decode_len,
            max_encode_len=args.max_encode_len,
            beam_width=args.beam_width,
            length_penalty=args.length_penalty,
            tgt_type_id=tgt_type_id)

        for ids in output_ids.tolist():
            if eos_id in ids:
                ids = ids[:ids.index(eos_id)]
            evaluated_sentences_ids.append(ids)

        for ids in raw_tgt_labels.numpy().tolist():
            ids = ids[:ids.index(eos_id)]
            reference_sentences_ids.append(ids)

    score1 = rouge1.score(evaluated_sentences_ids, reference_sentences_ids)
    score2 = rouge2.score(evaluated_sentences_ids, reference_sentences_ids)

    logger.info("Rouge-1: %.5f ,Rouge-2: %.5f" % (score1 * 100, score2 * 100))

    evaluated_sentences = []
    reference_sentences = []
    for ids in reference_sentences_ids[:5]:
        reference_sentences.append(''.join(
            map(post_process, vocab.to_tokens(ids))))
    for ids in evaluated_sentences_ids[:5]:
        evaluated_sentences.append(''.join(
            map(post_process, vocab.to_tokens(ids))))
    logger.debug(reference_sentences)
    logger.debug(evaluated_sentences)

    model.train()


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

    model = ErnieForGeneration.from_pretrained(args.model_name_or_path)
    if "ernie-tiny" in args.model_name_or_path:
        tokenizer = ErnieTinyTokenizer.from_pretrained(args.model_name_or_path)
    elif "ernie" in args.model_name_or_path:
        tokenizer = ErnieTokenizer.from_pretrained(args.model_name_or_path)
    elif "roberta" in args.model_name_or_path or "rbt" in args.model_name_or_path:
        tokenizer = RobertaTokenizer.from_pretrained(args.model_name_or_path)
    elif "electra" in args.model_name_or_path:
        tokenizer = ElectraTokenizer.from_pretrained(args.model_name_or_path)
    else:
        tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path)
    if args.init_checkpoint:
        model_state = paddle.load(args.init_checkpoint)
        model.set_state_dict(model_state)

    train_dataset, dev_dataset = Poetry.get_datasets(['train', 'dev'])
    attn_id = tokenizer.vocab[
        '[ATTN]'] if '[ATTN]' in tokenizer.vocab else tokenizer.vocab['[MASK]']
    tgt_type_id = model.sent_emb.weight.shape[0] - 1

    trans_func = convert_example(
        tokenizer=tokenizer,
        attn_id=attn_id,
        tgt_type_id=tgt_type_id,
        max_encode_len=args.max_encode_len,
        max_decode_len=args.max_decode_len,
        noise_prob=args.noise_prob,
        use_random_noice=args.use_random_noice)

    train_dataset = train_dataset.apply(trans_func, lazy=True)
    train_batch_sampler = paddle.io.DistributedBatchSampler(
        train_dataset, batch_size=args.batch_size, shuffle=True)
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # src_ids
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # src_pids
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # src_sids
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # tgt_ids
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # tgt_pids
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # tgt_sids
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # attn_ids
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # tgt_labels
    ): after_padding(fn(samples))
    train_data_loader = DataLoader(
        dataset=train_dataset,
        batch_sampler=train_batch_sampler,
        collate_fn=batchify_fn,
        num_workers=0,
        return_list=True)

    dev_dataset = dev_dataset.apply(trans_func, lazy=True)
    dev_batch_sampler = paddle.io.BatchSampler(
        dev_dataset, batch_size=args.batch_size, shuffle=False)
    dev_data_loader = DataLoader(
        dataset=dev_dataset,
        batch_sampler=dev_batch_sampler,
        collate_fn=batchify_fn,
        num_workers=0,
        return_list=True)

    label_num = model.word_emb.weight.shape[0]
    if paddle.distributed.get_world_size() > 1:
        model = paddle.DataParallel(model)

    max_steps = (len(train_data_loader) * args.num_epochs)
    lr_scheduler = paddle.optimizer.lr.LambdaDecay(
        args.learning_rate,
        lambda current_step, num_warmup_steps=max_steps*args.warmup_proportion,
        num_training_steps=max_steps: float(
            current_step) / float(max(1, num_warmup_steps))
        if current_step < num_warmup_steps else max(
            0.0,
            float(num_training_steps - current_step) / float(
                max(1, num_training_steps - num_warmup_steps))))

    optimizer = paddle.optimizer.AdamW(
        learning_rate=lr_scheduler,
        epsilon=args.adam_epsilon,
        parameters=model.parameters(),
        weight_decay=args.weight_decay,
        grad_clip=nn.ClipGradByGlobalNorm(1.0),
        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"])
        ])

    rouge1 = Rouge1()
    rouge2 = Rouge2()

    global_step = 1
    tic_train = time.time()
    for epoch in range(args.num_epochs):
        for step, batch in enumerate(train_data_loader, start=1):
            (src_ids, src_sids, src_pids, tgt_ids, tgt_sids, tgt_pids, attn_ids,
             mask_src_2_src, mask_tgt_2_srctgt, mask_attn_2_srctgtattn,
             tgt_labels, _) = batch
            # import pdb; pdb.set_trace()
            _, __, info = model(
                src_ids,
                sent_ids=src_sids,
                pos_ids=src_pids,
                attn_bias=mask_src_2_src,
                encode_only=True)
            cached_k, cached_v = info['caches']
            _, __, info = model(
                tgt_ids,
                sent_ids=tgt_sids,
                pos_ids=tgt_pids,
                attn_bias=mask_tgt_2_srctgt,
                past_cache=(cached_k, cached_v),
                encode_only=True)
            cached_k2, cached_v2 = info['caches']
            past_cache_k = [
                paddle.concat([k, k2], 1) for k, k2 in zip(cached_k, cached_k2)
            ]
            past_cache_v = [
                paddle.concat([v, v2], 1) for v, v2 in zip(cached_v, cached_v2)
            ]
            if args.label_smooth > 0.:
                tgt_labels = nn.functional.label_smooth(
                    nn.functional.one_hot(tgt_labels, label_num),
                    epsilon=args.label_smooth)
            loss, _, __ = model(
                attn_ids,
                sent_ids=tgt_sids,
                pos_ids=tgt_pids,
                attn_bias=mask_attn_2_srctgtattn,
                past_cache=(past_cache_k, past_cache_v),
                tgt_labels=tgt_labels,
                tgt_pos=paddle.nonzero(attn_ids == attn_id))
            if global_step % args.logging_steps == 0:
                if (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0:
                    logger.info(
                        "global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s, lr: %.3e"
                        % (global_step, epoch, step, loss, args.logging_steps /
                           (time.time() - tic_train), lr_scheduler.get_lr()))
                tic_train = time.time()

            loss.backward()
            optimizer.step()
            lr_scheduler.step()
            optimizer.clear_gradients()
            if global_step % args.save_steps == 0 and (
                (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0):
                evaluate(model, dev_data_loader, tokenizer, rouge1, rouge2,
                         attn_id, tgt_type_id, args)
                output_dir = os.path.join(args.output_dir,
                                          "model_%d" % global_step)
                if not os.path.exists(output_dir):
                    os.makedirs(output_dir)
                model_to_save = model._layers if isinstance(
                    model, paddle.DataParallel) else model
                model_to_save.save_pretrained(output_dir)
                tokenizer.save_pretrained(output_dir)
            global_step += 1


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