wehub-resource-sync
193 lines
7.8 KiB
Python
193 lines
7.8 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import random
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import time
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from functools import partial
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import numpy as np
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import paddle
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from data import convert_pairwise_example as convert_example
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from data import create_dataloader, gen_pair
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from model import PairwiseMatching
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from paddlenlp.data import Pad, Stack, Tuple
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from paddlenlp.datasets import load_dataset
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from paddlenlp.transformers import AutoModel, AutoTokenizer, LinearDecayWithWarmup
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# fmt: off
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parser = argparse.ArgumentParser()
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parser.add_argument("--margin", default=0.2, type=float, help="Margin for pos_score and neg_score.")
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parser.add_argument("--save_dir", default='./checkpoint', type=str, help="The output directory where the model checkpoints will be written.")
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parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.")
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parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
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parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
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parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
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parser.add_argument("--epochs", default=3, type=int, help="Total number of training epochs to perform.")
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parser.add_argument("--eval_step", default=100, type=int, help="Step interval for evaluation.")
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parser.add_argument('--save_step', default=10000, type=int, help="Step interval for saving checkpoint.")
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parser.add_argument('--max_step', default=10000, type=int, help="Max steps for training.")
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parser.add_argument("--warmup_proportion", default=0.0, type=float, help="Linear warmup proportion over the training process.")
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parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")
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parser.add_argument("--seed", type=int, default=1000, help="Random seed for initialization.")
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parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
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args = parser.parse_args()
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# fmt: on
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def set_seed(seed):
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"""sets random seed"""
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random.seed(seed)
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np.random.seed(seed)
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paddle.seed(seed)
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@paddle.no_grad()
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def evaluate(model, metric, data_loader, phase="dev"):
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"""
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Given a dataset, it evals model and computes the metric.
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Args:
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model(obj:`paddle.nn.Layer`): A model to classify texts.
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data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
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metric(obj:`paddle.metric.Metric`): The evaluation metric.
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"""
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model.eval()
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metric.reset()
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for idx, batch in enumerate(data_loader):
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input_ids, token_type_ids, labels = batch
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pos_probs = model.predict(input_ids=input_ids, token_type_ids=token_type_ids)
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neg_probs = 1.0 - pos_probs
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preds = np.concatenate((neg_probs, pos_probs), axis=1)
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metric.update(preds=preds, labels=labels)
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print("eval_{} auc:{:.2}".format(phase, metric.accumulate()))
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metric.reset()
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model.train()
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def do_train():
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paddle.set_device(args.device)
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rank = paddle.distributed.get_rank()
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if paddle.distributed.get_world_size() > 1:
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paddle.distributed.init_parallel_env()
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set_seed(args.seed)
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train_ds, dev_ds = load_dataset("lcqmc", splits=["train", "dev"])
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train_ds = gen_pair(train_ds)
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pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh")
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tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh")
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trans_func_train = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length)
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trans_func_eval = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length, phase="eval")
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batchify_fn_train = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=tokenizer.pad_token_id), # pos_pair_input
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # pos_pair_segment
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Pad(axis=0, pad_val=tokenizer.pad_token_id), # neg_pair_input
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # neg_pair_segment
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): [data for data in fn(samples)]
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batchify_fn_eval = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=tokenizer.pad_token_id), # pair_input
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # pair_segment
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Stack(dtype="int64"), # label
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): [data for data in fn(samples)]
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train_data_loader = create_dataloader(
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train_ds, mode="train", batch_size=args.batch_size, batchify_fn=batchify_fn_train, trans_fn=trans_func_train
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)
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dev_data_loader = create_dataloader(
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dev_ds, mode="dev", batch_size=args.batch_size, batchify_fn=batchify_fn_eval, trans_fn=trans_func_eval
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)
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model = PairwiseMatching(pretrained_model, margin=args.margin)
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if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):
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state_dict = paddle.load(args.init_from_ckpt)
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model.set_dict(state_dict)
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num_training_steps = len(train_data_loader) * args.epochs
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lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, args.warmup_proportion)
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# Generate parameter names needed to perform weight decay.
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# All bias and LayerNorm parameters are excluded.
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decay_params = [p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"])]
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optimizer = paddle.optimizer.AdamW(
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learning_rate=lr_scheduler,
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parameters=model.parameters(),
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weight_decay=args.weight_decay,
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apply_decay_param_fun=lambda x: x in decay_params,
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)
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metric = paddle.metric.Auc()
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global_step = 0
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tic_train = time.time()
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for epoch in range(1, args.epochs + 1):
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for step, batch in enumerate(train_data_loader, start=1):
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pos_input_ids, pos_token_type_ids, neg_input_ids, neg_token_type_ids = batch
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loss = model(
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pos_input_ids=pos_input_ids,
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neg_input_ids=neg_input_ids,
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pos_token_type_ids=pos_token_type_ids,
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neg_token_type_ids=neg_token_type_ids,
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)
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global_step += 1
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if global_step > args.max_step:
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print("Training steps have achieved max_step, training is stopped.")
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return
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if global_step % 10 == 0 and rank == 0:
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print(
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"global step %d, epoch: %d, batch: %d, loss: %.5f, speed: %.2f step/s"
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% (global_step, epoch, step, loss, 10 / (time.time() - tic_train))
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)
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tic_train = time.time()
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loss.backward()
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optimizer.step()
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lr_scheduler.step()
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optimizer.clear_grad()
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if global_step % args.eval_step == 0 and rank == 0:
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evaluate(model, metric, dev_data_loader, "dev")
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if global_step % args.save_step == 0 and rank == 0:
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save_dir = os.path.join(args.save_dir, "model_%d" % global_step)
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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save_param_path = os.path.join(save_dir, "model_state.pdparams")
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paddle.save(model.state_dict(), save_param_path)
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tokenizer.save_pretrained(save_dir)
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if __name__ == "__main__":
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do_train()
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