wehub-resource-sync
240 lines
11 KiB
Python
240 lines
11 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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import paddle.nn.functional as F
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from data import convert_example, create_dataloader, read_text_pair
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from gradient_cache.model import SemanticIndexCacheNeg
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import paddlenlp as ppnlp
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from paddlenlp.data import Pad, Tuple
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from paddlenlp.datasets import load_dataset
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from paddlenlp.transformers import LinearDecayWithWarmup
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# yapf: disable
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parser = argparse.ArgumentParser()
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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("--output_emb_size", default=None, type=int, help="output_embedding_size.")
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parser.add_argument("--learning_rate", default=1e-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=10, type=int, help="Total number of training epochs to perform.")
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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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parser.add_argument('--save_steps', type=int, default=10000, help="Interval steps to save checkpoint.")
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parser.add_argument("--train_set_file", type=str, required=True, help="The full path of train_set_file.")
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parser.add_argument("--margin", default=0.3, type=float, help="Margin between pos_sample and neg_samples.")
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parser.add_argument("--scale", default=30, type=int, help="Scale for pair-wise margin_rank_loss")
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parser.add_argument("--use_amp", action="store_true", help="Whether to use AMP.")
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parser.add_argument("--amp_loss_scale", default=32768, type=float, help="The value of scale_loss for fp16. This is only used for AMP training.")
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parser.add_argument("--chunk_numbers", type=int, default=50, help="The number of the chunks for model")
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args = parser.parse_args()
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# yapf: enable
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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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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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random.seed(args.seed)
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np.random.seed(args.seed)
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paddle.seed(args.seed)
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train_ds = load_dataset(read_text_pair, data_path=args.train_set_file, lazy=False)
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# If you wanna use bert/roberta pretrained model,
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# pretrained_model = ppnlp.transformers.BertModel.from_pretrained('bert-base-chinese')
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# pretrained_model = ppnlp.transformers.RobertaModel.from_pretrained('roberta-wwm-ext')
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pretrained_model = ppnlp.transformers.ErnieModel.from_pretrained("ernie-1.0")
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# If you wanna use bert/roberta pretrained model,
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# tokenizer = ppnlp.transformers.BertTokenizer.from_pretrained('bert-base-chinese')
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# tokenizer = ppnlp.transformers.RobertaTokenizer.from_pretrained('roberta-wwm-ext')
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tokenizer = ppnlp.transformers.ErnieTokenizer.from_pretrained("ernie-1.0")
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trans_func = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length)
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batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"), # query_input
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"), # query_# query_segment
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Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"), # query_# title_input
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"), # title_segment
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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, trans_fn=trans_func
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)
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model = SemanticIndexCacheNeg(
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pretrained_model, margin=args.margin, scale=args.scale, output_emb_size=args.output_emb_size
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)
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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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print("warmup from:{}".format(args.init_from_ckpt))
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model = paddle.DataParallel(model)
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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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if args.use_amp:
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scaler = paddle.amp.GradScaler(init_loss_scaling=args.amp_loss_scale)
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if args.batch_size % args.chunk_numbers == 0:
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chunk_numbers = args.chunk_numbers
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def split(inputs, chunk_numbers, axis=0):
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if inputs.shape[0] % chunk_numbers == 0:
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return paddle.split(inputs, chunk_numbers, axis=0)
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else:
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return paddle.split(inputs, inputs.shape[0], axis=0)
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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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chunked_x = [split(t, chunk_numbers, axis=0) for t in batch]
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sub_batchs = [list(s) for s in zip(*chunked_x)]
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all_reps = []
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all_grads = []
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all_labels = []
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all_CUDA_rnd_state = []
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all_query = []
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all_title = []
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for sub_batch in sub_batchs:
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all_reps = []
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all_labels = []
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(
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sub_query_input_ids,
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sub_query_token_type_ids,
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sub_title_input_ids,
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sub_title_token_type_ids,
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) = sub_batch
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with paddle.amp.auto_cast(args.use_amp, custom_white_list=["layer_norm", "softmax", "gelu"]):
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with paddle.no_grad():
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sub_CUDA_rnd_state = paddle.framework.random.get_cuda_rng_state()
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all_CUDA_rnd_state.append(sub_CUDA_rnd_state)
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sub_cosine_sim, sub_label, query_embedding, title_embedding = model(
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query_input_ids=sub_query_input_ids,
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title_input_ids=sub_title_input_ids,
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query_token_type_ids=sub_query_token_type_ids,
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title_token_type_ids=sub_title_token_type_ids,
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)
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all_reps.append(sub_cosine_sim)
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all_labels.append(sub_label)
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all_title.append(title_embedding)
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all_query.append(query_embedding)
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model_reps = paddle.concat(all_reps, axis=0)
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model_title = paddle.concat(all_title)
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model_query = paddle.concat(all_query)
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model_title = model_title.detach()
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model_query = model_query.detach()
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model_query.stop_gtadient = False
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model_title.stop_gradient = False
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model_reps.stop_gradient = False
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model_label = paddle.concat(all_labels, axis=0)
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loss = F.cross_entropy(input=model_reps, label=model_label)
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loss.backward()
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all_grads.append(model_reps.grad)
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for sub_batch, CUDA_state, grad in zip(sub_batchs, all_CUDA_rnd_state, all_grads):
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(
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sub_query_input_ids,
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sub_query_token_type_ids,
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sub_title_input_ids,
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sub_title_token_type_ids,
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) = sub_batch
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paddle.framework.random.set_cuda_rng_state(CUDA_state)
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cosine_sim, _ = model(
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query_input_ids=sub_query_input_ids,
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title_input_ids=sub_title_input_ids,
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query_token_type_ids=sub_query_token_type_ids,
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title_token_type_ids=sub_title_token_type_ids,
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)
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surrogate = paddle.dot(cosine_sim, grad)
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if args.use_amp:
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scaled = scaler.scale(surrogate)
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scaled.backward()
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else:
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surrogate.backward()
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if args.use_amp:
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scaler.minimize(optimizer, scaled)
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else:
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optimizer.step()
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global_step += 1
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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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lr_scheduler.step()
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optimizer.clear_grad()
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if global_step % args.save_steps == 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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