link_prediction.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import argparse
import io
import os
import random
import time
from functools import partial

import numpy as np
import paddle
import pgl
import yaml
from data import GraphDataLoader, PredictData, TrainData, batch_fn
from easydict import EasyDict as edict
from models import ErnieSageForLinkPrediction

from paddlenlp.transformers import ErnieTinyTokenizer, ErnieTokenizer
from paddlenlp.utils.log import logger

MODEL_CLASSES = {
    "ernie-tiny": (ErnieSageForLinkPrediction, ErnieTinyTokenizer),
    "ernie-1.0": (ErnieSageForLinkPrediction, ErnieTokenizer),
}


def set_seed(config):
    random.seed(config.seed)
    np.random.seed(config.seed)
    paddle.seed(config.seed)


def load_data(graph_data_path):
    base_graph = pgl.Graph.load(graph_data_path)
    term_ids = np.load(os.path.join(graph_data_path, "term_ids.npy"), mmap_mode="r")
    return base_graph, term_ids


def do_train(config):
    paddle.set_device(config.device)
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()
    set_seed(config)

    base_graph, term_ids = load_data(config.graph_work_path)
    collate_fn = partial(batch_fn, samples=config.samples, base_graph=base_graph, term_ids=term_ids)

    # mode = "train"
    train_ds = TrainData(config.graph_work_path)

    model_class, tokenizer_class = MODEL_CLASSES[config.model_name_or_path]
    tokenizer = tokenizer_class.from_pretrained(config.model_name_or_path)
    config.cls_token_id = tokenizer.cls_token_id

    model = model_class.from_pretrained(config.model_name_or_path, config_file=config)
    model = paddle.DataParallel(model)

    train_loader = GraphDataLoader(
        train_ds, batch_size=config.batch_size, shuffle=True, num_workers=config.sample_workers, collate_fn=collate_fn
    )

    optimizer = paddle.optimizer.Adam(learning_rate=config.lr, parameters=model.parameters())

    rank = paddle.distributed.get_rank()
    global_step = 0
    tic_train = time.time()
    for epoch in range(config.epoch):
        for step, (graphs, datas) in enumerate(train_loader):
            global_step += 1
            loss, outputs = model(graphs, datas)
            if global_step % config.log_per_step == 0:
                logger.info(
                    "global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
                    % (global_step, epoch, step, loss, config.log_per_step / (time.time() - tic_train))
                )
                tic_train = time.time()
            loss.backward()
            optimizer.step()
            optimizer.clear_grad()
            if global_step % config.save_per_step == 0:
                if rank == 0:
                    output_dir = os.path.join(config.output_path, "model_%d" % global_step)
                    if not os.path.exists(output_dir):
                        os.makedirs(output_dir)
                    model._layers.save_pretrained(output_dir)
    if rank == 0:
        output_dir = os.path.join(config.output_path, "last")
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)
        model._layers.save_pretrained(output_dir)


def tostr(data_array):
    return " ".join(["%.5lf" % d for d in data_array])


@paddle.no_grad()
def do_predict(config):
    paddle.set_device(config.device)
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()
    set_seed(config)

    # mode = "predict"
    num_nodes = int(np.load(os.path.join(config.graph_work_path, "num_nodes.npy")))

    base_graph, term_ids = load_data(config.graph_work_path)
    collate_fn = partial(batch_fn, samples=config.samples, base_graph=base_graph, term_ids=term_ids)

    model_class, tokenizer_class = MODEL_CLASSES[config.model_name_or_path]
    tokenizer = tokenizer_class.from_pretrained(config.model_name_or_path)
    config.cls_token_id = tokenizer.cls_token_id

    model = model_class.from_pretrained(config.infer_model, config_file=config)

    model = paddle.DataParallel(model)
    predict_ds = PredictData(num_nodes)

    predict_loader = GraphDataLoader(
        predict_ds,
        batch_size=config.infer_batch_size,
        shuffle=True,
        num_workers=config.sample_workers,
        collate_fn=collate_fn,
    )

    trainer_id = paddle.distributed.get_rank()
    id2str = io.open(os.path.join(config.graph_work_path, "terms.txt"), encoding=config.encoding).readlines()
    if not os.path.exists(config.output_path):
        os.mkdir(config.output_path)
    fout = io.open("%s/part-%s" % (config.output_path, trainer_id), "w", encoding="utf8")

    global_step = 0
    epoch = 0
    tic_train = time.time()
    model.eval()
    for step, (graphs, datas) in enumerate(predict_loader):
        global_step += 1
        loss, outputs = model(graphs, datas)
        for user_feat, user_real_index in zip(outputs[0].numpy(), outputs[3].numpy()):
            sri = id2str[int(user_real_index)].strip("\n")
            line = "{}\t{}\n".format(sri, tostr(user_feat))
            fout.write(line)
        if global_step % config.log_per_step == 0:
            logger.info(
                "predict step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
                % (global_step, epoch, step, loss, config.log_per_step / (time.time() - tic_train))
            )
            tic_train = time.time()
    fout.close()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="main")
    parser.add_argument("--conf", type=str, default="./config.yaml")
    parser.add_argument("--do_predict", action="store_true", default=False)
    args = parser.parse_args()
    config = edict(yaml.load(open(args.conf), Loader=yaml.FullLoader))

    assert config.device in ["gpu", "cpu"], "Device should be gpu/cpu, but got %s." % config.device
    logger.info(config)
    if args.do_predict:
        do_predict(config)
    else:
        do_train(config)