Train
(env)
| 65 | |
| 66 | |
| 67 | def train(env): |
| 68 | """Train""" |
| 69 | args = env.args |
| 70 | |
| 71 | logging.info("loading data.") |
| 72 | train = Corpus.load(args.train_data_path, env.fields) |
| 73 | dev = Corpus.load(args.valid_data_path, env.fields) |
| 74 | test = Corpus.load(args.test_data_path, env.fields) |
| 75 | logging.info("init dataset.") |
| 76 | train = TextDataset(train, env.fields, args.buckets) |
| 77 | dev = TextDataset(dev, env.fields, args.buckets) |
| 78 | test = TextDataset(test, env.fields, args.buckets) |
| 79 | logging.info("set the data loaders.") |
| 80 | train.loader = batchify(train, args.batch_size, args.use_data_parallel, True) |
| 81 | dev.loader = batchify(dev, args.batch_size) |
| 82 | test.loader = batchify(test, args.batch_size) |
| 83 | |
| 84 | logging.info("{:6} {:5} sentences, ".format('train:', len(train)) + "{:3} batches, ".format(len(train.loader)) + |
| 85 | "{} buckets".format(len(train.buckets))) |
| 86 | logging.info("{:6} {:5} sentences, ".format('dev:', len(dev)) + "{:3} batches, ".format(len(dev.loader)) + |
| 87 | "{} buckets".format(len(dev.buckets))) |
| 88 | logging.info("{:6} {:5} sentences, ".format('test:', len(test)) + "{:3} batches, ".format(len(test.loader)) + |
| 89 | "{} buckets".format(len(test.buckets))) |
| 90 | |
| 91 | logging.info("Create the model") |
| 92 | model = Model(args) |
| 93 | |
| 94 | # init parallel strategy |
| 95 | if args.use_data_parallel: |
| 96 | dist.init_parallel_env() |
| 97 | model = paddle.DataParallel(model) |
| 98 | |
| 99 | if args.encoding_model.startswith( |
| 100 | "ernie") and args.encoding_model != "ernie-lstm" or args.encoding_model == 'transformer': |
| 101 | args['lr'] = args.ernie_lr |
| 102 | else: |
| 103 | args['lr'] = args.lstm_lr |
| 104 | |
| 105 | if args.encoding_model.startswith("ernie") and args.encoding_model != "ernie-lstm": |
| 106 | max_steps = 100 * len(train.loader) |
| 107 | decay = LinearDecay(args.lr, int(args.warmup_proportion * max_steps), max_steps) |
| 108 | else: |
| 109 | decay = dygraph.ExponentialDecay(learning_rate=args.lr, decay_steps=args.decay_steps, decay_rate=args.decay) |
| 110 | |
| 111 | grad_clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=args.clip) |
| 112 | |
| 113 | if args.encoding_model.startswith("ernie") and args.encoding_model != "ernie-lstm": |
| 114 | optimizer = AdamW( |
| 115 | learning_rate=decay, |
| 116 | parameter_list=model.parameters(), |
| 117 | weight_decay=args.weight_decay, |
| 118 | grad_clip=grad_clip, |
| 119 | ) |
| 120 | else: |
| 121 | optimizer = fluid.optimizer.AdamOptimizer( |
| 122 | learning_rate=decay, |
| 123 | beta1=args.mu, |
| 124 | beta2=args.nu, |
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