diff --git a/run_classifier_pytorch.py b/run_classifier_pytorch.py index b3e3612542c..52bb8d36bc8 100644 --- a/run_classifier_pytorch.py +++ b/run_classifier_pytorch.py @@ -115,7 +115,11 @@ parser.add_argument("--iterations_per_loop", default = 1000, type = int, help = "How many steps to make in each estimator call.") - + +parser.add_argument("--use_gpu", + default = True, + type = bool, + help = "Whether to use GPU") ### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ### parser.add_argument("--use_tpu", default = False, @@ -416,25 +420,18 @@ def input_fn_builder(features, seq_length, is_training, drop_remainder): batch_size = params["batch_size"] num_examples = len(features) - - # This is for demo purposes and does NOT scale to large data sets. We do - # not use Dataset.from_generator() because that uses tf.py_func which is - # not TPU compatible. The right way to load data is with TFRecordReader. - d = tf.data.Dataset.from_tensor_slices({ - "input_ids": - torch.Tensor(all_input_ids, size=[num_examples, seq_length], - dtype=torch.int32, requires_grad=False), - "input_mask": - torch.Tensor(all_input_mask, size=[num_examples, seq_length], - dtype=torch.int32, requires_grad=False), - "segment_ids": - torch.Tensor(all_segment_ids, size=[num_examples, seq_length], - dtype=torch.int32, requires_grad=False), - "label_ids": - torch.Tensor(all_label_ids, size=[num_examples], - dtype=torch.int32, requires_grad=False) - }) - + + device = torch.device("cuda") if args.use_gpu else torch.device("cpu") + d = {"input_ids": + torch.IntTensor(all_input_ids, device = device), #Requires_grad=False by default + "input_mask": + torch.IntTensor(all_input_mask, device = device), + "segment_ids": + torch.IntTensor(all_segment_ids, device = device), + "label_ids": + torch.IntTensor(all_label_ids, device = device) + } + if is_training: d = d.repeat() d = d.shuffle(buffer_size=100) @@ -443,3 +440,136 @@ def input_fn_builder(features, seq_length, is_training, drop_remainder): return d return input_fn + + +def main(_): + processors = { + "cola": ColaProcessor, + "mnli": MnliProcessor, + "mrpc": MrpcProcessor, + } + + if not args.do_train and not args.do_eval: + raise ValueError("At least one of `do_train` or `do_eval` must be True.") + + bert_config = modeling.BertConfig.from_json_file(args.bert_config_file) + + if args.max_seq_length > bert_config.max_position_embeddings: + raise ValueError( + "Cannot use sequence length %d because the BERT model " + "was only trained up to sequence length %d" % + (args.max_seq_length, bert_config.max_position_embeddings)) + + if os.path.exists(args.output_dir) and os.listdir(args.output_dir): + raise ConfigurationError(f"Output directory ({args.output_dir}) already exists and is " + f"not empty.") + os.makedirs(args.output_dir, exist_ok=True) + + task_name = args.task_name.lower() + + if task_name not in processors: + raise ValueError("Task not found: %s" % (task_name)) + + processor = processors[task_name]() + + label_list = processor.get_labels() + + tokenizer = tokenization.FullTokenizer( + vocab_file=args.vocab_file, do_lower_case=args.do_lower_case) + + # tpu_cluster_resolver = None + # if FLAGS.use_tpu and FLAGS.tpu_name: + # tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( + # FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) + + # is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 + # run_config = tf.contrib.tpu.RunConfig( + # cluster=tpu_cluster_resolver, + # master=FLAGS.master, + # model_dir=FLAGS.output_dir, + # save_checkpoints_steps=FLAGS.save_checkpoints_steps, + # tpu_config=tf.contrib.tpu.TPUConfig( + # iterations_per_loop=FLAGS.iterations_per_loop, + # num_shards=FLAGS.num_tpu_cores, + # per_host_input_for_training=is_per_host)) + + train_examples = None + num_train_steps = None + num_warmup_steps = None + if args.do_train: + train_examples = processor.get_train_examples(args.data_dir) + num_train_steps = int( + len(train_examples) / args.train_batch_size * args.num_train_epochs) + num_warmup_steps = int(num_train_steps * args.warmup_proportion) + + model_fn = model_fn_builder( + bert_config=bert_config, + num_labels=len(label_list), + init_checkpoint=args.init_checkpoint, + learning_rate=args.learning_rate, + num_train_steps=num_train_steps, + num_warmup_steps=num_warmup_steps, + use_gpu=args.use_gpu, + use_one_hot_embeddings=args.use_gpu) ### TO DO - to check when model_fn is written) + + # If TPU is not available, this will fall back to normal Estimator on CPU + # or GPU. - TO DO + for batch in + estimator = tf.contrib.tpu.TPUEstimator( + use_tpu=args.use_tpu, + model_fn=model_fn, + config=run_config, + train_batch_size=args.train_batch_size, + eval_batch_size=args.eval_batch_size) + + if args.do_train: + train_features = convert_examples_to_features( + train_examples, label_list, args.max_seq_length, tokenizer) + logger.info("***** Running training *****") + logger.info(" Num examples = %d", len(train_examples)) + logger.info(" Batch size = %d", args.train_batch_size) + logger.info(" Num steps = %d", num_train_steps) + train_input_fn = input_fn_builder( + features=train_features, + seq_length=args.max_seq_length, + is_training=True, + drop_remainder=True) + estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) + + if args.do_eval: + eval_examples = processor.get_dev_examples(args.data_dir) + eval_features = convert_examples_to_features( + eval_examples, label_list, args.max_seq_length, tokenizer) + + tf.logging.info("***** Running evaluation *****") + tf.logging.info(" Num examples = %d", len(eval_examples)) + tf.logging.info(" Batch size = %d", args.eval_batch_size) + + # This tells the estimator to run through the entire set. + eval_steps = None + # However, if running eval on the TPU, you will need to specify the + # number of steps. + if args.use_tpu: + # Eval will be slightly WRONG on the TPU because it will truncate + # the last batch. + eval_steps = int(len(eval_examples) / args.eval_batch_size) + + eval_drop_remainder = True if args.use_tpu else False + eval_input_fn = input_fn_builder( + features=eval_features, + seq_length=args.max_seq_length, + is_training=False, + drop_remainder=eval_drop_remainder) + + result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps) + + output_eval_file = os.path.join(args.output_dir, "eval_results.txt") + with tf.gfile.GFile(output_eval_file, "w") as writer: + tf.logging.info("***** Eval results *****") + for key in sorted(result.keys()): + tf.logging.info(" %s = %s", key, str(result[key])) + writer.write("%s = %s\n" % (key, str(result[key]))) + +if __name__ == "__main__": + main() + return None \ No newline at end of file