import argparse import logging import os import sys import time import tensorflow as tf from datasets import load_dataset from tqdm import tqdm from transformers import AutoTokenizer, TFAutoModelForSequenceClassification from transformers.utils import is_sagemaker_dp_enabled if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled(): SDP_ENABLED = True os.environ["SAGEMAKER_INSTANCE_TYPE"] = "p3dn.24xlarge" import smdistributed.dataparallel.tensorflow as sdp else: SDP_ENABLED = False def fit(model, loss, opt, train_dataset, epochs, train_batch_size, max_steps=None): pbar = tqdm(train_dataset) for i, batch in enumerate(pbar): with tf.GradientTape() as tape: inputs, targets = batch outputs = model(batch) loss_value = loss(targets, outputs.logits) if SDP_ENABLED: tape = sdp.DistributedGradientTape(tape, sparse_as_dense=True) grads = tape.gradient(loss_value, model.trainable_variables) opt.apply_gradients(zip(grads, model.trainable_variables)) pbar.set_description(f"Loss: {loss_value:.4f}") if SDP_ENABLED and i == 0: sdp.broadcast_variables(model.variables, root_rank=0) sdp.broadcast_variables(opt.variables(), root_rank=0) if max_steps and i >= max_steps: break train_results = {"loss": loss_value.numpy()} return train_results def get_datasets(tokenizer, train_batch_size, eval_batch_size): # Load dataset train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"]) # Preprocess train dataset train_dataset = train_dataset.map( lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True ) train_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"]) train_features = { x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length]) for x in ["input_ids", "attention_mask"] } tf_train_dataset = tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])) # Preprocess test dataset test_dataset = test_dataset.map( lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True ) test_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"]) test_features = { x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length]) for x in ["input_ids", "attention_mask"] } tf_test_dataset = tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])) if SDP_ENABLED: tf_train_dataset = tf_train_dataset.shard(sdp.size(), sdp.rank()) tf_test_dataset = tf_test_dataset.shard(sdp.size(), sdp.rank()) tf_train_dataset = tf_train_dataset.batch(train_batch_size, drop_remainder=True) tf_test_dataset = tf_test_dataset.batch(eval_batch_size, drop_remainder=True) return tf_train_dataset, tf_test_dataset if __name__ == "__main__": parser = argparse.ArgumentParser() # Hyperparameters sent by the client are passed as command-line arguments to the script. parser.add_argument("--epochs", type=int, default=3) parser.add_argument("--per_device_train_batch_size", type=int, default=16) parser.add_argument("--per_device_eval_batch_size", type=int, default=8) parser.add_argument("--model_name_or_path", type=str) parser.add_argument("--learning_rate", type=str, default=5e-5) parser.add_argument("--do_train", type=bool, default=True) parser.add_argument("--do_eval", type=bool, default=True) parser.add_argument("--output_dir", type=str) parser.add_argument("--max_steps", type=int, default=None) # Data, model, and output directories parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"]) parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"]) parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"]) args, _ = parser.parse_known_args() # Set up logging logger = logging.getLogger(__name__) logging.basicConfig( level=logging.getLevelName("INFO"), handlers=[logging.StreamHandler(sys.stdout)], format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) if SDP_ENABLED: sdp.init() gpus = tf.config.experimental.list_physical_devices("GPU") for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) if gpus: tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU") # Load model and tokenizer model = TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path) tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path) # get datasets tf_train_dataset, tf_test_dataset = get_datasets( tokenizer=tokenizer, train_batch_size=args.per_device_train_batch_size, eval_batch_size=args.per_device_eval_batch_size, ) # fine optimizer and loss optimizer = tf.keras.optimizers.Adam(learning_rate=args.learning_rate) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) metrics = [tf.keras.metrics.SparseCategoricalAccuracy()] model.compile(optimizer=optimizer, loss=loss, metrics=metrics) # Training if args.do_train: # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size) start_train_time = time.time() train_results = fit( model, loss, optimizer, tf_train_dataset, args.epochs, args.per_device_train_batch_size, max_steps=args.max_steps, ) end_train_time = time.time() - start_train_time logger.info("*** Train ***") logger.info(f"train_runtime = {end_train_time}") output_eval_file = os.path.join(args.output_dir, "train_results.txt") if not SDP_ENABLED or sdp.rank() == 0: with open(output_eval_file, "w") as writer: logger.info("***** Train results *****") logger.info(train_results) for key, value in train_results.items(): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") # Evaluation if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0): result = model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True) logger.info("*** Evaluate ***") output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") logger.info(result) for key, value in result.items(): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") # Save result if SDP_ENABLED: if sdp.rank() == 0: model.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) else: model.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir)