Fix typo in val_batch_size and remove unused imports

Fix typo in val_batch_size and remove unused imports

Author: debbywehner

3.test_cases/pytorch/FSDP/src/train.py

@@ -1,35 +1,24 @@
 # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 # SPDX-License-Identifier: MIT-0
 
-import datetime
 import functools
 import math
-import re
 import time
 
-import numpy as np
 import torch
 from torch import optim
 import torch.distributed as dist
 import torch.utils.data
 
-import transformers
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from datasets import load_dataset
-
 from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
 from torch.distributed.fsdp import MixedPrecision
 from torch.distributed.fsdp import ShardingStrategy
 from torch.distributed.fsdp import CPUOffload
-from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy
-from torch.utils.data import DataLoader
+from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
 
-from model_utils.concat_dataset import ConcatTokensDataset
 from model_utils.train_utils import (get_model_config, 
                                    compute_num_params,
                                    get_transformer_layer,
-                                   get_sharding_strategy,
-                                   get_backward_fetch_policy,
                                    apply_activation_checkpoint,
                                    get_param_groups_by_weight_decay,
                                    get_logger,
@@ -268,7 +257,7 @@ def main(args):
     val_dataloader = create_streaming_dataloader(args.dataset, 
                                                   args.tokenizer, 
                                                   name=args.dataset_config_name, 
-                                                  batch_size=args.train_batch_size, 
+                                                  batch_size=args.val_batch_size, 
                                                   split='validation')
 
     train(model,