Fix issue with validation dataloader

megatron/data/ict_dataset.py

@@ -35,7 +35,7 @@ class InverseClozeDataset(Dataset):
 
     def __getitem__(self, idx):
         # get rng state corresponding to index (allows deterministic random pair)
-        rng = random.Random(idx + self.seed)
+        rng = random.Random(idx + 20000 + self.seed)
         np_rng = np.random.RandomState(seed=[rng.randint(0, 2**32-1) for _ in range(16)])
 
         # get seq length. Save 2 tokens for beginning and end

megatron/training.py

@@ -98,7 +98,6 @@ def pretrain(train_val_test_data_provider, model_provider, forward_step_func,
 
     iteration = 0
     if args.do_train and args.train_iters > 0:
-        if args.do_train:
         iteration, _ = train(forward_step_func,
                              model, optimizer, lr_scheduler,
                              train_data_iterator, val_data_iterator)
@@ -485,8 +484,8 @@ def get_train_val_test_data_iterators(train_data, val_data, test_data):
     if val_data is not None:
         start_iter_val = (args.iteration // args.eval_interval) * \
                          args.eval_iters
-        val_data.batch_sampler.start_iter = start_iter_val % \
-                                            len(val_data)
+        val_data.batch_sampler.start_iter = 0
+
         print_rank_0('setting validation data start iteration to {}'.
                      format(val_data.batch_sampler.start_iter))
 

megatron/utils.py

@@ -24,7 +24,7 @@ from megatron import get_adlr_autoresume
 from megatron import mpu
 from megatron import print_rank_0
 from megatron.checkpointing import save_checkpoint
-from megatron.data.samplers import DistributedBatchSampler
+from megatron.data.samplers import DistributedBatchSampler, RandomSampler
 from megatron.fp16 import FP16_Optimizer
 
 
@@ -102,12 +102,16 @@ def make_data_loader(dataset):
     num_workers = args.num_workers
 
     # Use a simple sampler with distributed batch sampler.
-    sampler = torch.utils.data.SequentialSampler(dataset)
+    #sampler = torch.utils.data.SequentialSampler(dataset)
+    sampler = RandomSampler(dataset,
+                            replacement=True,
+                            num_samples=global_batch_size*args.train_iters)
     batch_sampler = DistributedBatchSampler(sampler=sampler,
                                             batch_size=global_batch_size,
                                             drop_last=True,
                                             rank=rank,
-                                            world_size=world_size)
+                                            world_size=world_size,
+                                            wrap_last=True)
     # Torch dataloader.
     return torch.utils.data.DataLoader(dataset,
                                        batch_sampler=batch_sampler,

pretrain_bert_ict.py

@@ -115,7 +115,7 @@ def get_train_val_test_data():
 
         # Number of train/valid/test samples.
         train_iters = args.train_iters
-        eval_iters = (train_iters // args.eval_interval + 1) * args.eval_iters
+        eval_iters = args.eval_iters
         test_iters = args.eval_iters
         train_val_test_num_samples = [train_iters * global_batch_size,
                                       eval_iters * global_batch_size,
@@ -159,7 +159,7 @@ def get_train_val_test_data():
     args.do_valid = flags[1].item()
     args.do_test = flags[2].item()
 
-    return train_data, val_data, test_data
+    return train_data, valid_data, test_data
 
 
 if __name__ == "__main__":