removed unused values in samplers

megatron/data/samplers.py

@@ -15,8 +15,6 @@
 
 """Batch samplers that work with either random or sequential data samplers."""
 
-import numpy as np
-
 import torch
 from torch.utils import data
 
@@ -100,7 +98,6 @@ class DistributedBatchSampler(data.sampler.BatchSampler):
 
     def __iter__(self):
         batch = []
-        last_batch = None
         i = 0
         for idx in self.data_iterator(self.sampler, wrap_around=False):
             batch.append(idx)
@@ -110,7 +107,6 @@ class DistributedBatchSampler(data.sampler.BatchSampler):
                     yield tbatch
                     self.start_iter = 0
                 i += 1
-                last_batch = np.array(list(tbatch))
                 batch = []
         batch_len = len(batch)
         if batch_len > 0 and not self.drop_last:
@@ -118,15 +114,6 @@ class DistributedBatchSampler(data.sampler.BatchSampler):
                 self.sampler.wrap_around -= (self.batch_size)
                 self.wrap_around += (len(batch))
                 self.wrap_around %= self.batch_size
-                if isinstance(self.sampler, TransposedSampler):
-                    for i, idx in enumerate(self.data_iterator(
-                            self.sampler, wrap_around=True)):
-                        if i == 0:
-                            continue
-                        batch.append(idx)
-                        new_batch_len = len(batch)
-                        if len(batch) == self.batch_size:
-                            break
             yield self._batch(batch)
         if self.wrap_last:
             self.sampler.wrap_around += self.batch_size