Merge branch 'master' into master_params_sharing

megatron/data/samplers.py

@@ -80,10 +80,20 @@ class DistributedBatchSampler(data.sampler.BatchSampler):
     implementation is at the batch sampler level, instead of just the
     sampler level. This allows wrapping of arbitrary data samplers
     (sequential, random, WeightedRandomSampler, etc.) with this batch
-    sampler."""
+    sampler.
+    
+    The `interleave` argument specifies how to distribute a batch. A value
+    of True combined with the above random sampler is equivalent to pytorch's
+    torch.utils.data.distributed.DistributedSampler.
+
+    For the following batch [0,1,2,3,4,5,6,7] and data parallelism of 2 
+    specifying True will result in the following samples for each gpu:
+        GPU0: [0,2,4,6] GPU1: [1,3,5,7]
+    specifying False will result in the following samples:
+        GPU0: [0,1,2,3] GPU1: [4,5,6,7]"""
 
     def __init__(self, sampler, batch_size, drop_last, rank=-1,
-                 world_size=2, wrap_last=False):
+                 world_size=2, wrap_last=False, interleave=False):
         super(DistributedBatchSampler, self).__init__(sampler, batch_size,
                                                       drop_last)
         if rank == -1:
@@ -95,6 +105,7 @@ class DistributedBatchSampler(data.sampler.BatchSampler):
         self.wrap_around = 0
         self.wrap_last = wrap_last
         self.start_iter = 0
+        self.interleave = interleave
 
     def __iter__(self):
         batch = []
@@ -130,6 +141,8 @@ class DistributedBatchSampler(data.sampler.BatchSampler):
 
     def _batch(self, batch):
         """extracts samples only pertaining to this worker's batch"""
+        if self.interleave:
+            return batch[self.rank:self.batch_size:self.world_size]
         start = self.rank * self.batch_size // self.world_size
         end = (self.rank + 1) * self.batch_size // self.world_size
         return batch[start:end]