Pad inputs of different shape when performing batching

trax/data/inputs.py

@@ -681,7 +681,7 @@ def batch(generator, batch_size):
       # buf is a list of tuples, e.g., [(in1, tgt1), (in2, tgt2), (in3, tgt3)]
       # batch is a tuple of arrays: ([in1, in2, in3], [tgt1, tgt2, tgt3])
       try:
-        batched_example = tuple(np.stack(x) for x in zip(*buf))
+        batched_example = tuple(pad_to_max_dims([np.asarray(tensor) for tensor in x]) for x in zip(*buf))
       except ValueError as e:
         for j in range(len(buf)):
           logging.error('Batch[%d][%d] input shape: %r output shape: %r',

trax/data/inputs_test.py

@@ -83,6 +83,13 @@ def test_batch_data(self):
     self.assertLen(batch, 2)
     self.assertEqual(batch[0].shape, (10,))
 
+  def test_batch_data_padding(self):
+    dataset = (([1] * (10 - i), i+1) for i in range(10))
+    batches = data.batch(dataset, 10)
+    batch = next(batches)
+    self.assertEqual(batch[0].shape, (10, 10))
+    self.assertTrue(np.array_equal(batch[0][-1], np.asarray([1] + 9 * [0])))
+
   def test_batch_exception_size(self):
     dataset = ((i, i + 1) for i in range(10))
     with self.assertRaises(ValueError):