revert tile.py & sink_size = get_dataset_size()

mindspore/ops/_op_impl/tbe/tile.py

@@ -26,8 +26,9 @@ tile_op_info = TBERegOp("Tile") \
     .attr("multiples", "optional", "listInt", "all")\
     .input(0, "x1", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
-    .dtype_format(DataType.None_None, DataType.None_None) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
     .get_op_info()
 
 

mindspore/train/model.py

@@ -548,6 +548,8 @@ class Model:
             >>> model.train(2, dataset)
         """
         check_bool(dataset_sink_mode)
+        if sink_size == -1:
+            sink_size = train_dataset.get_dataset_size()
         check_int(sink_size)
         if sink_size < -1 or sink_size == 0:
             raise ValueError("The sink_size must be -1 or positive, but got sink_size {}.".format(sink_size))

tests/dataset_mock.py

@@ -21,7 +21,7 @@ from mindspore import Tensor
 class MindData:
     """ Stub for MindData """
 
-    def __init__(self, size=None, batch_size=None, repeat_count=1,
+    def __init__(self, size=1, batch_size=None, repeat_count=1,
                  np_types=None, output_shapes=None, input_indexs=()):
         self._size = size
         self._batch_size = batch_size

tests/ut/python/parallel/test_loss_scale.py

@@ -113,7 +113,7 @@ class TrainOneStepWithLossScaleCell(nn.Cell):
 
 class DatasetLenet(MindData):
     def __init__(self, predict, label, length=3):
-        super(DatasetLenet, self).__init__()
+        super(DatasetLenet, self).__init__(size=length)
         self.predict = predict
         self.label = label
         self.index = 0