* fix bool index

* change slice setitem to mixed procedure
* add testcase for slice assignment

mindspore/ops/composite/multitype_ops/_compile_utils.py

@@ -167,12 +167,13 @@ def _tensor_getitem(self, index):
         return tensor_index_by_tensor(self, index)
     if isinstance(index, tuple):
         return tensor_index_by_tuple(self, index)
+    # bool type should be judged before int
+    if isinstance(index, bool):
+        return _tensor_index_by_bool(self, index)
     if isinstance(index, int):
         return _tensor_index_by_integer(self, index)
     if isinstance(index, slice):
         return tensor_index_by_slice(self, index)
-    if isinstance(index, bool):
-        return _tensor_index_by_bool(self, index)
     if index is None:
         return F.expand_dims(self, 0)
     if index is ...:
@@ -206,7 +207,8 @@ def tensor_index_by_slice(data, slice_index):
     """Tensor getitem by a single slice"""
     shape = F.shape(data)
     if not shape:
-        const_utils.raise_index_error("When tensor is indexed by a slice, the dimension of the tensor cannot be 0.")
+        const_utils.raise_index_error("When tensor is indexed by a slice, the dimension of the tensor\
+                                       cannot be 0.")
     begin_strides, end_strides, step_strides = const_utils.get_stride_info_from_slice(shape, slice_index)
     return F.strided_slice(data, begin_strides, end_strides, step_strides)
 
@@ -215,7 +217,11 @@ def _tensor_index_by_integer(data, number):
     """Tensor getitem by a single integer number"""
     shape = F.shape(data)
     if not shape:
-        const_utils.raise_index_error("When tensor is indexed by an integer, the dimension of the tensor cannot be 0.")
+        return const_utils.raise_index_error("When tensor is indexed by an integer,\
+                                             the dimension of the tensor cannot be 0.")
+    if number >= shape[0]:
+        return const_utils.raise_index_error("index {} is out of bounds for axis 0 with size {}".format(
+            number, shape[0]))
     begin_strides, end_strides, step_strides = const_utils.get_stride_info_from_integer(shape, number)
     shrink_axis_mask = 1
     return P.StridedSlice(0, 0, 0, 0, shrink_axis_mask)(data, begin_strides, end_strides, step_strides)
@@ -427,8 +433,6 @@ def tensor_setitem_by_tuple_with_number(data, tuple_index, value):
     indexes_types = hyper_map(F.typeof, tuple_index)
     index_elements_type = const_utils.tuple_index_elements_type(indexes_types, const_utils.TENSOR_SETITEM)
 
-    if index_elements_type == const_utils.NO_TENSOR:
-        return tensor_setitem_by_slice_with_number(data, tuple_index, value)
     if index_elements_type == const_utils.ALL_TENSOR:
         indices = _generate_indices_from_tuple_of_tensor(data,
                                                          tuple_index,
@@ -488,8 +492,6 @@ def tensor_setitem_by_tuple_with_tensor(data, tuple_index, value):
     indexes_types = hyper_map(F.typeof, tuple_index)
     index_elements_type = const_utils.tuple_index_elements_type(indexes_types, const_utils.TENSOR_SETITEM)
 
-    if index_elements_type == const_utils.NO_TENSOR:
-        return tensor_setitem_by_slice_with_tensor(data, tuple_index, value)
     if index_elements_type == const_utils.ALL_TENSOR:
         indices = _generate_indices_from_tuple_of_tensor(data,
                                                          tuple_index,

mindspore/ops/composite/multitype_ops/_constexpr_utils.py

@@ -339,6 +339,8 @@ def check_tensors_dtype_same(data_dtype, value_dtype, op_name):
 @constexpr
 def generate_broadcast_shape(shapes, op_name):
     """Generate broadcast shape for a tuple of shape."""
+    if not shapes:
+        return ()
     broadcast_shape = shapes[0]
     for i, shape in enumerate(shapes):
         logger.debug(f"Broadcasts the {i}th tensor, the shape is {shape}.")
@@ -541,6 +543,11 @@ def generate_index_info_from_tuple_of_mixed_tensors(data_shape,
                               slice_indexes[slice_count].step)
             # Use list to represent slicing result.
             indexes_info[pos] = list(range(data_shape[pos]))[slice_obj]
+            if not indexes_info[pos]:
+                raise IndexError("An empty slice is not supported, got {}:{}:{}".format(
+                    slice_indexes[slice_count].start,
+                    slice_indexes[slice_count].stop,
+                    slice_indexes[slice_count].step))
             slice_count += 1
         elif isinstance(ele_type, mstype.ellipsis_type):
             if ellipsis_num != 0:

tests/st/pynative/test_tensor_index.py

@@ -646,7 +646,7 @@ class TensorAssignWithSlice2(Cell):
 class TensorAssignWithSlice(Cell):
     def __init__(self):
         super(TensorAssignWithSlice, self).__init__()
-        self.c = 2
+        self.c = 2.0
 
     def construct(self, a, b, ck):
         a[1:3, ::] = b
@@ -661,7 +661,47 @@ class TensorAssignWithSlice(Cell):
         return z
 
 
-def test_tensor_assign():
+def test_tensor_assign_slice_value_1():
+    net = TensorAssignWithSlice()
+    a = np.arange(60).reshape(3, 4, 5)
+    ck = np.arange(60).reshape(3, 4, 5)
+    b = np.array([1]).astype(np.float32)  # Tensor([1], dtype=mstype.float32)
+    tb = Tensor(b, dtype=mstype.float32)
+    ta = Tensor(a, dtype=mstype.float32)
+    tck = Tensor(ck, dtype=mstype.float32)
+    out = net(ta, tb, tck)
+    a[1:3, ::] = b
+    a[2:3:, 3:] = b
+    a[::] = b
+    a[::] = 2.0
+    a[::, ::] = b
+    a[::, ::] = 2.0
+    a[2:3:, 0:, 4:1:-1] = b
+    a[2:3:, 0:, 4:1:-1] = 2.0
+    z = a + ck
+    assert np.all(z == out.asnumpy())
+
+
+def test_tensor_assign_slice_value_2():
+    net2 = TensorAssignWithSlice2()
+    a = np.array([1, 2, 3, 4, 5, 6, 7, 8])
+    ck = np.array([1, 2, 3, 4, 5, 6, 7, 8])
+    b = np.array([1]).astype(np.float32)  # Tensor([1], dtype=mstype.float32)
+    tb = Tensor(b, dtype=mstype.float32)
+    ta = Tensor(a, dtype=mstype.float32)
+    tck = Tensor(ck, dtype=mstype.float32)
+    out = net2(ta, tb, tck)
+    a[1:5] = b
+    a[3:4] = 5
+    a[-1:1:-1] = b
+    a[-1:3:-1] = 5
+    a[::] = b
+    a[::] = 9
+    z = a + ck
+    assert np.all(z == out.asnumpy())
+
+
+def test_tensor_assign_exception():
     net = TensorAssignWithSlice()
     net2 = TensorAssignWithSlice2()
     net_e1 = TensorAssignWithSliceError1()
@@ -677,8 +717,6 @@ def test_tensor_assign():
     Tc = Tensor([], dtype=mstype.float32)
     t = Tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=mstype.float32)
     tck = Tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=mstype.float32)
-    net(Ta, b, Tck)
-    net2(t, b, tck)
     # Error for A[Slice] = Number
     # 1. A[Slice] = Number,  Slice error
     with pytest.raises(IndexError):
@@ -744,9 +782,6 @@ def test_tensor_assign():
     # 2. A[::, 1:, ...] = scalar/tensor
     net = TensorAssignWithTupleEllipsis()
     net(Ta, b)
-    Tc = Tensor(1, mstype.float32)
-    with pytest.raises(ValueError):
-        net(Ta, Tc)
     with pytest.raises(ValueError):
         net(Ta, Tb)
 
@@ -765,7 +800,7 @@ class TensorAssignWithTupleEllipsis(Cell):
         super(TensorAssignWithTupleEllipsis, self).__init__()
 
     def construct(self, a, b):
-        a[:2, ...] = 1
+        a[:2, ...] = 1.0
         a[1:, ...] = b
         return a
 
@@ -955,3 +990,16 @@ def Xtest_tensor_slice_reduce_out_of_bounds_positive():
     with pytest.raises(ValueError) as ex:
         net(input_tensor)
     assert "For 'StridedSlice' the `begin[0]` should be an int and must less than 6, but got `6`" in str(ex.value)
+
+
+def test_tensor_range():
+    a = np.arange(4*5*6).reshape(4, 5, 6).astype(np.float32)
+    ta = Tensor(a, mstype.float32)
+    ms_out = []
+    for item in ta:
+        ms_out.append(item)
+    np_out = []
+    for item in a:
+        np_out.append(item)
+    for i, elem in enumerate(ms_out):
+        assert np.all(elem.asnumpy() == np_out[i])

tests/ut/python/ops/test_tensor_slice.py

@@ -130,7 +130,7 @@ class TensorAssignWithSlice2(Cell):
 class TensorAssignWithSlice(Cell):
     def __init__(self):
         super(TensorAssignWithSlice, self).__init__()
-        self.c = 2
+        self.c = 2.0
 
     def construct(self, a, b, ck):
         a[1:3, ::] = b
@@ -528,8 +528,7 @@ def test_tensor_assign():
     net = TensorAssignWithTupleEllipsis()
     net(Ta, b)
     Tc = Tensor(1, mstype.float32)
-    with pytest.raises(ValueError):
-        net(Ta, Tc)
+    net(Ta, Tc)
     with pytest.raises(ValueError):
         net(Ta, Tb)
 
@@ -548,7 +547,7 @@ class TensorAssignWithTupleEllipsis(Cell):
         super(TensorAssignWithTupleEllipsis, self).__init__()
 
     def construct(self, a, b):
-        a[:2, ...] = 1
+        a[:2, ...] = 1.0
         a[1:, ...] = b
         return a
 
@@ -579,10 +578,10 @@ class TensorAssignWithTupleInteger(Cell):
         super(TensorAssignWithTupleInteger, self).__init__()
 
     def construct(self, a, b, ck):
-        a[(1)] = 1
+        a[(1)] = 1.0
         a[(1)] = b
         a[(1, 1)] = b
-        a[(1, 1)] = 1
+        a[(1, 1)] = 1.0
         z = a + ck
         return z