Tensor assign with integer

Signed-off-by: Ncandanzg <zhangshucheng@huawei.com>

mindspore/ops/composite/multitype_ops/_multitype_ops_util.py

@@ -15,6 +15,7 @@
 
 """constexpr util"""
 
+from functools import reduce
 import numpy as np
 from ...primitive import constexpr
 from ....common.tensor import Tensor
@@ -23,26 +24,27 @@ from ...._extends.utils import Slice
 
 @constexpr
 def check_equal(param1, param2, msg="{},{}"):
+    """Checks whether the two parameters are equal or not."""
     if param1 != param2:
         raise ValueError(msg.format(param1, param2))
     return param1
 
 @constexpr
 def check_tensor_setitem_index(index, element_type=None):
-    """Check tuple index type of tensor assignment."""
+    """Checks tuple index type of tensor assignment."""
     if index is None:
         raise ValueError("Tensor's index cannot be None.")
     # eg. Tensor[Slice] = u
     if isinstance(index, Slice):
         return True
-    # eg. Tensor[Tuple] = u
+    # eg. Tensor[tuple] = u
     if isinstance(index, tuple):
         if not index:
             raise ValueError("Tensor's index cannot be empty.")
-        # eg. Tensor[Tuple(Slice...)] = u
-        if not isinstance(index[0], Slice):
-            raise ValueError("Index of type '{}' is not supported yet.".format(type(index[0])))
-        return True
+        # eg. Tensor[tuple(Slice...)] = u
+        if isinstance(index[0], (Slice, int)):
+            return True
+        raise ValueError("Index of type '{}' is not supported yet.".format(type(index[0])))
     # eg. Tensor[Tensor[dtype=bool]] = u
     if index == mstype.tensor:
         if element_type is None or element_type != mstype.bool_:
@@ -57,7 +59,7 @@ def check_tensor_setitem_index(index, element_type=None):
 @constexpr
 def is_same_type(inst, type_):
     """
-    Check whether an object is an instance of a target type.
+    Checks whether an object is an instance of a target type.
 
     Inputs:
         inst (mindspore.dtype): Inspected type.
@@ -69,34 +71,23 @@ def is_same_type(inst, type_):
     return inst == type_
 
 
-@constexpr
-def error_msg(msg="", format_values=""):
-    """
-    Used to throw exception information.
-
-    Inputs:
-        msg (str): information content.
-    """
-
-    raise ValueError(msg.format(*format_values))
-
 def slice_expand(input_slices, shape):
     """
-    Convert slice to indices.
+    Converts slice to indices.
 
     Inputs:
-        slices (List or Tuple(List, ...)): Slice tuple or slice.
-        shape (Tuple): The shape of a sensor is an integer element tuple.
+        slices (Union[Slice, tuple[Slice]]): Slice tuple or slice.
+        shape (tuple): The shape of a sensor is an integer element tuple.
 
     Outputs:
-        (List, List, List), This is expressed as (begins, ends, strides).
+        tuple[list], This is expressed as (begins, ends, strides).
     """
     begin = []
     end = []
     strides = []
     index = 0
     slices = None
-    # Slice or Tuple(Slice...)
+    # Slice or tuple(Slice...)
     if isinstance(input_slices, Slice):
         slices = (input_slices,)
     elif isinstance(input_slices, (tuple, list)) and input_slices and isinstance(input_slices[0], Slice):
@@ -119,14 +110,15 @@ def slice_expand(input_slices, shape):
         index += 1
     return begin, end, strides
 
+
 @constexpr
 def slice2indices(input_slices, shape):
     """
-    Convert slice to indices.
+    Converts slice to indices.
 
     Inputs:
-        slices (List or Tuple(List, ...)): Slice tuple or slice.
-        shape (Tuple): The shape of a sensor is an integer element tuple.
+        slices (Union[Slice, tuple[Slice]]): Slice tuple or slice.
+        shape (tuple): The shape of a tensor is an integer element tuple.
 
     Outputs:
         Tensor, the shape is (n, 1).
@@ -145,6 +137,7 @@ def slice2indices(input_slices, shape):
 
 @constexpr
 def check_indices(indices_size, index):
+    """Checks indices whether is empty."""
     if indices_size < 1:
         raise ValueError("The tensor's index is unreasonable. index:{}".format(index))
     return indices_size
@@ -152,6 +145,7 @@ def check_indices(indices_size, index):
 
 @constexpr
 def check_indices_value_size(indices_size, value_size):
+    """Checks if the sizes are already matched."""
     if value_size < 1:
         raise ValueError("The value assigned to tensor cannot be empty.")
     if value_size > 1:
@@ -160,3 +154,30 @@ def check_indices_value_size(indices_size, value_size):
                 "The value given to tensor does not match the index size. \
                 value size:{}, indics size:{}".format(value_size, indices_size))
     return value_size
+
+@constexpr
+def integer_to_indices(index, shape):
+    """Converts int or tuple[int] to indices."""
+    size = reduce(lambda x, y: x * y, shape)
+    range_ = np.arange(size).reshape(shape)
+    value = range_[index]
+    value = value.reshape(-1, 1)
+    return Tensor(value, dtype=mstype.int32)
+
+@constexpr
+def tuple_element_is_slice(indexs):
+    """Judges tuple element type."""
+    if not indexs:
+        raise ValueError("Tensor's index cannot be empty.")
+    if isinstance(indexs, tuple) and isinstance(indexs[0], Slice):
+        return True
+    return False
+
+@constexpr
+def tuple_element_is_int(indexs):
+    """Judges tuple element type."""
+    if not indexs:
+        raise ValueError("Tensor's index cannot be empty.")
+    if isinstance(indexs, tuple) and isinstance(indexs[0], int):
+        return True
+    return False

mindspore/ops/composite/multitype_ops/setitem_impl.py

@@ -25,15 +25,14 @@ setitem = base.MultitypeFuncGraph('setitem')
 @setitem.register("List", "Number", "String")
 def _list_setitem_with_string(data, number_index, value):
     """
-    Assign value to list.
+    Assigns value to list.
 
     Inputs:
         data (list): Data of type lis.
         number_index (Number): Index of data.
-        value (String): Value given.
 
     Outputs:
-        List, type is same as the element type of data.
+        list, type is same as the element type of data.
     """
     return F.list_setitem(data, number_index, value)
 
@@ -41,7 +40,7 @@ def _list_setitem_with_string(data, number_index, value):
 @setitem.register("List", "Number", "Number")
 def _list_setitem_with_number(data, number_index, value):
     """
-    Assign value to list.
+    Assigns value to list.
 
     Inputs:
         data (list): Data of type lis.
@@ -49,7 +48,7 @@ def _list_setitem_with_number(data, number_index, value):
         value (Number): Value given.
 
     Outputs:
-        List, type is same as the element type of data.
+        list, type is same as the element type of data.
     """
     return F.list_setitem(data, number_index, value)
 
@@ -57,7 +56,7 @@ def _list_setitem_with_number(data, number_index, value):
 @setitem.register("List", "Number", "Tensor")
 def _list_setitem_with_Tensor(data, number_index, value):
     """
-    Assign value to list.
+    Assigns value to list.
 
     Inputs:
         data (list): Data of type lis.
@@ -65,7 +64,7 @@ def _list_setitem_with_Tensor(data, number_index, value):
         value (Tensor): Value given.
 
     Outputs:
-        List, type is same as the element type of data.
+        list, type is same as the element type of data.
     """
     return F.list_setitem(data, number_index, value)
 
@@ -73,15 +72,15 @@ def _list_setitem_with_Tensor(data, number_index, value):
 @setitem.register("List", "Number", "List")
 def _list_setitem_with_List(data, number_index, value):
     """
-    Assign value to list.
+    Assigns value to list.
 
     Inputs:
         data (list): Data of type lis.
         number_index (Number): Index of data.
-        value (List): Value given.
+        value (list): Value given.
 
     Outputs:
-        List, type is same as the element type of data.
+        list, type is same as the element type of data.
     """
     return F.list_setitem(data, number_index, value)
 
@@ -89,15 +88,15 @@ def _list_setitem_with_List(data, number_index, value):
 @setitem.register("Dictionary", "String", "Tensor")
 def _dict_setitem_with_tensor(data, key, value):
     """
-    Assign value to dictionary.
+    Assigns value to dictionary.
 
     Inputs:
-        data (Dictionary): Data of type dict.
+        data (dict): Data of type dict.
         key (str): Key of the data.
         value (Tensor): Value given.
 
     Outputs:
-        Dict, type is as same as the element type of data.
+        dict, type is as same as the element type of data.
     """
     return F.dict_setitem(data, key, value)
 
@@ -105,15 +104,15 @@ def _dict_setitem_with_tensor(data, key, value):
 @setitem.register("Dictionary", "String", "Number")
 def _dict_setitem_with_number(data, key, value):
     """
-    Assign value to dictionary.
+    Assigns value to dictionary.
 
     Inputs:
-        data (Dictionary): Data of type dict.
+        data (dict): Data of type dict.
         key (str): Key of the data.
         value (Number): Value given.
 
     Outputs:
-        Dict, type is as same as the element type of data.
+        dict, type is as same as the element type of data.
     """
     return F.dict_setitem(data, key, value)
 
@@ -219,14 +218,14 @@ def _tensor_setitem_with_slice_v4(data, input_slice, value):
     Tensor assignment.
 
     Note:
-        Syntax support: A[Slice] = U
+        Syntax support: A[tuple(Slice)] = U, and A[tuple(Number)] = U
         Restraint condition: A is a Tensor
                              Slice like "1:3, ::, :4:-1"
                              U is a Tensor(size=1) or Tensor(size>1)
 
     Inputs:
         data (Tensor): Assigned tensor.
-        input_slice (Tuple(Slice)): Slice expression.
+        input_slice (Union[tuple[Slice], tuple[Number]]): Slice expression.
         value (Number): Assignment value.
 
     Outputs:
@@ -236,39 +235,43 @@ def _tensor_setitem_with_slice_v4(data, input_slice, value):
 
 
 def _tensor_assgin_tensor(data, input_slice, value):
-    """Given a tensor value assign to tensor by slice"""
-    # 1. condition
+    """Assigns a tensor value to the tensor by slice."""
     result = None
     check_result = mult_util.check_tensor_setitem_index(input_slice)
     if check_result:
         data_shape = F.shape(data)
-        data_size = F.size(data)
-        data_dtype = F.dtype(data)
         indices = mult_util.slice2indices(input_slice, data_shape)
-        indices_size = F.size(indices)
-        indices_size = mult_util.check_indices(indices_size, input_slice)
-        update = F.fill(data_dtype, (indices_size,), 1)
-        condition_1d = F.scatter_nd(indices, update, (data_size,))
-        condition_1d = F.cast(condition_1d, mstype.bool_)
-        condition = F.reshape(condition_1d, data_shape)
-        # 2. u
-        value_fill = None
-        value_size = F.size(value)
-
-        value_size = mult_util.check_indices_value_size(indices_size, value_size)
-        if value_size == 1:
-            value_fill = F.fill(data_dtype, (indices_size,), 1)
-            value = F.cast(value, data_dtype)
-            value_fill = F.tensor_mul(value_fill, value)
-        elif value_size > 1:
-            value_fill = F.reshape(value, (indices_size,))
-        value_1d = F.scatter_nd(indices, value_fill, (data_size,))
-        u = F.reshape(value_1d, data_shape)
-        # A[slice]= u -> A[B]=U -> select(B, U, A)
-        result = F.select(condition, u, data)
+        is_tuple_int = mult_util.tuple_element_is_int(input_slice)
+        if is_tuple_int:
+            indices = mult_util.integer_to_indices(input_slice, data_shape)
+        result = _tensor_indices_tensor(data, data_shape, input_slice, indices, value)
     return result
 
 
+def _tensor_indices_tensor(data, data_shape, index, indices, value):
+    """Assigns a tensor value to the tensor."""
+    data_size = F.size(data)
+    data_dtype = F.dtype(data)
+    indices_size = F.size(indices)
+    indices_size = mult_util.check_indices(indices_size, index)
+    update = F.fill(data_dtype, (indices_size,), 1)
+    condition_1d = F.scatter_nd(indices, update, (data_size,))
+    condition_1d = F.cast(condition_1d, mstype.bool_)
+    condition = F.reshape(condition_1d, data_shape)
+    value_fill = None
+    value_size = F.size(value)
+
+    value_size = mult_util.check_indices_value_size(indices_size, value_size)
+    if value_size == 1:
+        value_fill = F.fill(data_dtype, (indices_size,), 1)
+        value = F.cast(value, data_dtype)
+        value_fill = F.tensor_mul(value_fill, value)
+    elif value_size > 1:
+        value_fill = F.reshape(value, (indices_size,))
+    value_1d = F.scatter_nd(indices, value_fill, (data_size,))
+    u = F.reshape(value_1d, data_shape)
+    return F.select(condition, u, data)
+
 @setitem.register("Tensor", "Slice", "Number")
 def _tensor_setitem_with_slice_v1(data, input_slice, value):
     """
@@ -297,14 +300,14 @@ def _tensor_setitem_with_slice_v2(data, input_slice, value):
     Tensor assignment.
 
     Note:
-        Syntax support: A[Slice] = u
+        Syntax support: A[tuple(Slice)] = u, and A[tuple(Number)] = u
         Restraint condition: A is a Tensor.
                              Slice like "1:3, ::, :4:-1"
                              u is a scalar
 
     Inputs:
         data (Tensor): Assigned tensor.
-        input_slice (Tuple(Slice)): slice expression.
+        input_slice (Union[tuple[Slice], tuple[Number]]): slice expression.
         value (Number): Assignment value.
 
     Outputs:
@@ -314,25 +317,46 @@ def _tensor_setitem_with_slice_v2(data, input_slice, value):
 
 
 def _tensor_assgin_number(data, input_slice, value):
-    """Given a scalar assign to tensor by slice"""
-    # 1. condition
+    """Givens a scalar assign to tensor by slice"""
     check_result = mult_util.check_tensor_setitem_index(input_slice)
     result = None
     if check_result:
         data_shape = F.shape(data)
-        data_size = F.size(data)
-        data_dtype = F.dtype(data)
         indices = mult_util.slice2indices(input_slice, data_shape)
-        indices_size = F.size(indices)
-        indices_size = mult_util.check_indices(indices_size, input_slice)
-        update = F.fill(data_dtype, (indices_size,), 1)
-        condition_1d = F.scatter_nd(indices, update, (data_size,))
-        condition_1d = F.cast(condition_1d, mstype.bool_)
-        condition = F.reshape(condition_1d, data_shape)
-        # 2. u
-        value_fill = F.fill(data_dtype, (indices_size,), value)
-        value_1d = F.scatter_nd(indices, value_fill, (data_size,))
-        u = F.reshape(value_1d, data_shape)
-        # A[slice]= u -> A[B]=U -> select(B, U, A)
-        result = F.select(condition, u, data)
+        is_tuple_int = mult_util.tuple_element_is_int(input_slice)
+        if is_tuple_int:
+            indices = mult_util.integer_to_indices(input_slice, data_shape)
+        result = _tensor_indices_number(data, data_shape, input_slice, indices, value)
     return result
+
+
+def _tensor_indices_number(data, data_shape, index, indices, value):
+    """Assigns a scalar value to the tensor."""
+    data_size = F.size(data)
+    data_dtype = F.dtype(data)
+    indices_size = F.size(indices)
+    indices_size = mult_util.check_indices(indices_size, index)
+    update = F.fill(data_dtype, (indices_size,), 1)
+    condition_1d = F.scatter_nd(indices, update, (data_size,))
+    condition_1d = F.cast(condition_1d, mstype.bool_)
+    condition = F.reshape(condition_1d, data_shape)
+    value_fill = F.fill(data_dtype, (indices_size,), value)
+    value_1d = F.scatter_nd(indices, value_fill, (data_size,))
+    u = F.reshape(value_1d, data_shape)
+    return F.select(condition, u, data)
+
+
+@setitem.register("Tensor", "Number", "Number")
+def _tensor_setitem_with_int_v1(data, index, value):
+    """Syntax: A[1] = 3"""
+    data_shape = F.shape(data)
+    indices = mult_util.integer_to_indices(index, data_shape)
+    return _tensor_indices_number(data, data_shape, index, indices, value)
+
+
+@setitem.register("Tensor", "Number", "Tensor")
+def _tensor_setitem_with_int_v2(data, index, value):
+    """Syntax: A[1] = Tensor"""
+    data_shape = F.shape(data)
+    indices = mult_util.integer_to_indices(index, data_shape)
+    return _tensor_indices_tensor(data, data_shape, index, indices, value)

tests/ut/python/ops/test_tensor_slice.py

@@ -138,7 +138,7 @@ class TensorAssignWithSlice(Cell):
         z = a
         return z
 
-def test_tensor_assign_with_slice():
+def test_tensor_assign():
     context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
     net = TensorAssignWithSlice()
     net2= TensorAssignWithSlice2()
@@ -147,6 +147,7 @@ def test_tensor_assign_with_slice():
     a = np.arange(60).reshape(3,4,5)
     b = Tensor([1])
     Ta = Tensor(a)
+    Ta4d = Tensor(a.reshape(1,3,4,5))
     Tb= Tensor([1,3])
     Tc= Tensor([])
     t = Tensor([1, 2, 3, 4, 5, 6, 7, 8])
@@ -184,6 +185,47 @@ def test_tensor_assign_with_slice():
     with pytest.raises(ValueError):
         net_e1(Ta, 2)
 
+    net = TensorAssignWithInteger()
+    # Error for A[Number] = scalar/Tensor
+    # 1. A[Number] = U, U is a Tensor, u.size not match
+    with pytest.raises(ValueError):
+        net(Ta, Tb)
+    with pytest.raises(ValueError):
+        net(Ta, Tc)
+    # 2. A[Number] = U, the number index error
+    with pytest.raises(IndexError):
+        net(Ta4d, b)
+
+    # Error for A[(n,m)] = scalar/Tensor
+    # 1. A[(n,m)] = U, U is a tensor. u.size not match
+    net = TensorAssignWithTupleInteger()
+    with pytest.raises(ValueError):
+        net(Ta, Tc)
+    with pytest.raises(ValueError):
+        net(Ta, Tb)
+    # 2. A[(n,m)] = U, the number index error
+    with pytest.raises(IndexError):
+        net(Ta4d, b)
+
+class TensorAssignWithInteger(Cell):
+    def __init__(self):
+        super(TensorAssignWithInteger, self).__init__()
+
+    def construct(self, a, b):
+        a[1] = 1
+        a[0] = b
+        return a
+
+class TensorAssignWithTupleInteger(Cell):
+    def __init__(self):
+        super(TensorAssignWithTupleInteger, self).__init__()
+
+    def construct(self, a, b):
+        a[(1)] = 1
+        a[(1)] = b
+        a[(1,1)] = b
+        a[(1,1)] = 1
+        return a
 
 class TensorAssignWithBoolTensorIndex(Cell):
     def __init__(self):
@@ -273,6 +315,14 @@ def test_tensor_assign_bool_index():
         net4(Ta, u_scalar)
 
 test_cases = [
+    ('TensorAssignWithTupleInteger', {
+        'block': TensorAssignWithTupleInteger(),
+        'desc_inputs': [Ta,  u_tensor],
+    }),
+    ('TensorAssignWithInteger', {
+        'block': TensorAssignWithInteger(),
+        'desc_inputs': [Ta,  u_tensor],
+    }),
     ('TensorAssignWithSlice', {
         'block': TensorAssignWithSlice(),
         'desc_inputs': [Ta,  u_tensor],