tensor assign with slice index

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

mindspore/_extends/parse/__init__.py

@@ -18,7 +18,7 @@ Interfaces for parser module in c++.
 
 from .parser import (Parser, create_obj_instance, generate_scope,
                      get_bprop_method_of_class, get_class_instance_type,
-                     get_class_member_namespace_symbol,
+                     get_class_member_namespace_symbol, create_slice_obj,
                      get_dataclass_attributes, get_dataclass_methods,
                      get_module_namespace, get_obj_type, get_object_key,
                      get_parse_method_of_class, get_scope_name,
@@ -29,4 +29,4 @@ __all__ = ['parse_cb', 'get_parse_method_of_class', 'get_bprop_method_of_class',
            'get_object_key', 'get_class_instance_type', 'is_class_member', 'get_obj_type',
            'create_obj_instance', 'get_module_namespace', 'get_class_member_namespace_symbol',
            'Parser', 'get_dataclass_attributes', 'get_dataclass_methods', 'dump_obj', 'load_obj',
-           'get_dataclass_methods', 'get_scope_name']
+           'get_dataclass_methods', 'get_scope_name', 'create_slice_obj']

mindspore/_extends/parse/parser.py

@@ -29,6 +29,7 @@ from mindspore.common.dtype import pytype_to_dtype
 from mindspore.common.api import _MindSporeFunction
 from .namespace import CellNamespace, ClosureNamespace, ClassMemberNamespace
 from .resources import parse_object_map, convert_object_map, trope_ns, SYMBOL_UNDEFINE, NO_IMPLEMENT
+from ..utils import Slice
 
 # define return value
 RET_SUCCESS = 0
@@ -69,6 +70,10 @@ parse_expr_statement_white_list = (
     "append",
 )
 
+def create_slice_obj(start, end, step):
+    """Create Slice object"""
+    return Slice(start, end, step)
+
 
 def parse_cb(func, parse_method=None):
     """Implements the function of parse."""

mindspore/_extends/utils.py

@@ -19,6 +19,7 @@ import logging
 import os
 import inspect
 from functools import wraps
+from dataclasses import dataclass
 
 
 def cal_sha256(file_path):
@@ -99,3 +100,13 @@ def cell_attr_register(fn=None, attrs=None):
     if fn is not None:
         return wrap_cell(fn)
     return wrap_cell
+
+
+@dataclass
+class Slice:
+    """
+    Slice class
+    """
+    start: int
+    end: int
+    step: int

mindspore/ccsrc/ir/value.h

@@ -123,6 +123,9 @@ class ValueSlice : public Value {
 
   abstract::AbstractBasePtr ToAbstract() override;
   std::string DumpText() const override { return ToString(); }
+  ValuePtr start() const { return start_; }
+  ValuePtr stop() const { return stop_; }
+  ValuePtr step() const { return step_; }
 
  private:
   ValuePtr start_;

mindspore/ccsrc/pipeline/parse/parse_base.h

@@ -79,6 +79,8 @@ const char PYTHON_PARSE_EXPAND_EXPR_STATEMENT[] = "expand_expr_statement";
 const char PYTHON_PARSE_GENERATE_SCOPE[] = "generate_scope";
 const char PYTHON_PARSE_GET_SCOPE_NAME[] = "get_scope_name";
 
+const char PYTHON_PARSE_CLASS_SLICE[] = "create_slice_obj";
+
 // define the common name
 const char NAMED_PRIMITIVE_ITER[] = "iter";
 const char NAMED_PRIMITIVE_NEXT[] = "next";

mindspore/ccsrc/pipeline/static_analysis/prim.cc

@@ -289,6 +289,13 @@ py::dict ConvertAbstractToPython(const AbstractBasePtr &abs_base) {
     dic["shape"] = shape;
     dic["dtype"] = abs_base->BuildType();
     dic["value"] = BuildValue(abs_base->BuildValue());
+  } else if (abs_base->isa<AbstractSlice>()) {
+    auto arg_slice = dyn_cast<AbstractSlice>(abs_base);
+    std::vector<int> shape;
+    dic["shape"] = shape;
+    dic["dtype"] = arg_slice->BuildType();
+    dic["value"] = BuildValue(arg_slice->BuildValue());
+
   } else if (abs_base->isa<AbstractTuple>()) {
     auto arg_tuple = dyn_cast<AbstractTuple>(abs_base);
     size_t len = arg_tuple->size();

mindspore/ccsrc/utils/convert_utils.cc

@@ -28,6 +28,7 @@
 
 #include "ir/meta_tensor.h"
 #include "pipeline/parse/parse.h"
+#include "pipeline/parse/parse_base.h"
 #include "ir/value.h"
 
 namespace mindspore {
@@ -97,6 +98,13 @@ py::object ValuePtrToPyData(const ValuePtr &value) {
       i++;
     }
     ret = rets;
+  } else if (value->isa<ValueSlice>()) {
+    auto slice = value->cast<ValueSlicePtr>();
+    auto start = ValuePtrToPyData(slice->start());
+    auto end = ValuePtrToPyData(slice->stop());
+    auto step = ValuePtrToPyData(slice->step());
+    ret = parse::python_adapter::CallPyFn(parse::PYTHON_MOD_PARSE_MODULE, parse::PYTHON_PARSE_CLASS_SLICE, start, end,
+                                          step);
   } else if (value->isa<Type>()) {
     py::tuple v(1);
     v[0] = value->cast<TypePtr>();

mindspore/ops/composite/multitype_ops/_multitype_ops_util.py

@@ -15,7 +15,43 @@
 
 """constexpr util"""
 
+import numpy as np
 from ...primitive import constexpr
+from ....common.tensor import Tensor
+from ....common import dtype as mstype
+from ...._extends.utils import Slice
+
+@constexpr
+def check_equal(param1, param2, msg="{},{}"):
+    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."""
+    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
+    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[Tensor[dtype=bool]] = u
+    if index == mstype.tensor:
+        if element_type is None or element_type != mstype.bool_:
+            raise ValueError(
+                "The index of tensor should be a bool type tensor. \
+                {} type is not supported yet.".format(element_type))
+        return True
+
+    raise ValueError("Index of type '{}' is not supported yet.".format(type(index)))
 
 
 @constexpr
@@ -43,3 +79,84 @@ def error_msg(msg="", format_values=""):
     """
 
     raise ValueError(msg.format(*format_values))
+
+def slice_expand(input_slices, shape):
+    """
+    Convert 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.
+
+    Outputs:
+        (List, List, List), This is expressed as (begins, ends, strides).
+    """
+    begin = []
+    end = []
+    strides = []
+    index = 0
+    slices = None
+    # 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):
+        slices = input_slices
+    else:
+        raise ValueError("Tensor's index type is not supported yet.")
+
+    for s in slices:
+        start = 0 if (s.start is None) else s.start
+        stop = shape[index] if (s.end is None) else s.end
+        step = 1 if (s.step is None) else s.step
+        begin.append(start)
+        end.append(stop)
+        strides.append(step)
+        index += 1
+    while index < len(shape):
+        begin.append(0)
+        end.append(shape[index])
+        strides.append(1)
+        index += 1
+    return begin, end, strides
+
+@constexpr
+def slice2indices(input_slices, shape):
+    """
+    Convert 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.
+
+    Outputs:
+        Tensor, the shape is (n, 1).
+    """
+    begin, end, strides = slice_expand(input_slices, shape)
+    np_r = []
+    for i, element in enumerate(shape):
+        s = begin[i] if (begin[i] >= 0) else (element + begin[i])
+        e = end[i] if (end[i] >= 0) else (element + end[i])
+        np_r.append(np.r_[s:e:strides[i]])
+    # Reference: np.ravel_multi_index((np.ix_(np.r_[1:3:1], np.r_[0:4:1], np.r_[4:0:-1])), a.shape)
+    np_ix = np.ix_(*np_r)
+    ravel = np.ravel_multi_index(np_ix, shape)
+    ravel = Tensor(ravel.reshape(-1, 1), dtype=mstype.int32)
+    return ravel
+
+@constexpr
+def check_indices(indices_size, index):
+    if indices_size < 1:
+        raise ValueError("The tensor's index is unreasonable. index:{}".format(index))
+    return indices_size
+
+
+@constexpr
+def check_indices_value_size(indices_size, value_size):
+    if value_size < 1:
+        raise ValueError("The value assigned to tensor cannot be empty.")
+    if value_size > 1:
+        if value_size != indices_size:
+            raise ValueError(
+                "The value given to tensor does not match the index size. \
+                value size:{}, indics size:{}".format(value_size, indices_size))
+    return value_size

mindspore/ops/composite/multitype_ops/setitem_impl.py

@@ -138,25 +138,23 @@ def _tensor_setitem_by_tensor_v1(data, index, value_tensor):
     Outputs:
         Tensor, element type and shape is same as data.
     """
+    result = None
     index_dtype = F.dtype(index)
     index_shape = F.shape(index)
-    is_bool = mult_util.is_same_type(index_dtype, mstype.bool_)
-    if not is_bool:
-        return mult_util.error_msg(
-            "The tensor index should be a bool type tensor. {} type tensor is not supported yet.", (index_dtype,))
-    data_shape = F.shape(data)
-    if index_shape != data_shape:
-        return mult_util.error_msg(
-            "The tensor(shape={}) and tensor index(shape={}) should be the same shape.", (data_shape, index_shape))
-    size = F.size(value_tensor)
-    if size != 1:
-        return mult_util.error_msg(
-            "When assign value is a tensor, its size should be 1, but current size is {}.", (size,))
-    dtype = F.dtype(data)
-    u_cast = F.cast(value_tensor, dtype)
-    one_data = F.ones_like(data)
-    u = F.tensor_mul(one_data, u_cast)
-    return F.select(index, u, data)
+    check_result = mult_util.check_tensor_setitem_index(mstype.tensor, index_dtype)
+    if check_result:
+        data_shape = F.shape(data)
+        data_shape = mult_util.check_equal(data_shape, index_shape,
+                                           "The tensor(shape={}) and tensor index(shape={}) should be the same shape.")
+        size = F.size(value_tensor)
+        size = mult_util.check_equal(1, size,
+                                     "When assign value is a tensor, its size should be {}, but current size is {}.")
+        dtype = F.dtype(data)
+        u_cast = F.cast(value_tensor, dtype)
+        one_data = F.ones_like(data)
+        u = F.tensor_mul(one_data, u_cast)
+        result = F.select(index, u, data)
+    return result
 
 
 @setitem.register("Tensor", "Tensor", "Number")
@@ -179,16 +177,162 @@ def _tensor_setitem_by_tensor_v2(data, index, value):
     Outputs:
         Tensor, element type and shape is same as data.
     """
+    result = None
     index_dtype = F.dtype(index)
     index_shape = F.shape(index)
-    is_bool = mult_util.is_same_type(index_dtype, mstype.bool_)
-    if not is_bool:
-        return mult_util.error_msg(
-            "The tensor index should be a bool type tensor. {} type tensor is not supported yet.", (index_dtype,))
-    shape = F.shape(data)
-    if index_shape != shape:
-        return mult_util.error_msg(
-            "The tensor(shape={}) and tensor index(shape={}) should be the same shape.", (shape, index_shape))
-    dtype = F.dtype(data)
-    u = F.fill(dtype, shape, value)
-    return F.select(index, u, data)
+    check_result = mult_util.check_tensor_setitem_index(mstype.tensor, index_dtype)
+    if check_result:
+        shape = F.shape(data)
+        shape = mult_util.check_equal(
+            shape, index_shape, "The tensor(shape={}) and tensor index(shape={}) should be the same shape.")
+        dtype = F.dtype(data)
+        u = F.fill(dtype, shape, value)
+        result = F.select(index, u, data)
+    return result
+
+
+@setitem.register("Tensor", "Slice", "Tensor")
+def _tensor_setitem_with_slice_v3(data, input_slice, value):
+    """
+    Tensor assignment.
+
+    Note:
+        Syntax support: A[Slice] = U
+        Restraint condition: A is a Tensor
+                             Slice like "1:3"
+                             U is a Tensor(size=1) or Tensor(size>1)
+
+    Inputs:
+        data (Tensor): Assigned tensor.
+        input_slice (Slice): Slice expression.
+        value (Number): Assignment value.
+
+    Outputs:
+        Tensor, element type and shape is same as data.
+    """
+    return _tensor_assgin_tensor(data, input_slice, value)
+
+
+@setitem.register("Tensor", "Tuple", "Tensor")
+def _tensor_setitem_with_slice_v4(data, input_slice, value):
+    """
+    Tensor assignment.
+
+    Note:
+        Syntax support: A[Slice] = 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.
+        value (Number): Assignment value.
+
+    Outputs:
+        Tensor, element type and shape is same as data.
+    """
+    return _tensor_assgin_tensor(data, input_slice, value)
+
+
+def _tensor_assgin_tensor(data, input_slice, value):
+    """Given a tensor value assign to tensor by slice"""
+    # 1. condition
+    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)
+    return result
+
+
+@setitem.register("Tensor", "Slice", "Number")
+def _tensor_setitem_with_slice_v1(data, input_slice, value):
+    """
+    Tensor assignment.
+
+    Note:
+        Syntax support: A[Slice] = u
+        Restraint condition: A is a Tensor.
+                             Slice like "1:3"
+                             u is a scalar
+
+    Inputs:
+        data (Tensor): Assigned tensor.
+        input_slice (Slice): slice expression.
+        value (Number): Assignment value.
+
+    Outputs:
+        Tensor, element type and shape is same as data.
+    """
+    return _tensor_assgin_number(data, input_slice, value)
+
+
+@setitem.register("Tensor", "Tuple", "Number")
+def _tensor_setitem_with_slice_v2(data, input_slice, value):
+    """
+    Tensor assignment.
+
+    Note:
+        Syntax support: A[Slice] = 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.
+        value (Number): Assignment value.
+
+    Outputs:
+        Tensor, element type and shape is same as data.
+    """
+    return _tensor_assgin_number(data, input_slice, value)
+
+
+def _tensor_assgin_number(data, input_slice, value):
+    """Given a scalar assign to tensor by slice"""
+    # 1. condition
+    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)
+    return result

mindspore/ops/functional.py

@@ -68,6 +68,7 @@ tuple_to_array = P.TupleToArray()
 scalar_cast = P.ScalarCast()
 print_ = P.Print()
 expand_dims = P.ExpandDims()
+scatter_nd = P.ScatterNd()
 
 tuple_setitem = Primitive('tuple_setitem')
 tuple_getitem = Primitive('tuple_getitem')

tests/ut/python/ops/test_tensor_slice.py

@@ -94,10 +94,101 @@ class NetWorkReduceToScalar(Cell):
         return ret
 
 
+class TensorAssignWithSliceError1(Cell):
+    def __init__(self):
+        super(TensorAssignWithSliceError1, self).__init__()
+
+    def construct(self, a, b):
+        a[1:3:-1,::] = b
+        return a
+
+class TensorAssignWithSliceError2(Cell):
+    def __init__(self):
+        super(TensorAssignWithSliceError2, self).__init__()
+
+    def construct(self, a, b):
+        a[1:3:-1] = b
+        return a
+class TensorAssignWithSlice2(Cell):
+    def __init__(self):
+        super(TensorAssignWithSlice2, self).__init__()
+
+    def construct(self, a, b):
+        a[1:5] = b
+        a[3:4] = 5
+        a[-1:1:-1] = b
+        a[-1:3:-1] = 5
+        a[::] = b
+        a[::] = 9
+        return a
+class TensorAssignWithSlice(Cell):
+    def __init__(self):
+        super(TensorAssignWithSlice, self).__init__()
+        self.c = 2
+
+    def construct(self, a, b):
+        a[1:3,::] = b
+        a[2:3:,3:] = b
+        a[::] = b
+        a[::] = self.c
+        a[::,::] = b
+        a[::,::] = self.c
+        a[2:3:,0:, 4:1:-1] = b
+        a[2:3:,0:, 4:1:-1] = self.c
+        z = a
+        return z
+
+def test_tensor_assign_with_slice():
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
+    net = TensorAssignWithSlice()
+    net2= TensorAssignWithSlice2()
+    net_e1 = TensorAssignWithSliceError1()
+    net_e2 = TensorAssignWithSliceError2()
+    a = np.arange(60).reshape(3,4,5)
+    b = Tensor([1])
+    Ta = Tensor(a)
+    Tb= Tensor([1,3])
+    Tc= Tensor([])
+    t = Tensor([1, 2, 3, 4, 5, 6, 7, 8])
+    net(Ta, b)
+    net2(t, b)
+    # Error for A[Slice] = Number
+    # 1. A[Slice] = Number,  Slice error
+    with pytest.raises(ValueError):
+        net_e2(t, 2)
+
+    # Error for A[Slice] = U, U is a Tensor
+    # 1. A[Slice] = U,  u.size is error
+    with pytest.raises(ValueError):
+        net2(t, Tb)
+    # 2. A[Slice] = U, U is empty
+    with pytest.raises(ValueError):
+        net2(t, Tc)
+    # 3. A[Slice] = U, U.size error
+    with pytest.raises(ValueError):
+        net2(t, Tb)
+
+    # Error for A[Tuple(Slice...)] = Tensor
+    # 1. A[Tuple(Slice...)] = U, U is empty
+    with pytest.raises(ValueError):
+        net(Ta, Tc)
+    # 2. A[Tuple(Slice...)] = U, U.size error
+    with pytest.raises(ValueError):
+        net(Ta, Tb)
+    # 3. A[Tuple(Slice...)] = U,  Slice error
+    with pytest.raises(ValueError):
+        net_e1(Ta, b)
+
+    # Error for A[Tuple(Slice...)] = Number
+    # 1. A[Tuple(Slice...)] = Number,  Slice error
+    with pytest.raises(ValueError):
+        net_e1(Ta, 2)
+
+
 class TensorAssignWithBoolTensorIndex(Cell):
     def __init__(self):
         super(TensorAssignWithBoolTensorIndex, self).__init__()
-        self.t = Tensor(np.arange(6).reshape([2, 3]), dtype=mstype.float64)
+        self.t = Tensor(np.arange(60).reshape([3,4,5]), dtype = mstype.float64)
 
     def construct(self, a, b, c, u_tensor, _scalar):
         a[c] = u_scalar
@@ -119,6 +210,7 @@ class TensorAssignWithBoolTensorIndex2(Cell):
     def __init__(self):
         super(TensorAssignWithBoolTensorIndex2, self).__init__()
         self.t = Tensor(np.arange(6).reshape([2, 3]), dtype=mstype.float64)
+        self.t = Tensor(np.arange(60).reshape([3,4,5]), dtype = mstype.float64)
 
     def construct(self, a, u_tensor, _scalar):
         a[a > 8] = u_tensor
@@ -139,7 +231,7 @@ class TensorAssignWithBoolTensorIndex2Error(Cell):
         return a
 
 
-a = np.random.uniform(1, 10, [2, 3])
+a = np.random.uniform(1,10,[3,4,5])
 b = a > 5
 c = a < 3
 Ta = Tensor(a)
@@ -148,13 +240,13 @@ Tc = Tensor(c)
 Td = Tensor([True, True])
 u_tensor = Tensor([1])
 u_tensor_error = Tensor([1, 2])
+t_1d = Tensor([1, 2, 3, 4, 5, 6, 7, 8])
 u_scalar = 5
 
-
 def test_tensor_assign_bool_index():
     net1 = TensorAssignWithBoolTensorIndex()
     net2 = TensorAssignWithBoolTensorIndex2()
-
+    net1(Ta, Tb, Tc, u_tensor, u_scalar)
     net1(Ta, Tb, Tc, u_tensor, u_scalar)
     with pytest.raises(ValueError):
         net1(Ta, Td, Tc, u_tensor, u_scalar)
@@ -180,8 +272,15 @@ def test_tensor_assign_bool_index():
     with pytest.raises(AttributeError):
         net4(Ta, u_scalar)
 
-
 test_cases = [
+    ('TensorAssignWithSlice', {
+        'block': TensorAssignWithSlice(),
+        'desc_inputs': [Ta,  u_tensor],
+    }),
+    ('TensorAssignWithSlice2', {
+        'block': TensorAssignWithSlice2(),
+        'desc_inputs': [t_1d,  u_tensor],
+    }),
     ('TensorAssignWithBoolTensorIndex', {
         'block': TensorAssignWithBoolTensorIndex(),
         'desc_inputs': [Ta, Tb, Tc, u_tensor, u_scalar],