fix shape bug.

mindspore/core/ir/tensor.cc

@@ -446,7 +446,9 @@ Tensor::Tensor(const Tensor &tensor)
       event_(tensor.event_),
       sync_status_(tensor.sync_status_),
       device_sync_(tensor.device_sync_),
-      padding_type_(tensor.padding_type()) {}
+      padding_type_(tensor.padding_type()) {
+  CheckShape(tensor.shape_);
+}
 
 Tensor::Tensor(const Tensor &tensor, TypeId data_type)
     : MetaTensor(data_type, tensor.shape_),
@@ -456,29 +458,43 @@ Tensor::Tensor(const Tensor &tensor, TypeId data_type)
       event_(tensor.event_),
       sync_status_(tensor.sync_status_),
       device_sync_(tensor.device_sync_),
-      padding_type_(tensor.padding_type()) {}
+      padding_type_(tensor.padding_type()) {
+  CheckShape(tensor.shape_);
+}
 
 Tensor::Tensor(TypeId data_type, const ShapeVector &shape, TensorDataPtr data)
-    : MetaTensor(data_type, shape), data_(std::move(data)), id_(MakeId()) {}
+    : MetaTensor(data_type, shape), data_(std::move(data)), id_(MakeId()) {
+  CheckShape(shape);
+}
 
 Tensor::Tensor(TypeId data_type, const ShapeVector &shape)
-    : Tensor(data_type, shape, MakeTensorData(data_type, shape)) {}
+    : Tensor(data_type, shape, MakeTensorData(data_type, shape)) {
+  CheckShape(shape);
+}
 
 Tensor::Tensor(TypeId data_type, const ShapeVector &shape, void *data, size_t data_len)
-    : Tensor(data_type, shape, MakeTensorData(data_type, shape, data, data_len)) {}
+    : Tensor(data_type, shape, MakeTensorData(data_type, shape, data, data_len)) {
+  CheckShape(shape);
+}
 
 Tensor::Tensor(TypeId data_type, const ShapeVector &shape, void *data, TypeId src_data_type)
-    : Tensor(data_type, shape, MakeTensorData(data_type, shape, data, src_data_type)) {}
+    : Tensor(data_type, shape, MakeTensorData(data_type, shape, data, src_data_type)) {
+  CheckShape(shape);
+}
 
 Tensor::Tensor(const std::vector<int64_t> &input, const TypePtr &data_type)
     : MetaTensor(TypeIdOf(data_type, kNumberTypeInt32), {static_cast<int>(input.size())}),
       data_(MakeTensorData(data_type_, shape_, input.data(), input.size())),
-      id_(MakeId()) {}
+      id_(MakeId()) {
+  CheckShape(shape_);
+}
 
 Tensor::Tensor(const std::vector<double> &input, const TypePtr &data_type)
     : MetaTensor(TypeIdOf(data_type, kNumberTypeFloat32), {static_cast<int>(input.size())}),
       data_(MakeTensorData(data_type_, shape_, input.data(), input.size())),
-      id_(MakeId()) {}
+      id_(MakeId()) {
+  CheckShape(shape_);
+}
 
 Tensor::Tensor(int64_t input, const TypePtr &data_type)
     : MetaTensor(TypeIdOf(data_type, kNumberTypeInt32), {}),
@@ -497,6 +513,7 @@ bool Tensor::operator==(const Tensor &tensor) const {
 bool Tensor::ValueEqual(const Tensor &tensor) const {
   return (&tensor == this || (MetaTensor::operator==(tensor) && data_->equals(*tensor.data_)));
 }
+
 // assgin value to this tensor
 Tensor &Tensor::AssignValue(const Tensor &tensor) {
   if (this != &tensor) {
@@ -573,6 +590,17 @@ std::string Tensor::ToStringRepr() const {
   return buf.str();
 }
 
+void Tensor::CheckShape(const ShapeVector &shape) const {
+  // Check tensor's shape, ignore one-dimensional tensor, including empty tensor with shape=(0,).
+  if (shape.size() > 1) {
+    for (const auto &s : shape) {
+      if (s == 0) {
+        MS_EXCEPTION(ValueError) << "Zero is not supported in the shape of Tensor !";
+      }
+    }
+  }
+}
+
 void Tensor::data_sync(bool need_wait) const {
   if (need_wait) {
     Wait();

mindspore/core/ir/tensor.h

@@ -261,6 +261,8 @@ class Tensor : public MetaTensor {
 
   std::string ToStringRepr() const;
 
+  void CheckShape(const ShapeVector &shape) const;
+
   bool is_init() const { return init_flag_; }
   void set_init_flag(bool flag) { init_flag_ = flag; }
 

tests/st/ops/gpu/test_tensoradd.py

@@ -33,11 +33,6 @@ class TensroAdd(nn.Cell):
 
         self.add = P.TensorAdd()
 
-        self.x = Parameter(initializer(
-            Tensor(np.random.randn(2, 0).astype(np.float32)), [2, 0]), name='x')
-        self.y = Parameter(initializer(
-            Tensor(np.random.randn(2, 1).astype(np.float32)), [2, 1]), name='y')
-
         self.x1 = Parameter(initializer(
             Tensor(np.arange(3).reshape(3).astype(np.float32)), [3]), name='x1')
         self.y1 = Parameter(initializer(
@@ -55,20 +50,17 @@ class TensroAdd(nn.Cell):
 
     @ms_function
     def construct(self):
-        return (
-            self.add(self.x, self.y), self.add(self.x1, self.y1), self.add(self.x2, self.y2),
-            self.add(self.x3, self.y3))
+        return (self.add(self.x1, self.y1), self.add(self.x2, self.y2), self.add(self.x3, self.y3))
 
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
-def test_TensroAdd():
+def test_TensorAdd():
     add = TensroAdd()
     output = add()
-    expect0 = np.array([])
-    expect1 = np.array([2, 3, 4])
-    expect2 = np.array(
+    expect0 = np.array([2, 3, 4])
+    expect1 = np.array(
         [[[[0., 2., 4.],
            [6., 8., 10.],
            [12., 14., 16.]],
@@ -96,7 +88,7 @@ def test_TensroAdd():
           [[144., 146., 148.],
            [150., 152., 154.],
            [156., 158., 160.]]]])
-    expect3 = np.array(
+    expect2 = np.array(
         [[[[0., 2., 4.],
            [6., 8., 10.],
            [12., 14., 16.]],
@@ -128,4 +120,26 @@ def test_TensroAdd():
     assert (output[0].asnumpy() == expect0).all()
     assert (output[1].asnumpy() == expect1).all()
     assert (output[2].asnumpy() == expect2).all()
-    assert (output[3].asnumpy() == expect3).all()
+
+
+class TensorAdd2(nn.Cell):
+    def __init__(self):
+        super(TensorAdd2, self).__init__()
+        self.add = P.TensorAdd()
+        self.x = Parameter(initializer(
+            Tensor(np.random.randn(2, 0).astype(np.float32)), [2, 0]), name='x')
+        self.y = Parameter(initializer(
+            Tensor(np.random.randn(2, 1).astype(np.float32)), [2, 1]), name='y')
+
+    @ms_function
+    def construct(self):
+        return self.add(self.x, self.y)
+
+
+# Constructing a tensor with 0 in shape is not support, excluding empty tensor.
+@pytest.mark.skip(reason='0 in shape is not support')
+def test_TensorAdd_shape_has_zero():
+    add = TensorAdd2()
+    output = add()
+    expect = np.array([])
+    assert (output.asnumpy() == expect).all()

tests/st/pynative/test_tensor_index.py

@@ -786,8 +786,8 @@ def test_tensor_assign_exception():
     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)
     # Error for A[Slice] = Number
-    # 1. A[Slice] = Number,  Slice error
-    with pytest.raises(IndexError):
+    # 1. A[Slice] = Number,  0 in shape
+    with pytest.raises(ValueError):
         net_e2(t, 2)
 
     # Error for A[Slice] = U, U is a Tensor

tests/ut/python/ir/test_tensor.py

@@ -68,6 +68,18 @@ def test_tensor():
     assert t4.dtype == ms.int64
 
 
+def test_tensor_empty():
+    t = ms.Tensor(np.ones(0), ms.float32)
+    assert isinstance(t, ms.Tensor)
+    assert t.shape == (0,)
+
+
+def test_tensor_shape_has_zero():
+    with pytest.raises(ValueError):
+        t = ms.Tensor(np.ones((1, 0)), ms.float32)
+        print(t)
+
+
 def test_tensor_type_float16():
     t_float16 = ms.Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float16))
     assert isinstance(t_float16, ms.Tensor)

tests/ut/python/ops/test_ops.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """ test ops """
 import functools
+import pytest
 
 import numpy as np
 
@@ -770,6 +771,8 @@ class StridedSliceNet(nn.Cell):
         return out_0, out_1, out_2, out_3
 
 
+# Constructing a tensor with 0 in shape is not support, excluding empty tensor.
+@pytest.mark.skip(reason='0 in shape is not support')
 def test_strided_slice_const():
     class StridedSLiceConstNet(nn.Cell):
         """StridedSLiceConstNet net definition"""

tests/ut/python/ops/test_tensor_slice.py

@@ -464,8 +464,8 @@ def test_tensor_assign():
     net(Ta, b, Tck)
     net2(t, b, tck)
     # Error for A[Slice] = Number
-    # 1. A[Slice] = Number,  Slice error
-    with pytest.raises(IndexError):
+    # 1. A[Slice] = Number,  0 in shape
+    with pytest.raises(ValueError):
         net_e2(t, Tensor(2, mstype.int32))
 
     # Error for A[Slice] = U, U is a Tensor