support len of a Tensor

mindspore/core/abstract/prim_structures.cc

@@ -291,9 +291,17 @@ AbstractBasePtr InferImplListLen(const AnalysisEnginePtr &, const PrimitivePtr &
   return InferTupleOrListOrDictLen<AbstractList>(primitive->name(), args_spec_list);
 }
 
-AbstractBasePtr InferImplArrayLen(const AnalysisEnginePtr &, const PrimitivePtr &,
+AbstractBasePtr InferImplArrayLen(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                   const AbstractBasePtrList &args_spec_list) {
-  return std::make_shared<AbstractScalar>(kAnyValue, kInt32);
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 1);
+  auto arg_abs = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
+  auto shape = arg_abs->BuildShape()->cast<ShapePtr>();
+  MS_EXCEPTION_IF_NULL(shape);
+  if (shape->shape().empty()) {
+    MS_EXCEPTION(TypeError) << "Not support len of a 0-D tensor.";
+  }
+  return std::make_shared<AbstractScalar>(shape->shape()[0]);
 }
 }  // namespace abstract
 }  // namespace mindspore

tests/ut/python/pipeline/parse/test_len_of_array.py

@@ -0,0 +1,53 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" test len of array"""
+import pytest
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+def test_len_a_3D_tensor():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x, y):
+            return len(x), len(y)
+
+    net = Net()
+    x = Tensor(np.ones((5, 6, 7)))
+    y = Tensor(np.ones((100, 6, 7)))
+    ret = net(x, y)
+    assert ret == (len(x), len(y)) == (5, 100)
+
+
+def test_len_a_0D_tensor():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            return len(x)
+
+    net = Net()
+    x = Tensor(np.array(100))
+    with pytest.raises(TypeError) as err:
+        _ = net(x)
+    assert "Not support len of a 0-D tensor." in str(err.value)