Update Square Op to support Dynamic Shape

mindspore/ops/operations/math_ops.py

@@ -1319,7 +1319,7 @@ class SquaredDifference(_MathBinaryOp):
         return _MathBinaryOp.do_infer_dtype(x_dtype, y_dtype, valid_type, self.name)
 
 
-class Square(PrimitiveWithInfer):
+class Square(PrimitiveWithCheck):
     """
     Returns square of a tensor element-wise.
 
@@ -1345,12 +1345,9 @@ class Square(PrimitiveWithInfer):
         """Initialize Square"""
         self.init_prim_io_names(inputs=['input_x'], outputs=['output'])
 
-    def infer_shape(self, x_shape):
-        return x_shape
-
-    def infer_dtype(self, x_dtype):
+    def __check__(self, x):
+        x_dtype = x["dtype"]
         validator.check_tensor_dtype_valid("x", x_dtype, mstype.number_type, self.name)
-        return x_dtype
 
     def infer_value(self, x):
         if x is not None:

tests/st/ops/gpu/test_square_op.py

@@ -0,0 +1,74 @@
+# 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.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops.operations import _inner_ops as inner
+from mindspore.ops import operations as P
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_square_normal():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    x_np = np.random.rand(2, 3, 4, 4).astype(np.float32)
+    output_ms = P.Square()(Tensor(x_np))
+    output_np = np.square(x_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+    x_np = np.random.rand(2, 3, 1, 5, 4, 4).astype(np.float32)
+    output_ms = P.Square()(Tensor(x_np))
+    output_np = np.square(x_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+    x_np = np.random.rand(2,).astype(np.float32)
+    output_ms = P.Square()(Tensor(x_np))
+    output_np = np.square(x_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+
+# Dynamic Shape Testing
+class SqaureNetDynamic(nn.Cell):
+    def __init__(self):
+        super(SqaureNetDynamic, self).__init__()
+        self.square = P.Square()
+        self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
+
+    def construct(self, x):
+        x_dyn = self.gpu_convert_to_dynamic_shape(x)
+        return self.square(x_dyn)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_square_dynamic():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    net = SqaureNetDynamic()
+    x_np = np.random.rand(1, 3, 4, 4, 1).astype(np.float32)
+    output_ms = net(Tensor(x_np))
+    output_np = np.square(x_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+    x_np = np.random.rand(2, 3, 4, 4, 8, 9).astype(np.float16)
+    output_ms = net(Tensor(x_np))
+    output_np = np.square(x_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+    x_np = np.random.rand(1).astype(np.float32)
+    output_ms = net(Tensor(x_np))
+    output_np = np.square(x_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)