!10559 update L1Regularizer operation

From: @dangjiaqi1
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/nn/layer/basic.py

@@ -47,7 +47,7 @@ class L1Regularizer(Cell):
         scale(regularization factor) should be a number which greater than 0
 
     Args:
-        scale (int, float)： l1 regularization factor which greater than 0.
+        scale (int, float): l1 regularization factor which greater than 0.
 
     Raises:
         ValueError: If `scale(regularization factor)` is not greater than 0.
@@ -57,7 +57,8 @@ class L1Regularizer(Cell):
         - **weights** (Tensor) - The input tensor
 
     Outputs:
-        Tensor, which dtype is Float and shape is ()
+        Tensor, which dtype is higher precision data type between mindspore.float32 and weights dtype,
+        and Tensor shape is ()
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -77,13 +78,13 @@ class L1Regularizer(Cell):
         if scale <= 0:
             raise ValueError("scale should be a number which greater than 0")
         if math.isinf(scale) or math.isnan(scale):
-            raise ValueError("scale is INF or NAN")
+            raise ValueError("scale can not be INF or NAN")
         self.abs = P.Abs()
         self.reduce_sum = P.ReduceSum()
         self.scale = Tensor(scale, dtype=mstype.float32)
 
     def construct(self, weights):
-        const_utils.check_valid_type(weights.dtype, mstype.number_type, 'weights')
+        const_utils.check_valid_type(F.dtype(weights), mstype.number_type, 'weights')
         l1_regularization = self.scale * self.reduce_sum(self.abs(weights))
         return l1_regularization
 

tests/st/ops/cpu/test_l1_regularizer_op.py

@@ -58,3 +58,31 @@ def test_l1_regularizer08():
     expect = 5.0
     print("output : ", output.asnumpy())
     assert np.all(output.asnumpy() == expect)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_l1_regularizer_input_int():
+    scale = 0.5
+    net = nn.L1Regularizer(scale)
+    weights = 2
+    try:
+        output = net(weights)
+        print("output : ", output.asnumpy())
+    except TypeError:
+        assert True
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_l1_regularizer_input_tuple():
+    scale = 0.5
+    net = nn.L1Regularizer(scale)
+    weights = (1, 2, 3, 4)
+    try:
+        output = net(weights)
+        print("output : ", output.asnumpy())
+    except TypeError:
+        assert True