modify func dropout

docs/api/api_python/ops/mindspore.ops.func_dropout.rst

@@ -1,22 +1,19 @@
 mindspore.ops.dropout
 ======================
 
-.. py:function:: mindspore.ops.dropout(x, p=0.5, seed0=0, seed1=0)
+.. py:function:: mindspore.ops.dropout(input, p=0.5)
 
     在训练期间，以服从伯努利分布的概率 `p` 随机将输入Tensor的某些值归零，起到减少神经元相关性的作用，避免过拟合。此概率与 `ops.Dropout` 和 `nn.Dropout` 中的含义相反。
 
     参数：
-        - **x** (Tensor) - dropout的输入，任意维度的Tensor，其数据类型为float16或float32。
+        - **input** (Tensor) - dropout的输入，任意维度的Tensor，其数据类型为float16或float32。
         - **p** (float，可选) - 输入神经元丢弃概率，数值范围在0到1之间。例如，p=0.1，删除10%的神经元。默认值：0.5。
-        - **seed0** (int，可选) - 算子层的随机种子，用于生成随机数。默认值：0。
-        - **seed1** (int，可选) - 全局的随机种子，和算子层的随机种子共同决定最终生成的随机数。默认值：0。
 
     返回：
-        - **output** (Tensor) - 归零后的Tensor，shape和数据类型与 `x` 相同。
+        - **output** (Tensor) - 归零后的Tensor，shape和数据类型与 `input` 相同。
         - **mask** (Tensor) - 用于归零的掩码，内部会按位压缩与对齐。
 
     异常：
         - **TypeError** - `p` 不是float。
-        - **TypeError** - `seed0` 或 `seed1` 不是int。
-        - **TypeError** - `x` 的数据类型既不是float16也不是float32。
-        - **TypeError** - `x` 不是Tensor。
+        - **TypeError** - `input` 的数据类型既不是float16也不是float32。
+        - **TypeError** - `input` 不是Tensor。

mindspore/python/mindspore/nn/layer/basic.py

@@ -252,10 +252,7 @@ class Dropout1d(Cell):
         self.prob = p
 
     def construct(self, x):
-        if not self.training:
-            return x
-
-        if self.prob == 0:
+        if not self.training or self.prob == 0:
             return x
 
         out = F.dropout1d(x, self.prob)
@@ -299,10 +296,7 @@ class Dropout2d(Cell):
         self.dropout2d = P.Dropout2D(self.keep_prob)
 
     def construct(self, x):
-        if not self.training:
-            return x
-
-        if self.keep_prob == 1:
+        if not self.training or self.keep_prob == 1:
             return x
 
         out, _ = self.dropout2d(x)
@@ -350,10 +344,7 @@ class Dropout3d(Cell):
         self.dropout3d = P.Dropout3D(self.keep_prob)
 
     def construct(self, x):
-        if not self.training:
-            return x
-
-        if self.keep_prob == 1:
+        if not self.training or self.keep_prob == 1:
             return x
 
         out, _ = self.dropout3d(x)

mindspore/python/mindspore/ops/function/nn_func.py

@@ -1158,7 +1158,7 @@ def binary_cross_entropy_with_logits(logits, label, weight, pos_weight, reductio
     return bce_with_logits_loss_op(logits, label, weight, pos_weight)
 
 
-def dropout(x, p=0.5, seed0=0, seed1=0):
+def dropout(input, p=0.5):
     """
     During training, randomly zeroes some of the elements of the input tensor
     with probability `p` from a Bernoulli distribution. It plays the role of
@@ -1166,34 +1166,31 @@ def dropout(x, p=0.5, seed0=0, seed1=0):
     here is opposite to that in `ops.Dropout` and `nn.Dropout`.
 
     Args:
-        x (Tensor): The input of Dropout, a Tensor of any shape with data type of float16 or float32.
+        input (Tensor): The input of Dropout, a Tensor of any shape with data type of float16 or float32.
         p (float, optional): The dropping rate, between 0 and 1, e.g. p = 0.1,
             means dropping out 10% of input units. Default: 0.5.
-        seed0 (int, optional): seed0 value for random generating. Default: 0.
-        seed1 (int, optional): seed1 value for random generating. Default: 0.
 
     Returns:
-        - **output** (Tensor) - Zeroed tensor, with the same shape and data type as `x`.
+        - **output** (Tensor) - Zeroed tensor, with the same shape and data type as `input`.
         - **mask** (Tensor) - Mask for zeroing, bitwise compression and alignment are performed internally.
 
     Raises:
         TypeError: If `p` is not a float.
-        TypeError: If `seed0` or `seed1` is not an int.
-        TypeError: If dtype of `x` is neither float16 nor float32.
-        TypeError: If `x` is not a Tensor.
+        TypeError: If dtype of `input` is neither float16 nor float32.
+        TypeError: If `input` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
-        >>> x = Tensor(((20, 16), (50, 50)), mindspore.float32)
-        >>> output, mask = ops.dropout(x, p=0.5)
-        >>> print(output.shape, mask.shape, mask.dtype)
-        (2, 2) (16,) UInt8
+        >>> input = Tensor(((20, 16), (50, 50)), mindspore.float32)
+        >>> output = ops.dropout(input, p=0.5)
+        >>> print(output.shape)
+        (2, 2)
     """
     keep_prob = 1 - p
-    dropout_ = P.Dropout(keep_prob=keep_prob, Seed0=seed0, Seed1=seed1)
-    return dropout_(x)
+    out, _ = P.Dropout(keep_prob=keep_prob)(input)
+    return out
 
 
 def celu(x, alpha=1.0):

tests/st/ops/cpu/test_dropout_op.py

@@ -125,12 +125,10 @@ def test_op1():
     Expectation: No exception.
     """
     x = Tensor(np.arange(0, 12).reshape(3, 4).astype(np.float16))
-    output1, mask1 = ops.dropout(x, p=0.5, seed0=1, seed1=100)
-    output2, mask2 = ops.dropout(x, p=0.5, seed0=1, seed1=100)
+    output1 = ops.dropout(x, p=0.5)
+    output2 = ops.dropout(x, p=0.5)
 
-    assert mask1.shape == mask2.shape
     assert np.allclose(output1.asnumpy(), output2.asnumpy())
-    assert np.allclose(mask1.asnumpy(), mask2.asnumpy())
 
 
 @pytest.mark.level0